diff --git a/master/_images/parameter_types_11_0.png b/master/_images/parameter_types_11_0.png
new file mode 100644
index 00000000..4bea406c
Binary files /dev/null and b/master/_images/parameter_types_11_0.png differ
diff --git a/master/_images/parameter_types_17_0.png b/master/_images/parameter_types_17_0.png
new file mode 100644
index 00000000..111c7dd9
Binary files /dev/null and b/master/_images/parameter_types_17_0.png differ
diff --git a/master/_images/parameter_types_3_0.png b/master/_images/parameter_types_3_0.png
new file mode 100644
index 00000000..e7a4b21e
Binary files /dev/null and b/master/_images/parameter_types_3_0.png differ
diff --git a/master/_sources/advanced-tour.ipynb.txt b/master/_sources/advanced-tour.ipynb.txt
index dc72e40e..9e93d09d 100644
--- a/master/_sources/advanced-tour.ipynb.txt
+++ b/master/_sources/advanced-tour.ipynb.txt
@@ -96,7 +96,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Next point to probe is: {'x': -0.331911981189704, 'y': 1.3219469606529486}\n"
+      "Next point to probe is: {'x': np.float64(-0.331911981189704), 'y': np.float64(1.3219469606529486)}\n"
      ]
     }
    ],
@@ -167,12 +167,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "-18.503835804889988 {'x': 1.953072105336, 'y': -2.9609778030491904}\n",
-      "-1.0819533157901717 {'x': 0.22703572807626315, 'y': 2.4249238905875123}\n",
-      "-6.50219704520679 {'x': -1.9991881984624875, 'y': 2.872282989383577}\n",
-      "-5.747604713731052 {'x': -1.994467585936897, 'y': -0.664242699361514}\n",
-      "-2.9682431497650823 {'x': 1.9737252084307952, 'y': 1.269540259274744}\n",
-      "{'target': 0.7861845912690544, 'params': {'x': -0.331911981189704, 'y': 1.3219469606529486}}\n"
+      "-18.707136686093495 {'x': np.float64(1.9261486197444082), 'y': np.float64(-2.9996360060323246)}\n",
+      "0.750594563473972 {'x': np.float64(-0.3763326769822668), 'y': np.float64(1.328297354179696)}\n",
+      "-6.559031075654336 {'x': np.float64(1.979183535803597), 'y': np.float64(2.9083667381450318)}\n",
+      "-6.915481333972961 {'x': np.float64(-1.9686133847781613), 'y': np.float64(-1.009985740060171)}\n",
+      "-6.8600832617014085 {'x': np.float64(-1.9763198875239296), 'y': np.float64(2.9885278383464513)}\n",
+      "{'target': np.float64(0.7861845912690544), 'params': {'x': np.float64(-0.331911981189704), 'y': np.float64(1.3219469606529486)}}\n"
      ]
     }
    ],
@@ -190,112 +190,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 2. Dealing with discrete parameters\n",
-    "\n",
-    "**There is no principled way of dealing with discrete parameters using this package.**\n",
-    "\n",
-    "Ok, now that we got that out of the way, how do you do it? You're bound to be in a situation where some of your function's parameters may only take on discrete values. Unfortunately, the nature of bayesian optimization with gaussian processes doesn't allow for an easy/intuitive way of dealing with discrete parameters - but that doesn't mean it is impossible. The example below showcases a simple, yet reasonably adequate, way to dealing with discrete parameters."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def func_with_discrete_params(x, y, d):\n",
-    "    # Simulate necessity of having d being discrete.\n",
-    "    assert type(d) == int\n",
-    "    \n",
-    "    return ((x + y + d) // (1 + d)) / (1 + (x + y) ** 2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def function_to_be_optimized(x, y, w):\n",
-    "    d = int(w)\n",
-    "    return func_with_discrete_params(x, y, d)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "optimizer = BayesianOptimization(\n",
-    "    f=function_to_be_optimized,\n",
-    "    pbounds={'x': (-10, 10), 'y': (-10, 10), 'w': (0, 5)},\n",
-    "    verbose=2,\n",
-    "    random_state=1,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "|   iter    |  target   |     w     |     x     |     y     |\n",
-      "-------------------------------------------------------------\n",
-      "| \u001b[30m1         | \u001b[30m-0.06199  | \u001b[30m2.085     | \u001b[30m4.406     | \u001b[30m-9.998    |\n",
-      "| \u001b[35m2         | \u001b[35m-0.0344   | \u001b[35m1.512     | \u001b[35m-7.065    | \u001b[35m-8.153    |\n",
-      "| \u001b[30m3         | \u001b[30m-0.2177   | \u001b[30m0.9313    | \u001b[30m-3.089    | \u001b[30m-2.065    |\n",
-      "| \u001b[35m4         | \u001b[35m0.1865    | \u001b[35m2.694     | \u001b[35m-1.616    | \u001b[35m3.704     |\n",
-      "| \u001b[30m5         | \u001b[30m-0.2187   | \u001b[30m1.022     | \u001b[30m7.562     | \u001b[30m-9.452    |\n",
-      "| \u001b[35m6         | \u001b[35m0.2488    | \u001b[35m2.684     | \u001b[35m-2.188    | \u001b[35m3.925     |\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "| \u001b[35m7         | \u001b[35m0.2948    | \u001b[35m2.683     | \u001b[35m-2.534    | \u001b[35m4.08      |\n",
-      "| \u001b[35m8         | \u001b[35m0.3202    | \u001b[35m2.514     | \u001b[35m-3.83     | \u001b[35m5.287     |\n",
-      "| \u001b[30m9         | \u001b[30m0.0       | \u001b[30m4.057     | \u001b[30m-4.458    | \u001b[30m3.928     |\n",
-      "| \u001b[35m10        | \u001b[35m0.4802    | \u001b[35m2.296     | \u001b[35m-3.518    | \u001b[35m4.558     |\n",
-      "| \u001b[30m11        | \u001b[30m0.0       | \u001b[30m1.084     | \u001b[30m-3.737    | \u001b[30m4.472     |\n",
-      "| \u001b[30m12        | \u001b[30m0.0       | \u001b[30m2.649     | \u001b[30m-3.861    | \u001b[30m4.353     |\n",
-      "| \u001b[30m13        | \u001b[30m0.0       | \u001b[30m2.442     | \u001b[30m-3.658    | \u001b[30m4.599     |\n",
-      "| \u001b[30m14        | \u001b[30m-0.05801  | \u001b[30m1.935     | \u001b[30m-0.4758   | \u001b[30m-8.755    |\n",
-      "| \u001b[30m15        | \u001b[30m0.0       | \u001b[30m2.337     | \u001b[30m7.973     | \u001b[30m-8.96     |\n",
-      "| \u001b[30m16        | \u001b[30m0.07699   | \u001b[30m0.6926    | \u001b[30m5.59      | \u001b[30m6.854     |\n",
-      "| \u001b[30m17        | \u001b[30m-0.02025  | \u001b[30m3.534     | \u001b[30m-8.943    | \u001b[30m1.987     |\n",
-      "| \u001b[30m18        | \u001b[30m0.0       | \u001b[30m2.59      | \u001b[30m-7.339    | \u001b[30m5.941     |\n",
-      "| \u001b[30m19        | \u001b[30m0.0929    | \u001b[30m2.237     | \u001b[30m-4.535    | \u001b[30m9.065     |\n",
-      "| \u001b[30m20        | \u001b[30m0.1538    | \u001b[30m0.477     | \u001b[30m2.931     | \u001b[30m2.683     |\n",
-      "| \u001b[30m21        | \u001b[30m0.0       | \u001b[30m0.9999    | \u001b[30m4.397     | \u001b[30m-3.971    |\n",
-      "| \u001b[30m22        | \u001b[30m-0.01894  | \u001b[30m3.764     | \u001b[30m-7.043    | \u001b[30m-3.184    |\n",
-      "| \u001b[30m23        | \u001b[30m0.03683   | \u001b[30m1.851     | \u001b[30m5.783     | \u001b[30m7.966     |\n",
-      "| \u001b[30m24        | \u001b[30m-0.04359  | \u001b[30m1.615     | \u001b[30m-5.133    | \u001b[30m-6.556    |\n",
-      "| \u001b[30m25        | \u001b[30m0.02617   | \u001b[30m3.863     | \u001b[30m0.1052    | \u001b[30m8.579     |\n",
-      "| \u001b[30m26        | \u001b[30m-0.1071   | \u001b[30m0.8131    | \u001b[30m-0.7949   | \u001b[30m-9.292    |\n",
-      "| \u001b[30m27        | \u001b[30m0.0       | \u001b[30m4.969     | \u001b[30m8.778     | \u001b[30m-8.467    |\n",
-      "| \u001b[30m28        | \u001b[30m-0.1372   | \u001b[30m0.9475    | \u001b[30m-1.019    | \u001b[30m-7.018    |\n",
-      "| \u001b[30m29        | \u001b[30m0.08078   | \u001b[30m1.917     | \u001b[30m-0.2606   | \u001b[30m6.272     |\n",
-      "| \u001b[30m30        | \u001b[30m0.02003   | \u001b[30m4.278     | \u001b[30m3.8       | \u001b[30m8.398     |\n",
-      "=============================================================\n"
-     ]
-    }
-   ],
-   "source": [
-    "optimizer.set_gp_params(alpha=1e-3)\n",
-    "optimizer.maximize()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 3. Tuning the underlying Gaussian Process\n",
+    "## 2. Tuning the underlying Gaussian Process\n",
     "\n",
     "The bayesian optimization algorithm works by performing a gaussian process regression of the observed combination of parameters and their associated target values. The predicted parameter $\\rightarrow$ target hyper-surface (and its uncertainty) is then used to guide the next best point to probe."
    ]
@@ -304,14 +199,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 3.1 Passing parameter to the GP\n",
+    "### 2.1 Passing parameter to the GP\n",
     "\n",
     "Depending on the problem it could be beneficial to change the default parameters of the underlying GP. You can use the `optimizer.set_gp_params` method to do this:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -320,12 +215,12 @@
      "text": [
       "|   iter    |  target   |     x     |     y     |\n",
       "-------------------------------------------------\n",
-      "| \u001b[30m1         | \u001b[30m0.7862    | \u001b[30m-0.3319   | \u001b[30m1.322     |\n",
-      "| \u001b[30m2         | \u001b[30m-18.19    | \u001b[30m1.957     | \u001b[30m-2.919    |\n",
-      "| \u001b[30m3         | \u001b[30m-12.05    | \u001b[30m-1.969    | \u001b[30m-2.029    |\n",
-      "| \u001b[30m4         | \u001b[30m-7.463    | \u001b[30m0.6032    | \u001b[30m-1.846    |\n",
-      "| \u001b[30m5         | \u001b[30m-1.093    | \u001b[30m1.444     | \u001b[30m1.096     |\n",
-      "| \u001b[35m6         | \u001b[35m0.8586    | \u001b[35m-0.2165   | \u001b[35m1.307     |\n",
+      "| \u001b[39m1        \u001b[39m | \u001b[39m0.7862   \u001b[39m | \u001b[39m-0.331911\u001b[39m | \u001b[39m1.3219469\u001b[39m |\n",
+      "| \u001b[39m2        \u001b[39m | \u001b[39m-18.34   \u001b[39m | \u001b[39m1.9021640\u001b[39m | \u001b[39m-2.965222\u001b[39m |\n",
+      "| \u001b[35m3        \u001b[39m | \u001b[35m0.8731   \u001b[39m | \u001b[35m-0.298167\u001b[39m | \u001b[35m1.1948749\u001b[39m |\n",
+      "| \u001b[39m4        \u001b[39m | \u001b[39m-6.497   \u001b[39m | \u001b[39m1.9876938\u001b[39m | \u001b[39m2.8830942\u001b[39m |\n",
+      "| \u001b[39m5        \u001b[39m | \u001b[39m-4.286   \u001b[39m | \u001b[39m-1.995643\u001b[39m | \u001b[39m-0.141769\u001b[39m |\n",
+      "| \u001b[39m6        \u001b[39m | \u001b[39m-6.781   \u001b[39m | \u001b[39m-1.953302\u001b[39m | \u001b[39m2.9913127\u001b[39m |\n",
       "=================================================\n"
      ]
     }
@@ -348,7 +243,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 3.2 Tuning the `alpha` parameter\n",
+    "### 2.2 Tuning the `alpha` parameter\n",
     "\n",
     "When dealing with functions with discrete parameters,or particularly erratic target space it might be beneficial to increase the value of the `alpha` parameter. This parameters controls how much noise the GP can handle, so increase it whenever you think that extra flexibility is needed."
    ]
@@ -358,7 +253,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 3.3 Changing kernels\n",
+    "### 2.3 Changing kernels\n",
     "\n",
     "By default this package uses the Matern 2.5 kernel. Depending on your use case you may find that tuning the GP kernel could be beneficial. You're on your own here since these are very specific solutions to very specific problems. You should start with the [scikit learn docs](https://scikit-learn.org/stable/modules/gaussian_process.html#kernels-for-gaussian-processes)."
    ]
@@ -376,7 +271,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -385,7 +280,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -399,7 +294,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -411,7 +306,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -433,7 +328,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -449,7 +344,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -476,7 +371,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -485,7 +380,7 @@
        "['optimization:start', 'optimization:step', 'optimization:end']"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -497,7 +392,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "bayesian-optimization-t6LLJ9me-py3.10",
    "language": "python",
    "name": "python3"
   },
@@ -511,7 +406,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.1.undefined"
+   "version": "3.10.13"
   },
   "nbdime-conflicts": {
    "local_diff": [
diff --git a/master/_sources/basic-tour.ipynb.txt b/master/_sources/basic-tour.ipynb.txt
index 3cbcbd40..4ecd8329 100644
--- a/master/_sources/basic-tour.ipynb.txt
+++ b/master/_sources/basic-tour.ipynb.txt
@@ -252,7 +252,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Or as an iterable. Beware that the order has to be alphabetical. You can usee `optimizer.space.keys` for guidance"
+    "Or as an iterable. Beware that the order has to match the order of the initial `pbounds` dictionary. You can usee `optimizer.space.keys` for guidance"
    ]
   },
   {
diff --git a/master/_sources/index.rst.txt b/master/_sources/index.rst.txt
index ac664a58..5c198c6f 100644
--- a/master/_sources/index.rst.txt
+++ b/master/_sources/index.rst.txt
@@ -11,6 +11,7 @@
    Basic Tour </basic-tour>
    Advanced Tour </advanced-tour>
    Constrained Bayesian Optimization </constraints>
+   Parameter Types </parameter_types>
    Sequential Domain Reduction </domain_reduction>
    Acquisition Functions </acquisition_functions>
    Exploration vs. Exploitation </exploitation_vs_exploration>
@@ -26,6 +27,7 @@
    reference/constraint
    reference/domain_reduction
    reference/target_space
+   reference/parameter
    reference/exception
    reference/other
 
@@ -121,11 +123,13 @@ section. We suggest that you:
    to learn how to use the package's most important features.
 -  Take a look at the `advanced tour
    notebook <advanced-tour.html>`__
-   to learn how to make the package more flexible, how to deal with
-   categorical parameters, how to use observers, and more.
+   to learn how to make the package more flexible or how to use observers.
 -  To learn more about acquisition functions, a central building block
    of bayesian optimization, see the `acquisition functions
    notebook <acquisition_functions.html>`__
+-  If you want to optimize over integer-valued or categorical
+   parameters, see the `parameter types
+   notebook <parameter_types.html>`__.
 -  Check out this
    `notebook <visualization.html>`__
    with a step by step visualization of how this method works.
@@ -195,6 +199,20 @@ For constrained optimization:
        year={2014}
    }
 
+For optimization over non-float parameters:
+
+::
+
+   @article{garrido2020dealing,
+       title={Dealing with categorical and integer-valued variables in bayesian optimization with gaussian processes},
+       author={Garrido-Merch{\'a}n, Eduardo C and Hern{\'a}ndez-Lobato, Daniel},
+       journal={Neurocomputing},
+       volume={380},
+       pages={20--35},
+       year={2020},
+       publisher={Elsevier}
+   }
+
 .. |tests| image:: https://github.com/bayesian-optimization/BayesianOptimization/actions/workflows/run_tests.yml/badge.svg
 .. |Codecov| image:: https://codecov.io/github/bayesian-optimization/BayesianOptimization/badge.svg?branch=master&service=github
    :target: https://codecov.io/github/bayesian-optimization/BayesianOptimization?branch=master
diff --git a/master/_sources/parameter_types.ipynb.txt b/master/_sources/parameter_types.ipynb.txt
new file mode 100644
index 00000000..3d668300
--- /dev/null
+++ b/master/_sources/parameter_types.ipynb.txt
@@ -0,0 +1,756 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Optimizing over non-float Parameters\n",
+    "\n",
+    "Sometimes, you need to optimize a target that is not just a function of floating-point values, but relies on integer or categorical parameters. This notebook shows how such problems are handled by following an approach from [\"Dealing with categorical and integer-valued variables in Bayesian Optimization with Gaussian processes\" by Garrido-Merchán and Hernández-Lobato](https://arxiv.org/abs/1805.03463). One simple way of handling an integer-valued parameter is to run the optimization as normal, but then round to the nearest integer after a point has been suggested. This method is similar, except that the rounding is performed in the _kernel_. Why does this matter? It means that the kernel is aware that two parameters, that map the to same point but are potentially distinct before this transformation are the same."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import warnings\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from bayes_opt import BayesianOptimization\n",
+    "from bayes_opt import acquisition\n",
+    "\n",
+    "from sklearn.gaussian_process.kernels import Matern\n",
+    "\n",
+    "# suppress warnings about this being an experimental feature\n",
+    "warnings.filterwarnings(action=\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Simple integer-valued function\n",
+    "Let's look at a simple, one-dimensional, integer-valued target function and compare a typed optimizer and a continuous optimizer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1000x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def target_function_1d(x):\n",
+    "    return np.sin(np.round(x)) - np.abs(np.round(x) / 5)\n",
+    "\n",
+    "c_pbounds = {'x': (-10, 10)}\n",
+    "bo_cont = BayesianOptimization(target_function_1d, c_pbounds, verbose=0, random_state=1)\n",
+    "\n",
+    "# one way of constructing an integer-valued parameter is to add a third element to the tuple\n",
+    "d_pbounds = {'x': (-10, 10, int)}\n",
+    "bo_disc = BayesianOptimization(target_function_1d, d_pbounds, verbose=0, random_state=1)\n",
+    "\n",
+    "fig, axs = plt.subplots(2, 1, figsize=(10, 6), sharex=True, sharey=True)\n",
+    "\n",
+    "bo_cont.maximize(init_points=2, n_iter=10)\n",
+    "bo_cont.acquisition_function._fit_gp(bo_cont._gp, bo_cont.space)\n",
+    "\n",
+    "y_mean, y_std = bo_cont._gp.predict(np.linspace(-10, 10, 1000).reshape(-1, 1), return_std=True)\n",
+    "axs[0].set_title('Continuous')\n",
+    "axs[0].plot(np.linspace(-10, 10, 1000), target_function_1d(np.linspace(-10, 10, 1000)), 'k--', label='True function')\n",
+    "axs[0].plot(np.linspace(-10, 10, 1000), y_mean, label='Predicted mean')\n",
+    "axs[0].fill_between(np.linspace(-10, 10, 1000), y_mean - y_std, y_mean + y_std, alpha=0.3, label='Predicted std')\n",
+    "axs[0].plot(bo_cont.space.params, bo_cont.space.target, 'ro')\n",
+    "\n",
+    "bo_disc.maximize(init_points=2, n_iter=10)\n",
+    "bo_disc.acquisition_function._fit_gp(bo_disc._gp, bo_disc.space)\n",
+    "\n",
+    "y_mean, y_std = bo_disc._gp.predict(np.linspace(-10, 10, 1000).reshape(-1, 1), return_std=True)\n",
+    "axs[1].set_title('Discrete')\n",
+    "axs[1].plot(np.linspace(-10, 10, 1000), target_function_1d(np.linspace(-10, 10, 1000)), 'k--', label='True function')\n",
+    "axs[1].plot(np.linspace(-10, 10, 1000), y_mean, label='Predicted mean')\n",
+    "axs[1].fill_between(np.linspace(-10, 10, 1000), y_mean - y_std, y_mean + y_std, alpha=0.3, label='Predicted std')\n",
+    "axs[1].plot(bo_disc.space.params, bo_disc.space.target, 'ro')\n",
+    "\n",
+    "for ax in axs:\n",
+    "    ax.grid(True)\n",
+    "fig.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can see, that the discrete optimizer is aware that the function is discrete and does not try to predict values between the integers. The continuous optimizer tries to predict values between the integers, despite the fact that these are known.\n",
+    "We can also see that the discrete optimizer predicts blocky mean and standard deviations, which is a result of the discrete nature of the function."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Mixed-parameter optimization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def discretized_function(x, y):\n",
+    "    y = np.round(y)\n",
+    "    return (-1*np.cos(x)**np.abs(y) + -1*np.cos(y)) + 0.1 * (x + y) - 0.01 * (x**2 + y**2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Bounded region of parameter space\n",
+    "c_pbounds = {'x': (-5, 5), 'y': (-5, 5)}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "labels = [\"All-float Optimizer\", \"Typed Optimizer\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "continuous_optimizer = BayesianOptimization(\n",
+    "    f=discretized_function,\n",
+    "    acquisition_function=acquisition.ExpectedImprovement(xi=0.01, random_state=1),\n",
+    "    pbounds=c_pbounds,\n",
+    "    verbose=2,\n",
+    "    random_state=1,\n",
+    ")\n",
+    "\n",
+    "continuous_optimizer.set_gp_params(kernel=Matern(nu=2.5, length_scale=np.ones(2)))\n",
+    "\n",
+    "d_pbounds = {'x': (-5, 5), 'y': (-5, 5, int)}\n",
+    "discrete_optimizer = BayesianOptimization(\n",
+    "    f=discretized_function,\n",
+    "    acquisition_function=acquisition.ExpectedImprovement(xi=0.01, random_state=1),\n",
+    "    pbounds=d_pbounds,\n",
+    "    verbose=2,\n",
+    "    random_state=1,\n",
+    ")\n",
+    "\n",
+    "discrete_optimizer.set_gp_params(kernel=Matern(nu=2.5, length_scale=np.ones(2)));"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "====================  All-float Optimizer  ====================\n",
+      "\n",
+      "|   iter    |  target   |     x     |     y     |\n",
+      "-------------------------------------------------\n",
+      "| \u001b[39m1        \u001b[39m | \u001b[39m0.03061  \u001b[39m | \u001b[39m-0.829779\u001b[39m | \u001b[39m2.2032449\u001b[39m |\n",
+      "| \u001b[39m2        \u001b[39m | \u001b[39m-0.6535  \u001b[39m | \u001b[39m-4.998856\u001b[39m | \u001b[39m-1.976674\u001b[39m |\n",
+      "| \u001b[35m3        \u001b[39m | \u001b[35m0.8025   \u001b[39m | \u001b[35m-0.829779\u001b[39m | \u001b[35m2.6549696\u001b[39m |\n",
+      "| \u001b[35m4        \u001b[39m | \u001b[35m0.9203   \u001b[39m | \u001b[35m-0.981065\u001b[39m | \u001b[35m2.6644394\u001b[39m |\n",
+      "| \u001b[35m5        \u001b[39m | \u001b[35m1.008    \u001b[39m | \u001b[35m-1.652553\u001b[39m | \u001b[35m2.7133425\u001b[39m |\n",
+      "| \u001b[39m6        \u001b[39m | \u001b[39m0.9926   \u001b[39m | \u001b[39m-1.119714\u001b[39m | \u001b[39m2.8358733\u001b[39m |\n",
+      "| \u001b[35m7        \u001b[39m | \u001b[35m1.322    \u001b[39m | \u001b[35m-2.418942\u001b[39m | \u001b[35m3.4600371\u001b[39m |\n",
+      "| \u001b[39m8        \u001b[39m | \u001b[39m-0.5063  \u001b[39m | \u001b[39m-3.092074\u001b[39m | \u001b[39m3.7368226\u001b[39m |\n",
+      "| \u001b[39m9        \u001b[39m | \u001b[39m-0.6432  \u001b[39m | \u001b[39m-4.089558\u001b[39m | \u001b[39m-0.560384\u001b[39m |\n",
+      "| \u001b[39m10       \u001b[39m | \u001b[39m1.267    \u001b[39m | \u001b[39m-2.360726\u001b[39m | \u001b[39m3.3725022\u001b[39m |\n",
+      "| \u001b[39m11       \u001b[39m | \u001b[39m0.4649   \u001b[39m | \u001b[39m-2.247113\u001b[39m | \u001b[39m3.7419056\u001b[39m |\n",
+      "| \u001b[39m12       \u001b[39m | \u001b[39m1.0      \u001b[39m | \u001b[39m-1.740988\u001b[39m | \u001b[39m3.4854116\u001b[39m |\n",
+      "| \u001b[39m13       \u001b[39m | \u001b[39m0.986    \u001b[39m | \u001b[39m1.2164322\u001b[39m | \u001b[39m4.4938459\u001b[39m |\n",
+      "| \u001b[39m14       \u001b[39m | \u001b[39m-2.27    \u001b[39m | \u001b[39m-2.213867\u001b[39m | \u001b[39m0.3585570\u001b[39m |\n",
+      "| \u001b[39m15       \u001b[39m | \u001b[39m-1.853   \u001b[39m | \u001b[39m1.7935035\u001b[39m | \u001b[39m-0.377351\u001b[39m |\n",
+      "=================================================\n",
+      "Max: 1.321554535694256\n",
+      "\n",
+      "\n",
+      "====================  Typed Optimizer  ====================\n",
+      "\n",
+      "|   iter    |  target   |     x     |     y     |\n",
+      "-------------------------------------------------\n",
+      "| \u001b[39m1        \u001b[39m | \u001b[39m0.8025   \u001b[39m | \u001b[39m-0.829779\u001b[39m | \u001b[39m3        \u001b[39m |\n",
+      "| \u001b[39m2        \u001b[39m | \u001b[39m-2.75    \u001b[39m | \u001b[39m-4.998856\u001b[39m | \u001b[39m0        \u001b[39m |\n",
+      "| \u001b[39m3        \u001b[39m | \u001b[39m0.8007   \u001b[39m | \u001b[39m-0.827713\u001b[39m | \u001b[39m3        \u001b[39m |\n",
+      "| \u001b[39m4        \u001b[39m | \u001b[39m-0.749   \u001b[39m | \u001b[39m2.2682240\u001b[39m | \u001b[39m-5       \u001b[39m |\n",
+      "| \u001b[39m5        \u001b[39m | \u001b[39m0.3718   \u001b[39m | \u001b[39m-2.339072\u001b[39m | \u001b[39m4        \u001b[39m |\n",
+      "| \u001b[39m6        \u001b[39m | \u001b[39m0.2146   \u001b[39m | \u001b[39m4.9971028\u001b[39m | \u001b[39m5        \u001b[39m |\n",
+      "| \u001b[39m7        \u001b[39m | \u001b[39m0.7473   \u001b[39m | \u001b[39m4.9970839\u001b[39m | \u001b[39m2        \u001b[39m |\n",
+      "| \u001b[35m8        \u001b[39m | \u001b[35m0.8275   \u001b[39m | \u001b[35m4.9986856\u001b[39m | \u001b[35m-3       \u001b[39m |\n",
+      "| \u001b[39m9        \u001b[39m | \u001b[39m0.3464   \u001b[39m | \u001b[39m4.9987136\u001b[39m | \u001b[39m-2       \u001b[39m |\n",
+      "| \u001b[39m10       \u001b[39m | \u001b[39m-0.7852  \u001b[39m | \u001b[39m4.9892216\u001b[39m | \u001b[39m-5       \u001b[39m |\n",
+      "| \u001b[39m11       \u001b[39m | \u001b[39m-0.6627  \u001b[39m | \u001b[39m-4.999635\u001b[39m | \u001b[39m-4       \u001b[39m |\n",
+      "| \u001b[39m12       \u001b[39m | \u001b[39m-0.1697  \u001b[39m | \u001b[39m-4.992664\u001b[39m | \u001b[39m-3       \u001b[39m |\n",
+      "| \u001b[35m13       \u001b[39m | \u001b[35m1.428    \u001b[39m | \u001b[35m4.9950290\u001b[39m | \u001b[35m3        \u001b[39m |\n",
+      "| \u001b[39m14       \u001b[39m | \u001b[39m1.137    \u001b[39m | \u001b[39m4.9970984\u001b[39m | \u001b[39m4        \u001b[39m |\n",
+      "| \u001b[35m15       \u001b[39m | \u001b[35m1.641    \u001b[39m | \u001b[35m4.0889271\u001b[39m | \u001b[35m3        \u001b[39m |\n",
+      "=================================================\n",
+      "Max: 1.6407143853831352\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "for lbl, optimizer in zip(labels, [continuous_optimizer, discrete_optimizer]):\n",
+    "    print(f\"====================  {lbl}  ====================\\n\")\n",
+    "    optimizer.maximize(\n",
+    "        init_points=2,\n",
+    "        n_iter=13\n",
+    "    )\n",
+    "    print(f\"Max: {optimizer.max['target']}\\n\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.linspace(c_pbounds['x'][0], c_pbounds['x'][1], 1000)\n",
+    "y = np.linspace(c_pbounds['y'][0], c_pbounds['y'][1], 1000)\n",
+    "\n",
+    "X, Y = np.meshgrid(x, y)\n",
+    "\n",
+    "Z = discretized_function(X, Y)\n",
+    "\n",
+    "params = [{'x': x_i, 'y': y_j} for y_j in y for x_i in x]\n",
+    "array_params = [continuous_optimizer._space.params_to_array(p) for p in params]\n",
+    "c_pred = continuous_optimizer._gp.predict(array_params).reshape(X.shape)\n",
+    "d_pred = discrete_optimizer._gp.predict(array_params).reshape(X.shape)\n",
+    "\n",
+    "vmin = np.min([np.min(Z), np.min(c_pred), np.min(d_pred)])\n",
+    "vmax = np.max([np.max(Z), np.max(c_pred), np.max(d_pred)])\n",
+    "\n",
+    "fig, axs = plt.subplots(1, 3)\n",
+    "\n",
+    "axs[0].set_title('Actual function')\n",
+    "axs[0].contourf(X, Y, Z, cmap=plt.cm.coolwarm, vmin=vmin, vmax=vmax)\n",
+    "\n",
+    "\n",
+    "axs[1].set_title(labels[0])\n",
+    "axs[1].contourf(X, Y, c_pred, cmap=plt.cm.coolwarm, vmin=vmin, vmax=vmax)\n",
+    "axs[1].scatter(continuous_optimizer._space.params[:,0], continuous_optimizer._space.params[:,1], c='k')\n",
+    "\n",
+    "axs[2].set_title(labels[1])\n",
+    "axs[2].contourf(X, Y, d_pred, cmap=plt.cm.coolwarm, vmin=vmin, vmax=vmax)\n",
+    "axs[2].scatter(discrete_optimizer._space.params[:,0], discrete_optimizer._space.params[:,1], c='k')\n",
+    "\n",
+    "def make_plot_fancy(ax: plt.Axes):\n",
+    "    ax.set_aspect(\"equal\")\n",
+    "    ax.set_xlabel('x (float)')\n",
+    "    ax.set_xticks([-5.0, -2.5, 0., 2.5, 5.0])\n",
+    "    ax.set_ylabel('y (int)')\n",
+    "    ax.set_yticks([-4, -2, 0, 2, 4])\n",
+    "\n",
+    "for ax in axs:\n",
+    "    make_plot_fancy(ax)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Categorical variables\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also handle categorical variables! This is done under-the-hood by constructing parameters in a one-hot-encoding representation, with a transformation in the kernel rounding to the nearest one-hot representation. If you want to use this, you can specify a collection of strings as options.\n",
+    "\n",
+    "NB: As internally, the categorical variables are within a range of `[0, 1]` and the GP used for BO is by default isotropic, you might want to ensure your other features are similarly scaled to a range of `[0, 1]` or use an anisotropic GP."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def f1(x1, x2):\n",
+    "    return -1*(x1 - np.sqrt(x1**2 + x2**2) * np.cos(np.sqrt(x1**2 + x2**2))**2 + 0.5 * np.sqrt(x1**2 + x2**2))\n",
+    "\n",
+    "def f2(x1, x2):\n",
+    "    return -1*(x2 - np.sqrt(x1**2 + x2**2) * np.sin(np.sqrt(x1**2 + x2**2))**2 + 0.5 * np.sqrt(x1**2 + x2**2))\n",
+    "\n",
+    "def SPIRAL(x1, x2, k):\n",
+    "    \"\"\"cf Ladislav-Luksan\n",
+    "    \"\"\"\n",
+    "    if k=='1':\n",
+    "        return f1(10 * x1, 10 * x2)\n",
+    "    elif k=='2':\n",
+    "        return f2(10 * x1, 10 * x2)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "|   iter    |  target   |    x1     |    x2     |     k     |\n",
+      "-------------------------------------------------------------\n",
+      "| \u001b[39m1        \u001b[39m | \u001b[39m-2.052   \u001b[39m | \u001b[39m-0.165955\u001b[39m | \u001b[39m0.4406489\u001b[39m | \u001b[39m2        \u001b[39m |\n",
+      "| \u001b[35m2        \u001b[39m | \u001b[35m13.49    \u001b[39m | \u001b[35m-0.743751\u001b[39m | \u001b[35m0.9980810\u001b[39m | \u001b[35m1        \u001b[39m |\n",
+      "| \u001b[39m3        \u001b[39m | \u001b[39m-14.49   \u001b[39m | \u001b[39m-0.743433\u001b[39m | \u001b[39m0.9709879\u001b[39m | \u001b[39m2        \u001b[39m |\n",
+      "| \u001b[39m4        \u001b[39m | \u001b[39m-13.33   \u001b[39m | \u001b[39m0.9950794\u001b[39m | \u001b[39m-0.352913\u001b[39m | \u001b[39m1        \u001b[39m |\n",
+      "| \u001b[39m5        \u001b[39m | \u001b[39m9.674    \u001b[39m | \u001b[39m0.5436849\u001b[39m | \u001b[39m-0.574376\u001b[39m | \u001b[39m2        \u001b[39m |\n",
+      "| \u001b[39m6        \u001b[39m | \u001b[39m9.498    \u001b[39m | \u001b[39m-0.218693\u001b[39m | \u001b[39m-0.709177\u001b[39m | \u001b[39m2        \u001b[39m |\n",
+      "| \u001b[39m7        \u001b[39m | \u001b[39m11.43    \u001b[39m | \u001b[39m-0.918642\u001b[39m | \u001b[39m-0.648372\u001b[39m | \u001b[39m2        \u001b[39m |\n",
+      "| \u001b[39m8        \u001b[39m | \u001b[39m0.4882   \u001b[39m | \u001b[39m-0.218182\u001b[39m | \u001b[39m-0.012177\u001b[39m | \u001b[39m2        \u001b[39m |\n",
+      "| \u001b[39m9        \u001b[39m | \u001b[39m7.542    \u001b[39m | \u001b[39m-0.787692\u001b[39m | \u001b[39m0.3452580\u001b[39m | \u001b[39m1        \u001b[39m |\n",
+      "| \u001b[39m10       \u001b[39m | \u001b[39m-2.161   \u001b[39m | \u001b[39m0.1392349\u001b[39m | \u001b[39m-0.125728\u001b[39m | \u001b[39m1        \u001b[39m |\n",
+      "| \u001b[39m11       \u001b[39m | \u001b[39m-0.8336  \u001b[39m | \u001b[39m0.1206357\u001b[39m | \u001b[39m-0.543264\u001b[39m | \u001b[39m1        \u001b[39m |\n",
+      "| \u001b[39m12       \u001b[39m | \u001b[39m-8.413   \u001b[39m | \u001b[39m0.4981209\u001b[39m | \u001b[39m0.6434939\u001b[39m | \u001b[39m1        \u001b[39m |\n",
+      "| \u001b[39m13       \u001b[39m | \u001b[39m6.372    \u001b[39m | \u001b[39m0.0587256\u001b[39m | \u001b[39m-0.892371\u001b[39m | \u001b[39m2        \u001b[39m |\n",
+      "| \u001b[39m14       \u001b[39m | \u001b[39m-12.71   \u001b[39m | \u001b[39m0.7529885\u001b[39m | \u001b[39m-0.780621\u001b[39m | \u001b[39m1        \u001b[39m |\n",
+      "| \u001b[39m15       \u001b[39m | \u001b[39m-1.521   \u001b[39m | \u001b[39m0.4118274\u001b[39m | \u001b[39m-0.517960\u001b[39m | \u001b[39m1        \u001b[39m |\n",
+      "| \u001b[39m16       \u001b[39m | \u001b[39m11.88    \u001b[39m | \u001b[39m-0.755390\u001b[39m | \u001b[39m-0.533137\u001b[39m | \u001b[39m1        \u001b[39m |\n",
+      "| \u001b[39m17       \u001b[39m | \u001b[39m0.6373   \u001b[39m | \u001b[39m0.2249733\u001b[39m | \u001b[39m-0.053787\u001b[39m | \u001b[39m2        \u001b[39m |\n",
+      "| \u001b[39m18       \u001b[39m | \u001b[39m2.154    \u001b[39m | \u001b[39m0.0583506\u001b[39m | \u001b[39m0.6550869\u001b[39m | \u001b[39m1        \u001b[39m |\n",
+      "| \u001b[35m19       \u001b[39m | \u001b[35m13.69    \u001b[39m | \u001b[35m-0.741717\u001b[39m | \u001b[35m-0.820073\u001b[39m | \u001b[35m2        \u001b[39m |\n",
+      "| \u001b[39m20       \u001b[39m | \u001b[39m1.615    \u001b[39m | \u001b[39m-0.663312\u001b[39m | \u001b[39m-0.905925\u001b[39m | \u001b[39m1        \u001b[39m |\n",
+      "=============================================================\n"
+     ]
+    }
+   ],
+   "source": [
+    "pbounds = {'x1': (-1, 1), 'x2': (-1, 1), 'k': ('1', '2')}\n",
+    "\n",
+    "categorical_optimizer = BayesianOptimization(\n",
+    "    f=SPIRAL,\n",
+    "    acquisition_function=acquisition.ExpectedImprovement(1e-2),\n",
+    "    pbounds=pbounds,\n",
+    "    verbose=2,\n",
+    "    random_state=1,\n",
+    ")\n",
+    "discrete_optimizer.set_gp_params(alpha=1e-3)\n",
+    "\n",
+    "categorical_optimizer.maximize(\n",
+    "        init_points=2,\n",
+    "        n_iter=18,\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "res = categorical_optimizer._space.res()\n",
+    "k1 = np.array([[p['params']['x1'], p['params']['x2']] for p in res if p['params']['k']=='1'])\n",
+    "k2 = np.array([[p['params']['x1'], p['params']['x2']] for p in res if p['params']['k']=='2'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x1 = np.linspace(pbounds['x1'][0], pbounds['x1'][1], 1000)\n",
+    "x2 = np.linspace(pbounds['x2'][0], pbounds['x2'][1], 1000)\n",
+    "\n",
+    "X1, X2 = np.meshgrid(x1, x2)\n",
+    "Z1 = SPIRAL(X1, X2, '1')\n",
+    "Z2 = SPIRAL(X1, X2, '2')\n",
+    "\n",
+    "fig, axs = plt.subplots(1, 2)\n",
+    "\n",
+    "vmin = np.min([np.min(Z1), np.min(Z2)])\n",
+    "vmax = np.max([np.max(Z1), np.max(Z2)])\n",
+    "\n",
+    "axs[0].contourf(X1, X2, Z1, vmin=vmin, vmax=vmax)\n",
+    "axs[0].set_aspect(\"equal\")\n",
+    "axs[0].scatter(k1[:,0], k1[:,1], c='k')\n",
+    "axs[1].contourf(X1, X2, Z2, vmin=vmin, vmax=vmax)\n",
+    "axs[1].scatter(k2[:,0], k2[:,1], c='k')\n",
+    "axs[1].set_aspect(\"equal\")\n",
+    "axs[0].set_title('k=1')\n",
+    "axs[1].set_title('k=2')\n",
+    "fig.tight_layout()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Use in ML"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A typical usecase for integer and categorical parameters is optimizing the hyperparameters of a machine learning model. Below you can find an example where the hyperparameters of an SVM are optimized."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "|   iter    |  target   |  kernel   |  log10_C  |\n",
+      "-------------------------------------------------\n",
+      "| \u001b[39m1        \u001b[39m | \u001b[39m-0.2361  \u001b[39m | \u001b[39mpoly2    \u001b[39m | \u001b[39m0.9943696\u001b[39m |\n",
+      "| \u001b[39m2        \u001b[39m | \u001b[39m-0.2864  \u001b[39m | \u001b[39mrbf      \u001b[39m | \u001b[39m-0.999771\u001b[39m |\n",
+      "| \u001b[39m3        \u001b[39m | \u001b[39m-0.2625  \u001b[39m | \u001b[39mpoly3    \u001b[39m | \u001b[39m0.7449728\u001b[39m |\n",
+      "| \u001b[35m4        \u001b[39m | \u001b[35m-0.2361  \u001b[39m | \u001b[35mpoly2    \u001b[39m | \u001b[35m0.9944598\u001b[39m |\n",
+      "| \u001b[39m5        \u001b[39m | \u001b[39m-0.298   \u001b[39m | \u001b[39mpoly3    \u001b[39m | \u001b[39m-0.999625\u001b[39m |\n",
+      "| \u001b[35m6        \u001b[39m | \u001b[35m-0.2361  \u001b[39m | \u001b[35mpoly2    \u001b[39m | \u001b[35m0.9945010\u001b[39m |\n",
+      "| \u001b[35m7        \u001b[39m | \u001b[35m-0.2152  \u001b[39m | \u001b[35mrbf      \u001b[39m | \u001b[35m0.9928960\u001b[39m |\n",
+      "| \u001b[39m8        \u001b[39m | \u001b[39m-0.2153  \u001b[39m | \u001b[39mrbf      \u001b[39m | \u001b[39m0.9917667\u001b[39m |\n",
+      "| \u001b[39m9        \u001b[39m | \u001b[39m-0.2362  \u001b[39m | \u001b[39mpoly2    \u001b[39m | \u001b[39m0.9897298\u001b[39m |\n",
+      "| \u001b[39m10       \u001b[39m | \u001b[39m-0.2362  \u001b[39m | \u001b[39mpoly2    \u001b[39m | \u001b[39m0.9874217\u001b[39m |\n",
+      "=================================================\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import load_breast_cancer\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.metrics import log_loss\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from bayes_opt import BayesianOptimization\n",
+    "\n",
+    "data = load_breast_cancer()\n",
+    "X_train, y_train = data['data'], data['target']\n",
+    "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2, random_state=1)\n",
+    "kernels = ['rbf', 'poly']\n",
+    "\n",
+    "def f_target(kernel, log10_C):\n",
+    "    if kernel == 'poly2':\n",
+    "        kernel = 'poly'\n",
+    "        degree = 2\n",
+    "    elif kernel == 'poly3':\n",
+    "        kernel = 'poly'\n",
+    "        degree = 3\n",
+    "    elif kernel == 'rbf':\n",
+    "        degree = 3 # not used, equal to default\n",
+    "\n",
+    "    C = 10**log10_C\n",
+    "\n",
+    "    model = SVC(C=C, kernel=kernel, degree=degree, probability=True, random_state=1)\n",
+    "    model.fit(X_train, y_train)\n",
+    "\n",
+    "    # Package looks for maximum, so we return -1 * log_loss\n",
+    "    loss = -1 * log_loss(y_val, model.predict_proba(X_val))\n",
+    "    return loss\n",
+    "\n",
+    "\n",
+    "params_svm ={\n",
+    "    'kernel': ['rbf', 'poly2', 'poly3'],\n",
+    "    'log10_C':(-1, +1),\n",
+    "}\n",
+    "\n",
+    "optimizer = BayesianOptimization(\n",
+    "    f_target,\n",
+    "    params_svm,\n",
+    "    random_state=1,\n",
+    "    verbose=2\n",
+    ")\n",
+    "\n",
+    "kernel = Matern(nu=2.5, length_scale=np.ones(optimizer.space.dim))\n",
+    "discrete_optimizer.set_gp_params(kernel=kernel)\n",
+    "optimizer.maximize(init_points=2, n_iter=8)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. Defining your own Parameter\n",
+    "\n",
+    "Maybe you want to optimize over another form of parameters, which does not align with `float`, `int` or categorical. For this purpose, you can create your own, custom parameter. A simple example is a parameter that is discrete, but still admits a distance representation (like an integer) while not being uniformly spaced.\n",
+    "\n",
+    "However, you can go further even and encode constraints and even symmetries in your parameter. Let's consider the problem of finding a triangle which maximizes an area given its sides $a, b, c$ with a constraint that the perimeter is fixed, i.e. $a + b + c=s$.\n",
+    "\n",
+    "We will create a parameter that encodes such a triangle, and via it's kernel transform ensures that the sides sum to the required length $s$. As you might expect, the solution to this problem is an equilateral triangle, i.e. $a=b=c=s/3$.\n",
+    "\n",
+    "To define the parameter, we need to subclass `BayesParameter` and define a few important functions/properties.\n",
+    "\n",
+    "- `is_continuous` is a property which denotes whether a parameter is continuous. When optimizing the acquisition function, non-continuous parameters will not be optimized using gradient-based methods, but only via random sampling.\n",
+    "- `random_sample` is a function that samples randomly from the space of the parameter.\n",
+    "- `to_float` transforms the canonical representation of a parameter into float values for the target space to store. There is a one-to-one correspondence between valid float representations produced by this function and canonical representations of the parameter. This function is most important when working with parameters that use a non-numeric canonical representation, such as categorical parameters.\n",
+    "- `to_param` performs the inverse of `to_float`: Given a float-based representation, it creates a canonical representation. This function should perform binning whenever appropriate, e.g. in the case of the `IntParameter`, this function would round any float values supplied to it.\n",
+    "- `kernel_transform` is the most important function of the Parameter and defines how to represent a value in the kernel space. In contrast to `to_float`, this function expects both the input, as well as the output to be float-representations of the value.\n",
+    "- `to_string` produces a stringified version of the parameter, which allows users to define custom pretty-print rules for ththe ScreenLogger use.\n",
+    "- `dim` is a property which defines the dimensionality of the parameter. In most cases, this will be 1, but e.g. for categorical parameters it is equivalent to the cardinality of the category space.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from bayes_opt.logger import ScreenLogger\n",
+    "from bayes_opt.parameter import BayesParameter\n",
+    "from bayes_opt.event import Events\n",
+    "from bayes_opt.util import ensure_rng\n",
+    "\n",
+    "\n",
+    "class FixedPerimeterTriangleParameter(BayesParameter):\n",
+    "    def __init__(self, name: str, bounds, perimeter) -> None:\n",
+    "        super().__init__(name, bounds)\n",
+    "        self.perimeter = perimeter\n",
+    "\n",
+    "    @property\n",
+    "    def is_continuous(self):\n",
+    "        return True\n",
+    "    \n",
+    "    def random_sample(self, n_samples: int, random_state):\n",
+    "        random_state = ensure_rng(random_state)\n",
+    "        samples = []\n",
+    "        while len(samples) < n_samples:\n",
+    "            samples_ = random_state.dirichlet(np.ones(3), n_samples)\n",
+    "            samples_ = samples_ * self.perimeter # scale samples by perimeter\n",
+    "\n",
+    "            samples_ = samples_[np.all((self.bounds[:, 0] <= samples_) & (samples_ <= self.bounds[:, 1]), axis=-1)]\n",
+    "            samples.extend(np.atleast_2d(samples_))\n",
+    "        samples = np.array(samples[:n_samples])\n",
+    "        return samples\n",
+    "    \n",
+    "    def to_float(self, value):\n",
+    "        return value\n",
+    "    \n",
+    "    def to_param(self, value):\n",
+    "        return value * self.perimeter / sum(value)\n",
+    "\n",
+    "    def kernel_transform(self, value):\n",
+    "        return value * self.perimeter / np.sum(value, axis=-1, keepdims=True)\n",
+    "\n",
+    "    def to_string(self, value, str_len: int) -> str:\n",
+    "        len_each = (str_len - 2) // 3\n",
+    "        str_ = '|'.join([f\"{float(np.round(value[i], 4))}\"[:len_each] for i in range(3)])\n",
+    "        return str_.ljust(str_len)\n",
+    "\n",
+    "    @property\n",
+    "    def dim(self):\n",
+    "        return 3 # as we have three float values, each representing the length of one side.\n",
+    "\n",
+    "def area_of_triangle(sides):\n",
+    "    a, b, c = sides\n",
+    "    s = np.sum(sides, axis=-1) # perimeter\n",
+    "    A = np.sqrt(s * (s-a) * (s-b) * (s-c))\n",
+    "    return A\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "|      iter       |     target      |      sides      |\n",
+      "-------------------------------------------------------\n",
+      "| \u001b[39m1              \u001b[39m | \u001b[39m0.4572         \u001b[39m | \u001b[39m0.29|0.70|0.00 \u001b[39m |\n",
+      "| \u001b[35m2              \u001b[39m | \u001b[35m0.5096         \u001b[39m | \u001b[35m0.58|0.25|0.15 \u001b[39m |\n",
+      "| \u001b[39m3              \u001b[39m | \u001b[39m0.5081         \u001b[39m | \u001b[39m0.58|0.25|0.15 \u001b[39m |\n",
+      "| \u001b[35m4              \u001b[39m | \u001b[35m0.5386         \u001b[39m | \u001b[35m0.44|0.28|0.26 \u001b[39m |\n",
+      "| \u001b[39m5              \u001b[39m | \u001b[39m0.5279         \u001b[39m | \u001b[39m0.38|0.14|0.47 \u001b[39m |\n",
+      "| \u001b[39m6              \u001b[39m | \u001b[39m0.5328         \u001b[39m | \u001b[39m0.18|0.36|0.45 \u001b[39m |\n",
+      "| \u001b[39m7              \u001b[39m | \u001b[39m0.4366         \u001b[39m | \u001b[39m0.02|0.22|0.74 \u001b[39m |\n",
+      "| \u001b[39m8              \u001b[39m | \u001b[39m0.4868         \u001b[39m | \u001b[39m0.00|0.61|0.37 \u001b[39m |\n",
+      "| \u001b[39m9              \u001b[39m | \u001b[39m0.4977         \u001b[39m | \u001b[39m0.56|0.01|0.42 \u001b[39m |\n",
+      "| \u001b[35m10             \u001b[39m | \u001b[35m0.5418         \u001b[39m | \u001b[35m0.29|0.40|0.30 \u001b[39m |\n",
+      "| \u001b[39m11             \u001b[39m | \u001b[39m0.3361         \u001b[39m | \u001b[39m0.06|0.87|0.06 \u001b[39m |\n",
+      "| \u001b[39m12             \u001b[39m | \u001b[39m0.06468        \u001b[39m | \u001b[39m0.99|0.00|0.00 \u001b[39m |\n",
+      "| \u001b[39m13             \u001b[39m | \u001b[39m0.01589        \u001b[39m | \u001b[39m0.0|0.00|0.99  \u001b[39m |\n",
+      "| \u001b[39m14             \u001b[39m | \u001b[39m0.4999         \u001b[39m | \u001b[39m0.21|0.16|0.61 \u001b[39m |\n",
+      "| \u001b[39m15             \u001b[39m | \u001b[39m0.499          \u001b[39m | \u001b[39m0.53|0.46|0.00 \u001b[39m |\n",
+      "| \u001b[39m16             \u001b[39m | \u001b[39m0.4937         \u001b[39m | \u001b[39m0.00|0.41|0.58 \u001b[39m |\n",
+      "| \u001b[39m17             \u001b[39m | \u001b[39m0.5233         \u001b[39m | \u001b[39m0.33|0.51|0.14 \u001b[39m |\n",
+      "| \u001b[39m18             \u001b[39m | \u001b[39m0.5204         \u001b[39m | \u001b[39m0.17|0.54|0.28 \u001b[39m |\n",
+      "| \u001b[39m19             \u001b[39m | \u001b[39m0.5235         \u001b[39m | \u001b[39m0.51|0.15|0.32 \u001b[39m |\n",
+      "| \u001b[39m20             \u001b[39m | \u001b[39m0.5412         \u001b[39m | \u001b[39m0.31|0.27|0.41 \u001b[39m |\n",
+      "| \u001b[39m21             \u001b[39m | \u001b[39m0.4946         \u001b[39m | \u001b[39m0.41|0.00|0.57 \u001b[39m |\n",
+      "| \u001b[39m22             \u001b[39m | \u001b[39m0.5355         \u001b[39m | \u001b[39m0.41|0.39|0.19 \u001b[39m |\n",
+      "| \u001b[35m23             \u001b[39m | \u001b[35m0.5442         \u001b[39m | \u001b[35m0.35|0.32|0.32 \u001b[39m |\n",
+      "| \u001b[39m24             \u001b[39m | \u001b[39m0.5192         \u001b[39m | \u001b[39m0.16|0.28|0.54 \u001b[39m |\n",
+      "| \u001b[39m25             \u001b[39m | \u001b[39m0.5401         \u001b[39m | \u001b[39m0.39|0.23|0.36 \u001b[39m |\n",
+      "=======================================================\n"
+     ]
+    }
+   ],
+   "source": [
+    "param = FixedPerimeterTriangleParameter(\n",
+    "    name='sides',\n",
+    "    bounds=np.array([[0., 1.], [0., 1.], [0., 1.]]),\n",
+    "    perimeter=1.\n",
+    ")\n",
+    "\n",
+    "pbounds = {'sides': param}\n",
+    "optimizer = BayesianOptimization(\n",
+    "    area_of_triangle,\n",
+    "    pbounds,\n",
+    "    random_state=1,\n",
+    ")\n",
+    "\n",
+    "# Increase the cell size to accommodate the three float values\n",
+    "logger = ScreenLogger(verbose=2, is_constrained=False)\n",
+    "logger._default_cell_size = 15\n",
+    "\n",
+    "for e in [Events.OPTIMIZATION_START, Events.OPTIMIZATION_STEP, Events.OPTIMIZATION_END]:\n",
+    "    optimizer.subscribe(e, logger)\n",
+    "\n",
+    "optimizer.maximize(init_points=2, n_iter=23)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This seems to work decently well, but we can improve it significantly if we consider the symmetries inherent in the problem: This problem is permutation invariant, i.e. we do not care which side specifically is denoted as $a$, $b$ or $c$. Instead, we can, without loss of generality, decide that the shortest side will always be denoted as $a$, and the longest always as $c$. If we enhance our kernel transform with this symmetry, the performance improves significantly. This can be easily done by sub-classing the previously created triangle parameter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "|      iter       |     target      |      sides      |\n",
+      "-------------------------------------------------------\n",
+      "| \u001b[39m1              \u001b[39m | \u001b[39m0.4572         \u001b[39m | \u001b[39m0.00|0.29|0.70 \u001b[39m |\n",
+      "| \u001b[35m2              \u001b[39m | \u001b[35m0.5096         \u001b[39m | \u001b[35m0.15|0.25|0.58 \u001b[39m |\n",
+      "| \u001b[39m3              \u001b[39m | \u001b[39m0.498          \u001b[39m | \u001b[39m0.06|0.33|0.60 \u001b[39m |\n",
+      "| \u001b[35m4              \u001b[39m | \u001b[35m0.5097         \u001b[39m | \u001b[35m0.13|0.27|0.58 \u001b[39m |\n",
+      "| \u001b[35m5              \u001b[39m | \u001b[35m0.5358         \u001b[39m | \u001b[35m0.19|0.36|0.43 \u001b[39m |\n",
+      "| \u001b[35m6              \u001b[39m | \u001b[35m0.5443         \u001b[39m | \u001b[35m0.33|0.33|0.33 \u001b[39m |\n",
+      "| \u001b[39m7              \u001b[39m | \u001b[39m0.5405         \u001b[39m | \u001b[39m0.28|0.28|0.42 \u001b[39m |\n",
+      "| \u001b[39m8              \u001b[39m | \u001b[39m0.5034         \u001b[39m | \u001b[39m0.01|0.49|0.49 \u001b[39m |\n",
+      "| \u001b[39m9              \u001b[39m | \u001b[39m0.4977         \u001b[39m | \u001b[39m0.01|0.42|0.56 \u001b[39m |\n",
+      "| \u001b[39m10             \u001b[39m | \u001b[39m0.5427         \u001b[39m | \u001b[39m0.27|0.36|0.36 \u001b[39m |\n",
+      "=======================================================\n"
+     ]
+    }
+   ],
+   "source": [
+    "class SortingFixedPerimeterTriangleParameter(FixedPerimeterTriangleParameter):\n",
+    "    def __init__(self, name: str, bounds, perimeter) -> None:\n",
+    "        super().__init__(name, bounds, perimeter)\n",
+    "\n",
+    "    def to_param(self, value):\n",
+    "        value = np.sort(value, axis=-1)\n",
+    "        return super().to_param(value)\n",
+    "\n",
+    "    def kernel_transform(self, value):\n",
+    "        value = np.sort(value, axis=-1)\n",
+    "        return super().kernel_transform(value)\n",
+    "\n",
+    "param = SortingFixedPerimeterTriangleParameter(\n",
+    "    name='sides',\n",
+    "    bounds=np.array([[0., 1.], [0., 1.], [0., 1.]]),\n",
+    "    perimeter=1.\n",
+    ")\n",
+    "\n",
+    "pbounds = {'sides': param}\n",
+    "optimizer = BayesianOptimization(\n",
+    "    area_of_triangle,\n",
+    "    pbounds,\n",
+    "    random_state=1,\n",
+    ")\n",
+    "\n",
+    "logger = ScreenLogger(verbose=2, is_constrained=False)\n",
+    "logger._default_cell_size = 15\n",
+    "\n",
+    "for e in [Events.OPTIMIZATION_START, Events.OPTIMIZATION_STEP, Events.OPTIMIZATION_END]:\n",
+    "    optimizer.subscribe(e, logger)\n",
+    "\n",
+    "optimizer.maximize(init_points=2, n_iter=8)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "bayesian-optimization-tb9vsVm6-py3.9",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/master/_sources/reference/parameter.rst.txt b/master/_sources/reference/parameter.rst.txt
new file mode 100644
index 00000000..91b8f2e9
--- /dev/null
+++ b/master/_sources/reference/parameter.rst.txt
@@ -0,0 +1,5 @@
+:py:mod:`bayes_opt.parameter`
+--------------------------------
+
+.. automodule:: bayes_opt.parameter
+   :members: 
diff --git a/master/acquisition_functions.html b/master/acquisition_functions.html
index 997614cf..d7f1ccca 100644
--- a/master/acquisition_functions.html
+++ b/master/acquisition_functions.html
@@ -321,6 +321,21 @@
   
   
   
+    <li class="md-nav__item">
+      <a href="parameter_types.html" class="md-nav__link">
+        <span title="/parameter_types.ipynb (reference label)" class="md-ellipsis">Parameter Types</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
     <li class="md-nav__item">
       <a href="domain_reduction.html" class="md-nav__link">
         <span title="/domain_reduction.ipynb (reference label)" class="md-ellipsis">Sequential Domain Reduction</span>
@@ -500,6 +515,21 @@
   
   
   
+    <li class="md-nav__item">
+      <a href="reference/parameter.html" class="md-nav__link">
+        <span title="/reference/parameter.rst (reference label)" class="md-ellipsis">bayes_<wbr>opt.<wbr>parameter</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
     <li class="md-nav__item">
       <a href="reference/exception.html" class="md-nav__link">
         <span title="/reference/exception.rst (reference label)" class="md-ellipsis">bayes_<wbr>opt.<wbr>exception</span>
@@ -698,8 +728,12 @@ <h1 id="Acquisition-functions">Acquisition functions<a class="headerlink" href="
 </div>
 </div>
 <p>As we can see, the greedy policy is <em>too</em> greedy and does not explore enough to find the maximum.</p>
-<p>What if our acquisition function does not depend solely on <code class="docutils literal notranslate"><span class="pre">mean</span></code> and <code class="docutils literal notranslate"><span class="pre">std</span></code>? In that case, we can intervene at a deeper level, and overwrite the <code class="docutils literal notranslate"><span class="pre">._get_acq</span></code> function. The built-in version of <code class="docutils literal notranslate"><span class="pre">._get_acq</span></code> is a higher-order function which returns a function accepting an array-like <code class="docutils literal notranslate"><span class="pre">x</span></code> and performing the following: 1. Evaluate the GP mean <span class="math notranslate nohighlight">\(\mu\)</span> and std <span class="math notranslate nohighlight">\(\sigma\)</span> at points <code class="docutils literal notranslate"><span class="pre">x</span></code>. 2. If applicapable, evaluate the constraint fulfilment probability <span class="math notranslate nohighlight">\(p_c\)</span> at x. 3. Return
-<code class="docutils literal notranslate"><span class="pre">-1</span> <span class="pre">*</span> <span class="pre">base_acq(mean,</span> <span class="pre">std)</span></code> or <code class="docutils literal notranslate"><span class="pre">-1</span> <span class="pre">*</span> <span class="pre">base_acq(mean,</span> <span class="pre">std)</span> <span class="pre">*</span> <span class="pre">p_c</span></code></p>
+<p>What if our acquisition function does not depend solely on <code class="docutils literal notranslate"><span class="pre">mean</span></code> and <code class="docutils literal notranslate"><span class="pre">std</span></code>? In that case, we can intervene at a deeper level, and overwrite the <code class="docutils literal notranslate"><span class="pre">._get_acq</span></code> function. The built-in version of <code class="docutils literal notranslate"><span class="pre">._get_acq</span></code> is a higher-order function which returns a function accepting an array-like <code class="docutils literal notranslate"><span class="pre">x</span></code> and performing the following:</p>
+<ol class="arabic simple">
+<li><p>Evaluate the GP mean <span class="math notranslate nohighlight">\(\mu\)</span> and std <span class="math notranslate nohighlight">\(\sigma\)</span> at points <code class="docutils literal notranslate"><span class="pre">x</span></code>.</p></li>
+<li><p>If applicapable, evaluate the constraint fulfilment probability <span class="math notranslate nohighlight">\(p_c\)</span> at x.</p></li>
+<li><p>Return <code class="docutils literal notranslate"><span class="pre">-1</span> <span class="pre">*</span> <span class="pre">base_acq(mean,</span> <span class="pre">std)</span></code> or <code class="docutils literal notranslate"><span class="pre">-1</span> <span class="pre">*</span> <span class="pre">base_acq(mean,</span> <span class="pre">std)</span> <span class="pre">*</span> <span class="pre">p_c</span></code></p></li>
+</ol>
 <p>An example of such an acquisition function is Thompson Sampling. If you consider a Gaussian Process as a prior over functions, Thompson Sampling works by sampling a function from this prior and then selecting the argmax as next point of interest. This is a somewhat noisy version of the greedy acquisition, which will encourage some exploration.</p>
 <p>While we usually find the argmax of the acquisition function through a combination of random sampling and gradient-based optimization, we will skip the gradient-based optimization here, as it is quite expensive and would require us to fix the multivariate normal. We can do this by additionally overwriting <code class="docutils literal notranslate"><span class="pre">.suggest</span></code>, specifically by changing the default argument of <code class="docutils literal notranslate"><span class="pre">n_l_bfgs_b</span></code> to <code class="docutils literal notranslate"><span class="pre">0</span></code>.</p>
 <div class="nbinput nblast docutils container">
diff --git a/master/advanced-tour.html b/master/advanced-tour.html
index 060b2aad..e679801f 100644
--- a/master/advanced-tour.html
+++ b/master/advanced-tour.html
@@ -350,37 +350,30 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#2.-Dealing-with-discrete-parameters" class="md-nav__link">
-    <span title="/advanced-tour.ipynb#2.-dealing-with-discrete-parameters (reference label)" class="md-ellipsis">2.<wbr> Dealing with discrete parameters</span>
+  <a href="#2.-Tuning-the-underlying-Gaussian-Process" class="md-nav__link">
+    <span title="/advanced-tour.ipynb#2.-tuning-the-underlying-gaussian-process (reference label)" class="md-ellipsis">2.<wbr> Tuning the underlying Gaussian Process</span>
   </a>
   
-</li>
-      
-        <li class="md-nav__item">
-  <a href="#3.-Tuning-the-underlying-Gaussian-Process" class="md-nav__link">
-    <span title="/advanced-tour.ipynb#3.-tuning-the-underlying-gaussian-process (reference label)" class="md-ellipsis">3.<wbr> Tuning the underlying Gaussian Process</span>
-  </a>
-  
-    <nav class="md-nav" aria-label="3. Tuning the underlying Gaussian Process">
+    <nav class="md-nav" aria-label="2. Tuning the underlying Gaussian Process">
       <ul class="md-nav__list">
         
           <li class="md-nav__item">
-  <a href="#3.1-Passing-parameter-to-the-GP" class="md-nav__link">
-    <span title="/advanced-tour.ipynb#3.1-passing-parameter-to-the-gp (reference label)" class="md-ellipsis">3.<wbr>1 Passing parameter to the GP</span>
+  <a href="#2.1-Passing-parameter-to-the-GP" class="md-nav__link">
+    <span title="/advanced-tour.ipynb#2.1-passing-parameter-to-the-gp (reference label)" class="md-ellipsis">2.<wbr>1 Passing parameter to the GP</span>
   </a>
   
 </li>
         
           <li class="md-nav__item">
-  <a href="#3.2-Tuning-the-alpha-parameter" class="md-nav__link">
-    <span title="/advanced-tour.ipynb#3.2-tuning-the-alpha-parameter (reference label)" class="md-ellipsis">3.<wbr>2 Tuning the alpha parameter</span>
+  <a href="#2.2-Tuning-the-alpha-parameter" class="md-nav__link">
+    <span title="/advanced-tour.ipynb#2.2-tuning-the-alpha-parameter (reference label)" class="md-ellipsis">2.<wbr>2 Tuning the alpha parameter</span>
   </a>
   
 </li>
         
           <li class="md-nav__item">
-  <a href="#3.3-Changing-kernels" class="md-nav__link">
-    <span title="/advanced-tour.ipynb#3.3-changing-kernels (reference label)" class="md-ellipsis">3.<wbr>3 Changing kernels</span>
+  <a href="#2.3-Changing-kernels" class="md-nav__link">
+    <span title="/advanced-tour.ipynb#2.3-changing-kernels (reference label)" class="md-ellipsis">2.<wbr>3 Changing kernels</span>
   </a>
   
 </li>
@@ -427,6 +420,21 @@
   
   
   
+    <li class="md-nav__item">
+      <a href="parameter_types.html" class="md-nav__link">
+        <span title="/parameter_types.ipynb (reference label)" class="md-ellipsis">Parameter Types</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
     <li class="md-nav__item">
       <a href="domain_reduction.html" class="md-nav__link">
         <span title="/domain_reduction.ipynb (reference label)" class="md-ellipsis">Sequential Domain Reduction</span>
@@ -598,6 +606,21 @@
   
   
   
+    <li class="md-nav__item">
+      <a href="reference/parameter.html" class="md-nav__link">
+        <span title="/reference/parameter.rst (reference label)" class="md-ellipsis">bayes_<wbr>opt.<wbr>parameter</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
     <li class="md-nav__item">
       <a href="reference/exception.html" class="md-nav__link">
         <span title="/reference/exception.rst (reference label)" class="md-ellipsis">bayes_<wbr>opt.<wbr>exception</span>
@@ -675,37 +698,30 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#2.-Dealing-with-discrete-parameters" class="md-nav__link">
-    <span title="/advanced-tour.ipynb#2.-dealing-with-discrete-parameters (reference label)" class="md-ellipsis">2.<wbr> Dealing with discrete parameters</span>
-  </a>
-  
-</li>
-      
-        <li class="md-nav__item">
-  <a href="#3.-Tuning-the-underlying-Gaussian-Process" class="md-nav__link">
-    <span title="/advanced-tour.ipynb#3.-tuning-the-underlying-gaussian-process (reference label)" class="md-ellipsis">3.<wbr> Tuning the underlying Gaussian Process</span>
+  <a href="#2.-Tuning-the-underlying-Gaussian-Process" class="md-nav__link">
+    <span title="/advanced-tour.ipynb#2.-tuning-the-underlying-gaussian-process (reference label)" class="md-ellipsis">2.<wbr> Tuning the underlying Gaussian Process</span>
   </a>
   
-    <nav class="md-nav" aria-label="3. Tuning the underlying Gaussian Process">
+    <nav class="md-nav" aria-label="2. Tuning the underlying Gaussian Process">
       <ul class="md-nav__list">
         
           <li class="md-nav__item">
-  <a href="#3.1-Passing-parameter-to-the-GP" class="md-nav__link">
-    <span title="/advanced-tour.ipynb#3.1-passing-parameter-to-the-gp (reference label)" class="md-ellipsis">3.<wbr>1 Passing parameter to the GP</span>
+  <a href="#2.1-Passing-parameter-to-the-GP" class="md-nav__link">
+    <span title="/advanced-tour.ipynb#2.1-passing-parameter-to-the-gp (reference label)" class="md-ellipsis">2.<wbr>1 Passing parameter to the GP</span>
   </a>
   
 </li>
         
           <li class="md-nav__item">
-  <a href="#3.2-Tuning-the-alpha-parameter" class="md-nav__link">
-    <span title="/advanced-tour.ipynb#3.2-tuning-the-alpha-parameter (reference label)" class="md-ellipsis">3.<wbr>2 Tuning the alpha parameter</span>
+  <a href="#2.2-Tuning-the-alpha-parameter" class="md-nav__link">
+    <span title="/advanced-tour.ipynb#2.2-tuning-the-alpha-parameter (reference label)" class="md-ellipsis">2.<wbr>2 Tuning the alpha parameter</span>
   </a>
   
 </li>
         
           <li class="md-nav__item">
-  <a href="#3.3-Changing-kernels" class="md-nav__link">
-    <span title="/advanced-tour.ipynb#3.3-changing-kernels (reference label)" class="md-ellipsis">3.<wbr>3 Changing kernels</span>
+  <a href="#2.3-Changing-kernels" class="md-nav__link">
+    <span title="/advanced-tour.ipynb#2.3-changing-kernels (reference label)" class="md-ellipsis">2.<wbr>3 Changing kernels</span>
   </a>
   
