Network_Anomaly_Detection_6750_6870_6952.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/alaahossam/Networks-Anomaly-Detection/blob/master/Network_Anomaly_Detection_6750_6870_6952.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Network Anomaly Detection\n",
        "\n"
      ],
      "metadata": {
        "id": "6UM7CxrnffuX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data preprocessing"
      ],
      "metadata": {
        "id": "Isk5m9K2OXuG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Import needed libraries."
      ],
      "metadata": {
        "id": "2pY53IEq40lq"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xY-DzodZ4PYw"
      },
      "outputs": [],
      "source": [
        "import sys\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from copy import deepcopy\n",
        "from sklearn import metrics\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder,OrdinalEncoder,OneHotEncoder,normalize\n",
        "from sklearn.metrics import classification_report, accuracy_score,f1_score, confusion_matrix, ConfusionMatrixDisplay\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.compose import make_column_transformer\n",
        "import math"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Generate training and test sets"
      ],
      "metadata": {
        "id": "UP-Sk0xzBcUm"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xkn39uKyuaIo",
        "outputId": "68bace9e-abc2-4ad2-d64f-efa8f9343ffd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "path1 = '/content/drive/MyDrive/kddcup.data_10_percent/kddcup.data_10_percent'\n",
        "path2 = '/content/drive/MyDrive/corrected/corrected'\n",
        "train_df = pd.read_csv(path1)\n",
        "test_df = pd.read_csv(path2)\n",
        "#train_nodup= train_df.drop_duplicates()\n",
        "#test_nodup= test_df.drop_duplicates()"
      ],
      "metadata": {
        "id": "bNUgYrpMEaGN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ytrain_df = train_df.iloc[:,-1]\n",
        "train_df = train_df.drop(train_df.columns[-1], axis=1)"
      ],
      "metadata": {
        "id": "AOGDhgQ8SFCK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "g = ytrain_df.unique()\n",
        "g = list(g)\n",
        "#print(g)\n",
        "#print(len(g))\n",
        "#test_df