 </li>
@@ -805,7 +821,7 @@ <h2 id="1.-Suggest-Evaluate-Register-Paradigm">1. Suggest-Evaluate-Register Para
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-Next point to probe is: {&#39;x&#39;: -0.331911981189704, &#39;y&#39;: 1.3219469606529486}
+Next point to probe is: {&#39;x&#39;: np.float64(-0.331911981189704), &#39;y&#39;: np.float64(1.3219469606529486)}
 </pre></div></div>
 </div>
 <p>You are now free to evaluate your function at the suggested point however/whenever you like.</p>
@@ -859,107 +875,20 @@ <h3 id="1.1-The-maximize-loop">1.1 The maximize loop<a class="headerlink" href="
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
--18.503835804889988 {&#39;x&#39;: 1.953072105336, &#39;y&#39;: -2.9609778030491904}
--1.0819533157901717 {&#39;x&#39;: 0.22703572807626315, &#39;y&#39;: 2.4249238905875123}
--6.50219704520679 {&#39;x&#39;: -1.9991881984624875, &#39;y&#39;: 2.872282989383577}
--5.747604713731052 {&#39;x&#39;: -1.994467585936897, &#39;y&#39;: -0.664242699361514}
--2.9682431497650823 {&#39;x&#39;: 1.9737252084307952, &#39;y&#39;: 1.269540259274744}
-{&#39;target&#39;: 0.7861845912690544, &#39;params&#39;: {&#39;x&#39;: -0.331911981189704, &#39;y&#39;: 1.3219469606529486}}
+-18.707136686093495 {&#39;x&#39;: np.float64(1.9261486197444082), &#39;y&#39;: np.float64(-2.9996360060323246)}
+0.750594563473972 {&#39;x&#39;: np.float64(-0.3763326769822668), &#39;y&#39;: np.float64(1.328297354179696)}
+-6.559031075654336 {&#39;x&#39;: np.float64(1.979183535803597), &#39;y&#39;: np.float64(2.9083667381450318)}
+-6.915481333972961 {&#39;x&#39;: np.float64(-1.9686133847781613), &#39;y&#39;: np.float64(-1.009985740060171)}
+-6.8600832617014085 {&#39;x&#39;: np.float64(-1.9763198875239296), &#39;y&#39;: np.float64(2.9885278383464513)}
+{&#39;target&#39;: np.float64(0.7861845912690544), &#39;params&#39;: {&#39;x&#39;: np.float64(-0.331911981189704), &#39;y&#39;: np.float64(1.3219469606529486)}}
 </pre></div></div>
 </div>
-<h2 id="2.-Dealing-with-discrete-parameters">2. Dealing with discrete parameters<a class="headerlink" href="#2.-Dealing-with-discrete-parameters" title="Link to this heading">¶</a></h2>
-<p><strong>There is no principled way of dealing with discrete parameters using this package.</strong></p>
-<p>Ok, now that we got that out of the way, how do you do it? You’re bound to be in a situation where some of your function’s parameters may only take on discrete values. Unfortunately, the nature of bayesian optimization with gaussian processes doesn’t allow for an easy/intuitive way of dealing with discrete parameters - but that doesn’t mean it is impossible. The example below showcases a simple, yet reasonably adequate, way to dealing with discrete parameters.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[9]:
-</code></pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><code><span class="k">def</span> <span class="nf">func_with_discrete_params</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">d</span><span class="p">):</span>
-    <span class="c1"># Simulate necessity of having d being discrete.</span>
-    <span class="k">assert</span> <span class="nb">type</span><span class="p">(</span><span class="n">d</span><span class="p">)</span> <span class="o">==</span> <span class="nb">int</span>
-
-    <span class="k">return</span> <span class="p">((</span><span class="n">x</span> <span class="o">+</span> <span class="n">y</span> <span class="o">+</span> <span class="n">d</span><span class="p">)</span> <span class="o">//</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">d</span><span class="p">))</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="p">(</span><span class="n">x</span> <span class="o">+</span> <span class="n">y</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span>
-</code></pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[10]:
-</code></pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><code><span class="k">def</span> <span class="nf">function_to_be_optimized</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">w</span><span class="p">):</span>
-    <span class="n">d</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">w</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">func_with_discrete_params</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">d</span><span class="p">)</span>
-</code></pre></div>
-</div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[11]:
-</code></pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><code><span class="n">optimizer</span> <span class="o">=</span> <span class="n">BayesianOptimization</span><span class="p">(</span>
-    <span class="n">f</span><span class="o">=</span><span class="n">function_to_be_optimized</span><span class="p">,</span>
-    <span class="n">pbounds</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;x&#39;</span><span class="p">:</span> <span class="p">(</span><span class="o">-</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="s1">&#39;y&#39;</span><span class="p">:</span> <span class="p">(</span><span class="o">-</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="s1">&#39;w&#39;</span><span class="p">:</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">5</span><span class="p">)},</span>
-    <span class="n">verbose</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
-    <span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-<span class="p">)</span>
-</code></pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[12]:
-</code></pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><code><span class="n">optimizer</span><span class="o">.</span><span class="n">set_gp_params</span><span class="p">(</span><span class="n">alpha</span><span class="o">=</span><span class="mf">1e-3</span><span class="p">)</span>
-<span class="n">optimizer</span><span class="o">.</span><span class="n">maximize</span><span class="p">()</span>
-</code></pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-|   iter    |  target   |     w     |     x     |     y     |
--------------------------------------------------------------
-| <span class="ansi-black-fg">1         | </span><span class="ansi-black-fg">-0.06199  | </span><span class="ansi-black-fg">2.085     | </span><span class="ansi-black-fg">4.406     | </span><span class="ansi-black-fg">-9.998    |
-| </span><span class="ansi-magenta-fg">2         | </span><span class="ansi-magenta-fg">-0.0344   | </span><span class="ansi-magenta-fg">1.512     | </span><span class="ansi-magenta-fg">-7.065    | </span><span class="ansi-magenta-fg">-8.153    |
-| </span><span class="ansi-black-fg">3         | </span><span class="ansi-black-fg">-0.2177   | </span><span class="ansi-black-fg">0.9313    | </span><span class="ansi-black-fg">-3.089    | </span><span class="ansi-black-fg">-2.065    |
-| </span><span class="ansi-magenta-fg">4         | </span><span class="ansi-magenta-fg">0.1865    | </span><span class="ansi-magenta-fg">2.694     | </span><span class="ansi-magenta-fg">-1.616    | </span><span class="ansi-magenta-fg">3.704     |
-| </span><span class="ansi-black-fg">5         | </span><span class="ansi-black-fg">-0.2187   | </span><span class="ansi-black-fg">1.022     | </span><span class="ansi-black-fg">7.562     | </span><span class="ansi-black-fg">-9.452    |
-| </span><span class="ansi-magenta-fg">6         | </span><span class="ansi-magenta-fg">0.2488    | </span><span class="ansi-magenta-fg">2.684     | </span><span class="ansi-magenta-fg">-2.188    | </span><span class="ansi-magenta-fg">3.925     |
-| </span><span class="ansi-magenta-fg">7         | </span><span class="ansi-magenta-fg">0.2948    | </span><span class="ansi-magenta-fg">2.683     | </span><span class="ansi-magenta-fg">-2.534    | </span><span class="ansi-magenta-fg">4.08      |
-| </span><span class="ansi-magenta-fg">8         | </span><span class="ansi-magenta-fg">0.3202    | </span><span class="ansi-magenta-fg">2.514     | </span><span class="ansi-magenta-fg">-3.83     | </span><span class="ansi-magenta-fg">5.287     |
-| </span><span class="ansi-black-fg">9         | </span><span class="ansi-black-fg">0.0       | </span><span class="ansi-black-fg">4.057     | </span><span class="ansi-black-fg">-4.458    | </span><span class="ansi-black-fg">3.928     |
-| </span><span class="ansi-magenta-fg">10        | </span><span class="ansi-magenta-fg">0.4802    | </span><span class="ansi-magenta-fg">2.296     | </span><span class="ansi-magenta-fg">-3.518    | </span><span class="ansi-magenta-fg">4.558     |
-| </span><span class="ansi-black-fg">11        | </span><span class="ansi-black-fg">0.0       | </span><span class="ansi-black-fg">1.084     | </span><span class="ansi-black-fg">-3.737    | </span><span class="ansi-black-fg">4.472     |
-| </span><span class="ansi-black-fg">12        | </span><span class="ansi-black-fg">0.0       | </span><span class="ansi-black-fg">2.649     | </span><span class="ansi-black-fg">-3.861    | </span><span class="ansi-black-fg">4.353     |
-| </span><span class="ansi-black-fg">13        | </span><span class="ansi-black-fg">0.0       | </span><span class="ansi-black-fg">2.442     | </span><span class="ansi-black-fg">-3.658    | </span><span class="ansi-black-fg">4.599     |
-| </span><span class="ansi-black-fg">14        | </span><span class="ansi-black-fg">-0.05801  | </span><span class="ansi-black-fg">1.935     | </span><span class="ansi-black-fg">-0.4758   | </span><span class="ansi-black-fg">-8.755    |
-| </span><span class="ansi-black-fg">15        | </span><span class="ansi-black-fg">0.0       | </span><span class="ansi-black-fg">2.337     | </span><span class="ansi-black-fg">7.973     | </span><span class="ansi-black-fg">-8.96     |
-| </span><span class="ansi-black-fg">16        | </span><span class="ansi-black-fg">0.07699   | </span><span class="ansi-black-fg">0.6926    | </span><span class="ansi-black-fg">5.59      | </span><span class="ansi-black-fg">6.854     |
-| </span><span class="ansi-black-fg">17        | </span><span class="ansi-black-fg">-0.02025  | </span><span class="ansi-black-fg">3.534     | </span><span class="ansi-black-fg">-8.943    | </span><span class="ansi-black-fg">1.987     |
-| </span><span class="ansi-black-fg">18        | </span><span class="ansi-black-fg">0.0       | </span><span class="ansi-black-fg">2.59      | </span><span class="ansi-black-fg">-7.339    | </span><span class="ansi-black-fg">5.941     |
-| </span><span class="ansi-black-fg">19        | </span><span class="ansi-black-fg">0.0929    | </span><span class="ansi-black-fg">2.237     | </span><span class="ansi-black-fg">-4.535    | </span><span class="ansi-black-fg">9.065     |
-| </span><span class="ansi-black-fg">20        | </span><span class="ansi-black-fg">0.1538    | </span><span class="ansi-black-fg">0.477     | </span><span class="ansi-black-fg">2.931     | </span><span class="ansi-black-fg">2.683     |
-| </span><span class="ansi-black-fg">21        | </span><span class="ansi-black-fg">0.0       | </span><span class="ansi-black-fg">0.9999    | </span><span class="ansi-black-fg">4.397     | </span><span class="ansi-black-fg">-3.971    |
-| </span><span class="ansi-black-fg">22        | </span><span class="ansi-black-fg">-0.01894  | </span><span class="ansi-black-fg">3.764     | </span><span class="ansi-black-fg">-7.043    | </span><span class="ansi-black-fg">-3.184    |
-| </span><span class="ansi-black-fg">23        | </span><span class="ansi-black-fg">0.03683   | </span><span class="ansi-black-fg">1.851     | </span><span class="ansi-black-fg">5.783     | </span><span class="ansi-black-fg">7.966     |
-| </span><span class="ansi-black-fg">24        | </span><span class="ansi-black-fg">-0.04359  | </span><span class="ansi-black-fg">1.615     | </span><span class="ansi-black-fg">-5.133    | </span><span class="ansi-black-fg">-6.556    |
-| </span><span class="ansi-black-fg">25        | </span><span class="ansi-black-fg">0.02617   | </span><span class="ansi-black-fg">3.863     | </span><span class="ansi-black-fg">0.1052    | </span><span class="ansi-black-fg">8.579     |
-| </span><span class="ansi-black-fg">26        | </span><span class="ansi-black-fg">-0.1071   | </span><span class="ansi-black-fg">0.8131    | </span><span class="ansi-black-fg">-0.7949   | </span><span class="ansi-black-fg">-9.292    |
-| </span><span class="ansi-black-fg">27        | </span><span class="ansi-black-fg">0.0       | </span><span class="ansi-black-fg">4.969     | </span><span class="ansi-black-fg">8.778     | </span><span class="ansi-black-fg">-8.467    |
-| </span><span class="ansi-black-fg">28        | </span><span class="ansi-black-fg">-0.1372   | </span><span class="ansi-black-fg">0.9475    | </span><span class="ansi-black-fg">-1.019    | </span><span class="ansi-black-fg">-7.018    |
-| </span><span class="ansi-black-fg">29        | </span><span class="ansi-black-fg">0.08078   | </span><span class="ansi-black-fg">1.917     | </span><span class="ansi-black-fg">-0.2606   | </span><span class="ansi-black-fg">6.272     |
-| </span><span class="ansi-black-fg">30        | </span><span class="ansi-black-fg">0.02003   | </span><span class="ansi-black-fg">4.278     | </span><span class="ansi-black-fg">3.8       | </span><span class="ansi-black-fg">8.398     |
-=============================================================</span>
-</pre></div></div>
-</div>
-<h2 id="3.-Tuning-the-underlying-Gaussian-Process">3. Tuning the underlying Gaussian Process<a class="headerlink" href="#3.-Tuning-the-underlying-Gaussian-Process" title="Link to this heading">¶</a></h2>
+<h2 id="2.-Tuning-the-underlying-Gaussian-Process">2. Tuning the underlying Gaussian Process<a class="headerlink" href="#2.-Tuning-the-underlying-Gaussian-Process" title="Link to this heading">¶</a></h2>
 <p>The bayesian optimization algorithm works by performing a gaussian process regression of the observed combination of parameters and their associated target values. The predicted parameter <span class="math notranslate nohighlight">\(\rightarrow\)</span> target hyper-surface (and its uncertainty) is then used to guide the next best point to probe.</p>
-<h3 id="3.1-Passing-parameter-to-the-GP">3.1 Passing parameter to the GP<a class="headerlink" href="#3.1-Passing-parameter-to-the-GP" title="Link to this heading">¶</a></h3>
+<h3 id="2.1-Passing-parameter-to-the-GP">2.1 Passing parameter to the GP<a class="headerlink" href="#2.1-Passing-parameter-to-the-GP" title="Link to this heading">¶</a></h3>
 <p>Depending on the problem it could be beneficial to change the default parameters of the underlying GP. You can use the <code class="docutils literal notranslate"><span class="pre">optimizer.set_gp_params</span></code> method to do this:</p>
 <div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[13]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[9]:
 </code></pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><code><span class="n">optimizer</span> <span class="o">=</span> <span class="n">BayesianOptimization</span><span class="p">(</span>
@@ -983,24 +912,24 @@ <h3 id="3.1-Passing-parameter-to-the-GP">3.1 Passing parameter to the GP<a class
 <div class="highlight"><pre>
 |   iter    |  target   |     x     |     y     |
 -------------------------------------------------
-| <span class="ansi-black-fg">1         | </span><span class="ansi-black-fg">0.7862    | </span><span class="ansi-black-fg">-0.3319   | </span><span class="ansi-black-fg">1.322     |
-| </span><span class="ansi-black-fg">2         | </span><span class="ansi-black-fg">-18.19    | </span><span class="ansi-black-fg">1.957     | </span><span class="ansi-black-fg">-2.919    |
-| </span><span class="ansi-black-fg">3         | </span><span class="ansi-black-fg">-12.05    | </span><span class="ansi-black-fg">-1.969    | </span><span class="ansi-black-fg">-2.029    |
-| </span><span class="ansi-black-fg">4         | </span><span class="ansi-black-fg">-7.463    | </span><span class="ansi-black-fg">0.6032    | </span><span class="ansi-black-fg">-1.846    |
-| </span><span class="ansi-black-fg">5         | </span><span class="ansi-black-fg">-1.093    | </span><span class="ansi-black-fg">1.444     | </span><span class="ansi-black-fg">1.096     |
-| </span><span class="ansi-magenta-fg">6         | </span><span class="ansi-magenta-fg">0.8586    | </span><span class="ansi-magenta-fg">-0.2165   | </span><span class="ansi-magenta-fg">1.307     |
-=================================================</span>
+| 1         | 0.7862    | -0.331911 | 1.3219469 |
+| 2         | -18.34    | 1.9021640 | -2.965222 |
+| <span class="ansi-magenta-fg">3        </span> | <span class="ansi-magenta-fg">0.8731   </span> | <span class="ansi-magenta-fg">-0.298167</span> | <span class="ansi-magenta-fg">1.1948749</span> |
+| 4         | -6.497    | 1.9876938 | 2.8830942 |
+| 5         | -4.286    | -1.995643 | -0.141769 |
+| 6         | -6.781    | -1.953302 | 2.9913127 |
+=================================================
 </pre></div></div>
 </div>
-<h3 id="3.2-Tuning-the-alpha-parameter">3.2 Tuning the <code class="docutils literal notranslate"><span class="pre">alpha</span></code> parameter<a class="headerlink" href="#3.2-Tuning-the-alpha-parameter" title="Link to this heading">¶</a></h3>
+<h3 id="2.2-Tuning-the-alpha-parameter">2.2 Tuning the <code class="docutils literal notranslate"><span class="pre">alpha</span></code> parameter<a class="headerlink" href="#2.2-Tuning-the-alpha-parameter" title="Link to this heading">¶</a></h3>
 <p>When dealing with functions with discrete parameters,or particularly erratic target space it might be beneficial to increase the value of the <code class="docutils literal notranslate"><span class="pre">alpha</span></code> parameter. This parameters controls how much noise the GP can handle, so increase it whenever you think that extra flexibility is needed.</p>
-<h3 id="3.3-Changing-kernels">3.3 Changing kernels<a class="headerlink" href="#3.3-Changing-kernels" title="Link to this heading">¶</a></h3>
+<h3 id="2.3-Changing-kernels">2.3 Changing kernels<a class="headerlink" href="#2.3-Changing-kernels" title="Link to this heading">¶</a></h3>
 <p>By default this package uses the Matern 2.5 kernel. Depending on your use case you may find that tuning the GP kernel could be beneficial. You’re on your own here since these are very specific solutions to very specific problems. You should start with the <a class="reference external" href="https://scikit-learn.org/stable/modules/gaussian_process.html#kernels-for-gaussian-processes">scikit learn docs</a>.</p>
 <h2 id="Observers-Continued">Observers Continued<a class="headerlink" href="#Observers-Continued" title="Link to this heading">¶</a></h2>
 <p>Observers are objects that subscribe and listen to particular events fired by the <code class="docutils literal notranslate"><span class="pre">BayesianOptimization</span></code> object.</p>
 <p>When an event gets fired a callback function is called with the event and the <code class="docutils literal notranslate"><span class="pre">BayesianOptimization</span></code> instance passed as parameters. The callback can be specified at the time of subscription. If none is given it will look for an <code class="docutils literal notranslate"><span class="pre">update</span></code> method from the observer.</p>
 <div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[14]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[10]:
 </code></pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><code><span class="kn">from</span> <span class="nn">bayes_opt.event</span> <span class="kn">import</span> <span class="n">DEFAULT_EVENTS</span><span class="p">,</span> <span class="n">Events</span>
@@ -1008,7 +937,7 @@ <h2 id="Observers-Continued">Observers Continued<a class="headerlink" href="#Obs
 </div>
 </div>
 <div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[15]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[11]:
 </code></pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><code><span class="n">optimizer</span> <span class="o">=</span> <span class="n">BayesianOptimization</span><span class="p">(</span>
@@ -1021,7 +950,7 @@ <h2 id="Observers-Continued">Observers Continued<a class="headerlink" href="#Obs
 </div>
 </div>
 <div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[16]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[12]:
 </code></pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><code><span class="k">class</span> <span class="nc">BasicObserver</span><span class="p">:</span>
@@ -1032,7 +961,7 @@ <h2 id="Observers-Continued">Observers Continued<a class="headerlink" href="#Obs
 </div>
 </div>
 <div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[17]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[13]:
 </code></pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><code><span class="n">my_observer</span> <span class="o">=</span> <span class="n">BasicObserver</span><span class="p">()</span>
@@ -1047,7 +976,7 @@ <h2 id="Observers-Continued">Observers Continued<a class="headerlink" href="#Obs
 </div>
 <p>Alternatively you have the option to pass a completely different callback.</p>
 <div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[18]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[14]:
 </code></pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><code><span class="k">def</span> <span class="nf">my_callback</span><span class="p">(</span><span class="n">event</span><span class="p">,</span> <span class="n">instance</span><span class="p">):</span>
@@ -1062,7 +991,7 @@ <h2 id="Observers-Continued">Observers Continued<a class="headerlink" href="#Obs
 </div>
 </div>
 <div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[19]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[15]:
 </code></pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><code><span class="n">optimizer</span><span class="o">.</span><span class="n">maximize</span><span class="p">(</span><span class="n">init_points</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">n_iter</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
@@ -1082,7 +1011,7 @@ <h2 id="Observers-Continued">Observers Continued<a class="headerlink" href="#Obs
 </div>
 <p>For a list of all default events you can checkout <code class="docutils literal notranslate"><span class="pre">DEFAULT_EVENTS</span></code></p>
 <div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[20]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[16]:
 </code></pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><code><span class="n">DEFAULT_EVENTS</span>
@@ -1090,7 +1019,7 @@ <h2 id="Observers-Continued">Observers Continued<a class="headerlink" href="#Obs
 </div>
 </div>
 <div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[20]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[16]:
 </code></pre></div>
 </div>
 <div class="output_area docutils container">
diff --git a/master/advanced-tour.ipynb b/master/advanced-tour.ipynb
index dc72e40e..9e93d09d 100644
--- a/master/advanced-tour.ipynb
+++ b/master/advanced-tour.ipynb
@@ -96,7 +96,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Next point to probe is: {'x': -0.331911981189704, 'y': 1.3219469606529486}\n"
+      "Next point to probe is: {'x': np.float64(-0.331911981189704), 'y': np.float64(1.3219469606529486)}\n"
      ]
     }
    ],
@@ -167,12 +167,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "-18.503835804889988 {'x': 1.953072105336, 'y': -2.9609778030491904}\n",
-      "-1.0819533157901717 {'x': 0.22703572807626315, 'y': 2.4249238905875123}\n",
-      "-6.50219704520679 {'x': -1.9991881984624875, 'y': 2.872282989383577}\n",
-      "-5.747604713731052 {'x': -1.994467585936897, 'y': -0.664242699361514}\n",
-      "-2.9682431497650823 {'x': 1.9737252084307952, 'y': 1.269540259274744}\n",
-      "{'target': 0.7861845912690544, 'params': {'x': -0.331911981189704, 'y': 1.3219469606529486}}\n"
+      "-18.707136686093495 {'x': np.float64(1.9261486197444082), 'y': np.float64(-2.9996360060323246)}\n",
+      "0.750594563473972 {'x': np.float64(-0.3763326769822668), 'y': np.float64(1.328297354179696)}\n",
+      "-6.559031075654336 {'x': np.float64(1.979183535803597), 'y': np.float64(2.9083667381450318)}\n",
+      "-6.915481333972961 {'x': np.float64(-1.9686133847781613), 'y': np.float64(-1.009985740060171)}\n",
+      "-6.8600832617014085 {'x': np.float64(-1.9763198875239296), 'y': np.float64(2.9885278383464513)}\n",
+      "{'target': np.float64(0.7861845912690544), 'params': {'x': np.float64(-0.331911981189704), 'y': np.float64(1.3219469606529486)}}\n"
      ]
     }
    ],
@@ -190,112 +190,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 2. Dealing with discrete parameters\n",
-    "\n",
-    "**There is no principled way of dealing with discrete parameters using this package.**\n",
-    "\n",
-    "Ok, now that we got that out of the way, how do you do it? You're bound to be in a situation where some of your function's parameters may only take on discrete values. Unfortunately, the nature of bayesian optimization with gaussian processes doesn't allow for an easy/intuitive way of dealing with discrete parameters - but that doesn't mean it is impossible. The example below showcases a simple, yet reasonably adequate, way to dealing with discrete parameters."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def func_with_discrete_params(x, y, d):\n",
-    "    # Simulate necessity of having d being discrete.\n",
-    "    assert type(d) == int\n",
-    "    \n",
-    "    return ((x + y + d) // (1 + d)) / (1 + (x + y) ** 2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def function_to_be_optimized(x, y, w):\n",
-    "    d = int(w)\n",
-    "    return func_with_discrete_params(x, y, d)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "optimizer = BayesianOptimization(\n",
-    "    f=function_to_be_optimized,\n",
-    "    pbounds={'x': (-10, 10), 'y': (-10, 10), 'w': (0, 5)},\n",
-    "    verbose=2,\n",
-    "    random_state=1,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "|   iter    |  target   |     w     |     x     |     y     |\n",
-      "-------------------------------------------------------------\n",
-      "| \u001b[30m1         | \u001b[30m-0.06199  | \u001b[30m2.085     | \u001b[30m4.406     | \u001b[30m-9.998    |\n",
-      "| \u001b[35m2         | \u001b[35m-0.0344   | \u001b[35m1.512     | \u001b[35m-7.065    | \u001b[35m-8.153    |\n",
-      "| \u001b[30m3         | \u001b[30m-0.2177   | \u001b[30m0.9313    | \u001b[30m-3.089    | \u001b[30m-2.065    |\n",
-      "| \u001b[35m4         | \u001b[35m0.1865    | \u001b[35m2.694     | \u001b[35m-1.616    | \u001b[35m3.704     |\n",
-      "| \u001b[30m5         | \u001b[30m-0.2187   | \u001b[30m1.022     | \u001b[30m7.562     | \u001b[30m-9.452    |\n",
-      "| \u001b[35m6         | \u001b[35m0.2488    | \u001b[35m2.684     | \u001b[35m-2.188    | \u001b[35m3.925     |\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "| \u001b[35m7         | \u001b[35m0.2948    | \u001b[35m2.683     | \u001b[35m-2.534    | \u001b[35m4.08      |\n",
-      "| \u001b[35m8         | \u001b[35m0.3202    | \u001b[35m2.514     | \u001b[35m-3.83     | \u001b[35m5.287     |\n",
-      "| \u001b[30m9         | \u001b[30m0.0       | \u001b[30m4.057     | \u001b[30m-4.458    | \u001b[30m3.928     |\n",
-      "| \u001b[35m10        | \u001b[35m0.4802    | \u001b[35m2.296     | \u001b[35m-3.518    | \u001b[35m4.558     |\n",
-      "| \u001b[30m11        | \u001b[30m0.0       | \u001b[30m1.084     | \u001b[30m-3.737    | \u001b[30m4.472     |\n",
-      "| \u001b[30m12        | \u001b[30m0.0       | \u001b[30m2.649     | \u001b[30m-3.861    | \u001b[30m4.353     |\n",
-      "| \u001b[30m13        | \u001b[30m0.0       | \u001b[30m2.442     | \u001b[30m-3.658    | \u001b[30m4.599     |\n",
-      "| \u001b[30m14        | \u001b[30m-0.05801  | \u001b[30m1.935     | \u001b[30m-0.4758   | \u001b[30m-8.755    |\n",
-      "| \u001b[30m15        | \u001b[30m0.0       | \u001b[30m2.337     | \u001b[30m7.973     | \u001b[30m-8.96     |\n",
-      "| \u001b[30m16        | \u001b[30m0.07699   | \u001b[30m0.6926    | \u001b[30m5.59      | \u001b[30m6.854     |\n",
-      "| \u001b[30m17        | \u001b[30m-0.02025  | \u001b[30m3.534     | \u001b[30m-8.943    | \u001b[30m1.987     |\n",
-      "| \u001b[30m18        | \u001b[30m0.0       | \u001b[30m2.59      | \u001b[30m-7.339    | \u001b[30m5.941     |\n",
-      "| \u001b[30m19        | \u001b[30m0.0929    | \u001b[30m2.237     | \u001b[30m-4.535    | \u001b[30m9.065     |\n",
-      "| \u001b[30m20        | \u001b[30m0.1538    | \u001b[30m0.477     | \u001b[30m2.931     | \u001b[30m2.683     |\n",
-      "| \u001b[30m21        | \u001b[30m0.0       | \u001b[30m0.9999    | \u001b[30m4.397     | \u001b[30m-3.971    |\n",
-      "| \u001b[30m22        | \u001b[30m-0.01894  | \u001b[30m3.764     | \u001b[30m-7.043    | \u001b[30m-3.184    |\n",
-      "| \u001b[30m23        | \u001b[30m0.03683   | \u001b[30m1.851     | \u001b[30m5.783     | \u001b[30m7.966     |\n",
-      "| \u001b[30m24        | \u001b[30m-0.04359  | \u001b[30m1.615     | \u001b[30m-5.133    | \u001b[30m-6.556    |\n",
-      "| \u001b[30m25        | \u001b[30m0.02617   | \u001b[30m3.863     | \u001b[30m0.1052    | \u001b[30m8.579     |\n",
-      "| \u001b[30m26        | \u001b[30m-0.1071   | \u001b[30m0.8131    | \u001b[30m-0.7949   | \u001b[30m-9.292    |\n",
-      "| \u001b[30m27        | \u001b[30m0.0       | \u001b[30m4.969     | \u001b[30m8.778     | \u001b[30m-8.467    |\n",
-      "| \u001b[30m28        | \u001b[30m-0.1372   | \u001b[30m0.9475    | \u001b[30m-1.019    | \u001b[30m-7.018    |\n",
-      "| \u001b[30m29        | \u001b[30m0.08078   | \u001b[30m1.917     | \u001b[30m-0.2606   | \u001b[30m6.272     |\n",
-      "| \u001b[30m30        | \u001b[30m0.02003   | \u001b[30m4.278     | \u001b[30m3.8       | \u001b[30m8.398     |\n",
-      "=============================================================\n"
-     ]
-    }
-   ],
-   "source": [
-    "optimizer.set_gp_params(alpha=1e-3)\n",
-    "optimizer.maximize()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 3. Tuning the underlying Gaussian Process\n",
+    "## 2. Tuning the underlying Gaussian Process\n",
     "\n",
     "The bayesian optimization algorithm works by performing a gaussian process regression of the observed combination of parameters and their associated target values. The predicted parameter $\\rightarrow$ target hyper-surface (and its uncertainty) is then used to guide the next best point to probe."
    ]
@@ -304,14 +199,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 3.1 Passing parameter to the GP\n",
+    "### 2.1 Passing parameter to the GP\n",
     "\n",
     "Depending on the problem it could be beneficial to change the default parameters of the underlying GP. You can use the `optimizer.set_gp_params` method to do this:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -320,12 +215,12 @@
      "text": [
       "|   iter    |  target   |     x     |     y     |\n",
       "-------------------------------------------------\n",
-      "| \u001b[30m1         | \u001b[30m0.7862    | \u001b[30m-0.3319   | \u001b[30m1.322     |\n",
-      "| \u001b[30m2         | \u001b[30m-18.19    | \u001b[30m1.957     | \u001b[30m-2.919    |\n",
-      "| \u001b[30m3         | \u001b[30m-12.05    | \u001b[30m-1.969    | \u001b[30m-2.029    |\n",
-      "| \u001b[30m4         | \u001b[30m-7.463    | \u001b[30m0.6032    | \u001b[30m-1.846    |\n",
-      "| \u001b[30m5         | \u001b[30m-1.093    | \u001b[30m1.444     | \u001b[30m1.096     |\n",
-      "| \u001b[35m6         | \u001b[35m0.8586    | \u001b[35m-0.2165   | \u001b[35m1.307     |\n",
+      "| \u001b[39m1        \u001b[39m | \u001b[39m0.7862   \u001b[39m | \u001b[39m-0.331911\u001b[39m | \u001b[39m1.3219469\u001b[39m |\n",
+      "| \u001b[39m2        \u001b[39m | \u001b[39m-18.34   \u001b[39m | \u001b[39m1.9021640\u001b[39m | \u001b[39m-2.965222\u001b[39m |\n",
+      "| \u001b[35m3        \u001b[39m | \u001b[35m0.8731   \u001b[39m | \u001b[35m-0.298167\u001b[39m | \u001b[35m1.1948749\u001b[39m |\n",
+      "| \u001b[39m4        \u001b[39m | \u001b[39m-6.497   \u001b[39m | \u001b[39m1.9876938\u001b[39m | \u001b[39m2.8830942\u001b[39m |\n",
+      "| \u001b[39m5        \u001b[39m | \u001b[39m-4.286   \u001b[39m | \u001b[39m-1.995643\u001b[39m | \u001b[39m-0.141769\u001b[39m |\n",
+      "| \u001b[39m6        \u001b[39m | \u001b[39m-6.781   \u001b[39m | \u001b[39m-1.953302\u001b[39m | \u001b[39m2.9913127\u001b[39m |\n",
       "=================================================\n"
      ]
     }
@@ -348,7 +243,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 3.2 Tuning the `alpha` parameter\n",
+    "### 2.2 Tuning the `alpha` parameter\n",
     "\n",
     "When dealing with functions with discrete parameters,or particularly erratic target space it might be beneficial to increase the value of the `alpha` parameter. This parameters controls how much noise the GP can handle, so increase it whenever you think that extra flexibility is needed."
    ]
@@ -358,7 +253,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 3.3 Changing kernels\n",
+    "### 2.3 Changing kernels\n",
     "\n",
     "By default this package uses the Matern 2.5 kernel. Depending on your use case you may find that tuning the GP kernel could be beneficial. You're on your own here since these are very specific solutions to very specific problems. You should start with the [scikit learn docs](https://scikit-learn.org/stable/modules/gaussian_process.html#kernels-for-gaussian-processes)."
    ]
@@ -376,7 +271,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -385,7 +280,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -399,7 +294,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -411,7 +306,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -433,7 +328,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -449,7 +344,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -476,7 +371,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -485,7 +380,7 @@
        "['optimization:start', 'optimization:step', 'optimization:end']"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -497,7 +392,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "bayesian-optimization-t6LLJ9me-py3.10",
    "language": "python",
    "name": "python3"
   },
@@ -511,7 +406,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.1.undefined"
+   "version": "3.10.13"
   },
   "nbdime-conflicts": {
    "local_diff": [
diff --git a/master/basic-tour.html b/master/basic-tour.html
index e968d599..c8e35f8c 100644
--- a/master/basic-tour.html
+++ b/master/basic-tour.html
@@ -427,6 +427,21 @@
   
   
   
+    <li class="md-nav__item">
+      <a href="parameter_types.html" class="md-nav__link">
+        <span title="/parameter_types.ipynb (reference label)" class="md-ellipsis">Parameter Types</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
     <li class="md-nav__item">
       <a href="domain_reduction.html" class="md-nav__link">
         <span title="/domain_reduction.ipynb (reference label)" class="md-ellipsis">Sequential Domain Reduction</span>
@@ -598,6 +613,21 @@
   
   
   
+    <li class="md-nav__item">
+      <a href="reference/parameter.html" class="md-nav__link">
+        <span title="/reference/parameter.rst (reference label)" class="md-ellipsis">bayes_<wbr>opt.<wbr>parameter</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
     <li class="md-nav__item">
       <a href="reference/exception.html" class="md-nav__link">
         <span title="/reference/exception.rst (reference label)" class="md-ellipsis">bayes_<wbr>opt.<wbr>exception</span>
@@ -798,7 +828,11 @@ <h2 id="2.-Getting-Started">2. Getting Started<a class="headerlink" href="#2.-Ge
 </div>
 </div>
 <p>The BayesianOptimization object will work out of the box without much tuning needed. The main method you should be aware of is <code class="docutils literal notranslate"><span class="pre">maximize</span></code>, which does exactly what you think it does.</p>
-<p>There are many parameters you can pass to maximize, nonetheless, the most important ones are: - <code class="docutils literal notranslate"><span class="pre">n_iter</span></code>: How many steps of bayesian optimization you want to perform. The more steps the more likely to find a good maximum you are. - <code class="docutils literal notranslate"><span class="pre">init_points</span></code>: How many steps of <strong>random</strong> exploration you want to perform. Random exploration can help by diversifying the exploration space.</p>
+<p>There are many parameters you can pass to maximize, nonetheless, the most important ones are:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">n_iter</span></code>: How many steps of bayesian optimization you want to perform. The more steps the more likely to find a good maximum you are.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">init_points</span></code>: How many steps of <strong>random</strong> exploration you want to perform. Random exploration can help by diversifying the exploration space.</p></li>
+</ul>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[5]:
 </code></pre></div>
@@ -919,7 +953,7 @@ <h2 id="3.-Guiding-the-optimization">3. Guiding the optimization<a class="header
 </code></pre></div>
 </div>
 </div>
-<p>Or as an iterable. Beware that the order has to be alphabetical. You can usee <code class="docutils literal notranslate"><span class="pre">optimizer.space.keys</span></code> for guidance</p>
+<p>Or as an iterable. Beware that the order has to match the order of the initial <code class="docutils literal notranslate"><span class="pre">pbounds</span></code> dictionary. You can usee <code class="docutils literal notranslate"><span class="pre">optimizer.space.keys</span></code> for guidance</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[11]:
 </code></pre></div>
@@ -979,7 +1013,11 @@ <h3 id="4.1-Saving-progress">4.1 Saving progress<a class="headerlink" href="#4.1
 </code></pre></div>
 </div>
 </div>
-<p>The observer paradigm works by: 1. Instantiating an observer object. 2. Tying the observer object to a particular event fired by an optimizer.</p>
+<p>The observer paradigm works by:</p>
+<ol class="arabic simple">
+<li><p>Instantiating an observer object.</p></li>
+<li><p>Tying the observer object to a particular event fired by an optimizer.</p></li>
+</ol>
 <p>The <code class="docutils literal notranslate"><span class="pre">BayesianOptimization</span></code> object fires a number of internal events during optimization, in particular, every time it probes the function and obtains a new parameter-target combination it will fire an <code class="docutils literal notranslate"><span class="pre">Events.OPTIMIZATION_STEP</span></code> event, which our logger will listen to.</p>
 <p><strong>Caveat:</strong> The logger will not look back at previously probed points.</p>
 <div class="nbinput nblast docutils container">
diff --git a/master/basic-tour.ipynb b/master/basic-tour.ipynb
index 3cbcbd40..4ecd8329 100644
--- a/master/basic-tour.ipynb
+++ b/master/basic-tour.ipynb
@@ -252,7 +252,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Or as an iterable. Beware that the order has to be alphabetical. You can usee `optimizer.space.keys` for guidance"
+    "Or as an iterable. Beware that the order has to match the order of the initial `pbounds` dictionary. You can usee `optimizer.space.keys` for guidance"
    ]
   },
   {
diff --git a/master/constraints.html b/master/constraints.html
index e899aa17..ae79d031 100644
--- a/master/constraints.html
+++ b/master/constraints.html
@@ -400,6 +400,21 @@
   
   
   
+    <li class="md-nav__item">
+      <a href="parameter_types.html" class="md-nav__link">
+        <span title="/parameter_types.ipynb (reference label)" class="md-ellipsis">Parameter Types</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
     <li class="md-nav__item">
       <a href="domain_reduction.html" class="md-nav__link">
         <span title="/domain_reduction.ipynb (reference label)" class="md-ellipsis">Sequential Domain Reduction</span>
@@ -571,6 +586,21 @@
   
   
   
+    <li class="md-nav__item">
+      <a href="reference/parameter.html" class="md-nav__link">
+        <span title="/reference/parameter.rst (reference label)" class="md-ellipsis">bayes_<wbr>opt.<wbr>parameter</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
     <li class="md-nav__item">
       <a href="reference/exception.html" class="md-nav__link">
         <span title="/reference/exception.rst (reference label)" class="md-ellipsis">bayes_<wbr>opt.<wbr>exception</span>
@@ -1183,13 +1213,13 @@ <h3 id="2.2-Multiple-Constraints">2.2 Multiple Constraints<a class="headerlink"
       
       
         
-        <a href="domain_reduction.html" class="md-footer__link md-footer__link--next" aria-label="Next: Sequential Domain Reduction" rel="next">
+        <a href="parameter_types.html" class="md-footer__link md-footer__link--next" aria-label="Next: Optimizing over non-float Parameters" rel="next">
           <div class="md-footer__title">
             <div class="md-ellipsis">
               <span class="md-footer__direction">
                 Next
               </span>
-              Sequential Domain Reduction
+              Optimizing over non-float Parameters
             </div>
           </div>
           <div class="md-footer__button md-icon">
diff --git a/master/domain_reduction.html b/master/domain_reduction.html
index 559393d7..d3b03847 100644
--- a/master/domain_reduction.html
+++ b/master/domain_reduction.html
@@ -324,6 +324,21 @@
               
   
   
+  
+  
+    <li class="md-nav__item">
+      <a href="parameter_types.html" class="md-nav__link">
+        <span title="/parameter_types.ipynb (reference label)" class="md-ellipsis">Parameter Types</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
     
   
     
@@ -551,6 +566,21 @@
   
   
   
+    <li class="md-nav__item">
+      <a href="reference/parameter.html" class="md-nav__link">
+        <span title="/reference/parameter.rst (reference label)" class="md-ellipsis">bayes_<wbr>opt.<wbr>parameter</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
     <li class="md-nav__item">
       <a href="reference/exception.html" class="md-nav__link">
         <span title="/reference/exception.rst (reference label)" class="md-ellipsis">bayes_<wbr>opt.<wbr>exception</span>
@@ -854,7 +884,7 @@ <h2 id="Parameters">Parameters<a class="headerlink" href="#Parameters" title="Li
     <nav class="md-footer__inner md-grid" aria-label="Footer" >
       
         
-        <a href="constraints.html" class="md-footer__link md-footer__link--prev" aria-label="Previous: Constrained Optimization" rel="prev">
+        <a href="parameter_types.html" class="md-footer__link md-footer__link--prev" aria-label="Previous: Optimizing over non-float Parameters" rel="prev">
           <div class="md-footer__button md-icon">
             <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11h12Z"/></svg>
           </div>
@@ -863,7 +893,7 @@ <h2 id="Parameters">Parameters<a class="headerlink" href="#Parameters" title="Li
               <span class="md-footer__direction">
                 Previous
               </span>
-              Constrained Optimization
+              Optimizing over non-float Parameters
             </div>
           </div>
         </a>
diff --git a/master/exploitation_vs_exploration.html b/master/exploitation_vs_exploration.html
index f790409f..61d25f6c 100644
--- a/master/exploitation_vs_exploration.html
+++ b/master/exploitation_vs_exploration.html
@@ -326,6 +326,21 @@
   
   
   
+    <li class="md-nav__item">
+      <a href="parameter_types.html" class="md-nav__link">
+        <span title="/parameter_types.ipynb (reference label)" class="md-ellipsis">Parameter Types</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
     <li class="md-nav__item">
       <a href="domain_reduction.html" class="md-nav__link">
         <span title="/domain_reduction.ipynb (reference label)" class="md-ellipsis">Sequential Domain Reduction</span>
@@ -631,6 +646,21 @@
   
   
   
+    <li class="md-nav__item">
+      <a href="reference/parameter.html" class="md-nav__link">
+        <span title="/reference/parameter.rst (reference label)" class="md-ellipsis">bayes_<wbr>opt.<wbr>parameter</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
     <li class="md-nav__item">
       <a href="reference/exception.html" class="md-nav__link">
         <span title="/reference/exception.rst (reference label)" class="md-ellipsis">bayes_<wbr>opt.<wbr>exception</span>
diff --git a/master/genindex.html b/master/genindex.html
index 8ff9ad04..fc17330d 100644
--- a/master/genindex.html
+++ b/master/genindex.html
@@ -318,6 +318,21 @@
   
   
   
+    <li class="md-nav__item">
+      <a href="parameter_types.html" class="md-nav__link">
+        <span title="/parameter_types.ipynb (reference label)" class="md-ellipsis">Parameter Types</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
     <li class="md-nav__item">
       <a href="domain_reduction.html" class="md-nav__link">
         <span title="/domain_reduction.ipynb (reference label)" class="md-ellipsis">Sequential Domain Reduction</span>
@@ -489,6 +504,21 @@
   
   
   
+    <li class="md-nav__item">
+      <a href="reference/parameter.html" class="md-nav__link">
+        <span title="/reference/parameter.rst (reference label)" class="md-ellipsis">bayes_<wbr>opt.<wbr>parameter</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
     <li class="md-nav__item">
       <a href="reference/exception.html" class="md-nav__link">
         <span title="/reference/exception.rst (reference label)" class="md-ellipsis">bayes_<wbr>opt.<wbr>exception</span>
diff --git a/master/index.html b/master/index.html
index 4622f600..c7354467 100644
--- a/master/index.html
+++ b/master/index.html
@@ -331,6 +331,21 @@
   
   
   
+    <li class="md-nav__item">
+      <a href="parameter_types.html" class="md-nav__link">
+        <span title="/parameter_types.ipynb (reference label)" class="md-ellipsis">Parameter Types</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
     <li class="md-nav__item">
       <a href="domain_reduction.html" class="md-nav__link">
         <span title="/domain_reduction.ipynb (reference label)" class="md-ellipsis">Sequential Domain Reduction</span>
@@ -502,6 +517,21 @@
   
   
   
+    <li class="md-nav__item">
+      <a href="reference/parameter.html" class="md-nav__link">
+        <span title="/reference/parameter.rst (reference label)" class="md-ellipsis">bayes_<wbr>opt.<wbr>parameter</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
     <li class="md-nav__item">
       <a href="reference/exception.html" class="md-nav__link">
         <span title="/reference/exception.rst (reference label)" class="md-ellipsis">bayes_<wbr>opt.<wbr>exception</span>
@@ -693,11 +723,13 @@ <h2 id="quick-index">Quick Index<a class="headerlink" href="#quick-index" title=
 to learn how to use the package’s most important features.</p></li>
 <li><p>Take a look at the <a class="reference external" href="advanced-tour.html">advanced tour
 notebook</a>
-to learn how to make the package more flexible, how to deal with
-categorical parameters, how to use observers, and more.</p></li>
+to learn how to make the package more flexible or how to use observers.</p></li>
 <li><p>To learn more about acquisition functions, a central building block
 of bayesian optimization, see the <a class="reference external" href="acquisition_functions.html">acquisition functions
 notebook</a></p></li>
+<li><p>If you want to optimize over integer-valued or categorical
+parameters, see the <a class="reference external" href="parameter_types.html">parameter types
+notebook</a>.</p></li>
 <li><p>Check out this
 <a class="reference external" href="visualization.html">notebook</a>
 with a step by step visualization of how this method works.</p></li>
@@ -757,6 +789,18 @@ <h2 id="citation">Citation<a class="headerlink" href="#citation" title="Link to
 <span class="p">}</span>
 </code></pre></div>
 </div>
+<p>For optimization over non-float parameters:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><code><span class="nd">@article</span><span class="p">{</span><span class="n">garrido2020dealing</span><span class="p">,</span>
+    <span class="n">title</span><span class="o">=</span><span class="p">{</span><span class="n">Dealing</span> <span class="k">with</span> <span class="n">categorical</span> <span class="ow">and</span> <span class="n">integer</span><span class="o">-</span><span class="n">valued</span> <span class="n">variables</span> <span class="ow">in</span> <span class="n">bayesian</span> <span class="n">optimization</span> <span class="k">with</span> <span class="n">gaussian</span> <span class="n">processes</span><span class="p">},</span>
+    <span class="n">author</span><span class="o">=</span><span class="p">{</span><span class="n">Garrido</span><span class="o">-</span><span class="n">Merch</span><span class="p">{</span>\<span class="s1">&#39;a}n, Eduardo C and Hern{</span><span class="se">\&#39;</span><span class="s1">a}ndez-Lobato, Daniel},</span>
+    <span class="n">journal</span><span class="o">=</span><span class="p">{</span><span class="n">Neurocomputing</span><span class="p">},</span>
+    <span class="n">volume</span><span class="o">=</span><span class="p">{</span><span class="mi">380</span><span class="p">},</span>
+    <span class="n">pages</span><span class="o">=</span><span class="p">{</span><span class="mi">20</span><span class="o">--</span><span class="mi">35</span><span class="p">},</span>
+    <span class="n">year</span><span class="o">=</span><span class="p">{</span><span class="mi">2020</span><span class="p">},</span>
+    <span class="n">publisher</span><span class="o">=</span><span class="p">{</span><span class="n">Elsevier</span><span class="p">}</span>
+<span class="p">}</span>
+</code></pre></div>
+</div>
 
 
 
diff --git a/master/objects.inv b/master/objects.inv
index e1e80f30..7a12b987 100644
Binary files a/master/objects.inv and b/master/objects.inv differ
diff --git a/master/parameter_types.html b/master/parameter_types.html
new file mode 100644
index 00000000..50081edc
--- /dev/null
+++ b/master/parameter_types.html
@@ -0,0 +1,1474 @@
+
+
+<!doctype html>
+<html lang="en" class="no-js">
+  <head>
+    
+      <meta charset="utf-8">
+      <meta name="viewport" content="width=device-width,initial-scale=1">
+      
+      
+      
+      <link rel="icon" href="_static/func.ico">
+    
+    
+      
+        <title>Optimizing over non-float Parameters - Bayesian Optimization</title>
+      
+    
+    
+      
+        
+        
+      
+      
+
+    
+    
+    
+      
+        
+        
+        <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
+      
+    
+        <link rel="stylesheet" type="text/css" href="_static/sphinx_immaterial_theme.4b4a8c74e2ff2ab5b.min.css?v=afa97f93" />
+        <link rel="stylesheet" type="text/css" href="_static/nbsphinx-code-cells.css?v=2aa19091" />
+    <script>__md_scope=new URL("/",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
+    
+      
+
+    
+    
+    
+  </head>
+  
+  
+    
+    
+      
+    
+    
+    
+    
+    <body dir="ltr" data-md-color-scheme="default" data-md-color-primary="light-blue" data-md-color-accent="light-green">
+  
+    
+    
+      <script>var palette=__md_get("__palette");if(palette&&"object"==typeof palette.color)for(var key of Object.keys(palette.color))document.body.setAttribute("data-md-color-"+key,palette.color[key])</script>
+    
+    <input class="md-toggle" data-md-toggle="drawer" type="checkbox" id="__drawer" autocomplete="off">
+    <input class="md-toggle" data-md-toggle="search" type="checkbox" id="__search" autocomplete="off">
+    <label class="md-overlay" for="__drawer"></label>
+    <div data-md-component="skip">
+      
+        
+        <a href="#1.-Simple-integer-valued-function" class="md-skip">
+          Skip to content
+        </a>
+      
+    </div>
+    <div data-md-component="announce">
+      
+    </div>
+    
+      <div data-md-component="outdated" hidden>
+        
+      </div>
+    
+    
+      
+
+<header class="md-header" data-md-component="header">
+  <nav class="md-header__inner md-grid" aria-label="Header">
+    <a href="index.html" title="Bayesian Optimization" class="md-header__button md-logo" aria-label="Bayesian Optimization" data-md-component="logo">
+      
+  
+  <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54Z"/></svg>
+    </a>
+    <label class="md-header__button md-icon" for="__drawer">
+      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg>
+    </label>
+    <div class="md-header__title" data-md-component="header-title">
+      <div class="md-header__ellipsis">
+        <div class="md-header__topic">
+          <span class="md-ellipsis">
+            Bayesian Optimization
+          </span>
+        </div>
+        <div class="md-header__topic" data-md-component="header-topic">
+          <span class="md-ellipsis">
+            
+              Optimizing over non-float Parameters
+            
+          </span>
+        </div>
+      </div>
+    </div>
+    
+      <form class="md-header__option" data-md-component="palette">
+        
+          
+          
+          <input class="md-option" data-md-color-media="(prefers-color-scheme: light)" data-md-color-scheme="default" data-md-color-primary="light-blue" data-md-color-accent="light-green"  aria-label="Switch to dark mode"  type="radio" name="__palette" id="__palette_1">
+          
+            <label class="md-header__button md-icon" title="Switch to dark mode" for="__palette_2" hidden>
+              <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 2a7 7 0 0 1 7 7c0 2.38-1.19 4.47-3 5.74V17a1 1 0 0 1-1 1H9a1 1 0 0 1-1-1v-2.26C6.19 13.47 5 11.38 5 9a7 7 0 0 1 7-7M9 21v-1h6v1a1 1 0 0 1-1 1h-4a1 1 0 0 1-1-1m3-17a5 5 0 0 0-5 5c0 2.05 1.23 3.81 3 4.58V16h4v-2.42c1.77-.77 3-2.53 3-4.58a5 5 0 0 0-5-5Z"/></svg>
+            </label>
+          
+        
+          
+          
+          <input class="md-option" data-md-color-media="(prefers-color-scheme: dark)" data-md-color-scheme="slate" data-md-color-primary="deep-orange" data-md-color-accent="lime"  aria-label="Switch to light mode"  type="radio" name="__palette" id="__palette_2">
+          
+            <label class="md-header__button md-icon" title="Switch to light mode" for="__palette_1" hidden>
+              <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 2a7 7 0 0 0-7 7c0 2.38 1.19 4.47 3 5.74V17a1 1 0 0 0 1 1h6a1 1 0 0 0 1-1v-2.26c1.81-1.27 3-3.36 3-5.74a7 7 0 0 0-7-7M9 21a1 1 0 0 0 1 1h4a1 1 0 0 0 1-1v-1H9v1Z"/></svg>
+            </label>
+          
+        
+      </form>
+    
+    
+    
+      <label class="md-header__button md-icon" for="__search">
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
+      </label>
+      <div class="md-search" data-md-component="search" role="dialog">
+  <label class="md-search__overlay" for="__search"></label>
+  <div class="md-search__inner" role="search">
+    <form class="md-search__form" name="search">
+      <input type="text" class="md-search__input" name="query" aria-label="Search" placeholder="Search" autocapitalize="off" autocorrect="off" autocomplete="off" spellcheck="false" data-md-component="search-query" required>
+      <label class="md-search__icon md-icon" for="__search">
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11h12Z"/></svg>
+      </label>
+      <nav class="md-search__options" aria-label="Search">
+        
+          <a href="javascript:void(0)" class="md-search__icon md-icon" title="Share" aria-label="Share" data-clipboard data-clipboard-text="" data-md-component="search-share" tabindex="-1">
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M18 16.08c-.76 0-1.44.3-1.96.77L8.91 12.7c.05-.23.09-.46.09-.7 0-.24-.04-.47-.09-.7l7.05-4.11c.54.5 1.25.81 2.04.81a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3c0 .24.04.47.09.7L8.04 9.81C7.5 9.31 6.79 9 6 9a3 3 0 0 0-3 3 3 3 0 0 0 3 3c.79 0 1.5-.31 2.04-.81l7.12 4.15c-.05.21-.08.43-.08.66 0 1.61 1.31 2.91 2.92 2.91 1.61 0 2.92-1.3 2.92-2.91A2.92 2.92 0 0 0 18 16.08Z"/></svg>
+          </a>
+        
+        <button type="reset" class="md-search__icon md-icon" title="Clear" aria-label="Clear" tabindex="-1">
+          <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12 19 6.41Z"/></svg>
+        </button>
+      </nav>
+      
+    </form>
+    <div class="md-search__output">
+      <div class="md-search__scrollwrap" data-md-scrollfix>
+        <div class="md-search-result" data-md-component="search-result">
+          <div class="md-search-result__meta">
+            Initializing search
+          </div>
+          <ol class="md-search-result__list"></ol>
+        </div>
+      </div>
+    </div>
+  </div>
+</div>
+    
+    
+      <div class="md-header__source">
+        <a href="https://github.com/bayesian-optimization/BayesianOptimization/" title="Go to repository" class="md-source" data-md-component="source">
+  <div class="md-source__icon md-icon">
+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg>
+  </div>
+  <div class="md-source__repository">
+    bayesian-optimization
+  </div>
+</a>
+      </div>
+    
+  </nav>
+  
+</header>
+    
+    <div class="md-container" data-md-component="container">
+      
+      
+      
+      
+        
+          
+        
+      
+      <main class="md-main" data-md-component="main">
+        <div class="md-main__inner md-grid">
+          
+            
+              
+              <div class="md-sidebar md-sidebar--primary" data-md-component="sidebar" data-md-type="navigation" >
+                <div class="md-sidebar__scrollwrap">
+                  <div class="md-sidebar__inner">
+                    
+
+
+<nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
+  <label class="md-nav__title" for="__drawer">
+    <a href="index.html" title="Bayesian Optimization" class="md-nav__button md-logo" aria-label="Bayesian Optimization" data-md-component="logo">
+      
+  
+  <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54Z"/></svg>
+    </a>
+    Bayesian Optimization
+  </label>
+  
+    <div class="md-nav__source">
+      <a href="https://github.com/bayesian-optimization/BayesianOptimization/" title="Go to repository" class="md-source" data-md-component="source">
+  <div class="md-source__icon md-icon">
+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg>
+  </div>
+  <div class="md-source__repository">
+    bayesian-optimization
+  </div>
+</a>
+    </div>
+  
+  <ul class="md-nav__list" data-md-scrollfix>
+    
+      
+      
+      
+
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="index.html" class="md-nav__link">
+        <span title="/index.rst (reference label)" class="md-ellipsis">Quickstart</span>
+      </a>
+    </li>
+  
+
+    
+      
+      
+      
+
+  
+  
+    
+  
+  
+    
+      
+    
+    <li class="md-nav__item md-nav__item--active md-nav__item--section md-nav__item--nested">
+      
+      
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_2" type="checkbox" id="__nav_2" checked>
+      
+      
+      
+        <label class="md-nav__link" for="__nav_2">
+          <span class="md-ellipsis">Example Notebooks:<wbr></span>
+          <span class="md-nav__icon md-icon"></span>
+        </label>
+      
+      <nav class="md-nav" aria-label="Example Notebooks:" data-md-level="1">
+        <label class="md-nav__title" for="__nav_2">
+          <span class="md-nav__icon md-icon"></span>
+          <span class="md-ellipsis">Example Notebooks:<wbr></span>
+        </label>
+        <ul class="md-nav__list" data-md-scrollfix>
+          
+            
+              
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="basic-tour.html" class="md-nav__link">
+        <span title="/basic-tour.ipynb (reference label)" class="md-ellipsis">Basic Tour</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="advanced-tour.html" class="md-nav__link">
+        <span title="/advanced-tour.ipynb (reference label)" class="md-ellipsis">Advanced Tour</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="constraints.html" class="md-nav__link">
+        <span title="/constraints.ipynb (reference label)" class="md-ellipsis">Constrained Bayesian Optimization</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+    
+  
+    
+  
+  
+    <li class="md-nav__item md-nav__item--active">
+      
+      
+      <input class="md-nav__toggle md-toggle" data-md-toggle="toc" type="checkbox" id="__toc">
+      
+        <label class="md-nav__link md-nav__link--active" for="__toc">
+          <span title="/parameter_types.ipynb (reference label)" class="md-ellipsis">Parameter Types</span>
+          <span class="md-nav__icon md-icon"></span>
+        </label>
+      
+      <a href="#" class="md-nav__link md-nav__link--active">
+        <span title="/parameter_types.ipynb (reference label)" class="md-ellipsis">Parameter Types</span>
+      </a>
+      
+        
+
+  
+
+<nav class="md-nav md-nav--secondary">
+  
+  
+    
+  
+  
+    <label class="md-nav__title" for="__toc">
+      <span class="md-nav__icon md-icon"></span>
+      <span title="/parameter_types.ipynb (reference label)" class="md-ellipsis">Parameter Types</span>
+    </label>
+    <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
+      
+        <li class="md-nav__item">
+  <a href="#1.-Simple-integer-valued-function" class="md-nav__link">
+    <span title="/parameter_types.ipynb#1.-simple-integer-valued-function (reference label)" class="md-ellipsis">1.<wbr> Simple integer-<wbr>valued function</span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#2.-Mixed-parameter-optimization" class="md-nav__link">
+    <span title="/parameter_types.ipynb#2.-mixed-parameter-optimization (reference label)" class="md-ellipsis">2.<wbr> Mixed-<wbr>parameter optimization</span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#3.-Categorical-variables" class="md-nav__link">
+    <span title="/parameter_types.ipynb#3.-categorical-variables (reference label)" class="md-ellipsis">3.<wbr> Categorical variables</span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#4.-Use-in-ML" class="md-nav__link">
+    <span title="/parameter_types.ipynb#4.-use-in-ml (reference label)" class="md-ellipsis">4.<wbr> Use in ML</span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#5.-Defining-your-own-Parameter" class="md-nav__link">
+    <span title="/parameter_types.ipynb#5.-defining-your-own-parameter (reference label)" class="md-ellipsis">5.<wbr> Defining your own Parameter</span>
+  </a>
+  
+</li>
+      
+    </ul>
+  
+</nav>
+      
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="domain_reduction.html" class="md-nav__link">
+        <span title="/domain_reduction.ipynb (reference label)" class="md-ellipsis">Sequential Domain Reduction</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="acquisition_functions.html" class="md-nav__link">
+        <span title="/acquisition_functions.ipynb (reference label)" class="md-ellipsis">Acquisition Functions</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="exploitation_vs_exploration.html" class="md-nav__link">
+        <span title="/exploitation_vs_exploration.ipynb (reference label)" class="md-ellipsis">Exploration vs.<wbr> Exploitation</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="visualization.html" class="md-nav__link">
+        <span title="/visualization.ipynb (reference label)" class="md-ellipsis">Visualization of a 1D-<wbr>Optimization</span>
+      </a>
+    </li>
+  
+
+            
+          
+        </ul>
+      </nav>
+    </li>
+  
+  
+
+    
+      
+      
+      
+
+  
+  
+  
+    
+      
+    
+    <li class="md-nav__item md-nav__item--section md-nav__item--nested">
+      
+      
+        <input class="md-nav__toggle md-toggle md-toggle--indeterminate" data-md-toggle="__nav_3" type="checkbox" id="__nav_3" checked>
+      
+      
+      
+        <label class="md-nav__link" for="__nav_3">
+          <span class="md-ellipsis">API reference:<wbr></span>
+          <span class="md-nav__icon md-icon"></span>
+        </label>
+      
+      <nav class="md-nav" aria-label="API reference:" data-md-level="1">
+        <label class="md-nav__title" for="__nav_3">
+          <span class="md-nav__icon md-icon"></span>
+          <span class="md-ellipsis">API reference:<wbr></span>
+        </label>
+        <ul class="md-nav__list" data-md-scrollfix>
+          
+            
+              
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="reference/bayes_opt.html" class="md-nav__link">
+        <span title="/reference/bayes_opt.rst (reference label)" class="md-ellipsis">bayes_<wbr>opt.<wbr>Bayesian<wbr>Optimization</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="reference/acquisition.html" class="md-nav__link">
+        <span title="/reference/acquisition.rst (reference label)" class="md-ellipsis">bayes_<wbr>opt.<wbr>acquisition</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="reference/constraint.html" class="md-nav__link">
+        <span title="/reference/constraint.rst (reference label)" class="md-ellipsis">bayes_<wbr>opt.<wbr>Constraint<wbr>Model</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="reference/domain_reduction.html" class="md-nav__link">
+        <span title="/reference/domain_reduction.rst (reference label)" class="md-ellipsis">bayes_<wbr>opt.<wbr>Sequential<wbr>Domain<wbr>Reduction<wbr>Transformer</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="reference/target_space.html" class="md-nav__link">
+        <span title="/reference/target_space.rst (reference label)" class="md-ellipsis">bayes_<wbr>opt.<wbr>Target<wbr>Space</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="reference/parameter.html" class="md-nav__link">
+        <span title="/reference/parameter.rst (reference label)" class="md-ellipsis">bayes_<wbr>opt.<wbr>parameter</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="reference/exception.html" class="md-nav__link">
+        <span title="/reference/exception.rst (reference label)" class="md-ellipsis">bayes_<wbr>opt.<wbr>exception</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="reference/other.html" class="md-nav__link">
+        <span title="/reference/other.rst (reference label)" class="md-ellipsis">Other</span>
+      </a>
+    </li>
+  
+
+            
+          
+        </ul>
+      </nav>
+    </li>
+  
+  
+
+    
+  </ul>
+</nav>
+                  </div>
+                </div>
+              </div>
+            
+            
+              
+              <div class="md-sidebar md-sidebar--secondary" data-md-component="sidebar" data-md-type="toc" >
+                <div class="md-sidebar__scrollwrap">
+                  <div class="md-sidebar__inner">
+                    
+
+  
+
+<nav class="md-nav md-nav--secondary">
+  
+  
+  
+    <label class="md-nav__title" for="__toc">
+      <span class="md-nav__icon md-icon"></span>
+      <span title="/parameter_types.ipynb (reference label)" class="md-ellipsis">Parameter Types</span>
+    </label>
+    <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
+      
+        <li class="md-nav__item">
+  <a href="#1.-Simple-integer-valued-function" class="md-nav__link">
+    <span title="/parameter_types.ipynb#1.-simple-integer-valued-function (reference label)" class="md-ellipsis">1.<wbr> Simple integer-<wbr>valued function</span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#2.-Mixed-parameter-optimization" class="md-nav__link">
+    <span title="/parameter_types.ipynb#2.-mixed-parameter-optimization (reference label)" class="md-ellipsis">2.<wbr> Mixed-<wbr>parameter optimization</span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#3.-Categorical-variables" class="md-nav__link">
+    <span title="/parameter_types.ipynb#3.-categorical-variables (reference label)" class="md-ellipsis">3.<wbr> Categorical variables</span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#4.-Use-in-ML" class="md-nav__link">
+    <span title="/parameter_types.ipynb#4.-use-in-ml (reference label)" class="md-ellipsis">4.<wbr> Use in ML</span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#5.-Defining-your-own-Parameter" class="md-nav__link">
+    <span title="/parameter_types.ipynb#5.-defining-your-own-parameter (reference label)" class="md-ellipsis">5.<wbr> Defining your own Parameter</span>
+  </a>
+  
+</li>
+      
+    </ul>
+  
+</nav>
+                  </div>
+                </div>
+              </div>
+            
+          
+          
+            <div class="md-content" data-md-component="content">
+              <article class="md-content__inner md-typeset" role="main">
+                  
+
+
+  <a href="https://github.com/bayesian-optimization/BayesianOptimization/blob/master/docsrc/parameter_types.ipynb" title="Edit this page" class="md-content__button md-icon">
+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M10 20H6V4h7v5h5v3.1l2-2V8l-6-6H6c-1.1 0-2 .9-2 2v16c0 1.1.9 2 2 2h4v-2m10.2-7c.1 0 .3.1.4.2l1.3 1.3c.2.2.2.6 0 .8l-1 1-2.1-2.1 1-1c.1-.1.2-.2.4-.2m0 3.9L14.1 23H12v-2.1l6.1-6.1 2.1 2.1Z"/></svg>
+  </a>
+
+<h1 id="Optimizing-over-non-float-Parameters">Optimizing over non-float Parameters<a class="headerlink" href="#Optimizing-over-non-float-Parameters" title="Link to this heading">¶</a></h1>
+<p>Sometimes, you need to optimize a target that is not just a function of floating-point values, but relies on integer or categorical parameters. This notebook shows how such problems are handled by following an approach from <a class="reference external" href="https://arxiv.org/abs/1805.03463">“Dealing with categorical and integer-valued variables in Bayesian Optimization with Gaussian processes” by Garrido-Merchán and Hernández-Lobato</a>. One simple way of handling an integer-valued parameter is to run the optimization as
+normal, but then round to the nearest integer after a point has been suggested. This method is similar, except that the rounding is performed in the <em>kernel</em>. Why does this matter? It means that the kernel is aware that two parameters, that map the to same point but are potentially distinct before this transformation are the same.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[1]:
+</code></pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><code><span class="kn">import</span> <span class="nn">warnings</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">from</span> <span class="nn">bayes_opt</span> <span class="kn">import</span> <span class="n">BayesianOptimization</span>
+<span class="kn">from</span> <span class="nn">bayes_opt</span> <span class="kn">import</span> <span class="n">acquisition</span>
+
+<span class="kn">from</span> <span class="nn">sklearn.gaussian_process.kernels</span> <span class="kn">import</span> <span class="n">Matern</span>
+
+<span class="c1"># suppress warnings about this being an experimental feature</span>
+<span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span><span class="n">action</span><span class="o">=</span><span class="s2">&quot;ignore&quot;</span><span class="p">)</span>
+</code></pre></div>
+</div>
+</div>
+<h2 id="1.-Simple-integer-valued-function">1. Simple integer-valued function<a class="headerlink" href="#1.-Simple-integer-valued-function" title="Link to this heading">¶</a></h2>
+<p>Let’s look at a simple, one-dimensional, integer-valued target function and compare a typed optimizer and a continuous optimizer.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[2]:
+</code></pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><code><span class="k">def</span> <span class="nf">target_function_1d</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
+    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">x</span><span class="p">))</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">/</span> <span class="mi">5</span><span class="p">)</span>
+
+<span class="n">c_pbounds</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;x&#39;</span><span class="p">:</span> <span class="p">(</span><span class="o">-</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">)}</span>
+<span class="n">bo_cont</span> <span class="o">=</span> <span class="n">BayesianOptimization</span><span class="p">(</span><span class="n">target_function_1d</span><span class="p">,</span> <span class="n">c_pbounds</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+
+<span class="c1"># one way of constructing an integer-valued parameter is to add a third element to the tuple</span>
+<span class="n">d_pbounds</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;x&#39;</span><span class="p">:</span> <span class="p">(</span><span class="o">-</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="nb">int</span><span class="p">)}</span>
+<span class="n">bo_disc</span> <span class="o">=</span> <span class="n">BayesianOptimization</span><span class="p">(</span><span class="n">target_function_1d</span><span class="p">,</span> <span class="n">d_pbounds</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+
+<span class="n">fig</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">6</span><span class="p">),</span> <span class="n">sharex</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">sharey</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="n">bo_cont</span><span class="o">.</span><span class="n">maximize</span><span class="p">(</span><span class="n">init_points</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">n_iter</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+<span class="n">bo_cont</span><span class="o">.</span><span class="n">acquisition_function</span><span class="o">.</span><span class="n">_fit_gp</span><span class="p">(</span><span class="n">bo_cont</span><span class="o">.</span><span class="n">_gp</span><span class="p">,</span> <span class="n">bo_cont</span><span class="o">.</span><span class="n">space</span><span class="p">)</span>
+
+<span class="n">y_mean</span><span class="p">,</span> <span class="n">y_std</span> <span class="o">=</span> <span class="n">bo_cont</span><span class="o">.</span><span class="n">_gp</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">1000</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">return_std</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Continuous&#39;</span><span class="p">)</span>
+<span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">1000</span><span class="p">),</span> <span class="n">target_function_1d</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">1000</span><span class="p">)),</span> <span class="s1">&#39;k--&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;True function&#39;</span><span class="p">)</span>
+<span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">1000</span><span class="p">),</span> <span class="n">y_mean</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Predicted mean&#39;</span><span class="p">)</span>
+<span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">fill_between</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">1000</span><span class="p">),</span> <span class="n">y_mean</span> <span class="o">-</span> <span class="n">y_std</span><span class="p">,</span> <span class="n">y_mean</span> <span class="o">+</span> <span class="n">y_std</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Predicted std&#39;</span><span class="p">)</span>
+<span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">bo_cont</span><span class="o">.</span><span class="n">space</span><span class="o">.</span><span class="n">params</span><span class="p">,</span> <span class="n">bo_cont</span><span class="o">.</span><span class="n">space</span><span class="o">.</span><span class="n">target</span><span class="p">,</span> <span class="s1">&#39;ro&#39;</span><span class="p">)</span>
+
+<span class="n">bo_disc</span><span class="o">.</span><span class="n">maximize</span><span class="p">(</span><span class="n">init_points</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">n_iter</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+<span class="n">bo_disc</span><span class="o">.</span><span class="n">acquisition_function</span><span class="o">.</span><span class="n">_fit_gp</span><span class="p">(</span><span class="n">bo_disc</span><span class="o">.</span><span class="n">_gp</span><span class="p">,</span> <span class="n">bo_disc</span><span class="o">.</span><span class="n">space</span><span class="p">)</span>
+
+<span class="n">y_mean</span><span class="p">,</span> <span class="n">y_std</span> <span class="o">=</span> <span class="n">bo_disc</span><span class="o">.</span><span class="n">_gp</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">1000</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">return_std</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Discrete&#39;</span><span class="p">)</span>
+<span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">1000</span><span class="p">),</span> <span class="n">target_function_1d</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">1000</span><span class="p">)),</span> <span class="s1">&#39;k--&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;True function&#39;</span><span class="p">)</span>
+<span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">1000</span><span class="p">),</span> <span class="n">y_mean</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Predicted mean&#39;</span><span class="p">)</span>
+<span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">fill_between</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">1000</span><span class="p">),</span> <span class="n">y_mean</span> <span class="o">-</span> <span class="n">y_std</span><span class="p">,</span> <span class="n">y_mean</span> <span class="o">+</span> <span class="n">y_std</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Predicted std&#39;</span><span class="p">)</span>
+<span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">bo_disc</span><span class="o">.</span><span class="n">space</span><span class="o">.</span><span class="n">params</span><span class="p">,</span> <span class="n">bo_disc</span><span class="o">.</span><span class="n">space</span><span class="o">.</span><span class="n">target</span><span class="p">,</span> <span class="s1">&#39;ro&#39;</span><span class="p">)</span>
+
+<span class="k">for</span> <span class="n">ax</span> <span class="ow">in</span> <span class="n">axs</span><span class="p">:</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">fig</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+</code></pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="_images/parameter_types_3_0.png" src="_images/parameter_types_3_0.png" />
+</div>
+</div>
+<p>We can see, that the discrete optimizer is aware that the function is discrete and does not try to predict values between the integers. The continuous optimizer tries to predict values between the integers, despite the fact that these are known. We can also see that the discrete optimizer predicts blocky mean and standard deviations, which is a result of the discrete nature of the function.</p>
+<h2 id="2.-Mixed-parameter-optimization">2. Mixed-parameter optimization<a class="headerlink" href="#2.-Mixed-parameter-optimization" title="Link to this heading">¶</a></h2>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[3]:
+</code></pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><code><span class="k">def</span> <span class="nf">discretized_function</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
+    <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
+    <span class="k">return</span> <span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="o">**</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">y</span><span class="p">)</span> <span class="o">+</span> <span class="o">-</span><span class="mi">1</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">y</span><span class="p">))</span> <span class="o">+</span> <span class="mf">0.1</span> <span class="o">*</span> <span class="p">(</span><span class="n">x</span> <span class="o">+</span> <span class="n">y</span><span class="p">)</span> <span class="o">-</span> <span class="mf">0.01</span> <span class="o">*</span> <span class="p">(</span><span class="n">x</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">y</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+</code></pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[4]:
+</code></pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><code><span class="c1"># Bounded region of parameter space</span>
+<span class="n">c_pbounds</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;x&#39;</span><span class="p">:</span> <span class="p">(</span><span class="o">-</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="s1">&#39;y&#39;</span><span class="p">:</span> <span class="p">(</span><span class="o">-</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">)}</span>
+</code></pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[5]:
+</code></pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><code><span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;All-float Optimizer&quot;</span><span class="p">,</span> <span class="s2">&quot;Typed Optimizer&quot;</span><span class="p">]</span>
+</code></pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[6]:
+</code></pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><code><span class="n">continuous_optimizer</span> <span class="o">=</span> <span class="n">BayesianOptimization</span><span class="p">(</span>
+    <span class="n">f</span><span class="o">=</span><span class="n">discretized_function</span><span class="p">,</span>
+    <span class="n">acquisition_function</span><span class="o">=</span><span class="n">acquisition</span><span class="o">.</span><span class="n">ExpectedImprovement</span><span class="p">(</span><span class="n">xi</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span>
+    <span class="n">pbounds</span><span class="o">=</span><span class="n">c_pbounds</span><span class="p">,</span>
+    <span class="n">verbose</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
+    <span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="n">continuous_optimizer</span><span class="o">.</span><span class="n">set_gp_params</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="n">Matern</span><span class="p">(</span><span class="n">nu</span><span class="o">=</span><span class="mf">2.5</span><span class="p">,</span> <span class="n">length_scale</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">2</span><span class="p">)))</span>
+
+<span class="n">d_pbounds</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;x&#39;</span><span class="p">:</span> <span class="p">(</span><span class="o">-</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="s1">&#39;y&#39;</span><span class="p">:</span> <span class="p">(</span><span class="o">-</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="nb">int</span><span class="p">)}</span>
+<span class="n">discrete_optimizer</span> <span class="o">=</span> <span class="n">BayesianOptimization</span><span class="p">(</span>
+    <span class="n">f</span><span class="o">=</span><span class="n">discretized_function</span><span class="p">,</span>
+    <span class="n">acquisition_function</span><span class="o">=</span><span class="n">acquisition</span><span class="o">.</span><span class="n">ExpectedImprovement</span><span class="p">(</span><span class="n">xi</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span>
+    <span class="n">pbounds</span><span class="o">=</span><span class="n">d_pbounds</span><span class="p">,</span>
+    <span class="n">verbose</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
+    <span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="n">discrete_optimizer</span><span class="o">.</span><span class="n">set_gp_params</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="n">Matern</span><span class="p">(</span><span class="n">nu</span><span class="o">=</span><span class="mf">2.5</span><span class="p">,</span> <span class="n">length_scale</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">2</span><span class="p">)));</span>
+</code></pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[7]:
+</code></pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><code><span class="k">for</span> <span class="n">lbl</span><span class="p">,</span> <span class="n">optimizer</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="p">[</span><span class="n">continuous_optimizer</span><span class="p">,</span> <span class="n">discrete_optimizer</span><span class="p">]):</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;====================  </span><span class="si">{</span><span class="n">lbl</span><span class="si">}</span><span class="s2">  ====================</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="n">optimizer</span><span class="o">.</span><span class="n">maximize</span><span class="p">(</span>
+        <span class="n">init_points</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
+        <span class="n">n_iter</span><span class="o">=</span><span class="mi">13</span>
+    <span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Max: </span><span class="si">{</span><span class="n">optimizer</span><span class="o">.</span><span class="n">max</span><span class="p">[</span><span class="s1">&#39;target&#39;</span><span class="p">]</span><span class="si">}</span><span class="se">\n\n</span><span class="s2">&quot;</span><span class="p">)</span>
+</code></pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+====================  All-float Optimizer  ====================
+
+|   iter    |  target   |     x     |     y     |
+-------------------------------------------------
+| 1         | 0.03061   | -0.829779 | 2.2032449 |
+| 2         | -0.6535   | -4.998856 | -1.976674 |
+| <span class="ansi-magenta-fg">3        </span> | <span class="ansi-magenta-fg">0.8025   </span> | <span class="ansi-magenta-fg">-0.829779</span> | <span class="ansi-magenta-fg">2.6549696</span> |
+| <span class="ansi-magenta-fg">4        </span> | <span class="ansi-magenta-fg">0.9203   </span> | <span class="ansi-magenta-fg">-0.981065</span> | <span class="ansi-magenta-fg">2.6644394</span> |
+| <span class="ansi-magenta-fg">5        </span> | <span class="ansi-magenta-fg">1.008    </span> | <span class="ansi-magenta-fg">-1.652553</span> | <span class="ansi-magenta-fg">2.7133425</span> |
+| 6         | 0.9926    | -1.119714 | 2.8358733 |
+| <span class="ansi-magenta-fg">7        </span> | <span class="ansi-magenta-fg">1.322    </span> | <span class="ansi-magenta-fg">-2.418942</span> | <span class="ansi-magenta-fg">3.4600371</span> |
+| 8         | -0.5063   | -3.092074 | 3.7368226 |
+| 9         | -0.6432   | -4.089558 | -0.560384 |
+| 10        | 1.267     | -2.360726 | 3.3725022 |
+| 11        | 0.4649    | -2.247113 | 3.7419056 |
+| 12        | 1.0       | -1.740988 | 3.4854116 |
+| 13        | 0.986     | 1.2164322 | 4.4938459 |
+| 14        | -2.27     | -2.213867 | 0.3585570 |
+| 15        | -1.853    | 1.7935035 | -0.377351 |
+=================================================
+Max: 1.321554535694256
+
+
+====================  Typed Optimizer  ====================
+
+|   iter    |  target   |     x     |     y     |
+-------------------------------------------------
+| 1         | 0.8025    | -0.829779 | 3         |
+| 2         | -2.75     | -4.998856 | 0         |
+| 3         | 0.8007    | -0.827713 | 3         |
+| 4         | -0.749    | 2.2682240 | -5        |
+| 5         | 0.3718    | -2.339072 | 4         |
+| 6         | 0.2146    | 4.9971028 | 5         |
+| 7         | 0.7473    | 4.9970839 | 2         |
+| <span class="ansi-magenta-fg">8        </span> | <span class="ansi-magenta-fg">0.8275   </span> | <span class="ansi-magenta-fg">4.9986856</span> | <span class="ansi-magenta-fg">-3       </span> |
+| 9         | 0.3464    | 4.9987136 | -2        |
+| 10        | -0.7852   | 4.9892216 | -5        |
+| 11        | -0.6627   | -4.999635 | -4        |
+| 12        | -0.1697   | -4.992664 | -3        |
+| <span class="ansi-magenta-fg">13       </span> | <span class="ansi-magenta-fg">1.428    </span> | <span class="ansi-magenta-fg">4.9950290</span> | <span class="ansi-magenta-fg">3        </span> |
+| 14        | 1.137     | 4.9970984 | 4         |
+| <span class="ansi-magenta-fg">15       </span> | <span class="ansi-magenta-fg">1.641    </span> | <span class="ansi-magenta-fg">4.0889271</span> | <span class="ansi-magenta-fg">3        </span> |
+=================================================
+Max: 1.6407143853831352
+
+
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[8]:
+</code></pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><code><span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="n">c_pbounds</span><span class="p">[</span><span class="s1">&#39;x&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">c_pbounds</span><span class="p">[</span><span class="s1">&#39;x&#39;</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span> <span class="mi">1000</span><span class="p">)</span>
+<span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="n">c_pbounds</span><span class="p">[</span><span class="s1">&#39;y&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">c_pbounds</span><span class="p">[</span><span class="s1">&#39;y&#39;</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span> <span class="mi">1000</span><span class="p">)</span>
+
+<span class="n">X</span><span class="p">,</span> <span class="n">Y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+
+<span class="n">Z</span> <span class="o">=</span> <span class="n">discretized_function</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">)</span>
+
+<span class="n">params</span> <span class="o">=</span> <span class="p">[{</span><span class="s1">&#39;x&#39;</span><span class="p">:</span> <span class="n">x_i</span><span class="p">,</span> <span class="s1">&#39;y&#39;</span><span class="p">:</span> <span class="n">y_j</span><span class="p">}</span> <span class="k">for</span> <span class="n">y_j</span> <span class="ow">in</span> <span class="n">y</span> <span class="k">for</span> <span class="n">x_i</span> <span class="ow">in</span> <span class="n">x</span><span class="p">]</span>
+<span class="n">array_params</span> <span class="o">=</span> <span class="p">[</span><span class="n">continuous_optimizer</span><span class="o">.</span><span class="n">_space</span><span class="o">.</span><span class="n">params_to_array</span><span class="p">(</span><span class="n">p</span><span class="p">)</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">params</span><span class="p">]</span>
+<span class="n">c_pred</span> <span class="o">=</span> <span class="n">continuous_optimizer</span><span class="o">.</span><span class="n">_gp</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">array_params</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+<span class="n">d_pred</span> <span class="o">=</span> <span class="n">discrete_optimizer</span><span class="o">.</span><span class="n">_gp</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">array_params</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+
+<span class="n">vmin</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">([</span><span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">Z</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">c_pred</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">d_pred</span><span class="p">)])</span>
+<span class="n">vmax</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">([</span><span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">Z</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">c_pred</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">d_pred</span><span class="p">)])</span>
+
+<span class="n">fig</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
+
+<span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Actual function&#39;</span><span class="p">)</span>
+<span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">contourf</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">coolwarm</span><span class="p">,</span> <span class="n">vmin</span><span class="o">=</span><span class="n">vmin</span><span class="p">,</span> <span class="n">vmax</span><span class="o">=</span><span class="n">vmax</span><span class="p">)</span>
+
+
+<span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">labels</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+<span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">contourf</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">c_pred</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">coolwarm</span><span class="p">,</span> <span class="n">vmin</span><span class="o">=</span><span class="n">vmin</span><span class="p">,</span> <span class="n">vmax</span><span class="o">=</span><span class="n">vmax</span><span class="p">)</span>
+<span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">continuous_optimizer</span><span class="o">.</span><span class="n">_space</span><span class="o">.</span><span class="n">params</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="n">continuous_optimizer</span><span class="o">.</span><span class="n">_space</span><span class="o">.</span><span class="n">params</span><span class="p">[:,</span><span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">)</span>
+
+<span class="n">axs</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">labels</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+<span class="n">axs</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">contourf</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">d_pred</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">coolwarm</span><span class="p">,</span> <span class="n">vmin</span><span class="o">=</span><span class="n">vmin</span><span class="p">,</span> <span class="n">vmax</span><span class="o">=</span><span class="n">vmax</span><span class="p">)</span>
+<span class="n">axs</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">discrete_optimizer</span><span class="o">.</span><span class="n">_space</span><span class="o">.</span><span class="n">params</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="n">discrete_optimizer</span><span class="o">.</span><span class="n">_space</span><span class="o">.</span><span class="n">params</span><span class="p">[:,</span><span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">)</span>
+
+<span class="k">def</span> <span class="nf">make_plot_fancy</span><span class="p">(</span><span class="n">ax</span><span class="p">:</span> <span class="n">plt</span><span class="o">.</span><span class="n">Axes</span><span class="p">):</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_aspect</span><span class="p">(</span><span class="s2">&quot;equal&quot;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;x (float)&#39;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">([</span><span class="o">-</span><span class="mf">5.0</span><span class="p">,</span> <span class="o">-</span><span class="mf">2.5</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">2.5</span><span class="p">,</span> <span class="mf">5.0</span><span class="p">])</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;y (int)&#39;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">([</span><span class="o">-</span><span class="mi">4</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">])</span>
+
+<span class="k">for</span> <span class="n">ax</span> <span class="ow">in</span> <span class="n">axs</span><span class="p">:</span>
+    <span class="n">make_plot_fancy</span><span class="p">(</span><span class="n">ax</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</code></pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="_images/parameter_types_11_0.png" src="_images/parameter_types_11_0.png" />
+</div>
+</div>
+<h2 id="3.-Categorical-variables">3. Categorical variables<a class="headerlink" href="#3.-Categorical-variables" title="Link to this heading">¶</a></h2>
+<p>We can also handle categorical variables! This is done under-the-hood by constructing parameters in a one-hot-encoding representation, with a transformation in the kernel rounding to the nearest one-hot representation. If you want to use this, you can specify a collection of strings as options.</p>
+<p>NB: As internally, the categorical variables are within a range of <code class="docutils literal notranslate"><span class="pre">[0,</span> <span class="pre">1]</span></code> and the GP used for BO is by default isotropic, you might want to ensure your other features are similarly scaled to a range of <code class="docutils literal notranslate"><span class="pre">[0,</span> <span class="pre">1]</span></code> or use an anisotropic GP.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[9]:
+</code></pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><code><span class="k">def</span> <span class="nf">f1</span><span class="p">(</span><span class="n">x1</span><span class="p">,</span> <span class="n">x2</span><span class="p">):</span>
+    <span class="k">return</span> <span class="o">-</span><span class="mi">1</span><span class="o">*</span><span class="p">(</span><span class="n">x1</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">x1</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">x2</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">x1</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">x2</span><span class="o">**</span><span class="mi">2</span><span class="p">))</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">x1</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">x2</span><span class="o">**</span><span class="mi">2</span><span class="p">))</span>
+
+<span class="k">def</span> <span class="nf">f2</span><span class="p">(</span><span class="n">x1</span><span class="p">,</span> <span class="n">x2</span><span class="p">):</span>
+    <span class="k">return</span> <span class="o">-</span><span class="mi">1</span><span class="o">*</span><span class="p">(</span><span class="n">x2</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">x1</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">x2</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">x1</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">x2</span><span class="o">**</span><span class="mi">2</span><span class="p">))</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">x1</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">x2</span><span class="o">**</span><span class="mi">2</span><span class="p">))</span>
+
+<span class="k">def</span> <span class="nf">SPIRAL</span><span class="p">(</span><span class="n">x1</span><span class="p">,</span> <span class="n">x2</span><span class="p">,</span> <span class="n">k</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;cf Ladislav-Luksan</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="n">k</span><span class="o">==</span><span class="s1">&#39;1&#39;</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">f1</span><span class="p">(</span><span class="mi">10</span> <span class="o">*</span> <span class="n">x1</span><span class="p">,</span> <span class="mi">10</span> <span class="o">*</span> <span class="n">x2</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="n">k</span><span class="o">==</span><span class="s1">&#39;2&#39;</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">f2</span><span class="p">(</span><span class="mi">10</span> <span class="o">*</span> <span class="n">x1</span><span class="p">,</span> <span class="mi">10</span> <span class="o">*</span> <span class="n">x2</span><span class="p">)</span>
+</code><br/></pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[10]:
+</code></pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><code><span class="n">pbounds</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;x1&#39;</span><span class="p">:</span> <span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="s1">&#39;x2&#39;</span><span class="p">:</span> <span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="s1">&#39;k&#39;</span><span class="p">:</span> <span class="p">(</span><span class="s1">&#39;1&#39;</span><span class="p">,</span> <span class="s1">&#39;2&#39;</span><span class="p">)}</span>
+
+<span class="n">categorical_optimizer</span> <span class="o">=</span> <span class="n">BayesianOptimization</span><span class="p">(</span>
+    <span class="n">f</span><span class="o">=</span><span class="n">SPIRAL</span><span class="p">,</span>
+    <span class="n">acquisition_function</span><span class="o">=</span><span class="n">acquisition</span><span class="o">.</span><span class="n">ExpectedImprovement</span><span class="p">(</span><span class="mf">1e-2</span><span class="p">),</span>
+    <span class="n">pbounds</span><span class="o">=</span><span class="n">pbounds</span><span class="p">,</span>
+    <span class="n">verbose</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
+    <span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">discrete_optimizer</span><span class="o">.</span><span class="n">set_gp_params</span><span class="p">(</span><span class="n">alpha</span><span class="o">=</span><span class="mf">1e-3</span><span class="p">)</span>
+
+<span class="n">categorical_optimizer</span><span class="o">.</span><span class="n">maximize</span><span class="p">(</span>
+        <span class="n">init_points</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
+        <span class="n">n_iter</span><span class="o">=</span><span class="mi">18</span><span class="p">,</span>
+    <span class="p">)</span>
+</code></pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+|   iter    |  target   |    x1     |    x2     |     k     |
+-------------------------------------------------------------
+| 1         | -2.052    | -0.165955 | 0.4406489 | 2         |
+| <span class="ansi-magenta-fg">2        </span> | <span class="ansi-magenta-fg">13.49    </span> | <span class="ansi-magenta-fg">-0.743751</span> | <span class="ansi-magenta-fg">0.9980810</span> | <span class="ansi-magenta-fg">1        </span> |
+| 3         | -14.49    | -0.743433 | 0.9709879 | 2         |
+| 4         | -13.33    | 0.9950794 | -0.352913 | 1         |
+| 5         | 9.674     | 0.5436849 | -0.574376 | 2         |
+| 6         | 9.498     | -0.218693 | -0.709177 | 2         |
+| 7         | 11.43     | -0.918642 | -0.648372 | 2         |
+| 8         | 0.4882    | -0.218182 | -0.012177 | 2         |
+| 9         | 7.542     | -0.787692 | 0.3452580 | 1         |
+| 10        | -2.161    | 0.1392349 | -0.125728 | 1         |
+| 11        | -0.8336   | 0.1206357 | -0.543264 | 1         |
+| 12        | -8.413    | 0.4981209 | 0.6434939 | 1         |
+| 13        | 6.372     | 0.0587256 | -0.892371 | 2         |
+| 14        | -12.71    | 0.7529885 | -0.780621 | 1         |
+| 15        | -1.521    | 0.4118274 | -0.517960 | 1         |
+| 16        | 11.88     | -0.755390 | -0.533137 | 1         |
+| 17        | 0.6373    | 0.2249733 | -0.053787 | 2         |
+| 18        | 2.154     | 0.0583506 | 0.6550869 | 1         |
+| <span class="ansi-magenta-fg">19       </span> | <span class="ansi-magenta-fg">13.69    </span> | <span class="ansi-magenta-fg">-0.741717</span> | <span class="ansi-magenta-fg">-0.820073</span> | <span class="ansi-magenta-fg">2        </span> |
+| 20        | 1.615     | -0.663312 | -0.905925 | 1         |
+=============================================================
+</pre></div></div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[11]:
+</code></pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><code><span class="n">res</span> <span class="o">=</span> <span class="n">categorical_optimizer</span><span class="o">.</span><span class="n">_space</span><span class="o">.</span><span class="n">res</span><span class="p">()</span>
+<span class="n">k1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="n">p</span><span class="p">[</span><span class="s1">&#39;params&#39;</span><span class="p">][</span><span class="s1">&#39;x1&#39;</span><span class="p">],</span> <span class="n">p</span><span class="p">[</span><span class="s1">&#39;params&#39;</span><span class="p">][</span><span class="s1">&#39;x2&#39;</span><span class="p">]]</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">res</span> <span class="k">if</span> <span class="n">p</span><span class="p">[</span><span class="s1">&#39;params&#39;</span><span class="p">][</span><span class="s1">&#39;k&#39;</span><span class="p">]</span><span class="o">==</span><span class="s1">&#39;1&#39;</span><span class="p">])</span>
+<span class="n">k2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="n">p</span><span class="p">[</span><span class="s1">&#39;params&#39;</span><span class="p">][</span><span class="s1">&#39;x1&#39;</span><span class="p">],</span> <span class="n">p</span><span class="p">[</span><span class="s1">&#39;params&#39;</span><span class="p">][</span><span class="s1">&#39;x2&#39;</span><span class="p">]]</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">res</span> <span class="k">if</span> <span class="n">p</span><span class="p">[</span><span class="s1">&#39;params&#39;</span><span class="p">][</span><span class="s1">&#39;k&#39;</span><span class="p">]</span><span class="o">==</span><span class="s1">&#39;2&#39;</span><span class="p">])</span>
+</code></pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[12]:
+</code></pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><code><span class="n">x1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="n">pbounds</span><span class="p">[</span><span class="s1">&#39;x1&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">pbounds</span><span class="p">[</span><span class="s1">&#39;x1&#39;</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span> <span class="mi">1000</span><span class="p">)</span>
+<span class="n">x2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="n">pbounds</span><span class="p">[</span><span class="s1">&#39;x2&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">pbounds</span><span class="p">[</span><span class="s1">&#39;x2&#39;</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span> <span class="mi">1000</span><span class="p">)</span>
+
+<span class="n">X1</span><span class="p">,</span> <span class="n">X2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">x1</span><span class="p">,</span> <span class="n">x2</span><span class="p">)</span>
+<span class="n">Z1</span> <span class="o">=</span> <span class="n">SPIRAL</span><span class="p">(</span><span class="n">X1</span><span class="p">,</span> <span class="n">X2</span><span class="p">,</span> <span class="s1">&#39;1&#39;</span><span class="p">)</span>
+<span class="n">Z2</span> <span class="o">=</span> <span class="n">SPIRAL</span><span class="p">(</span><span class="n">X1</span><span class="p">,</span> <span class="n">X2</span><span class="p">,</span> <span class="s1">&#39;2&#39;</span><span class="p">)</span>
+
+<span class="n">fig</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+
+<span class="n">vmin</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">([</span><span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">Z1</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">Z2</span><span class="p">)])</span>
+<span class="n">vmax</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">([</span><span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">Z1</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">Z2</span><span class="p">)])</span>
+
+<span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">contourf</span><span class="p">(</span><span class="n">X1</span><span class="p">,</span> <span class="n">X2</span><span class="p">,</span> <span class="n">Z1</span><span class="p">,</span> <span class="n">vmin</span><span class="o">=</span><span class="n">vmin</span><span class="p">,</span> <span class="n">vmax</span><span class="o">=</span><span class="n">vmax</span><span class="p">)</span>
+<span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_aspect</span><span class="p">(</span><span class="s2">&quot;equal&quot;</span><span class="p">)</span>
+<span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">k1</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="n">k1</span><span class="p">[:,</span><span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">)</span>
+<span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">contourf</span><span class="p">(</span><span class="n">X1</span><span class="p">,</span> <span class="n">X2</span><span class="p">,</span> <span class="n">Z2</span><span class="p">,</span> <span class="n">vmin</span><span class="o">=</span><span class="n">vmin</span><span class="p">,</span> <span class="n">vmax</span><span class="o">=</span><span class="n">vmax</span><span class="p">)</span>
+<span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">k2</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="n">k2</span><span class="p">[:,</span><span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">)</span>
+<span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_aspect</span><span class="p">(</span><span class="s2">&quot;equal&quot;</span><span class="p">)</span>
+<span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;k=1&#39;</span><span class="p">)</span>
+<span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;k=2&#39;</span><span class="p">)</span>
+<span class="n">fig</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+</code><br/></pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="_images/parameter_types_17_0.png" src="_images/parameter_types_17_0.png" />
+</div>
+</div>
+<h2 id="4.-Use-in-ML">4. Use in ML<a class="headerlink" href="#4.-Use-in-ML" title="Link to this heading">¶</a></h2>
+<p>A typical usecase for integer and categorical parameters is optimizing the hyperparameters of a machine learning model. Below you can find an example where the hyperparameters of an SVM are optimized.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[13]:
+</code></pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><code><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">load_breast_cancer</span>
+<span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="kn">import</span> <span class="n">SVC</span>
+<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">log_loss</span>
+<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
+<span class="kn">from</span> <span class="nn">bayes_opt</span> <span class="kn">import</span> <span class="n">BayesianOptimization</span>
+
+<span class="n">data</span> <span class="o">=</span> <span class="n">load_breast_cancer</span><span class="p">()</span>
+<span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="s1">&#39;data&#39;</span><span class="p">],</span> <span class="n">data</span><span class="p">[</span><span class="s1">&#39;target&#39;</span><span class="p">]</span>
+<span class="n">X_train</span><span class="p">,</span> <span class="n">X_val</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_val</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">kernels</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;rbf&#39;</span><span class="p">,</span> <span class="s1">&#39;poly&#39;</span><span class="p">]</span>
+
+<span class="k">def</span> <span class="nf">f_target</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span> <span class="n">log10_C</span><span class="p">):</span>
+    <span class="k">if</span> <span class="n">kernel</span> <span class="o">==</span> <span class="s1">&#39;poly2&#39;</span><span class="p">:</span>
+        <span class="n">kernel</span> <span class="o">=</span> <span class="s1">&#39;poly&#39;</span>
+        <span class="n">degree</span> <span class="o">=</span> <span class="mi">2</span>
+    <span class="k">elif</span> <span class="n">kernel</span> <span class="o">==</span> <span class="s1">&#39;poly3&#39;</span><span class="p">:</span>
+        <span class="n">kernel</span> <span class="o">=</span> <span class="s1">&#39;poly&#39;</span>
+        <span class="n">degree</span> <span class="o">=</span> <span class="mi">3</span>
+    <span class="k">elif</span> <span class="n">kernel</span> <span class="o">==</span> <span class="s1">&#39;rbf&#39;</span><span class="p">:</span>
+        <span class="n">degree</span> <span class="o">=</span> <span class="mi">3</span> <span class="c1"># not used, equal to default</span>
+
+    <span class="n">C</span> <span class="o">=</span> <span class="mi">10</span><span class="o">**</span><span class="n">log10_C</span>
+
+    <span class="n">model</span> <span class="o">=</span> <span class="n">SVC</span><span class="p">(</span><span class="n">C</span><span class="o">=</span><span class="n">C</span><span class="p">,</span> <span class="n">kernel</span><span class="o">=</span><span class="n">kernel</span><span class="p">,</span> <span class="n">degree</span><span class="o">=</span><span class="n">degree</span><span class="p">,</span> <span class="n">probability</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+
+    <span class="c1"># Package looks for maximum, so we return -1 * log_loss</span>
+    <span class="n">loss</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span> <span class="o">*</span> <span class="n">log_loss</span><span class="p">(</span><span class="n">y_val</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X_val</span><span class="p">))</span>
+    <span class="k">return</span> <span class="n">loss</span>
+
+
+<span class="n">params_svm</span> <span class="o">=</span><span class="p">{</span>
+    <span class="s1">&#39;kernel&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;rbf&#39;</span><span class="p">,</span> <span class="s1">&#39;poly2&#39;</span><span class="p">,</span> <span class="s1">&#39;poly3&#39;</span><span class="p">],</span>
+    <span class="s1">&#39;log10_C&#39;</span><span class="p">:(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">+</span><span class="mi">1</span><span class="p">),</span>
+<span class="p">}</span>
+
+<span class="n">optimizer</span> <span class="o">=</span> <span class="n">BayesianOptimization</span><span class="p">(</span>
+    <span class="n">f_target</span><span class="p">,</span>
+    <span class="n">params_svm</span><span class="p">,</span>
+    <span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+    <span class="n">verbose</span><span class="o">=</span><span class="mi">2</span>
+<span class="p">)</span>
+
+<span class="n">kernel</span> <span class="o">=</span> <span class="n">Matern</span><span class="p">(</span><span class="n">nu</span><span class="o">=</span><span class="mf">2.5</span><span class="p">,</span> <span class="n">length_scale</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">optimizer</span><span class="o">.</span><span class="n">space</span><span class="o">.</span><span class="n">dim</span><span class="p">))</span>
+<span class="n">discrete_optimizer</span><span class="o">.</span><span class="n">set_gp_params</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="n">kernel</span><span class="p">)</span>
+<span class="n">optimizer</span><span class="o">.</span><span class="n">maximize</span><span class="p">(</span><span class="n">init_points</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">n_iter</span><span class="o">=</span><span class="mi">8</span><span class="p">)</span>
+</code></pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+|   iter    |  target   |  kernel   |  log10_C  |
+-------------------------------------------------
+| 1         | -0.2361   | poly2     | 0.9943696 |
+| 2         | -0.2864   | rbf       | -0.999771 |
+| 3         | -0.2625   | poly3     | 0.7449728 |
+| <span class="ansi-magenta-fg">4        </span> | <span class="ansi-magenta-fg">-0.2361  </span> | <span class="ansi-magenta-fg">poly2    </span> | <span class="ansi-magenta-fg">0.9944598</span> |
+| 5         | -0.298    | poly3     | -0.999625 |
+| <span class="ansi-magenta-fg">6        </span> | <span class="ansi-magenta-fg">-0.2361  </span> | <span class="ansi-magenta-fg">poly2    </span> | <span class="ansi-magenta-fg">0.9945010</span> |
+| <span class="ansi-magenta-fg">7        </span> | <span class="ansi-magenta-fg">-0.2152  </span> | <span class="ansi-magenta-fg">rbf      </span> | <span class="ansi-magenta-fg">0.9928960</span> |
+| 8         | -0.2153   | rbf       | 0.9917667 |
+| 9         | -0.2362   | poly2     | 0.9897298 |
+| 10        | -0.2362   | poly2     | 0.9874217 |
+=================================================
+</pre></div></div>
+</div>
+<h2 id="5.-Defining-your-own-Parameter">5. Defining your own Parameter<a class="headerlink" href="#5.-Defining-your-own-Parameter" title="Link to this heading">¶</a></h2>
+<p>Maybe you want to optimize over another form of parameters, which does not align with <code class="docutils literal notranslate"><span class="pre">float</span></code>, <code class="docutils literal notranslate"><span class="pre">int</span></code> or categorical. For this purpose, you can create your own, custom parameter. A simple example is a parameter that is discrete, but still admits a distance representation (like an integer) while not being uniformly spaced.</p>
+<p>However, you can go further even and encode constraints and even symmetries in your parameter. Let’s consider the problem of finding a triangle which maximizes an area given its sides <span class="math notranslate nohighlight">\(a, b, c\)</span> with a constraint that the perimeter is fixed, i.e. <span class="math notranslate nohighlight">\(a + b + c=s\)</span>.</p>
+<p>We will create a parameter that encodes such a triangle, and via it’s kernel transform ensures that the sides sum to the required length <span class="math notranslate nohighlight">\(s\)</span>. As you might expect, the solution to this problem is an equilateral triangle, i.e. <span class="math notranslate nohighlight">\(a=b=c=s/3\)</span>.</p>
+<p>To define the parameter, we need to subclass <code class="docutils literal notranslate"><span class="pre">BayesParameter</span></code> and define a few important functions/properties.</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">is_continuous</span></code> is a property which denotes whether a parameter is continuous. When optimizing the acquisition function, non-continuous parameters will not be optimized using gradient-based methods, but only via random sampling.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">random_sample</span></code> is a function that samples randomly from the space of the parameter.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">to_float</span></code> transforms the canonical representation of a parameter into float values for the target space to store. There is a one-to-one correspondence between valid float representations produced by this function and canonical representations of the parameter. This function is most important when working with parameters that use a non-numeric canonical representation, such as categorical parameters.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">to_param</span></code> performs the inverse of <code class="docutils literal notranslate"><span class="pre">to_float</span></code>: Given a float-based representation, it creates a canonical representation. This function should perform binning whenever appropriate, e.g. in the case of the <code class="docutils literal notranslate"><span class="pre">IntParameter</span></code>, this function would round any float values supplied to it.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">kernel_transform</span></code> is the most important function of the Parameter and defines how to represent a value in the kernel space. In contrast to <code class="docutils literal notranslate"><span class="pre">to_float</span></code>, this function expects both the input, as well as the output to be float-representations of the value.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">to_string</span></code> produces a stringified version of the parameter, which allows users to define custom pretty-print rules for ththe ScreenLogger use.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">dim</span></code> is a property which defines the dimensionality of the parameter. In most cases, this will be 1, but e.g. for categorical parameters it is equivalent to the cardinality of the category space.</p></li>
+</ul>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[14]:
+</code></pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><code><span class="kn">from</span> <span class="nn">bayes_opt.logger</span> <span class="kn">import</span> <span class="n">ScreenLogger</span>
+<span class="kn">from</span> <span class="nn">bayes_opt.parameter</span> <span class="kn">import</span> <span class="n">BayesParameter</span>
+<span class="kn">from</span> <span class="nn">bayes_opt.event</span> <span class="kn">import</span> <span class="n">Events</span>
+<span class="kn">from</span> <span class="nn">bayes_opt.util</span> <span class="kn">import</span> <span class="n">ensure_rng</span>
+
+
+<span class="k">class</span> <span class="nc">FixedPerimeterTriangleParameter</span><span class="p">(</span><span class="n">BayesParameter</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">bounds</span><span class="p">,</span> <span class="n">perimeter</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">bounds</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">perimeter</span> <span class="o">=</span> <span class="n">perimeter</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">is_continuous</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="kc">True</span>
+
+    <span class="k">def</span> <span class="nf">random_sample</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n_samples</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">random_state</span><span class="p">):</span>
+        <span class="n">random_state</span> <span class="o">=</span> <span class="n">ensure_rng</span><span class="p">(</span><span class="n">random_state</span><span class="p">)</span>
+        <span class="n">samples</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">while</span> <span class="nb">len</span><span class="p">(</span><span class="n">samples</span><span class="p">)</span> <span class="o">&lt;</span> <span class="n">n_samples</span><span class="p">:</span>
+            <span class="n">samples_</span> <span class="o">=</span> <span class="n">random_state</span><span class="o">.</span><span class="n">dirichlet</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">3</span><span class="p">),</span> <span class="n">n_samples</span><span class="p">)</span>
+            <span class="n">samples_</span> <span class="o">=</span> <span class="n">samples_</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">perimeter</span> <span class="c1"># scale samples by perimeter</span>
+
+            <span class="n">samples_</span> <span class="o">=</span> <span class="n">samples_</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">all</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">bounds</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">samples_</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">samples_</span> <span class="o">&lt;=</span> <span class="bp">self</span><span class="o">.</span><span class="n">bounds</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]),</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)]</span>
+            <span class="n">samples</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">atleast_2d</span><span class="p">(</span><span class="n">samples_</span><span class="p">))</span>
+        <span class="n">samples</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">samples</span><span class="p">[:</span><span class="n">n_samples</span><span class="p">])</span>
+        <span class="k">return</span> <span class="n">samples</span>
+
+    <span class="k">def</span> <span class="nf">to_float</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">value</span>
+
+    <span class="k">def</span> <span class="nf">to_param</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">value</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">perimeter</span> <span class="o">/</span> <span class="nb">sum</span><span class="p">(</span><span class="n">value</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">kernel_transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">value</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">perimeter</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">keepdims</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">to_string</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span> <span class="n">str_len</span><span class="p">:</span> <span class="nb">int</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
+        <span class="n">len_each</span> <span class="o">=</span> <span class="p">(</span><span class="n">str_len</span> <span class="o">-</span> <span class="mi">2</span><span class="p">)</span> <span class="o">//</span> <span class="mi">3</span>
+        <span class="n">str_</span> <span class="o">=</span> <span class="s1">&#39;|&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="nb">float</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">value</span><span class="p">[</span><span class="n">i</span><span class="p">],</span><span class="w"> </span><span class="mi">4</span><span class="p">))</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">[:</span><span class="n">len_each</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">3</span><span class="p">)])</span>
+        <span class="k">return</span> <span class="n">str_</span><span class="o">.</span><span class="n">ljust</span><span class="p">(</span><span class="n">str_len</span><span class="p">)</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">dim</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="mi">3</span> <span class="c1"># as we have three float values, each representing the length of one side.</span>
+
+<span class="k">def</span> <span class="nf">area_of_triangle</span><span class="p">(</span><span class="n">sides</span><span class="p">):</span>
+    <span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span> <span class="o">=</span> <span class="n">sides</span>
+    <span class="n">s</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">sides</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span> <span class="c1"># perimeter</span>
+    <span class="n">A</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">s</span> <span class="o">*</span> <span class="p">(</span><span class="n">s</span><span class="o">-</span><span class="n">a</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">s</span><span class="o">-</span><span class="n">b</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">s</span><span class="o">-</span><span class="n">c</span><span class="p">))</span>
+    <span class="k">return</span> <span class="n">A</span>
+</code><br/></pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[15]:
+</code></pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><code><span class="n">param</span> <span class="o">=</span> <span class="n">FixedPerimeterTriangleParameter</span><span class="p">(</span>
+    <span class="n">name</span><span class="o">=</span><span class="s1">&#39;sides&#39;</span><span class="p">,</span>
+    <span class="n">bounds</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">]]),</span>
+    <span class="n">perimeter</span><span class="o">=</span><span class="mf">1.</span>
+<span class="p">)</span>
+
+<span class="n">pbounds</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;sides&#39;</span><span class="p">:</span> <span class="n">param</span><span class="p">}</span>
+<span class="n">optimizer</span> <span class="o">=</span> <span class="n">BayesianOptimization</span><span class="p">(</span>
+    <span class="n">area_of_triangle</span><span class="p">,</span>
+    <span class="n">pbounds</span><span class="p">,</span>
+    <span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="c1"># Increase the cell size to accommodate the three float values</span>
+<span class="n">logger</span> <span class="o">=</span> <span class="n">ScreenLogger</span><span class="p">(</span><span class="n">verbose</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">is_constrained</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<span class="n">logger</span><span class="o">.</span><span class="n">_default_cell_size</span> <span class="o">=</span> <span class="mi">15</span>
+
+<span class="k">for</span> <span class="n">e</span> <span class="ow">in</span> <span class="p">[</span><span class="n">Events</span><span class="o">.</span><span class="n">OPTIMIZATION_START</span><span class="p">,</span> <span class="n">Events</span><span class="o">.</span><span class="n">OPTIMIZATION_STEP</span><span class="p">,</span> <span class="n">Events</span><span class="o">.</span><span class="n">OPTIMIZATION_END</span><span class="p">]:</span>
+    <span class="n">optimizer</span><span class="o">.</span><span class="n">subscribe</span><span class="p">(</span><span class="n">e</span><span class="p">,</span> <span class="n">logger</span><span class="p">)</span>
+
+<span class="n">optimizer</span><span class="o">.</span><span class="n">maximize</span><span class="p">(</span><span class="n">init_points</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">n_iter</span><span class="o">=</span><span class="mi">23</span><span class="p">)</span>
+</code></pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+|      iter       |     target      |      sides      |
+-------------------------------------------------------
+| 1               | 0.4572          | 0.29|0.70|0.00  |
+| <span class="ansi-magenta-fg">2              </span> | <span class="ansi-magenta-fg">0.5096         </span> | <span class="ansi-magenta-fg">0.58|0.25|0.15 </span> |
+| 3               | 0.5081          | 0.58|0.25|0.15  |
+| <span class="ansi-magenta-fg">4              </span> | <span class="ansi-magenta-fg">0.5386         </span> | <span class="ansi-magenta-fg">0.44|0.28|0.26 </span> |
+| 5               | 0.5279          | 0.38|0.14|0.47  |
+| 6               | 0.5328          | 0.18|0.36|0.45  |
+| 7               | 0.4366          | 0.02|0.22|0.74  |
+| 8               | 0.4868          | 0.00|0.61|0.37  |
+| 9               | 0.4977          | 0.56|0.01|0.42  |
+| <span class="ansi-magenta-fg">10             </span> | <span class="ansi-magenta-fg">0.5418         </span> | <span class="ansi-magenta-fg">0.29|0.40|0.30 </span> |
+| 11              | 0.3361          | 0.06|0.87|0.06  |
+| 12              | 0.06468         | 0.99|0.00|0.00  |
+| 13              | 0.01589         | 0.0|0.00|0.99   |
+| 14              | 0.4999          | 0.21|0.16|0.61  |
+| 15              | 0.499           | 0.53|0.46|0.00  |
+| 16              | 0.4937          | 0.00|0.41|0.58  |
+| 17              | 0.5233          | 0.33|0.51|0.14  |
+| 18              | 0.5204          | 0.17|0.54|0.28  |
+| 19              | 0.5235          | 0.51|0.15|0.32  |
+| 20              | 0.5412          | 0.31|0.27|0.41  |
+| 21              | 0.4946          | 0.41|0.00|0.57  |
+| 22              | 0.5355          | 0.41|0.39|0.19  |
+| <span class="ansi-magenta-fg">23             </span> | <span class="ansi-magenta-fg">0.5442         </span> | <span class="ansi-magenta-fg">0.35|0.32|0.32 </span> |
+| 24              | 0.5192          | 0.16|0.28|0.54  |
+| 25              | 0.5401          | 0.39|0.23|0.36  |
+=======================================================
+</pre></div></div>
+</div>
+<p>This seems to work decently well, but we can improve it significantly if we consider the symmetries inherent in the problem: This problem is permutation invariant, i.e. we do not care which side specifically is denoted as <span class="math notranslate nohighlight">\(a\)</span>, <span class="math notranslate nohighlight">\(b\)</span> or <span class="math notranslate nohighlight">\(c\)</span>. Instead, we can, without loss of generality, decide that the shortest side will always be denoted as <span class="math notranslate nohighlight">\(a\)</span>, and the longest always as <span class="math notranslate nohighlight">\(c\)</span>. If we enhance our kernel transform with this symmetry, the performance improves significantly.
+This can be easily done by sub-classing the previously created triangle parameter.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[16]:
+</code></pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><code><span class="k">class</span> <span class="nc">SortingFixedPerimeterTriangleParameter</span><span class="p">(</span><span class="n">FixedPerimeterTriangleParameter</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">bounds</span><span class="p">,</span> <span class="n">perimeter</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">bounds</span><span class="p">,</span> <span class="n">perimeter</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">to_param</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
+        <span class="n">value</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sort</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+        <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">to_param</span><span class="p">(</span><span class="n">value</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">kernel_transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
+        <span class="n">value</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sort</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+        <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">kernel_transform</span><span class="p">(</span><span class="n">value</span><span class="p">)</span>
+
+<span class="n">param</span> <span class="o">=</span> <span class="n">SortingFixedPerimeterTriangleParameter</span><span class="p">(</span>
+    <span class="n">name</span><span class="o">=</span><span class="s1">&#39;sides&#39;</span><span class="p">,</span>
+    <span class="n">bounds</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">]]),</span>
+    <span class="n">perimeter</span><span class="o">=</span><span class="mf">1.</span>
+<span class="p">)</span>
+
+<span class="n">pbounds</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;sides&#39;</span><span class="p">:</span> <span class="n">param</span><span class="p">}</span>
+<span class="n">optimizer</span> <span class="o">=</span> <span class="n">BayesianOptimization</span><span class="p">(</span>
+    <span class="n">area_of_triangle</span><span class="p">,</span>
+    <span class="n">pbounds</span><span class="p">,</span>
+    <span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="n">logger</span> <span class="o">=</span> <span class="n">ScreenLogger</span><span class="p">(</span><span class="n">verbose</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">is_constrained</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<span class="n">logger</span><span class="o">.</span><span class="n">_default_cell_size</span> <span class="o">=</span> <span class="mi">15</span>
+
+<span class="k">for</span> <span class="n">e</span> <span class="ow">in</span> <span class="p">[</span><span class="n">Events</span><span class="o">.</span><span class="n">OPTIMIZATION_START</span><span class="p">,</span> <span class="n">Events</span><span class="o">.</span><span class="n">OPTIMIZATION_STEP</span><span class="p">,</span> <span class="n">Events</span><span class="o">.</span><span class="n">OPTIMIZATION_END</span><span class="p">]:</span>
+    <span class="n">optimizer</span><span class="o">.</span><span class="n">subscribe</span><span class="p">(</span><span class="n">e</span><span class="p">,</span> <span class="n">logger</span><span class="p">)</span>
+
+<span class="n">optimizer</span><span class="o">.</span><span class="n">maximize</span><span class="p">(</span><span class="n">init_points</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">n_iter</span><span class="o">=</span><span class="mi">8</span><span class="p">)</span>
+</code></pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+|      iter       |     target      |      sides      |
+-------------------------------------------------------
+| 1               | 0.4572          | 0.00|0.29|0.70  |
+| <span class="ansi-magenta-fg">2              </span> | <span class="ansi-magenta-fg">0.5096         </span> | <span class="ansi-magenta-fg">0.15|0.25|0.58 </span> |
+| 3               | 0.498           | 0.06|0.33|0.60  |
+| <span class="ansi-magenta-fg">4              </span> | <span class="ansi-magenta-fg">0.5097         </span> | <span class="ansi-magenta-fg">0.13|0.27|0.58 </span> |
+| <span class="ansi-magenta-fg">5              </span> | <span class="ansi-magenta-fg">0.5358         </span> | <span class="ansi-magenta-fg">0.19|0.36|0.43 </span> |
+| <span class="ansi-magenta-fg">6              </span> | <span class="ansi-magenta-fg">0.5443         </span> | <span class="ansi-magenta-fg">0.33|0.33|0.33 </span> |
+| 7               | 0.5405          | 0.28|0.28|0.42  |
+| 8               | 0.5034          | 0.01|0.49|0.49  |
+| 9               | 0.4977          | 0.01|0.42|0.56  |
+| 10              | 0.5427          | 0.27|0.36|0.36  |
+=======================================================
+</pre></div></div>
+</div>
+
+
+
+
+
+
+                
+              </article>
+            </div>
+          
+          
+  <script>var tabs=__md_get("__tabs");if(Array.isArray(tabs))e:for(var set of document.querySelectorAll(".tabbed-set")){var tab,labels=set.querySelector(".tabbed-labels");for(tab of tabs)for(var label of labels.getElementsByTagName("label"))if(label.innerText.trim()===tab){var input=document.getElementById(label.htmlFor);input.checked=!0;continue e}}</script>
+
+        </div>
+        
+          <a href="#" class="md-top md-icon" data-md-component="top" hidden>
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8v12Z"/></svg>
+            Back to top
+          </a>
+        
+      </main>
+      
+        <footer class="md-footer">
+  
+    
+    <nav class="md-footer__inner md-grid" aria-label="Footer" >
+      
+        
+        <a href="constraints.html" class="md-footer__link md-footer__link--prev" aria-label="Previous: Constrained Optimization" rel="prev">
+          <div class="md-footer__button md-icon">
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11h12Z"/></svg>
+          </div>
+          <div class="md-footer__title">
+            <div class="md-ellipsis">
+              <span class="md-footer__direction">
+                Previous
+              </span>
+              Constrained Optimization
+            </div>
+          </div>
+        </a>
+      
+      
+        
+        <a href="domain_reduction.html" class="md-footer__link md-footer__link--next" aria-label="Next: Sequential Domain Reduction" rel="next">
+          <div class="md-footer__title">
+            <div class="md-ellipsis">
+              <span class="md-footer__direction">
+                Next
+              </span>
+              Sequential Domain Reduction
+            </div>
+          </div>
+          <div class="md-footer__button md-icon">
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M4 11v2h12l-5.5 5.5 1.42 1.42L19.84 12l-7.92-7.92L10.5 5.5 16 11H4Z"/></svg>
+          </div>
+        </a>
+      
+    </nav>
+  
+  
+  
+  <div class="md-footer-meta md-typeset">
+    
+    <div class="md-footer-meta__inner md-grid">
+      <div class="md-copyright">
+  
+    <div class="md-footer-copyright__highlight">
+        &#169; Copyright 2024, Fernando Nogueira and the bayesian-optimization developers.
+        
+    </div>
+  
+    Created using
+    <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
+    8.1.3.
+     and
+    <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
+  
+</div>
+      
+        <div class="md-social">
+  
+    
+    
+    <a href="https://github.com/bayesian-optimization/BayesianOptimization" target="_blank" rel="noopener" title="Source on github.com" class="md-social__link">
+      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg>
+    </a>
+  
+    
+    
+      
+      
+    
+    <a href="https://pypi.org/project/bayesian-optimization/" target="_blank" rel="noopener" title="pypi.org" class="md-social__link">
+      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.8 200.5c-7.7-30.9-22.3-54.2-53.4-54.2h-40.1v47.4c0 36.8-31.2 67.8-66.8 67.8H172.7c-29.2 0-53.4 25-53.4 54.3v101.8c0 29 25.2 46 53.4 54.3 33.8 9.9 66.3 11.7 106.8 0 26.9-7.8 53.4-23.5 53.4-54.3v-40.7H226.2v-13.6h160.2c31.1 0 42.6-21.7 53.4-54.2 11.2-33.5 10.7-65.7 0-108.6zM286.2 404c11.1 0 20.1 9.1 20.1 20.3 0 11.3-9 20.4-20.1 20.4-11 0-20.1-9.2-20.1-20.4.1-11.3 9.1-20.3 20.1-20.3zM167.8 248.1h106.8c29.7 0 53.4-24.5 53.4-54.3V91.9c0-29-24.4-50.7-53.4-55.6-35.8-5.9-74.7-5.6-106.8.1-45.2 8-53.4 24.7-53.4 55.6v40.7h106.9v13.6h-147c-31.1 0-58.3 18.7-66.8 54.2-9.8 40.7-10.2 66.1 0 108.6 7.6 31.6 25.7 54.2 56.8 54.2H101v-48.8c0-35.3 30.5-66.4 66.8-66.4zm-6.7-142.6c-11.1 0-20.1-9.1-20.1-20.3.1-11.3 9-20.4 20.1-20.4 11 0 20.1 9.2 20.1 20.4s-9 20.3-20.1 20.3z"/></svg>
+    </a>
+  
+    
+    
+      
+      
+    
+    <a href="https://anaconda.org/conda-forge/bayesian-optimization" target="_blank" rel="noopener" title="anaconda.org" class="md-social__link">
+      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.8 200.5c-7.7-30.9-22.3-54.2-53.4-54.2h-40.1v47.4c0 36.8-31.2 67.8-66.8 67.8H172.7c-29.2 0-53.4 25-53.4 54.3v101.8c0 29 25.2 46 53.4 54.3 33.8 9.9 66.3 11.7 106.8 0 26.9-7.8 53.4-23.5 53.4-54.3v-40.7H226.2v-13.6h160.2c31.1 0 42.6-21.7 53.4-54.2 11.2-33.5 10.7-65.7 0-108.6zM286.2 404c11.1 0 20.1 9.1 20.1 20.3 0 11.3-9 20.4-20.1 20.4-11 0-20.1-9.2-20.1-20.4.1-11.3 9.1-20.3 20.1-20.3zM167.8 248.1h106.8c29.7 0 53.4-24.5 53.4-54.3V91.9c0-29-24.4-50.7-53.4-55.6-35.8-5.9-74.7-5.6-106.8.1-45.2 8-53.4 24.7-53.4 55.6v40.7h106.9v13.6h-147c-31.1 0-58.3 18.7-66.8 54.2-9.8 40.7-10.2 66.1 0 108.6 7.6 31.6 25.7 54.2 56.8 54.2H101v-48.8c0-35.3 30.5-66.4 66.8-66.4zm-6.7-142.6c-11.1 0-20.1-9.1-20.1-20.3.1-11.3 9-20.4 20.1-20.4 11 0 20.1 9.2 20.1 20.4s-9 20.3-20.1 20.3z"/></svg>
+    </a>
+  
+</div>
+      
+    </div>
+    
+  </div>
+</footer>
+      
+    </div>
+    <div class="md-dialog" data-md-component="dialog">
+      <div class="md-dialog__inner md-typeset"></div>
+    </div>
+    
+    <script id="__config" type="application/json">{"base": ".", "features": ["navigation.expand", "navigation.sections", "navigation.top", "search.share", "toc.follow", "toc.sticky", "content.tabs.link", "announce.dismiss"], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": null, "versionPath": "../versions.json"}}</script>
+    
+      
+        <script src="_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
+        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+        <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+        <script id="MathJax-script" src="_static/mathjax/tex-mml-chtml.js?v=cadf963e"></script>
+    
+  </body>
+</html>
\ No newline at end of file
diff --git a/master/parameter_types.ipynb b/master/parameter_types.ipynb
new file mode 100644
index 00000000..3d668300
--- /dev/null
+++ b/master/parameter_types.ipynb
@@ -0,0 +1,756 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Optimizing over non-float Parameters\n",
+    "\n",
+    "Sometimes, you need to optimize a target that is not just a function of floating-point values, but relies on integer or categorical parameters. This notebook shows how such problems are handled by following an approach from [\"Dealing with categorical and integer-valued variables in Bayesian Optimization with Gaussian processes\" by Garrido-Merchán and Hernández-Lobato](https://arxiv.org/abs/1805.03463). One simple way of handling an integer-valued parameter is to run the optimization as normal, but then round to the nearest integer after a point has been suggested. This method is similar, except that the rounding is performed in the _kernel_. Why does this matter? It means that the kernel is aware that two parameters, that map the to same point but are potentially distinct before this transformation are the same."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import warnings\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from bayes_opt import BayesianOptimization\n",
+    "from bayes_opt import acquisition\n",
+    "\n",
+    "from sklearn.gaussian_process.kernels import Matern\n",
+    "\n",
+    "# suppress warnings about this being an experimental feature\n",
+    "warnings.filterwarnings(action=\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Simple integer-valued function\n",
+    "Let's look at a simple, one-dimensional, integer-valued target function and compare a typed optimizer and a continuous optimizer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1000x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def target_function_1d(x):\n",
+    "    return np.sin(np.round(x)) - np.abs(np.round(x) / 5)\n",
+    "\n",
+    "c_pbounds = {'x': (-10, 10)}\n",
+    "bo_cont = BayesianOptimization(target_function_1d, c_pbounds, verbose=0, random_state=1)\n",
+    "\n",
+    "# one way of constructing an integer-valued parameter is to add a third element to the tuple\n",
+    "d_pbounds = {'x': (-10, 10, int)}\n",
+    "bo_disc = BayesianOptimization(target_function_1d, d_pbounds, verbose=0, random_state=1)\n",
+    "\n",
+    "fig, axs = plt.subplots(2, 1, figsize=(10, 6), sharex=True, sharey=True)\n",
+    "\n",
+    "bo_cont.maximize(init_points=2, n_iter=10)\n",
+    "bo_cont.acquisition_function._fit_gp(bo_cont._gp, bo_cont.space)\n",
+    "\n",
+    "y_mean, y_std = bo_cont._gp.predict(np.linspace(-10, 10, 1000).reshape(-1, 1), return_std=True)\n",
+    "axs[0].set_title('Continuous')\n",
+    "axs[0].plot(np.linspace(-10, 10, 1000), target_function_1d(np.linspace(-10, 10, 1000)), 'k--', label='True function')\n",
+    "axs[0].plot(np.linspace(-10, 10, 1000), y_mean, label='Predicted mean')\n",
+    "axs[0].fill_between(np.linspace(-10, 10, 1000), y_mean - y_std, y_mean + y_std, alpha=0.3, label='Predicted std')\n",
+    "axs[0].plot(bo_cont.space.params, bo_cont.space.target, 'ro')\n",
+    "\n",
+    "bo_disc.maximize(init_points=2, n_iter=10)\n",
+    "bo_disc.acquisition_function._fit_gp(bo_disc._gp, bo_disc.space)\n",
+    "\n",
+    "y_mean, y_std = bo_disc._gp.predict(np.linspace(-10, 10, 1000).reshape(-1, 1), return_std=True)\n",
+    "axs[1].set_title('Discrete')\n",
+    "axs[1].plot(np.linspace(-10, 10, 1000), target_function_1d(np.linspace(-10, 10, 1000)), 'k--', label='True function')\n",
+    "axs[1].plot(np.linspace(-10, 10, 1000), y_mean, label='Predicted mean')\n",
+    "axs[1].fill_between(np.linspace(-10, 10, 1000), y_mean - y_std, y_mean + y_std, alpha=0.3, label='Predicted std')\n",
+    "axs[1].plot(bo_disc.space.params, bo_disc.space.target, 'ro')\n",
+    "\n",
+    "for ax in axs:\n",
+    "    ax.grid(True)\n",
+    "fig.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can see, that the discrete optimizer is aware that the function is discrete and does not try to predict values between the integers. The continuous optimizer tries to predict values between the integers, despite the fact that these are known.\n",
+    "We can also see that the discrete optimizer predicts blocky mean and standard deviations, which is a result of the discrete nature of the function."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Mixed-parameter optimization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def discretized_function(x, y):\n",
+    "    y = np.round(y)\n",
+    "    return (-1*np.cos(x)**np.abs(y) + -1*np.cos(y)) + 0.1 * (x + y) - 0.01 * (x**2 + y**2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Bounded region of parameter space\n",
+    "c_pbounds = {'x': (-5, 5), 'y': (-5, 5)}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "labels = [\"All-float Optimizer\", \"Typed Optimizer\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "continuous_optimizer = BayesianOptimization(\n",
+    "    f=discretized_function,\n",
+    "    acquisition_function=acquisition.ExpectedImprovement(xi=0.01, random_state=1),\n",
+    "    pbounds=c_pbounds,\n",
+    "    verbose=2,\n",
+    "    random_state=1,\n",
+    ")\n",
+    "\n",
+    "continuous_optimizer.set_gp_params(kernel=Matern(nu=2.5, length_scale=np.ones(2)))\n",
+    "\n",
+    "d_pbounds = {'x': (-5, 5), 'y': (-5, 5, int)}\n",
+    "discrete_optimizer = BayesianOptimization(\n",
+    "    f=discretized_function,\n",
+    "    acquisition_function=acquisition.ExpectedImprovement(xi=0.01, random_state=1),\n",
+    "    pbounds=d_pbounds,\n",
+    "    verbose=2,\n",
+    "    random_state=1,\n",
+    ")\n",
+    "\n",
+    "discrete_optimizer.set_gp_params(kernel=Matern(nu=2.5, length_scale=np.ones(2)));"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "====================  All-float Optimizer  ====================\n",
+      "\n",
+      "|   iter    |  target   |     x     |     y     |\n",
+      "-------------------------------------------------\n",
+      "| \u001b[39m1        \u001b[39m | \u001b[39m0.03061  \u001b[39m | \u001b[39m-0.829779\u001b[39m | \u001b[39m2.2032449\u001b[39m |\n",
+      "| \u001b[39m2        \u001b[39m | \u001b[39m-0.6535  \u001b[39m | \u001b[39m-4.998856\u001b[39m | \u001b[39m-1.976674\u001b[39m |\n",
+      "| \u001b[35m3        \u001b[39m | \u001b[35m0.8025   \u001b[39m | \u001b[35m-0.829779\u001b[39m | \u001b[35m2.6549696\u001b[39m |\n",
+      "| \u001b[35m4        \u001b[39m | \u001b[35m0.9203   \u001b[39m | \u001b[35m-0.981065\u001b[39m | \u001b[35m2.6644394\u001b[39m |\n",
+      "| \u001b[35m5        \u001b[39m | \u001b[35m1.008    \u001b[39m | \u001b[35m-1.652553\u001b[39m | \u001b[35m2.7133425\u001b[39m |\n",
+      "| \u001b[39m6        \u001b[39m | \u001b[39m0.9926   \u001b[39m | \u001b[39m-1.119714\u001b[39m | \u001b[39m2.8358733\u001b[39m |\n",
+      "| \u001b[35m7        \u001b[39m | \u001b[35m1.322    \u001b[39m | \u001b[35m-2.418942\u001b[39m | \u001b[35m3.4600371\u001b[39m |\n",
+      "| \u001b[39m8        \u001b[39m | \u001b[39m-0.5063  \u001b[39m | \u001b[39m-3.092074\u001b[39m | \u001b[39m3.7368226\u001b[39m |\n",
+      "| \u001b[39m9        \u001b[39m | \u001b[39m-0.6432  \u001b[39m | \u001b[39m-4.089558\u001b[39m | \u001b[39m-0.560384\u001b[39m |\n",
+      "| \u001b[39m10       \u001b[39m | \u001b[39m1.267    \u001b[39m | \u001b[39m-2.360726\u001b[39m | \u001b[39m3.3725022\u001b[39m |\n",
+      "| \u001b[39m11       \u001b[39m | \u001b[39m0.4649   \u001b[39m | \u001b[39m-2.247113\u001b[39m | \u001b[39m3.7419056\u001b[39m |\n",
+      "| \u001b[39m12       \u001b[39m | \u001b[39m1.0      \u001b[39m | \u001b[39m-1.740988\u001b[39m | \u001b[39m3.4854116\u001b[39m |\n",
+      "| \u001b[39m13       \u001b[39m | \u001b[39m0.986    \u001b[39m | \u001b[39m1.2164322\u001b[39m | \u001b[39m4.4938459\u001b[39m |\n",
+      "| \u001b[39m14       \u001b[39m | \u001b[39m-2.27    \u001b[39m | \u001b[39m-2.213867\u001b[39m | \u001b[39m0.3585570\u001b[39m |\n",
+      "| \u001b[39m15       \u001b[39m | \u001b[39m-1.853   \u001b[39m | \u001b[39m1.7935035\u001b[39m | \u001b[39m-0.377351\u001b[39m |\n",
+      "=================================================\n",
+      "Max: 1.321554535694256\n",
+      "\n",
+      "\n",
+      "====================  Typed Optimizer  ====================\n",
+      "\n",
+      "|   iter    |  target   |     x     |     y     |\n",
+      "-------------------------------------------------\n",
+      "| \u001b[39m1        \u001b[39m | \u001b[39m0.8025   \u001b[39m | \u001b[39m-0.829779\u001b[39m | \u001b[39m3        \u001b[39m |\n",
+      "| \u001b[39m2        \u001b[39m | \u001b[39m-2.75    \u001b[39m | \u001b[39m-4.998856\u001b[39m | \u001b[39m0        \u001b[39m |\n",
+      "| \u001b[39m3        \u001b[39m | \u001b[39m0.8007   \u001b[39m | \u001b[39m-0.827713\u001b[39m | \u001b[39m3        \u001b[39m |\n",
+      "| \u001b[39m4        \u001b[39m | \u001b[39m-0.749   \u001b[39m | \u001b[39m2.2682240\u001b[39m | \u001b[39m-5       \u001b[39m |\n",
+      "| \u001b[39m5        \u001b[39m | \u001b[39m0.3718   \u001b[39m | \u001b[39m-2.339072\u001b[39m | \u001b[39m4        \u001b[39m |\n",
+      "| \u001b[39m6        \u001b[39m | \u001b[39m0.2146   \u001b[39m | \u001b[39m4.9971028\u001b[39m | \u001b[39m5        \u001b[39m |\n",
+      "| \u001b[39m7        \u001b[39m | \u001b[39m0.7473   \u001b[39m | \u001b[39m4.9970839\u001b[39m | \u001b[39m2        \u001b[39m |\n",
+      "| \u001b[35m8        \u001b[39m | \u001b[35m0.8275   \u001b[39m | \u001b[35m4.9986856\u001b[39m | \u001b[35m-3       \u001b[39m |\n",
+      "| \u001b[39m9        \u001b[39m | \u001b[39m0.3464   \u001b[39m | \u001b[39m4.9987136\u001b[39m | \u001b[39m-2       \u001b[39m |\n",
+      "| \u001b[39m10       \u001b[39m | \u001b[39m-0.7852  \u001b[39m | \u001b[39m4.9892216\u001b[39m | \u001b[39m-5       \u001b[39m |\n",
+      "| \u001b[39m11       \u001b[39m | \u001b[39m-0.6627  \u001b[39m | \u001b[39m-4.999635\u001b[39m | \u001b[39m-4       \u001b[39m |\n",
+      "| \u001b[39m12       \u001b[39m | \u001b[39m-0.1697  \u001b[39m | \u001b[39m-4.992664\u001b[39m | \u001b[39m-3       \u001b[39m |\n",
+      "| \u001b[35m13       \u001b[39m | \u001b[35m1.428    \u001b[39m | \u001b[35m4.9950290\u001b[39m | \u001b[35m3        \u001b[39m |\n",
+      "| \u001b[39m14       \u001b[39m | \u001b[39m1.137    \u001b[39m | \u001b[39m4.9970984\u001b[39m | \u001b[39m4        \u001b[39m |\n",
+      "| \u001b[35m15       \u001b[39m | \u001b[35m1.641    \u001b[39m | \u001b[35m4.0889271\u001b[39m | \u001b[35m3        \u001b[39m |\n",
+      "=================================================\n",
+      "Max: 1.6407143853831352\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "for lbl, optimizer in zip(labels, [continuous_optimizer, discrete_optimizer]):\n",
+    "    print(f\"====================  {lbl}  ====================\\n\")\n",
+    "    optimizer.maximize(\n",
+    "        init_points=2,\n",
+    "        n_iter=13\n",
+    "    )\n",
+    "    print(f\"Max: {optimizer.max['target']}\\n\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.linspace(c_pbounds['x'][0], c_pbounds['x'][1], 1000)\n",
+    "y = np.linspace(c_pbounds['y'][0], c_pbounds['y'][1], 1000)\n",
+    "\n",
+    "X, Y = np.meshgrid(x, y)\n",
+    "\n",
+    "Z = discretized_function(X, Y)\n",
+    "\n",
+    "params = [{'x': x_i, 'y': y_j} for y_j in y for x_i in x]\n",
+    "array_params = [continuous_optimizer._space.params_to_array(p) for p in params]\n",
+    "c_pred = continuous_optimizer._gp.predict(array_params).reshape(X.shape)\n",
+    "d_pred = discrete_optimizer._gp.predict(array_params).reshape(X.shape)\n",
+    "\n",
+    "vmin = np.min([np.min(Z), np.min(c_pred), np.min(d_pred)])\n",
+    "vmax = np.max([np.max(Z), np.max(c_pred), np.max(d_pred)])\n",
+    "\n",
+    "fig, axs = plt.subplots(1, 3)\n",
+    "\n",
+    "axs[0].set_title('Actual function')\n",
+    "axs[0].contourf(X, Y, Z, cmap=plt.cm.coolwarm, vmin=vmin, vmax=vmax)\n",
+    "\n",
+    "\n",
+    "axs[1].set_title(labels[0])\n",
+    "axs[1].contourf(X, Y, c_pred, cmap=plt.cm.coolwarm, vmin=vmin, vmax=vmax)\n",
+    "axs[1].scatter(continuous_optimizer._space.params[:,0], continuous_optimizer._space.params[:,1], c='k')\n",
+    "\n",
+    "axs[2].set_title(labels[1])\n",
+    "axs[2].contourf(X, Y, d_pred, cmap=plt.cm.coolwarm, vmin=vmin, vmax=vmax)\n",
+    "axs[2].scatter(discrete_optimizer._space.params[:,0], discrete_optimizer._space.params[:,1], c='k')\n",
+    "\n",
+    "def make_plot_fancy(ax: plt.Axes):\n",
+    "    ax.set_aspect(\"equal\")\n",
+    "    ax.set_xlabel('x (float)')\n",
+    "    ax.set_xticks([-5.0, -2.5, 0., 2.5, 5.0])\n",
+    "    ax.set_ylabel('y (int)')\n",
+    "    ax.set_yticks([-4, -2, 0, 2, 4])\n",
+    "\n",
+    "for ax in axs:\n",
+    "    make_plot_fancy(ax)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Categorical variables\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also handle categorical variables! This is done under-the-hood by constructing parameters in a one-hot-encoding representation, with a transformation in the kernel rounding to the nearest one-hot representation. If you want to use this, you can specify a collection of strings as options.\n",
+    "\n",
+    "NB: As internally, the categorical variables are within a range of `[0, 1]` and the GP used for BO is by default isotropic, you might want to ensure your other features are similarly scaled to a range of `[0, 1]` or use an anisotropic GP."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def f1(x1, x2):\n",
+    "    return -1*(x1 - np.sqrt(x1**2 + x2**2) * np.cos(np.sqrt(x1**2 + x2**2))**2 + 0.5 * np.sqrt(x1**2 + x2**2))\n",
+    "\n",
+    "def f2(x1, x2):\n",
+    "    return -1*(x2 - np.sqrt(x1**2 + x2**2) * np.sin(np.sqrt(x1**2 + x2**2))**2 + 0.5 * np.sqrt(x1**2 + x2**2))\n",
+    "\n",
+    "def SPIRAL(x1, x2, k):\n",
+    "    \"\"\"cf Ladislav-Luksan\n",
+    "    \"\"\"\n",
+    "    if k=='1':\n",
+    "        return f1(10 * x1, 10 * x2)\n",
+    "    elif k=='2':\n",
+    "        return f2(10 * x1, 10 * x2)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "|   iter    |  target   |    x1     |    x2     |     k     |\n",
+      "-------------------------------------------------------------\n",
+      "| \u001b[39m1        \u001b[39m | \u001b[39m-2.052   \u001b[39m | \u001b[39m-0.165955\u001b[39m | \u001b[39m0.4406489\u001b[39m | \u001b[39m2        \u001b[39m |\n",
+      "| \u001b[35m2        \u001b[39m | \u001b[35m13.49    \u001b[39m | \u001b[35m-0.743751\u001b[39m | \u001b[35m0.9980810\u001b[39m | \u001b[35m1        \u001b[39m |\n",
+      "| \u001b[39m3        \u001b[39m | \u001b[39m-14.49   \u001b[39m | \u001b[39m-0.743433\u001b[39m | \u001b[39m0.9709879\u001b[39m | \u001b[39m2        \u001b[39m |\n",
+      "| \u001b[39m4        \u001b[39m | \u001b[39m-13.33   \u001b[39m | \u001b[39m0.9950794\u001b[39m | \u001b[39m-0.352913\u001b[39m | \u001b[39m1        \u001b[39m |\n",
+      "| \u001b[39m5        \u001b[39m | \u001b[39m9.674    \u001b[39m | \u001b[39m0.5436849\u001b[39m | \u001b[39m-0.574376\u001b[39m | \u001b[39m2        \u001b[39m |\n",
+      "| \u001b[39m6        \u001b[39m | \u001b[39m9.498    \u001b[39m | \u001b[39m-0.218693\u001b[39m | \u001b[39m-0.709177\u001b[39m | \u001b[39m2        \u001b[39m |\n",
+      "| \u001b[39m7        \u001b[39m | \u001b[39m11.43    \u001b[39m | \u001b[39m-0.918642\u001b[39m | \u001b[39m-0.648372\u001b[39m | \u001b[39m2        \u001b[39m |\n",
+      "| \u001b[39m8        \u001b[39m | \u001b[39m0.4882   \u001b[39m | \u001b[39m-0.218182\u001b[39m | \u001b[39m-0.012177\u001b[39m | \u001b[39m2        \u001b[39m |\n",
+      "| \u001b[39m9        \u001b[39m | \u001b[39m7.542    \u001b[39m | \u001b[39m-0.787692\u001b[39m | \u001b[39m0.3452580\u001b[39m | \u001b[39m1        \u001b[39m |\n",
+      "| \u001b[39m10       \u001b[39m | \u001b[39m-2.161   \u001b[39m | \u001b[39m0.1392349\u001b[39m | \u001b[39m-0.125728\u001b[39m | \u001b[39m1        \u001b[39m |\n",
+      "| \u001b[39m11       \u001b[39m | \u001b[39m-0.8336  \u001b[39m | \u001b[39m0.1206357\u001b[39m | \u001b[39m-0.543264\u001b[39m | \u001b[39m1        \u001b[39m |\n",
+      "| \u001b[39m12       \u001b[39m | \u001b[39m-8.413   \u001b[39m | \u001b[39m0.4981209\u001b[39m | \u001b[39m0.6434939\u001b[39m | \u001b[39m1        \u001b[39m |\n",
+      "| \u001b[39m13       \u001b[39m | \u001b[39m6.372    \u001b[39m | \u001b[39m0.0587256\u001b[39m | \u001b[39m-0.892371\u001b[39m | \u001b[39m2        \u001b[39m |\n",
+      "| \u001b[39m14       \u001b[39m | \u001b[39m-12.71   \u001b[39m | \u001b[39m0.7529885\u001b[39m | \u001b[39m-0.780621\u001b[39m | \u001b[39m1        \u001b[39m |\n",
+      "| \u001b[39m15       \u001b[39m | \u001b[39m-1.521   \u001b[39m | \u001b[39m0.4118274\u001b[39m | \u001b[39m-0.517960\u001b[39m | \u001b[39m1        \u001b[39m |\n",
+      "| \u001b[39m16       \u001b[39m | \u001b[39m11.88    \u001b[39m | \u001b[39m-0.755390\u001b[39m | \u001b[39m-0.533137\u001b[39m | \u001b[39m1        \u001b[39m |\n",
+      "| \u001b[39m17       \u001b[39m | \u001b[39m0.6373   \u001b[39m | \u001b[39m0.2249733\u001b[39m | \u001b[39m-0.053787\u001b[39m | \u001b[39m2        \u001b[39m |\n",
+      "| \u001b[39m18       \u001b[39m | \u001b[39m2.154    \u001b[39m | \u001b[39m0.0583506\u001b[39m | \u001b[39m0.6550869\u001b[39m | \u001b[39m1        \u001b[39m |\n",
+      "| \u001b[35m19       \u001b[39m | \u001b[35m13.69    \u001b[39m | \u001b[35m-0.741717\u001b[39m | \u001b[35m-0.820073\u001b[39m | \u001b[35m2        \u001b[39m |\n",
+      "| \u001b[39m20       \u001b[39m | \u001b[39m1.615    \u001b[39m | \u001b[39m-0.663312\u001b[39m | \u001b[39m-0.905925\u001b[39m | \u001b[39m1        \u001b[39m |\n",
+      "=============================================================\n"
+     ]
+    }
+   ],
+   "source": [
+    "pbounds = {'x1': (-1, 1), 'x2': (-1, 1), 'k': ('1', '2')}\n",
+    "\n",
+    "categorical_optimizer = BayesianOptimization(\n",
+    "    f=SPIRAL,\n",
+    "    acquisition_function=acquisition.ExpectedImprovement(1e-2),\n",
+    "    pbounds=pbounds,\n",
+    "    verbose=2,\n",
+    "    random_state=1,\n",
+    ")\n",
+    "discrete_optimizer.set_gp_params(alpha=1e-3)\n",
+    "\n",
+    "categorical_optimizer.maximize(\n",
+    "        init_points=2,\n",
+    "        n_iter=18,\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "res = categorical_optimizer._space.res()\n",
+    "k1 = np.array([[p['params']['x1'], p['params']['x2']] for p in res if p['params']['k']=='1'])\n",
+    "k2 = np.array([[p['params']['x1'], p['params']['x2']] for p in res if p['params']['k']=='2'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x1 = np.linspace(pbounds['x1'][0], pbounds['x1'][1], 1000)\n",
+    "x2 = np.linspace(pbounds['x2'][0], pbounds['x2'][1], 1000)\n",
+    "\n",
+    "X1, X2 = np.meshgrid(x1, x2)\n",
+    "Z1 = SPIRAL(X1, X2, '1')\n",
+    "Z2 = SPIRAL(X1, X2, '2')\n",
+    "\n",
+    "fig, axs = plt.subplots(1, 2)\n",
+    "\n",
+    "vmin = np.min([np.min(Z1), np.min(Z2)])\n",
+    "vmax = np.max([np.max(Z1), np.max(Z2)])\n",
+    "\n",
+    "axs[0].contourf(X1, X2, Z1, vmin=vmin, vmax=vmax)\n",
+    "axs[0].set_aspect(\"equal\")\n",
+    "axs[0].scatter(k1[:,0], k1[:,1], c='k')\n",
+    "axs[1].contourf(X1, X2, Z2, vmin=vmin, vmax=vmax)\n",
+    "axs[1].scatter(k2[:,0], k2[:,1], c='k')\n",
+    "axs[1].set_aspect(\"equal\")\n",
+    "axs[0].set_title('k=1')\n",
+    "axs[1].set_title('k=2')\n",
+    "fig.tight_layout()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Use in ML"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A typical usecase for integer and categorical parameters is optimizing the hyperparameters of a machine learning model. Below you can find an example where the hyperparameters of an SVM are optimized."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "|   iter    |  target   |  kernel   |  log10_C  |\n",
+      "-------------------------------------------------\n",
+      "| \u001b[39m1        \u001b[39m | \u001b[39m-0.2361  \u001b[39m | \u001b[39mpoly2    \u001b[39m | \u001b[39m0.9943696\u001b[39m |\n",
+      "| \u001b[39m2        \u001b[39m | \u001b[39m-0.2864  \u001b[39m | \u001b[39mrbf      \u001b[39m | \u001b[39m-0.999771\u001b[39m |\n",
+      "| \u001b[39m3        \u001b[39m | \u001b[39m-0.2625  \u001b[39m | \u001b[39mpoly3    \u001b[39m | \u001b[39m0.7449728\u001b[39m |\n",
+      "| \u001b[35m4        \u001b[39m | \u001b[35m-0.2361  \u001b[39m | \u001b[35mpoly2    \u001b[39m | \u001b[35m0.9944598\u001b[39m |\n",
+      "| \u001b[39m5        \u001b[39m | \u001b[39m-0.298   \u001b[39m | \u001b[39mpoly3    \u001b[39m | \u001b[39m-0.999625\u001b[39m |\n",
+      "| \u001b[35m6        \u001b[39m | \u001b[35m-0.2361  \u001b[39m | \u001b[35mpoly2    \u001b[39m | \u001b[35m0.9945010\u001b[39m |\n",
+      "| \u001b[35m7        \u001b[39m | \u001b[35m-0.2152  \u001b[39m | \u001b[35mrbf      \u001b[39m | \u001b[35m0.9928960\u001b[39m |\n",
+      "| \u001b[39m8        \u001b[39m | \u001b[39m-0.2153  \u001b[39m | \u001b[39mrbf      \u001b[39m | \u001b[39m0.9917667\u001b[39m |\n",
+      "| \u001b[39m9        \u001b[39m | \u001b[39m-0.2362  \u001b[39m | \u001b[39mpoly2    \u001b[39m | \u001b[39m0.9897298\u001b[39m |\n",
+      "| \u001b[39m10       \u001b[39m | \u001b[39m-0.2362  \u001b[39m | \u001b[39mpoly2    \u001b[39m | \u001b[39m0.9874217\u001b[39m |\n",
+      "=================================================\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import load_breast_cancer\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.metrics import log_loss\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from bayes_opt import BayesianOptimization\n",
+    "\n",
+    "data = load_breast_cancer()\n",
+    "X_train, y_train = data['data'], data['target']\n",
+    "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2, random_state=1)\n",
+    "kernels = ['rbf', 'poly']\n",
+    "\n",
+    "def f_target(kernel, log10_C):\n",
+    "    if kernel == 'poly2':\n",
+    "        kernel = 'poly'\n",
+    "        degree = 2\n",
+    "    elif kernel == 'poly3':\n",
+    "        kernel = 'poly'\n",
+    "        degree = 3\n",
+    "    elif kernel == 'rbf':\n",
+    "        degree = 3 # not used, equal to default\n",
+    "\n",
+    "    C = 10**log10_C\n",
+    "\n",
+    "    model = SVC(C=C, kernel=kernel, degree=degree, probability=True, random_state=1)\n",
+    "    model.fit(X_train, y_train)\n",
+    "\n",
+    "    # Package looks for maximum, so we return -1 * log_loss\n",
+    "    loss = -1 * log_loss(y_val, model.predict_proba(X_val))\n",
+    "    return loss\n",
+    "\n",
+    "\n",
+    "params_svm ={\n",
+    "    'kernel': ['rbf', 'poly2', 'poly3'],\n",
+    "    'log10_C':(-1, +1),\n",
+    "}\n",
+    "\n",
+    "optimizer = BayesianOptimization(\n",
+    "    f_target,\n",
+    "    params_svm,\n",
+    "    random_state=1,\n",
+    "    verbose=2\n",
+    ")\n",
+    "\n",
+    "kernel = Matern(nu=2.5, length_scale=np.ones(optimizer.space.dim))\n",
+    "discrete_optimizer.set_gp_params(kernel=kernel)\n",
+    "optimizer.maximize(init_points=2, n_iter=8)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. Defining your own Parameter\n",
+    "\n",
+    "Maybe you want to optimize over another form of parameters, which does not align with `float`, `int` or categorical. For this purpose, you can create your own, custom parameter. A simple example is a parameter that is discrete, but still admits a distance representation (like an integer) while not being uniformly spaced.\n",
+    "\n",
+    "However, you can go further even and encode constraints and even symmetries in your parameter. Let's consider the problem of finding a triangle which maximizes an area given its sides $a, b, c$ with a constraint that the perimeter is fixed, i.e. $a + b + c=s$.\n",
+    "\n",
+    "We will create a parameter that encodes such a triangle, and via it's kernel transform ensures that the sides sum to the required length $s$. As you might expect, the solution to this problem is an equilateral triangle, i.e. $a=b=c=s/3$.\n",
+    "\n",
+    "To define the parameter, we need to subclass `BayesParameter` and define a few important functions/properties.\n",
+    "\n",
+    "- `is_continuous` is a property which denotes whether a parameter is continuous. When optimizing the acquisition function, non-continuous parameters will not be optimized using gradient-based methods, but only via random sampling.\n",
+    "- `random_sample` is a function that samples randomly from the space of the parameter.\n",
+    "- `to_float` transforms the canonical representation of a parameter into float values for the target space to store. There is a one-to-one correspondence between valid float representations produced by this function and canonical representations of the parameter. This function is most important when working with parameters that use a non-numeric canonical representation, such as categorical parameters.\n",
+    "- `to_param` performs the inverse of `to_float`: Given a float-based representation, it creates a canonical representation. This function should perform binning whenever appropriate, e.g. in the case of the `IntParameter`, this function would round any float values supplied to it.\n",
+    "- `kernel_transform` is the most important function of the Parameter and defines how to represent a value in the kernel space. In contrast to `to_float`, this function expects both the input, as well as the output to be float-representations of the value.\n",
+    "- `to_string` produces a stringified version of the parameter, which allows users to define custom pretty-print rules for ththe ScreenLogger use.\n",
+    "- `dim` is a property which defines the dimensionality of the parameter. In most cases, this will be 1, but e.g. for categorical parameters it is equivalent to the cardinality of the category space.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from bayes_opt.logger import ScreenLogger\n",
+    "from bayes_opt.parameter import BayesParameter\n",
+    "from bayes_opt.event import Events\n",
+    "from bayes_opt.util import ensure_rng\n",
+    "\n",
+    "\n",
+    "class FixedPerimeterTriangleParameter(BayesParameter):\n",
+    "    def __init__(self, name: str, bounds, perimeter) -> None:\n",
+    "        super().__init__(name, bounds)\n",
+    "        self.perimeter = perimeter\n",
+    "\n",
+    "    @property\n",
+    "    def is_continuous(self):\n",
+    "        return True\n",
+    "    \n",
+    "    def random_sample(self, n_samples: int, random_state):\n",
+    "        random_state = ensure_rng(random_state)\n",
+    "        samples = []\n",
+    "        while len(samples) < n_samples:\n",
+    "            samples_ = random_state.dirichlet(np.ones(3), n_samples)\n",
+    "            samples_ = samples_ * self.perimeter # scale samples by perimeter\n",
+    "\n",
+    "            samples_ = samples_[np.all((self.bounds[:, 0] <= samples_) & (samples_ <= self.bounds[:, 1]), axis=-1)]\n",
+    "            samples.extend(np.atleast_2d(samples_))\n",
+    "        samples = np.array(samples[:n_samples])\n",
+    "        return samples\n",
+    "    \n",
+    "    def to_float(self, value):\n",
+    "        return value\n",
+    "    \n",
+    "    def to_param(self, value):\n",
+    "        return value * self.perimeter / sum(value)\n",
+    "\n",
+    "    def kernel_transform(self, value):\n",
+    "        return value * self.perimeter / np.sum(value, axis=-1, keepdims=True)\n",
+    "\n",
+    "    def to_string(self, value, str_len: int) -> str:\n",
+    "        len_each = (str_len - 2) // 3\n",
+    "        str_ = '|'.join([f\"{float(np.round(value[i], 4))}\"[:len_each] for i in range(3)])\n",
+    "        return str_.ljust(str_len)\n",
+    "\n",
+    "    @property\n",
+    "    def dim(self):\n",
+    "        return 3 # as we have three float values, each representing the length of one side.\n",
+    "\n",
+    "def area_of_triangle(sides):\n",
+    "    a, b, c = sides\n",
+    "    s = np.sum(sides, axis=-1) # perimeter\n",
+    "    A = np.sqrt(s * (s-a) * (s-b) * (s-c))\n",
+    "    return A\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "|      iter       |     target      |      sides      |\n",
+      "-------------------------------------------------------\n",
+      "| \u001b[39m1              \u001b[39m | \u001b[39m0.4572         \u001b[39m | \u001b[39m0.29|0.70|0.00 \u001b[39m |\n",
+      "| \u001b[35m2              \u001b[39m | \u001b[35m0.5096         \u001b[39m | \u001b[35m0.58|0.25|0.15 \u001b[39m |\n",
+      "| \u001b[39m3              \u001b[39m | \u001b[39m0.5081         \u001b[39m | \u001b[39m0.58|0.25|0.15 \u001b[39m |\n",
+      "| \u001b[35m4              \u001b[39m | \u001b[35m0.5386         \u001b[39m | \u001b[35m0.44|0.28|0.26 \u001b[39m |\n",
+      "| \u001b[39m5              \u001b[39m | \u001b[39m0.5279         \u001b[39m | \u001b[39m0.38|0.14|0.47 \u001b[39m |\n",
+      "| \u001b[39m6              \u001b[39m | \u001b[39m0.5328         \u001b[39m | \u001b[39m0.18|0.36|0.45 \u001b[39m |\n",
+      "| \u001b[39m7              \u001b[39m | \u001b[39m0.4366         \u001b[39m | \u001b[39m0.02|0.22|0.74 \u001b[39m |\n",
+      "| \u001b[39m8              \u001b[39m | \u001b[39m0.4868         \u001b[39m | \u001b[39m0.00|0.61|0.37 \u001b[39m |\n",
+      "| \u001b[39m9              \u001b[39m | \u001b[39m0.4977         \u001b[39m | \u001b[39m0.56|0.01|0.42 \u001b[39m |\n",
+      "| \u001b[35m10             \u001b[39m | \u001b[35m0.5418         \u001b[39m | \u001b[35m0.29|0.40|0.30 \u001b[39m |\n",
+      "| \u001b[39m11             \u001b[39m | \u001b[39m0.3361         \u001b[39m | \u001b[39m0.06|0.87|0.06 \u001b[39m |\n",
+      "| \u001b[39m12             \u001b[39m | \u001b[39m0.06468        \u001b[39m | \u001b[39m0.99|0.00|0.00 \u001b[39m |\n",
+      "| \u001b[39m13             \u001b[39m | \u001b[39m0.01589        \u001b[39m | \u001b[39m0.0|0.00|0.99  \u001b[39m |\n",
+      "| \u001b[39m14             \u001b[39m | \u001b[39m0.4999         \u001b[39m | \u001b[39m0.21|0.16|0.61 \u001b[39m |\n",
+      "| \u001b[39m15             \u001b[39m | \u001b[39m0.499          \u001b[39m | \u001b[39m0.53|0.46|0.00 \u001b[39m |\n",
+      "| \u001b[39m16             \u001b[39m | \u001b[39m0.4937         \u001b[39m | \u001b[39m0.00|0.41|0.58 \u001b[39m |\n",
+      "| \u001b[39m17             \u001b[39m | \u001b[39m0.5233         \u001b[39m | \u001b[39m0.33|0.51|0.14 \u001b[39m |\n",
+      "| \u001b[39m18             \u001b[39m | \u001b[39m0.5204         \u001b[39m | \u001b[39m0.17|0.54|0.28 \u001b[39m |\n",
+      "| \u001b[39m19             \u001b[39m | \u001b[39m0.5235         \u001b[39m | \u001b[39m0.51|0.15|0.32 \u001b[39m |\n",
+      "| \u001b[39m20             \u001b[39m | \u001b[39m0.5412         \u001b[39m | \u001b[39m0.31|0.27|0.41 \u001b[39m |\n",
+      "| \u001b[39m21             \u001b[39m | \u001b[39m0.4946         \u001b[39m | \u001b[39m0.41|0.00|0.57 \u001b[39m |\n",
+      "| \u001b[39m22             \u001b[39m | \u001b[39m0.5355         \u001b[39m | \u001b[39m0.41|0.39|0.19 \u001b[39m |\n",
+      "| \u001b[35m23             \u001b[39m | \u001b[35m0.5442         \u001b[39m | \u001b[35m0.35|0.32|0.32 \u001b[39m |\n",
+      "| \u001b[39m24             \u001b[39m | \u001b[39m0.5192         \u001b[39m | \u001b[39m0.16|0.28|0.54 \u001b[39m |\n",
+      "| \u001b[39m25             \u001b[39m | \u001b[39m0.5401         \u001b[39m | \u001b[39m0.39|0.23|0.36 \u001b[39m |\n",
+      "=======================================================\n"
+     ]
+    }
+   ],
+   "source": [
+    "param = FixedPerimeterTriangleParameter(\n",
+    "    name='sides',\n",
+    "    bounds=np.array([[0., 1.], [0., 1.], [0., 1.]]),\n",
+    "    perimeter=1.\n",
+    ")\n",
+    "\n",
+    "pbounds = {'sides': param}\n",
+    "optimizer = BayesianOptimization(\n",
+    "    area_of_triangle,\n",
+    "    pbounds,\n",
+    "    random_state=1,\n",
+    ")\n",
+    "\n",
+    "# Increase the cell size to accommodate the three float values\n",
+    "logger = ScreenLogger(verbose=2, is_constrained=False)\n",
+    "logger._default_cell_size = 15\n",
+    "\n",
+    "for e in [Events.OPTIMIZATION_START, Events.OPTIMIZATION_STEP, Events.OPTIMIZATION_END]:\n",
+    "    optimizer.subscribe(e, logger)\n",
+    "\n",
+    "optimizer.maximize(init_points=2, n_iter=23)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This seems to work decently well, but we can improve it significantly if we consider the symmetries inherent in the problem: This problem is permutation invariant, i.e. we do not care which side specifically is denoted as $a$, $b$ or $c$. Instead, we can, without loss of generality, decide that the shortest side will always be denoted as $a$, and the longest always as $c$. If we enhance our kernel transform with this symmetry, the performance improves significantly. This can be easily done by sub-classing the previously created triangle parameter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "|      iter       |     target      |      sides      |\n",
+      "-------------------------------------------------------\n",
+      "| \u001b[39m1              \u001b[39m | \u001b[39m0.4572         \u001b[39m | \u001b[39m0.00|0.29|0.70 \u001b[39m |\n",
+      "| \u001b[35m2              \u001b[39m | \u001b[35m0.5096         \u001b[39m | \u001b[35m0.15|0.25|0.58 \u001b[39m |\n",
+      "| \u001b[39m3              \u001b[39m | \u001b[39m0.498          \u001b[39m | \u001b[39m0.06|0.33|0.60 \u001b[39m |\n",
+      "| \u001b[35m4              \u001b[39m | \u001b[35m0.5097         \u001b[39m | \u001b[35m0.13|0.27|0.58 \u001b[39m |\n",
+      "| \u001b[35m5              \u001b[39m | \u001b[35m0.5358         \u001b[39m | \u001b[35m0.19|0.36|0.43 \u001b[39m |\n",
+      "| \u001b[35m6              \u001b[39m | \u001b[35m0.5443         \u001b[39m | \u001b[35m0.33|0.33|0.33 \u001b[39m |\n",
+      "| \u001b[39m7              \u001b[39m | \u001b[39m0.5405         \u001b[39m | \u001b[39m0.28|0.28|0.42 \u001b[39m |\n",
+      "| \u001b[39m8              \u001b[39m | \u001b[39m0.5034         \u001b[39m | \u001b[39m0.01|0.49|0.49 \u001b[39m |\n",
+      "| \u001b[39m9              \u001b[39m | \u001b[39m0.4977         \u001b[39m | \u001b[39m0.01|0.42|0.56 \u001b[39m |\n",
+      "| \u001b[39m10             \u001b[39m | \u001b[39m0.5427         \u001b[39m | \u001b[39m0.27|0.36|0.36 \u001b[39m |\n",
+      "=======================================================\n"
+     ]
+    }
+   ],
+   "source": [
+    "class SortingFixedPerimeterTriangleParameter(FixedPerimeterTriangleParameter):\n",
+    "    def __init__(self, name: str, bounds, perimeter) -> None:\n",
+    "        super().__init__(name, bounds, perimeter)\n",
+    "\n",
+    "    def to_param(self, value):\n",
+    "        value = np.sort(value, axis=-1)\n",
+    "        return super().to_param(value)\n",
+    "\n",
+    "    def kernel_transform(self, value):\n",
+    "        value = np.sort(value, axis=-1)\n",
+    "        return super().kernel_transform(value)\n",
+    "\n",
+    "param = SortingFixedPerimeterTriangleParameter(\n",
+    "    name='sides',\n",
+    "    bounds=np.array([[0., 1.], [0., 1.], [0., 1.]]),\n",
+    "    perimeter=1.\n",
+    ")\n",
+    "\n",
+    "pbounds = {'sides': param}\n",
+    "optimizer = BayesianOptimization(\n",
+    "    area_of_triangle,\n",
+    "    pbounds,\n",
+    "    random_state=1,\n",
+    ")\n",
+    "\n",
+    "logger = ScreenLogger(verbose=2, is_constrained=False)\n",
+    "logger._default_cell_size = 15\n",
+    "\n",
+    "for e in [Events.OPTIMIZATION_START, Events.OPTIMIZATION_STEP, Events.OPTIMIZATION_END]:\n",
+    "    optimizer.subscribe(e, logger)\n",
+    "\n",
+    "optimizer.maximize(init_points=2, n_iter=8)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "bayesian-optimization-tb9vsVm6-py3.9",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/master/py-modindex.html b/master/py-modindex.html
index bb9b84ee..b7b8eed4 100644
--- a/master/py-modindex.html
+++ b/master/py-modindex.html
@@ -321,6 +321,21 @@
   
   
   
+    <li class="md-nav__item">
+      <a href="parameter_types.html" class="md-nav__link">
+        <span title="/parameter_types.ipynb (reference label)" class="md-ellipsis">Parameter Types</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
     <li class="md-nav__item">
       <a href="domain_reduction.html" class="md-nav__link">
         <span title="/domain_reduction.ipynb (reference label)" class="md-ellipsis">Sequential Domain Reduction</span>
@@ -492,6 +507,21 @@
   
   
   
+    <li class="md-nav__item">
+      <a href="reference/parameter.html" class="md-nav__link">
+        <span title="/reference/parameter.rst (reference label)" class="md-ellipsis">bayes_<wbr>opt.<wbr>parameter</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
     <li class="md-nav__item">
       <a href="reference/exception.html" class="md-nav__link">
         <span title="/reference/exception.rst (reference label)" class="md-ellipsis">bayes_<wbr>opt.<wbr>exception</span>
diff --git a/master/reference/acquisition.html b/master/reference/acquisition.html
index 496c0351..5766b3eb 100644
--- a/master/reference/acquisition.html
+++ b/master/reference/acquisition.html
@@ -323,6 +323,21 @@
   
   
   
+    <li class="md-nav__item">
+      <a href="../parameter_types.html" class="md-nav__link">
+        <span title="/parameter_types.ipynb (reference label)" class="md-ellipsis">Parameter Types</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
     <li class="md-nav__item">
       <a href="../domain_reduction.html" class="md-nav__link">
         <span title="/domain_reduction.ipynb (reference label)" class="md-ellipsis">Sequential Domain Reduction</span>
@@ -824,6 +839,21 @@
   
   
   
+    <li class="md-nav__item">
+      <a href="parameter.html" class="md-nav__link">
+        <span title="/reference/parameter.rst (reference label)" class="md-ellipsis">bayes_<wbr>opt.<wbr>parameter</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
     <li class="md-nav__item">
       <a href="exception.html" class="md-nav__link">
         <span title="/reference/exception.rst (reference label)" class="md-ellipsis">bayes_<wbr>opt.<wbr>exception</span>
diff --git a/master/reference/acquisition/ConstantLiar.html b/master/reference/acquisition/ConstantLiar.html
index bea843ba..7052e331 100644
--- a/master/reference/acquisition/ConstantLiar.html
+++ b/master/reference/acquisition/ConstantLiar.html
@@ -323,6 +323,21 @@
   
   
   
+    <li class="md-nav__item">
+      <a href="../../parameter_types.html" class="md-nav__link">
+        <span title="/parameter_types.ipynb (reference label)" class="md-ellipsis">Parameter Types</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
     <li class="md-nav__item">
       <a href="../../domain_reduction.html" class="md-nav__link">
         <span title="/domain_reduction.ipynb (reference label)" class="md-ellipsis">Sequential Domain Reduction</span>
@@ -819,6 +834,21 @@
   
   
   
+    <li class="md-nav__item">
+      <a href="../parameter.html" class="md-nav__link">
+        <span title="/reference/parameter.rst (reference label)" class="md-ellipsis">bayes_<wbr>opt.<wbr>parameter</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
     <li class="md-nav__item">
       <a href="../exception.html" class="md-nav__link">
         <span title="/reference/exception.rst (reference label)" class="md-ellipsis">bayes_<wbr>opt.<wbr>exception</span>
diff --git a/master/reference/acquisition/ExpectedImprovement.html b/master/reference/acquisition/ExpectedImprovement.html
index c0355de4..1673cc91 100644
--- a/master/reference/acquisition/ExpectedImprovement.html
+++ b/master/reference/acquisition/ExpectedImprovement.html
@@ -323,6 +323,21 @@
   
   
   
+    <li class="md-nav__item">
+      <a href="../../parameter_types.html" class="md-nav__link">
+        <span title="/parameter_types.ipynb (reference label)" class="md-ellipsis">Parameter Types</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
     <li class="md-nav__item">
       <a href="../../domain_reduction.html" class="md-nav__link">
         <span title="/domain_reduction.ipynb (reference label)" class="md-ellipsis">Sequential Domain Reduction</span>
@@ -839,6 +854,21 @@
   
   
   
+    <li class="md-nav__item">
+      <a href="../parameter.html" class="md-nav__link">
+        <span title="/reference/parameter.rst (reference label)" class="md-ellipsis">bayes_<wbr>opt.<wbr>parameter</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
     <li class="md-nav__item">
       <a href="../exception.html" class="md-nav__link">
         <span title="/reference/exception.rst (reference label)" class="md-ellipsis">bayes_<wbr>opt.<wbr>exception</span>
diff --git a/master/reference/acquisition/GPHedge.html b/master/reference/acquisition/GPHedge.html
index eb23c6b6..e12a01da 100644
--- a/master/reference/acquisition/GPHedge.html
+++ b/master/reference/acquisition/GPHedge.html
@@ -323,6 +323,21 @@
   
   
   
+    <li class="md-nav__item">
+      <a href="../../parameter_types.html" class="md-nav__link">
+        <span title="/parameter_types.ipynb (reference label)" class="md-ellipsis">Parameter Types</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
     <li class="md-nav__item">
       <a href="../../domain_reduction.html" class="md-nav__link">
         <span title="/domain_reduction.ipynb (reference label)" class="md-ellipsis">Sequential Domain Reduction</span>
@@ -791,6 +806,21 @@
   
   
   
+    <li class="md-nav__item">
+      <a href="../parameter.html" class="md-nav__link">
+        <span title="/reference/parameter.rst (reference label)" class="md-ellipsis">bayes_<wbr>opt.<wbr>parameter</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
     <li class="md-nav__item">
       <a href="../exception.html" class="md-nav__link">
         <span title="/reference/exception.rst (reference label)" class="md-ellipsis">bayes_<wbr>opt.<wbr>exception</span>
diff --git a/master/reference/acquisition/ProbabilityOfImprovement.html b/master/reference/acquisition/ProbabilityOfImprovement.html
index 88657cda..619239b3 100644
--- a/master/reference/acquisition/ProbabilityOfImprovement.html
+++ b/master/reference/acquisition/ProbabilityOfImprovement.html
@@ -323,6 +323,21 @@
   
   
   
+    <li class="md-nav__item">
+      <a href="../../parameter_types.html" class="md-nav__link">
+        <span title="/parameter_types.ipynb (reference label)" class="md-ellipsis">Parameter Types</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
     <li class="md-nav__item">
       <a href="../../domain_reduction.html" class="md-nav__link">
         <span title="/domain_reduction.ipynb (reference label)" class="md-ellipsis">Sequential Domain Reduction</span>
@@ -839,6 +854,21 @@
   
   
   
+    <li class="md-nav__item">
+      <a href="../parameter.html" class="md-nav__link">
+        <span title="/reference/parameter.rst (reference label)" class="md-ellipsis">bayes_<wbr>opt.<wbr>parameter</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
     <li class="md-nav__item">
       <a href="../exception.html" class="md-nav__link">
         <span title="/reference/exception.rst (reference label)" class="md-ellipsis">bayes_<wbr>opt.<wbr>exception</span>
diff --git a/master/reference/acquisition/UpperConfidenceBound.html b/master/reference/acquisition/UpperConfidenceBound.html
index fd8b6ef9..df86f6fc 100644
--- a/master/reference/acquisition/UpperConfidenceBound.html
+++ b/master/reference/acquisition/UpperConfidenceBound.html
@@ -323,6 +323,21 @@
   
   
   
+    <li class="md-nav__item">
+      <a href="../../parameter_types.html" class="md-nav__link">
+        <span title="/parameter_types.ipynb (reference label)" class="md-ellipsis">Parameter Types</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
     <li class="md-nav__item">
       <a href="../../domain_reduction.html" class="md-nav__link">
         <span title="/domain_reduction.ipynb (reference label)" class="md-ellipsis">Sequential Domain Reduction</span>
@@ -832,6 +847,21 @@
   
   
   
+    <li class="md-nav__item">
+      <a href="../parameter.html" class="md-nav__link">
+        <span title="/reference/parameter.rst (reference label)" class="md-ellipsis">bayes_<wbr>opt.<wbr>parameter</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
     <li class="md-nav__item">
       <a href="../exception.html" class="md-nav__link">
         <span title="/reference/exception.rst (reference label)" class="md-ellipsis">bayes_<wbr>opt.<wbr>exception</span>
diff --git a/master/reference/bayes_opt.html b/master/reference/bayes_opt.html
index b2c575cc..c51d1a37 100644
--- a/master/reference/bayes_opt.html
+++ b/master/reference/bayes_opt.html
@@ -323,6 +323,21 @@
   
   
   
+    <li class="md-nav__item">
+      <a href="../parameter_types.html" class="md-nav__link">
+        <span title="/parameter_types.ipynb (reference label)" class="md-ellipsis">Parameter Types</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
     <li class="md-nav__item">
       <a href="../domain_reduction.html" class="md-nav__link">
         <span title="/domain_reduction.ipynb (reference label)" class="md-ellipsis">Sequential Domain Reduction</span>
@@ -894,6 +909,21 @@
   
   
   
+    <li class="md-nav__item">
+      <a href="parameter.html" class="md-nav__link">
+        <span title="/reference/parameter.rst (reference label)" class="md-ellipsis">bayes_<wbr>opt.<wbr>parameter</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
     <li class="md-nav__item">
       <a href="exception.html" class="md-nav__link">
         <span title="/reference/exception.rst (reference label)" class="md-ellipsis">bayes_<wbr>opt.<wbr>exception</span>
@@ -1421,7 +1451,7 @@ <h1 id="bayes-opt-bayesianoptimization"><a class="reference internal" href="#bay
 
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="bayes_opt.BayesianOptimization.probe">
-<span class="sig-name descname"><span class="pre">probe</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.BayesianOptimization.probe.params" title="bayes_opt.BayesianOptimization.probe.params (Python parameter) — The parameters where the optimizer will evaluate the function."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Mapping" title="(in Python v3.13)"><span class="pre">Mapping</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.13)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><span class="pre">dtype</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.floating" title="(in NumPy v2.2)"><span class="pre">floating</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.BayesianOptimization.probe.lazy" title="bayes_opt.BayesianOptimization.probe.lazy (Python parameter) — If True, the optimizer will evaluate the points when calling maximize()."><span class="n"><span class="pre">lazy</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span></span><a class="headerlink" href="#bayes_opt.BayesianOptimization.probe" title="Link to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">probe</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.BayesianOptimization.probe.params" title="bayes_opt.BayesianOptimization.probe.params (Python parameter) — The parameters where the optimizer will evaluate the function."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Mapping" title="(in Python v3.13)"><span class="pre">Mapping</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.13)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><span class="pre">dtype</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.floating" title="(in NumPy v2.2)"><span class="pre">floating</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.BayesianOptimization.probe.lazy" title="bayes_opt.BayesianOptimization.probe.lazy (Python parameter) — If True, the optimizer will evaluate the points when calling maximize()."><span class="n"><span class="pre">lazy</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span></span><a class="headerlink" href="#bayes_opt.BayesianOptimization.probe" title="Link to this definition">¶</a></dt>
 <dd><p>Evaluate the function at the given points.</p>
 <p>Useful to guide the optimizer.</p>
 <dl class="field-list simple">
@@ -1442,7 +1472,7 @@ <h1 id="bayes-opt-bayesianoptimization"><a class="reference internal" href="#bay
 
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="bayes_opt.BayesianOptimization.register">
-<span class="sig-name descname"><span class="pre">register</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.BayesianOptimization.register.params" title="bayes_opt.BayesianOptimization.register.params (Python parameter) — The parameters associated with the observation."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Mapping" title="(in Python v3.13)"><span class="pre">Mapping</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.13)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><span class="pre">dtype</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.floating" title="(in NumPy v2.2)"><span class="pre">floating</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.BayesianOptimization.register.target" title="bayes_opt.BayesianOptimization.register.target (Python parameter) — Value of the target function at the observation."><span class="n"><span class="pre">target</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.BayesianOptimization.register.constraint_value" title="bayes_opt.BayesianOptimization.register.constraint_value (Python parameter) — Value of the constraint function at the observation, if any."><span class="n"><span class="pre">constraint_value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><span class="pre">dtype</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.floating" title="(in NumPy v2.2)"><span class="pre">floating</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span></span><a class="headerlink" href="#bayes_opt.BayesianOptimization.register" title="Link to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.BayesianOptimization.register.params" title="bayes_opt.BayesianOptimization.register.params (Python parameter) — The parameters associated with the observation."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Mapping" title="(in Python v3.13)"><span class="pre">Mapping</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.13)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><span class="pre">dtype</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.floating" title="(in NumPy v2.2)"><span class="pre">floating</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.BayesianOptimization.register.target" title="bayes_opt.BayesianOptimization.register.target (Python parameter) — Value of the target function at the observation."><span class="n"><span class="pre">target</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.BayesianOptimization.register.constraint_value" title="bayes_opt.BayesianOptimization.register.constraint_value (Python parameter) — Value of the constraint function at the observation, if any."><span class="n"><span class="pre">constraint_value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><span class="pre">dtype</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.floating" title="(in NumPy v2.2)"><span class="pre">floating</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span></span><a class="headerlink" href="#bayes_opt.BayesianOptimization.register" title="Link to this definition">¶</a></dt>
 <dd><p>Register an observation with known target.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="bayes_opt.BayesianOptimization.register-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#bayes_opt.BayesianOptimization.register-parameters" title="Permalink to this headline">¶</a></dt>
@@ -1470,7 +1500,7 @@ <h1 id="bayes-opt-bayesianoptimization"><a class="reference internal" href="#bay
 
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="bayes_opt.BayesianOptimization.set_bounds">
-<span class="sig-name descname"><span class="pre">set_bounds</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.BayesianOptimization.set_bounds.new_bounds" title="bayes_opt.BayesianOptimization.set_bounds.new_bounds (Python parameter) — A dictionary with the parameter name and its new bounds"><span class="n"><span class="pre">new_bounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Mapping" title="(in Python v3.13)"><span class="pre">Mapping</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><span class="pre">dtype</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.floating" title="(in NumPy v2.2)"><span class="pre">floating</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.13)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span></span><a class="headerlink" href="#bayes_opt.BayesianOptimization.set_bounds" title="Link to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_bounds</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.BayesianOptimization.set_bounds.new_bounds" title="bayes_opt.BayesianOptimization.set_bounds.new_bounds (Python parameter) — A dictionary with the parameter name and its new bounds"><span class="n"><span class="pre">new_bounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Mapping" title="(in Python v3.13)"><span class="pre">Mapping</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.13)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.13)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#type" title="(in Python v3.13)"><span class="pre">type</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.13)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#type" title="(in Python v3.13)"><span class="pre">type</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.13)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span></span><a class="headerlink" href="#bayes_opt.BayesianOptimization.set_bounds" title="Link to this definition">¶</a></dt>
 <dd><p>Modify the bounds of the search space.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="bayes_opt.BayesianOptimization.set_bounds-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#bayes_opt.BayesianOptimization.set_bounds-parameters" title="Permalink to this headline">¶</a></dt>
@@ -1510,11 +1540,11 @@ <h1 id="bayes-opt-bayesianoptimization"><a class="reference internal" href="#bay
 
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="bayes_opt.BayesianOptimization.suggest">
-<span class="sig-name descname"><span class="pre">suggest</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="headerlink" href="#bayes_opt.BayesianOptimization.suggest" title="Link to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">suggest</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><span class="pre">dtype</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.floating" title="(in NumPy v2.2)"><span class="pre">floating</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></span><a class="headerlink" href="#bayes_opt.BayesianOptimization.suggest" title="Link to this definition">¶</a></dt>
 <dd><p>Suggest a promising point to probe next.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="bayes_opt.BayesianOptimization.suggest-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#bayes_opt.BayesianOptimization.suggest-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><span class="sphinx_autodoc_typehints-type"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>[<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></a>, <a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a>]</span></p>
+<dd class="field-odd"><p><span class="sphinx_autodoc_typehints-type"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>[<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></a>, <a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a> | <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>[<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><code class="xref py py-data docutils literal notranslate"><span class="pre">Any</span></code></a>, <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dtype</span></code></a>[<a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.floating" title="(in NumPy v2.2)"><code class="xref py py-class docutils literal notranslate"><span class="pre">floating</span></code></a>[<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><code class="xref py py-data docutils literal notranslate"><span class="pre">Any</span></code></a>]]]]</span></p>
 </dd>
 </dl>
 </dd></dl>
diff --git a/master/reference/constraint.html b/master/reference/constraint.html
index 20a82384..bcf00175 100644
--- a/master/reference/constraint.html
+++ b/master/reference/constraint.html
@@ -323,6 +323,21 @@
   
   
   
+    <li class="md-nav__item">
+      <a href="../parameter_types.html" class="md-nav__link">
+        <span title="/parameter_types.ipynb (reference label)" class="md-ellipsis">Parameter Types</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
     <li class="md-nav__item">
       <a href="../domain_reduction.html" class="md-nav__link">
         <span title="/domain_reduction.ipynb (reference label)" class="md-ellipsis">Sequential Domain Reduction</span>
@@ -526,6 +541,13 @@
     <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.constraint.ConstraintModel.__init__.random_state (Python parameter) — Random state to use." class="md-ellipsis">random_<wbr>state</span>
   </a>
   
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.constraint.ConstraintModel.__init__.transform" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.constraint.ConstraintModel.__init__.transform (Python parameter)" class="md-ellipsis">transform</span>
+  </a>
+  
 </li>
         
       </ul>
@@ -840,6 +862,21 @@
   
   
   
+    <li class="md-nav__item">
+      <a href="parameter.html" class="md-nav__link">
+        <span title="/reference/parameter.rst (reference label)" class="md-ellipsis">bayes_<wbr>opt.<wbr>parameter</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
     <li class="md-nav__item">
       <a href="exception.html" class="md-nav__link">
         <span title="/reference/exception.rst (reference label)" class="md-ellipsis">bayes_<wbr>opt.<wbr>exception</span>
@@ -938,6 +975,13 @@
     <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.constraint.ConstraintModel.__init__.random_state (Python parameter) — Random state to use." class="md-ellipsis">random_<wbr>state</span>
   </a>
   
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.constraint.ConstraintModel.__init__.transform" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.constraint.ConstraintModel.__init__.transform (Python parameter)" class="md-ellipsis">transform</span>
+  </a>
+  
 </li>
         
       </ul>
@@ -1230,7 +1274,7 @@ <h1 id="bayes-opt-constraintmodel"><code class="xref py py-class docutils litera
 <p>See the <a class="reference external" href="../constraints.html#2.-Advanced-Constrained-Optimization">Constrained Optimization notebook</a> for a complete example.</p>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="bayes_opt.constraint.ConstraintModel">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">bayes_opt.constraint.</span></span><span class="sig-name descname"><span class="pre">ConstraintModel</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.constraint.ConstraintModel.__init__.fun" title="bayes_opt.constraint.ConstraintModel.__init__.fun (Python parameter) — The constraint function."><span class="n"><span class="pre">fun</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.13)"><span class="pre">collections.abc.Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.13)"><span class="pre">collections.abc.Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">NDArray</span><span class="p"><span class="pre">[</span></span><span class="pre">Float</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.constraint.ConstraintModel.__init__.lb" title="bayes_opt.constraint.ConstraintModel.__init__.lb (Python parameter) — The lower bound on the constraints."><span class="n"><span class="pre">lb</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">NDArray</span><span class="p"><span class="pre">[</span></span><span class="pre">Float</span><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.constraint.ConstraintModel.__init__.ub" title="bayes_opt.constraint.ConstraintModel.__init__.ub (Python parameter) — The upper bound on the constraints."><span class="n"><span class="pre">ub</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">NDArray</span><span class="p"><span class="pre">[</span></span><span class="pre">Float</span><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.constraint.ConstraintModel.__init__.random_state" title="bayes_opt.constraint.ConstraintModel.__init__.random_state (Python parameter) — Random state to use."><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">RandomState</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span></span><a class="headerlink" href="#bayes_opt.constraint.ConstraintModel" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">bayes_opt.constraint.</span></span><span class="sig-name descname"><span class="pre">ConstraintModel</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.constraint.ConstraintModel.__init__.fun" title="bayes_opt.constraint.ConstraintModel.__init__.fun (Python parameter) — The constraint function."><span class="n"><span class="pre">fun</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.13)"><span class="pre">collections.abc.Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.13)"><span class="pre">collections.abc.Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">NDArray</span><span class="p"><span class="pre">[</span></span><span class="pre">Float</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.constraint.ConstraintModel.__init__.lb" title="bayes_opt.constraint.ConstraintModel.__init__.lb (Python parameter) — The lower bound on the constraints."><span class="n"><span class="pre">lb</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">NDArray</span><span class="p"><span class="pre">[</span></span><span class="pre">Float</span><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.constraint.ConstraintModel.__init__.ub" title="bayes_opt.constraint.ConstraintModel.__init__.ub (Python parameter) — The upper bound on the constraints."><span class="n"><span class="pre">ub</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">NDArray</span><span class="p"><span class="pre">[</span></span><span class="pre">Float</span><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.constraint.ConstraintModel.__init__.transform" title="bayes_opt.constraint.ConstraintModel.__init__.transform (Python parameter)"><span class="n"><span class="pre">transform</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.13)"><span class="pre">collections.abc.Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.constraint.ConstraintModel.__init__.random_state" title="bayes_opt.constraint.ConstraintModel.__init__.random_state (Python parameter) — Random state to use."><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">RandomState</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span></span><a class="headerlink" href="#bayes_opt.constraint.ConstraintModel" title="Link to this definition">¶</a></dt>
 <dd><p>Model constraints using GP regressors.</p>
 <p>This class takes the function to optimize as well as the parameters bounds
 in order to find which values for the parameters yield the maximum value
@@ -1250,6 +1294,8 @@ <h1 id="bayes-opt-constraintmodel"><code class="xref py py-class docutils litera
 </dd>
 <dt id="bayes_opt.constraint.ConstraintModel.__init__.random_state"><span class="sig-name descname">random_state</span><span class="p"> : </span><span class="desctype"><span class="desctype">np.random.RandomState or <a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)">int</a> or None, default=None</span></span><a class="headerlink" href="#bayes_opt.constraint.ConstraintModel.__init__.random_state" title="Permalink to this definition">¶</a></dt><dd><p>Random state to use.</p>
 </dd>
+<dt id="bayes_opt.constraint.ConstraintModel.__init__.transform"><span class="sig-name descname">transform</span><span class="p"> : </span><span class="desctype"><span class="desctype">Callable[[Any], Any] | None</span></span><a class="headerlink" href="#bayes_opt.constraint.ConstraintModel.__init__.transform" title="Permalink to this definition">¶</a></dt><dd><p></p>
+</dd>
 </dl>
 </dd>
 </dl>
diff --git a/master/reference/domain_reduction.html b/master/reference/domain_reduction.html
index f5f83d3d..ed78bb9b 100644
--- a/master/reference/domain_reduction.html
+++ b/master/reference/domain_reduction.html
@@ -323,6 +323,21 @@
   
   
   
+    <li class="md-nav__item">
+      <a href="../parameter_types.html" class="md-nav__link">
+        <span title="/parameter_types.ipynb (reference label)" class="md-ellipsis">Parameter Types</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
     <li class="md-nav__item">
       <a href="../domain_reduction.html" class="md-nav__link">
         <span title="/domain_reduction.ipynb (reference label)" class="md-ellipsis">Sequential Domain Reduction</span>
@@ -541,6 +556,13 @@
     <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.SequentialDomainReductionTransformer.__init__.minimum_window (Python parameter) — Minimum window size for each parameter." class="md-ellipsis">minimum_<wbr>window</span>
   </a>
   
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.SequentialDomainReductionTransformer.__init__.parameters" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.SequentialDomainReductionTransformer.__init__.parameters (Python parameter)" class="md-ellipsis">parameters</span>
+  </a>
+  
 </li>
         
       </ul>
@@ -670,6 +692,21 @@
   
   
   
+    <li class="md-nav__item">
+      <a href="parameter.html" class="md-nav__link">
+        <span title="/reference/parameter.rst (reference label)" class="md-ellipsis">bayes_<wbr>opt.<wbr>parameter</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
     <li class="md-nav__item">
       <a href="exception.html" class="md-nav__link">
         <span title="/reference/exception.rst (reference label)" class="md-ellipsis">bayes_<wbr>opt.<wbr>exception</span>
@@ -768,6 +805,13 @@
     <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.SequentialDomainReductionTransformer.__init__.minimum_window (Python parameter) — Minimum window size for each parameter." class="md-ellipsis">minimum_<wbr>window</span>
   </a>
   
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.SequentialDomainReductionTransformer.__init__.parameters" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.SequentialDomainReductionTransformer.__init__.parameters (Python parameter)" class="md-ellipsis">parameters</span>
+  </a>
+  
 </li>
         
       </ul>
@@ -890,7 +934,7 @@ <h1 id="bayes-opt-sequentialdomainreductiontransformer"><a class="reference inte
 <p>See the <a class="reference external" href="../domain_reduction.html">Sequential Domain Reduction notebook</a> for a complete example.</p>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="bayes_opt.SequentialDomainReductionTransformer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">bayes_opt.</span></span><span class="sig-name descname"><span class="pre">SequentialDomainReductionTransformer</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.SequentialDomainReductionTransformer.__init__.gamma_osc" title="bayes_opt.SequentialDomainReductionTransformer.__init__.gamma_osc (Python parameter) — Parameter used to scale (typically dampen) oscillations."><span class="n"><span class="pre">gamma_osc</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.7</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.SequentialDomainReductionTransformer.__init__.gamma_pan" title="bayes_opt.SequentialDomainReductionTransformer.__init__.gamma_pan (Python parameter) — Parameter used to scale (typically unitary) panning."><span class="n"><span class="pre">gamma_pan</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">1.0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.SequentialDomainReductionTransformer.__init__.eta" title="bayes_opt.SequentialDomainReductionTransformer.__init__.eta (Python parameter) — Zooming parameter used to shrink the region of interest."><span class="n"><span class="pre">eta</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.9</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.SequentialDomainReductionTransformer.__init__.minimum_window" title="bayes_opt.SequentialDomainReductionTransformer.__init__.minimum_window (Python parameter) — Minimum window size for each parameter."><span class="n"><span class="pre">minimum_window</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="pre">NDArray</span><span class="p"><span class="pre">[</span></span><span class="pre">Float</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.13)"><span class="pre">collections.abc.Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Mapping</span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.0</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span></span><a class="headerlink" href="#bayes_opt.SequentialDomainReductionTransformer" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">bayes_opt.</span></span><span class="sig-name descname"><span class="pre">SequentialDomainReductionTransformer</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.SequentialDomainReductionTransformer.__init__.parameters" title="bayes_opt.SequentialDomainReductionTransformer.__init__.parameters (Python parameter)"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="pre">Iterable</span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.SequentialDomainReductionTransformer.__init__.gamma_osc" title="bayes_opt.SequentialDomainReductionTransformer.__init__.gamma_osc (Python parameter) — Parameter used to scale (typically dampen) oscillations."><span class="n"><span class="pre">gamma_osc</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.7</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.SequentialDomainReductionTransformer.__init__.gamma_pan" title="bayes_opt.SequentialDomainReductionTransformer.__init__.gamma_pan (Python parameter) — Parameter used to scale (typically unitary) panning."><span class="n"><span class="pre">gamma_pan</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">1.0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.SequentialDomainReductionTransformer.__init__.eta" title="bayes_opt.SequentialDomainReductionTransformer.__init__.eta (Python parameter) — Zooming parameter used to shrink the region of interest."><span class="n"><span class="pre">eta</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.9</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.SequentialDomainReductionTransformer.__init__.minimum_window" title="bayes_opt.SequentialDomainReductionTransformer.__init__.minimum_window (Python parameter) — Minimum window size for each parameter."><span class="n"><span class="pre">minimum_window</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="pre">NDArray</span><span class="p"><span class="pre">[</span></span><span class="pre">Float</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.13)"><span class="pre">collections.abc.Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Mapping</span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.0</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span></span><a class="headerlink" href="#bayes_opt.SequentialDomainReductionTransformer" title="Link to this definition">¶</a></dt>
 <dd><p>Reduce the searchable space.</p>
 <p>A sequential domain reduction transformer based on the work by Stander, N. and Craig, K:
 “On the robustness of a simple domain reduction scheme for simulation-based optimization”</p>
@@ -906,6 +950,8 @@ <h1 id="bayes-opt-sequentialdomainreductiontransformer"><a class="reference inte
 <dt id="bayes_opt.SequentialDomainReductionTransformer.__init__.minimum_window"><span class="sig-name descname">minimum_window</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)">float</a> or np.ndarray or <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)">dict</a>, default=0.0</span></span><a class="headerlink" href="#bayes_opt.SequentialDomainReductionTransformer.__init__.minimum_window" title="Permalink to this definition">¶</a></dt><dd><p>Minimum window size for each parameter. If a float is provided,
 the same value is used for all parameters.</p>
 </dd>
+<dt id="bayes_opt.SequentialDomainReductionTransformer.__init__.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">Iterable[<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)">str</a>] | None</span></span><a class="headerlink" href="#bayes_opt.SequentialDomainReductionTransformer.__init__.parameters" title="Permalink to this definition">¶</a></dt><dd><p></p>
+</dd>
 </dl>
 </dd>
 </dl>
diff --git a/master/reference/exception.html b/master/reference/exception.html
index 2b33a47c..d42b3c7f 100644
--- a/master/reference/exception.html
+++ b/master/reference/exception.html
@@ -323,6 +323,21 @@
   
   
   
+    <li class="md-nav__item">
+      <a href="../parameter_types.html" class="md-nav__link">
+        <span title="/parameter_types.ipynb (reference label)" class="md-ellipsis">Parameter Types</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
     <li class="md-nav__item">
       <a href="../domain_reduction.html" class="md-nav__link">
         <span title="/domain_reduction.ipynb (reference label)" class="md-ellipsis">Sequential Domain Reduction</span>
@@ -494,6 +509,21 @@
               
   
   
+  
+  
+    <li class="md-nav__item">
+      <a href="parameter.html" class="md-nav__link">
+        <span title="/reference/parameter.rst (reference label)" class="md-ellipsis">bayes_<wbr>opt.<wbr>parameter</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
     
   
     
@@ -734,7 +764,7 @@ <h1 id="module-bayes_opt.exception"><span id="bayes-opt-exception"></span><a cla
     <nav class="md-footer__inner md-grid" aria-label="Footer" >
       
         
-        <a href="target_space.html" class="md-footer__link md-footer__link--prev" aria-label="Previous: bayes_opt.TargetSpace" rel="prev">
+        <a href="parameter.html" class="md-footer__link md-footer__link--prev" aria-label="Previous: bayes_opt.parameter" rel="prev">
           <div class="md-footer__button md-icon">
             <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11h12Z"/></svg>
           </div>
@@ -743,7 +773,7 @@ <h1 id="module-bayes_opt.exception"><span id="bayes-opt-exception"></span><a cla
               <span class="md-footer__direction">
                 Previous
               </span>
-              bayes_opt.TargetSpace
+              bayes_opt.parameter
             </div>
           </div>
         </a>
diff --git a/master/reference/other.html b/master/reference/other.html
index c0709189..345c95e9 100644
--- a/master/reference/other.html
+++ b/master/reference/other.html
@@ -323,6 +323,21 @@
   
   
   
+    <li class="md-nav__item">
+      <a href="../parameter_types.html" class="md-nav__link">
+        <span title="/parameter_types.ipynb (reference label)" class="md-ellipsis">Parameter Types</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
     <li class="md-nav__item">
       <a href="../domain_reduction.html" class="md-nav__link">
         <span title="/domain_reduction.ipynb (reference label)" class="md-ellipsis">Sequential Domain Reduction</span>
@@ -496,6 +511,21 @@
   
   
   
+    <li class="md-nav__item">
+      <a href="parameter.html" class="md-nav__link">
+        <span title="/reference/parameter.rst (reference label)" class="md-ellipsis">bayes_<wbr>opt.<wbr>parameter</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
     <li class="md-nav__item">
       <a href="exception.html" class="md-nav__link">
         <span title="/reference/exception.rst (reference label)" class="md-ellipsis">bayes_<wbr>opt.<wbr>exception</span>
diff --git a/master/reference/parameter.html b/master/reference/parameter.html
new file mode 100644
index 00000000..9bc243d2
--- /dev/null
+++ b/master/reference/parameter.html
@@ -0,0 +1,3637 @@
+
+
+<!doctype html>
+<html lang="en" class="no-js">
+  <head>
+    
+      <meta charset="utf-8">
+      <meta name="viewport" content="width=device-width,initial-scale=1">
+      
+      
+      
+      <link rel="icon" href="../_static/func.ico">
+    
+    
+      
+        <title>bayes_opt.parameter - Bayesian Optimization</title>
+      
+    
+    
+      
+        
+        
+      
+      
+
+    
+    
+    
+      
+        
+        
+        <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
+      
+    
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.4b4a8c74e2ff2ab5b.min.css?v=afa97f93" />
+    <script>__md_scope=new URL("/",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
+    
+      
+
+    
+    
+    
+  </head>
+  
+  
+    
+    
+      
+    
+    
+    
+    
+    <body dir="ltr" data-md-color-scheme="default" data-md-color-primary="light-blue" data-md-color-accent="light-green">
+  
+    
+    
+      <script>var palette=__md_get("__palette");if(palette&&"object"==typeof palette.color)for(var key of Object.keys(palette.color))document.body.setAttribute("data-md-color-"+key,palette.color[key])</script>
+    
+    <input class="md-toggle" data-md-toggle="drawer" type="checkbox" id="__drawer" autocomplete="off">
+    <input class="md-toggle" data-md-toggle="search" type="checkbox" id="__search" autocomplete="off">
+    <label class="md-overlay" for="__drawer"></label>
+    <div data-md-component="skip">
+      
+        
+        <a href="#bayes_opt.parameter.BayesParameter" class="md-skip">
+          Skip to content
+        </a>
+      
+    </div>
+    <div data-md-component="announce">
+      
+    </div>
+    
+      <div data-md-component="outdated" hidden>
+        
+      </div>
+    
+    
+      
+
+<header class="md-header" data-md-component="header">
+  <nav class="md-header__inner md-grid" aria-label="Header">
+    <a href="../index.html" title="Bayesian Optimization" class="md-header__button md-logo" aria-label="Bayesian Optimization" data-md-component="logo">
+      
+  
+  <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54Z"/></svg>
+    </a>
+    <label class="md-header__button md-icon" for="__drawer">
+      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg>
+    </label>
+    <div class="md-header__title" data-md-component="header-title">
+      <div class="md-header__ellipsis">
+        <div class="md-header__topic">
+          <span class="md-ellipsis">
+            Bayesian Optimization
+          </span>
+        </div>
+        <div class="md-header__topic" data-md-component="header-topic">
+          <span class="md-ellipsis">
+            
+              bayes_opt.parameter
+            
+          </span>
+        </div>
+      </div>
+    </div>
+    
+      <form class="md-header__option" data-md-component="palette">
+        
+          
+          
+          <input class="md-option" data-md-color-media="(prefers-color-scheme: light)" data-md-color-scheme="default" data-md-color-primary="light-blue" data-md-color-accent="light-green"  aria-label="Switch to dark mode"  type="radio" name="__palette" id="__palette_1">
+          
+            <label class="md-header__button md-icon" title="Switch to dark mode" for="__palette_2" hidden>
+              <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 2a7 7 0 0 1 7 7c0 2.38-1.19 4.47-3 5.74V17a1 1 0 0 1-1 1H9a1 1 0 0 1-1-1v-2.26C6.19 13.47 5 11.38 5 9a7 7 0 0 1 7-7M9 21v-1h6v1a1 1 0 0 1-1 1h-4a1 1 0 0 1-1-1m3-17a5 5 0 0 0-5 5c0 2.05 1.23 3.81 3 4.58V16h4v-2.42c1.77-.77 3-2.53 3-4.58a5 5 0 0 0-5-5Z"/></svg>
+            </label>
+          
+        
+          
+          
+          <input class="md-option" data-md-color-media="(prefers-color-scheme: dark)" data-md-color-scheme="slate" data-md-color-primary="deep-orange" data-md-color-accent="lime"  aria-label="Switch to light mode"  type="radio" name="__palette" id="__palette_2">
+          
+            <label class="md-header__button md-icon" title="Switch to light mode" for="__palette_1" hidden>
+              <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 2a7 7 0 0 0-7 7c0 2.38 1.19 4.47 3 5.74V17a1 1 0 0 0 1 1h6a1 1 0 0 0 1-1v-2.26c1.81-1.27 3-3.36 3-5.74a7 7 0 0 0-7-7M9 21a1 1 0 0 0 1 1h4a1 1 0 0 0 1-1v-1H9v1Z"/></svg>
+            </label>
+          
+        
+      </form>
+    
+    
+    
+      <label class="md-header__button md-icon" for="__search">
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
+      </label>
+      <div class="md-search" data-md-component="search" role="dialog">
+  <label class="md-search__overlay" for="__search"></label>
+  <div class="md-search__inner" role="search">
+    <form class="md-search__form" name="search">
+      <input type="text" class="md-search__input" name="query" aria-label="Search" placeholder="Search" autocapitalize="off" autocorrect="off" autocomplete="off" spellcheck="false" data-md-component="search-query" required>
+      <label class="md-search__icon md-icon" for="__search">
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11h12Z"/></svg>
+      </label>
+      <nav class="md-search__options" aria-label="Search">
+        
+          <a href="javascript:void(0)" class="md-search__icon md-icon" title="Share" aria-label="Share" data-clipboard data-clipboard-text="" data-md-component="search-share" tabindex="-1">
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M18 16.08c-.76 0-1.44.3-1.96.77L8.91 12.7c.05-.23.09-.46.09-.7 0-.24-.04-.47-.09-.7l7.05-4.11c.54.5 1.25.81 2.04.81a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3c0 .24.04.47.09.7L8.04 9.81C7.5 9.31 6.79 9 6 9a3 3 0 0 0-3 3 3 3 0 0 0 3 3c.79 0 1.5-.31 2.04-.81l7.12 4.15c-.05.21-.08.43-.08.66 0 1.61 1.31 2.91 2.92 2.91 1.61 0 2.92-1.3 2.92-2.91A2.92 2.92 0 0 0 18 16.08Z"/></svg>
+          </a>
+        
+        <button type="reset" class="md-search__icon md-icon" title="Clear" aria-label="Clear" tabindex="-1">
+          <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12 19 6.41Z"/></svg>
+        </button>
+      </nav>
+      
+    </form>
+    <div class="md-search__output">
+      <div class="md-search__scrollwrap" data-md-scrollfix>
+        <div class="md-search-result" data-md-component="search-result">
+          <div class="md-search-result__meta">
+            Initializing search
+          </div>
+          <ol class="md-search-result__list"></ol>
+        </div>
+      </div>
+    </div>
+  </div>
+</div>
+    
+    
+      <div class="md-header__source">
+        <a href="https://github.com/bayesian-optimization/BayesianOptimization/" title="Go to repository" class="md-source" data-md-component="source">
+  <div class="md-source__icon md-icon">
+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg>
+  </div>
+  <div class="md-source__repository">
+    bayesian-optimization
+  </div>
+</a>
+      </div>
+    
+  </nav>
+  
+</header>
+    
+    <div class="md-container" data-md-component="container">
+      
+      
+      
+      
+        
+          
+        
+      
+      <main class="md-main" data-md-component="main">
+        <div class="md-main__inner md-grid">
+          
+            
+              
+              <div class="md-sidebar md-sidebar--primary" data-md-component="sidebar" data-md-type="navigation" >
+                <div class="md-sidebar__scrollwrap">
+                  <div class="md-sidebar__inner">
+                    
+
+
+<nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
+  <label class="md-nav__title" for="__drawer">
+    <a href="../index.html" title="Bayesian Optimization" class="md-nav__button md-logo" aria-label="Bayesian Optimization" data-md-component="logo">
+      
+  
+  <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54Z"/></svg>
+    </a>
+    Bayesian Optimization
+  </label>
+  
+    <div class="md-nav__source">
+      <a href="https://github.com/bayesian-optimization/BayesianOptimization/" title="Go to repository" class="md-source" data-md-component="source">
+  <div class="md-source__icon md-icon">
+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg>
+  </div>
+  <div class="md-source__repository">
+    bayesian-optimization
+  </div>
+</a>
+    </div>
+  
+  <ul class="md-nav__list" data-md-scrollfix>
+    
+      
+      
+      
+
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../index.html" class="md-nav__link">
+        <span title="/index.rst (reference label)" class="md-ellipsis">Quickstart</span>
+      </a>
+    </li>
+  
+
+    
+      
+      
+      
+
+  
+  
+  
+    
+      
+    
+    <li class="md-nav__item md-nav__item--section md-nav__item--nested">
+      
+      
+        <input class="md-nav__toggle md-toggle md-toggle--indeterminate" data-md-toggle="__nav_2" type="checkbox" id="__nav_2" checked>
+      
+      
+      
+        <label class="md-nav__link" for="__nav_2">
+          <span class="md-ellipsis">Example Notebooks:<wbr></span>
+          <span class="md-nav__icon md-icon"></span>
+        </label>
+      
+      <nav class="md-nav" aria-label="Example Notebooks:" data-md-level="1">
+        <label class="md-nav__title" for="__nav_2">
+          <span class="md-nav__icon md-icon"></span>
+          <span class="md-ellipsis">Example Notebooks:<wbr></span>
+        </label>
+        <ul class="md-nav__list" data-md-scrollfix>
+          
+            
+              
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../basic-tour.html" class="md-nav__link">
+        <span title="/basic-tour.ipynb (reference label)" class="md-ellipsis">Basic Tour</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../advanced-tour.html" class="md-nav__link">
+        <span title="/advanced-tour.ipynb (reference label)" class="md-ellipsis">Advanced Tour</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../constraints.html" class="md-nav__link">
+        <span title="/constraints.ipynb (reference label)" class="md-ellipsis">Constrained Bayesian Optimization</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../parameter_types.html" class="md-nav__link">
+        <span title="/parameter_types.ipynb (reference label)" class="md-ellipsis">Parameter Types</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../domain_reduction.html" class="md-nav__link">
+        <span title="/domain_reduction.ipynb (reference label)" class="md-ellipsis">Sequential Domain Reduction</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../acquisition_functions.html" class="md-nav__link">
+        <span title="/acquisition_functions.ipynb (reference label)" class="md-ellipsis">Acquisition Functions</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../exploitation_vs_exploration.html" class="md-nav__link">
+        <span title="/exploitation_vs_exploration.ipynb (reference label)" class="md-ellipsis">Exploration vs.<wbr> Exploitation</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../visualization.html" class="md-nav__link">
+        <span title="/visualization.ipynb (reference label)" class="md-ellipsis">Visualization of a 1D-<wbr>Optimization</span>
+      </a>
+    </li>
+  
+
+            
+          
+        </ul>
+      </nav>
+    </li>
+  
+  
+
+    
+      
+      
+      
+
+  
+  
+    
+  
+  
+    
+      
+    
+    <li class="md-nav__item md-nav__item--active md-nav__item--section md-nav__item--nested">
+      
+      
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_3" type="checkbox" id="__nav_3" checked>
+      
+      
+      
+        <label class="md-nav__link" for="__nav_3">
+          <span class="md-ellipsis">API reference:<wbr></span>
+          <span class="md-nav__icon md-icon"></span>
+        </label>
+      
+      <nav class="md-nav" aria-label="API reference:" data-md-level="1">
+        <label class="md-nav__title" for="__nav_3">
+          <span class="md-nav__icon md-icon"></span>
+          <span class="md-ellipsis">API reference:<wbr></span>
+        </label>
+        <ul class="md-nav__list" data-md-scrollfix>
+          
+            
+              
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="bayes_opt.html" class="md-nav__link">
+        <span title="/reference/bayes_opt.rst (reference label)" class="md-ellipsis">bayes_<wbr>opt.<wbr>Bayesian<wbr>Optimization</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="acquisition.html" class="md-nav__link">
+        <span title="/reference/acquisition.rst (reference label)" class="md-ellipsis">bayes_<wbr>opt.<wbr>acquisition</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="constraint.html" class="md-nav__link">
+        <span title="/reference/constraint.rst (reference label)" class="md-ellipsis">bayes_<wbr>opt.<wbr>Constraint<wbr>Model</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="domain_reduction.html" class="md-nav__link">
+        <span title="/reference/domain_reduction.rst (reference label)" class="md-ellipsis">bayes_<wbr>opt.<wbr>Sequential<wbr>Domain<wbr>Reduction<wbr>Transformer</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="target_space.html" class="md-nav__link">
+        <span title="/reference/target_space.rst (reference label)" class="md-ellipsis">bayes_<wbr>opt.<wbr>Target<wbr>Space</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+    
+  
+    
+  
+  
+    <li class="md-nav__item md-nav__item--active">
+      
+      
+      <input class="md-nav__toggle md-toggle" data-md-toggle="toc" type="checkbox" id="__toc">
+      
+        <label class="md-nav__link md-nav__link--active" for="__toc">
+          <span title="/reference/parameter.rst (reference label)" class="md-ellipsis">bayes_<wbr>opt.<wbr>parameter</span>
+          <span class="md-nav__icon md-icon"></span>
+        </label>
+      
+      <a href="#" class="md-nav__link md-nav__link--active">
+        <span title="/reference/parameter.rst (reference label)" class="md-ellipsis">bayes_<wbr>opt.<wbr>parameter</span>
+      </a>
+      
+        
+
+  
+
+<nav class="md-nav md-nav--secondary">
+  
+  
+    
+  
+  
+    <label class="md-nav__title" for="__toc">
+      <span class="md-nav__icon md-icon"></span>
+      <span title="/reference/parameter.rst (reference label)" class="md-ellipsis">bayes_<wbr>opt.<wbr>parameter</span>
+    </label>
+    <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
+      
+        <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.BayesParameter" class="md-nav__link">
+    <span aria-label="Python class" class="objinfo-icon objinfo-icon__data" title="Python class">C</span><span title="bayes_opt.parameter.BayesParameter (Python class) — Base class for Bayesian optimization parameters." class="md-ellipsis">bayes_<wbr>opt.<wbr>parameter.<wbr>Bayes<wbr>Parameter</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="bayes_opt.parameter.BayesParameter">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.BayesParameter-parameters" class="md-nav__link">
+    <span class="md-ellipsis">Parameters</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="Parameters">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.BayesParameter.__init__.name" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.BayesParameter.__init__.name (Python parameter) — The name of the parameter." class="md-ellipsis">name</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.BayesParameter.__init__.bounds" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.BayesParameter.__init__.bounds (Python parameter)" class="md-ellipsis">bounds</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.BayesParameter.bounds" class="md-nav__link">
+    <span aria-label="Python property" class="objinfo-icon objinfo-icon__alias" title="Python property">P</span><span title="bayes_opt.parameter.BayesParameter.bounds (Python property) — The bounds of the parameter in float space." class="md-ellipsis">bounds</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.BayesParameter.dim" class="md-nav__link">
+    <span aria-label="Python property" class="objinfo-icon objinfo-icon__alias" title="Python property">P</span><span title="bayes_opt.parameter.BayesParameter.dim (Python property) — The dimensionality of the parameter." class="md-ellipsis">dim</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.BayesParameter.is_continuous" class="md-nav__link">
+    <span aria-label="Python property" class="objinfo-icon objinfo-icon__alias" title="Python property">P</span><span title="bayes_opt.parameter.BayesParameter.is_continuous (Python property) — Whether the parameter is continuous." class="md-ellipsis">is_<wbr>continuous</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.BayesParameter.kernel_transform" class="md-nav__link">
+    <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="bayes_opt.parameter.BayesParameter.kernel_transform (Python method) — Transform a parameter value for use in a kernel." class="md-ellipsis">kernel_<wbr>transform</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="kernel_transform">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.BayesParameter.kernel_transform-parameters" class="md-nav__link">
+    <span class="md-ellipsis">Parameters</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="Parameters">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.BayesParameter.kernel_transform.value" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.BayesParameter.kernel_transform.value (Python parameter) — The value(s) to transform, should be a float." class="md-ellipsis">value</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.BayesParameter.kernel_transform-return-type" class="md-nav__link">
+    <span class="md-ellipsis">Return type</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.BayesParameter.kernel_transform-returns" class="md-nav__link">
+    <span class="md-ellipsis">Returns</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.BayesParameter.random_sample" class="md-nav__link">
+    <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="bayes_opt.parameter.BayesParameter.random_sample (Python method) — Generate random samples from the parameter." class="md-ellipsis">random_<wbr>sample</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="random_sample">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.BayesParameter.random_sample-parameters" class="md-nav__link">
+    <span class="md-ellipsis">Parameters</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="Parameters">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.BayesParameter.random_sample.n_samples" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.BayesParameter.random_sample.n_samples (Python parameter) — The number of samples to generate." class="md-ellipsis">n_<wbr>samples</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.BayesParameter.random_sample.random_state" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.BayesParameter.random_sample.random_state (Python parameter) — The random state to use for sampling." class="md-ellipsis">random_<wbr>state</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.BayesParameter.random_sample-return-type" class="md-nav__link">
+    <span class="md-ellipsis">Return type</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.BayesParameter.random_sample-returns" class="md-nav__link">
+    <span class="md-ellipsis">Returns</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.BayesParameter.to_float" class="md-nav__link">
+    <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="bayes_opt.parameter.BayesParameter.to_float (Python method) — Convert a parameter value to a float." class="md-ellipsis">to_<wbr>float</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="to_float">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.BayesParameter.to_float-parameters" class="md-nav__link">
+    <span class="md-ellipsis">Parameters</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="Parameters">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.BayesParameter.to_float.value" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.BayesParameter.to_float.value (Python parameter) — The value to convert, should be the canonical representation of the parameter." class="md-ellipsis">value</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.BayesParameter.to_float-return-type" class="md-nav__link">
+    <span class="md-ellipsis">Return type</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.BayesParameter.to_param" class="md-nav__link">
+    <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="bayes_opt.parameter.BayesParameter.to_param (Python method) — Convert a float value to a parameter." class="md-ellipsis">to_<wbr>param</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="to_param">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.BayesParameter.to_param-parameters" class="md-nav__link">
+    <span class="md-ellipsis">Parameters</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="Parameters">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.BayesParameter.to_param.value" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.BayesParameter.to_param.value (Python parameter) — The value to convert, should be a float." class="md-ellipsis">value</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.BayesParameter.to_param-return-type" class="md-nav__link">
+    <span class="md-ellipsis">Return type</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.BayesParameter.to_param-returns" class="md-nav__link">
+    <span class="md-ellipsis">Returns</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.BayesParameter.to_string" class="md-nav__link">
+    <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="bayes_opt.parameter.BayesParameter.to_string (Python method) — Represent a parameter value as a string." class="md-ellipsis">to_<wbr>string</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="to_string">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.BayesParameter.to_string-parameters" class="md-nav__link">
+    <span class="md-ellipsis">Parameters</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="Parameters">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.BayesParameter.to_string.value" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.BayesParameter.to_string.value (Python parameter) — The value to represent." class="md-ellipsis">value</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.BayesParameter.to_string.str_len" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.BayesParameter.to_string.str_len (Python parameter) — The maximum length of the string representation." class="md-ellipsis">str_<wbr>len</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.BayesParameter.to_string-return-type" class="md-nav__link">
+    <span class="md-ellipsis">Return type</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.BayesParameter.to_string-returns" class="md-nav__link">
+    <span class="md-ellipsis">Returns</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.CategoricalParameter" class="md-nav__link">
+    <span aria-label="Python class" class="objinfo-icon objinfo-icon__data" title="Python class">C</span><span title="bayes_opt.parameter.CategoricalParameter (Python class) — A parameter with categorical values." class="md-ellipsis">bayes_<wbr>opt.<wbr>parameter.<wbr>Categorical<wbr>Parameter</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="bayes_opt.parameter.CategoricalParameter">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.CategoricalParameter-parameters" class="md-nav__link">
+    <span class="md-ellipsis">Parameters</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="Parameters">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.CategoricalParameter.__init__.name" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.CategoricalParameter.__init__.name (Python parameter) — The name of the parameter." class="md-ellipsis">name</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.CategoricalParameter.__init__.categories" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.CategoricalParameter.__init__.categories (Python parameter) — The categories of the parameter." class="md-ellipsis">categories</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.CategoricalParameter.dim" class="md-nav__link">
+    <span aria-label="Python property" class="objinfo-icon objinfo-icon__alias" title="Python property">P</span><span title="bayes_opt.parameter.CategoricalParameter.dim (Python property) — The dimensionality of the parameter." class="md-ellipsis">dim</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.CategoricalParameter.is_continuous" class="md-nav__link">
+    <span aria-label="Python property" class="objinfo-icon objinfo-icon__alias" title="Python property">P</span><span title="bayes_opt.parameter.CategoricalParameter.is_continuous (Python property) — Whether the parameter is continuous." class="md-ellipsis">is_<wbr>continuous</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.CategoricalParameter.kernel_transform" class="md-nav__link">
+    <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="bayes_opt.parameter.CategoricalParameter.kernel_transform (Python method) — Transform a parameter value for use in a kernel." class="md-ellipsis">kernel_<wbr>transform</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="kernel_transform">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.CategoricalParameter.kernel_transform-parameters" class="md-nav__link">
+    <span class="md-ellipsis">Parameters</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="Parameters">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.CategoricalParameter.kernel_transform.value" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.CategoricalParameter.kernel_transform.value (Python parameter) — The value(s) to transform, should be a float." class="md-ellipsis">value</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.CategoricalParameter.kernel_transform-return-type" class="md-nav__link">
+    <span class="md-ellipsis">Return type</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.CategoricalParameter.kernel_transform-returns" class="md-nav__link">
+    <span class="md-ellipsis">Returns</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.CategoricalParameter.random_sample" class="md-nav__link">
+    <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="bayes_opt.parameter.CategoricalParameter.random_sample (Python method) — Generate random float-format samples from the parameter." class="md-ellipsis">random_<wbr>sample</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="random_sample">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.CategoricalParameter.random_sample-parameters" class="md-nav__link">
+    <span class="md-ellipsis">Parameters</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="Parameters">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.CategoricalParameter.random_sample.n_samples" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.CategoricalParameter.random_sample.n_samples (Python parameter) — The number of samples to generate." class="md-ellipsis">n_<wbr>samples</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.CategoricalParameter.random_sample.random_state" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.CategoricalParameter.random_sample.random_state (Python parameter) — The random state to use for sampling." class="md-ellipsis">random_<wbr>state</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.CategoricalParameter.random_sample-return-type" class="md-nav__link">
+    <span class="md-ellipsis">Return type</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.CategoricalParameter.random_sample-returns" class="md-nav__link">
+    <span class="md-ellipsis">Returns</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.CategoricalParameter.to_float" class="md-nav__link">
+    <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="bayes_opt.parameter.CategoricalParameter.to_float (Python method) — Convert a parameter value to a float." class="md-ellipsis">to_<wbr>float</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="to_float">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.CategoricalParameter.to_float-parameters" class="md-nav__link">
+    <span class="md-ellipsis">Parameters</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="Parameters">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.CategoricalParameter.to_float.value" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.CategoricalParameter.to_float.value (Python parameter) — The value to convert, should be the canonical representation of the parameter." class="md-ellipsis">value</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.CategoricalParameter.to_float-return-type" class="md-nav__link">
+    <span class="md-ellipsis">Return type</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.CategoricalParameter.to_param" class="md-nav__link">
+    <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="bayes_opt.parameter.CategoricalParameter.to_param (Python method) — Convert a float value to a parameter." class="md-ellipsis">to_<wbr>param</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="to_param">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.CategoricalParameter.to_param-parameters" class="md-nav__link">
+    <span class="md-ellipsis">Parameters</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="Parameters">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.CategoricalParameter.to_param.value" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.CategoricalParameter.to_param.value (Python parameter) — The value to convert, should be a float." class="md-ellipsis">value</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.CategoricalParameter.to_param-return-type" class="md-nav__link">
+    <span class="md-ellipsis">Return type</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.CategoricalParameter.to_param-returns" class="md-nav__link">
+    <span class="md-ellipsis">Returns</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.CategoricalParameter.to_string" class="md-nav__link">
+    <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="bayes_opt.parameter.CategoricalParameter.to_string (Python method) — Represent a parameter value as a string." class="md-ellipsis">to_<wbr>string</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="to_string">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.CategoricalParameter.to_string-parameters" class="md-nav__link">
+    <span class="md-ellipsis">Parameters</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="Parameters">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.CategoricalParameter.to_string.value" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.CategoricalParameter.to_string.value (Python parameter) — The value to represent." class="md-ellipsis">value</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.CategoricalParameter.to_string.str_len" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.CategoricalParameter.to_string.str_len (Python parameter) — The maximum length of the string representation." class="md-ellipsis">str_<wbr>len</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.CategoricalParameter.to_string-return-type" class="md-nav__link">
+    <span class="md-ellipsis">Return type</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.CategoricalParameter.to_string-returns" class="md-nav__link">
+    <span class="md-ellipsis">Returns</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.FloatParameter" class="md-nav__link">
+    <span aria-label="Python class" class="objinfo-icon objinfo-icon__data" title="Python class">C</span><span title="bayes_opt.parameter.FloatParameter (Python class) — A parameter with float values." class="md-ellipsis">bayes_<wbr>opt.<wbr>parameter.<wbr>Float<wbr>Parameter</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="bayes_opt.parameter.FloatParameter">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.FloatParameter-parameters" class="md-nav__link">
+    <span class="md-ellipsis">Parameters</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="Parameters">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.FloatParameter.__init__.name" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.FloatParameter.__init__.name (Python parameter) — The name of the parameter." class="md-ellipsis">name</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.FloatParameter.__init__.bounds" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.FloatParameter.__init__.bounds (Python parameter) — The bounds of the parameter." class="md-ellipsis">bounds</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.FloatParameter.dim" class="md-nav__link">
+    <span aria-label="Python property" class="objinfo-icon objinfo-icon__alias" title="Python property">P</span><span title="bayes_opt.parameter.FloatParameter.dim (Python property) — The dimensionality of the parameter." class="md-ellipsis">dim</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.FloatParameter.is_continuous" class="md-nav__link">
+    <span aria-label="Python property" class="objinfo-icon objinfo-icon__alias" title="Python property">P</span><span title="bayes_opt.parameter.FloatParameter.is_continuous (Python property) — Whether the parameter is continuous." class="md-ellipsis">is_<wbr>continuous</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.FloatParameter.kernel_transform" class="md-nav__link">
+    <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="bayes_opt.parameter.FloatParameter.kernel_transform (Python method) — Transform a parameter value for use in a kernel." class="md-ellipsis">kernel_<wbr>transform</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="kernel_transform">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.FloatParameter.kernel_transform-parameters" class="md-nav__link">
+    <span class="md-ellipsis">Parameters</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="Parameters">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.FloatParameter.kernel_transform.value" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.FloatParameter.kernel_transform.value (Python parameter) — The value(s) to transform, should be a float." class="md-ellipsis">value</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.FloatParameter.kernel_transform-return-type" class="md-nav__link">
+    <span class="md-ellipsis">Return type</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.FloatParameter.kernel_transform-returns" class="md-nav__link">
+    <span class="md-ellipsis">Returns</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.FloatParameter.to_float" class="md-nav__link">
+    <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="bayes_opt.parameter.FloatParameter.to_float (Python method) — Convert a parameter value to a float." class="md-ellipsis">to_<wbr>float</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="to_float">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.FloatParameter.to_float-parameters" class="md-nav__link">
+    <span class="md-ellipsis">Parameters</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="Parameters">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.FloatParameter.to_float.value" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.FloatParameter.to_float.value (Python parameter) — The value to convert, should be the canonical representation of the parameter." class="md-ellipsis">value</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.FloatParameter.to_float-return-type" class="md-nav__link">
+    <span class="md-ellipsis">Return type</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.FloatParameter.to_param" class="md-nav__link">
+    <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="bayes_opt.parameter.FloatParameter.to_param (Python method) — Convert a float value to a parameter." class="md-ellipsis">to_<wbr>param</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="to_param">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.FloatParameter.to_param-parameters" class="md-nav__link">
+    <span class="md-ellipsis">Parameters</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="Parameters">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.FloatParameter.to_param.value" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.FloatParameter.to_param.value (Python parameter) — The value to convert, should be a float." class="md-ellipsis">value</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.FloatParameter.to_param-return-type" class="md-nav__link">
+    <span class="md-ellipsis">Return type</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.FloatParameter.to_param-returns" class="md-nav__link">
+    <span class="md-ellipsis">Returns</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.FloatParameter.to_string" class="md-nav__link">
+    <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="bayes_opt.parameter.FloatParameter.to_string (Python method) — Represent a parameter value as a string." class="md-ellipsis">to_<wbr>string</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="to_string">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.FloatParameter.to_string-parameters" class="md-nav__link">
+    <span class="md-ellipsis">Parameters</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="Parameters">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.FloatParameter.to_string.value" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.FloatParameter.to_string.value (Python parameter) — The value to represent." class="md-ellipsis">value</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.FloatParameter.to_string.str_len" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.FloatParameter.to_string.str_len (Python parameter) — The maximum length of the string representation." class="md-ellipsis">str_<wbr>len</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.FloatParameter.to_string-return-type" class="md-nav__link">
+    <span class="md-ellipsis">Return type</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.FloatParameter.to_string-returns" class="md-nav__link">
+    <span class="md-ellipsis">Returns</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.IntParameter" class="md-nav__link">
+    <span aria-label="Python class" class="objinfo-icon objinfo-icon__data" title="Python class">C</span><span title="bayes_opt.parameter.IntParameter (Python class) — A parameter with int values." class="md-ellipsis">bayes_<wbr>opt.<wbr>parameter.<wbr>Int<wbr>Parameter</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="bayes_opt.parameter.IntParameter">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.IntParameter-parameters" class="md-nav__link">
+    <span class="md-ellipsis">Parameters</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="Parameters">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.IntParameter.__init__.name" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.IntParameter.__init__.name (Python parameter) — The name of the parameter." class="md-ellipsis">name</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.IntParameter.__init__.bounds" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.IntParameter.__init__.bounds (Python parameter) — The bounds of the parameter." class="md-ellipsis">bounds</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.IntParameter.dim" class="md-nav__link">
+    <span aria-label="Python property" class="objinfo-icon objinfo-icon__alias" title="Python property">P</span><span title="bayes_opt.parameter.IntParameter.dim (Python property) — The dimensionality of the parameter." class="md-ellipsis">dim</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.IntParameter.is_continuous" class="md-nav__link">
+    <span aria-label="Python property" class="objinfo-icon objinfo-icon__alias" title="Python property">P</span><span title="bayes_opt.parameter.IntParameter.is_continuous (Python property) — Whether the parameter is continuous." class="md-ellipsis">is_<wbr>continuous</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.IntParameter.kernel_transform" class="md-nav__link">
+    <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="bayes_opt.parameter.IntParameter.kernel_transform (Python method) — Transform a parameter value for use in a kernel." class="md-ellipsis">kernel_<wbr>transform</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="kernel_transform">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.IntParameter.kernel_transform-parameters" class="md-nav__link">
+    <span class="md-ellipsis">Parameters</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="Parameters">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.IntParameter.kernel_transform.value" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.IntParameter.kernel_transform.value (Python parameter) — The value(s) to transform, should be a float." class="md-ellipsis">value</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.IntParameter.kernel_transform-return-type" class="md-nav__link">
+    <span class="md-ellipsis">Return type</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.IntParameter.kernel_transform-returns" class="md-nav__link">
+    <span class="md-ellipsis">Returns</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.IntParameter.random_sample" class="md-nav__link">
+    <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="bayes_opt.parameter.IntParameter.random_sample (Python method) — Generate random samples from the parameter." class="md-ellipsis">random_<wbr>sample</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="random_sample">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.IntParameter.random_sample-parameters" class="md-nav__link">
+    <span class="md-ellipsis">Parameters</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="Parameters">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.IntParameter.random_sample.n_samples" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.IntParameter.random_sample.n_samples (Python parameter) — The number of samples to generate." class="md-ellipsis">n_<wbr>samples</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.IntParameter.random_sample.random_state" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.IntParameter.random_sample.random_state (Python parameter) — The random state to use for sampling." class="md-ellipsis">random_<wbr>state</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.IntParameter.random_sample-return-type" class="md-nav__link">
+    <span class="md-ellipsis">Return type</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.IntParameter.random_sample-returns" class="md-nav__link">
+    <span class="md-ellipsis">Returns</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.IntParameter.to_float" class="md-nav__link">
+    <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="bayes_opt.parameter.IntParameter.to_float (Python method) — Convert a parameter value to a float." class="md-ellipsis">to_<wbr>float</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="to_float">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.IntParameter.to_float-parameters" class="md-nav__link">
+    <span class="md-ellipsis">Parameters</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="Parameters">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.IntParameter.to_float.value" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.IntParameter.to_float.value (Python parameter) — The value to convert, should be the canonical representation of the parameter." class="md-ellipsis">value</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.IntParameter.to_float-return-type" class="md-nav__link">
+    <span class="md-ellipsis">Return type</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.IntParameter.to_param" class="md-nav__link">
+    <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="bayes_opt.parameter.IntParameter.to_param (Python method) — Convert a float value to a parameter." class="md-ellipsis">to_<wbr>param</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="to_param">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.IntParameter.to_param-parameters" class="md-nav__link">
+    <span class="md-ellipsis">Parameters</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="Parameters">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.IntParameter.to_param.value" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.IntParameter.to_param.value (Python parameter) — The value to convert, should be a float." class="md-ellipsis">value</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.IntParameter.to_param-return-type" class="md-nav__link">
+    <span class="md-ellipsis">Return type</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.IntParameter.to_param-returns" class="md-nav__link">
+    <span class="md-ellipsis">Returns</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.is_numeric" class="md-nav__link">
+    <span aria-label="Python function" class="objinfo-icon objinfo-icon__procedure" title="Python function">F</span><span title="bayes_opt.parameter.is_numeric (Python function) — Check if a value is numeric." class="md-ellipsis">bayes_<wbr>opt.<wbr>parameter.<wbr>is_<wbr>numeric</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="bayes_opt.parameter.is_numeric">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.is_numeric-return-type" class="md-nav__link">
+    <span class="md-ellipsis">Return type</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.is_numeric-parameters" class="md-nav__link">
+    <span class="md-ellipsis">Parameters</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="Parameters">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.is_numeric.value" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.is_numeric.value (Python parameter)" class="md-ellipsis">value</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.wrap_kernel" class="md-nav__link">
+    <span aria-label="Python function" class="objinfo-icon objinfo-icon__procedure" title="Python function">F</span><span title="bayes_opt.parameter.wrap_kernel (Python function) — Wrap a kernel to transform input data before passing it to the kernel." class="md-ellipsis">bayes_<wbr>opt.<wbr>parameter.<wbr>wrap_<wbr>kernel</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="bayes_opt.parameter.wrap_kernel">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.wrap_kernel-parameters" class="md-nav__link">
+    <span class="md-ellipsis">Parameters</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="Parameters">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.wrap_kernel.kernel" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.wrap_kernel.kernel (Python parameter) — The kernel to wrap." class="md-ellipsis">kernel</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.wrap_kernel.transform" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.wrap_kernel.transform (Python parameter) — The transformation function to apply to the input data." class="md-ellipsis">transform</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.wrap_kernel-return-type" class="md-nav__link">
+    <span class="md-ellipsis">Return type</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.wrap_kernel-returns" class="md-nav__link">
+    <span class="md-ellipsis">Returns</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+    </ul>
+  
+</nav>
+      
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="exception.html" class="md-nav__link">
+        <span title="/reference/exception.rst (reference label)" class="md-ellipsis">bayes_<wbr>opt.<wbr>exception</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="other.html" class="md-nav__link">
+        <span title="/reference/other.rst (reference label)" class="md-ellipsis">Other</span>
+      </a>
+    </li>
+  
+
+            
+          
+        </ul>
+      </nav>
+    </li>
+  
+  
+
+    
+  </ul>
+</nav>
+                  </div>
+                </div>
+              </div>
+            
+            
+              
+              <div class="md-sidebar md-sidebar--secondary" data-md-component="sidebar" data-md-type="toc" >
+                <div class="md-sidebar__scrollwrap">
+                  <div class="md-sidebar__inner">
+                    
+
+  
+
+<nav class="md-nav md-nav--secondary">
+  
+  
+  
+    <label class="md-nav__title" for="__toc">
+      <span class="md-nav__icon md-icon"></span>
+      <span title="/reference/parameter.rst (reference label)" class="md-ellipsis">bayes_<wbr>opt.<wbr>parameter</span>
+    </label>
+    <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
+      
+        <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.BayesParameter" class="md-nav__link">
+    <span aria-label="Python class" class="objinfo-icon objinfo-icon__data" title="Python class">C</span><span title="bayes_opt.parameter.BayesParameter (Python class) — Base class for Bayesian optimization parameters." class="md-ellipsis">bayes_<wbr>opt.<wbr>parameter.<wbr>Bayes<wbr>Parameter</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="bayes_opt.parameter.BayesParameter">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.BayesParameter-parameters" class="md-nav__link">
+    <span class="md-ellipsis">Parameters</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="Parameters">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.BayesParameter.__init__.name" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.BayesParameter.__init__.name (Python parameter) — The name of the parameter." class="md-ellipsis">name</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.BayesParameter.__init__.bounds" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.BayesParameter.__init__.bounds (Python parameter)" class="md-ellipsis">bounds</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.BayesParameter.bounds" class="md-nav__link">
+    <span aria-label="Python property" class="objinfo-icon objinfo-icon__alias" title="Python property">P</span><span title="bayes_opt.parameter.BayesParameter.bounds (Python property) — The bounds of the parameter in float space." class="md-ellipsis">bounds</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.BayesParameter.dim" class="md-nav__link">
+    <span aria-label="Python property" class="objinfo-icon objinfo-icon__alias" title="Python property">P</span><span title="bayes_opt.parameter.BayesParameter.dim (Python property) — The dimensionality of the parameter." class="md-ellipsis">dim</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.BayesParameter.is_continuous" class="md-nav__link">
+    <span aria-label="Python property" class="objinfo-icon objinfo-icon__alias" title="Python property">P</span><span title="bayes_opt.parameter.BayesParameter.is_continuous (Python property) — Whether the parameter is continuous." class="md-ellipsis">is_<wbr>continuous</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.BayesParameter.kernel_transform" class="md-nav__link">
+    <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="bayes_opt.parameter.BayesParameter.kernel_transform (Python method) — Transform a parameter value for use in a kernel." class="md-ellipsis">kernel_<wbr>transform</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="kernel_transform">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.BayesParameter.kernel_transform-parameters" class="md-nav__link">
+    <span class="md-ellipsis">Parameters</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="Parameters">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.BayesParameter.kernel_transform.value" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.BayesParameter.kernel_transform.value (Python parameter) — The value(s) to transform, should be a float." class="md-ellipsis">value</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.BayesParameter.kernel_transform-return-type" class="md-nav__link">
+    <span class="md-ellipsis">Return type</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.BayesParameter.kernel_transform-returns" class="md-nav__link">
+    <span class="md-ellipsis">Returns</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.BayesParameter.random_sample" class="md-nav__link">
+    <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="bayes_opt.parameter.BayesParameter.random_sample (Python method) — Generate random samples from the parameter." class="md-ellipsis">random_<wbr>sample</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="random_sample">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.BayesParameter.random_sample-parameters" class="md-nav__link">
+    <span class="md-ellipsis">Parameters</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="Parameters">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.BayesParameter.random_sample.n_samples" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.BayesParameter.random_sample.n_samples (Python parameter) — The number of samples to generate." class="md-ellipsis">n_<wbr>samples</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.BayesParameter.random_sample.random_state" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.BayesParameter.random_sample.random_state (Python parameter) — The random state to use for sampling." class="md-ellipsis">random_<wbr>state</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.BayesParameter.random_sample-return-type" class="md-nav__link">
+    <span class="md-ellipsis">Return type</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.BayesParameter.random_sample-returns" class="md-nav__link">
+    <span class="md-ellipsis">Returns</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.BayesParameter.to_float" class="md-nav__link">
+    <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="bayes_opt.parameter.BayesParameter.to_float (Python method) — Convert a parameter value to a float." class="md-ellipsis">to_<wbr>float</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="to_float">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.BayesParameter.to_float-parameters" class="md-nav__link">
+    <span class="md-ellipsis">Parameters</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="Parameters">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.BayesParameter.to_float.value" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.BayesParameter.to_float.value (Python parameter) — The value to convert, should be the canonical representation of the parameter." class="md-ellipsis">value</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.BayesParameter.to_float-return-type" class="md-nav__link">
+    <span class="md-ellipsis">Return type</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.BayesParameter.to_param" class="md-nav__link">
+    <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="bayes_opt.parameter.BayesParameter.to_param (Python method) — Convert a float value to a parameter." class="md-ellipsis">to_<wbr>param</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="to_param">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.BayesParameter.to_param-parameters" class="md-nav__link">
+    <span class="md-ellipsis">Parameters</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="Parameters">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.BayesParameter.to_param.value" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.BayesParameter.to_param.value (Python parameter) — The value to convert, should be a float." class="md-ellipsis">value</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.BayesParameter.to_param-return-type" class="md-nav__link">
+    <span class="md-ellipsis">Return type</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.BayesParameter.to_param-returns" class="md-nav__link">
+    <span class="md-ellipsis">Returns</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.BayesParameter.to_string" class="md-nav__link">
+    <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="bayes_opt.parameter.BayesParameter.to_string (Python method) — Represent a parameter value as a string." class="md-ellipsis">to_<wbr>string</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="to_string">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.BayesParameter.to_string-parameters" class="md-nav__link">
+    <span class="md-ellipsis">Parameters</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="Parameters">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.BayesParameter.to_string.value" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.BayesParameter.to_string.value (Python parameter) — The value to represent." class="md-ellipsis">value</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.BayesParameter.to_string.str_len" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.BayesParameter.to_string.str_len (Python parameter) — The maximum length of the string representation." class="md-ellipsis">str_<wbr>len</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.BayesParameter.to_string-return-type" class="md-nav__link">
+    <span class="md-ellipsis">Return type</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.BayesParameter.to_string-returns" class="md-nav__link">
+    <span class="md-ellipsis">Returns</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.CategoricalParameter" class="md-nav__link">
+    <span aria-label="Python class" class="objinfo-icon objinfo-icon__data" title="Python class">C</span><span title="bayes_opt.parameter.CategoricalParameter (Python class) — A parameter with categorical values." class="md-ellipsis">bayes_<wbr>opt.<wbr>parameter.<wbr>Categorical<wbr>Parameter</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="bayes_opt.parameter.CategoricalParameter">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.CategoricalParameter-parameters" class="md-nav__link">
+    <span class="md-ellipsis">Parameters</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="Parameters">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.CategoricalParameter.__init__.name" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.CategoricalParameter.__init__.name (Python parameter) — The name of the parameter." class="md-ellipsis">name</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.CategoricalParameter.__init__.categories" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.CategoricalParameter.__init__.categories (Python parameter) — The categories of the parameter." class="md-ellipsis">categories</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.CategoricalParameter.dim" class="md-nav__link">
+    <span aria-label="Python property" class="objinfo-icon objinfo-icon__alias" title="Python property">P</span><span title="bayes_opt.parameter.CategoricalParameter.dim (Python property) — The dimensionality of the parameter." class="md-ellipsis">dim</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.CategoricalParameter.is_continuous" class="md-nav__link">
+    <span aria-label="Python property" class="objinfo-icon objinfo-icon__alias" title="Python property">P</span><span title="bayes_opt.parameter.CategoricalParameter.is_continuous (Python property) — Whether the parameter is continuous." class="md-ellipsis">is_<wbr>continuous</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.CategoricalParameter.kernel_transform" class="md-nav__link">
+    <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="bayes_opt.parameter.CategoricalParameter.kernel_transform (Python method) — Transform a parameter value for use in a kernel." class="md-ellipsis">kernel_<wbr>transform</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="kernel_transform">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.CategoricalParameter.kernel_transform-parameters" class="md-nav__link">
+    <span class="md-ellipsis">Parameters</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="Parameters">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.CategoricalParameter.kernel_transform.value" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.CategoricalParameter.kernel_transform.value (Python parameter) — The value(s) to transform, should be a float." class="md-ellipsis">value</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.CategoricalParameter.kernel_transform-return-type" class="md-nav__link">
+    <span class="md-ellipsis">Return type</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.CategoricalParameter.kernel_transform-returns" class="md-nav__link">
+    <span class="md-ellipsis">Returns</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.CategoricalParameter.random_sample" class="md-nav__link">
+    <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="bayes_opt.parameter.CategoricalParameter.random_sample (Python method) — Generate random float-format samples from the parameter." class="md-ellipsis">random_<wbr>sample</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="random_sample">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.CategoricalParameter.random_sample-parameters" class="md-nav__link">
+    <span class="md-ellipsis">Parameters</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="Parameters">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.CategoricalParameter.random_sample.n_samples" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.CategoricalParameter.random_sample.n_samples (Python parameter) — The number of samples to generate." class="md-ellipsis">n_<wbr>samples</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.CategoricalParameter.random_sample.random_state" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.CategoricalParameter.random_sample.random_state (Python parameter) — The random state to use for sampling." class="md-ellipsis">random_<wbr>state</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.CategoricalParameter.random_sample-return-type" class="md-nav__link">
+    <span class="md-ellipsis">Return type</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.CategoricalParameter.random_sample-returns" class="md-nav__link">
+    <span class="md-ellipsis">Returns</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.CategoricalParameter.to_float" class="md-nav__link">
+    <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="bayes_opt.parameter.CategoricalParameter.to_float (Python method) — Convert a parameter value to a float." class="md-ellipsis">to_<wbr>float</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="to_float">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.CategoricalParameter.to_float-parameters" class="md-nav__link">
+    <span class="md-ellipsis">Parameters</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="Parameters">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.CategoricalParameter.to_float.value" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.CategoricalParameter.to_float.value (Python parameter) — The value to convert, should be the canonical representation of the parameter." class="md-ellipsis">value</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.CategoricalParameter.to_float-return-type" class="md-nav__link">
+    <span class="md-ellipsis">Return type</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.CategoricalParameter.to_param" class="md-nav__link">
+    <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="bayes_opt.parameter.CategoricalParameter.to_param (Python method) — Convert a float value to a parameter." class="md-ellipsis">to_<wbr>param</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="to_param">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.CategoricalParameter.to_param-parameters" class="md-nav__link">
+    <span class="md-ellipsis">Parameters</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="Parameters">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.CategoricalParameter.to_param.value" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.CategoricalParameter.to_param.value (Python parameter) — The value to convert, should be a float." class="md-ellipsis">value</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.CategoricalParameter.to_param-return-type" class="md-nav__link">
+    <span class="md-ellipsis">Return type</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.CategoricalParameter.to_param-returns" class="md-nav__link">
+    <span class="md-ellipsis">Returns</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.CategoricalParameter.to_string" class="md-nav__link">
+    <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="bayes_opt.parameter.CategoricalParameter.to_string (Python method) — Represent a parameter value as a string." class="md-ellipsis">to_<wbr>string</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="to_string">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.CategoricalParameter.to_string-parameters" class="md-nav__link">
+    <span class="md-ellipsis">Parameters</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="Parameters">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.CategoricalParameter.to_string.value" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.CategoricalParameter.to_string.value (Python parameter) — The value to represent." class="md-ellipsis">value</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.CategoricalParameter.to_string.str_len" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.CategoricalParameter.to_string.str_len (Python parameter) — The maximum length of the string representation." class="md-ellipsis">str_<wbr>len</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.CategoricalParameter.to_string-return-type" class="md-nav__link">
+    <span class="md-ellipsis">Return type</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.CategoricalParameter.to_string-returns" class="md-nav__link">
+    <span class="md-ellipsis">Returns</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.FloatParameter" class="md-nav__link">
+    <span aria-label="Python class" class="objinfo-icon objinfo-icon__data" title="Python class">C</span><span title="bayes_opt.parameter.FloatParameter (Python class) — A parameter with float values." class="md-ellipsis">bayes_<wbr>opt.<wbr>parameter.<wbr>Float<wbr>Parameter</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="bayes_opt.parameter.FloatParameter">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.FloatParameter-parameters" class="md-nav__link">
+    <span class="md-ellipsis">Parameters</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="Parameters">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.FloatParameter.__init__.name" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.FloatParameter.__init__.name (Python parameter) — The name of the parameter." class="md-ellipsis">name</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.FloatParameter.__init__.bounds" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.FloatParameter.__init__.bounds (Python parameter) — The bounds of the parameter." class="md-ellipsis">bounds</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.FloatParameter.dim" class="md-nav__link">
+    <span aria-label="Python property" class="objinfo-icon objinfo-icon__alias" title="Python property">P</span><span title="bayes_opt.parameter.FloatParameter.dim (Python property) — The dimensionality of the parameter." class="md-ellipsis">dim</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.FloatParameter.is_continuous" class="md-nav__link">
+    <span aria-label="Python property" class="objinfo-icon objinfo-icon__alias" title="Python property">P</span><span title="bayes_opt.parameter.FloatParameter.is_continuous (Python property) — Whether the parameter is continuous." class="md-ellipsis">is_<wbr>continuous</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.FloatParameter.kernel_transform" class="md-nav__link">
+    <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="bayes_opt.parameter.FloatParameter.kernel_transform (Python method) — Transform a parameter value for use in a kernel." class="md-ellipsis">kernel_<wbr>transform</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="kernel_transform">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.FloatParameter.kernel_transform-parameters" class="md-nav__link">
+    <span class="md-ellipsis">Parameters</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="Parameters">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.FloatParameter.kernel_transform.value" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.FloatParameter.kernel_transform.value (Python parameter) — The value(s) to transform, should be a float." class="md-ellipsis">value</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.FloatParameter.kernel_transform-return-type" class="md-nav__link">
+    <span class="md-ellipsis">Return type</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.FloatParameter.kernel_transform-returns" class="md-nav__link">
+    <span class="md-ellipsis">Returns</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.FloatParameter.to_float" class="md-nav__link">
+    <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="bayes_opt.parameter.FloatParameter.to_float (Python method) — Convert a parameter value to a float." class="md-ellipsis">to_<wbr>float</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="to_float">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.FloatParameter.to_float-parameters" class="md-nav__link">
+    <span class="md-ellipsis">Parameters</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="Parameters">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.FloatParameter.to_float.value" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.FloatParameter.to_float.value (Python parameter) — The value to convert, should be the canonical representation of the parameter." class="md-ellipsis">value</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.FloatParameter.to_float-return-type" class="md-nav__link">
+    <span class="md-ellipsis">Return type</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.FloatParameter.to_param" class="md-nav__link">
+    <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="bayes_opt.parameter.FloatParameter.to_param (Python method) — Convert a float value to a parameter." class="md-ellipsis">to_<wbr>param</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="to_param">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.FloatParameter.to_param-parameters" class="md-nav__link">
+    <span class="md-ellipsis">Parameters</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="Parameters">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.FloatParameter.to_param.value" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.FloatParameter.to_param.value (Python parameter) — The value to convert, should be a float." class="md-ellipsis">value</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.FloatParameter.to_param-return-type" class="md-nav__link">
+    <span class="md-ellipsis">Return type</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.FloatParameter.to_param-returns" class="md-nav__link">
+    <span class="md-ellipsis">Returns</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.FloatParameter.to_string" class="md-nav__link">
+    <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="bayes_opt.parameter.FloatParameter.to_string (Python method) — Represent a parameter value as a string." class="md-ellipsis">to_<wbr>string</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="to_string">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.FloatParameter.to_string-parameters" class="md-nav__link">
+    <span class="md-ellipsis">Parameters</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="Parameters">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.FloatParameter.to_string.value" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.FloatParameter.to_string.value (Python parameter) — The value to represent." class="md-ellipsis">value</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.FloatParameter.to_string.str_len" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.FloatParameter.to_string.str_len (Python parameter) — The maximum length of the string representation." class="md-ellipsis">str_<wbr>len</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.FloatParameter.to_string-return-type" class="md-nav__link">
+    <span class="md-ellipsis">Return type</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.FloatParameter.to_string-returns" class="md-nav__link">
+    <span class="md-ellipsis">Returns</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.IntParameter" class="md-nav__link">
+    <span aria-label="Python class" class="objinfo-icon objinfo-icon__data" title="Python class">C</span><span title="bayes_opt.parameter.IntParameter (Python class) — A parameter with int values." class="md-ellipsis">bayes_<wbr>opt.<wbr>parameter.<wbr>Int<wbr>Parameter</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="bayes_opt.parameter.IntParameter">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.IntParameter-parameters" class="md-nav__link">
+    <span class="md-ellipsis">Parameters</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="Parameters">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.IntParameter.__init__.name" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.IntParameter.__init__.name (Python parameter) — The name of the parameter." class="md-ellipsis">name</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.IntParameter.__init__.bounds" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.IntParameter.__init__.bounds (Python parameter) — The bounds of the parameter." class="md-ellipsis">bounds</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.IntParameter.dim" class="md-nav__link">
+    <span aria-label="Python property" class="objinfo-icon objinfo-icon__alias" title="Python property">P</span><span title="bayes_opt.parameter.IntParameter.dim (Python property) — The dimensionality of the parameter." class="md-ellipsis">dim</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.IntParameter.is_continuous" class="md-nav__link">
+    <span aria-label="Python property" class="objinfo-icon objinfo-icon__alias" title="Python property">P</span><span title="bayes_opt.parameter.IntParameter.is_continuous (Python property) — Whether the parameter is continuous." class="md-ellipsis">is_<wbr>continuous</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.IntParameter.kernel_transform" class="md-nav__link">
+    <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="bayes_opt.parameter.IntParameter.kernel_transform (Python method) — Transform a parameter value for use in a kernel." class="md-ellipsis">kernel_<wbr>transform</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="kernel_transform">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.IntParameter.kernel_transform-parameters" class="md-nav__link">
+    <span class="md-ellipsis">Parameters</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="Parameters">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.IntParameter.kernel_transform.value" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.IntParameter.kernel_transform.value (Python parameter) — The value(s) to transform, should be a float." class="md-ellipsis">value</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.IntParameter.kernel_transform-return-type" class="md-nav__link">
+    <span class="md-ellipsis">Return type</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.IntParameter.kernel_transform-returns" class="md-nav__link">
+    <span class="md-ellipsis">Returns</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.IntParameter.random_sample" class="md-nav__link">
+    <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="bayes_opt.parameter.IntParameter.random_sample (Python method) — Generate random samples from the parameter." class="md-ellipsis">random_<wbr>sample</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="random_sample">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.IntParameter.random_sample-parameters" class="md-nav__link">
+    <span class="md-ellipsis">Parameters</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="Parameters">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.IntParameter.random_sample.n_samples" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.IntParameter.random_sample.n_samples (Python parameter) — The number of samples to generate." class="md-ellipsis">n_<wbr>samples</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.IntParameter.random_sample.random_state" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.IntParameter.random_sample.random_state (Python parameter) — The random state to use for sampling." class="md-ellipsis">random_<wbr>state</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.IntParameter.random_sample-return-type" class="md-nav__link">
+    <span class="md-ellipsis">Return type</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.IntParameter.random_sample-returns" class="md-nav__link">
+    <span class="md-ellipsis">Returns</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.IntParameter.to_float" class="md-nav__link">
+    <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="bayes_opt.parameter.IntParameter.to_float (Python method) — Convert a parameter value to a float." class="md-ellipsis">to_<wbr>float</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="to_float">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.IntParameter.to_float-parameters" class="md-nav__link">
+    <span class="md-ellipsis">Parameters</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="Parameters">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.IntParameter.to_float.value" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.IntParameter.to_float.value (Python parameter) — The value to convert, should be the canonical representation of the parameter." class="md-ellipsis">value</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.IntParameter.to_float-return-type" class="md-nav__link">
+    <span class="md-ellipsis">Return type</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.IntParameter.to_param" class="md-nav__link">
+    <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="bayes_opt.parameter.IntParameter.to_param (Python method) — Convert a float value to a parameter." class="md-ellipsis">to_<wbr>param</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="to_param">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.IntParameter.to_param-parameters" class="md-nav__link">
+    <span class="md-ellipsis">Parameters</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="Parameters">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.IntParameter.to_param.value" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.IntParameter.to_param.value (Python parameter) — The value to convert, should be a float." class="md-ellipsis">value</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.IntParameter.to_param-return-type" class="md-nav__link">
+    <span class="md-ellipsis">Return type</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.IntParameter.to_param-returns" class="md-nav__link">
+    <span class="md-ellipsis">Returns</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.is_numeric" class="md-nav__link">
+    <span aria-label="Python function" class="objinfo-icon objinfo-icon__procedure" title="Python function">F</span><span title="bayes_opt.parameter.is_numeric (Python function) — Check if a value is numeric." class="md-ellipsis">bayes_<wbr>opt.<wbr>parameter.<wbr>is_<wbr>numeric</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="bayes_opt.parameter.is_numeric">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.is_numeric-return-type" class="md-nav__link">
+    <span class="md-ellipsis">Return type</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.is_numeric-parameters" class="md-nav__link">
+    <span class="md-ellipsis">Parameters</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="Parameters">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.is_numeric.value" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.is_numeric.value (Python parameter)" class="md-ellipsis">value</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.wrap_kernel" class="md-nav__link">
+    <span aria-label="Python function" class="objinfo-icon objinfo-icon__procedure" title="Python function">F</span><span title="bayes_opt.parameter.wrap_kernel (Python function) — Wrap a kernel to transform input data before passing it to the kernel." class="md-ellipsis">bayes_<wbr>opt.<wbr>parameter.<wbr>wrap_<wbr>kernel</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="bayes_opt.parameter.wrap_kernel">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.wrap_kernel-parameters" class="md-nav__link">
+    <span class="md-ellipsis">Parameters</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="Parameters">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.wrap_kernel.kernel" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.wrap_kernel.kernel (Python parameter) — The kernel to wrap." class="md-ellipsis">kernel</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.wrap_kernel.transform" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.parameter.wrap_kernel.transform (Python parameter) — The transformation function to apply to the input data." class="md-ellipsis">transform</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.wrap_kernel-return-type" class="md-nav__link">
+    <span class="md-ellipsis">Return type</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.parameter.wrap_kernel-returns" class="md-nav__link">
+    <span class="md-ellipsis">Returns</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+    </ul>
+  
+</nav>
+                  </div>
+                </div>
+              </div>
+            
+          
+          
+            <div class="md-content" data-md-component="content">
+              <article class="md-content__inner md-typeset" role="main">
+                  
+
+
+  <a href="https://github.com/bayesian-optimization/BayesianOptimization/blob/master/docsrc/reference/parameter.rst" title="Edit this page" class="md-content__button md-icon">
+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M10 20H6V4h7v5h5v3.1l2-2V8l-6-6H6c-1.1 0-2 .9-2 2v16c0 1.1.9 2 2 2h4v-2m10.2-7c.1 0 .3.1.4.2l1.3 1.3c.2.2.2.6 0 .8l-1 1-2.1-2.1 1-1c.1-.1.2-.2.4-.2m0 3.9L14.1 23H12v-2.1l6.1-6.1 2.1 2.1Z"/></svg>
+  </a>
+
+<h1 id="module-bayes_opt.parameter"><span id="bayes-opt-parameter"></span><a class="reference internal" href="#module-bayes_opt.parameter" title="bayes_opt.parameter (Python module)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">bayes_opt.parameter</span></code></a><a class="headerlink" href="#module-bayes_opt.parameter" title="Link to this heading">¶</a></h1>
+<p>Parameter classes for Bayesian optimization.</p>
+<dl class="py class objdesc">
+<dt class="sig sig-object highlight sig-wrap py" id="bayes_opt.parameter.BayesParameter">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">bayes_opt.parameter.</span></span><span class="sig-name descname"><span class="pre">BayesParameter</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.parameter.BayesParameter.__init__.name" title="bayes_opt.parameter.BayesParameter.__init__.name (Python parameter) — The name of the parameter."><span class="n"><span class="pre">name</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.parameter.BayesParameter.__init__.bounds" title="bayes_opt.parameter.BayesParameter.__init__.bounds (Python parameter)"><span class="n"><span class="pre">bounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><span class="pre">dtype</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span></span><a class="headerlink" href="#bayes_opt.parameter.BayesParameter" title="Link to this definition">¶</a></dt>
+<dd><p>Base class for Bayesian optimization parameters.</p>
+<dl class="field-list simple">
+<dt class="field-odd" id="bayes_opt.parameter.BayesParameter-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#bayes_opt.parameter.BayesParameter-parameters" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><dl class="simple api-field highlight">
+<dt id="bayes_opt.parameter.BayesParameter.__init__.name"><span class="sig-name descname">name</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)">str</a></span></span><a class="headerlink" href="#bayes_opt.parameter.BayesParameter.__init__.name" title="Permalink to this definition">¶</a></dt><dd><p>The name of the parameter.</p>
+</dd>
+<dt id="bayes_opt.parameter.BayesParameter.__init__.bounds"><span class="sig-name descname">bounds</span><span class="p"> : </span><span class="desctype"><span class="desctype">NDArray[Any]</span></span><a class="headerlink" href="#bayes_opt.parameter.BayesParameter.__init__.bounds" title="Permalink to this definition">¶</a></dt><dd><p></p>
+</dd>
+</dl>
+</dd>
+</dl>
+<dl class="py property objdesc">
+<dt class="sig sig-object highlight py" id="bayes_opt.parameter.BayesParameter.bounds">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">bounds</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><span class="pre">dtype</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><a class="headerlink" href="#bayes_opt.parameter.BayesParameter.bounds" title="Link to this definition">¶</a></dt>
+<dd><p>The bounds of the parameter in float space.</p>
+</dd></dl>
+
+<dl class="py property objdesc">
+<dt class="sig sig-object highlight py" id="bayes_opt.parameter.BayesParameter.dim">
+<em class="property"><span class="pre">abstract</span><span class="w"> </span><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">dim</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span><a class="headerlink" href="#bayes_opt.parameter.BayesParameter.dim" title="Link to this definition">¶</a></dt>
+<dd><p>The dimensionality of the parameter.</p>
+</dd></dl>
+
+<dl class="py property objdesc">
+<dt class="sig sig-object highlight py" id="bayes_opt.parameter.BayesParameter.is_continuous">
+<em class="property"><span class="pre">abstract</span><span class="w"> </span><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">is_continuous</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><a class="headerlink" href="#bayes_opt.parameter.BayesParameter.is_continuous" title="Link to this definition">¶</a></dt>
+<dd><p>Whether the parameter is continuous.</p>
+</dd></dl>
+
+<dl class="py method objdesc">
+<dt class="sig sig-object highlight sig-wrap py" id="bayes_opt.parameter.BayesParameter.kernel_transform">
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">kernel_transform</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.parameter.BayesParameter.kernel_transform.value" title="bayes_opt.parameter.BayesParameter.kernel_transform.value (Python parameter) — The value(s) to transform, should be a float."><span class="n"><span class="pre">value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><span class="pre">dtype</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.floating" title="(in NumPy v2.2)"><span class="pre">floating</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><span class="pre">dtype</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.floating" title="(in NumPy v2.2)"><span class="pre">floating</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></span><a class="headerlink" href="#bayes_opt.parameter.BayesParameter.kernel_transform" title="Link to this definition">¶</a></dt>
+<dd><p>Transform a parameter value for use in a kernel.</p>
+<dl class="field-list simple">
+<dt class="field-odd" id="bayes_opt.parameter.BayesParameter.kernel_transform-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#bayes_opt.parameter.BayesParameter.kernel_transform-parameters" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><dl class="simple api-field highlight">
+<dt id="bayes_opt.parameter.BayesParameter.kernel_transform.value"><span class="sig-name descname">value</span><span class="p"> : </span><span class="desctype"><span class="desctype">np.ndarray</span></span><a class="headerlink" href="#bayes_opt.parameter.BayesParameter.kernel_transform.value" title="Permalink to this definition">¶</a></dt><dd><p>The value(s) to transform, should be a float.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even" id="bayes_opt.parameter.BayesParameter.kernel_transform-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#bayes_opt.parameter.BayesParameter.kernel_transform-return-type" title="Permalink to this headline">¶</a></dt>
+<dd class="field-even"><p><span class="sphinx_autodoc_typehints-type"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>[<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><code class="xref py py-data docutils literal notranslate"><span class="pre">Any</span></code></a>, <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dtype</span></code></a>[<a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.floating" title="(in NumPy v2.2)"><code class="xref py py-class docutils literal notranslate"><span class="pre">floating</span></code></a>[<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><code class="xref py py-data docutils literal notranslate"><span class="pre">Any</span></code></a>]]]</span></p>
+</dd>
+<dt class="field-odd" id="bayes_opt.parameter.BayesParameter.kernel_transform-returns">Returns<span class="colon">:</span><a class="headerlink" href="#bayes_opt.parameter.BayesParameter.kernel_transform-returns" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><p><em>np.ndarray</em></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method objdesc">
+<dt class="sig sig-object highlight sig-wrap py" id="bayes_opt.parameter.BayesParameter.random_sample">
+<span class="sig-name descname"><span class="pre">random_sample</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.parameter.BayesParameter.random_sample.n_samples" title="bayes_opt.parameter.BayesParameter.random_sample.n_samples (Python parameter) — The number of samples to generate."><span class="n"><span class="pre">n_samples</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.parameter.BayesParameter.random_sample.random_state" title="bayes_opt.parameter.BayesParameter.random_sample.random_state (Python parameter) — The random state to use for sampling."><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v2.2)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><span class="pre">dtype</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.floating" title="(in NumPy v2.2)"><span class="pre">floating</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></span><a class="headerlink" href="#bayes_opt.parameter.BayesParameter.random_sample" title="Link to this definition">¶</a></dt>
+<dd><p>Generate random samples from the parameter.</p>
+<dl class="field-list simple">
+<dt class="field-odd" id="bayes_opt.parameter.BayesParameter.random_sample-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#bayes_opt.parameter.BayesParameter.random_sample-parameters" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><dl class="simple api-field highlight">
+<dt id="bayes_opt.parameter.BayesParameter.random_sample.n_samples"><span class="sig-name descname">n_samples</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)">int</a></span></span><a class="headerlink" href="#bayes_opt.parameter.BayesParameter.random_sample.n_samples" title="Permalink to this definition">¶</a></dt><dd><p>The number of samples to generate.</p>
+</dd>
+<dt id="bayes_opt.parameter.BayesParameter.random_sample.random_state"><span class="sig-name descname">random_state</span><span class="p"> : </span><span class="desctype"><span class="desctype">np.random.RandomState | <a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)">int</a> | None</span></span><a class="headerlink" href="#bayes_opt.parameter.BayesParameter.random_sample.random_state" title="Permalink to this definition">¶</a></dt><dd><p>The random state to use for sampling.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even" id="bayes_opt.parameter.BayesParameter.random_sample-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#bayes_opt.parameter.BayesParameter.random_sample-return-type" title="Permalink to this headline">¶</a></dt>
+<dd class="field-even"><p><span class="sphinx_autodoc_typehints-type"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>[<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><code class="xref py py-data docutils literal notranslate"><span class="pre">Any</span></code></a>, <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dtype</span></code></a>[<a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.floating" title="(in NumPy v2.2)"><code class="xref py py-class docutils literal notranslate"><span class="pre">floating</span></code></a>[<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><code class="xref py py-data docutils literal notranslate"><span class="pre">Any</span></code></a>]]]</span></p>
+</dd>
+<dt class="field-odd" id="bayes_opt.parameter.BayesParameter.random_sample-returns">Returns<span class="colon">:</span><a class="headerlink" href="#bayes_opt.parameter.BayesParameter.random_sample-returns" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><p><em>np.ndarray</em> – The samples.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method objdesc">
+<dt class="sig sig-object highlight sig-wrap py" id="bayes_opt.parameter.BayesParameter.to_float">
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">to_float</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.parameter.BayesParameter.to_float.value" title="bayes_opt.parameter.BayesParameter.to_float.value (Python parameter) — The value to convert, should be the canonical representation of the parameter."><span class="n"><span class="pre">value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><span class="pre">dtype</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.floating" title="(in NumPy v2.2)"><span class="pre">floating</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></span><a class="headerlink" href="#bayes_opt.parameter.BayesParameter.to_float" title="Link to this definition">¶</a></dt>
+<dd><p>Convert a parameter value to a float.</p>
+<dl class="field-list simple">
+<dt class="field-odd" id="bayes_opt.parameter.BayesParameter.to_float-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#bayes_opt.parameter.BayesParameter.to_float-parameters" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><dl class="simple api-field highlight">
+<dt id="bayes_opt.parameter.BayesParameter.to_float.value"><span class="sig-name descname">value</span><span class="p"> : </span><span class="desctype"><span class="desctype">Any</span></span><a class="headerlink" href="#bayes_opt.parameter.BayesParameter.to_float.value" title="Permalink to this definition">¶</a></dt><dd><p>The value to convert, should be the canonical representation of the parameter.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even" id="bayes_opt.parameter.BayesParameter.to_float-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#bayes_opt.parameter.BayesParameter.to_float-return-type" title="Permalink to this headline">¶</a></dt>
+<dd class="field-even"><p><span class="sphinx_autodoc_typehints-type"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a> | <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>[<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><code class="xref py py-data docutils literal notranslate"><span class="pre">Any</span></code></a>, <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dtype</span></code></a>[<a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.floating" title="(in NumPy v2.2)"><code class="xref py py-class docutils literal notranslate"><span class="pre">floating</span></code></a>[<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><code class="xref py py-data docutils literal notranslate"><span class="pre">Any</span></code></a>]]]</span></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method objdesc">
+<dt class="sig sig-object highlight sig-wrap py" id="bayes_opt.parameter.BayesParameter.to_param">
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">to_param</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.parameter.BayesParameter.to_param.value" title="bayes_opt.parameter.BayesParameter.to_param.value (Python parameter) — The value to convert, should be a float."><span class="n"><span class="pre">value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><span class="pre">dtype</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.floating" title="(in NumPy v2.2)"><span class="pre">floating</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a></span></span></span><a class="headerlink" href="#bayes_opt.parameter.BayesParameter.to_param" title="Link to this definition">¶</a></dt>
+<dd><p>Convert a float value to a parameter.</p>
+<dl class="field-list simple">
+<dt class="field-odd" id="bayes_opt.parameter.BayesParameter.to_param-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#bayes_opt.parameter.BayesParameter.to_param-parameters" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><dl class="simple api-field highlight">
+<dt id="bayes_opt.parameter.BayesParameter.to_param.value"><span class="sig-name descname">value</span><span class="p"> : </span><span class="desctype"><span class="desctype">np.ndarray</span></span><a class="headerlink" href="#bayes_opt.parameter.BayesParameter.to_param.value" title="Permalink to this definition">¶</a></dt><dd><p>The value to convert, should be a float.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even" id="bayes_opt.parameter.BayesParameter.to_param-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#bayes_opt.parameter.BayesParameter.to_param-return-type" title="Permalink to this headline">¶</a></dt>
+<dd class="field-even"><p><span class="sphinx_autodoc_typehints-type"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><code class="xref py py-data docutils literal notranslate"><span class="pre">Any</span></code></a></span></p>
+</dd>
+<dt class="field-odd" id="bayes_opt.parameter.BayesParameter.to_param-returns">Returns<span class="colon">:</span><a class="headerlink" href="#bayes_opt.parameter.BayesParameter.to_param-returns" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><p><em>Any</em> – The canonical representation of the parameter.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method objdesc">
+<dt class="sig sig-object highlight py" id="bayes_opt.parameter.BayesParameter.to_string">
+<span class="sig-name descname"><span class="pre">to_string</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.parameter.BayesParameter.to_string.value" title="bayes_opt.parameter.BayesParameter.to_string.value (Python parameter) — The value to represent."><span class="n"><span class="pre">value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.parameter.BayesParameter.to_string.str_len" title="bayes_opt.parameter.BayesParameter.to_string.str_len (Python parameter) — The maximum length of the string representation."><span class="n"><span class="pre">str_len</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></span></span><a class="headerlink" href="#bayes_opt.parameter.BayesParameter.to_string" title="Link to this definition">¶</a></dt>
+<dd><p>Represent a parameter value as a string.</p>
+<dl class="field-list simple">
+<dt class="field-odd" id="bayes_opt.parameter.BayesParameter.to_string-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#bayes_opt.parameter.BayesParameter.to_string-parameters" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><dl class="simple api-field highlight">
+<dt id="bayes_opt.parameter.BayesParameter.to_string.value"><span class="sig-name descname">value</span><span class="p"> : </span><span class="desctype"><span class="desctype">Any</span></span><a class="headerlink" href="#bayes_opt.parameter.BayesParameter.to_string.value" title="Permalink to this definition">¶</a></dt><dd><p>The value to represent.</p>
+</dd>
+<dt id="bayes_opt.parameter.BayesParameter.to_string.str_len"><span class="sig-name descname">str_len</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)">int</a></span></span><a class="headerlink" href="#bayes_opt.parameter.BayesParameter.to_string.str_len" title="Permalink to this definition">¶</a></dt><dd><p>The maximum length of the string representation.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even" id="bayes_opt.parameter.BayesParameter.to_string-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#bayes_opt.parameter.BayesParameter.to_string-return-type" title="Permalink to this headline">¶</a></dt>
+<dd class="field-even"><p><span class="sphinx_autodoc_typehints-type"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></a></span></p>
+</dd>
+<dt class="field-odd" id="bayes_opt.parameter.BayesParameter.to_string-returns">Returns<span class="colon">:</span><a class="headerlink" href="#bayes_opt.parameter.BayesParameter.to_string-returns" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><p><em>str</em></p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class objdesc">
+<dt class="sig sig-object highlight sig-wrap py" id="bayes_opt.parameter.CategoricalParameter">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">bayes_opt.parameter.</span></span><span class="sig-name descname"><span class="pre">CategoricalParameter</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.parameter.CategoricalParameter.__init__.name" title="bayes_opt.parameter.CategoricalParameter.__init__.name (Python parameter) — The name of the parameter."><span class="n"><span class="pre">name</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.parameter.CategoricalParameter.__init__.categories" title="bayes_opt.parameter.CategoricalParameter.__init__.categories (Python parameter) — The categories of the parameter."><span class="n"><span class="pre">categories</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.13)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span></span><a class="headerlink" href="#bayes_opt.parameter.CategoricalParameter" title="Link to this definition">¶</a></dt>
+<dd><p>A parameter with categorical values.</p>
+<dl class="field-list simple">
+<dt class="field-odd" id="bayes_opt.parameter.CategoricalParameter-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#bayes_opt.parameter.CategoricalParameter-parameters" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><dl class="simple api-field highlight">
+<dt id="bayes_opt.parameter.CategoricalParameter.__init__.name"><span class="sig-name descname">name</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)">str</a></span></span><a class="headerlink" href="#bayes_opt.parameter.CategoricalParameter.__init__.name" title="Permalink to this definition">¶</a></dt><dd><p>The name of the parameter.</p>
+</dd>
+<dt id="bayes_opt.parameter.CategoricalParameter.__init__.categories"><span class="sig-name descname">categories</span><span class="p"> : </span><span class="desctype"><span class="desctype">Sequence[Any]</span></span><a class="headerlink" href="#bayes_opt.parameter.CategoricalParameter.__init__.categories" title="Permalink to this definition">¶</a></dt><dd><p>The categories of the parameter.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<dl class="py property objdesc">
+<dt class="sig sig-object highlight py" id="bayes_opt.parameter.CategoricalParameter.dim">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">dim</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span><a class="headerlink" href="#bayes_opt.parameter.CategoricalParameter.dim" title="Link to this definition">¶</a></dt>
+<dd><p>The dimensionality of the parameter.</p>
+</dd></dl>
+
+<dl class="py property objdesc">
+<dt class="sig sig-object highlight py" id="bayes_opt.parameter.CategoricalParameter.is_continuous">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">is_continuous</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><a class="headerlink" href="#bayes_opt.parameter.CategoricalParameter.is_continuous" title="Link to this definition">¶</a></dt>
+<dd><p>Whether the parameter is continuous.</p>
+</dd></dl>
+
+<dl class="py method objdesc">
+<dt class="sig sig-object highlight sig-wrap py" id="bayes_opt.parameter.CategoricalParameter.kernel_transform">
+<span class="sig-name descname"><span class="pre">kernel_transform</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.parameter.CategoricalParameter.kernel_transform.value" title="bayes_opt.parameter.CategoricalParameter.kernel_transform.value (Python parameter) — The value(s) to transform, should be a float."><span class="n"><span class="pre">value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><span class="pre">dtype</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.floating" title="(in NumPy v2.2)"><span class="pre">floating</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><span class="pre">dtype</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.floating" title="(in NumPy v2.2)"><span class="pre">floating</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></span><a class="headerlink" href="#bayes_opt.parameter.CategoricalParameter.kernel_transform" title="Link to this definition">¶</a></dt>
+<dd><p>Transform a parameter value for use in a kernel.</p>
+<dl class="field-list simple">
+<dt class="field-odd" id="bayes_opt.parameter.CategoricalParameter.kernel_transform-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#bayes_opt.parameter.CategoricalParameter.kernel_transform-parameters" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><dl class="simple api-field highlight">
+<dt id="bayes_opt.parameter.CategoricalParameter.kernel_transform.value"><span class="sig-name descname">value</span><span class="p"> : </span><span class="desctype"><span class="desctype">np.ndarray</span></span><a class="headerlink" href="#bayes_opt.parameter.CategoricalParameter.kernel_transform.value" title="Permalink to this definition">¶</a></dt><dd><p>The value(s) to transform, should be a float.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even" id="bayes_opt.parameter.CategoricalParameter.kernel_transform-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#bayes_opt.parameter.CategoricalParameter.kernel_transform-return-type" title="Permalink to this headline">¶</a></dt>
+<dd class="field-even"><p><span class="sphinx_autodoc_typehints-type"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>[<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><code class="xref py py-data docutils literal notranslate"><span class="pre">Any</span></code></a>, <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dtype</span></code></a>[<a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.floating" title="(in NumPy v2.2)"><code class="xref py py-class docutils literal notranslate"><span class="pre">floating</span></code></a>[<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><code class="xref py py-data docutils literal notranslate"><span class="pre">Any</span></code></a>]]]</span></p>
+</dd>
+<dt class="field-odd" id="bayes_opt.parameter.CategoricalParameter.kernel_transform-returns">Returns<span class="colon">:</span><a class="headerlink" href="#bayes_opt.parameter.CategoricalParameter.kernel_transform-returns" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><p><em>np.ndarray</em></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method objdesc">
+<dt class="sig sig-object highlight sig-wrap py" id="bayes_opt.parameter.CategoricalParameter.random_sample">
+<span class="sig-name descname"><span class="pre">random_sample</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.parameter.CategoricalParameter.random_sample.n_samples" title="bayes_opt.parameter.CategoricalParameter.random_sample.n_samples (Python parameter) — The number of samples to generate."><span class="n"><span class="pre">n_samples</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.parameter.CategoricalParameter.random_sample.random_state" title="bayes_opt.parameter.CategoricalParameter.random_sample.random_state (Python parameter) — The random state to use for sampling."><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v2.2)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><span class="pre">dtype</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.floating" title="(in NumPy v2.2)"><span class="pre">floating</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></span><a class="headerlink" href="#bayes_opt.parameter.CategoricalParameter.random_sample" title="Link to this definition">¶</a></dt>
+<dd><p>Generate random float-format samples from the parameter.</p>
+<dl class="field-list simple">
+<dt class="field-odd" id="bayes_opt.parameter.CategoricalParameter.random_sample-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#bayes_opt.parameter.CategoricalParameter.random_sample-parameters" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><dl class="simple api-field highlight">
+<dt id="bayes_opt.parameter.CategoricalParameter.random_sample.n_samples"><span class="sig-name descname">n_samples</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)">int</a></span></span><a class="headerlink" href="#bayes_opt.parameter.CategoricalParameter.random_sample.n_samples" title="Permalink to this definition">¶</a></dt><dd><p>The number of samples to generate.</p>
+</dd>
+<dt id="bayes_opt.parameter.CategoricalParameter.random_sample.random_state"><span class="sig-name descname">random_state</span><span class="p"> : </span><span class="desctype"><span class="desctype">np.random.RandomState | <a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)">int</a> | None</span></span><a class="headerlink" href="#bayes_opt.parameter.CategoricalParameter.random_sample.random_state" title="Permalink to this definition">¶</a></dt><dd><p>The random state to use for sampling.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even" id="bayes_opt.parameter.CategoricalParameter.random_sample-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#bayes_opt.parameter.CategoricalParameter.random_sample-return-type" title="Permalink to this headline">¶</a></dt>
+<dd class="field-even"><p><span class="sphinx_autodoc_typehints-type"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>[<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><code class="xref py py-data docutils literal notranslate"><span class="pre">Any</span></code></a>, <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dtype</span></code></a>[<a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.floating" title="(in NumPy v2.2)"><code class="xref py py-class docutils literal notranslate"><span class="pre">floating</span></code></a>[<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><code class="xref py py-data docutils literal notranslate"><span class="pre">Any</span></code></a>]]]</span></p>
+</dd>
+<dt class="field-odd" id="bayes_opt.parameter.CategoricalParameter.random_sample-returns">Returns<span class="colon">:</span><a class="headerlink" href="#bayes_opt.parameter.CategoricalParameter.random_sample-returns" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><p><em>np.ndarray</em> – The samples.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method objdesc">
+<dt class="sig sig-object highlight py" id="bayes_opt.parameter.CategoricalParameter.to_float">
+<span class="sig-name descname"><span class="pre">to_float</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.parameter.CategoricalParameter.to_float.value" title="bayes_opt.parameter.CategoricalParameter.to_float.value (Python parameter) — The value to convert, should be the canonical representation of the parameter."><span class="n"><span class="pre">value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><span class="pre">dtype</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.floating" title="(in NumPy v2.2)"><span class="pre">floating</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></span><a class="headerlink" href="#bayes_opt.parameter.CategoricalParameter.to_float" title="Link to this definition">¶</a></dt>
+<dd><p>Convert a parameter value to a float.</p>
+<dl class="field-list simple">
+<dt class="field-odd" id="bayes_opt.parameter.CategoricalParameter.to_float-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#bayes_opt.parameter.CategoricalParameter.to_float-parameters" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><dl class="simple api-field highlight">
+<dt id="bayes_opt.parameter.CategoricalParameter.to_float.value"><span class="sig-name descname">value</span><span class="p"> : </span><span class="desctype"><span class="desctype">Any</span></span><a class="headerlink" href="#bayes_opt.parameter.CategoricalParameter.to_float.value" title="Permalink to this definition">¶</a></dt><dd><p>The value to convert, should be the canonical representation of the parameter.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even" id="bayes_opt.parameter.CategoricalParameter.to_float-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#bayes_opt.parameter.CategoricalParameter.to_float-return-type" title="Permalink to this headline">¶</a></dt>
+<dd class="field-even"><p><span class="sphinx_autodoc_typehints-type"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>[<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><code class="xref py py-data docutils literal notranslate"><span class="pre">Any</span></code></a>, <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dtype</span></code></a>[<a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.floating" title="(in NumPy v2.2)"><code class="xref py py-class docutils literal notranslate"><span class="pre">floating</span></code></a>[<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><code class="xref py py-data docutils literal notranslate"><span class="pre">Any</span></code></a>]]]</span></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method objdesc">
+<dt class="sig sig-object highlight py" id="bayes_opt.parameter.CategoricalParameter.to_param">
+<span class="sig-name descname"><span class="pre">to_param</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.parameter.CategoricalParameter.to_param.value" title="bayes_opt.parameter.CategoricalParameter.to_param.value (Python parameter) — The value to convert, should be a float."><span class="n"><span class="pre">value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><span class="pre">dtype</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.floating" title="(in NumPy v2.2)"><span class="pre">floating</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a></span></span></span><a class="headerlink" href="#bayes_opt.parameter.CategoricalParameter.to_param" title="Link to this definition">¶</a></dt>
+<dd><p>Convert a float value to a parameter.</p>
+<dl class="field-list simple">
+<dt class="field-odd" id="bayes_opt.parameter.CategoricalParameter.to_param-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#bayes_opt.parameter.CategoricalParameter.to_param-parameters" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><dl class="simple api-field highlight">
+<dt id="bayes_opt.parameter.CategoricalParameter.to_param.value"><span class="sig-name descname">value</span><span class="p"> : </span><span class="desctype"><span class="desctype">np.ndarray</span></span><a class="headerlink" href="#bayes_opt.parameter.CategoricalParameter.to_param.value" title="Permalink to this definition">¶</a></dt><dd><p>The value to convert, should be a float.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even" id="bayes_opt.parameter.CategoricalParameter.to_param-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#bayes_opt.parameter.CategoricalParameter.to_param-return-type" title="Permalink to this headline">¶</a></dt>
+<dd class="field-even"><p><span class="sphinx_autodoc_typehints-type"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><code class="xref py py-data docutils literal notranslate"><span class="pre">Any</span></code></a></span></p>
+</dd>
+<dt class="field-odd" id="bayes_opt.parameter.CategoricalParameter.to_param-returns">Returns<span class="colon">:</span><a class="headerlink" href="#bayes_opt.parameter.CategoricalParameter.to_param-returns" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><p><em>Any</em> – The canonical representation of the parameter.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method objdesc">
+<dt class="sig sig-object highlight py" id="bayes_opt.parameter.CategoricalParameter.to_string">
+<span class="sig-name descname"><span class="pre">to_string</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.parameter.CategoricalParameter.to_string.value" title="bayes_opt.parameter.CategoricalParameter.to_string.value (Python parameter) — The value to represent."><span class="n"><span class="pre">value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.parameter.CategoricalParameter.to_string.str_len" title="bayes_opt.parameter.CategoricalParameter.to_string.str_len (Python parameter) — The maximum length of the string representation."><span class="n"><span class="pre">str_len</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></span></span><a class="headerlink" href="#bayes_opt.parameter.CategoricalParameter.to_string" title="Link to this definition">¶</a></dt>
+<dd><p>Represent a parameter value as a string.</p>
+<dl class="field-list simple">
+<dt class="field-odd" id="bayes_opt.parameter.CategoricalParameter.to_string-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#bayes_opt.parameter.CategoricalParameter.to_string-parameters" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><dl class="simple api-field highlight">
+<dt id="bayes_opt.parameter.CategoricalParameter.to_string.value"><span class="sig-name descname">value</span><span class="p"> : </span><span class="desctype"><span class="desctype">Any</span></span><a class="headerlink" href="#bayes_opt.parameter.CategoricalParameter.to_string.value" title="Permalink to this definition">¶</a></dt><dd><p>The value to represent.</p>
+</dd>
+<dt id="bayes_opt.parameter.CategoricalParameter.to_string.str_len"><span class="sig-name descname">str_len</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)">int</a></span></span><a class="headerlink" href="#bayes_opt.parameter.CategoricalParameter.to_string.str_len" title="Permalink to this definition">¶</a></dt><dd><p>The maximum length of the string representation.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even" id="bayes_opt.parameter.CategoricalParameter.to_string-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#bayes_opt.parameter.CategoricalParameter.to_string-return-type" title="Permalink to this headline">¶</a></dt>
+<dd class="field-even"><p><span class="sphinx_autodoc_typehints-type"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></a></span></p>
+</dd>
+<dt class="field-odd" id="bayes_opt.parameter.CategoricalParameter.to_string-returns">Returns<span class="colon">:</span><a class="headerlink" href="#bayes_opt.parameter.CategoricalParameter.to_string-returns" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><p><em>str</em></p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class objdesc">
+<dt class="sig sig-object highlight sig-wrap py" id="bayes_opt.parameter.FloatParameter">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">bayes_opt.parameter.</span></span><span class="sig-name descname"><span class="pre">FloatParameter</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.parameter.FloatParameter.__init__.name" title="bayes_opt.parameter.FloatParameter.__init__.name (Python parameter) — The name of the parameter."><span class="n"><span class="pre">name</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.parameter.FloatParameter.__init__.bounds" title="bayes_opt.parameter.FloatParameter.__init__.bounds (Python parameter) — The bounds of the parameter."><span class="n"><span class="pre">bounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.13)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span></span><a class="headerlink" href="#bayes_opt.parameter.FloatParameter" title="Link to this definition">¶</a></dt>
+<dd><p>A parameter with float values.</p>
+<dl class="field-list simple">
+<dt class="field-odd" id="bayes_opt.parameter.FloatParameter-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#bayes_opt.parameter.FloatParameter-parameters" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><dl class="simple api-field highlight">
+<dt id="bayes_opt.parameter.FloatParameter.__init__.name"><span class="sig-name descname">name</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)">str</a></span></span><a class="headerlink" href="#bayes_opt.parameter.FloatParameter.__init__.name" title="Permalink to this definition">¶</a></dt><dd><p>The name of the parameter.</p>
+</dd>
+<dt id="bayes_opt.parameter.FloatParameter.__init__.bounds"><span class="sig-name descname">bounds</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.13)">tuple</a>[<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)">float</a>, <a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)">float</a>]</span></span><a class="headerlink" href="#bayes_opt.parameter.FloatParameter.__init__.bounds" title="Permalink to this definition">¶</a></dt><dd><p>The bounds of the parameter.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<dl class="py property objdesc">
+<dt class="sig sig-object highlight py" id="bayes_opt.parameter.FloatParameter.dim">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">dim</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span><a class="headerlink" href="#bayes_opt.parameter.FloatParameter.dim" title="Link to this definition">¶</a></dt>
+<dd><p>The dimensionality of the parameter.</p>
+</dd></dl>
+
+<dl class="py property objdesc">
+<dt class="sig sig-object highlight py" id="bayes_opt.parameter.FloatParameter.is_continuous">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">is_continuous</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><a class="headerlink" href="#bayes_opt.parameter.FloatParameter.is_continuous" title="Link to this definition">¶</a></dt>
+<dd><p>Whether the parameter is continuous.</p>
+</dd></dl>
+
+<dl class="py method objdesc">
+<dt class="sig sig-object highlight sig-wrap py" id="bayes_opt.parameter.FloatParameter.kernel_transform">
+<span class="sig-name descname"><span class="pre">kernel_transform</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.parameter.FloatParameter.kernel_transform.value" title="bayes_opt.parameter.FloatParameter.kernel_transform.value (Python parameter) — The value(s) to transform, should be a float."><span class="n"><span class="pre">value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><span class="pre">dtype</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.floating" title="(in NumPy v2.2)"><span class="pre">floating</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><span class="pre">dtype</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.floating" title="(in NumPy v2.2)"><span class="pre">floating</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></span><a class="headerlink" href="#bayes_opt.parameter.FloatParameter.kernel_transform" title="Link to this definition">¶</a></dt>
+<dd><p>Transform a parameter value for use in a kernel.</p>
+<dl class="field-list simple">
+<dt class="field-odd" id="bayes_opt.parameter.FloatParameter.kernel_transform-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#bayes_opt.parameter.FloatParameter.kernel_transform-parameters" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><dl class="simple api-field highlight">
+<dt id="bayes_opt.parameter.FloatParameter.kernel_transform.value"><span class="sig-name descname">value</span><span class="p"> : </span><span class="desctype"><span class="desctype">np.ndarray</span></span><a class="headerlink" href="#bayes_opt.parameter.FloatParameter.kernel_transform.value" title="Permalink to this definition">¶</a></dt><dd><p>The value(s) to transform, should be a float.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even" id="bayes_opt.parameter.FloatParameter.kernel_transform-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#bayes_opt.parameter.FloatParameter.kernel_transform-return-type" title="Permalink to this headline">¶</a></dt>
+<dd class="field-even"><p><span class="sphinx_autodoc_typehints-type"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>[<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><code class="xref py py-data docutils literal notranslate"><span class="pre">Any</span></code></a>, <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dtype</span></code></a>[<a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.floating" title="(in NumPy v2.2)"><code class="xref py py-class docutils literal notranslate"><span class="pre">floating</span></code></a>[<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><code class="xref py py-data docutils literal notranslate"><span class="pre">Any</span></code></a>]]]</span></p>
+</dd>
+<dt class="field-odd" id="bayes_opt.parameter.FloatParameter.kernel_transform-returns">Returns<span class="colon">:</span><a class="headerlink" href="#bayes_opt.parameter.FloatParameter.kernel_transform-returns" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><p><em>np.ndarray</em></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method objdesc">
+<dt class="sig sig-object highlight py" id="bayes_opt.parameter.FloatParameter.to_float">
+<span class="sig-name descname"><span class="pre">to_float</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.parameter.FloatParameter.to_float.value" title="bayes_opt.parameter.FloatParameter.to_float.value (Python parameter) — The value to convert, should be the canonical representation of the parameter."><span class="n"><span class="pre">value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a></span></span></span><a class="headerlink" href="#bayes_opt.parameter.FloatParameter.to_float" title="Link to this definition">¶</a></dt>
+<dd><p>Convert a parameter value to a float.</p>
+<dl class="field-list simple">
+<dt class="field-odd" id="bayes_opt.parameter.FloatParameter.to_float-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#bayes_opt.parameter.FloatParameter.to_float-parameters" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><dl class="simple api-field highlight">
+<dt id="bayes_opt.parameter.FloatParameter.to_float.value"><span class="sig-name descname">value</span><span class="p"> : </span><span class="desctype"><span class="desctype">Any</span></span><a class="headerlink" href="#bayes_opt.parameter.FloatParameter.to_float.value" title="Permalink to this definition">¶</a></dt><dd><p>The value to convert, should be the canonical representation of the parameter.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even" id="bayes_opt.parameter.FloatParameter.to_float-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#bayes_opt.parameter.FloatParameter.to_float-return-type" title="Permalink to this headline">¶</a></dt>
+<dd class="field-even"><p><span class="sphinx_autodoc_typehints-type"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></span></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method objdesc">
+<dt class="sig sig-object highlight py" id="bayes_opt.parameter.FloatParameter.to_param">
+<span class="sig-name descname"><span class="pre">to_param</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.parameter.FloatParameter.to_param.value" title="bayes_opt.parameter.FloatParameter.to_param.value (Python parameter) — The value to convert, should be a float."><span class="n"><span class="pre">value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><span class="pre">dtype</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.floating" title="(in NumPy v2.2)"><span class="pre">floating</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a></span></span></span><a class="headerlink" href="#bayes_opt.parameter.FloatParameter.to_param" title="Link to this definition">¶</a></dt>
+<dd><p>Convert a float value to a parameter.</p>
+<dl class="field-list simple">
+<dt class="field-odd" id="bayes_opt.parameter.FloatParameter.to_param-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#bayes_opt.parameter.FloatParameter.to_param-parameters" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><dl class="simple api-field highlight">
+<dt id="bayes_opt.parameter.FloatParameter.to_param.value"><span class="sig-name descname">value</span><span class="p"> : </span><span class="desctype"><span class="desctype">np.ndarray</span></span><a class="headerlink" href="#bayes_opt.parameter.FloatParameter.to_param.value" title="Permalink to this definition">¶</a></dt><dd><p>The value to convert, should be a float.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even" id="bayes_opt.parameter.FloatParameter.to_param-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#bayes_opt.parameter.FloatParameter.to_param-return-type" title="Permalink to this headline">¶</a></dt>
+<dd class="field-even"><p><span class="sphinx_autodoc_typehints-type"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></span></p>
+</dd>
+<dt class="field-odd" id="bayes_opt.parameter.FloatParameter.to_param-returns">Returns<span class="colon">:</span><a class="headerlink" href="#bayes_opt.parameter.FloatParameter.to_param-returns" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><p><em>Any</em> – The canonical representation of the parameter.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method objdesc">
+<dt class="sig sig-object highlight py" id="bayes_opt.parameter.FloatParameter.to_string">
+<span class="sig-name descname"><span class="pre">to_string</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.parameter.FloatParameter.to_string.value" title="bayes_opt.parameter.FloatParameter.to_string.value (Python parameter) — The value to represent."><span class="n"><span class="pre">value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.parameter.FloatParameter.to_string.str_len" title="bayes_opt.parameter.FloatParameter.to_string.str_len (Python parameter) — The maximum length of the string representation."><span class="n"><span class="pre">str_len</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></span></span><a class="headerlink" href="#bayes_opt.parameter.FloatParameter.to_string" title="Link to this definition">¶</a></dt>
+<dd><p>Represent a parameter value as a string.</p>
+<dl class="field-list simple">
+<dt class="field-odd" id="bayes_opt.parameter.FloatParameter.to_string-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#bayes_opt.parameter.FloatParameter.to_string-parameters" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><dl class="simple api-field highlight">
+<dt id="bayes_opt.parameter.FloatParameter.to_string.value"><span class="sig-name descname">value</span><span class="p"> : </span><span class="desctype"><span class="desctype">Any</span></span><a class="headerlink" href="#bayes_opt.parameter.FloatParameter.to_string.value" title="Permalink to this definition">¶</a></dt><dd><p>The value to represent.</p>
+</dd>
+<dt id="bayes_opt.parameter.FloatParameter.to_string.str_len"><span class="sig-name descname">str_len</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)">int</a></span></span><a class="headerlink" href="#bayes_opt.parameter.FloatParameter.to_string.str_len" title="Permalink to this definition">¶</a></dt><dd><p>The maximum length of the string representation.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even" id="bayes_opt.parameter.FloatParameter.to_string-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#bayes_opt.parameter.FloatParameter.to_string-return-type" title="Permalink to this headline">¶</a></dt>
+<dd class="field-even"><p><span class="sphinx_autodoc_typehints-type"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></a></span></p>
+</dd>
+<dt class="field-odd" id="bayes_opt.parameter.FloatParameter.to_string-returns">Returns<span class="colon">:</span><a class="headerlink" href="#bayes_opt.parameter.FloatParameter.to_string-returns" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><p><em>str</em></p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class objdesc">
+<dt class="sig sig-object highlight sig-wrap py" id="bayes_opt.parameter.IntParameter">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">bayes_opt.parameter.</span></span><span class="sig-name descname"><span class="pre">IntParameter</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.parameter.IntParameter.__init__.name" title="bayes_opt.parameter.IntParameter.__init__.name (Python parameter) — The name of the parameter."><span class="n"><span class="pre">name</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.parameter.IntParameter.__init__.bounds" title="bayes_opt.parameter.IntParameter.__init__.bounds (Python parameter) — The bounds of the parameter."><span class="n"><span class="pre">bounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.13)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span></span><a class="headerlink" href="#bayes_opt.parameter.IntParameter" title="Link to this definition">¶</a></dt>
+<dd><p>A parameter with int values.</p>
+<dl class="field-list simple">
+<dt class="field-odd" id="bayes_opt.parameter.IntParameter-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#bayes_opt.parameter.IntParameter-parameters" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><dl class="simple api-field highlight">
+<dt id="bayes_opt.parameter.IntParameter.__init__.name"><span class="sig-name descname">name</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)">str</a></span></span><a class="headerlink" href="#bayes_opt.parameter.IntParameter.__init__.name" title="Permalink to this definition">¶</a></dt><dd><p>The name of the parameter.</p>
+</dd>
+<dt id="bayes_opt.parameter.IntParameter.__init__.bounds"><span class="sig-name descname">bounds</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.13)">tuple</a>[<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)">int</a>, <a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)">int</a>]</span></span><a class="headerlink" href="#bayes_opt.parameter.IntParameter.__init__.bounds" title="Permalink to this definition">¶</a></dt><dd><p>The bounds of the parameter.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<dl class="py property objdesc">
+<dt class="sig sig-object highlight py" id="bayes_opt.parameter.IntParameter.dim">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">dim</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span><a class="headerlink" href="#bayes_opt.parameter.IntParameter.dim" title="Link to this definition">¶</a></dt>
+<dd><p>The dimensionality of the parameter.</p>
+</dd></dl>
+
+<dl class="py property objdesc">
+<dt class="sig sig-object highlight py" id="bayes_opt.parameter.IntParameter.is_continuous">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">is_continuous</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><a class="headerlink" href="#bayes_opt.parameter.IntParameter.is_continuous" title="Link to this definition">¶</a></dt>
+<dd><p>Whether the parameter is continuous.</p>
+</dd></dl>
+
+<dl class="py method objdesc">
+<dt class="sig sig-object highlight sig-wrap py" id="bayes_opt.parameter.IntParameter.kernel_transform">
+<span class="sig-name descname"><span class="pre">kernel_transform</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.parameter.IntParameter.kernel_transform.value" title="bayes_opt.parameter.IntParameter.kernel_transform.value (Python parameter) — The value(s) to transform, should be a float."><span class="n"><span class="pre">value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><span class="pre">dtype</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.floating" title="(in NumPy v2.2)"><span class="pre">floating</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><span class="pre">dtype</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.floating" title="(in NumPy v2.2)"><span class="pre">floating</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></span><a class="headerlink" href="#bayes_opt.parameter.IntParameter.kernel_transform" title="Link to this definition">¶</a></dt>
+<dd><p>Transform a parameter value for use in a kernel.</p>
+<dl class="field-list simple">
+<dt class="field-odd" id="bayes_opt.parameter.IntParameter.kernel_transform-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#bayes_opt.parameter.IntParameter.kernel_transform-parameters" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><dl class="simple api-field highlight">
+<dt id="bayes_opt.parameter.IntParameter.kernel_transform.value"><span class="sig-name descname">value</span><span class="p"> : </span><span class="desctype"><span class="desctype">np.ndarray</span></span><a class="headerlink" href="#bayes_opt.parameter.IntParameter.kernel_transform.value" title="Permalink to this definition">¶</a></dt><dd><p>The value(s) to transform, should be a float.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even" id="bayes_opt.parameter.IntParameter.kernel_transform-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#bayes_opt.parameter.IntParameter.kernel_transform-return-type" title="Permalink to this headline">¶</a></dt>
+<dd class="field-even"><p><span class="sphinx_autodoc_typehints-type"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>[<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><code class="xref py py-data docutils literal notranslate"><span class="pre">Any</span></code></a>, <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dtype</span></code></a>[<a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.floating" title="(in NumPy v2.2)"><code class="xref py py-class docutils literal notranslate"><span class="pre">floating</span></code></a>[<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><code class="xref py py-data docutils literal notranslate"><span class="pre">Any</span></code></a>]]]</span></p>
+</dd>
+<dt class="field-odd" id="bayes_opt.parameter.IntParameter.kernel_transform-returns">Returns<span class="colon">:</span><a class="headerlink" href="#bayes_opt.parameter.IntParameter.kernel_transform-returns" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><p><em>np.ndarray</em></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method objdesc">
+<dt class="sig sig-object highlight sig-wrap py" id="bayes_opt.parameter.IntParameter.random_sample">
+<span class="sig-name descname"><span class="pre">random_sample</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.parameter.IntParameter.random_sample.n_samples" title="bayes_opt.parameter.IntParameter.random_sample.n_samples (Python parameter) — The number of samples to generate."><span class="n"><span class="pre">n_samples</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.parameter.IntParameter.random_sample.random_state" title="bayes_opt.parameter.IntParameter.random_sample.random_state (Python parameter) — The random state to use for sampling."><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v2.2)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><span class="pre">dtype</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.floating" title="(in NumPy v2.2)"><span class="pre">floating</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></span><a class="headerlink" href="#bayes_opt.parameter.IntParameter.random_sample" title="Link to this definition">¶</a></dt>
+<dd><p>Generate random samples from the parameter.</p>
+<dl class="field-list simple">
+<dt class="field-odd" id="bayes_opt.parameter.IntParameter.random_sample-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#bayes_opt.parameter.IntParameter.random_sample-parameters" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><dl class="simple api-field highlight">
+<dt id="bayes_opt.parameter.IntParameter.random_sample.n_samples"><span class="sig-name descname">n_samples</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)">int</a></span></span><a class="headerlink" href="#bayes_opt.parameter.IntParameter.random_sample.n_samples" title="Permalink to this definition">¶</a></dt><dd><p>The number of samples to generate.</p>
+</dd>
+<dt id="bayes_opt.parameter.IntParameter.random_sample.random_state"><span class="sig-name descname">random_state</span><span class="p"> : </span><span class="desctype"><span class="desctype">np.random.RandomState | <a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)">int</a> | None</span></span><a class="headerlink" href="#bayes_opt.parameter.IntParameter.random_sample.random_state" title="Permalink to this definition">¶</a></dt><dd><p>The random state to use for sampling.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even" id="bayes_opt.parameter.IntParameter.random_sample-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#bayes_opt.parameter.IntParameter.random_sample-return-type" title="Permalink to this headline">¶</a></dt>
+<dd class="field-even"><p><span class="sphinx_autodoc_typehints-type"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>[<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><code class="xref py py-data docutils literal notranslate"><span class="pre">Any</span></code></a>, <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dtype</span></code></a>[<a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.floating" title="(in NumPy v2.2)"><code class="xref py py-class docutils literal notranslate"><span class="pre">floating</span></code></a>[<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><code class="xref py py-data docutils literal notranslate"><span class="pre">Any</span></code></a>]]]</span></p>
+</dd>
+<dt class="field-odd" id="bayes_opt.parameter.IntParameter.random_sample-returns">Returns<span class="colon">:</span><a class="headerlink" href="#bayes_opt.parameter.IntParameter.random_sample-returns" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><p><em>np.ndarray</em> – The samples.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method objdesc">
+<dt class="sig sig-object highlight py" id="bayes_opt.parameter.IntParameter.to_float">
+<span class="sig-name descname"><span class="pre">to_float</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.parameter.IntParameter.to_float.value" title="bayes_opt.parameter.IntParameter.to_float.value (Python parameter) — The value to convert, should be the canonical representation of the parameter."><span class="n"><span class="pre">value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a></span></span></span><a class="headerlink" href="#bayes_opt.parameter.IntParameter.to_float" title="Link to this definition">¶</a></dt>
+<dd><p>Convert a parameter value to a float.</p>
+<dl class="field-list simple">
+<dt class="field-odd" id="bayes_opt.parameter.IntParameter.to_float-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#bayes_opt.parameter.IntParameter.to_float-parameters" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><dl class="simple api-field highlight">
+<dt id="bayes_opt.parameter.IntParameter.to_float.value"><span class="sig-name descname">value</span><span class="p"> : </span><span class="desctype"><span class="desctype">Any</span></span><a class="headerlink" href="#bayes_opt.parameter.IntParameter.to_float.value" title="Permalink to this definition">¶</a></dt><dd><p>The value to convert, should be the canonical representation of the parameter.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even" id="bayes_opt.parameter.IntParameter.to_float-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#bayes_opt.parameter.IntParameter.to_float-return-type" title="Permalink to this headline">¶</a></dt>
+<dd class="field-even"><p><span class="sphinx_autodoc_typehints-type"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></span></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method objdesc">
+<dt class="sig sig-object highlight sig-wrap py" id="bayes_opt.parameter.IntParameter.to_param">
+<span class="sig-name descname"><span class="pre">to_param</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.parameter.IntParameter.to_param.value" title="bayes_opt.parameter.IntParameter.to_param.value (Python parameter) — The value to convert, should be a float."><span class="n"><span class="pre">value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><span class="pre">dtype</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.integer" title="(in NumPy v2.2)"><span class="pre">integer</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><span class="pre">dtype</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.floating" title="(in NumPy v2.2)"><span class="pre">floating</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span></span></span><a class="headerlink" href="#bayes_opt.parameter.IntParameter.to_param" title="Link to this definition">¶</a></dt>
+<dd><p>Convert a float value to a parameter.</p>
+<dl class="field-list simple">
+<dt class="field-odd" id="bayes_opt.parameter.IntParameter.to_param-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#bayes_opt.parameter.IntParameter.to_param-parameters" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><dl class="simple api-field highlight">
+<dt id="bayes_opt.parameter.IntParameter.to_param.value"><span class="sig-name descname">value</span><span class="p"> : </span><span class="desctype"><span class="desctype">np.ndarray</span></span><a class="headerlink" href="#bayes_opt.parameter.IntParameter.to_param.value" title="Permalink to this definition">¶</a></dt><dd><p>The value to convert, should be a float.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even" id="bayes_opt.parameter.IntParameter.to_param-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#bayes_opt.parameter.IntParameter.to_param-return-type" title="Permalink to this headline">¶</a></dt>
+<dd class="field-even"><p><span class="sphinx_autodoc_typehints-type"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code></a></span></p>
+</dd>
+<dt class="field-odd" id="bayes_opt.parameter.IntParameter.to_param-returns">Returns<span class="colon">:</span><a class="headerlink" href="#bayes_opt.parameter.IntParameter.to_param-returns" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><p><em>Any</em> – The canonical representation of the parameter.</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py function objdesc">
+<dt class="sig sig-object highlight py" id="bayes_opt.parameter.is_numeric">
+<span class="sig-prename descclassname"><span class="pre">bayes_opt.parameter.</span></span><span class="sig-name descname"><span class="pre">is_numeric</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.parameter.is_numeric.value" title="bayes_opt.parameter.is_numeric.value (Python parameter)"><span class="n"><span class="pre">value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span></span></span><a class="headerlink" href="#bayes_opt.parameter.is_numeric" title="Link to this definition">¶</a></dt>
+<dd><p>Check if a value is numeric.</p>
+<dl class="field-list simple">
+<dt class="field-odd" id="bayes_opt.parameter.is_numeric-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#bayes_opt.parameter.is_numeric-return-type" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><p><span class="sphinx_autodoc_typehints-type"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></a></span></p>
+</dd>
+<dt class="field-even" id="bayes_opt.parameter.is_numeric-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#bayes_opt.parameter.is_numeric-parameters" title="Permalink to this headline">¶</a></dt>
+<dd class="field-even"><dl class="simple api-field highlight">
+<dt id="bayes_opt.parameter.is_numeric.value"><span class="sig-name descname">value</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)">Any</a></span></span><a class="headerlink" href="#bayes_opt.parameter.is_numeric.value" title="Permalink to this definition">¶</a></dt><dd><p></p>
+</dd>
+</dl>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function objdesc">
+<dt class="sig sig-object highlight sig-wrap py" id="bayes_opt.parameter.wrap_kernel">
+<span class="sig-prename descclassname"><span class="pre">bayes_opt.parameter.</span></span><span class="sig-name descname"><span class="pre">wrap_kernel</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.parameter.wrap_kernel.kernel" title="bayes_opt.parameter.wrap_kernel.kernel (Python parameter) — The kernel to wrap."><span class="n"><span class="pre">kernel</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.kernels.Kernel.html#sklearn.gaussian_process.kernels.Kernel" title="(in scikit-learn v1.6)"><span class="pre">Kernel</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.parameter.wrap_kernel.transform" title="bayes_opt.parameter.wrap_kernel.transform (Python parameter) — The transformation function to apply to the input data."><span class="n"><span class="pre">transform</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.13)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.kernels.Kernel.html#sklearn.gaussian_process.kernels.Kernel" title="(in scikit-learn v1.6)"><span class="pre">Kernel</span></a></span></span></span><a class="headerlink" href="#bayes_opt.parameter.wrap_kernel" title="Link to this definition">¶</a></dt>
+<dd><p>Wrap a kernel to transform input data before passing it to the kernel.</p>
+<dl class="field-list simple">
+<dt class="field-odd" id="bayes_opt.parameter.wrap_kernel-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#bayes_opt.parameter.wrap_kernel-parameters" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><dl class="simple api-field highlight">
+<dt id="bayes_opt.parameter.wrap_kernel.kernel"><span class="sig-name descname">kernel</span><span class="p"> : </span><span class="desctype"><span class="desctype">kernels.Kernel</span></span><a class="headerlink" href="#bayes_opt.parameter.wrap_kernel.kernel" title="Permalink to this definition">¶</a></dt><dd><p>The kernel to wrap.</p>
+</dd>
+<dt id="bayes_opt.parameter.wrap_kernel.transform"><span class="sig-name descname">transform</span><span class="p"> : </span><span class="desctype"><span class="desctype">Callable</span></span><a class="headerlink" href="#bayes_opt.parameter.wrap_kernel.transform" title="Permalink to this definition">¶</a></dt><dd><p>The transformation function to apply to the input data.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even" id="bayes_opt.parameter.wrap_kernel-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#bayes_opt.parameter.wrap_kernel-return-type" title="Permalink to this headline">¶</a></dt>
+<dd class="field-even"><p><span class="sphinx_autodoc_typehints-type"><a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.kernels.Kernel.html#sklearn.gaussian_process.kernels.Kernel" title="(in scikit-learn v1.6)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Kernel</span></code></a></span></p>
+</dd>
+<dt class="field-odd" id="bayes_opt.parameter.wrap_kernel-returns">Returns<span class="colon">:</span><a class="headerlink" href="#bayes_opt.parameter.wrap_kernel-returns" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><p><em>kernels.Kernel</em> – The wrapped kernel.</p>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>See <a class="reference external" href="https://arxiv.org/abs/1805.03463">https://arxiv.org/abs/1805.03463</a> for more information.</p>
+</dd></dl>
+
+
+
+
+
+
+
+                
+              </article>
+            </div>
+          
+          
+  <script>var tabs=__md_get("__tabs");if(Array.isArray(tabs))e:for(var set of document.querySelectorAll(".tabbed-set")){var tab,labels=set.querySelector(".tabbed-labels");for(tab of tabs)for(var label of labels.getElementsByTagName("label"))if(label.innerText.trim()===tab){var input=document.getElementById(label.htmlFor);input.checked=!0;continue e}}</script>
+
+        </div>
+        
+          <a href="#" class="md-top md-icon" data-md-component="top" hidden>
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8v12Z"/></svg>
+            Back to top
+          </a>
+        
+      </main>
+      
+        <footer class="md-footer">
+  
+    
+    <nav class="md-footer__inner md-grid" aria-label="Footer" >
+      
+        
+        <a href="target_space.html" class="md-footer__link md-footer__link--prev" aria-label="Previous: bayes_opt.TargetSpace" rel="prev">
+          <div class="md-footer__button md-icon">
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11h12Z"/></svg>
+          </div>
+          <div class="md-footer__title">
+            <div class="md-ellipsis">
+              <span class="md-footer__direction">
+                Previous
+              </span>
+              bayes_opt.TargetSpace
+            </div>
+          </div>
+        </a>
+      
+      
+        
+        <a href="exception.html" class="md-footer__link md-footer__link--next" aria-label="Next: bayes_opt.exception" rel="next">
+          <div class="md-footer__title">
+            <div class="md-ellipsis">
+              <span class="md-footer__direction">
+                Next
+              </span>
+              bayes_opt.exception
+            </div>
+          </div>
+          <div class="md-footer__button md-icon">
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M4 11v2h12l-5.5 5.5 1.42 1.42L19.84 12l-7.92-7.92L10.5 5.5 16 11H4Z"/></svg>
+          </div>
+        </a>
+      
+    </nav>
+  
+  
+  
+  <div class="md-footer-meta md-typeset">
+    
+    <div class="md-footer-meta__inner md-grid">
+      <div class="md-copyright">
+  
+    <div class="md-footer-copyright__highlight">
+        &#169; Copyright 2024, Fernando Nogueira and the bayesian-optimization developers.
+        
+    </div>
+  
+    Created using
+    <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
+    8.1.3.
+     and
+    <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
+  
+</div>
+      
+        <div class="md-social">
+  
+    
+    
+    <a href="https://github.com/bayesian-optimization/BayesianOptimization" target="_blank" rel="noopener" title="Source on github.com" class="md-social__link">
+      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg>
+    </a>
+  
+    
+    
+      
+      
+    
+    <a href="https://pypi.org/project/bayesian-optimization/" target="_blank" rel="noopener" title="pypi.org" class="md-social__link">
+      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.8 200.5c-7.7-30.9-22.3-54.2-53.4-54.2h-40.1v47.4c0 36.8-31.2 67.8-66.8 67.8H172.7c-29.2 0-53.4 25-53.4 54.3v101.8c0 29 25.2 46 53.4 54.3 33.8 9.9 66.3 11.7 106.8 0 26.9-7.8 53.4-23.5 53.4-54.3v-40.7H226.2v-13.6h160.2c31.1 0 42.6-21.7 53.4-54.2 11.2-33.5 10.7-65.7 0-108.6zM286.2 404c11.1 0 20.1 9.1 20.1 20.3 0 11.3-9 20.4-20.1 20.4-11 0-20.1-9.2-20.1-20.4.1-11.3 9.1-20.3 20.1-20.3zM167.8 248.1h106.8c29.7 0 53.4-24.5 53.4-54.3V91.9c0-29-24.4-50.7-53.4-55.6-35.8-5.9-74.7-5.6-106.8.1-45.2 8-53.4 24.7-53.4 55.6v40.7h106.9v13.6h-147c-31.1 0-58.3 18.7-66.8 54.2-9.8 40.7-10.2 66.1 0 108.6 7.6 31.6 25.7 54.2 56.8 54.2H101v-48.8c0-35.3 30.5-66.4 66.8-66.4zm-6.7-142.6c-11.1 0-20.1-9.1-20.1-20.3.1-11.3 9-20.4 20.1-20.4 11 0 20.1 9.2 20.1 20.4s-9 20.3-20.1 20.3z"/></svg>
+    </a>
+  
+    
+    
+      
+      
+    
+    <a href="https://anaconda.org/conda-forge/bayesian-optimization" target="_blank" rel="noopener" title="anaconda.org" class="md-social__link">
+      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.8 200.5c-7.7-30.9-22.3-54.2-53.4-54.2h-40.1v47.4c0 36.8-31.2 67.8-66.8 67.8H172.7c-29.2 0-53.4 25-53.4 54.3v101.8c0 29 25.2 46 53.4 54.3 33.8 9.9 66.3 11.7 106.8 0 26.9-7.8 53.4-23.5 53.4-54.3v-40.7H226.2v-13.6h160.2c31.1 0 42.6-21.7 53.4-54.2 11.2-33.5 10.7-65.7 0-108.6zM286.2 404c11.1 0 20.1 9.1 20.1 20.3 0 11.3-9 20.4-20.1 20.4-11 0-20.1-9.2-20.1-20.4.1-11.3 9.1-20.3 20.1-20.3zM167.8 248.1h106.8c29.7 0 53.4-24.5 53.4-54.3V91.9c0-29-24.4-50.7-53.4-55.6-35.8-5.9-74.7-5.6-106.8.1-45.2 8-53.4 24.7-53.4 55.6v40.7h106.9v13.6h-147c-31.1 0-58.3 18.7-66.8 54.2-9.8 40.7-10.2 66.1 0 108.6 7.6 31.6 25.7 54.2 56.8 54.2H101v-48.8c0-35.3 30.5-66.4 66.8-66.4zm-6.7-142.6c-11.1 0-20.1-9.1-20.1-20.3.1-11.3 9-20.4 20.1-20.4 11 0 20.1 9.2 20.1 20.4s-9 20.3-20.1 20.3z"/></svg>
+    </a>
+  
+</div>
+      
+    </div>
+    
+  </div>
+</footer>
+      
+    </div>
+    <div class="md-dialog" data-md-component="dialog">
+      <div class="md-dialog__inner md-typeset"></div>
+    </div>
+    
+    <script id="__config" type="application/json">{"base": "..", "features": ["navigation.expand", "navigation.sections", "navigation.top", "search.share", "toc.follow", "toc.sticky", "content.tabs.link", "announce.dismiss"], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": null, "versionPath": "../versions.json"}}</script>
+    
+      
+        <script src="../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
+        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+    
+  </body>
+</html>
\ No newline at end of file
diff --git a/master/reference/target_space.html b/master/reference/target_space.html
index f4f92d73..069404aa 100644
--- a/master/reference/target_space.html
+++ b/master/reference/target_space.html
@@ -323,6 +323,21 @@
   
   
   
+    <li class="md-nav__item">
+      <a href="../parameter_types.html" class="md-nav__link">
+        <span title="/parameter_types.ipynb (reference label)" class="md-ellipsis">Parameter Types</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
     <li class="md-nav__item">
       <a href="../domain_reduction.html" class="md-nav__link">
         <span title="/domain_reduction.ipynb (reference label)" class="md-ellipsis">Sequential Domain Reduction</span>
@@ -635,6 +650,26 @@
       </ul>
     </nav>
   
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.TargetSpace.calculate_bounds" class="md-nav__link">
+    <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="bayes_opt.TargetSpace.calculate_bounds (Python method) — Calculate the float bounds of the parameter space." class="md-ellipsis">calculate_<wbr>bounds</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="calculate_bounds">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.TargetSpace.calculate_bounds-return-type" class="md-nav__link">
+    <span class="md-ellipsis">Return type</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
 </li>
         
           <li class="md-nav__item">
@@ -675,6 +710,26 @@
       </ul>
     </nav>
   
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.TargetSpace.continuous_dimensions" class="md-nav__link">
+    <span aria-label="Python property" class="objinfo-icon objinfo-icon__alias" title="Python property">P</span><span title="bayes_opt.TargetSpace.continuous_dimensions (Python property) — Get the continuous parameters." class="md-ellipsis">continuous_<wbr>dimensions</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="continuous_dimensions">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.TargetSpace.continuous_dimensions-returns" class="md-nav__link">
+    <span class="md-ellipsis">Returns</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
 </li>
         
           <li class="md-nav__item">
@@ -715,6 +770,46 @@
       </ul>
     </nav>
   
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.TargetSpace.kernel_transform" class="md-nav__link">
+    <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="bayes_opt.TargetSpace.kernel_transform (Python method) — Transform floating-point suggestions to values used in the kernel." class="md-ellipsis">kernel_<wbr>transform</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="kernel_transform">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.TargetSpace.kernel_transform-return-type" class="md-nav__link">
+    <span class="md-ellipsis">Return type</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.TargetSpace.kernel_transform-parameters" class="md-nav__link">
+    <span class="md-ellipsis">Parameters</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="Parameters">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.TargetSpace.kernel_transform.value" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.TargetSpace.kernel_transform.value (Python parameter)" class="md-ellipsis">value</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
 </li>
         
           <li class="md-nav__item">
@@ -735,6 +830,80 @@
       </ul>
     </nav>
   
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.TargetSpace.make_masks" class="md-nav__link">
+    <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="bayes_opt.TargetSpace.make_masks (Python method) — Create a dictionary of masks for the parameters." class="md-ellipsis">make_<wbr>masks</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="make_masks">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.TargetSpace.make_masks-return-type" class="md-nav__link">
+    <span class="md-ellipsis">Return type</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.TargetSpace.make_masks-returns" class="md-nav__link">
+    <span class="md-ellipsis">Returns</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.TargetSpace.make_params" class="md-nav__link">
+    <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="bayes_opt.TargetSpace.make_params (Python method) — Create a dictionary of parameters from a dictionary of bounds." class="md-ellipsis">make_<wbr>params</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="make_params">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.TargetSpace.make_params-parameters" class="md-nav__link">
+    <span class="md-ellipsis">Parameters</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="Parameters">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.TargetSpace.make_params.pbounds" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.TargetSpace.make_params.pbounds (Python parameter) — A dictionary with the parameter names as keys and a tuple with minimum and maximum values." class="md-ellipsis">pbounds</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.TargetSpace.make_params-return-type" class="md-nav__link">
+    <span class="md-ellipsis">Return type</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.TargetSpace.make_params-returns" class="md-nav__link">
+    <span class="md-ellipsis">Returns</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
 </li>
         
           <li class="md-nav__item">
@@ -755,6 +924,26 @@
       </ul>
     </nav>
   
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.TargetSpace.masks" class="md-nav__link">
+    <span aria-label="Python property" class="objinfo-icon objinfo-icon__alias" title="Python property">P</span><span title="bayes_opt.TargetSpace.masks (Python property) — Get the masks for the parameters." class="md-ellipsis">masks</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="masks">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.TargetSpace.masks-returns" class="md-nav__link">
+    <span class="md-ellipsis">Returns</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
 </li>
         
           <li class="md-nav__item">
@@ -802,6 +991,13 @@
       </ul>
     </nav>
   
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.TargetSpace.params_config" class="md-nav__link">
+    <span aria-label="Python property" class="objinfo-icon objinfo-icon__alias" title="Python property">P</span><span title="bayes_opt.TargetSpace.params_config (Python property) — Get the parameters configuration." class="md-ellipsis">params_<wbr>config</span>
+  </a>
+  
 </li>
         
           <li class="md-nav__item">
@@ -906,6 +1102,33 @@
     <nav class="md-nav" aria-label="random_sample">
       <ul class="md-nav__list">
         
+          <li class="md-nav__item">
+  <a href="#bayes_opt.TargetSpace.random_sample-parameters" class="md-nav__link">
+    <span class="md-ellipsis">Parameters</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="Parameters">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.TargetSpace.random_sample.n_samples" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.TargetSpace.random_sample.n_samples (Python parameter) — Number of samples to draw." class="md-ellipsis">n_<wbr>samples</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.TargetSpace.random_sample.random_state" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.TargetSpace.random_sample.random_state (Python parameter) — The random state to use for sampling." class="md-ellipsis">random_<wbr>state</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
           <li class="md-nav__item">
   <a href="#bayes_opt.TargetSpace.random_sample-return-type" class="md-nav__link">
     <span class="md-ellipsis">Return type</span>
@@ -1093,6 +1316,21 @@
   
   
   
+    <li class="md-nav__item">
+      <a href="parameter.html" class="md-nav__link">
+        <span title="/reference/parameter.rst (reference label)" class="md-ellipsis">bayes_<wbr>opt.<wbr>parameter</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
     <li class="md-nav__item">
       <a href="exception.html" class="md-nav__link">
         <span title="/reference/exception.rst (reference label)" class="md-ellipsis">bayes_<wbr>opt.<wbr>exception</span>
@@ -1270,6 +1508,26 @@
       </ul>
     </nav>
   
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.TargetSpace.calculate_bounds" class="md-nav__link">
+    <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="bayes_opt.TargetSpace.calculate_bounds (Python method) — Calculate the float bounds of the parameter space." class="md-ellipsis">calculate_<wbr>bounds</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="calculate_bounds">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.TargetSpace.calculate_bounds-return-type" class="md-nav__link">
+    <span class="md-ellipsis">Return type</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
 </li>
         
           <li class="md-nav__item">
@@ -1310,6 +1568,26 @@
       </ul>
     </nav>
   
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.TargetSpace.continuous_dimensions" class="md-nav__link">
+    <span aria-label="Python property" class="objinfo-icon objinfo-icon__alias" title="Python property">P</span><span title="bayes_opt.TargetSpace.continuous_dimensions (Python property) — Get the continuous parameters." class="md-ellipsis">continuous_<wbr>dimensions</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="continuous_dimensions">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.TargetSpace.continuous_dimensions-returns" class="md-nav__link">
+    <span class="md-ellipsis">Returns</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
 </li>
         
           <li class="md-nav__item">
@@ -1350,6 +1628,46 @@
       </ul>
     </nav>
   
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.TargetSpace.kernel_transform" class="md-nav__link">
+    <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="bayes_opt.TargetSpace.kernel_transform (Python method) — Transform floating-point suggestions to values used in the kernel." class="md-ellipsis">kernel_<wbr>transform</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="kernel_transform">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.TargetSpace.kernel_transform-return-type" class="md-nav__link">
+    <span class="md-ellipsis">Return type</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.TargetSpace.kernel_transform-parameters" class="md-nav__link">
+    <span class="md-ellipsis">Parameters</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="Parameters">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.TargetSpace.kernel_transform.value" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.TargetSpace.kernel_transform.value (Python parameter)" class="md-ellipsis">value</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
 </li>
         
           <li class="md-nav__item">
@@ -1370,6 +1688,80 @@
       </ul>
     </nav>
   
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.TargetSpace.make_masks" class="md-nav__link">
+    <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="bayes_opt.TargetSpace.make_masks (Python method) — Create a dictionary of masks for the parameters." class="md-ellipsis">make_<wbr>masks</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="make_masks">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.TargetSpace.make_masks-return-type" class="md-nav__link">
+    <span class="md-ellipsis">Return type</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.TargetSpace.make_masks-returns" class="md-nav__link">
+    <span class="md-ellipsis">Returns</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.TargetSpace.make_params" class="md-nav__link">
+    <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="bayes_opt.TargetSpace.make_params (Python method) — Create a dictionary of parameters from a dictionary of bounds." class="md-ellipsis">make_<wbr>params</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="make_params">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.TargetSpace.make_params-parameters" class="md-nav__link">
+    <span class="md-ellipsis">Parameters</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="Parameters">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.TargetSpace.make_params.pbounds" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.TargetSpace.make_params.pbounds (Python parameter) — A dictionary with the parameter names as keys and a tuple with minimum and maximum values." class="md-ellipsis">pbounds</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.TargetSpace.make_params-return-type" class="md-nav__link">
+    <span class="md-ellipsis">Return type</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.TargetSpace.make_params-returns" class="md-nav__link">
+    <span class="md-ellipsis">Returns</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
 </li>
         
           <li class="md-nav__item">
@@ -1390,6 +1782,26 @@
       </ul>
     </nav>
   
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.TargetSpace.masks" class="md-nav__link">
+    <span aria-label="Python property" class="objinfo-icon objinfo-icon__alias" title="Python property">P</span><span title="bayes_opt.TargetSpace.masks (Python property) — Get the masks for the parameters." class="md-ellipsis">masks</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="masks">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.TargetSpace.masks-returns" class="md-nav__link">
+    <span class="md-ellipsis">Returns</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
 </li>
         
           <li class="md-nav__item">
@@ -1437,6 +1849,13 @@
       </ul>
     </nav>
   
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.TargetSpace.params_config" class="md-nav__link">
+    <span aria-label="Python property" class="objinfo-icon objinfo-icon__alias" title="Python property">P</span><span title="bayes_opt.TargetSpace.params_config (Python property) — Get the parameters configuration." class="md-ellipsis">params_<wbr>config</span>
+  </a>
+  
 </li>
         
           <li class="md-nav__item">
@@ -1541,6 +1960,33 @@
     <nav class="md-nav" aria-label="random_sample">
       <ul class="md-nav__list">
         
+          <li class="md-nav__item">
+  <a href="#bayes_opt.TargetSpace.random_sample-parameters" class="md-nav__link">
+    <span class="md-ellipsis">Parameters</span>
+  </a>
+  
+    <nav class="md-nav" aria-label="Parameters">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.TargetSpace.random_sample.n_samples" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.TargetSpace.random_sample.n_samples (Python parameter) — Number of samples to draw." class="md-ellipsis">n_<wbr>samples</span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#bayes_opt.TargetSpace.random_sample.random_state" class="md-nav__link">
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="bayes_opt.TargetSpace.random_sample.random_state (Python parameter) — The random state to use for sampling." class="md-ellipsis">random_<wbr>state</span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+        
           <li class="md-nav__item">
   <a href="#bayes_opt.TargetSpace.random_sample-return-type" class="md-nav__link">
     <span class="md-ellipsis">Return type</span>
@@ -1735,7 +2181,7 @@
 <h1 id="bayes-opt-targetspace"><a class="reference internal" href="#bayes_opt.TargetSpace" title="bayes_opt.TargetSpace (Python class) — Holds the param-space coordinates (X) and target values (Y)."><code class="xref py py-class docutils literal notranslate"><span class="pre">bayes_opt.TargetSpace</span></code></a><a class="headerlink" href="#bayes-opt-targetspace" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="bayes_opt.TargetSpace">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">bayes_opt.</span></span><span class="sig-name descname"><span class="pre">TargetSpace</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.TargetSpace.__init__.target_func" title="bayes_opt.TargetSpace.__init__.target_func (Python parameter) — Function to be maximized."><span class="n"><span class="pre">target_func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.13)"><span class="pre">collections.abc.Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.TargetSpace.__init__.pbounds" title="bayes_opt.TargetSpace.__init__.pbounds (Python parameter) — Dictionary with parameters names as keys and a tuple with minimum and maximum values."><span class="n"><span class="pre">pbounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="pre">Mapping</span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.13)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.TargetSpace.__init__.constraint" title="bayes_opt.TargetSpace.__init__.constraint (Python parameter)"><span class="n"><span class="pre">constraint</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="constraint.html#bayes_opt.constraint.ConstraintModel" title="bayes_opt.constraint.ConstraintModel (Python class) — Model constraints using GP regressors."><span class="pre">ConstraintModel</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.TargetSpace.__init__.random_state" title="bayes_opt.TargetSpace.__init__.random_state (Python parameter) — optionally specify a seed for a random number generator"><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">RandomState</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.TargetSpace.__init__.allow_duplicate_points" title="bayes_opt.TargetSpace.__init__.allow_duplicate_points (Python parameter) — If True, the optimizer will allow duplicate points to be registered. This behavior may be desired in high noise situations where repeatedly probing the same point will give different answers."><span class="n"><span class="pre">allow_duplicate_points</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span></span><a class="headerlink" href="#bayes_opt.TargetSpace" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">bayes_opt.</span></span><span class="sig-name descname"><span class="pre">TargetSpace</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.TargetSpace.__init__.target_func" title="bayes_opt.TargetSpace.__init__.target_func (Python parameter) — Function to be maximized."><span class="n"><span class="pre">target_func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.13)"><span class="pre">collections.abc.Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.TargetSpace.__init__.pbounds" title="bayes_opt.TargetSpace.__init__.pbounds (Python parameter) — Dictionary with parameters names as keys and a tuple with minimum and maximum values."><span class="n"><span class="pre">pbounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="pre">BoundsMapping</span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.TargetSpace.__init__.constraint" title="bayes_opt.TargetSpace.__init__.constraint (Python parameter)"><span class="n"><span class="pre">constraint</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="constraint.html#bayes_opt.constraint.ConstraintModel" title="bayes_opt.constraint.ConstraintModel (Python class) — Model constraints using GP regressors."><span class="pre">ConstraintModel</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.TargetSpace.__init__.random_state" title="bayes_opt.TargetSpace.__init__.random_state (Python parameter) — optionally specify a seed for a random number generator"><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">RandomState</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.TargetSpace.__init__.allow_duplicate_points" title="bayes_opt.TargetSpace.__init__.allow_duplicate_points (Python parameter) — If True, the optimizer will allow duplicate points to be registered. This behavior may be desired in high noise situations where repeatedly probing the same point will give different answers."><span class="n"><span class="pre">allow_duplicate_points</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span></span><a class="headerlink" href="#bayes_opt.TargetSpace" title="Link to this definition">¶</a></dt>
 <dd><p>Holds the param-space coordinates (X) and target values (Y).</p>
 <p>Allows for constant-time appends.</p>
 <dl class="field-list simple">
@@ -1772,7 +2218,7 @@ <h1 id="bayes-opt-targetspace"><a class="reference internal" href="#bayes_opt.Ta
 </div>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="bayes_opt.TargetSpace.array_to_params">
-<span class="sig-name descname"><span class="pre">array_to_params</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.TargetSpace.array_to_params.x" title="bayes_opt.TargetSpace.array_to_params.x (Python parameter) — a single point, with len(x) == self.dim."><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><span class="pre">dtype</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.floating" title="(in NumPy v2.2)"><span class="pre">floating</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="headerlink" href="#bayes_opt.TargetSpace.array_to_params" title="Link to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">array_to_params</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.TargetSpace.array_to_params.x" title="bayes_opt.TargetSpace.array_to_params.x (Python parameter) — a single point, with len(x) == self.dim."><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><span class="pre">dtype</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.floating" title="(in NumPy v2.2)"><span class="pre">floating</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><span class="pre">dtype</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.floating" title="(in NumPy v2.2)"><span class="pre">floating</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></span><a class="headerlink" href="#bayes_opt.TargetSpace.array_to_params" title="Link to this definition">¶</a></dt>
 <dd><p>Convert an array representation of parameters into a dict version.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="bayes_opt.TargetSpace.array_to_params-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#bayes_opt.TargetSpace.array_to_params-parameters" title="Permalink to this headline">¶</a></dt>
@@ -1782,7 +2228,7 @@ <h1 id="bayes-opt-targetspace"><a class="reference internal" href="#bayes_opt.Ta
 </dl>
 </dd>
 <dt class="field-even" id="bayes_opt.TargetSpace.array_to_params-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#bayes_opt.TargetSpace.array_to_params-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><span class="sphinx_autodoc_typehints-type"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>[<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></a>, <a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a>]</span></p>
+<dd class="field-even"><p><span class="sphinx_autodoc_typehints-type"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>[<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></a>, <a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a> | <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>[<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><code class="xref py py-data docutils literal notranslate"><span class="pre">Any</span></code></a>, <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dtype</span></code></a>[<a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.floating" title="(in NumPy v2.2)"><code class="xref py py-class docutils literal notranslate"><span class="pre">floating</span></code></a>[<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><code class="xref py py-data docutils literal notranslate"><span class="pre">Any</span></code></a>]]]]</span></p>
 </dd>
 <dt class="field-odd" id="bayes_opt.TargetSpace.array_to_params-returns">Returns<span class="colon">:</span><a class="headerlink" href="#bayes_opt.TargetSpace.array_to_params-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p><em>dict</em> – Representation of the parameters as dictionary.</p>
@@ -1801,6 +2247,17 @@ <h1 id="bayes-opt-targetspace"><a class="reference internal" href="#bayes_opt.Ta
 </dl>
 </dd></dl>
 
+<dl class="py method objdesc">
+<dt class="sig sig-object highlight py" id="bayes_opt.TargetSpace.calculate_bounds">
+<span class="sig-name descname"><span class="pre">calculate_bounds</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><span class="pre">dtype</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.floating" title="(in NumPy v2.2)"><span class="pre">floating</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></span><a class="headerlink" href="#bayes_opt.TargetSpace.calculate_bounds" title="Link to this definition">¶</a></dt>
+<dd><p>Calculate the float bounds of the parameter space.</p>
+<dl class="field-list simple">
+<dt class="field-odd" id="bayes_opt.TargetSpace.calculate_bounds-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#bayes_opt.TargetSpace.calculate_bounds-return-type" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><p><span class="sphinx_autodoc_typehints-type"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>[<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><code class="xref py py-data docutils literal notranslate"><span class="pre">Any</span></code></a>, <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dtype</span></code></a>[<a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.floating" title="(in NumPy v2.2)"><code class="xref py py-class docutils literal notranslate"><span class="pre">floating</span></code></a>[<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><code class="xref py py-data docutils literal notranslate"><span class="pre">Any</span></code></a>]]]</span></p>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="bayes_opt.TargetSpace.constraint">
 <em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">constraint</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="constraint.html#bayes_opt.constraint.ConstraintModel" title="bayes_opt.constraint.ConstraintModel (Python class) — Model constraints using GP regressors."><span class="pre">ConstraintModel</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span><a class="headerlink" href="#bayes_opt.TargetSpace.constraint" title="Link to this definition">¶</a></dt>
@@ -1823,6 +2280,17 @@ <h1 id="bayes-opt-targetspace"><a class="reference internal" href="#bayes_opt.Ta
 </dl>
 </dd></dl>
 
+<dl class="py property objdesc">
+<dt class="sig sig-object highlight py" id="bayes_opt.TargetSpace.continuous_dimensions">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">continuous_dimensions</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><span class="pre">dtype</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.bool" title="(in NumPy v2.2)"><span class="pre">bool</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><a class="headerlink" href="#bayes_opt.TargetSpace.continuous_dimensions" title="Link to this definition">¶</a></dt>
+<dd><p>Get the continuous parameters.</p>
+<dl class="field-list simple">
+<dt class="field-odd" id="bayes_opt.TargetSpace.continuous_dimensions-returns">Returns<span class="colon">:</span><a class="headerlink" href="#bayes_opt.TargetSpace.continuous_dimensions-returns" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><p><em>dict</em></p>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="bayes_opt.TargetSpace.dim">
 <em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">dim</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span><a class="headerlink" href="#bayes_opt.TargetSpace.dim" title="Link to this definition">¶</a></dt>
@@ -1845,6 +2313,24 @@ <h1 id="bayes-opt-targetspace"><a class="reference internal" href="#bayes_opt.Ta
 </dl>
 </dd></dl>
 
+<dl class="py method objdesc">
+<dt class="sig sig-object highlight sig-wrap py" id="bayes_opt.TargetSpace.kernel_transform">
+<span class="sig-name descname"><span class="pre">kernel_transform</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.TargetSpace.kernel_transform.value" title="bayes_opt.TargetSpace.kernel_transform.value (Python parameter)"><span class="n"><span class="pre">value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><span class="pre">dtype</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.floating" title="(in NumPy v2.2)"><span class="pre">floating</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><span class="pre">dtype</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.floating" title="(in NumPy v2.2)"><span class="pre">floating</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></span><a class="headerlink" href="#bayes_opt.TargetSpace.kernel_transform" title="Link to this definition">¶</a></dt>
+<dd><p>Transform floating-point suggestions to values used in the kernel.</p>
+<p>Vectorized.</p>
+<dl class="field-list simple">
+<dt class="field-odd" id="bayes_opt.TargetSpace.kernel_transform-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#bayes_opt.TargetSpace.kernel_transform-return-type" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><p><span class="sphinx_autodoc_typehints-type"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>[<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><code class="xref py py-data docutils literal notranslate"><span class="pre">Any</span></code></a>, <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dtype</span></code></a>[<a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.floating" title="(in NumPy v2.2)"><code class="xref py py-class docutils literal notranslate"><span class="pre">floating</span></code></a>[<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><code class="xref py py-data docutils literal notranslate"><span class="pre">Any</span></code></a>]]]</span></p>
+</dd>
+<dt class="field-even" id="bayes_opt.TargetSpace.kernel_transform-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#bayes_opt.TargetSpace.kernel_transform-parameters" title="Permalink to this headline">¶</a></dt>
+<dd class="field-even"><dl class="simple api-field highlight">
+<dt id="bayes_opt.TargetSpace.kernel_transform.value"><span class="sig-name descname">value</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)">ndarray</a>[<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)">Any</a>, <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)">dtype</a>[<a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.floating" title="(in NumPy v2.2)">floating</a>[<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)">Any</a>]]]</span></span><a class="headerlink" href="#bayes_opt.TargetSpace.kernel_transform.value" title="Permalink to this definition">¶</a></dt><dd><p></p>
+</dd>
+</dl>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="bayes_opt.TargetSpace.keys">
 <em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">keys</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span><a class="headerlink" href="#bayes_opt.TargetSpace.keys" title="Link to this definition">¶</a></dt>
@@ -1856,6 +2342,44 @@ <h1 id="bayes-opt-targetspace"><a class="reference internal" href="#bayes_opt.Ta
 </dl>
 </dd></dl>
 
+<dl class="py method objdesc">
+<dt class="sig sig-object highlight py" id="bayes_opt.TargetSpace.make_masks">
+<span class="sig-name descname"><span class="pre">make_masks</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><span class="pre">dtype</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.bool" title="(in NumPy v2.2)"><span class="pre">bool</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></span><a class="headerlink" href="#bayes_opt.TargetSpace.make_masks" title="Link to this definition">¶</a></dt>
+<dd><p>Create a dictionary of masks for the parameters.</p>
+<p>The mask can be used to select the corresponding parameters from an array.</p>
+<dl class="field-list simple">
+<dt class="field-odd" id="bayes_opt.TargetSpace.make_masks-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#bayes_opt.TargetSpace.make_masks-return-type" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><p><span class="sphinx_autodoc_typehints-type"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>[<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></a>, <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>[<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><code class="xref py py-data docutils literal notranslate"><span class="pre">Any</span></code></a>, <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dtype</span></code></a>[<a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.bool" title="(in NumPy v2.2)"><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></a>]]]</span></p>
+</dd>
+<dt class="field-even" id="bayes_opt.TargetSpace.make_masks-returns">Returns<span class="colon">:</span><a class="headerlink" href="#bayes_opt.TargetSpace.make_masks-returns" title="Permalink to this headline">¶</a></dt>
+<dd class="field-even"><p><em>dict</em> – A dictionary with the parameter names as keys and the corresponding
+mask as values.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method objdesc">
+<dt class="sig sig-object highlight sig-wrap py" id="bayes_opt.TargetSpace.make_params">
+<span class="sig-name descname"><span class="pre">make_params</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.TargetSpace.make_params.pbounds" title="bayes_opt.TargetSpace.make_params.pbounds (Python parameter) — A dictionary with the parameter names as keys and a tuple with minimum and maximum values."><span class="n"><span class="pre">pbounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Mapping" title="(in Python v3.13)"><span class="pre">Mapping</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.13)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.13)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#type" title="(in Python v3.13)"><span class="pre">type</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.13)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#type" title="(in Python v3.13)"><span class="pre">type</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.13)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="parameter.html#bayes_opt.parameter.BayesParameter" title="bayes_opt.parameter.BayesParameter (Python class) — Base class for Bayesian optimization parameters."><span class="pre">BayesParameter</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="headerlink" href="#bayes_opt.TargetSpace.make_params" title="Link to this definition">¶</a></dt>
+<dd><p>Create a dictionary of parameters from a dictionary of bounds.</p>
+<dl class="field-list simple">
+<dt class="field-odd" id="bayes_opt.TargetSpace.make_params-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#bayes_opt.TargetSpace.make_params-parameters" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><dl class="simple api-field highlight">
+<dt id="bayes_opt.TargetSpace.make_params.pbounds"><span class="sig-name descname">pbounds</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)">dict</a></span></span><a class="headerlink" href="#bayes_opt.TargetSpace.make_params.pbounds" title="Permalink to this definition">¶</a></dt><dd><p>A dictionary with the parameter names as keys and a tuple with minimum
+and maximum values.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even" id="bayes_opt.TargetSpace.make_params-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#bayes_opt.TargetSpace.make_params-return-type" title="Permalink to this headline">¶</a></dt>
+<dd class="field-even"><p><span class="sphinx_autodoc_typehints-type"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>[<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></a>, <a class="reference internal" href="parameter.html#bayes_opt.parameter.BayesParameter" title="bayes_opt.parameter.BayesParameter (Python class) — Base class for Bayesian optimization parameters."><code class="xref py py-class docutils literal notranslate"><span class="pre">BayesParameter</span></code></a>]</span></p>
+</dd>
+<dt class="field-odd" id="bayes_opt.TargetSpace.make_params-returns">Returns<span class="colon">:</span><a class="headerlink" href="#bayes_opt.TargetSpace.make_params-returns" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><p><em>dict</em> – A dictionary with the parameter names as keys and the corresponding
+parameter objects as values.</p>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="bayes_opt.TargetSpace.mask">
 <em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">mask</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><span class="pre">dtype</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.bool" title="(in NumPy v2.2)"><span class="pre">bool</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><a class="headerlink" href="#bayes_opt.TargetSpace.mask" title="Link to this definition">¶</a></dt>
@@ -1868,6 +2392,17 @@ <h1 id="bayes-opt-targetspace"><a class="reference internal" href="#bayes_opt.Ta
 </dl>
 </dd></dl>
 
+<dl class="py property objdesc">
+<dt class="sig sig-object highlight py" id="bayes_opt.TargetSpace.masks">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">masks</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><span class="pre">dtype</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.bool" title="(in NumPy v2.2)"><span class="pre">bool</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><a class="headerlink" href="#bayes_opt.TargetSpace.masks" title="Link to this definition">¶</a></dt>
+<dd><p>Get the masks for the parameters.</p>
+<dl class="field-list simple">
+<dt class="field-odd" id="bayes_opt.TargetSpace.masks-returns">Returns<span class="colon">:</span><a class="headerlink" href="#bayes_opt.TargetSpace.masks-returns" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><p><em>dict</em></p>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="bayes_opt.TargetSpace.max">
 <span class="sig-name descname"><span class="pre">max</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span></span><a class="headerlink" href="#bayes_opt.TargetSpace.max" title="Link to this definition">¶</a></dt>
@@ -1898,9 +2433,15 @@ <h1 id="bayes-opt-targetspace"><a class="reference internal" href="#bayes_opt.Ta
 </dl>
 </dd></dl>
 
+<dl class="py property objdesc">
+<dt class="sig sig-object highlight py" id="bayes_opt.TargetSpace.params_config">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">params_config</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="parameter.html#bayes_opt.parameter.BayesParameter" title="bayes_opt.parameter.BayesParameter (Python class) — Base class for Bayesian optimization parameters."><span class="pre">BayesParameter</span></a><span class="p"><span class="pre">]</span></span></span><a class="headerlink" href="#bayes_opt.TargetSpace.params_config" title="Link to this definition">¶</a></dt>
+<dd><p>Get the parameters configuration.</p>
+</dd></dl>
+
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="bayes_opt.TargetSpace.params_to_array">
-<span class="sig-name descname"><span class="pre">params_to_array</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.TargetSpace.params_to_array.params" title="bayes_opt.TargetSpace.params_to_array.params (Python parameter) — a single point, with len(x) == self.dim."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Mapping" title="(in Python v3.13)"><span class="pre">Mapping</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><span class="pre">dtype</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.floating" title="(in NumPy v2.2)"><span class="pre">floating</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></span><a class="headerlink" href="#bayes_opt.TargetSpace.params_to_array" title="Link to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">params_to_array</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.TargetSpace.params_to_array.params" title="bayes_opt.TargetSpace.params_to_array.params (Python parameter) — a single point, with len(x) == self.dim."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Mapping" title="(in Python v3.13)"><span class="pre">Mapping</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><span class="pre">dtype</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.floating" title="(in NumPy v2.2)"><span class="pre">floating</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><span class="pre">dtype</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.floating" title="(in NumPy v2.2)"><span class="pre">floating</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></span><a class="headerlink" href="#bayes_opt.TargetSpace.params_to_array" title="Link to this definition">¶</a></dt>
 <dd><p>Convert a dict representation of parameters into an array version.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="bayes_opt.TargetSpace.params_to_array-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#bayes_opt.TargetSpace.params_to_array-parameters" title="Permalink to this headline">¶</a></dt>
@@ -1920,7 +2461,7 @@ <h1 id="bayes-opt-targetspace"><a class="reference internal" href="#bayes_opt.Ta
 
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="bayes_opt.TargetSpace.probe">
-<span class="sig-name descname"><span class="pre">probe</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.TargetSpace.probe.params" title="bayes_opt.TargetSpace.probe.params (Python parameter) — a single point, with len(x) == self.dim"><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Mapping" title="(in Python v3.13)"><span class="pre">Mapping</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.13)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><span class="pre">dtype</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.floating" title="(in NumPy v2.2)"><span class="pre">floating</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.13)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><span class="pre">dtype</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.floating" title="(in NumPy v2.2)"><span class="pre">floating</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></span><a class="headerlink" href="#bayes_opt.TargetSpace.probe" title="Link to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">probe</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.TargetSpace.probe.params" title="bayes_opt.TargetSpace.probe.params (Python parameter) — a single point, with len(x) == self.dim"><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Mapping" title="(in Python v3.13)"><span class="pre">Mapping</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.13)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><span class="pre">dtype</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.floating" title="(in NumPy v2.2)"><span class="pre">floating</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.13)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><span class="pre">dtype</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.floating" title="(in NumPy v2.2)"><span class="pre">floating</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></span><a class="headerlink" href="#bayes_opt.TargetSpace.probe" title="Link to this definition">¶</a></dt>
 <dd><p>Evaluate the target function on a point and register the result.</p>
 <p class="rubric">Notes</p>
 <p>If <cite>params</cite> has been previously seen and duplicate points are not allowed,
@@ -1951,15 +2492,25 @@ <h1 id="bayes-opt-targetspace"><a class="reference internal" href="#bayes_opt.Ta
 </dd></dl>
 
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="bayes_opt.TargetSpace.random_sample">
-<span class="sig-name descname"><span class="pre">random_sample</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><span class="pre">dtype</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.floating" title="(in NumPy v2.2)"><span class="pre">floating</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></span><a class="headerlink" href="#bayes_opt.TargetSpace.random_sample" title="Link to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="bayes_opt.TargetSpace.random_sample">
+<span class="sig-name descname"><span class="pre">random_sample</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.TargetSpace.random_sample.n_samples" title="bayes_opt.TargetSpace.random_sample.n_samples (Python parameter) — Number of samples to draw."><span class="n"><span class="pre">n_samples</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.TargetSpace.random_sample.random_state" title="bayes_opt.TargetSpace.random_sample.random_state (Python parameter) — The random state to use for sampling."><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v2.2)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><span class="pre">dtype</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.floating" title="(in NumPy v2.2)"><span class="pre">floating</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></span><a class="headerlink" href="#bayes_opt.TargetSpace.random_sample" title="Link to this definition">¶</a></dt>
 <dd><p>Sample a random point from within the bounds of the space.</p>
 <dl class="field-list simple">
-<dt class="field-odd" id="bayes_opt.TargetSpace.random_sample-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#bayes_opt.TargetSpace.random_sample-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><span class="sphinx_autodoc_typehints-type"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>[<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><code class="xref py py-data docutils literal notranslate"><span class="pre">Any</span></code></a>, <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dtype</span></code></a>[<a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.floating" title="(in NumPy v2.2)"><code class="xref py py-class docutils literal notranslate"><span class="pre">floating</span></code></a>[<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><code class="xref py py-data docutils literal notranslate"><span class="pre">Any</span></code></a>]]]</span></p>
+<dt class="field-odd" id="bayes_opt.TargetSpace.random_sample-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#bayes_opt.TargetSpace.random_sample-parameters" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><dl class="simple api-field highlight">
+<dt id="bayes_opt.TargetSpace.random_sample.n_samples"><span class="sig-name descname">n_samples</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)">int</a>, optional</span></span><a class="headerlink" href="#bayes_opt.TargetSpace.random_sample.n_samples" title="Permalink to this definition">¶</a></dt><dd><p>Number of samples to draw. If 0, a single sample is drawn,
+and a 1D array is returned. If n_samples &gt; 0, an array of
+shape (n_samples, dim) is returned.</p>
+</dd>
+<dt id="bayes_opt.TargetSpace.random_sample.random_state"><span class="sig-name descname">random_state</span><span class="p"> : </span><span class="desctype"><span class="desctype">np.random.RandomState | <a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)">int</a> | None</span></span><a class="headerlink" href="#bayes_opt.TargetSpace.random_sample.random_state" title="Permalink to this definition">¶</a></dt><dd><p>The random state to use for sampling.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even" id="bayes_opt.TargetSpace.random_sample-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#bayes_opt.TargetSpace.random_sample-return-type" title="Permalink to this headline">¶</a></dt>
+<dd class="field-even"><p><span class="sphinx_autodoc_typehints-type"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>[<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><code class="xref py py-data docutils literal notranslate"><span class="pre">Any</span></code></a>, <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dtype</span></code></a>[<a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.floating" title="(in NumPy v2.2)"><code class="xref py py-class docutils literal notranslate"><span class="pre">floating</span></code></a>[<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><code class="xref py py-data docutils literal notranslate"><span class="pre">Any</span></code></a>]]]</span></p>
 </dd>
-<dt class="field-even" id="bayes_opt.TargetSpace.random_sample-returns">Returns<span class="colon">:</span><a class="headerlink" href="#bayes_opt.TargetSpace.random_sample-returns" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><strong>data</strong> (<em>ndarray</em>) – [1 x dim] array with dimensions corresponding to <cite>self._keys</cite></p>
+<dt class="field-odd" id="bayes_opt.TargetSpace.random_sample-returns">Returns<span class="colon">:</span><a class="headerlink" href="#bayes_opt.TargetSpace.random_sample-returns" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><p><strong>data</strong> (<em>ndarray</em>) – [1 x dim] array with dimensions corresponding to <cite>self._keys</cite></p>
 </dd>
 </dl>
 <p class="example rubric">Examples</p>
@@ -1974,7 +2525,7 @@ <h1 id="bayes-opt-targetspace"><a class="reference internal" href="#bayes_opt.Ta
 
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="bayes_opt.TargetSpace.register">
-<span class="sig-name descname"><span class="pre">register</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.TargetSpace.register.params" title="bayes_opt.TargetSpace.register.params (Python parameter) — a single point, with len(x) == self.dim."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Mapping" title="(in Python v3.13)"><span class="pre">Mapping</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.13)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><span class="pre">dtype</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.floating" title="(in NumPy v2.2)"><span class="pre">floating</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.TargetSpace.register.target" title="bayes_opt.TargetSpace.register.target (Python parameter) — target function value"><span class="n"><span class="pre">target</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.TargetSpace.register.constraint_value" title="bayes_opt.TargetSpace.register.constraint_value (Python parameter) — Constraint function value"><span class="n"><span class="pre">constraint_value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><span class="pre">dtype</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.floating" title="(in NumPy v2.2)"><span class="pre">floating</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span></span><a class="headerlink" href="#bayes_opt.TargetSpace.register" title="Link to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.TargetSpace.register.params" title="bayes_opt.TargetSpace.register.params (Python parameter) — a single point, with len(x) == self.dim."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Mapping" title="(in Python v3.13)"><span class="pre">Mapping</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.13)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><span class="pre">dtype</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.floating" title="(in NumPy v2.2)"><span class="pre">floating</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.TargetSpace.register.target" title="bayes_opt.TargetSpace.register.target (Python parameter) — target function value"><span class="n"><span class="pre">target</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.TargetSpace.register.constraint_value" title="bayes_opt.TargetSpace.register.constraint_value (Python parameter) — Constraint function value"><span class="n"><span class="pre">constraint_value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><span class="pre">dtype</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.floating" title="(in NumPy v2.2)"><span class="pre">floating</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span></span><a class="headerlink" href="#bayes_opt.TargetSpace.register" title="Link to this definition">¶</a></dt>
 <dd><p>Append a point and its target value to the known data.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="bayes_opt.TargetSpace.register-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#bayes_opt.TargetSpace.register-parameters" title="Permalink to this headline">¶</a></dt>
@@ -2033,7 +2584,7 @@ <h1 id="bayes-opt-targetspace"><a class="reference internal" href="#bayes_opt.Ta
 
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="bayes_opt.TargetSpace.set_bounds">
-<span class="sig-name descname"><span class="pre">set_bounds</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.TargetSpace.set_bounds.new_bounds" title="bayes_opt.TargetSpace.set_bounds.new_bounds (Python parameter) — A dictionary with the parameter name and its new bounds"><span class="n"><span class="pre">new_bounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Mapping" title="(in Python v3.13)"><span class="pre">Mapping</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.2)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype" title="(in NumPy v2.2)"><span class="pre">dtype</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.floating" title="(in NumPy v2.2)"><span class="pre">floating</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.13)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span></span><a class="headerlink" href="#bayes_opt.TargetSpace.set_bounds" title="Link to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_bounds</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#bayes_opt.TargetSpace.set_bounds.new_bounds" title="bayes_opt.TargetSpace.set_bounds.new_bounds (Python parameter) — A dictionary with the parameter name and its new bounds"><span class="n"><span class="pre">new_bounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Mapping" title="(in Python v3.13)"><span class="pre">Mapping</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.13)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.13)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#type" title="(in Python v3.13)"><span class="pre">type</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.13)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#type" title="(in Python v3.13)"><span class="pre">type</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.13)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span></span><a class="headerlink" href="#bayes_opt.TargetSpace.set_bounds" title="Link to this definition">¶</a></dt>
 <dd><p>Change the lower and upper search bounds.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="bayes_opt.TargetSpace.set_bounds-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#bayes_opt.TargetSpace.set_bounds-parameters" title="Permalink to this headline">¶</a></dt>
@@ -2105,13 +2656,13 @@ <h1 id="bayes-opt-targetspace"><a class="reference internal" href="#bayes_opt.Ta
       
       
         
-        <a href="exception.html" class="md-footer__link md-footer__link--next" aria-label="Next: bayes_opt.exception" rel="next">
+        <a href="parameter.html" class="md-footer__link md-footer__link--next" aria-label="Next: bayes_opt.parameter" rel="next">
           <div class="md-footer__title">
             <div class="md-ellipsis">
               <span class="md-footer__direction">
                 Next
               </span>
-              bayes_opt.exception
+              bayes_opt.parameter
             </div>
           </div>
           <div class="md-footer__button md-icon">
diff --git a/master/searchindex.js b/master/searchindex.js
index 91084dd7..b8ff2c33 100644
--- a/master/searchindex.js
+++ b/master/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"alltitles": {"1. Simple Constrained Optimization": [[3, "1.-Simple-Constrained-Optimization"]], "1. Specifying the function to be optimized": [[2, "1.-Specifying-the-function-to-be-optimized"]], "1. Suggest-Evaluate-Register Paradigm": [[1, "1.-Suggest-Evaluate-Register-Paradigm"]], "1.1 The maximize loop": [[1, "1.1-The-maximize-loop"]], "2. Advanced Constrained Optimization": [[3, "2.-Advanced-Constrained-Optimization"]], "2. Dealing with discrete parameters": [[1, "2.-Dealing-with-discrete-parameters"]], "2. Getting Started": [[2, "2.-Getting-Started"]], "2.1 Changing bounds": [[2, "2.1-Changing-bounds"]], "2.1 Simple, single constraint": [[3, "2.1-Simple,-single-constraint"]], "2.2 Multiple Constraints": [[3, "2.2-Multiple-Constraints"]], "3. Guiding the optimization": [[2, "3.-Guiding-the-optimization"]], "3. Tuning the underlying Gaussian Process": [[1, "3.-Tuning-the-underlying-Gaussian-Process"]], "3.1 Passing parameter to the GP": [[1, "3.1-Passing-parameter-to-the-GP"]], "3.2 Tuning the alpha parameter": [[1, "3.2-Tuning-the-alpha-parameter"]], "3.3 Changing kernels": [[1, "3.3-Changing-kernels"]], "4. Saving, loading and restarting": [[2, "4.-Saving,-loading-and-restarting"]], "4.1 Saving progress": [[2, "4.1-Saving-progress"]], "4.2 Loading progress": [[2, "4.2-Loading-progress"]], "Acquisition Function \u201cExpected Improvement\u201d": [[5, "Acquisition-Function-%22Expected-Improvement%22"]], "Acquisition Function \u201cProbability of Improvement\u201d": [[5, "Acquisition-Function-%22Probability-of-Improvement%22"]], "Acquisition Function \u201cUpper Confidence Bound\u201d": [[5, "Acquisition-Function-%22Upper-Confidence-Bound%22"]], "Acquisition functions": [[0, null]], "Advanced tour of the Bayesian Optimization package": [[1, null]], "After five steps of GP (and two random points)": [[19, "After-five-steps-of-GP-(and-two-random-points)"]], "After four steps of GP (and two random points)": [[19, "After-four-steps-of-GP-(and-two-random-points)"]], "After one step of GP (and two random points)": [[19, "After-one-step-of-GP-(and-two-random-points)"]], "After seven steps of GP (and two random points)": [[19, "After-seven-steps-of-GP-(and-two-random-points)"]], "After six steps of GP (and two random points)": [[19, "After-six-steps-of-GP-(and-two-random-points)"]], "After three steps of GP (and two random points)": [[19, "After-three-steps-of-GP-(and-two-random-points)"]], "After two steps of GP (and two random points)": [[19, "After-two-steps-of-GP-(and-two-random-points)"]], "Background": [[4, "Background"]], "Basic tour of the Bayesian Optimization package": [[2, null]], "Basics": [[4, "Basics"]], "Bayesian Optimization": [[6, null]], "Citation": [[6, "citation"]], "Conda (via conda-forge)": [[6, "conda-via-conda-forge"]], "Constrained Optimization": [[3, null]], "Create a BayesianOptimization Object": [[19, "Create-a-BayesianOptimization-Object"]], "Exploitation vs Exploration": [[5, null]], "How does it work?": [[6, "how-does-it-work"]], "Installation": [[6, "installation"]], "Let\u2019s first define a couple functions to make plotting easier": [[19, "Let's-first-define-a-couple-functions-to-make-plotting-easier"]], "Next Steps": [[2, "Next-Steps"]], "Note": [[9, null], [11, null], [12, null], [14, null], [14, null]], "Observers Continued": [[1, "Observers-Continued"]], "Other": [[17, null]], "Parameters": [[4, "Parameters"]], "Plotting and visualizing the algorithm at each step": [[19, "Plotting-and-visualizing-the-algorithm-at-each-step"]], "Prefer exploitation (kappa=0.1)": [[5, "Prefer-exploitation-(kappa=0.1)"]], "Prefer exploitation (xi=0.0)": [[5, "Prefer-exploitation-(xi=0.0)"]], "Prefer exploitation (xi=1e-4)": [[5, "Prefer-exploitation-(xi=1e-4)"]], "Prefer exploration (kappa=10)": [[5, "Prefer-exploration-(kappa=10)"]], "Prefer exploration (xi=0.1)": [[5, "Prefer-exploration-(xi=0.1)"], [5, "id1"]], "Quick Index": [[6, "quick-index"]], "Sequential Domain Reduction": [[4, null]], "Simulation 2": [[3, "Simulation-2"]], "Stopping": [[19, "Stopping"]], "Target function": [[5, "Target-function"]], "Two random points": [[19, "Two-random-points"]], "Utility function for plotting": [[5, "Utility-function-for-plotting"]], "Visualization": [[19, null]], "Warning": [[13, null]], "bayes_opt.BayesianOptimization": [[13, null]], "bayes_opt.ConstraintModel": [[14, null]], "bayes_opt.SequentialDomainReductionTransformer": [[15, null]], "bayes_opt.TargetSpace": [[18, null]], "bayes_opt.acquisition": [[7, null]], "bayes_opt.acquisition.ConstantLiar": [[8, null]], "bayes_opt.acquisition.ExpectedImprovement": [[9, null]], "bayes_opt.acquisition.GPHedge": [[10, null]], "bayes_opt.acquisition.ProbabilityOfImprovement": [[11, null]], "bayes_opt.acquisition.UpperConfidenceBound": [[12, null]], "bayes_opt.exception": [[16, null]], "pip (via PyPI)": [[6, "pip-via-pypi"]]}, "docurls": ["acquisition_functions.html", "advanced-tour.html", "basic-tour.html", "constraints.html", "domain_reduction.html", "exploitation_vs_exploration.html", "index.html", "reference/acquisition.html", "reference/acquisition/ConstantLiar.html", "reference/acquisition/ExpectedImprovement.html", "reference/acquisition/GPHedge.html", "reference/acquisition/ProbabilityOfImprovement.html", "reference/acquisition/UpperConfidenceBound.html", "reference/bayes_opt.html", "reference/constraint.html", "reference/domain_reduction.html", "reference/exception.html", "reference/other.html", "reference/target_space.html", "visualization.html"], "envversion": {"nbsphinx": 4, "sphinx": 64, "sphinx.domains.c": 3, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 9, "sphinx.domains.index": 1, "sphinx.domains.javascript": 3, "sphinx.domains.math": 2, "sphinx.domains.python": 4, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "sphinx.ext.intersphinx": 1}, "indexentries": {"acquisition_function (bayes_opt.bayesianoptimization property)": [[13, "bayes_opt.BayesianOptimization.acquisition_function", false]], "acquisitionfunction (class in bayes_opt.acquisition)": [[7, "bayes_opt.acquisition.AcquisitionFunction", false]], "allowed() (bayes_opt.constraint.constraintmodel method)": [[14, "bayes_opt.constraint.ConstraintModel.allowed", false]], "approx() (bayes_opt.constraint.constraintmodel method)": [[14, "bayes_opt.constraint.ConstraintModel.approx", false]], "array_to_params() (bayes_opt.targetspace method)": [[18, "bayes_opt.TargetSpace.array_to_params", false]], "base_acq() (bayes_opt.acquisition.acquisitionfunction method)": [[7, "bayes_opt.acquisition.AcquisitionFunction.base_acq", false]], "base_acq() (bayes_opt.acquisition.constantliar method)": [[8, "bayes_opt.acquisition.ConstantLiar.base_acq", false]], "base_acq() (bayes_opt.acquisition.expectedimprovement method)": [[9, "bayes_opt.acquisition.ExpectedImprovement.base_acq", false]], "base_acq() (bayes_opt.acquisition.gphedge method)": [[10, "bayes_opt.acquisition.GPHedge.base_acq", false]], "base_acq() (bayes_opt.acquisition.probabilityofimprovement method)": [[11, "bayes_opt.acquisition.ProbabilityOfImprovement.base_acq", false]], "base_acq() (bayes_opt.acquisition.upperconfidencebound method)": [[12, "bayes_opt.acquisition.UpperConfidenceBound.base_acq", false]], "bayes_opt.acquisition": [[7, "module-bayes_opt.acquisition", false]], "bayes_opt.exception": [[16, "module-bayes_opt.exception", false]], "bayesianoptimization (class in bayes_opt)": [[13, "bayes_opt.BayesianOptimization", false]], "bayesianoptimizationerror": [[16, "bayes_opt.exception.BayesianOptimizationError", false]], "bounds (bayes_opt.targetspace property)": [[18, "bayes_opt.TargetSpace.bounds", false]], "constantliar (class in bayes_opt.acquisition)": [[8, "bayes_opt.acquisition.ConstantLiar", false]], "constraint (bayes_opt.bayesianoptimization property)": [[13, "bayes_opt.BayesianOptimization.constraint", false]], "constraint (bayes_opt.targetspace property)": [[18, "bayes_opt.TargetSpace.constraint", false]], "constraint_values (bayes_opt.targetspace property)": [[18, "bayes_opt.TargetSpace.constraint_values", false]], "constraintmodel (class in bayes_opt.constraint)": [[14, "bayes_opt.constraint.ConstraintModel", false]], "constraintnotsupportederror": [[16, "bayes_opt.exception.ConstraintNotSupportedError", false]], "decay_exploration() (bayes_opt.acquisition.expectedimprovement method)": [[9, "bayes_opt.acquisition.ExpectedImprovement.decay_exploration", false]], "decay_exploration() (bayes_opt.acquisition.probabilityofimprovement method)": [[11, "bayes_opt.acquisition.ProbabilityOfImprovement.decay_exploration", false]], "decay_exploration() (bayes_opt.acquisition.upperconfidencebound method)": [[12, "bayes_opt.acquisition.UpperConfidenceBound.decay_exploration", false]], "dim (bayes_opt.targetspace property)": [[18, "bayes_opt.TargetSpace.dim", false]], "empty (bayes_opt.targetspace property)": [[18, "bayes_opt.TargetSpace.empty", false]], "eval() (bayes_opt.constraint.constraintmodel method)": [[14, "bayes_opt.constraint.ConstraintModel.eval", false]], "events (class in bayes_opt)": [[17, "bayes_opt.Events", false]], "expectedimprovement (class in bayes_opt.acquisition)": [[9, "bayes_opt.acquisition.ExpectedImprovement", false]], "fit() (bayes_opt.constraint.constraintmodel method)": [[14, "bayes_opt.constraint.ConstraintModel.fit", false]], "gphedge (class in bayes_opt.acquisition)": [[10, "bayes_opt.acquisition.GPHedge", false]], "initialize() (bayes_opt.sequentialdomainreductiontransformer method)": [[15, "bayes_opt.SequentialDomainReductionTransformer.initialize", false]], "is_constrained (bayes_opt.screenlogger property)": [[17, "bayes_opt.ScreenLogger.is_constrained", false]], "jsonlogger (class in bayes_opt)": [[17, "bayes_opt.JSONLogger", false]], "keys (bayes_opt.targetspace property)": [[18, "bayes_opt.TargetSpace.keys", false]], "lb (bayes_opt.constraint.constraintmodel property)": [[14, "bayes_opt.constraint.ConstraintModel.lb", false]], "mask (bayes_opt.targetspace property)": [[18, "bayes_opt.TargetSpace.mask", false]], "max (bayes_opt.bayesianoptimization property)": [[13, "bayes_opt.BayesianOptimization.max", false]], "max() (bayes_opt.targetspace method)": [[18, "bayes_opt.TargetSpace.max", false]], "maximize() (bayes_opt.bayesianoptimization method)": [[13, "bayes_opt.BayesianOptimization.maximize", false]], "model (bayes_opt.constraint.constraintmodel property)": [[14, "bayes_opt.constraint.ConstraintModel.model", false]], "module": [[7, "module-bayes_opt.acquisition", false], [16, "module-bayes_opt.exception", false]], "notuniqueerror": [[16, "bayes_opt.exception.NotUniqueError", false]], "novalidpointregisterederror": [[16, "bayes_opt.exception.NoValidPointRegisteredError", false]], "params (bayes_opt.targetspace property)": [[18, "bayes_opt.TargetSpace.params", false]], "params_to_array() (bayes_opt.targetspace method)": [[18, "bayes_opt.TargetSpace.params_to_array", false]], "predict() (bayes_opt.constraint.constraintmodel method)": [[14, "bayes_opt.constraint.ConstraintModel.predict", false]], "probabilityofimprovement (class in bayes_opt.acquisition)": [[11, "bayes_opt.acquisition.ProbabilityOfImprovement", false]], "probe() (bayes_opt.bayesianoptimization method)": [[13, "bayes_opt.BayesianOptimization.probe", false]], "probe() (bayes_opt.targetspace method)": [[18, "bayes_opt.TargetSpace.probe", false]], "random_sample() (bayes_opt.targetspace method)": [[18, "bayes_opt.TargetSpace.random_sample", false]], "register() (bayes_opt.bayesianoptimization method)": [[13, "bayes_opt.BayesianOptimization.register", false]], "register() (bayes_opt.targetspace method)": [[18, "bayes_opt.TargetSpace.register", false]], "res (bayes_opt.bayesianoptimization property)": [[13, "bayes_opt.BayesianOptimization.res", false]], "res() (bayes_opt.targetspace method)": [[18, "bayes_opt.TargetSpace.res", false]], "screenlogger (class in bayes_opt)": [[17, "bayes_opt.ScreenLogger", false]], "sequentialdomainreductiontransformer (class in bayes_opt)": [[15, "bayes_opt.SequentialDomainReductionTransformer", false]], "set_bounds() (bayes_opt.bayesianoptimization method)": [[13, "bayes_opt.BayesianOptimization.set_bounds", false]], "set_bounds() (bayes_opt.targetspace method)": [[18, "bayes_opt.TargetSpace.set_bounds", false]], "set_gp_params() (bayes_opt.bayesianoptimization method)": [[13, "bayes_opt.BayesianOptimization.set_gp_params", false]], "space (bayes_opt.bayesianoptimization property)": [[13, "bayes_opt.BayesianOptimization.space", false]], "suggest() (bayes_opt.acquisition.acquisitionfunction method)": [[7, "bayes_opt.acquisition.AcquisitionFunction.suggest", false]], "suggest() (bayes_opt.acquisition.constantliar method)": [[8, "bayes_opt.acquisition.ConstantLiar.suggest", false]], "suggest() (bayes_opt.acquisition.expectedimprovement method)": [[9, "bayes_opt.acquisition.ExpectedImprovement.suggest", false]], "suggest() (bayes_opt.acquisition.gphedge method)": [[10, "bayes_opt.acquisition.GPHedge.suggest", false]], "suggest() (bayes_opt.acquisition.probabilityofimprovement method)": [[11, "bayes_opt.acquisition.ProbabilityOfImprovement.suggest", false]], "suggest() (bayes_opt.acquisition.upperconfidencebound method)": [[12, "bayes_opt.acquisition.UpperConfidenceBound.suggest", false]], "suggest() (bayes_opt.bayesianoptimization method)": [[13, "bayes_opt.BayesianOptimization.suggest", false]], "target (bayes_opt.targetspace property)": [[18, "bayes_opt.TargetSpace.target", false]], "targetspace (class in bayes_opt)": [[18, "bayes_opt.TargetSpace", false]], "targetspaceemptyerror": [[16, "bayes_opt.exception.TargetSpaceEmptyError", false]], "transform() (bayes_opt.sequentialdomainreductiontransformer method)": [[15, "bayes_opt.SequentialDomainReductionTransformer.transform", false]], "ub (bayes_opt.constraint.constraintmodel property)": [[14, "bayes_opt.constraint.ConstraintModel.ub", false]], "update() (bayes_opt.jsonlogger method)": [[17, "bayes_opt.JSONLogger.update", false]], "update() (bayes_opt.screenlogger method)": [[17, "bayes_opt.ScreenLogger.update", false]], "upperconfidencebound (class in bayes_opt.acquisition)": [[12, "bayes_opt.acquisition.UpperConfidenceBound", false]], "verbose (bayes_opt.screenlogger property)": [[17, "bayes_opt.ScreenLogger.verbose", false]]}, "objects": {"bayes_opt": [[13, 0, 1, 0, "BayesianOptimization", "Handle optimization of a target function over a specific target space."], [17, 0, 1, 0, "Events", "Define optimization events."], [17, 0, 1, 0, "JSONLogger", "Logger that outputs steps in JSON format."], [17, 0, 1, 0, "ScreenLogger", "Logger that outputs text, e.g. to log to a terminal."], [15, 0, 1, 0, "SequentialDomainReductionTransformer", "Reduce the searchable space."], [18, 0, 1, 0, "TargetSpace", "Holds the param-space coordinates (X) and target values (Y)."], [7, 4, 0, 1, "acquisition", ""], [16, 4, 0, 1, "exception", ""]], "bayes_opt.BayesianOptimization": [[13, 2, 1, 0, "acquisition_function", "Return the acquisition function associated with the optimizer."], [13, 2, 1, 0, "constraint", "Return the constraint associated with the optimizer, if any."], [13, 2, 1, 0, "max", "Get maximum target value found and corresponding parameters."], [13, 3, 1, 0, "maximize", "Maximize the given function over the target space."], [13, 3, 1, 0, "probe", "Evaluate the function at the given points."], [13, 3, 1, 0, "register", "Register an observation with known target."], [13, 2, 1, 0, "res", "Get all target values and constraint fulfillment for all parameters."], [13, 3, 1, 0, "set_bounds", "Modify the bounds of the search space."], [13, 3, 1, 0, "set_gp_params", "Set parameters of the internal Gaussian Process Regressor."], [13, 2, 1, 0, "space", "Return the target space associated with the optimizer."], [13, 3, 1, 0, "suggest", "Suggest a promising point to probe next."]], "bayes_opt.BayesianOptimization.__init__": [[13, 1, 2, 0, "acquisition_function", ""], [13, 1, 2, 0, "allow_duplicate_points", "If True, the optimizer will allow duplicate points to be registered. This behavior may be desired in high noise situations where repeatedly probing the same point will give different answers."], [13, 1, 2, 0, "bounds_transformer", "If provided, the transformation is applied to the bounds."], [13, 1, 2, 0, "constraint", "Note that the names of arguments of the constraint function and of f need to be the same."], [13, 1, 2, 0, "f", "Function to be maximized."], [13, 1, 2, 0, "pbounds", "Dictionary with parameters names as keys and a tuple with minimum and maximum values."], [13, 1, 2, 0, "random_state", "If the value is an integer, it is used as the seed for creating a numpy.random.RandomState."], [13, 1, 2, 0, "verbose", "The level of verbosity."]], "bayes_opt.BayesianOptimization.maximize": [[13, 1, 2, 0, "init_points", "Number of random points to probe before starting the optimization."], [13, 1, 2, 0, "n_iter", "Number of iterations where the method attempts to find the maximum value."]], "bayes_opt.BayesianOptimization.probe": [[13, 1, 2, 0, "lazy", "If True, the optimizer will evaluate the points when calling maximize()."], [13, 1, 2, 0, "params", "The parameters where the optimizer will evaluate the function."]], "bayes_opt.BayesianOptimization.register": [[13, 1, 2, 0, "constraint_value", "Value of the constraint function at the observation, if any."], [13, 1, 2, 0, "params", "The parameters associated with the observation."], [13, 1, 2, 0, "target", "Value of the target function at the observation."]], "bayes_opt.BayesianOptimization.set_bounds": [[13, 1, 2, 0, "new_bounds", "A dictionary with the parameter name and its new bounds"]], "bayes_opt.BayesianOptimization.set_gp_params": [[13, 1, 2, 0, "params", ""]], "bayes_opt.JSONLogger": [[17, 3, 1, 0, "update", "Handle incoming events."]], "bayes_opt.JSONLogger.__init__": [[17, 1, 2, 0, "path", "Path to the file to write to."], [17, 1, 2, 0, "reset", "Whether to overwrite the file if it already exists."]], "bayes_opt.JSONLogger.update": [[17, 1, 2, 0, "event", "One of the values associated with Events.OPTIMIZATION_START, Events.OPTIMIZATION_STEP or Events.OPTIMIZATION_END."], [17, 1, 2, 0, "instance", "The instance associated with the step."]], "bayes_opt.ScreenLogger": [[17, 2, 1, 0, "is_constrained", "Return whether the logger is constrained."], [17, 3, 1, 0, "update", "Handle incoming events."], [17, 2, 1, 0, "verbose", "Return the verbosity level."]], "bayes_opt.ScreenLogger.__init__": [[17, 1, 2, 0, "is_constrained", "Whether the logger is associated with a constrained optimization instance."], [17, 1, 2, 0, "verbose", "Verbosity level of the logger."]], "bayes_opt.ScreenLogger.update": [[17, 1, 2, 0, "event", "One of the values associated with Events.OPTIMIZATION_START, Events.OPTIMIZATION_STEP or Events.OPTIMIZATION_END."], [17, 1, 2, 0, "instance", "The instance associated with the step."]], "bayes_opt.SequentialDomainReductionTransformer": [[15, 3, 1, 0, "initialize", "Initialize all of the parameters."], [15, 3, 1, 0, "transform", "Transform the bounds of the target space."]], "bayes_opt.SequentialDomainReductionTransformer.__init__": [[15, 1, 2, 0, "eta", "Zooming parameter used to shrink the region of interest."], [15, 1, 2, 0, "gamma_osc", "Parameter used to scale (typically dampen) oscillations."], [15, 1, 2, 0, "gamma_pan", "Parameter used to scale (typically unitary) panning."], [15, 1, 2, 0, "minimum_window", "Minimum window size for each parameter."]], "bayes_opt.SequentialDomainReductionTransformer.initialize": [[15, 1, 2, 0, "target_space", "TargetSpace this DomainTransformer operates on."]], "bayes_opt.SequentialDomainReductionTransformer.transform": [[15, 1, 2, 0, "target_space", "TargetSpace this DomainTransformer operates on."]], "bayes_opt.TargetSpace": [[18, 3, 1, 0, "array_to_params", "Convert an array representation of parameters into a dict version."], [18, 2, 1, 0, "bounds", "Get the bounds of this TargetSpace."], [18, 2, 1, 0, "constraint", "Get the constraint model."], [18, 2, 1, 0, "constraint_values", "Get the constraint values registered to this TargetSpace."], [18, 2, 1, 0, "dim", "Get the number of parameter names."], [18, 2, 1, 0, "empty", "Check if anything has been registered."], [18, 2, 1, 0, "keys", "Get the keys (or parameter names)."], [18, 2, 1, 0, "mask", "Return a boolean array of valid points."], [18, 3, 1, 0, "max", "Get maximum target value found and corresponding parameters."], [18, 2, 1, 0, "params", "Get the parameter values registered to this TargetSpace."], [18, 3, 1, 0, "params_to_array", "Convert a dict representation of parameters into an array version."], [18, 3, 1, 0, "probe", "Evaluate the target function on a point and register the result."], [18, 3, 1, 0, "random_sample", "Sample a random point from within the bounds of the space."], [18, 3, 1, 0, "register", "Append a point and its target value to the known data."], [18, 3, 1, 0, "res", "Get all target values and constraint fulfillment for all parameters."], [18, 3, 1, 0, "set_bounds", "Change the lower and upper search bounds."], [18, 2, 1, 0, "target", "Get the target function values registered to this TargetSpace."]], "bayes_opt.TargetSpace.__init__": [[18, 1, 2, 0, "allow_duplicate_points", "If True, the optimizer will allow duplicate points to be registered. This behavior may be desired in high noise situations where repeatedly probing the same point will give different answers."], [18, 1, 2, 0, "constraint", ""], [18, 1, 2, 0, "pbounds", "Dictionary with parameters names as keys and a tuple with minimum and maximum values."], [18, 1, 2, 0, "random_state", "optionally specify a seed for a random number generator"], [18, 1, 2, 0, "target_func", "Function to be maximized."]], "bayes_opt.TargetSpace.array_to_params": [[18, 1, 2, 0, "x", "a single point, with len(x) == self.dim."]], "bayes_opt.TargetSpace.params_to_array": [[18, 1, 2, 0, "params", "a single point, with len(x) == self.dim."]], "bayes_opt.TargetSpace.probe": [[18, 1, 2, 0, "params", "a single point, with len(x) == self.dim"]], "bayes_opt.TargetSpace.register": [[18, 1, 2, 0, "constraint_value", "Constraint function value"], [18, 1, 2, 0, "params", "a single point, with len(x) == self.dim."], [18, 1, 2, 0, "target", "target function value"]], "bayes_opt.TargetSpace.set_bounds": [[18, 1, 2, 0, "new_bounds", "A dictionary with the parameter name and its new bounds"]], "bayes_opt.acquisition": [[7, 0, 1, 0, "AcquisitionFunction", "Base class for acquisition functions."], [8, 0, 1, 0, "ConstantLiar", "Constant Liar acquisition function."], [9, 0, 1, 0, "ExpectedImprovement", "Expected Improvement acqusition function."], [10, 0, 1, 0, "GPHedge", "GPHedge acquisition function."], [11, 0, 1, 0, "ProbabilityOfImprovement", "Probability of Improvement acqusition function."], [12, 0, 1, 0, "UpperConfidenceBound", "Upper Confidence Bound acquisition function."]], "bayes_opt.acquisition.AcquisitionFunction": [[7, 3, 1, 0, "base_acq", "Provide access to the base acquisition function."], [7, 3, 1, 0, "suggest", "Suggest a promising point to probe next."]], "bayes_opt.acquisition.AcquisitionFunction.__init__": [[7, 1, 2, 0, "random_state", "Set the random state for reproducibility."]], "bayes_opt.acquisition.AcquisitionFunction.base_acq": [[7, 1, 2, 0, "args", ""], [7, 1, 2, 0, "kwargs", ""]], "bayes_opt.acquisition.AcquisitionFunction.suggest": [[7, 1, 2, 0, "fit_gp", "Whether to fit the Gaussian Process to the target space. Set to False if the GP is already fitted."], [7, 1, 2, 0, "gp", "A fitted Gaussian Process."], [7, 1, 2, 0, "n_l_bfgs_b", "Number of starting points for the L-BFGS-B optimizer."], [7, 1, 2, 0, "n_random", "Number of random samples to use."], [7, 1, 2, 0, "target_space", "The target space to probe."]], "bayes_opt.acquisition.ConstantLiar": [[8, 3, 1, 0, "base_acq", "Calculate the acquisition function."], [8, 3, 1, 0, "suggest", "Suggest a promising point to probe next."]], "bayes_opt.acquisition.ConstantLiar.__init__": [[8, 1, 2, 0, "atol", "Absolute tolerance to eliminate a dummy point."], [8, 1, 2, 0, "base_acquisition", "The acquisition function to use."], [8, 1, 2, 0, "random_state", "Set the random state for reproducibility."], [8, 1, 2, 0, "rtol", "Relative tolerance to eliminate a dummy point."], [8, 1, 2, 0, "strategy", "Strategy to use for the constant liar."]], "bayes_opt.acquisition.ConstantLiar.base_acq": [[8, 1, 2, 0, "args", ""], [8, 1, 2, 0, "kwargs", ""]], "bayes_opt.acquisition.ConstantLiar.suggest": [[8, 1, 2, 0, "fit_gp", "Whether to fit the Gaussian Process to the target space. Set to False if the GP is already fitted."], [8, 1, 2, 0, "gp", "A fitted Gaussian Process."], [8, 1, 2, 0, "n_l_bfgs_b", "Number of starting points for the L-BFGS-B optimizer."], [8, 1, 2, 0, "n_random", "Number of random samples to use."], [8, 1, 2, 0, "target_space", "The target space to probe."]], "bayes_opt.acquisition.ExpectedImprovement": [[9, 3, 1, 0, "base_acq", "Calculate the expected improvement."], [9, 3, 1, 0, "decay_exploration", "Decay xi by a constant rate."], [9, 3, 1, 0, "suggest", "Suggest a promising point to probe next."]], "bayes_opt.acquisition.ExpectedImprovement.__init__": [[9, 1, 2, 0, "exploration_decay", "Decay rate for xi."], [9, 1, 2, 0, "exploration_decay_delay", ""], [9, 1, 2, 0, "random_state", "Set the random state for reproducibility."], [9, 1, 2, 0, "xi", "Governs the exploration/exploitation tradeoff."]], "bayes_opt.acquisition.ExpectedImprovement.base_acq": [[9, 1, 2, 0, "mean", "Mean of the predictive distribution."], [9, 1, 2, 0, "std", "Standard deviation of the predictive distribution."]], "bayes_opt.acquisition.ExpectedImprovement.suggest": [[9, 1, 2, 0, "fit_gp", "Whether to fit the Gaussian Process to the target space. Set to False if the GP is already fitted."], [9, 1, 2, 0, "gp", "A fitted Gaussian Process."], [9, 1, 2, 0, "n_l_bfgs_b", "Number of starting points for the L-BFGS-B optimizer."], [9, 1, 2, 0, "n_random", "Number of random samples to use."], [9, 1, 2, 0, "target_space", "The target space to probe."]], "bayes_opt.acquisition.GPHedge": [[10, 3, 1, 0, "base_acq", "Raise an error, since the base acquisition function is ambiguous."], [10, 3, 1, 0, "suggest", "Suggest a promising point to probe next."]], "bayes_opt.acquisition.GPHedge.__init__": [[10, 1, 2, 0, "base_acquisitions", "Sequence of base acquisition functions."], [10, 1, 2, 0, "random_state", "Set the random state for reproducibility."]], "bayes_opt.acquisition.GPHedge.base_acq": [[10, 1, 2, 0, "args", ""], [10, 1, 2, 0, "kwargs", ""]], "bayes_opt.acquisition.GPHedge.suggest": [[10, 1, 2, 0, "fit_gp", "Whether to fit the Gaussian Process to the target space. Set to False if the GP is already fitted."], [10, 1, 2, 0, "gp", "A fitted Gaussian Process."], [10, 1, 2, 0, "n_l_bfgs_b", "Number of starting points for the L-BFGS-B optimizer."], [10, 1, 2, 0, "n_random", "Number of random samples to use."], [10, 1, 2, 0, "target_space", "The target space to probe."]], "bayes_opt.acquisition.ProbabilityOfImprovement": [[11, 3, 1, 0, "base_acq", "Calculate the probability of improvement."], [11, 3, 1, 0, "decay_exploration", "Decay xi by a constant rate."], [11, 3, 1, 0, "suggest", "Suggest a promising point to probe next."]], "bayes_opt.acquisition.ProbabilityOfImprovement.__init__": [[11, 1, 2, 0, "exploration_decay", "Decay rate for xi."], [11, 1, 2, 0, "exploration_decay_delay", "Delay for decay."], [11, 1, 2, 0, "random_state", "Set the random state for reproducibility."], [11, 1, 2, 0, "xi", "Governs the exploration/exploitation tradeoff."]], "bayes_opt.acquisition.ProbabilityOfImprovement.base_acq": [[11, 1, 2, 0, "mean", "Mean of the predictive distribution."], [11, 1, 2, 0, "std", "Standard deviation of the predictive distribution."]], "bayes_opt.acquisition.ProbabilityOfImprovement.suggest": [[11, 1, 2, 0, "fit_gp", "Whether to fit the Gaussian Process to the target space. Set to False if the GP is already fitted."], [11, 1, 2, 0, "gp", "A fitted Gaussian Process."], [11, 1, 2, 0, "n_l_bfgs_b", "Number of starting points for the L-BFGS-B optimizer."], [11, 1, 2, 0, "n_random", "Number of random samples to use."], [11, 1, 2, 0, "target_space", "The target space to probe."]], "bayes_opt.acquisition.UpperConfidenceBound": [[12, 3, 1, 0, "base_acq", "Calculate the upper confidence bound."], [12, 3, 1, 0, "decay_exploration", "Decay kappa by a constant rate."], [12, 3, 1, 0, "suggest", "Suggest a promising point to probe next."]], "bayes_opt.acquisition.UpperConfidenceBound.__init__": [[12, 1, 2, 0, "exploration_decay", "Decay rate for kappa."], [12, 1, 2, 0, "exploration_decay_delay", "Delay for decay."], [12, 1, 2, 0, "kappa", "Governs the exploration/exploitation tradeoff."], [12, 1, 2, 0, "random_state", "Set the random state for reproducibility."]], "bayes_opt.acquisition.UpperConfidenceBound.base_acq": [[12, 1, 2, 0, "mean", "Mean of the predictive distribution."], [12, 1, 2, 0, "std", "Standard deviation of the predictive distribution."]], "bayes_opt.acquisition.UpperConfidenceBound.suggest": [[12, 1, 2, 0, "fit_gp", "Whether to fit the Gaussian Process to the target space. Set to False if the GP is already fitted."], [12, 1, 2, 0, "gp", "A fitted Gaussian Process."], [12, 1, 2, 0, "n_l_bfgs_b", "Number of starting points for the L-BFGS-B optimizer."], [12, 1, 2, 0, "n_random", "Number of random samples to use."], [12, 1, 2, 0, "target_space", "The target space to probe."]], "bayes_opt.constraint": [[14, 0, 1, 0, "ConstraintModel", "Model constraints using GP regressors."]], "bayes_opt.constraint.ConstraintModel": [[14, 3, 1, 0, "allowed", "Check whether constraint_values fulfills the specified limits."], [14, 3, 1, 0, "approx", "Approximate the constraint function using the internal GPR model."], [14, 3, 1, 0, "eval", "Evaluate the constraint function."], [14, 3, 1, 0, "fit", "Fit internal GPRs to the data."], [14, 2, 1, 0, "lb", "Return lower bounds."], [14, 2, 1, 0, "model", "Return GP regressors of the constraint function."], [14, 3, 1, 0, "predict", "Calculate the probability that the constraint is fulfilled at X."], [14, 2, 1, 0, "ub", "Return upper bounds."]], "bayes_opt.constraint.ConstraintModel.__init__": [[14, 1, 2, 0, "fun", "The constraint function."], [14, 1, 2, 0, "lb", "The lower bound on the constraints."], [14, 1, 2, 0, "random_state", "Random state to use."], [14, 1, 2, 0, "ub", "The upper bound on the constraints."]], "bayes_opt.constraint.ConstraintModel.allowed": [[14, 1, 2, 0, "constraint_values", "The values of the constraint function."]], "bayes_opt.constraint.ConstraintModel.approx": [[14, 1, 2, 0, "X", "Parameters for which to estimate the constraint function value."]], "bayes_opt.constraint.ConstraintModel.eval": [[14, 1, 2, "bayes_opt.constraint.ConstraintModel.eval", "kwargs", "Evaluate the constraint function."]], "bayes_opt.constraint.ConstraintModel.fit": [[14, 1, 2, 0, "X", "Parameters of the constraint function."], [14, 1, 2, 0, "Y", "Values of the constraint function."]], "bayes_opt.constraint.ConstraintModel.predict": [[14, 1, 2, 0, "X", "Parameters for which to predict the probability of constraint fulfilment."]], "bayes_opt.exception": [[16, 5, 1, 0, "BayesianOptimizationError", "Base class for exceptions in the Bayesian Optimization."], [16, 5, 1, 0, "ConstraintNotSupportedError", "Raised when constrained optimization is not supported."], [16, 5, 1, 0, "NoValidPointRegisteredError", "Raised when an acquisition function depends on previous points but none are registered."], [16, 5, 1, 0, "NotUniqueError", "A point is non-unique."], [16, 5, 1, 0, "TargetSpaceEmptyError", "Raised when the target space is empty."]]}, "objnames": {"0": ["py", "class", "Python class"], "1": ["py", "parameter", "Python parameter"], "2": ["py", "property", "Python property"], "3": ["py", "method", "Python method"], "4": ["py", "module", "Python module"], "5": ["py", "exception", "Python exception"]}, "objtypes": {"0": "py:class", "1": "py:parameter", "2": "py:property", "3": "py:method", "4": "py:module", "5": "py:exception"}, "terms": {"": [0, 1, 3, 4, 5, 6, 8], "0": [0, 1, 2, 3, 4, 15, 18, 19], "000000000116973": 3, "0002287496346898": 2, "0008684793556073": 3, "005532": 2, "0084": 2, "0087879313090253": 3, "01": [0, 19], "015": 2, "0154397119682423": 2, "016": [0, 2], "018": 1, "01894": 1, "019": 1, "02003": 1, "02025": 1, "021": 19, "021127762033562": 3, "022": 1, "02272": 2, "024": 0, "025": 0, "026": 0, "02617": 1, "02644400210430190": 6, "027": 0, "02816": 2, "0288343947238228": 2, "029": [1, 2], "033": 19, "034": 2, "0344": 1, "03683": 1, "039": 0, "043": [1, 2], "04359": 1, "045": 2, "04749": 2, "05": [1, 8], "057": 1, "05801": 1, "06": 6, "06199": 1, "065": 1, "069": 2, "06916": 2, "07374": 2, "076": 0, "07699": 1, "07993": 2, "08": [1, 8], "08078": 1, "0819533157901717": 1, "084": 1, "085": 1, "089": 1, "0929": 1, "093": [1, 2], "096": 1, "0x23439267280": 4, "0x23439267fd0": 4, "1": [0, 4, 15, 18, 19], "10": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 19], "100": [0, 3, 18], "1000": [0, 3], "10000": [0, 5, 7, 8, 9, 10, 11, 12, 19], "1009": 10, "1052": 1, "1071": 1, "109": 19, "10_000": [7, 8, 9, 10, 11, 12], "11": [1, 2, 3, 4, 19], "1108": 6, "1166": 2, "12": [1, 2, 3, 19], "13": [0, 1, 2, 3, 19], "132": 0, "133": 1, "135": 2, "135455292718879": 2, "1372": 1, "1396": 2, "13961016009280103": 2, "14": [1, 2, 3, 19], "15": [0, 1, 2, 3, 19], "1501": 2, "153": 1, "1538": 1, "154": 19, "158": 19, "16": [0, 1, 2, 3, 5, 19], "168": 2, "17": [1, 2, 3], "173": 0, "175": 2, "18": [1, 2], "184": 1, "186": 2, "1860045642089614": 2, "1865": 1, "188": 1, "19": [1, 2, 6], "193": 2, "1_000": 0, "1e": [0, 1, 8], "2": [0, 4, 5, 12, 13, 14, 17, 18, 19], "20": [0, 1, 2, 4, 19], "2002": [4, 6], "2014": [3, 6], "2017": 19, "2094": 19, "21": [1, 2], "2122": 19, "2165": 1, "2177": 1, "2187": 1, "22": 1, "22703572807626315": 1, "229": 2, "23": 1, "230305457787476": 3, "235835023240657": 2, "2361": 0, "237": 1, "24": [1, 2], "2488": 1, "25": [1, 13], "26": 1, "2606": 1, "266": 2, "267": 0, "269540259274744": 1, "27": [1, 19], "271": 2, "272": 1, "278": 1, "28": 1, "287": 1, "29": 1, "292": 1, "2948": 1, "296": 1, "2975037081163068": 3, "3": [0, 3, 4, 5, 19], "30": [0, 1, 19], "301617813728339": 2, "302": 2, "307": 1, "3202": 1, "3219469606529486": [1, 2], "322": [1, 2], "3319": 1, "331911981189704": 1, "33253689": 18, "3338": 2, "337": 1, "339": 1, "35": 2, "353": 1, "374": 19, "378": 2, "39": [1, 2, 3], "397": 1, "398": 1, "4": [0, 1, 3, 4, 18, 19], "406": 1, "4178": 2, "42": [0, 5], "4249238905875123": 1, "442": 1, "444": 1, "446": 2, "45": 0, "452": 1, "458": 1, "463": 1, "467": 1, "472": 1, "4758": 1, "477": 1, "48": 2, "4802": 1, "5": [0, 1, 2, 3, 4, 5, 8, 13, 18, 19], "50": [4, 5], "50219704520679": 1, "503835804889988": 1, "5044": 2, "511": 0, "512": 1, "514": 1, "518": 1, "523": 0, "525": 0, "534": 1, "535": 1, "5419": 10, "5426": 0, "54488318": 18, "55": 18, "556": 1, "558": 1, "562": 1, "565": 2, "5721": 0, "576": [12, 19], "579": 1, "582": [2, 19], "5822074982517598": 2, "59": 1, "599": 1, "6": [0, 1, 2, 3, 4, 5, 18, 19], "6032": 1, "615": 1, "616": 1, "6164": 2, "617": 0, "64727683929071": 3, "649": 1, "651": 2, "6535": 2, "658": 1, "66": 2, "6605": 2, "664242699361514": 1, "683": 1, "684": 1, "6926": 1, "694": 1, "7": [0, 1, 2, 3, 4, 5, 15, 19], "704": 1, "705": 19, "7146": 2, "7342": 0, "737": 1, "746": 19, "747604713731052": 1, "755": 1, "758": 2, "764": 1, "775": 19, "778": 1, "779531005607566": 2, "78": 2, "7821": 2, "783": 1, "7861845912690544": 1, "7862": 1, "7949": 1, "7985": 0, "8": [0, 1, 2, 3, 4, 5, 8, 19], "80": 3, "801": 0, "813": 2, "8131": 1, "819": 19, "8198": 19, "82": 0, "83": [1, 2], "834": 2, "8340440094051482": 2, "8427": 2, "8458666395359906": 2, "846": [1, 2], "851": 1, "854": [1, 2], "857": 0, "8586": 1, "861": 1, "863": 1, "872282989383577": 1, "875": 0, "8809": 0, "8877": 0, "8917": 2, "9": [0, 1, 2, 3, 4, 5, 15, 18, 19], "917": 1, "919": 1, "9227": 0, "925": 1, "9266": 2, "928": 1, "931": 1, "9313": 1, "932": 0, "934": 0, "935": 1, "937": 6, "941": 1, "9421": 2, "943": 1, "9434": 2, "945": 6, "9471": 2, "9475": 1, "95": [0, 19], "953": 0, "953072105336": 1, "954": 0, "955": 0, "957": 1, "96": 1, "9600": [0, 19], "9609778030491904": 1, "961": 0, "962": 0, "964": 0, "966": 1, "966506881014352": 2, "967": 2, "9682431497650823": 1, "969": 1, "971": 1, "973": 1, "9734131381980626": 3, "9737252084307952": 1, "974": 2, "975": 0, "9757": 2, "976": 2, "9778": 2, "9819": 2, "987": 1, "987234": 5, "988": 2, "99": 0, "9912": 2, "994467585936897": 1, "995": 2, "9958": 0, "997": 2, "9973": 0, "998": [1, 19], "9988": 2, "9991881984624875": 1, "99963711795217": 2, "9997": 2, "9999": 1, "9999891845990944": 3, "A": [3, 7, 8, 9, 10, 11, 12, 13, 15, 16, 18, 19], "And": 1, "As": [0, 2, 6], "At": [2, 6, 10], "By": [1, 2], "For": [0, 1, 2, 3, 6, 10], "If": [0, 1, 2, 6, 8, 9, 11, 12, 13, 14, 15, 18], "In": [0, 1, 3, 13, 14, 18, 19], "It": [2, 4, 8, 19], "NOT": 1, "On": [4, 6, 15], "One": [0, 1, 7, 17], "Or": 2, "That": 14, "The": [0, 2, 3, 4, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19], "Then": 10, "There": [1, 2, 3, 4], "These": 4, "To": [2, 3, 6], "Will": 1, "_": 1, "__init__": 0, "_fit_gp": [0, 19], "_get_acq": [0, 19], "_gp": [0, 3, 5, 13, 19], "_kei": 18, "_space": [0, 19], "a_": 3, "ab": 10, "abc": [13, 14, 15, 18], "abl": [2, 3, 19], "about": [2, 6], "abov": [1, 3], "absolut": 8, "abstract": 7, "accept": 0, "access": [0, 2, 3, 7], "accordingli": 3, "account": [2, 6], "acklei": 4, "acq": [0, 1, 3, 19], "acq_i": 10, "acquisit": [1, 2, 3, 6, 13, 16, 18, 19], "acquisition_funct": [0, 1, 5, 13, 19], "acquisition_function_": 0, "acquisitionfunct": [0, 1, 7, 8, 10, 13], "acqusit": [9, 11], "across": 5, "activ": 6, "actual": [3, 4], "ad": 0, "addit": [3, 6], "addition": [0, 3, 6, 19], "adequ": [1, 2, 6], "adjust": [0, 9, 11, 12], "admittedli": 3, "advanc": [2, 6], "advantag": 4, "after": [0, 4, 10, 13], "again": 8, "aid": 6, "al": [3, 10], "albeit": [2, 6], "alert": 2, "algorithm": [1, 2, 6, 8], "all": [0, 1, 2, 7, 13, 15, 18], "alloc": 10, "allow": [1, 2, 3, 13, 14, 18, 19], "allow_duplicate_point": [3, 13, 18], "alon": 7, "along": 3, "alpha": [0, 5, 19], "alphabet": 2, "alreadi": [3, 7, 8, 9, 10, 11, 12, 17], "also": [0, 2, 9], "alter": 2, "altern": 1, "alwai": [0, 7, 8], "ambigu": 10, "amort": 18, "an": [0, 1, 2, 3, 4, 6, 10, 13, 16, 17, 18], "ani": [1, 2, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18], "anoth": [1, 3], "answer": [13, 18], "anyth": [6, 18], "append": 18, "appli": [4, 9, 11, 12, 13], "applicap": 0, "approach": 3, "approx": [3, 14], "approxim": [3, 14], "ar": [0, 1, 2, 3, 4, 5, 6, 10, 14, 16, 18], "area": 3, "arg": [7, 8, 10], "arg1": 4, "arg2": 4, "argmax": [0, 19], "argument": [0, 3, 13, 14], "around": [1, 4, 5], "arrai": [0, 3, 14, 18, 19], "array_to_param": 18, "articl": 6, "arxiv": 10, "assert": [0, 1, 18], "assess": 3, "associ": [1, 2, 13, 17], "assum": [3, 14], "async_optim": 1, "asynchron": 8, "atleast_2d": 0, "atol": 8, "attempt": [2, 6, 13], "author": [4, 6], "automat": [3, 9, 11, 12], "avail": 2, "avoid": 3, "awai": 0, "awar": 2, "ax": [0, 3], "ax2": 0, "axi": [0, 4, 19], "b": [4, 7, 8, 9, 10, 11, 12], "back": [1, 2], "bad": 3, "balanc": [2, 6, 19], "base": [0, 3, 4, 6, 7, 8, 10, 13, 15, 16], "base_acq": [0, 7, 8, 9, 10, 11, 12], "base_acquisit": [8, 10], "basic": [6, 7], "basicobserv": 1, "bayes_opt": [0, 1, 2, 3, 4, 5, 17, 19], "bayesian": [3, 7, 10, 13, 14, 16, 19], "bayesian_optim": 17, "bayesianoptim": [0, 1, 2, 3, 4, 5, 6, 17], "bayesianoptimizationerror": 16, "bbox_to_anchor": [0, 19], "becaus": 3, "becom": [0, 2, 6], "been": [3, 8, 18], "befor": [2, 3, 13], "begin": 0, "behav": 17, "behavior": [3, 7, 13, 18], "being": [1, 19], "below": [1, 2, 3, 6], "benefici": 1, "best": [0, 1, 2, 3, 6, 19], "better": 1, "between": [2, 3, 6, 19], "bewar": 2, "bfg": [7, 8, 9, 10, 11, 12], "bit": 3, "black": 3, "black_box_funct": [1, 2], "black_box_function_no_constraint": 3, "black_box_function_with_constraint": 3, "blackbox": 1, "block": 6, "bo": [2, 5], "bold": 19, "booktitl": 6, "bool": [0, 7, 8, 9, 10, 11, 12, 13, 14, 17, 18], "boolean": 18, "borderaxespad": [0, 19], "both": [3, 4, 18], "bottom": 3, "bound": [1, 3, 4, 6, 12, 13, 14, 15, 18, 19], "bound_transform": 4, "boundari": 18, "bounds_transform": [4, 13], "bounds_transformers_iter": 4, "box": [0, 2], "brochu": 10, "brows": 2, "bug": 6, "build": 6, "built": [0, 2, 4, 6], "c": [0, 3, 5, 6, 14, 19], "c_": 3, "c_1": 3, "c_2": 3, "c_est": 3, "c_label": 3, "c_n": 3, "cach": 18, "calcul": [4, 8, 9, 11, 12, 14], "call": [0, 1, 2, 3, 8, 9, 11, 12, 13], "callabl": [13, 14, 18], "callback": 1, "can": [0, 1, 2, 3, 4, 6, 7, 17, 19], "candid": 10, "carri": 1, "case": [0, 1, 2, 3, 14, 19], "catch_warn": 0, "categor": 6, "caveat": 2, "cdf": [9, 11], "central": 6, "certain": [2, 3, 6], "chang": [0, 6, 18], "charg": 1, "cheap": [0, 3], "cheaper": [2, 6], "check": [3, 6, 14, 18], "checkout": [1, 2], "chemistri": 1, "choos": 19, "chosen": 2, "cite": 6, "class": [0, 1, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18], "clear": [1, 2], "close": [2, 3, 6, 19], "cm": 3, "cmap": 3, "co": [3, 4], "code": 3, "codifi": 3, "collect": [13, 14, 15, 18], "color": [0, 19], "com": 6, "combin": [0, 1, 2, 3, 6, 7], "come": 0, "common": [2, 6], "complet": [1, 4, 13, 14, 15], "complex": 3, "comput": [0, 2, 3, 6], "concaten": [0, 19], "concurr": 1, "condit": [3, 14, 18], "confid": [0, 2, 6, 12, 19], "configur": 3, "consequ": 3, "conserv": 19, "consid": [0, 3, 9], "constant": [8, 9, 11, 12, 18], "constantliar": 7, "constitut": 3, "constrain": [0, 2, 6, 14, 16, 17], "constrained_layout": 3, "constraint": [0, 6, 13, 14, 18], "constraint_funct": 3, "constraint_function_2_dim": 3, "constraint_limit": 3, "constraint_low": 3, "constraint_upp": 3, "constraint_valu": [13, 14, 18], "constraint_vbound": 3, "constraintmodel": [3, 13, 18], "constraintnotsupportederror": [0, 16], "construct": [2, 3, 6, 19], "contain": [3, 16], "contour": 3, "contourf": 3, "contract": 4, "control": [1, 2, 6, 19], "converg": 4, "convert": 18, "coolwarm": 3, "coordin": 18, "copi": 8, "core": 1, "correct": 6, "correspond": [2, 6, 13, 18, 19], "cost": [0, 2, 4, 6], "could": [0, 1, 2, 3], "cours": 2, "cov": 0, "cover": 2, "craig": [4, 6, 15], "creat": [3, 4, 13], "cross": 6, "cumul": 10, "cunningham": 6, "current": [0, 19], "custom": 16, "d": [0, 1, 19], "dampen": 15, "data": [14, 18, 19], "date": 4, "dc": 14, "deal": 6, "decai": [9, 11, 12], "decay_explor": [9, 11, 12], "deeper": 0, "def": [0, 1, 2, 3, 4, 5, 18, 19], "default": [0, 1, 2, 3, 4, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18], "default_ev": 1, "defin": [1, 3, 4, 17], "definit": 0, "delai": [11, 12], "demonstr": 3, "depend": [0, 1, 2, 16], "describ": [2, 3, 6], "design": [2, 6], "desir": [3, 13, 18], "detail": 6, "determin": [2, 6, 7], "develop": 6, "deviat": [0, 9, 11, 12], "dict": [13, 15, 18], "dictat": [0, 7], "dictionari": [2, 13, 18], "differ": [0, 1, 3, 13, 18, 19], "dim": [0, 18], "dimens": 18, "dimension": [3, 14], "directli": [0, 1, 3], "disallow": 3, "disclaim": 2, "discourag": [7, 8], "discuss": [2, 6], "disregard": 0, "distinct": 3, "distribut": [1, 2, 6, 8, 9, 11, 12, 19], "diversifi": 2, "divid": 0, "do": [0, 1, 2, 3, 6], "doc": 1, "document": 4, "doe": [0, 1, 2, 14, 18], "doesn": 1, "doi": 6, "domain": [3, 6, 15], "domaintransform": [13, 15], "don": [1, 2, 14], "dot": 3, "dtype": [4, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18], "due": [3, 19], "dummi": 8, "duplic": [3, 13, 18], "dure": 2, "dynam": 6, "e": [2, 3, 4, 13, 17, 19], "each": [2, 3, 6, 9, 10, 11, 12, 15], "earlier": 17, "easi": [0, 1], "easiest": 2, "ec": [0, 19], "eddi": 6, "edgecolor": 3, "ei": [0, 2, 6, 9], "eipu": 0, "either": 19, "elimin": 8, "els": [0, 3], "embed": 3, "emploi": [2, 3, 6], "empti": [16, 18], "encourag": 0, "end": [1, 9, 11, 12], "endeavor": [2, 6], "eng": 6, "engin": 6, "enough": [0, 2], "enter": 19, "enum": 17, "enumer": 2, "equal": [0, 3], "errat": 1, "error": [3, 10], "estim": [0, 3, 14], "et": [3, 10], "eta": [4, 15], "etc": 1, "eval": 14, "evalu": [0, 2, 3, 7, 8, 10, 13, 14, 18], "even": [0, 1, 3], "event": [1, 2, 17], "everi": [2, 4], "exactli": 2, "exampl": [0, 1, 2, 3, 4, 6, 14, 15, 18, 19], "except": [3, 4], "execut": 1, "exemplifi": 6, "exist": [2, 17], "exp": [0, 4, 5, 19], "expect": [0, 2, 6, 9, 10], "expectedimprov": [0, 5, 7], "expectedimprovementperunitcost": 0, "expens": [0, 2, 3, 4, 6], "experi": [0, 1], "explicitli": 3, "exploit": [2, 6, 9, 11, 12, 19], "explor": [0, 2, 6, 9, 11, 12, 19], "exploration_decai": [0, 9, 11, 12], "exploration_decay_delai": [0, 9, 11, 12], "extra": 1, "extract": 3, "f": [0, 1, 2, 3, 4, 5, 13, 19], "factor": 0, "fall": 3, "fals": [3, 7, 8, 9, 10, 11, 12, 13, 17, 18], "fashion": [1, 6], "fc": [0, 19], "feasibl": 3, "featur": [2, 6], "feel": 2, "fernando": 6, "few": [0, 2, 3, 6, 19], "fig": [0, 3, 19], "figsiz": [0, 3, 5, 19], "figur": [0, 3, 5, 19], "file": [2, 6, 17], "fill": [0, 19], "fill_between": 5, "final": 6, "find": [0, 1, 2, 3, 6, 13, 14], "fire": [1, 2], "first": 2, "fit": [2, 6, 7, 8, 9, 10, 11, 12, 13, 14, 19], "fit_gp": [0, 7, 8, 9, 10, 11, 12], "fix": 0, "flatten": [0, 5, 19], "flexibl": [1, 6], "float": [4, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18], "fly": 3, "folder": [1, 2], "follow": [0, 2, 3, 6, 7, 19], "fontdict": [0, 19], "fontsiz": [0, 19], "form": 3, "format": [0, 1, 2, 17, 19], "found": [1, 2, 3, 4, 6, 13, 18], "frac": [9, 11, 19], "free": [1, 19], "from": [0, 1, 2, 3, 4, 5, 8, 10, 11, 12, 17, 18, 19], "fromit": 4, "fulfil": [0, 3, 13, 14, 18], "fun": [3, 14], "func_with_discrete_param": 1, "function": [1, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 18], "function_to_be_optim": 1, "fundament": 19, "further": [0, 19], "g": [0, 13, 17, 19], "gain": 10, "gamma_": 4, "gamma_osc": 15, "gamma_pan": 15, "gardner": [3, 6], "gardner2014bayesian": 6, "gaussian": [0, 2, 3, 6, 7, 8, 9, 10, 11, 12, 13, 19], "gaussianprocessregressor": [7, 8, 9, 10, 11, 12, 14], "gener": [2, 3, 13, 18], "get": [0, 1, 3, 13, 18, 19], "get_legend_handles_label": 0, "gif": [2, 6], "github": 6, "give": [1, 13, 18, 19], "given": [1, 3, 13, 14], "global": [2, 6], "go": [1, 3, 6, 19], "gold": [0, 19], "good": [2, 3], "got": 1, "govern": [9, 11, 12], "gp": [0, 7, 8, 9, 10, 11, 12, 13, 14], "gphedg": 7, "gpr": 14, "gradient": 0, "greedi": 0, "greedyacquisit": 0, "green": 3, "grid": [0, 3, 19], "gridspec": [0, 19], "group": 7, "grow": [2, 6, 19], "gt": [3, 4], "guess": [0, 19], "guid": [1, 3, 13], "guidanc": 2, "ha": [2, 3, 4, 18, 19], "handl": [1, 3, 13, 17], "happen": 2, "hard": [2, 6], "hashabl": 1, "hasn": 1, "hault": 1, "have": [1, 2, 3, 4, 8, 14, 19], "heavili": 4, "height_ratio": [0, 19], "help": 2, "here": [0, 1, 2, 3, 19], "high": [2, 6, 13, 18], "higher": [0, 9, 11, 12], "hold": 18, "hone": 3, "how": [0, 1, 2, 3, 4, 19], "howev": [1, 2], "http": [6, 10], "hyper": 1, "i": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19], "icml": 6, "idea": [2, 3, 6], "ident": 4, "ignor": [0, 1], "illustr": 3, "implement": [0, 2, 6, 7], "impli": 3, "import": [0, 1, 2, 3, 4, 5, 6, 19], "imposs": 1, "improv": [0, 1, 2, 6, 9, 11], "includ": 8, "incom": 17, "increas": 1, "independ": [3, 14], "individu": 14, "inequ": 6, "inf": 3, "infeas": 3, "infer": [2, 6], "inform": [2, 4, 6, 10, 13, 19], "ingredi": [1, 2], "init_point": [0, 1, 2, 3, 4, 13, 19], "initi": [4, 15, 19], "inlin": 19, "inproceed": 6, "instanc": [1, 2, 3, 17], "instanti": [1, 2], "int": [1, 7, 8, 9, 10, 11, 12, 13, 14, 17, 18], "int_": 14, "integ": 13, "intend": 13, "intent": 2, "interest": [0, 3, 4, 15, 19], "intern": [1, 2, 3, 6, 13, 14], "interv": [0, 19], "interven": 0, "intuit": 1, "investig": 0, "invok": [1, 2], "is_constrain": 17, "isn": 3, "issu": 6, "iter": [0, 1, 2, 4, 6, 10, 13, 19], "its": [0, 1, 2, 6, 13, 18, 19], "j": [3, 6], "jacob": 6, "john": 6, "joint": 14, "journal": 6, "json": 17, "jsonlogg": [2, 17], "just": [2, 19], "k": [0, 4, 15, 19], "kappa": [1, 12, 19], "kei": [2, 13, 14, 18], "kenneth": 6, "keyword": 3, "kick": 19, "kilian": 6, "know": [2, 3, 6], "known": [2, 3, 6, 13, 18, 19], "kusner": 6, "kwarg": [4, 7, 8, 10, 14], "l": [7, 8, 9, 10, 11, 12], "label": [0, 3, 4, 19], "labels2": 0, "labels_bot": 3, "labels_top": 3, "lambda": [0, 18], "languag": 1, "larger": 19, "last": [1, 3], "last_x": 0, "later": 0, "lazi": [2, 13], "lazili": 2, "lb": [3, 14], "learn": [1, 3, 6], "left": [1, 9, 11, 14], "legend": [0, 4, 19], "len": [0, 2, 4, 18, 19], "leq": [3, 14], "less": [8, 19], "let": [0, 1, 3, 4, 6], "level": [0, 13, 17], "liar": 8, "lie": [2, 3], "like": [0, 1, 2, 3, 8, 19], "lim": 3, "limit": [3, 14], "line": 0, "linear": 4, "lines2": 0, "linewidth": [0, 19], "linspac": [0, 3, 5, 19], "list": [1, 2, 4, 13, 14, 18], "listen": [1, 2], "literatur": 1, "ll": 4, "load": 0, "load_log": 2, "loc": [0, 19], "local": 19, "log": [1, 2, 17], "logger": [2, 17], "long": 1, "look": [1, 2, 3, 6], "loop": 13, "low": [3, 14], "lower": [3, 4, 8, 9, 11, 12, 14, 18], "lt": 4, "machin": [1, 6], "magnitud": [3, 9], "mai": [1, 2, 3, 13, 18], "main": 2, "make": [6, 13], "mani": [0, 2, 7], "manual": 13, "map": [13, 15, 18], "markeredgecolor": [0, 19], "markeredgewidth": [0, 19], "markerfacecolor": [0, 19], "markers": [0, 19], "mask": [3, 18], "match": 14, "matern": 1, "mathcal": 14, "mathemat": 3, "matplotlib": [0, 3, 4, 5, 19], "matrix": 0, "matt": 6, "max": [0, 1, 2, 3, 8, 9, 11, 13, 18, 19], "max_": 3, "maxim": [0, 2, 3, 4, 5, 13, 18, 19], "maxima": [3, 19], "maximum": [0, 2, 3, 6, 8, 13, 14, 18, 19], "mayb": 1, "mean": [0, 1, 2, 3, 5, 8, 9, 11, 12, 13, 14], "mechan": 0, "meshgrid": 3, "meta": 7, "method": [0, 1, 2, 4, 6, 8, 9, 11, 12, 13], "might": [0, 1, 2, 3], "min": [3, 8], "minim": [2, 6], "minima": 19, "minimum": [2, 3, 8, 13, 15, 18, 19], "minimum_window": [4, 15], "minut": 0, "misc": 6, "model": [3, 6, 13, 14, 18], "modifi": [3, 7, 13], "modul": [7, 16], "moment": 13, "month": 6, "more": [0, 1, 2, 4, 5, 6, 10, 13, 19], "most": [0, 2, 4, 5, 6, 7], "msg": 0, "mu": [0, 9, 11, 12, 14, 19], "much": [1, 2, 3], "multi": 3, "multipl": [7, 8, 14, 19], "multivari": 0, "multivariate_norm": 0, "must": [0, 2, 3], "mutat": 4, "mutating_optim": 4, "my_callback": 1, "my_observ": 1, "n": [2, 3, 4, 14, 15], "n_constraint": [3, 14], "n_featur": 14, "n_iter": [0, 1, 2, 3, 4, 5, 13, 19], "n_l_bfgs_b": [0, 7, 8, 9, 10, 11, 12], "n_plots_per_row": 3, "n_random": [0, 7, 8, 9, 10, 11, 12], "n_restarts_optim": 1, "n_sampl": 14, "name": [3, 13, 14, 18], "nan": 3, "nativ": 2, "natur": [1, 2, 3], "ndarrai": [7, 8, 9, 10, 11, 12, 13, 14, 15, 18], "ndarrrai": 14, "necess": 1, "necessari": 19, "need": [1, 2, 3, 4, 6, 13, 14], "new": [2, 13, 15, 18], "new_bound": [2, 13, 18], "new_optim": 2, "next": [0, 1, 3, 6, 7, 8, 9, 10, 11, 12, 13, 19], "next_point": 1, "next_point_to_prob": 1, "nielen": 6, "nogueira": 6, "nois": [1, 13, 18], "noisi": 0, "non": [3, 4, 16], "none": [0, 1, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19], "nonetheless": 2, "nonlinearconstraint": [3, 13], "noreturn": 10, "normal": [0, 3, 9, 11], "note": [3, 5, 13, 18], "notebook": [0, 2, 3, 6, 7, 14, 15], "notic": [1, 19], "notuniqueerror": [16, 18], "novalidpointregisterederror": 16, "now": [1, 2, 3, 4], "np": [0, 3, 4, 5, 7, 8, 9, 10, 11, 12, 14, 15, 18, 19], "number": [2, 3, 6, 7, 8, 9, 10, 11, 12, 13, 18, 19], "numpi": [0, 3, 4, 5, 13, 19], "nut": 1, "o": 17, "object": [1, 2, 3, 4], "observ": [0, 2, 3, 6, 13, 19], "obtain": [2, 19], "obviou": 4, "obvious": 2, "occasion": [3, 13, 18], "off": [9, 11, 12, 19], "often": 2, "ok": [1, 3], "one": [0, 3, 4, 7], "ones": 2, "onli": [1, 2, 3, 4, 13, 19], "onto": [2, 4], "open": 6, "oper": [0, 3, 8, 15], "opt": 0, "optim": [0, 4, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19], "optimization_end": 17, "optimization_start": [1, 17], "optimization_step": [1, 2, 17], "option": [1, 13, 18], "orang": 0, "order": [0, 2, 3, 13, 14], "org": 10, "origin": 3, "osc": 4, "oscil": [4, 15], "other": [0, 2, 3, 6, 13, 18], "otherwis": 13, "our": [0, 1, 2, 3, 4, 19], "out": [0, 1, 2, 5, 6], "output": [2, 17], "over": [0, 1, 2, 6, 13], "overal": 14, "overwrit": [0, 17], "own": [1, 7], "p": [6, 14], "p1": 18, "p2": 18, "p_allow": 3, "p_c": 0, "packag": [0, 3, 4, 6, 19], "page": 6, "pain": 3, "pan": [4, 15], "paper": 3, "paradigm": 2, "parallel": 7, "param": [0, 1, 2, 3, 4, 5, 13, 18, 19], "paramet": [0, 2, 3, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19], "params_to_arrai": 18, "particular": [0, 1, 2, 7], "particularli": [1, 2, 4, 6], "pass": 2, "path": [2, 17], "pathlik": 17, "pbound": [1, 2, 3, 4, 5, 13, 18], "pdf": 9, "peak": 5, "per": 0, "perform": [0, 1, 2, 4], "phase": 4, "phi": [9, 11], "pi": 4, "pictur": [2, 6], "place": 3, "pleas": [2, 6], "plot": [0, 3, 4], "plot_bo": 5, "plot_constrained_opt": 3, "plot_gp": [0, 19], "plt": [0, 3, 4, 5, 19], "poi": 11, "point": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 16, 18], "polici": 0, "portfolio": 10, "posit": [9, 11], "possibl": [2, 6], "posterior": [0, 2, 6, 19], "pr": 14, "practic": [0, 19], "predict": [0, 1, 3, 5, 8, 9, 11, 12, 14, 19], "prefer": [3, 9, 11, 12], "present": [6, 14, 18], "pretti": [3, 19], "previou": 16, "previous": [2, 6, 7, 8, 18], "principl": 1, "print": [0, 1, 2, 3], "prior": 0, "probabilityofimprov": [5, 7, 9], "probabl": [0, 3, 9, 11, 14], "probe": [1, 2, 3, 7, 8, 9, 10, 11, 12, 13, 18, 19], "problem": [1, 2, 3, 4, 6, 7], "proce": 3, "procedur": 19, "process": [0, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 19], "product": 14, "progress": 1, "project": 6, "promis": [7, 8, 9, 10, 11, 12, 13], "proper": [2, 6], "properti": [2, 3, 13, 14, 17, 18], "provid": [3, 7, 13, 15], "proxi": [2, 6], "public": 6, "pure": 6, "purpl": [0, 19], "purpos": 2, "push": 0, "py": 1, "pyplot": [0, 3, 4, 5, 19], "python": 6, "q": 6, "quadrat": 3, "qualiti": 1, "quick": 4, "quickli": 3, "quit": [0, 4, 19], "r": [0, 3, 6, 19], "rais": [0, 3, 9, 10, 11, 14, 16, 18], "random": [0, 1, 2, 5, 7, 8, 9, 10, 11, 12, 13, 14, 18], "random_sampl": 18, "random_st": [0, 1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 18, 19], "randomst": [7, 8, 9, 10, 11, 12, 13, 14, 18], "rang": [0, 1, 3, 4, 5, 19], "rate": [9, 11, 12], "re": [0, 1, 2, 3, 13, 18, 19], "read": 3, "real": 2, "realiz": 2, "reason": [1, 3], "receiv": 0, "recent": [0, 4], "recreat": 1, "red": [3, 5], "reduc": [0, 4, 9, 11, 12, 15], "reduct": [6, 15], "refer": [2, 3, 4, 6], "reformul": 3, "region": [2, 3, 4, 6, 15], "regist": [3, 8, 13, 16, 18], "regress": 1, "regressor": [3, 13, 14], "rel": [4, 8], "remov": 0, "repeat": 1, "repeatedli": [3, 13, 18], "report": 18, "represent": 18, "reproduc": [7, 8, 9, 10, 11, 12], "requir": [0, 2, 4, 6], "rerun": 3, "research": 6, "reset": 17, "reshap": [0, 3, 5, 19], "respect": [3, 14], "restart": 17, "restrict": 19, "result": [1, 3, 17, 18], "return": [0, 1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19], "return_cov": 0, "return_std": [0, 5, 19], "reward": 10, "right": [9, 11, 14], "rightarrow": 1, "robust": [4, 6, 15], "routin": [4, 13], "row": 3, "rtol": 8, "rtype": [9, 11, 12], "run": [0, 1, 3, 18], "sai": 0, "same": [3, 8, 13, 14, 15, 18], "sampl": [0, 2, 3, 6, 7, 8, 9, 10, 11, 12, 18], "satisfi": 18, "scale": 15, "scatter": [3, 5], "scenario": 2, "scheme": [4, 6, 15], "scikit": 1, "scipi": 3, "screenlogg": 17, "script": [1, 6], "search": [4, 6, 13, 18], "searchabl": 15, "section": 6, "see": [0, 2, 3, 4, 6, 7, 10, 13, 14, 15], "seed": [5, 13, 18], "seem": 3, "seen": [2, 4, 6, 18], "select": [0, 10], "self": [0, 1, 18], "sens": [2, 6], "sequenc": [10, 13, 15, 18], "sequenti": [15, 19], "sequentialdomainreductiontransform": 4, "sequentialdomaintransform": 6, "serv": 2, "server": 1, "set": [0, 2, 3, 4, 7, 8, 9, 10, 11, 12, 13, 19], "set_aspect": 3, "set_bound": [2, 13, 18], "set_gp_param": [1, 13], "set_titl": 3, "set_xlabel": [0, 19], "set_xlim": [0, 19], "set_ylabel": [0, 19], "set_ylim": [0, 19], "setup": 3, "sever": 0, "shape": [0, 3, 14], "shift": 4, "should": [1, 2, 3, 6, 14, 19], "shouldn": 2, "show": [3, 5], "showcas": [0, 1], "shrink": [4, 15], "shrinkag": 4, "sigma": [0, 5, 9, 11, 12, 14, 19], "sign": 3, "silent": [2, 3], "similar": [3, 9, 17], "similarli": 3, "simpl": [0, 1, 4, 6, 15], "simplefilt": 0, "simpli": [1, 3, 14], "simul": [1, 4, 6, 15], "sin": 3, "sinc": [0, 1, 3, 8, 10], "singl": 18, "situat": [0, 1, 2, 3, 6, 13, 18], "size": [0, 3, 15, 19], "skip": 0, "slightli": 3, "so": [1, 2, 3, 6], "softwar": 6, "sole": 0, "solut": [1, 3], "solv": 3, "some": [0, 1, 2, 3], "somewhat": 0, "sourc": 6, "space": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 18, 19], "special": 0, "specif": [0, 1, 2, 3, 13], "specifi": [1, 14, 18], "sped": 6, "spread": 5, "sqrt": 4, "squar": 0, "squeez": 3, "standard": [0, 3, 4, 9, 11, 12], "standard_optim": 4, "stander": [4, 6, 15], "start": [1, 3, 4, 7, 8, 9, 10, 11, 12, 13, 19], "state": [7, 8, 9, 10, 11, 12, 13, 14, 17], "statement": 0, "std": [0, 9, 11, 12], "step": [0, 1, 4, 6, 7, 10, 17], "still": [2, 6], "stop": 1, "store": [2, 3], "str": [8, 13, 15, 17, 18], "strategi": [2, 6, 8], "stuck": 19, "subclass": 7, "subj": 3, "submodul": 2, "subplot": [0, 3, 19], "subscrib": [1, 2], "subscript": 1, "succes": 19, "suggest": [0, 6, 7, 8, 9, 10, 11, 12, 13], "suit": [2, 6], "suitabl": 7, "super": 0, "super_acq": 0, "support": [0, 16], "suptitl": [0, 19], "sure": 13, "surfac": 1, "surrog": 3, "surrogate_object": 3, "swapax": 3, "switch": 3, "t": [1, 2, 3, 14], "take": [0, 1, 2, 3, 6, 13, 14], "target": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19], "target_func": 18, "target_funct": 3, "target_spac": [0, 7, 8, 9, 10, 11, 12, 15], "target_vbound": 3, "targetspac": [7, 8, 9, 10, 11, 12, 13, 15], "targetspaceemptyerror": 16, "techniqu": [2, 4, 6], "tell": 1, "termin": 17, "test": 19, "text": [3, 9, 11, 12, 14, 17], "thei": [1, 7, 18], "them": [2, 3], "therefor": [2, 4, 6], "thi": [0, 1, 2, 3, 4, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 18, 19], "thing": [0, 1], "think": [1, 2], "thompson": 0, "thompsonsampl": 0, "three": 4, "through": 0, "thu": 3, "tild": 14, "time": [1, 2, 3, 4, 18], "tip": [2, 6], "titl": [3, 4, 6], "toler": 8, "too": [0, 19], "tool": [2, 6], "top": 3, "tour": 6, "trade": [9, 11, 12, 19], "tradeoff": [9, 11, 12], "transform": [4, 13, 15], "tri": 3, "true": [0, 2, 3, 5, 7, 8, 9, 10, 11, 12, 13, 17, 18, 19], "try": [3, 14, 19], "tune": [2, 6], "tupl": [13, 18], "twinx": 0, "two": [3, 4], "ty": 2, "type": [0, 1, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18], "typeerror": 14, "typic": [3, 4, 15], "u": [0, 6, 19], "ub": 14, "ucb": [2, 6, 12, 19], "uncertainti": [1, 19], "under": 6, "understand": [1, 6, 7], "unev": 19, "unfeas": 3, "unfortun": 1, "uniqu": [16, 18], "unit": 0, "unitari": 15, "unknown": [2, 6, 19], "unnecessari": 3, "unpack": 3, "unseed": 13, "until": 3, "up": [0, 3, 4, 6, 14], "updat": [1, 3, 4, 10, 17], "upon": [2, 6], "upper": [2, 4, 6, 12, 14, 18, 19], "upperconfidencebound": [1, 5, 7, 19], "url": 6, "us": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 19], "usag": [2, 6], "use": 2, "user": [2, 19], "usual": [0, 3], "util": [0, 2, 19], "utility_funct": 19, "valid": [6, 18], "valu": [0, 1, 2, 3, 4, 6, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18], "valueerror": [9, 11], "variabl": [4, 19], "varianc": [8, 14], "variou": 0, "verbos": [1, 2, 3, 4, 5, 13, 17], "veri": [0, 1, 2, 6], "version": [0, 18], "via": [2, 3], "violat": 3, "visual": [3, 6], "vmax": 3, "vmin": 3, "volum": 6, "w": 1, "wa": [1, 19], "wai": [1, 2, 3, 7], "want": [1, 2, 3, 6], "warn": 0, "we": [0, 1, 2, 3, 4, 6, 14, 19], "weinberg": 6, "well": [3, 13, 14], "were": 3, "what": [0, 1, 2, 3, 6], "whatev": 1, "when": [1, 2, 3, 4, 6, 13, 16], "whenev": [1, 3], "where": [0, 1, 2, 3, 4, 6, 9, 11, 13, 18], "wheter": 14, "whether": [3, 7, 8, 9, 10, 11, 12, 14, 17], "which": [0, 2, 3, 4, 6, 13, 14, 19], "while": [0, 1, 2], "white": 3, "whole": 5, "why": 1, "window": 15, "wish": 2, "within": 18, "without": 2, "word": 3, "work": [0, 1, 2, 3, 15, 19], "worker": 8, "wors": 3, "worst": 3, "worth": [2, 6], "would": [0, 3], "wrapper": 1, "write": [0, 3, 17], "written": 1, "x": [0, 1, 2, 3, 4, 5, 9, 11, 12, 14, 18, 19], "x_": 3, "x_i": 3, "x_max_bound": 4, "x_min_bound": 4, "x_ob": [0, 19], "x_reduc": 0, "x_train_": 0, "xi": [0, 9, 11], "xu": 6, "xy": 3, "y": [0, 1, 2, 3, 4, 14, 18, 19], "y_": [3, 9, 11], "y_cov": 0, "y_j": 3, "y_max": [9, 11], "y_mean": 0, "y_ob": [0, 19], "y_sampl": 0, "y_sample_argmax": 0, "year": 6, "yet": [1, 7, 8], "yield": [13, 14], "you": [0, 1, 2, 3, 6, 7, 13], "your": [0, 1, 2, 3, 6, 7], "yourself": 3, "z": 3, "z_est": 3, "z_mask": 3, "zhixiang": 6, "zoom": [4, 15], "zorder": 5}, "titles": ["Acquisition functions", "Advanced tour of the Bayesian Optimization package", "Basic tour of the Bayesian Optimization package", "Constrained Optimization", "Sequential Domain Reduction", "Exploitation vs Exploration", "Bayesian Optimization", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">bayes_opt.acquisition</span></code>", "<code class=\"xref py py-class docutils literal notranslate\"><span class=\"pre\">bayes_opt.acquisition.ConstantLiar</span></code>", "<code class=\"xref py py-class docutils literal notranslate\"><span class=\"pre\">bayes_opt.acquisition.ExpectedImprovement</span></code>", "<code class=\"xref py py-class docutils literal notranslate\"><span class=\"pre\">bayes_opt.acquisition.GPHedge</span></code>", "<code class=\"xref py py-class docutils literal notranslate\"><span class=\"pre\">bayes_opt.acquisition.ProbabilityOfImprovement</span></code>", "<code class=\"xref py py-class docutils literal notranslate\"><span class=\"pre\">bayes_opt.acquisition.UpperConfidenceBound</span></code>", "<code class=\"xref py py-class docutils literal notranslate\"><span class=\"pre\">bayes_opt.BayesianOptimization</span></code>", "<code class=\"xref py py-class docutils literal notranslate\"><span class=\"pre\">bayes_opt.ConstraintModel</span></code>", "<code class=\"xref py py-class docutils literal notranslate\"><span class=\"pre\">bayes_opt.SequentialDomainReductionTransformer</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">bayes_opt.exception</span></code>", "Other", "<code class=\"xref py py-class docutils literal notranslate\"><span class=\"pre\">bayes_opt.TargetSpace</span></code>", "Visualization"], "titleterms": {"": 19, "0": 5, "1": [1, 2, 3, 5], "10": 5, "1e": 5, "2": [1, 2, 3], "3": [1, 2], "4": [2, 5], "The": 1, "acquisit": [0, 5, 7, 8, 9, 10, 11, 12], "advanc": [1, 3], "after": 19, "algorithm": 19, "alpha": 1, "background": 4, "basic": [2, 4], "bayes_opt": [7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18], "bayesian": [1, 2, 6], "bayesianoptim": [13, 19], "bound": [2, 5], "chang": [1, 2], "citat": 6, "conda": 6, "confid": 5, "constantliar": 8, "constrain": 3, "constraint": 3, "constraintmodel": 14, "continu": 1, "coupl": 19, "creat": 19, "deal": 1, "defin": 19, "discret": 1, "doe": 6, "domain": 4, "each": 19, "easier": 19, "evalu": 1, "except": 16, "expect": 5, "expectedimprov": 9, "exploit": 5, "explor": 5, "first": 19, "five": 19, "forg": 6, "four": 19, "function": [0, 2, 5, 19], "gaussian": 1, "get": 2, "gp": [1, 19], "gphedg": 10, "guid": 2, "how": 6, "improv": 5, "index": 6, "instal": 6, "kappa": 5, "kernel": 1, "let": 19, "load": 2, "loop": 1, "make": 19, "maxim": 1, "multipl": 3, "next": 2, "note": [9, 11, 12, 14], "object": 19, "observ": 1, "one": 19, "optim": [1, 2, 3, 6], "other": 17, "packag": [1, 2], "paradigm": 1, "paramet": [1, 4], "pass": 1, "pip": 6, "plot": [5, 19], "point": 19, "prefer": 5, "probabilityofimprov": 11, "probabl": 5, "process": 1, "progress": 2, "pypi": 6, "quick": 6, "random": 19, "reduct": 4, "regist": 1, "restart": 2, "save": 2, "sequenti": 4, "sequentialdomainreductiontransform": 15, "seven": 19, "simpl": 3, "simul": 3, "singl": 3, "six": 19, "specifi": 2, "start": 2, "step": [2, 19], "stop": 19, "suggest": 1, "target": 5, "targetspac": 18, "three": 19, "tour": [1, 2], "tune": 1, "two": 19, "underli": 1, "upper": 5, "upperconfidencebound": 12, "util": 5, "v": 5, "via": 6, "visual": 19, "warn": 13, "work": 6, "xi": 5}})
\ No newline at end of file
+Search.setIndex({"alltitles": {"1. Simple Constrained Optimization": [[3, "1.-Simple-Constrained-Optimization"]], "1. Simple integer-valued function": [[7, "1.-Simple-integer-valued-function"]], "1. Specifying the function to be optimized": [[2, "1.-Specifying-the-function-to-be-optimized"]], "1. Suggest-Evaluate-Register Paradigm": [[1, "1.-Suggest-Evaluate-Register-Paradigm"]], "1.1 The maximize loop": [[1, "1.1-The-maximize-loop"]], "2. Advanced Constrained Optimization": [[3, "2.-Advanced-Constrained-Optimization"]], "2. Getting Started": [[2, "2.-Getting-Started"]], "2. Mixed-parameter optimization": [[7, "2.-Mixed-parameter-optimization"]], "2. Tuning the underlying Gaussian Process": [[1, "2.-Tuning-the-underlying-Gaussian-Process"]], "2.1 Changing bounds": [[2, "2.1-Changing-bounds"]], "2.1 Passing parameter to the GP": [[1, "2.1-Passing-parameter-to-the-GP"]], "2.1 Simple, single constraint": [[3, "2.1-Simple,-single-constraint"]], "2.2 Multiple Constraints": [[3, "2.2-Multiple-Constraints"]], "2.2 Tuning the alpha parameter": [[1, "2.2-Tuning-the-alpha-parameter"]], "2.3 Changing kernels": [[1, "2.3-Changing-kernels"]], "3. Categorical variables": [[7, "3.-Categorical-variables"]], "3. Guiding the optimization": [[2, "3.-Guiding-the-optimization"]], "4. Saving, loading and restarting": [[2, "4.-Saving,-loading-and-restarting"]], "4. Use in ML": [[7, "4.-Use-in-ML"]], "4.1 Saving progress": [[2, "4.1-Saving-progress"]], "4.2 Loading progress": [[2, "4.2-Loading-progress"]], "5. Defining your own Parameter": [[7, "5.-Defining-your-own-Parameter"]], "Acquisition Function \u201cExpected Improvement\u201d": [[5, "Acquisition-Function-%22Expected-Improvement%22"]], "Acquisition Function \u201cProbability of Improvement\u201d": [[5, "Acquisition-Function-%22Probability-of-Improvement%22"]], "Acquisition Function \u201cUpper Confidence Bound\u201d": [[5, "Acquisition-Function-%22Upper-Confidence-Bound%22"]], "Acquisition functions": [[0, null]], "Advanced tour of the Bayesian Optimization package": [[1, null]], "After five steps of GP (and two random points)": [[21, "After-five-steps-of-GP-(and-two-random-points)"]], "After four steps of GP (and two random points)": [[21, "After-four-steps-of-GP-(and-two-random-points)"]], "After one step of GP (and two random points)": [[21, "After-one-step-of-GP-(and-two-random-points)"]], "After seven steps of GP (and two random points)": [[21, "After-seven-steps-of-GP-(and-two-random-points)"]], "After six steps of GP (and two random points)": [[21, "After-six-steps-of-GP-(and-two-random-points)"]], "After three steps of GP (and two random points)": [[21, "After-three-steps-of-GP-(and-two-random-points)"]], "After two steps of GP (and two random points)": [[21, "After-two-steps-of-GP-(and-two-random-points)"]], "Background": [[4, "Background"]], "Basic tour of the Bayesian Optimization package": [[2, null]], "Basics": [[4, "Basics"]], "Bayesian Optimization": [[6, null]], "Citation": [[6, "citation"]], "Conda (via conda-forge)": [[6, "conda-via-conda-forge"]], "Constrained Optimization": [[3, null]], "Create a BayesianOptimization Object": [[21, "Create-a-BayesianOptimization-Object"]], "Exploitation vs Exploration": [[5, null]], "How does it work?": [[6, "how-does-it-work"]], "Installation": [[6, "installation"]], "Let\u2019s first define a couple functions to make plotting easier": [[21, "Let's-first-define-a-couple-functions-to-make-plotting-easier"]], "Next Steps": [[2, "Next-Steps"]], "Note": [[10, null], [12, null], [13, null], [15, null], [15, null]], "Observers Continued": [[1, "Observers-Continued"]], "Optimizing over non-float Parameters": [[7, null]], "Other": [[18, null]], "Parameters": [[4, "Parameters"]], "Plotting and visualizing the algorithm at each step": [[21, "Plotting-and-visualizing-the-algorithm-at-each-step"]], "Prefer exploitation (kappa=0.1)": [[5, "Prefer-exploitation-(kappa=0.1)"]], "Prefer exploitation (xi=0.0)": [[5, "Prefer-exploitation-(xi=0.0)"]], "Prefer exploitation (xi=1e-4)": [[5, "Prefer-exploitation-(xi=1e-4)"]], "Prefer exploration (kappa=10)": [[5, "Prefer-exploration-(kappa=10)"]], "Prefer exploration (xi=0.1)": [[5, "Prefer-exploration-(xi=0.1)"], [5, "id1"]], "Quick Index": [[6, "quick-index"]], "Sequential Domain Reduction": [[4, null]], "Simulation 2": [[3, "Simulation-2"]], "Stopping": [[21, "Stopping"]], "Target function": [[5, "Target-function"]], "Two random points": [[21, "Two-random-points"]], "Utility function for plotting": [[5, "Utility-function-for-plotting"]], "Visualization": [[21, null]], "Warning": [[14, null]], "bayes_opt.BayesianOptimization": [[14, null]], "bayes_opt.ConstraintModel": [[15, null]], "bayes_opt.SequentialDomainReductionTransformer": [[16, null]], "bayes_opt.TargetSpace": [[20, null]], "bayes_opt.acquisition": [[8, null]], "bayes_opt.acquisition.ConstantLiar": [[9, null]], "bayes_opt.acquisition.ExpectedImprovement": [[10, null]], "bayes_opt.acquisition.GPHedge": [[11, null]], "bayes_opt.acquisition.ProbabilityOfImprovement": [[12, null]], "bayes_opt.acquisition.UpperConfidenceBound": [[13, null]], "bayes_opt.exception": [[17, null]], "bayes_opt.parameter": [[19, null]], "pip (via PyPI)": [[6, "pip-via-pypi"]]}, "docurls": ["acquisition_functions.html", "advanced-tour.html", "basic-tour.html", "constraints.html", "domain_reduction.html", "exploitation_vs_exploration.html", "index.html", "parameter_types.html", "reference/acquisition.html", "reference/acquisition/ConstantLiar.html", "reference/acquisition/ExpectedImprovement.html", "reference/acquisition/GPHedge.html", "reference/acquisition/ProbabilityOfImprovement.html", "reference/acquisition/UpperConfidenceBound.html", "reference/bayes_opt.html", "reference/constraint.html", "reference/domain_reduction.html", "reference/exception.html", "reference/other.html", "reference/parameter.html", "reference/target_space.html", "visualization.html"], "envversion": {"nbsphinx": 4, "sphinx": 64, "sphinx.domains.c": 3, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 9, "sphinx.domains.index": 1, "sphinx.domains.javascript": 3, "sphinx.domains.math": 2, "sphinx.domains.python": 4, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "sphinx.ext.intersphinx": 1}, "indexentries": {"acquisition_function (bayes_opt.bayesianoptimization property)": [[14, "bayes_opt.BayesianOptimization.acquisition_function", false]], "acquisitionfunction (class in bayes_opt.acquisition)": [[8, "bayes_opt.acquisition.AcquisitionFunction", false]], "allowed() (bayes_opt.constraint.constraintmodel method)": [[15, "bayes_opt.constraint.ConstraintModel.allowed", false]], "approx() (bayes_opt.constraint.constraintmodel method)": [[15, "bayes_opt.constraint.ConstraintModel.approx", false]], "array_to_params() (bayes_opt.targetspace method)": [[20, "bayes_opt.TargetSpace.array_to_params", false]], "base_acq() (bayes_opt.acquisition.acquisitionfunction method)": [[8, "bayes_opt.acquisition.AcquisitionFunction.base_acq", false]], "base_acq() (bayes_opt.acquisition.constantliar method)": [[9, "bayes_opt.acquisition.ConstantLiar.base_acq", false]], "base_acq() (bayes_opt.acquisition.expectedimprovement method)": [[10, "bayes_opt.acquisition.ExpectedImprovement.base_acq", false]], "base_acq() (bayes_opt.acquisition.gphedge method)": [[11, "bayes_opt.acquisition.GPHedge.base_acq", false]], "base_acq() (bayes_opt.acquisition.probabilityofimprovement method)": [[12, "bayes_opt.acquisition.ProbabilityOfImprovement.base_acq", false]], "base_acq() (bayes_opt.acquisition.upperconfidencebound method)": [[13, "bayes_opt.acquisition.UpperConfidenceBound.base_acq", false]], "bayes_opt.acquisition": [[8, "module-bayes_opt.acquisition", false]], "bayes_opt.exception": [[17, "module-bayes_opt.exception", false]], "bayes_opt.parameter": [[19, "module-bayes_opt.parameter", false]], "bayesianoptimization (class in bayes_opt)": [[14, "bayes_opt.BayesianOptimization", false]], "bayesianoptimizationerror": [[17, "bayes_opt.exception.BayesianOptimizationError", false]], "bayesparameter (class in bayes_opt.parameter)": [[19, "bayes_opt.parameter.BayesParameter", false]], "bounds (bayes_opt.parameter.bayesparameter property)": [[19, "bayes_opt.parameter.BayesParameter.bounds", false]], "bounds (bayes_opt.targetspace property)": [[20, "bayes_opt.TargetSpace.bounds", false]], "calculate_bounds() (bayes_opt.targetspace method)": [[20, "bayes_opt.TargetSpace.calculate_bounds", false]], "categoricalparameter (class in bayes_opt.parameter)": [[19, "bayes_opt.parameter.CategoricalParameter", false]], "constantliar (class in bayes_opt.acquisition)": [[9, "bayes_opt.acquisition.ConstantLiar", false]], "constraint (bayes_opt.bayesianoptimization property)": [[14, "bayes_opt.BayesianOptimization.constraint", false]], "constraint (bayes_opt.targetspace property)": [[20, "bayes_opt.TargetSpace.constraint", false]], "constraint_values (bayes_opt.targetspace property)": [[20, "bayes_opt.TargetSpace.constraint_values", false]], "constraintmodel (class in bayes_opt.constraint)": [[15, "bayes_opt.constraint.ConstraintModel", false]], "constraintnotsupportederror": [[17, "bayes_opt.exception.ConstraintNotSupportedError", false]], "continuous_dimensions (bayes_opt.targetspace property)": [[20, "bayes_opt.TargetSpace.continuous_dimensions", false]], "decay_exploration() (bayes_opt.acquisition.expectedimprovement method)": [[10, "bayes_opt.acquisition.ExpectedImprovement.decay_exploration", false]], "decay_exploration() (bayes_opt.acquisition.probabilityofimprovement method)": [[12, "bayes_opt.acquisition.ProbabilityOfImprovement.decay_exploration", false]], "decay_exploration() (bayes_opt.acquisition.upperconfidencebound method)": [[13, "bayes_opt.acquisition.UpperConfidenceBound.decay_exploration", false]], "dim (bayes_opt.parameter.bayesparameter property)": [[19, "bayes_opt.parameter.BayesParameter.dim", false]], "dim (bayes_opt.parameter.categoricalparameter property)": [[19, "bayes_opt.parameter.CategoricalParameter.dim", false]], "dim (bayes_opt.parameter.floatparameter property)": [[19, "bayes_opt.parameter.FloatParameter.dim", false]], "dim (bayes_opt.parameter.intparameter property)": [[19, "bayes_opt.parameter.IntParameter.dim", false]], "dim (bayes_opt.targetspace property)": [[20, "bayes_opt.TargetSpace.dim", false]], "empty (bayes_opt.targetspace property)": [[20, "bayes_opt.TargetSpace.empty", false]], "eval() (bayes_opt.constraint.constraintmodel method)": [[15, "bayes_opt.constraint.ConstraintModel.eval", false]], "events (class in bayes_opt)": [[18, "bayes_opt.Events", false]], "expectedimprovement (class in bayes_opt.acquisition)": [[10, "bayes_opt.acquisition.ExpectedImprovement", false]], "fit() (bayes_opt.constraint.constraintmodel method)": [[15, "bayes_opt.constraint.ConstraintModel.fit", false]], "floatparameter (class in bayes_opt.parameter)": [[19, "bayes_opt.parameter.FloatParameter", false]], "gphedge (class in bayes_opt.acquisition)": [[11, "bayes_opt.acquisition.GPHedge", false]], "initialize() (bayes_opt.sequentialdomainreductiontransformer method)": [[16, "bayes_opt.SequentialDomainReductionTransformer.initialize", false]], "intparameter (class in bayes_opt.parameter)": [[19, "bayes_opt.parameter.IntParameter", false]], "is_constrained (bayes_opt.screenlogger property)": [[18, "bayes_opt.ScreenLogger.is_constrained", false]], "is_continuous (bayes_opt.parameter.bayesparameter property)": [[19, "bayes_opt.parameter.BayesParameter.is_continuous", false]], "is_continuous (bayes_opt.parameter.categoricalparameter property)": [[19, "bayes_opt.parameter.CategoricalParameter.is_continuous", false]], "is_continuous (bayes_opt.parameter.floatparameter property)": [[19, "bayes_opt.parameter.FloatParameter.is_continuous", false]], "is_continuous (bayes_opt.parameter.intparameter property)": [[19, "bayes_opt.parameter.IntParameter.is_continuous", false]], "is_numeric() (in module bayes_opt.parameter)": [[19, "bayes_opt.parameter.is_numeric", false]], "jsonlogger (class in bayes_opt)": [[18, "bayes_opt.JSONLogger", false]], "kernel_transform() (bayes_opt.parameter.bayesparameter method)": [[19, "bayes_opt.parameter.BayesParameter.kernel_transform", false]], "kernel_transform() (bayes_opt.parameter.categoricalparameter method)": [[19, "bayes_opt.parameter.CategoricalParameter.kernel_transform", false]], "kernel_transform() (bayes_opt.parameter.floatparameter method)": [[19, "bayes_opt.parameter.FloatParameter.kernel_transform", false]], "kernel_transform() (bayes_opt.parameter.intparameter method)": [[19, "bayes_opt.parameter.IntParameter.kernel_transform", false]], "kernel_transform() (bayes_opt.targetspace method)": [[20, "bayes_opt.TargetSpace.kernel_transform", false]], "keys (bayes_opt.targetspace property)": [[20, "bayes_opt.TargetSpace.keys", false]], "lb (bayes_opt.constraint.constraintmodel property)": [[15, "bayes_opt.constraint.ConstraintModel.lb", false]], "make_masks() (bayes_opt.targetspace method)": [[20, "bayes_opt.TargetSpace.make_masks", false]], "make_params() (bayes_opt.targetspace method)": [[20, "bayes_opt.TargetSpace.make_params", false]], "mask (bayes_opt.targetspace property)": [[20, "bayes_opt.TargetSpace.mask", false]], "masks (bayes_opt.targetspace property)": [[20, "bayes_opt.TargetSpace.masks", false]], "max (bayes_opt.bayesianoptimization property)": [[14, "bayes_opt.BayesianOptimization.max", false]], "max() (bayes_opt.targetspace method)": [[20, "bayes_opt.TargetSpace.max", false]], "maximize() (bayes_opt.bayesianoptimization method)": [[14, "bayes_opt.BayesianOptimization.maximize", false]], "model (bayes_opt.constraint.constraintmodel property)": [[15, "bayes_opt.constraint.ConstraintModel.model", false]], "module": [[8, "module-bayes_opt.acquisition", false], [17, "module-bayes_opt.exception", false], [19, "module-bayes_opt.parameter", false]], "notuniqueerror": [[17, "bayes_opt.exception.NotUniqueError", false]], "novalidpointregisterederror": [[17, "bayes_opt.exception.NoValidPointRegisteredError", false]], "params (bayes_opt.targetspace property)": [[20, "bayes_opt.TargetSpace.params", false]], "params_config (bayes_opt.targetspace property)": [[20, "bayes_opt.TargetSpace.params_config", false]], "params_to_array() (bayes_opt.targetspace method)": [[20, "bayes_opt.TargetSpace.params_to_array", false]], "predict() (bayes_opt.constraint.constraintmodel method)": [[15, "bayes_opt.constraint.ConstraintModel.predict", false]], "probabilityofimprovement (class in bayes_opt.acquisition)": [[12, "bayes_opt.acquisition.ProbabilityOfImprovement", false]], "probe() (bayes_opt.bayesianoptimization method)": [[14, "bayes_opt.BayesianOptimization.probe", false]], "probe() (bayes_opt.targetspace method)": [[20, "bayes_opt.TargetSpace.probe", false]], "random_sample() (bayes_opt.parameter.bayesparameter method)": [[19, "bayes_opt.parameter.BayesParameter.random_sample", false]], "random_sample() (bayes_opt.parameter.categoricalparameter method)": [[19, "bayes_opt.parameter.CategoricalParameter.random_sample", false]], "random_sample() (bayes_opt.parameter.intparameter method)": [[19, "bayes_opt.parameter.IntParameter.random_sample", false]], "random_sample() (bayes_opt.targetspace method)": [[20, "bayes_opt.TargetSpace.random_sample", false]], "register() (bayes_opt.bayesianoptimization method)": [[14, "bayes_opt.BayesianOptimization.register", false]], "register() (bayes_opt.targetspace method)": [[20, "bayes_opt.TargetSpace.register", false]], "res (bayes_opt.bayesianoptimization property)": [[14, "bayes_opt.BayesianOptimization.res", false]], "res() (bayes_opt.targetspace method)": [[20, "bayes_opt.TargetSpace.res", false]], "screenlogger (class in bayes_opt)": [[18, "bayes_opt.ScreenLogger", false]], "sequentialdomainreductiontransformer (class in bayes_opt)": [[16, "bayes_opt.SequentialDomainReductionTransformer", false]], "set_bounds() (bayes_opt.bayesianoptimization method)": [[14, "bayes_opt.BayesianOptimization.set_bounds", false]], "set_bounds() (bayes_opt.targetspace method)": [[20, "bayes_opt.TargetSpace.set_bounds", false]], "set_gp_params() (bayes_opt.bayesianoptimization method)": [[14, "bayes_opt.BayesianOptimization.set_gp_params", false]], "space (bayes_opt.bayesianoptimization property)": [[14, "bayes_opt.BayesianOptimization.space", false]], "suggest() (bayes_opt.acquisition.acquisitionfunction method)": [[8, "bayes_opt.acquisition.AcquisitionFunction.suggest", false]], "suggest() (bayes_opt.acquisition.constantliar method)": [[9, "bayes_opt.acquisition.ConstantLiar.suggest", false]], "suggest() (bayes_opt.acquisition.expectedimprovement method)": [[10, "bayes_opt.acquisition.ExpectedImprovement.suggest", false]], "suggest() (bayes_opt.acquisition.gphedge method)": [[11, "bayes_opt.acquisition.GPHedge.suggest", false]], "suggest() (bayes_opt.acquisition.probabilityofimprovement method)": [[12, "bayes_opt.acquisition.ProbabilityOfImprovement.suggest", false]], "suggest() (bayes_opt.acquisition.upperconfidencebound method)": [[13, "bayes_opt.acquisition.UpperConfidenceBound.suggest", false]], "suggest() (bayes_opt.bayesianoptimization method)": [[14, "bayes_opt.BayesianOptimization.suggest", false]], "target (bayes_opt.targetspace property)": [[20, "bayes_opt.TargetSpace.target", false]], "targetspace (class in bayes_opt)": [[20, "bayes_opt.TargetSpace", false]], "targetspaceemptyerror": [[17, "bayes_opt.exception.TargetSpaceEmptyError", false]], "to_float() (bayes_opt.parameter.bayesparameter method)": [[19, "bayes_opt.parameter.BayesParameter.to_float", false]], "to_float() (bayes_opt.parameter.categoricalparameter method)": [[19, "bayes_opt.parameter.CategoricalParameter.to_float", false]], "to_float() (bayes_opt.parameter.floatparameter method)": [[19, "bayes_opt.parameter.FloatParameter.to_float", false]], "to_float() (bayes_opt.parameter.intparameter method)": [[19, "bayes_opt.parameter.IntParameter.to_float", false]], "to_param() (bayes_opt.parameter.bayesparameter method)": [[19, "bayes_opt.parameter.BayesParameter.to_param", false]], "to_param() (bayes_opt.parameter.categoricalparameter method)": [[19, "bayes_opt.parameter.CategoricalParameter.to_param", false]], "to_param() (bayes_opt.parameter.floatparameter method)": [[19, "bayes_opt.parameter.FloatParameter.to_param", false]], "to_param() (bayes_opt.parameter.intparameter method)": [[19, "bayes_opt.parameter.IntParameter.to_param", false]], "to_string() (bayes_opt.parameter.bayesparameter method)": [[19, "bayes_opt.parameter.BayesParameter.to_string", false]], "to_string() (bayes_opt.parameter.categoricalparameter method)": [[19, "bayes_opt.parameter.CategoricalParameter.to_string", false]], "to_string() (bayes_opt.parameter.floatparameter method)": [[19, "bayes_opt.parameter.FloatParameter.to_string", false]], "transform() (bayes_opt.sequentialdomainreductiontransformer method)": [[16, "bayes_opt.SequentialDomainReductionTransformer.transform", false]], "ub (bayes_opt.constraint.constraintmodel property)": [[15, "bayes_opt.constraint.ConstraintModel.ub", false]], "update() (bayes_opt.jsonlogger method)": [[18, "bayes_opt.JSONLogger.update", false]], "update() (bayes_opt.screenlogger method)": [[18, "bayes_opt.ScreenLogger.update", false]], "upperconfidencebound (class in bayes_opt.acquisition)": [[13, "bayes_opt.acquisition.UpperConfidenceBound", false]], "verbose (bayes_opt.screenlogger property)": [[18, "bayes_opt.ScreenLogger.verbose", false]], "wrap_kernel() (in module bayes_opt.parameter)": [[19, "bayes_opt.parameter.wrap_kernel", false]]}, "objects": {"bayes_opt": [[14, 0, 1, 0, "BayesianOptimization", "Handle optimization of a target function over a specific target space."], [18, 0, 1, 0, "Events", "Define optimization events."], [18, 0, 1, 0, "JSONLogger", "Logger that outputs steps in JSON format."], [18, 0, 1, 0, "ScreenLogger", "Logger that outputs text, e.g. to log to a terminal."], [16, 0, 1, 0, "SequentialDomainReductionTransformer", "Reduce the searchable space."], [20, 0, 1, 0, "TargetSpace", "Holds the param-space coordinates (X) and target values (Y)."], [8, 4, 0, 1, "acquisition", ""], [17, 4, 0, 1, "exception", ""], [19, 4, 0, 1, "parameter", ""]], "bayes_opt.BayesianOptimization": [[14, 2, 1, 0, "acquisition_function", "Return the acquisition function associated with the optimizer."], [14, 2, 1, 0, "constraint", "Return the constraint associated with the optimizer, if any."], [14, 2, 1, 0, "max", "Get maximum target value found and corresponding parameters."], [14, 3, 1, 0, "maximize", "Maximize the given function over the target space."], [14, 3, 1, 0, "probe", "Evaluate the function at the given points."], [14, 3, 1, 0, "register", "Register an observation with known target."], [14, 2, 1, 0, "res", "Get all target values and constraint fulfillment for all parameters."], [14, 3, 1, 0, "set_bounds", "Modify the bounds of the search space."], [14, 3, 1, 0, "set_gp_params", "Set parameters of the internal Gaussian Process Regressor."], [14, 2, 1, 0, "space", "Return the target space associated with the optimizer."], [14, 3, 1, 0, "suggest", "Suggest a promising point to probe next."]], "bayes_opt.BayesianOptimization.__init__": [[14, 1, 2, 0, "acquisition_function", ""], [14, 1, 2, 0, "allow_duplicate_points", "If True, the optimizer will allow duplicate points to be registered. This behavior may be desired in high noise situations where repeatedly probing the same point will give different answers."], [14, 1, 2, 0, "bounds_transformer", "If provided, the transformation is applied to the bounds."], [14, 1, 2, 0, "constraint", "Note that the names of arguments of the constraint function and of f need to be the same."], [14, 1, 2, 0, "f", "Function to be maximized."], [14, 1, 2, 0, "pbounds", "Dictionary with parameters names as keys and a tuple with minimum and maximum values."], [14, 1, 2, 0, "random_state", "If the value is an integer, it is used as the seed for creating a numpy.random.RandomState."], [14, 1, 2, 0, "verbose", "The level of verbosity."]], "bayes_opt.BayesianOptimization.maximize": [[14, 1, 2, 0, "init_points", "Number of random points to probe before starting the optimization."], [14, 1, 2, 0, "n_iter", "Number of iterations where the method attempts to find the maximum value."]], "bayes_opt.BayesianOptimization.probe": [[14, 1, 2, 0, "lazy", "If True, the optimizer will evaluate the points when calling maximize()."], [14, 1, 2, 0, "params", "The parameters where the optimizer will evaluate the function."]], "bayes_opt.BayesianOptimization.register": [[14, 1, 2, 0, "constraint_value", "Value of the constraint function at the observation, if any."], [14, 1, 2, 0, "params", "The parameters associated with the observation."], [14, 1, 2, 0, "target", "Value of the target function at the observation."]], "bayes_opt.BayesianOptimization.set_bounds": [[14, 1, 2, 0, "new_bounds", "A dictionary with the parameter name and its new bounds"]], "bayes_opt.BayesianOptimization.set_gp_params": [[14, 1, 2, 0, "params", ""]], "bayes_opt.JSONLogger": [[18, 3, 1, 0, "update", "Handle incoming events."]], "bayes_opt.JSONLogger.__init__": [[18, 1, 2, 0, "path", "Path to the file to write to."], [18, 1, 2, 0, "reset", "Whether to overwrite the file if it already exists."]], "bayes_opt.JSONLogger.update": [[18, 1, 2, 0, "event", "One of the values associated with Events.OPTIMIZATION_START, Events.OPTIMIZATION_STEP or Events.OPTIMIZATION_END."], [18, 1, 2, 0, "instance", "The instance associated with the step."]], "bayes_opt.ScreenLogger": [[18, 2, 1, 0, "is_constrained", "Return whether the logger is constrained."], [18, 3, 1, 0, "update", "Handle incoming events."], [18, 2, 1, 0, "verbose", "Return the verbosity level."]], "bayes_opt.ScreenLogger.__init__": [[18, 1, 2, 0, "is_constrained", "Whether the logger is associated with a constrained optimization instance."], [18, 1, 2, 0, "verbose", "Verbosity level of the logger."]], "bayes_opt.ScreenLogger.update": [[18, 1, 2, 0, "event", "One of the values associated with Events.OPTIMIZATION_START, Events.OPTIMIZATION_STEP or Events.OPTIMIZATION_END."], [18, 1, 2, 0, "instance", "The instance associated with the step."]], "bayes_opt.SequentialDomainReductionTransformer": [[16, 3, 1, 0, "initialize", "Initialize all of the parameters."], [16, 3, 1, 0, "transform", "Transform the bounds of the target space."]], "bayes_opt.SequentialDomainReductionTransformer.__init__": [[16, 1, 2, 0, "eta", "Zooming parameter used to shrink the region of interest."], [16, 1, 2, 0, "gamma_osc", "Parameter used to scale (typically dampen) oscillations."], [16, 1, 2, 0, "gamma_pan", "Parameter used to scale (typically unitary) panning."], [16, 1, 2, 0, "minimum_window", "Minimum window size for each parameter."], [16, 1, 2, 0, "parameters", ""]], "bayes_opt.SequentialDomainReductionTransformer.initialize": [[16, 1, 2, 0, "target_space", "TargetSpace this DomainTransformer operates on."]], "bayes_opt.SequentialDomainReductionTransformer.transform": [[16, 1, 2, 0, "target_space", "TargetSpace this DomainTransformer operates on."]], "bayes_opt.TargetSpace": [[20, 3, 1, 0, "array_to_params", "Convert an array representation of parameters into a dict version."], [20, 2, 1, 0, "bounds", "Get the bounds of this TargetSpace."], [20, 3, 1, 0, "calculate_bounds", "Calculate the float bounds of the parameter space."], [20, 2, 1, 0, "constraint", "Get the constraint model."], [20, 2, 1, 0, "constraint_values", "Get the constraint values registered to this TargetSpace."], [20, 2, 1, 0, "continuous_dimensions", "Get the continuous parameters."], [20, 2, 1, 0, "dim", "Get the number of parameter names."], [20, 2, 1, 0, "empty", "Check if anything has been registered."], [20, 3, 1, 0, "kernel_transform", "Transform floating-point suggestions to values used in the kernel."], [20, 2, 1, 0, "keys", "Get the keys (or parameter names)."], [20, 3, 1, 0, "make_masks", "Create a dictionary of masks for the parameters."], [20, 3, 1, 0, "make_params", "Create a dictionary of parameters from a dictionary of bounds."], [20, 2, 1, 0, "mask", "Return a boolean array of valid points."], [20, 2, 1, 0, "masks", "Get the masks for the parameters."], [20, 3, 1, 0, "max", "Get maximum target value found and corresponding parameters."], [20, 2, 1, 0, "params", "Get the parameter values registered to this TargetSpace."], [20, 2, 1, 0, "params_config", "Get the parameters configuration."], [20, 3, 1, 0, "params_to_array", "Convert a dict representation of parameters into an array version."], [20, 3, 1, 0, "probe", "Evaluate the target function on a point and register the result."], [20, 3, 1, 0, "random_sample", "Sample a random point from within the bounds of the space."], [20, 3, 1, 0, "register", "Append a point and its target value to the known data."], [20, 3, 1, 0, "res", "Get all target values and constraint fulfillment for all parameters."], [20, 3, 1, 0, "set_bounds", "Change the lower and upper search bounds."], [20, 2, 1, 0, "target", "Get the target function values registered to this TargetSpace."]], "bayes_opt.TargetSpace.__init__": [[20, 1, 2, 0, "allow_duplicate_points", "If True, the optimizer will allow duplicate points to be registered. This behavior may be desired in high noise situations where repeatedly probing the same point will give different answers."], [20, 1, 2, 0, "constraint", ""], [20, 1, 2, 0, "pbounds", "Dictionary with parameters names as keys and a tuple with minimum and maximum values."], [20, 1, 2, 0, "random_state", "optionally specify a seed for a random number generator"], [20, 1, 2, 0, "target_func", "Function to be maximized."]], "bayes_opt.TargetSpace.array_to_params": [[20, 1, 2, 0, "x", "a single point, with len(x) == self.dim."]], "bayes_opt.TargetSpace.kernel_transform": [[20, 1, 2, 0, "value", ""]], "bayes_opt.TargetSpace.make_params": [[20, 1, 2, 0, "pbounds", "A dictionary with the parameter names as keys and a tuple with minimum and maximum values."]], "bayes_opt.TargetSpace.params_to_array": [[20, 1, 2, 0, "params", "a single point, with len(x) == self.dim."]], "bayes_opt.TargetSpace.probe": [[20, 1, 2, 0, "params", "a single point, with len(x) == self.dim"]], "bayes_opt.TargetSpace.random_sample": [[20, 1, 2, 0, "n_samples", "Number of samples to draw."], [20, 1, 2, 0, "random_state", "The random state to use for sampling."]], "bayes_opt.TargetSpace.register": [[20, 1, 2, 0, "constraint_value", "Constraint function value"], [20, 1, 2, 0, "params", "a single point, with len(x) == self.dim."], [20, 1, 2, 0, "target", "target function value"]], "bayes_opt.TargetSpace.set_bounds": [[20, 1, 2, 0, "new_bounds", "A dictionary with the parameter name and its new bounds"]], "bayes_opt.acquisition": [[8, 0, 1, 0, "AcquisitionFunction", "Base class for acquisition functions."], [9, 0, 1, 0, "ConstantLiar", "Constant Liar acquisition function."], [10, 0, 1, 0, "ExpectedImprovement", "Expected Improvement acqusition function."], [11, 0, 1, 0, "GPHedge", "GPHedge acquisition function."], [12, 0, 1, 0, "ProbabilityOfImprovement", "Probability of Improvement acqusition function."], [13, 0, 1, 0, "UpperConfidenceBound", "Upper Confidence Bound acquisition function."]], "bayes_opt.acquisition.AcquisitionFunction": [[8, 3, 1, 0, "base_acq", "Provide access to the base acquisition function."], [8, 3, 1, 0, "suggest", "Suggest a promising point to probe next."]], "bayes_opt.acquisition.AcquisitionFunction.__init__": [[8, 1, 2, 0, "random_state", "Set the random state for reproducibility."]], "bayes_opt.acquisition.AcquisitionFunction.base_acq": [[8, 1, 2, 0, "args", ""], [8, 1, 2, 0, "kwargs", ""]], "bayes_opt.acquisition.AcquisitionFunction.suggest": [[8, 1, 2, 0, "fit_gp", "Whether to fit the Gaussian Process to the target space. Set to False if the GP is already fitted."], [8, 1, 2, 0, "gp", "A fitted Gaussian Process."], [8, 1, 2, 0, "n_l_bfgs_b", "Number of starting points for the L-BFGS-B optimizer."], [8, 1, 2, 0, "n_random", "Number of random samples to use."], [8, 1, 2, 0, "target_space", "The target space to probe."]], "bayes_opt.acquisition.ConstantLiar": [[9, 3, 1, 0, "base_acq", "Calculate the acquisition function."], [9, 3, 1, 0, "suggest", "Suggest a promising point to probe next."]], "bayes_opt.acquisition.ConstantLiar.__init__": [[9, 1, 2, 0, "atol", "Absolute tolerance to eliminate a dummy point."], [9, 1, 2, 0, "base_acquisition", "The acquisition function to use."], [9, 1, 2, 0, "random_state", "Set the random state for reproducibility."], [9, 1, 2, 0, "rtol", "Relative tolerance to eliminate a dummy point."], [9, 1, 2, 0, "strategy", "Strategy to use for the constant liar."]], "bayes_opt.acquisition.ConstantLiar.base_acq": [[9, 1, 2, 0, "args", ""], [9, 1, 2, 0, "kwargs", ""]], "bayes_opt.acquisition.ConstantLiar.suggest": [[9, 1, 2, 0, "fit_gp", "Whether to fit the Gaussian Process to the target space. Set to False if the GP is already fitted."], [9, 1, 2, 0, "gp", "A fitted Gaussian Process."], [9, 1, 2, 0, "n_l_bfgs_b", "Number of starting points for the L-BFGS-B optimizer."], [9, 1, 2, 0, "n_random", "Number of random samples to use."], [9, 1, 2, 0, "target_space", "The target space to probe."]], "bayes_opt.acquisition.ExpectedImprovement": [[10, 3, 1, 0, "base_acq", "Calculate the expected improvement."], [10, 3, 1, 0, "decay_exploration", "Decay xi by a constant rate."], [10, 3, 1, 0, "suggest", "Suggest a promising point to probe next."]], "bayes_opt.acquisition.ExpectedImprovement.__init__": [[10, 1, 2, 0, "exploration_decay", "Decay rate for xi."], [10, 1, 2, 0, "exploration_decay_delay", ""], [10, 1, 2, 0, "random_state", "Set the random state for reproducibility."], [10, 1, 2, 0, "xi", "Governs the exploration/exploitation tradeoff."]], "bayes_opt.acquisition.ExpectedImprovement.base_acq": [[10, 1, 2, 0, "mean", "Mean of the predictive distribution."], [10, 1, 2, 0, "std", "Standard deviation of the predictive distribution."]], "bayes_opt.acquisition.ExpectedImprovement.suggest": [[10, 1, 2, 0, "fit_gp", "Whether to fit the Gaussian Process to the target space. Set to False if the GP is already fitted."], [10, 1, 2, 0, "gp", "A fitted Gaussian Process."], [10, 1, 2, 0, "n_l_bfgs_b", "Number of starting points for the L-BFGS-B optimizer."], [10, 1, 2, 0, "n_random", "Number of random samples to use."], [10, 1, 2, 0, "target_space", "The target space to probe."]], "bayes_opt.acquisition.GPHedge": [[11, 3, 1, 0, "base_acq", "Raise an error, since the base acquisition function is ambiguous."], [11, 3, 1, 0, "suggest", "Suggest a promising point to probe next."]], "bayes_opt.acquisition.GPHedge.__init__": [[11, 1, 2, 0, "base_acquisitions", "Sequence of base acquisition functions."], [11, 1, 2, 0, "random_state", "Set the random state for reproducibility."]], "bayes_opt.acquisition.GPHedge.base_acq": [[11, 1, 2, 0, "args", ""], [11, 1, 2, 0, "kwargs", ""]], "bayes_opt.acquisition.GPHedge.suggest": [[11, 1, 2, 0, "fit_gp", "Whether to fit the Gaussian Process to the target space. Set to False if the GP is already fitted."], [11, 1, 2, 0, "gp", "A fitted Gaussian Process."], [11, 1, 2, 0, "n_l_bfgs_b", "Number of starting points for the L-BFGS-B optimizer."], [11, 1, 2, 0, "n_random", "Number of random samples to use."], [11, 1, 2, 0, "target_space", "The target space to probe."]], "bayes_opt.acquisition.ProbabilityOfImprovement": [[12, 3, 1, 0, "base_acq", "Calculate the probability of improvement."], [12, 3, 1, 0, "decay_exploration", "Decay xi by a constant rate."], [12, 3, 1, 0, "suggest", "Suggest a promising point to probe next."]], "bayes_opt.acquisition.ProbabilityOfImprovement.__init__": [[12, 1, 2, 0, "exploration_decay", "Decay rate for xi."], [12, 1, 2, 0, "exploration_decay_delay", "Delay for decay."], [12, 1, 2, 0, "random_state", "Set the random state for reproducibility."], [12, 1, 2, 0, "xi", "Governs the exploration/exploitation tradeoff."]], "bayes_opt.acquisition.ProbabilityOfImprovement.base_acq": [[12, 1, 2, 0, "mean", "Mean of the predictive distribution."], [12, 1, 2, 0, "std", "Standard deviation of the predictive distribution."]], "bayes_opt.acquisition.ProbabilityOfImprovement.suggest": [[12, 1, 2, 0, "fit_gp", "Whether to fit the Gaussian Process to the target space. Set to False if the GP is already fitted."], [12, 1, 2, 0, "gp", "A fitted Gaussian Process."], [12, 1, 2, 0, "n_l_bfgs_b", "Number of starting points for the L-BFGS-B optimizer."], [12, 1, 2, 0, "n_random", "Number of random samples to use."], [12, 1, 2, 0, "target_space", "The target space to probe."]], "bayes_opt.acquisition.UpperConfidenceBound": [[13, 3, 1, 0, "base_acq", "Calculate the upper confidence bound."], [13, 3, 1, 0, "decay_exploration", "Decay kappa by a constant rate."], [13, 3, 1, 0, "suggest", "Suggest a promising point to probe next."]], "bayes_opt.acquisition.UpperConfidenceBound.__init__": [[13, 1, 2, 0, "exploration_decay", "Decay rate for kappa."], [13, 1, 2, 0, "exploration_decay_delay", "Delay for decay."], [13, 1, 2, 0, "kappa", "Governs the exploration/exploitation tradeoff."], [13, 1, 2, 0, "random_state", "Set the random state for reproducibility."]], "bayes_opt.acquisition.UpperConfidenceBound.base_acq": [[13, 1, 2, 0, "mean", "Mean of the predictive distribution."], [13, 1, 2, 0, "std", "Standard deviation of the predictive distribution."]], "bayes_opt.acquisition.UpperConfidenceBound.suggest": [[13, 1, 2, 0, "fit_gp", "Whether to fit the Gaussian Process to the target space. Set to False if the GP is already fitted."], [13, 1, 2, 0, "gp", "A fitted Gaussian Process."], [13, 1, 2, 0, "n_l_bfgs_b", "Number of starting points for the L-BFGS-B optimizer."], [13, 1, 2, 0, "n_random", "Number of random samples to use."], [13, 1, 2, 0, "target_space", "The target space to probe."]], "bayes_opt.constraint": [[15, 0, 1, 0, "ConstraintModel", "Model constraints using GP regressors."]], "bayes_opt.constraint.ConstraintModel": [[15, 3, 1, 0, "allowed", "Check whether constraint_values fulfills the specified limits."], [15, 3, 1, 0, "approx", "Approximate the constraint function using the internal GPR model."], [15, 3, 1, 0, "eval", "Evaluate the constraint function."], [15, 3, 1, 0, "fit", "Fit internal GPRs to the data."], [15, 2, 1, 0, "lb", "Return lower bounds."], [15, 2, 1, 0, "model", "Return GP regressors of the constraint function."], [15, 3, 1, 0, "predict", "Calculate the probability that the constraint is fulfilled at X."], [15, 2, 1, 0, "ub", "Return upper bounds."]], "bayes_opt.constraint.ConstraintModel.__init__": [[15, 1, 2, 0, "fun", "The constraint function."], [15, 1, 2, 0, "lb", "The lower bound on the constraints."], [15, 1, 2, 0, "random_state", "Random state to use."], [15, 1, 2, 0, "transform", ""], [15, 1, 2, 0, "ub", "The upper bound on the constraints."]], "bayes_opt.constraint.ConstraintModel.allowed": [[15, 1, 2, 0, "constraint_values", "The values of the constraint function."]], "bayes_opt.constraint.ConstraintModel.approx": [[15, 1, 2, 0, "X", "Parameters for which to estimate the constraint function value."]], "bayes_opt.constraint.ConstraintModel.eval": [[15, 1, 2, "bayes_opt.constraint.ConstraintModel.eval", "kwargs", "Evaluate the constraint function."]], "bayes_opt.constraint.ConstraintModel.fit": [[15, 1, 2, 0, "X", "Parameters of the constraint function."], [15, 1, 2, 0, "Y", "Values of the constraint function."]], "bayes_opt.constraint.ConstraintModel.predict": [[15, 1, 2, 0, "X", "Parameters for which to predict the probability of constraint fulfilment."]], "bayes_opt.exception": [[17, 5, 1, 0, "BayesianOptimizationError", "Base class for exceptions in the Bayesian Optimization."], [17, 5, 1, 0, "ConstraintNotSupportedError", "Raised when constrained optimization is not supported."], [17, 5, 1, 0, "NoValidPointRegisteredError", "Raised when an acquisition function depends on previous points but none are registered."], [17, 5, 1, 0, "NotUniqueError", "A point is non-unique."], [17, 5, 1, 0, "TargetSpaceEmptyError", "Raised when the target space is empty."]], "bayes_opt.parameter": [[19, 0, 1, 0, "BayesParameter", "Base class for Bayesian optimization parameters."], [19, 0, 1, 0, "CategoricalParameter", "A parameter with categorical values."], [19, 0, 1, 0, "FloatParameter", "A parameter with float values."], [19, 0, 1, 0, "IntParameter", "A parameter with int values."], [19, 6, 1, 0, "is_numeric", "Check if a value is numeric."], [19, 6, 1, 0, "wrap_kernel", "Wrap a kernel to transform input data before passing it to the kernel."]], "bayes_opt.parameter.BayesParameter": [[19, 2, 1, 0, "bounds", "The bounds of the parameter in float space."], [19, 2, 1, 0, "dim", "The dimensionality of the parameter."], [19, 2, 1, 0, "is_continuous", "Whether the parameter is continuous."], [19, 3, 1, 0, "kernel_transform", "Transform a parameter value for use in a kernel."], [19, 3, 1, 0, "random_sample", "Generate random samples from the parameter."], [19, 3, 1, 0, "to_float", "Convert a parameter value to a float."], [19, 3, 1, 0, "to_param", "Convert a float value to a parameter."], [19, 3, 1, 0, "to_string", "Represent a parameter value as a string."]], "bayes_opt.parameter.BayesParameter.__init__": [[19, 1, 2, 0, "bounds", ""], [19, 1, 2, 0, "name", "The name of the parameter."]], "bayes_opt.parameter.BayesParameter.kernel_transform": [[19, 1, 2, 0, "value", "The value(s) to transform, should be a float."]], "bayes_opt.parameter.BayesParameter.random_sample": [[19, 1, 2, 0, "n_samples", "The number of samples to generate."], [19, 1, 2, 0, "random_state", "The random state to use for sampling."]], "bayes_opt.parameter.BayesParameter.to_float": [[19, 1, 2, 0, "value", "The value to convert, should be the canonical representation of the parameter."]], "bayes_opt.parameter.BayesParameter.to_param": [[19, 1, 2, 0, "value", "The value to convert, should be a float."]], "bayes_opt.parameter.BayesParameter.to_string": [[19, 1, 2, 0, "str_len", "The maximum length of the string representation."], [19, 1, 2, 0, "value", "The value to represent."]], "bayes_opt.parameter.CategoricalParameter": [[19, 2, 1, 0, "dim", "The dimensionality of the parameter."], [19, 2, 1, 0, "is_continuous", "Whether the parameter is continuous."], [19, 3, 1, 0, "kernel_transform", "Transform a parameter value for use in a kernel."], [19, 3, 1, 0, "random_sample", "Generate random float-format samples from the parameter."], [19, 3, 1, 0, "to_float", "Convert a parameter value to a float."], [19, 3, 1, 0, "to_param", "Convert a float value to a parameter."], [19, 3, 1, 0, "to_string", "Represent a parameter value as a string."]], "bayes_opt.parameter.CategoricalParameter.__init__": [[19, 1, 2, 0, "categories", "The categories of the parameter."], [19, 1, 2, 0, "name", "The name of the parameter."]], "bayes_opt.parameter.CategoricalParameter.kernel_transform": [[19, 1, 2, 0, "value", "The value(s) to transform, should be a float."]], "bayes_opt.parameter.CategoricalParameter.random_sample": [[19, 1, 2, 0, "n_samples", "The number of samples to generate."], [19, 1, 2, 0, "random_state", "The random state to use for sampling."]], "bayes_opt.parameter.CategoricalParameter.to_float": [[19, 1, 2, 0, "value", "The value to convert, should be the canonical representation of the parameter."]], "bayes_opt.parameter.CategoricalParameter.to_param": [[19, 1, 2, 0, "value", "The value to convert, should be a float."]], "bayes_opt.parameter.CategoricalParameter.to_string": [[19, 1, 2, 0, "str_len", "The maximum length of the string representation."], [19, 1, 2, 0, "value", "The value to represent."]], "bayes_opt.parameter.FloatParameter": [[19, 2, 1, 0, "dim", "The dimensionality of the parameter."], [19, 2, 1, 0, "is_continuous", "Whether the parameter is continuous."], [19, 3, 1, 0, "kernel_transform", "Transform a parameter value for use in a kernel."], [19, 3, 1, 0, "to_float", "Convert a parameter value to a float."], [19, 3, 1, 0, "to_param", "Convert a float value to a parameter."], [19, 3, 1, 0, "to_string", "Represent a parameter value as a string."]], "bayes_opt.parameter.FloatParameter.__init__": [[19, 1, 2, 0, "bounds", "The bounds of the parameter."], [19, 1, 2, 0, "name", "The name of the parameter."]], "bayes_opt.parameter.FloatParameter.kernel_transform": [[19, 1, 2, 0, "value", "The value(s) to transform, should be a float."]], "bayes_opt.parameter.FloatParameter.to_float": [[19, 1, 2, 0, "value", "The value to convert, should be the canonical representation of the parameter."]], "bayes_opt.parameter.FloatParameter.to_param": [[19, 1, 2, 0, "value", "The value to convert, should be a float."]], "bayes_opt.parameter.FloatParameter.to_string": [[19, 1, 2, 0, "str_len", "The maximum length of the string representation."], [19, 1, 2, 0, "value", "The value to represent."]], "bayes_opt.parameter.IntParameter": [[19, 2, 1, 0, "dim", "The dimensionality of the parameter."], [19, 2, 1, 0, "is_continuous", "Whether the parameter is continuous."], [19, 3, 1, 0, "kernel_transform", "Transform a parameter value for use in a kernel."], [19, 3, 1, 0, "random_sample", "Generate random samples from the parameter."], [19, 3, 1, 0, "to_float", "Convert a parameter value to a float."], [19, 3, 1, 0, "to_param", "Convert a float value to a parameter."]], "bayes_opt.parameter.IntParameter.__init__": [[19, 1, 2, 0, "bounds", "The bounds of the parameter."], [19, 1, 2, 0, "name", "The name of the parameter."]], "bayes_opt.parameter.IntParameter.kernel_transform": [[19, 1, 2, 0, "value", "The value(s) to transform, should be a float."]], "bayes_opt.parameter.IntParameter.random_sample": [[19, 1, 2, 0, "n_samples", "The number of samples to generate."], [19, 1, 2, 0, "random_state", "The random state to use for sampling."]], "bayes_opt.parameter.IntParameter.to_float": [[19, 1, 2, 0, "value", "The value to convert, should be the canonical representation of the parameter."]], "bayes_opt.parameter.IntParameter.to_param": [[19, 1, 2, 0, "value", "The value to convert, should be a float."]], "bayes_opt.parameter.is_numeric": [[19, 1, 2, 0, "value", ""]], "bayes_opt.parameter.wrap_kernel": [[19, 1, 2, 0, "kernel", "The kernel to wrap."], [19, 1, 2, 0, "transform", "The transformation function to apply to the input data."]]}, "objnames": {"0": ["py", "class", "Python class"], "1": ["py", "parameter", "Python parameter"], "2": ["py", "property", "Python property"], "3": ["py", "method", "Python method"], "4": ["py", "module", "Python module"], "5": ["py", "exception", "Python exception"], "6": ["py", "function", "Python function"]}, "objtypes": {"0": "py:class", "1": "py:parameter", "2": "py:property", "3": "py:method", "4": "py:module", "5": "py:exception", "6": "py:function"}, "terms": {"": [0, 1, 3, 4, 5, 6, 7, 9, 19], "0": [0, 1, 2, 3, 4, 7, 16, 20, 21], "00": 7, "000000000116973": 3, "0002287496346898": 2, "0008684793556073": 3, "005532": 2, "008": 7, "0084": 2, "0087879313090253": 3, "009985740060171": 1, "01": [0, 7, 21], "012177": 7, "015": 2, "0154397119682423": 2, "01589": 7, "016": [0, 2], "02": 7, "021": 21, "021127762033562": 3, "02272": 2, "024": 0, "025": 0, "026": 0, "02644400210430190": 6, "027": 0, "02816": 2, "0288343947238228": 2, "029": 2, "03061": 7, "033": 21, "034": 2, "03463": 19, "039": 0, "043": 2, "045": 2, "04749": 2, "05": 9, "052": 7, "053787": 7, "0583506": 7, "0587256": 7, "06": [6, 7], "06468": 7, "069": 2, "06916": 2, "07374": 2, "076": 0, "07993": 2, "08": 9, "0889271": 7, "089558": 7, "092074": 7, "093": 2, "0x23439267280": 4, "0x23439267fd0": 4, "1": [0, 4, 16, 20, 21], "10": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 21], "100": [0, 3, 20], "1000": [0, 3, 7], "10000": [0, 5, 8, 9, 10, 11, 12, 13, 21], "1009": 11, "109": 21, "10_000": [8, 9, 10, 11, 12, 13], "11": [1, 2, 3, 4, 7, 21], "1108": 6, "1166": 2, "119714": 7, "12": [1, 2, 3, 7, 21], "1206357": 7, "125728": 7, "13": [0, 1, 2, 3, 7, 21], "132": 0, "135": 2, "135455292718879": 2, "137": 7, "1392349": 7, "1396": 2, "13961016009280103": 2, "14": [1, 2, 3, 7, 21], "141769": 1, "15": [0, 1, 2, 3, 7, 21], "1501": 2, "154": [7, 21], "158": 21, "16": [0, 1, 2, 3, 5, 7, 21], "161": 7, "165955": 7, "168": 2, "1697": 7, "17": [2, 3, 7], "173": 0, "175": 2, "18": [1, 2, 7], "1805": 19, "186": 2, "1860045642089614": 2, "19": [2, 6, 7], "193": 2, "1948749": 1, "1_000": 0, "1d": 20, "1e": [0, 1, 7, 9], "2": [0, 4, 5, 13, 14, 15, 18, 20, 21], "20": [0, 2, 4, 6, 7, 21], "2002": [4, 6], "2014": [3, 6], "2017": 21, "2020": 6, "2032449": 7, "2094": 21, "21": [2, 7], "2122": 21, "213867": 7, "2146": 7, "2152": 7, "2153": 7, "2164322": 7, "218182": 7, "218693": 7, "22": 7, "2249733": 7, "229": 2, "23": 7, "230305457787476": 3, "235835023240657": 2, "2361": [0, 7], "2362": 7, "24": [2, 7], "247113": 7, "25": [7, 14], "26": 7, "2625": 7, "266": 2, "267": [0, 7], "2682240": 7, "27": [7, 21], "271": 2, "28": 7, "286": 1, "2864": 7, "29": 7, "2975037081163068": 3, "298": 7, "298167": 1, "3": [0, 3, 4, 5, 21], "30": [0, 7, 21], "301617813728339": 2, "302": 2, "31": 7, "32": 7, "321554535694256": 7, "3219469": 1, "3219469606529486": [1, 2], "322": [2, 7], "328297354179696": 1, "33": 7, "331911": 1, "331911981189704": 1, "33253689": 20, "3338": 2, "3361": 7, "339072": 7, "34": 1, "3452580": 7, "3464": 7, "35": [2, 6, 7], "352913": 7, "3585570": 7, "36": 7, "360726": 7, "37": 7, "3718": 7, "372": 7, "3725022": 7, "374": 21, "3763326769822668": 1, "377351": 7, "378": 2, "38": 7, "380": 6, "39": [1, 2, 3, 7], "4": [0, 1, 3, 4, 20, 21], "40": 7, "41": 7, "4118274": 7, "413": 7, "4178": 2, "418942": 7, "42": [0, 5, 7], "428": 7, "43": 7, "4366": 7, "44": 7, "4406489": 7, "446": 2, "45": [0, 7], "4572": 7, "46": 7, "4600371": 7, "4649": 7, "47": 7, "48": 2, "4854116": 7, "4868": 7, "4882": 7, "49": 7, "4937": 7, "4938459": 7, "4946": 7, "497": 1, "4977": 7, "498": 7, "4981209": 7, "499": 7, "4999": 7, "5": [0, 1, 2, 3, 4, 5, 9, 14, 20, 21], "50": [4, 5], "5034": 7, "5044": 2, "5063": 7, "5081": 7, "5096": 7, "5097": 7, "51": 7, "511": 0, "517960": 7, "5192": 7, "5204": 7, "521": 7, "523": 0, "5233": 7, "5235": 7, "525": 0, "5279": 7, "53": 7, "5328": 7, "533137": 7, "5355": 7, "5358": 7, "5386": 7, "54": 7, "5401": 7, "5405": 7, "5412": 7, "5418": 7, "5419": 11, "542": 7, "5426": 0, "5427": 7, "543264": 7, "5436849": 7, "5442": 7, "5443": 7, "54488318": 20, "55": 20, "559031075654336": 1, "56": 7, "560384": 7, "565": 2, "57": 7, "5721": 0, "574376": 7, "576": [13, 21], "58": 7, "582": [2, 21], "5822074982517598": 2, "6": [0, 1, 2, 3, 4, 5, 7, 20, 21], "60": 7, "61": 7, "615": 7, "6164": 2, "617": 0, "6373": 7, "6407143853831352": 7, "641": 7, "6432": 7, "6434939": 7, "64727683929071": 3, "648372": 7, "651": 2, "652553": 7, "6535": [2, 7], "6549696": 7, "6550869": 7, "66": 2, "6605": 2, "6627": 7, "663312": 7, "6644394": 7, "674": 7, "69": 7, "7": [0, 1, 2, 3, 4, 5, 7, 16, 21], "70": 7, "705": 21, "707136686093495": 1, "709177": 7, "71": 7, "7133425": 7, "7146": 2, "7342": 0, "7368226": 7, "74": 7, "740988": 7, "741717": 7, "7419056": 7, "743433": 7, "743751": 7, "7449728": 7, "746": 21, "7473": 7, "749": 7, "75": 7, "750594563473972": 1, "7529885": 7, "755390": 7, "758": 2, "775": 21, "779531005607566": 2, "78": 2, "780621": 7, "781": 1, "7821": 2, "7852": 7, "7861845912690544": 1, "7862": 1, "787692": 7, "7935035": 7, "7985": 0, "8": [0, 1, 2, 3, 4, 5, 7, 9, 21], "80": 3, "8007": 7, "801": 0, "8025": 7, "813": 2, "819": 21, "8198": 21, "82": 0, "820073": 7, "8275": 7, "827713": 7, "829779": 7, "83": 2, "8336": 7, "834": 2, "8340440094051482": 2, "8358733": 7, "8427": 2, "8458666395359906": 2, "846": 2, "853": 7, "854": 2, "857": 0, "8600832617014085": 1, "87": 7, "8731": 1, "875": 0, "88": 7, "8809": 0, "8830942": 1, "8877": 0, "8917": 2, "892371": 7, "9": [0, 1, 2, 3, 4, 5, 7, 16, 20, 21], "9021640": 1, "905925": 7, "9083667381450318": 1, "915481333972961": 1, "918642": 7, "9203": 7, "9227": 0, "9261486197444082": 1, "9266": 2, "932": 0, "934": 0, "937": 6, "9421": 2, "9434": 2, "945": 6, "9471": 2, "95": [0, 21], "953": 0, "953302": 1, "954": 0, "955": 0, "9600": [0, 21], "961": 0, "962": 0, "964": 0, "965222": 1, "966506881014352": 2, "967": 2, "9686133847781613": 1, "9709879": 7, "9734131381980626": 3, "974": 2, "975": 0, "9757": 2, "976": 2, "9763198875239296": 1, "976674": 7, "9778": 2, "979183535803597": 1, "981065": 7, "9819": 2, "986": 7, "987234": 5, "9874217": 7, "9876938": 1, "988": 2, "9885278383464513": 1, "9892216": 7, "9897298": 7, "99": [0, 7], "9912": 2, "9913127": 1, "9917667": 7, "9926": 7, "992664": 7, "9928960": 7, "9943696": 7, "9944598": 7, "9945010": 7, "995": 2, "9950290": 7, "9950794": 7, "995643": 1, "9958": 0, "997": 2, "9970839": 7, "9970984": 7, "9971028": 7, "9973": 0, "998": 21, "9980810": 7, "9986856": 7, "9987136": 7, "9988": 2, "998856": 7, "999625": 7, "999635": 7, "9996360060323246": 1, "99963711795217": 2, "9997": 2, "999771": 7, "9999891845990944": 3, "A": [3, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 19, 20, 21], "And": 1, "As": [0, 2, 6, 7], "At": [2, 6, 11], "By": [1, 2], "For": [0, 1, 2, 3, 6, 7, 11], "If": [0, 1, 2, 6, 7, 9, 10, 12, 13, 14, 15, 16, 20], "In": [0, 1, 3, 7, 14, 15, 20, 21], "It": [2, 4, 7, 9, 21], "NOT": 1, "On": [4, 6, 16], "One": [0, 1, 7, 8, 18], "Or": 2, "That": 15, "The": [0, 2, 3, 4, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21], "Then": 11, "There": [2, 3, 4, 7], "These": 4, "To": [2, 3, 6, 7], "Will": 1, "_": 1, "__init__": [0, 7], "_default_cell_s": 7, "_fit_gp": [0, 7, 21], "_get_acq": [0, 21], "_gp": [0, 3, 5, 7, 14, 21], "_kei": 20, "_space": [0, 7, 21], "a_": 3, "ab": [7, 11, 19], "abc": [14, 15, 16, 20], "abl": [2, 3, 21], "about": [2, 6, 7], "abov": [1, 3], "absolut": 9, "abstract": [8, 19], "accept": 0, "access": [0, 2, 3, 8], "accommod": 7, "accordingli": 3, "account": [2, 6], "acklei": 4, "acq": [0, 1, 3, 21], "acq_i": 11, "acquisit": [1, 2, 3, 6, 7, 14, 17, 20, 21], "acquisition_funct": [0, 1, 5, 7, 14, 21], "acquisition_function_": 0, "acquisitionfunct": [0, 1, 8, 9, 11, 14], "acqusit": [10, 12], "across": 5, "action": 7, "activ": 6, "actual": [3, 4, 7], "ad": 0, "add": 7, "addit": [3, 6], "addition": [0, 3, 6, 21], "adequ": [2, 6], "adjust": [0, 10, 12, 13], "admit": 7, "admittedli": 3, "advanc": [2, 6], "advantag": 4, "after": [0, 4, 7, 11, 14], "again": 9, "aid": 6, "al": [3, 11], "albeit": [2, 6], "alert": 2, "algorithm": [1, 2, 6, 9], "align": 7, "all": [0, 1, 2, 7, 8, 14, 16, 20], "alloc": 11, "allow": [2, 3, 7, 14, 15, 20, 21], "allow_duplicate_point": [3, 14, 20], "alon": 8, "along": 3, "alpha": [0, 5, 7, 21], "alreadi": [3, 8, 9, 10, 11, 12, 13, 18], "also": [0, 2, 7, 10], "alter": 2, "altern": 1, "alwai": [0, 7, 8, 9], "ambigu": 11, "amort": 20, "an": [0, 1, 2, 3, 4, 6, 7, 11, 14, 17, 18, 20], "ani": [1, 2, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 19, 20], "anisotrop": 7, "anoth": [1, 3, 7], "answer": [14, 20], "anyth": [6, 20], "append": 20, "appli": [4, 10, 12, 13, 14, 19], "applicap": 0, "approach": [3, 7], "appropri": 7, "approx": [3, 15], "approxim": [3, 15], "ar": [0, 1, 2, 3, 4, 5, 6, 7, 11, 15, 17, 20], "area": [3, 7], "area_of_triangl": 7, "arg": [8, 9, 11], "arg1": 4, "arg2": 4, "argmax": [0, 21], "argument": [0, 3, 14, 15], "around": [1, 4, 5], "arrai": [0, 3, 7, 15, 20, 21], "array_param": 7, "array_to_param": 20, "articl": 6, "arxiv": [11, 19], "assert": [0, 20], "assess": 3, "associ": [1, 2, 14, 18], "assum": [3, 15], "async_optim": 1, "asynchron": 9, "atleast_2d": [0, 7], "atol": 9, "attempt": [2, 6, 14], "author": [4, 6], "automat": [3, 10, 12, 13], "avail": 2, "avoid": 3, "awai": 0, "awar": [2, 7], "ax": [0, 3, 7], "ax2": 0, "axi": [0, 4, 7, 21], "b": [4, 7, 8, 9, 10, 11, 12, 13], "back": [1, 2], "bad": 3, "balanc": [2, 6, 21], "base": [0, 3, 4, 6, 7, 8, 9, 11, 14, 16, 17, 19], "base_acq": [0, 8, 9, 10, 11, 12, 13], "base_acquisit": [9, 11], "basic": [6, 8], "basicobserv": 1, "bayes_opt": [0, 1, 2, 3, 4, 5, 7, 18, 21], "bayesian": [3, 7, 8, 11, 14, 15, 17, 19, 21], "bayesian_optim": 18, "bayesianoptim": [0, 1, 2, 3, 4, 5, 6, 7, 18], "bayesianoptimizationerror": 17, "bayesparamet": [7, 19, 20], "bbox_to_anchor": [0, 21], "becaus": 3, "becom": [0, 2, 6], "been": [3, 7, 9, 20], "befor": [2, 3, 7, 14, 19], "begin": 0, "behav": 18, "behavior": [3, 8, 14, 20], "being": [1, 7, 21], "below": [2, 3, 6, 7], "benefici": 1, "best": [0, 1, 2, 3, 6, 21], "better": 1, "between": [2, 3, 6, 7, 21], "bewar": 2, "bfg": [8, 9, 10, 11, 12, 13], "bin": 7, "bit": 3, "black": 3, "black_box_funct": [1, 2], "black_box_function_no_constraint": 3, "black_box_function_with_constraint": 3, "blackbox": 1, "block": 6, "blocki": 7, "bo": [2, 5, 7], "bo_cont": 7, "bo_disc": 7, "bold": 21, "booktitl": 6, "bool": [0, 8, 9, 10, 11, 12, 13, 14, 15, 18, 19, 20], "boolean": 20, "borderaxespad": [0, 21], "both": [3, 4, 7, 20], "bottom": 3, "bound": [1, 3, 4, 6, 7, 13, 14, 15, 16, 19, 20, 21], "bound_transform": 4, "boundari": 20, "bounds_transform": [4, 14], "bounds_transformers_iter": 4, "boundsmap": 20, "box": [0, 2], "brochu": 11, "brows": 2, "bug": 6, "build": 6, "built": [0, 2, 4, 6], "c": [0, 3, 5, 6, 7, 15, 21], "c_": 3, "c_1": 3, "c_2": 3, "c_est": 3, "c_label": 3, "c_n": 3, "c_pbound": 7, "c_pred": 7, "cach": 20, "calcul": [4, 9, 10, 12, 13, 15, 20], "calculate_bound": 20, "call": [0, 1, 2, 3, 9, 10, 12, 13, 14], "callabl": [14, 15, 19, 20], "callback": 1, "can": [0, 1, 2, 3, 4, 6, 7, 8, 18, 20, 21], "candid": 11, "canon": [7, 19], "cardin": 7, "care": 7, "carri": 1, "case": [0, 1, 2, 3, 7, 15, 21], "catch_warn": 0, "categor": [6, 19], "categori": [7, 19], "categorical_optim": 7, "categoricalparamet": 19, "caveat": 2, "cdf": [10, 12], "cell": 7, "central": 6, "certain": [2, 3, 6], "cf": 7, "chang": [0, 6, 20], "charg": 1, "cheap": [0, 3], "cheaper": [2, 6], "check": [3, 6, 15, 19, 20], "checkout": [1, 2], "chemistri": 1, "choos": 21, "chosen": 2, "cite": 6, "class": [0, 1, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20], "clear": [1, 2], "close": [2, 3, 6, 21], "cm": [3, 7], "cmap": [3, 7], "co": [3, 4, 7], "code": 3, "codifi": 3, "collect": [7, 14, 15, 16, 20], "color": [0, 21], "com": 6, "combin": [0, 1, 2, 3, 6, 8], "come": 0, "common": [2, 6], "compar": 7, "complet": [1, 4, 14, 15, 16], "complex": 3, "comput": [0, 2, 3, 6], "concaten": [0, 21], "concurr": 1, "condit": [3, 15, 20], "confid": [0, 2, 6, 13, 21], "configur": [3, 20], "consequ": 3, "conserv": 21, "consid": [0, 3, 7, 10], "constant": [9, 10, 12, 13, 20], "constantliar": 8, "constitut": 3, "constrain": [0, 2, 6, 15, 17, 18], "constrained_layout": 3, "constraint": [0, 6, 7, 14, 15, 20], "constraint_funct": 3, "constraint_function_2_dim": 3, "constraint_limit": 3, "constraint_low": 3, "constraint_upp": 3, "constraint_valu": [14, 15, 20], "constraint_vbound": 3, "constraintmodel": [3, 14, 20], "constraintnotsupportederror": [0, 17], "construct": [2, 3, 6, 7, 21], "contain": [3, 17], "continu": [7, 19, 20], "continuous_dimens": 20, "continuous_optim": 7, "contour": 3, "contourf": [3, 7], "contract": 4, "contrast": 7, "control": [1, 2, 6, 21], "converg": 4, "convert": [19, 20], "coolwarm": [3, 7], "coordin": 20, "copi": 9, "core": 1, "correct": 6, "correspond": [2, 6, 7, 14, 20, 21], "cost": [0, 2, 4, 6], "could": [0, 1, 2, 3], "cours": 2, "cov": 0, "cover": 2, "craig": [4, 6, 16], "creat": [3, 4, 7, 14, 20], "cross": 6, "cumul": 11, "cunningham": 6, "current": [0, 21], "custom": [7, 17], "d": [0, 21], "d_pbound": 7, "d_pred": 7, "dampen": 16, "daniel": 6, "data": [7, 15, 19, 20, 21], "dataset": 7, "date": 4, "dc": 15, "deal": [1, 6, 7], "decai": [10, 12, 13], "decay_explor": [10, 12, 13], "decent": 7, "decid": 7, "deeper": 0, "def": [0, 1, 2, 3, 4, 5, 7, 20, 21], "default": [0, 1, 2, 3, 4, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20], "default_ev": 1, "defin": [1, 3, 4, 18], "definit": 0, "degre": 7, "delai": [12, 13], "demonstr": 3, "denot": 7, "depend": [0, 1, 2, 17], "describ": [2, 3, 6], "design": [2, 6], "desir": [3, 14, 20], "despit": 7, "detail": 6, "determin": [2, 6, 8], "develop": 6, "deviat": [0, 7, 10, 12, 13], "dict": [14, 16, 20], "dictat": [0, 8], "dictionari": [2, 14, 20], "differ": [0, 1, 3, 14, 20, 21], "dim": [0, 7, 19, 20], "dimens": 20, "dimension": [3, 7, 15, 19], "directli": [0, 1, 3], "dirichlet": 7, "disallow": 3, "disclaim": 2, "discourag": [8, 9], "discret": [1, 7], "discrete_optim": 7, "discretized_funct": 7, "discuss": [2, 6], "disregard": 0, "distanc": 7, "distinct": [3, 7], "distribut": [1, 2, 6, 9, 10, 12, 13, 21], "diversifi": 2, "divid": 0, "do": [0, 1, 2, 3, 6, 7], "doc": 1, "document": 4, "doe": [0, 1, 2, 7, 15, 20], "doi": 6, "domain": [3, 6, 16], "domaintransform": [14, 16], "don": [1, 2, 15], "done": 7, "dot": 3, "draw": 20, "drawn": 20, "dtype": [4, 8, 9, 10, 11, 12, 13, 14, 15, 16, 19, 20], "due": [3, 21], "dummi": 9, "duplic": [3, 14, 20], "dure": 2, "dynam": 6, "e": [2, 3, 4, 7, 14, 18, 21], "each": [2, 3, 6, 7, 10, 11, 12, 13, 16], "earlier": 18, "easi": 0, "easiest": 2, "easili": 7, "ec": [0, 21], "eddi": 6, "edgecolor": 3, "eduardo": 6, "ei": [0, 2, 6, 10], "eipu": 0, "either": 21, "element": 7, "elif": 7, "elimin": 9, "els": [0, 3], "elsevi": 6, "embed": 3, "emploi": [2, 3, 6], "empti": [17, 20], "encod": 7, "encourag": 0, "end": [1, 10, 12, 13], "endeavor": [2, 6], "eng": 6, "engin": 6, "enhanc": 7, "enough": [0, 2], "ensur": 7, "ensure_rng": 7, "enter": 21, "enum": 18, "enumer": 2, "equal": [0, 3, 7], "equilater": 7, "equival": 7, "errat": 1, "error": [3, 11], "estim": [0, 3, 15], "et": [3, 11], "eta": [4, 16], "etc": 1, "eval": 15, "evalu": [0, 2, 3, 8, 9, 11, 14, 15, 20], "even": [0, 1, 3, 7], "event": [1, 2, 7, 18], "everi": [2, 4], "exactli": 2, "exampl": [0, 1, 2, 3, 4, 6, 7, 15, 16, 20, 21], "except": [3, 4, 7], "execut": 1, "exemplifi": 6, "exist": [2, 18], "exp": [0, 4, 5, 21], "expect": [0, 2, 6, 7, 10, 11], "expectedimprov": [0, 5, 7, 8], "expectedimprovementperunitcost": 0, "expens": [0, 2, 3, 4, 6], "experi": [0, 1], "experiment": 7, "explicitli": 3, "exploit": [2, 6, 10, 12, 13, 21], "explor": [0, 2, 6, 10, 12, 13, 21], "exploration_decai": [0, 10, 12, 13], "exploration_decay_delai": [0, 10, 12, 13], "extend": 7, "extra": 1, "extract": 3, "f": [0, 1, 2, 3, 4, 5, 7, 14, 21], "f1": 7, "f2": 7, "f_target": 7, "fact": 7, "factor": 0, "fall": 3, "fals": [3, 7, 8, 9, 10, 11, 12, 13, 14, 18, 20], "fashion": [1, 6], "fc": [0, 21], "feasibl": 3, "featur": [2, 6, 7], "feel": 2, "fernando": 6, "few": [0, 2, 3, 6, 7, 21], "fig": [0, 3, 7, 21], "figsiz": [0, 3, 5, 7, 21], "figur": [0, 3, 5, 21], "file": [2, 6, 18], "fill": [0, 21], "fill_between": [5, 7], "filterwarn": 7, "final": 6, "find": [0, 1, 2, 3, 6, 7, 14, 15], "fire": [1, 2], "first": 2, "fit": [2, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 21], "fit_gp": [0, 8, 9, 10, 11, 12, 13], "fix": [0, 7], "fixedperimetertriangleparamet": 7, "flatten": [0, 5, 21], "flexibl": [1, 6], "float": [4, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 19, 20], "float64": 1, "floatparamet": 19, "fly": 3, "folder": [1, 2], "follow": [0, 2, 3, 6, 7, 8, 21], "fontdict": [0, 21], "fontsiz": [0, 21], "form": [3, 7], "format": [0, 1, 2, 18, 19, 21], "found": [1, 2, 3, 4, 6, 14, 20], "frac": [10, 12, 21], "free": [1, 21], "from": [0, 1, 2, 3, 4, 5, 7, 9, 11, 12, 13, 18, 19, 20, 21], "fromit": 4, "fulfil": [0, 3, 14, 15, 20], "fun": [3, 15], "function": [1, 3, 4, 6, 8, 9, 10, 11, 12, 13, 14, 15, 17, 19, 20], "fundament": 21, "further": [0, 7, 21], "g": [0, 7, 14, 18, 21], "gain": 11, "gamma_": 4, "gamma_osc": 16, "gamma_pan": 16, "gardner": [3, 6], "gardner2014bayesian": 6, "garrido": [6, 7], "garrido2020d": 6, "gaussian": [0, 2, 3, 6, 7, 8, 9, 10, 11, 12, 13, 14, 21], "gaussian_process": 7, "gaussianprocessregressor": [8, 9, 10, 11, 12, 13, 15], "gener": [2, 3, 7, 14, 19, 20], "get": [0, 1, 3, 14, 20, 21], "get_legend_handles_label": 0, "gif": [2, 6], "github": 6, "give": [1, 14, 20, 21], "given": [1, 3, 7, 14, 15], "global": [2, 6], "go": [1, 3, 6, 7, 21], "gold": [0, 21], "good": [2, 3], "govern": [10, 12, 13], "gp": [0, 7, 8, 9, 10, 11, 12, 13, 14, 15], "gphedg": 8, "gpr": 15, "gradient": [0, 7], "greedi": 0, "greedyacquisit": 0, "green": 3, "grid": [0, 3, 7, 21], "gridspec": [0, 21], "group": 8, "grow": [2, 6, 21], "gt": [3, 4], "guess": [0, 21], "guid": [1, 3, 14], "guidanc": 2, "ha": [2, 3, 4, 7, 20, 21], "handl": [1, 3, 7, 14, 18], "happen": 2, "hard": [2, 6], "hashabl": 1, "hasn": 1, "hault": 1, "have": [1, 2, 3, 4, 7, 9, 15, 21], "heavili": 4, "height_ratio": [0, 21], "help": 2, "here": [0, 1, 2, 3, 21], "hern": 6, "hern\u00e1ndez": 7, "high": [2, 6, 14, 20], "higher": [0, 10, 12, 13], "hold": 20, "hone": 3, "hood": 7, "hot": 7, "how": [0, 1, 2, 3, 4, 7, 21], "howev": [1, 2, 7], "http": [6, 11, 19], "hyper": 1, "hyperparamet": 7, "i": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21], "icml": 6, "idea": [2, 3, 6], "ident": 4, "ignor": [0, 1, 7], "illustr": 3, "implement": [0, 2, 6, 8], "impli": 3, "import": [0, 1, 2, 3, 4, 5, 6, 7, 21], "improv": [0, 1, 2, 6, 7, 10, 12], "includ": 9, "incom": 18, "increas": [1, 7], "independ": [3, 15], "individu": 15, "inequ": 6, "inf": 3, "infeas": 3, "infer": [2, 6], "inform": [2, 4, 6, 11, 14, 19, 21], "ingredi": [1, 2], "inher": 7, "init_point": [0, 1, 2, 3, 4, 7, 14, 21], "initi": [2, 4, 16, 21], "inlin": 21, "inproceed": 6, "input": [7, 19], "instanc": [1, 2, 3, 18], "instanti": [1, 2], "instead": 7, "int": [7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 19, 20], "int_": 15, "integ": [6, 14, 19], "intend": 14, "intent": 2, "interest": [0, 3, 4, 16, 21], "intern": [1, 2, 3, 6, 7, 14, 15], "interv": [0, 21], "interven": 0, "intparamet": [7, 19], "invari": 7, "invers": 7, "investig": 0, "invok": [1, 2], "is_constrain": [7, 18], "is_continu": [7, 19], "is_numer": 19, "isn": 3, "isotrop": 7, "issu": 6, "iter": [0, 1, 2, 4, 6, 7, 11, 14, 16, 21], "its": [0, 1, 2, 6, 7, 14, 20, 21], "j": [3, 6], "jacob": 6, "john": 6, "join": 7, "joint": 15, "journal": 6, "json": 18, "jsonlogg": [2, 18], "just": [2, 7, 21], "k": [0, 4, 7, 16, 21], "k1": 7, "k2": 7, "kappa": [1, 13, 21], "keepdim": 7, "kei": [2, 14, 15, 20], "kenneth": 6, "kernel": [7, 19, 20], "kernel_transform": [7, 19, 20], "keyword": 3, "kick": 21, "kilian": 6, "know": [2, 3, 6], "known": [2, 3, 6, 7, 14, 20, 21], "kusner": 6, "kwarg": [4, 8, 9, 11, 15], "l": [8, 9, 10, 11, 12, 13], "label": [0, 3, 4, 7, 21], "labels2": 0, "labels_bot": 3, "labels_top": 3, "ladislav": 7, "lambda": [0, 20], "languag": 1, "larger": 21, "last": [1, 3], "last_x": 0, "later": 0, "lazi": [2, 14], "lazili": 2, "lb": [3, 15], "lbl": 7, "learn": [1, 3, 6, 7], "left": [1, 10, 12, 15], "legend": [0, 4, 21], "len": [0, 2, 4, 7, 20, 21], "len_each": 7, "length": [7, 19], "length_scal": 7, "leq": [3, 15], "less": [9, 21], "let": [0, 1, 3, 4, 6, 7], "level": [0, 14, 18], "liar": 9, "lie": [2, 3], "like": [0, 1, 2, 3, 7, 9, 21], "lim": 3, "limit": [3, 15], "line": 0, "linear": 4, "lines2": 0, "linewidth": [0, 21], "linspac": [0, 3, 5, 7, 21], "list": [1, 2, 4, 14, 15, 20], "listen": [1, 2], "literatur": 1, "ljust": 7, "ll": 4, "load": 0, "load_breast_canc": 7, "load_log": 2, "lobato": [6, 7], "loc": [0, 21], "local": 21, "log": [1, 2, 18], "log10_c": 7, "log_loss": 7, "logger": [2, 7, 18], "long": 1, "longest": 7, "look": [1, 2, 3, 6, 7], "loop": 14, "loss": 7, "low": [3, 15], "lower": [3, 4, 9, 10, 12, 13, 15, 20], "lt": 4, "luksan": 7, "machin": [1, 6, 7], "magnitud": [3, 10], "mai": [1, 2, 3, 14, 20], "main": 2, "make": [6, 14], "make_mask": 20, "make_param": 20, "make_plot_f": 7, "mani": [0, 2, 8], "manual": 14, "map": [7, 14, 16, 20], "markeredgecolor": [0, 21], "markeredgewidth": [0, 21], "markerfacecolor": [0, 21], "markers": [0, 21], "mask": [3, 20], "match": [2, 15], "matern": [1, 7], "mathcal": 15, "mathemat": 3, "matplotlib": [0, 3, 4, 5, 7, 21], "matrix": 0, "matt": 6, "matter": 7, "max": [0, 1, 2, 3, 7, 9, 10, 12, 14, 20, 21], "max_": 3, "maxim": [0, 2, 3, 4, 5, 7, 14, 20, 21], "maxima": [3, 21], "maximum": [0, 2, 3, 6, 7, 9, 14, 15, 19, 20, 21], "mayb": [1, 7], "mean": [0, 2, 3, 5, 7, 9, 10, 12, 13, 14, 15], "mechan": 0, "merch": 6, "merch\u00e1n": 7, "meshgrid": [3, 7], "meta": 8, "method": [0, 1, 2, 4, 6, 7, 9, 10, 12, 13, 14], "metric": 7, "might": [0, 1, 2, 3, 7], "min": [3, 7, 9], "minim": [2, 6], "minima": 21, "minimum": [2, 3, 9, 14, 16, 20, 21], "minimum_window": [4, 16], "minut": 0, "misc": 6, "model": [3, 6, 7, 14, 15, 20], "model_select": 7, "modifi": [3, 8, 14], "modul": [8, 17], "moment": 14, "month": 6, "more": [0, 1, 2, 4, 5, 6, 11, 14, 19, 21], "most": [0, 2, 4, 5, 6, 7, 8], "msg": 0, "mu": [0, 10, 12, 13, 15, 21], "much": [1, 2, 3], "multi": 3, "multipl": [8, 9, 15, 21], "multivari": 0, "multivariate_norm": 0, "must": [0, 2, 3], "mutat": 4, "mutating_optim": 4, "my_callback": 1, "my_observ": 1, "n": [2, 3, 4, 6, 7, 15, 16], "n_constraint": [3, 15], "n_featur": 15, "n_iter": [0, 1, 2, 3, 4, 5, 7, 14, 21], "n_l_bfgs_b": [0, 8, 9, 10, 11, 12, 13], "n_plots_per_row": 3, "n_random": [0, 8, 9, 10, 11, 12, 13], "n_restarts_optim": 1, "n_sampl": [7, 15, 19, 20], "name": [3, 7, 14, 15, 19, 20], "nan": 3, "nativ": 2, "natur": [2, 3, 7], "nb": 7, "ndarrai": [8, 9, 10, 11, 12, 13, 14, 15, 16, 19, 20], "ndarrrai": 15, "ndez": 6, "nearest": 7, "necessari": 21, "need": [1, 2, 3, 4, 6, 7, 14, 15], "neurocomput": 6, "new": [2, 14, 16, 20], "new_bound": [2, 14, 20], "new_optim": 2, "next": [0, 1, 3, 6, 8, 9, 10, 11, 12, 13, 14, 21], "next_point": 1, "next_point_to_prob": 1, "nielen": 6, "nogueira": 6, "nois": [1, 14, 20], "noisi": 0, "non": [3, 4, 6, 17], "none": [0, 1, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21], "nonetheless": 2, "nonlinearconstraint": [3, 14], "noreturn": 11, "normal": [0, 3, 7, 10, 12], "note": [3, 5, 14, 19, 20], "notebook": [0, 2, 3, 6, 7, 8, 15, 16], "notic": [1, 21], "notuniqueerror": [17, 20], "novalidpointregisterederror": 17, "now": [1, 2, 3, 4], "np": [0, 1, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 15, 16, 19, 20, 21], "nu": 7, "number": [2, 3, 6, 8, 9, 10, 11, 12, 13, 14, 19, 20, 21], "numer": [7, 19], "numpi": [0, 3, 4, 5, 7, 14, 21], "nut": 1, "o": 18, "object": [1, 2, 3, 4, 20], "observ": [0, 2, 3, 6, 14, 21], "obtain": [2, 21], "obviou": 4, "obvious": 2, "occasion": [3, 14, 20], "off": [10, 12, 13, 21], "often": 2, "ok": 3, "one": [0, 3, 4, 7, 8], "ones": [2, 7], "onli": [2, 3, 4, 7, 14, 21], "onto": [2, 4], "open": 6, "oper": [0, 3, 9, 16], "opt": 0, "optim": [0, 4, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21], "optimization_end": [7, 18], "optimization_start": [1, 7, 18], "optimization_step": [1, 2, 7, 18], "option": [1, 7, 14, 20], "orang": 0, "order": [0, 2, 3, 14, 15], "org": [11, 19], "origin": 3, "osc": 4, "oscil": [4, 16], "other": [0, 2, 3, 6, 7, 14, 20], "otherwis": 14, "our": [0, 1, 2, 3, 4, 7, 21], "out": [0, 1, 2, 5, 6], "output": [2, 7, 18], "over": [0, 1, 2, 6, 14], "overal": 15, "overwrit": [0, 18], "own": [1, 8], "p": [6, 7, 15], "p1": 20, "p2": 20, "p_allow": 3, "p_c": 0, "packag": [0, 3, 4, 6, 7, 21], "page": 6, "pain": 3, "pan": [4, 16], "paper": 3, "paradigm": 2, "parallel": 8, "param": [0, 1, 2, 3, 4, 5, 7, 14, 20, 21], "paramet": [0, 2, 3, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 21], "params_config": 20, "params_svm": 7, "params_to_arrai": [7, 20], "particular": [0, 1, 2, 8], "particularli": [1, 2, 4, 6], "pass": [2, 19], "path": [2, 18], "pathlik": 18, "pbound": [1, 2, 3, 4, 5, 7, 14, 20], "pdf": 10, "peak": 5, "per": 0, "perform": [0, 1, 2, 4, 7], "perimet": 7, "permut": 7, "phase": 4, "phi": [10, 12], "pi": 4, "pictur": [2, 6], "place": 3, "pleas": [2, 6], "plot": [0, 3, 4, 7], "plot_bo": 5, "plot_constrained_opt": 3, "plot_gp": [0, 21], "plt": [0, 3, 4, 5, 7, 21], "poi": 12, "point": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 17, 20], "poli": 7, "polici": 0, "poly2": 7, "poly3": 7, "portfolio": 11, "posit": [10, 12], "possibl": [2, 6], "posterior": [0, 2, 6, 21], "potenti": 7, "pr": 15, "practic": [0, 21], "predict": [0, 1, 3, 5, 7, 9, 10, 12, 13, 15, 21], "predict_proba": 7, "prefer": [3, 10, 12, 13], "present": [6, 15, 20], "pretti": [3, 7, 21], "previou": 17, "previous": [2, 6, 7, 8, 9, 20], "print": [0, 1, 2, 3, 7], "prior": 0, "probabilityofimprov": [5, 8, 10], "probabl": [0, 3, 7, 10, 12, 15], "probe": [1, 2, 3, 8, 9, 10, 11, 12, 13, 14, 20, 21], "problem": [1, 2, 3, 4, 6, 7, 8], "proce": 3, "procedur": 21, "process": [0, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 21], "produc": 7, "product": 15, "progress": 1, "project": 6, "promis": [8, 9, 10, 11, 12, 13, 14], "proper": [2, 6], "properti": [2, 3, 7, 14, 15, 18, 19, 20], "provid": [3, 8, 14, 16], "proxi": [2, 6], "public": 6, "publish": 6, "pure": 6, "purpl": [0, 21], "purpos": [2, 7], "push": 0, "py": 1, "pyplot": [0, 3, 4, 5, 7, 21], "python": 6, "q": 6, "quadrat": 3, "qualiti": 1, "quick": 4, "quickli": 3, "quit": [0, 4, 21], "r": [0, 3, 6, 21], "rais": [0, 3, 10, 11, 12, 15, 17, 20], "random": [0, 1, 2, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 19, 20], "random_sampl": [7, 19, 20], "random_st": [0, 1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 19, 20, 21], "randomli": 7, "randomst": [8, 9, 10, 11, 12, 13, 14, 15, 19, 20], "rang": [0, 1, 3, 4, 5, 7, 21], "rate": [10, 12, 13], "rbf": 7, "re": [0, 1, 2, 3, 7, 14, 20, 21], "read": 3, "real": 2, "realiz": 2, "reason": 3, "receiv": 0, "recent": [0, 4], "recreat": 1, "red": [3, 5], "reduc": [0, 4, 10, 12, 13, 16], "reduct": [6, 16], "refer": [2, 3, 4, 6], "reformul": 3, "region": [2, 3, 4, 6, 7, 16], "regist": [3, 9, 14, 17, 20], "regress": 1, "regressor": [3, 14, 15], "rel": [4, 9], "reli": 7, "remov": 0, "repeat": 1, "repeatedli": [3, 14, 20], "report": 20, "repres": [7, 19], "represent": [7, 19, 20], "reproduc": [8, 9, 10, 11, 12, 13], "requir": [0, 2, 4, 6, 7], "rerun": 3, "research": 6, "reset": 18, "reshap": [0, 3, 5, 7, 21], "respect": [3, 15], "restart": 18, "restrict": 21, "result": [1, 3, 7, 18, 20], "return": [0, 1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21], "return_cov": 0, "return_std": [0, 5, 7, 21], "reward": 11, "right": [10, 12, 15], "rightarrow": 1, "ro": 7, "robust": [4, 6, 16], "round": 7, "routin": [4, 14], "row": 3, "rtol": 9, "rtype": [10, 12, 13], "rule": 7, "run": [0, 1, 3, 7, 20], "sai": 0, "same": [3, 7, 9, 14, 15, 16, 20], "sampl": [0, 2, 3, 6, 7, 8, 9, 10, 11, 12, 13, 19, 20], "samples_": 7, "satisfi": 20, "scale": [7, 16], "scatter": [3, 5, 7], "scenario": 2, "scheme": [4, 6, 16], "scikit": 1, "scipi": 3, "screenlogg": [7, 18], "script": [1, 6], "search": [4, 6, 14, 20], "searchabl": 16, "section": 6, "see": [0, 2, 3, 4, 6, 7, 8, 11, 14, 15, 16, 19], "seed": [5, 14, 20], "seem": [3, 7], "seen": [2, 4, 6, 20], "select": [0, 11, 20], "self": [0, 1, 7, 20], "sens": [2, 6], "sequenc": [11, 14, 16, 19, 20], "sequenti": [16, 21], "sequentialdomainreductiontransform": 4, "sequentialdomaintransform": 6, "serv": 2, "server": 1, "set": [0, 2, 3, 4, 8, 9, 10, 11, 12, 13, 14, 21], "set_aspect": [3, 7], "set_bound": [2, 14, 20], "set_gp_param": [1, 7, 14], "set_titl": [3, 7], "set_xlabel": [0, 7, 21], "set_xlim": [0, 21], "set_xtick": 7, "set_ylabel": [0, 7, 21], "set_ylim": [0, 21], "set_ytick": 7, "setup": 3, "sever": 0, "shape": [0, 3, 7, 15, 20], "sharei": 7, "sharex": 7, "shift": 4, "shortest": 7, "should": [1, 2, 3, 6, 7, 15, 19, 21], "shouldn": 2, "show": [3, 5, 7], "showcas": 0, "shrink": [4, 16], "shrinkag": 4, "side": 7, "sigma": [0, 5, 10, 12, 13, 15, 21], "sign": 3, "significantli": 7, "silent": [2, 3], "similar": [3, 7, 10, 18], "similarli": [3, 7], "simpl": [0, 4, 6, 16], "simplefilt": 0, "simpli": [1, 3, 15], "simul": [1, 4, 6, 16], "sin": [3, 7], "sinc": [0, 1, 3, 9, 11], "singl": 20, "situat": [0, 1, 2, 3, 6, 14, 20], "size": [0, 3, 7, 16, 21], "skip": 0, "sklearn": 7, "slightli": 3, "so": [1, 2, 3, 6, 7], "softwar": 6, "sole": 0, "solut": [1, 3, 7], "solv": 3, "some": [0, 2, 3], "sometim": 7, "somewhat": 0, "sort": 7, "sortingfixedperimetertriangleparamet": 7, "sourc": 6, "space": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 19, 20, 21], "special": 0, "specif": [0, 1, 2, 3, 7, 14], "specifi": [1, 7, 15, 20], "sped": 6, "spiral": 7, "spread": 5, "sqrt": [4, 7], "squar": 0, "squeez": 3, "standard": [0, 3, 4, 7, 10, 12, 13], "standard_optim": 4, "stander": [4, 6, 16], "start": [1, 3, 4, 8, 9, 10, 11, 12, 13, 14, 21], "state": [8, 9, 10, 11, 12, 13, 14, 15, 18, 19, 20], "statement": 0, "std": [0, 7, 10, 12, 13], "step": [0, 1, 4, 6, 8, 11, 18], "still": [2, 6, 7], "stop": 1, "store": [2, 3, 7], "str": [7, 9, 14, 16, 18, 19, 20], "str_": 7, "str_len": [7, 19], "strategi": [2, 6, 9], "string": [7, 19], "stringifi": 7, "stuck": 21, "sub": 7, "subclass": [7, 8], "subj": 3, "submodul": 2, "subplot": [0, 3, 7, 21], "subscrib": [1, 2, 7], "subscript": 1, "succes": 21, "suggest": [0, 6, 7, 8, 9, 10, 11, 12, 13, 14, 20], "suit": [2, 6], "suitabl": 8, "sum": 7, "super": [0, 7], "super_acq": 0, "suppli": 7, "support": [0, 17], "suppress": 7, "suptitl": [0, 21], "sure": 14, "surfac": 1, "surrog": 3, "surrogate_object": 3, "svc": 7, "svm": 7, "swapax": 3, "switch": 3, "symmetri": 7, "t": [1, 2, 3, 15], "take": [0, 1, 2, 3, 6, 14, 15], "target": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 20, 21], "target_func": 20, "target_funct": 3, "target_function_1d": 7, "target_spac": [0, 8, 9, 10, 11, 12, 13, 16], "target_vbound": 3, "targetspac": [8, 9, 10, 11, 12, 13, 14, 16], "targetspaceemptyerror": 17, "techniqu": [2, 4, 6], "tell": 1, "termin": 18, "test": 21, "test_siz": 7, "text": [3, 10, 12, 13, 15, 18], "thei": [1, 8, 20], "them": [2, 3], "therefor": [2, 4, 6], "thi": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17, 20, 21], "thing": [0, 1], "think": [1, 2], "third": 7, "thompson": 0, "thompsonsampl": 0, "three": [4, 7], "through": 0, "ththe": 7, "thu": 3, "tight_layout": 7, "tild": 15, "time": [1, 2, 3, 4, 20], "tip": [2, 6], "titl": [3, 4, 6], "to_float": [7, 19], "to_param": [7, 19], "to_str": [7, 19], "toler": 9, "too": [0, 21], "tool": [2, 6], "top": 3, "tour": 6, "trade": [10, 12, 13, 21], "tradeoff": [10, 12, 13], "train_test_split": 7, "transform": [4, 7, 14, 15, 16, 19, 20], "tri": [3, 7], "triangl": 7, "true": [0, 2, 3, 5, 7, 8, 9, 10, 11, 12, 13, 14, 18, 20, 21], "try": [3, 7, 15, 21], "tune": [2, 6], "tupl": [7, 14, 19, 20], "twinx": 0, "two": [3, 4, 7], "ty": 2, "type": [0, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20], "typeerror": 15, "typic": [3, 4, 7, 16], "u": [0, 6, 21], "ub": 15, "ucb": [2, 6, 13, 21], "uncertainti": [1, 21], "under": [6, 7], "understand": [1, 6, 8], "unev": 21, "unfeas": 3, "uniformli": 7, "uniqu": [17, 20], "unit": 0, "unitari": 16, "unknown": [2, 6, 21], "unnecessari": 3, "unpack": 3, "unseed": 14, "until": 3, "up": [0, 3, 4, 6, 15], "updat": [1, 3, 4, 11, 18], "upon": [2, 6], "upper": [2, 4, 6, 13, 15, 20, 21], "upperconfidencebound": [1, 5, 8, 21], "url": 6, "us": [0, 1, 2, 3, 4, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21], "usag": [2, 6], "use": 2, "usecas": 7, "user": [2, 7, 21], "usual": [0, 3], "util": [0, 2, 7, 21], "utility_funct": 21, "valid": [6, 7, 20], "valu": [0, 1, 2, 3, 4, 6, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20], "valueerror": [10, 12], "variabl": [4, 6, 21], "varianc": [9, 15], "variou": 0, "vector": 20, "verbos": [1, 2, 3, 4, 5, 7, 14, 18], "veri": [0, 1, 2, 6], "version": [0, 7, 20], "via": [2, 3, 7], "violat": 3, "visual": [3, 6], "vmax": [3, 7], "vmin": [3, 7], "volum": 6, "wa": [1, 21], "wai": [2, 3, 7, 8], "want": [1, 2, 3, 6, 7], "warn": [0, 7], "we": [0, 1, 2, 3, 4, 6, 7, 15, 21], "weinberg": 6, "well": [3, 7, 14, 15], "were": 3, "what": [0, 1, 2, 3, 6], "whatev": 1, "when": [1, 2, 3, 4, 6, 7, 14, 17], "whenev": [1, 3, 7], "where": [0, 1, 2, 3, 4, 6, 7, 10, 12, 14, 20], "wheter": 15, "whether": [3, 7, 8, 9, 10, 11, 12, 13, 15, 18, 19], "which": [0, 2, 3, 4, 6, 7, 14, 15, 21], "while": [0, 1, 2, 7], "white": 3, "whole": 5, "why": [1, 7], "window": 16, "wish": 2, "within": [7, 20], "without": [2, 7], "word": 3, "work": [0, 1, 2, 3, 7, 16, 21], "worker": 9, "wors": 3, "worst": 3, "worth": [2, 6], "would": [0, 3, 7], "wrap": 19, "wrap_kernel": 19, "wrapper": 1, "write": [0, 3, 18], "written": 1, "x": [0, 1, 2, 3, 4, 5, 7, 10, 12, 13, 15, 20, 21], "x1": 7, "x2": 7, "x_": 3, "x_i": [3, 7], "x_max_bound": 4, "x_min_bound": 4, "x_ob": [0, 21], "x_reduc": 0, "x_train": 7, "x_train_": 0, "x_val": 7, "xi": [0, 7, 10, 12], "xu": 6, "xy": 3, "y": [0, 1, 2, 3, 4, 7, 15, 20, 21], "y_": [3, 10, 12], "y_cov": 0, "y_j": [3, 7], "y_max": [10, 12], "y_mean": [0, 7], "y_ob": [0, 21], "y_sampl": 0, "y_sample_argmax": 0, "y_std": 7, "y_train": 7, "y_val": 7, "year": 6, "yet": [8, 9], "yield": [14, 15], "you": [0, 1, 2, 3, 6, 7, 8, 14], "your": [0, 1, 2, 3, 6, 8], "yourself": 3, "z": [3, 7], "z1": 7, "z2": 7, "z_est": 3, "z_mask": 3, "zhixiang": 6, "zip": 7, "zoom": [4, 16], "zorder": 5}, "titles": ["Acquisition functions", "Advanced tour of the Bayesian Optimization package", "Basic tour of the Bayesian Optimization package", "Constrained Optimization", "Sequential Domain Reduction", "Exploitation vs Exploration", "Bayesian Optimization", "Optimizing over non-float Parameters", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">bayes_opt.acquisition</span></code>", "<code class=\"xref py py-class docutils literal notranslate\"><span class=\"pre\">bayes_opt.acquisition.ConstantLiar</span></code>", "<code class=\"xref py py-class docutils literal notranslate\"><span class=\"pre\">bayes_opt.acquisition.ExpectedImprovement</span></code>", "<code class=\"xref py py-class docutils literal notranslate\"><span class=\"pre\">bayes_opt.acquisition.GPHedge</span></code>", "<code class=\"xref py py-class docutils literal notranslate\"><span class=\"pre\">bayes_opt.acquisition.ProbabilityOfImprovement</span></code>", "<code class=\"xref py py-class docutils literal notranslate\"><span class=\"pre\">bayes_opt.acquisition.UpperConfidenceBound</span></code>", "<code class=\"xref py py-class docutils literal notranslate\"><span class=\"pre\">bayes_opt.BayesianOptimization</span></code>", "<code class=\"xref py py-class docutils literal notranslate\"><span class=\"pre\">bayes_opt.ConstraintModel</span></code>", "<code class=\"xref py py-class docutils literal notranslate\"><span class=\"pre\">bayes_opt.SequentialDomainReductionTransformer</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">bayes_opt.exception</span></code>", "Other", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">bayes_opt.parameter</span></code>", "<code class=\"xref py py-class docutils literal notranslate\"><span class=\"pre\">bayes_opt.TargetSpace</span></code>", "Visualization"], "titleterms": {"": 21, "0": 5, "1": [1, 2, 3, 5, 7], "10": 5, "1e": 5, "2": [1, 2, 3, 7], "3": [1, 2, 7], "4": [2, 5, 7], "5": 7, "The": 1, "acquisit": [0, 5, 8, 9, 10, 11, 12, 13], "advanc": [1, 3], "after": 21, "algorithm": 21, "alpha": 1, "background": 4, "basic": [2, 4], "bayes_opt": [8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 19, 20], "bayesian": [1, 2, 6], "bayesianoptim": [14, 21], "bound": [2, 5], "categor": 7, "chang": [1, 2], "citat": 6, "conda": 6, "confid": 5, "constantliar": 9, "constrain": 3, "constraint": 3, "constraintmodel": 15, "continu": 1, "coupl": 21, "creat": 21, "defin": [7, 21], "doe": 6, "domain": 4, "each": 21, "easier": 21, "evalu": 1, "except": 17, "expect": 5, "expectedimprov": 10, "exploit": 5, "explor": 5, "first": 21, "five": 21, "float": 7, "forg": 6, "four": 21, "function": [0, 2, 5, 7, 21], "gaussian": 1, "get": 2, "gp": [1, 21], "gphedg": 11, "guid": 2, "how": 6, "improv": 5, "index": 6, "instal": 6, "integ": 7, "kappa": 5, "kernel": 1, "let": 21, "load": 2, "loop": 1, "make": 21, "maxim": 1, "mix": 7, "ml": 7, "multipl": 3, "next": 2, "non": 7, "note": [10, 12, 13, 15], "object": 21, "observ": 1, "one": 21, "optim": [1, 2, 3, 6, 7], "other": 18, "over": 7, "own": 7, "packag": [1, 2], "paradigm": 1, "paramet": [1, 4, 7, 19], "pass": 1, "pip": 6, "plot": [5, 21], "point": 21, "prefer": 5, "probabilityofimprov": 12, "probabl": 5, "process": 1, "progress": 2, "pypi": 6, "quick": 6, "random": 21, "reduct": 4, "regist": 1, "restart": 2, "save": 2, "sequenti": 4, "sequentialdomainreductiontransform": 16, "seven": 21, "simpl": [3, 7], "simul": 3, "singl": 3, "six": 21, "specifi": 2, "start": 2, "step": [2, 21], "stop": 21, "suggest": 1, "target": 5, "targetspac": 20, "three": 21, "tour": [1, 2], "tune": 1, "two": 21, "underli": 1, "upper": 5, "upperconfidencebound": 13, "us": 7, "util": 5, "v": 5, "valu": 7, "variabl": 7, "via": 6, "visual": 21, "warn": 14, "work": 6, "xi": 5, "your": 7}})
\ No newline at end of file
diff --git a/master/sitemap.xml b/master/sitemap.xml
index b3d04286..8eb22eea 100644
--- a/master/sitemap.xml
+++ b/master/sitemap.xml
@@ -1 +1 @@
-<urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9"><url><loc>https://bayesian-optimization.github.io/BayesianOptimization/acquisition_functions.html</loc></url><url><loc>https://bayesian-optimization.github.io/BayesianOptimization/advanced-tour.html</loc></url><url><loc>https://bayesian-optimization.github.io/BayesianOptimization/basic-tour.html</loc></url><url><loc>https://bayesian-optimization.github.io/BayesianOptimization/constraints.html</loc></url><url><loc>https://bayesian-optimization.github.io/BayesianOptimization/domain_reduction.html</loc></url><url><loc>https://bayesian-optimization.github.io/BayesianOptimization/exploitation_vs_exploration.html</loc></url><url><loc>https://bayesian-optimization.github.io/BayesianOptimization/index.html</loc></url><url><loc>https://bayesian-optimization.github.io/BayesianOptimization/reference/acquisition.html</loc></url><url><loc>https://bayesian-optimization.github.io/BayesianOptimization/reference/acquisition/ConstantLiar.html</loc></url><url><loc>https://bayesian-optimization.github.io/BayesianOptimization/reference/acquisition/ExpectedImprovement.html</loc></url><url><loc>https://bayesian-optimization.github.io/BayesianOptimization/reference/acquisition/GPHedge.html</loc></url><url><loc>https://bayesian-optimization.github.io/BayesianOptimization/reference/acquisition/ProbabilityOfImprovement.html</loc></url><url><loc>https://bayesian-optimization.github.io/BayesianOptimization/reference/acquisition/UpperConfidenceBound.html</loc></url><url><loc>https://bayesian-optimization.github.io/BayesianOptimization/reference/bayes_opt.html</loc></url><url><loc>https://bayesian-optimization.github.io/BayesianOptimization/reference/constraint.html</loc></url><url><loc>https://bayesian-optimization.github.io/BayesianOptimization/reference/domain_reduction.html</loc></url><url><loc>https://bayesian-optimization.github.io/BayesianOptimization/reference/exception.html</loc></url><url><loc>https://bayesian-optimization.github.io/BayesianOptimization/reference/other.html</loc></url><url><loc>https://bayesian-optimization.github.io/BayesianOptimization/reference/target_space.html</loc></url><url><loc>https://bayesian-optimization.github.io/BayesianOptimization/visualization.html</loc></url><url><loc>https://bayesian-optimization.github.io/BayesianOptimization/genindex.html</loc></url><url><loc>https://bayesian-optimization.github.io/BayesianOptimization/py-modindex.html</loc></url></urlset>
\ No newline at end of file
+<urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9"><url><loc>https://bayesian-optimization.github.io/BayesianOptimization/acquisition_functions.html</loc></url><url><loc>https://bayesian-optimization.github.io/BayesianOptimization/advanced-tour.html</loc></url><url><loc>https://bayesian-optimization.github.io/BayesianOptimization/basic-tour.html</loc></url><url><loc>https://bayesian-optimization.github.io/BayesianOptimization/constraints.html</loc></url><url><loc>https://bayesian-optimization.github.io/BayesianOptimization/domain_reduction.html</loc></url><url><loc>https://bayesian-optimization.github.io/BayesianOptimization/exploitation_vs_exploration.html</loc></url><url><loc>https://bayesian-optimization.github.io/BayesianOptimization/index.html</loc></url><url><loc>https://bayesian-optimization.github.io/BayesianOptimization/parameter_types.html</loc></url><url><loc>https://bayesian-optimization.github.io/BayesianOptimization/reference/acquisition.html</loc></url><url><loc>https://bayesian-optimization.github.io/BayesianOptimization/reference/acquisition/ConstantLiar.html</loc></url><url><loc>https://bayesian-optimization.github.io/BayesianOptimization/reference/acquisition/ExpectedImprovement.html</loc></url><url><loc>https://bayesian-optimization.github.io/BayesianOptimization/reference/acquisition/GPHedge.html</loc></url><url><loc>https://bayesian-optimization.github.io/BayesianOptimization/reference/acquisition/ProbabilityOfImprovement.html</loc></url><url><loc>https://bayesian-optimization.github.io/BayesianOptimization/reference/acquisition/UpperConfidenceBound.html</loc></url><url><loc>https://bayesian-optimization.github.io/BayesianOptimization/reference/bayes_opt.html</loc></url><url><loc>https://bayesian-optimization.github.io/BayesianOptimization/reference/constraint.html</loc></url><url><loc>https://bayesian-optimization.github.io/BayesianOptimization/reference/domain_reduction.html</loc></url><url><loc>https://bayesian-optimization.github.io/BayesianOptimization/reference/exception.html</loc></url><url><loc>https://bayesian-optimization.github.io/BayesianOptimization/reference/other.html</loc></url><url><loc>https://bayesian-optimization.github.io/BayesianOptimization/reference/parameter.html</loc></url><url><loc>https://bayesian-optimization.github.io/BayesianOptimization/reference/target_space.html</loc></url><url><loc>https://bayesian-optimization.github.io/BayesianOptimization/visualization.html</loc></url><url><loc>https://bayesian-optimization.github.io/BayesianOptimization/genindex.html</loc></url><url><loc>https://bayesian-optimization.github.io/BayesianOptimization/py-modindex.html</loc></url></urlset>
\ No newline at end of file
diff --git a/master/visualization.html b/master/visualization.html
index adb96883..3b0cc500 100644
--- a/master/visualization.html
+++ b/master/visualization.html
@@ -326,6 +326,21 @@
   
   
   
+    <li class="md-nav__item">
+      <a href="parameter_types.html" class="md-nav__link">
+        <span title="/parameter_types.ipynb (reference label)" class="md-ellipsis">Parameter Types</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
     <li class="md-nav__item">
       <a href="domain_reduction.html" class="md-nav__link">
         <span title="/domain_reduction.ipynb (reference label)" class="md-ellipsis">Sequential Domain Reduction</span>
@@ -620,6 +635,21 @@
   
   
   
+    <li class="md-nav__item">
+      <a href="reference/parameter.html" class="md-nav__link">
+        <span title="/reference/parameter.rst (reference label)" class="md-ellipsis">bayes_<wbr>opt.<wbr>parameter</span>
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+  
     <li class="md-nav__item">
       <a href="reference/exception.html" class="md-nav__link">
         <span title="/reference/exception.rst (reference label)" class="md-ellipsis">bayes_<wbr>opt.<wbr>exception</span>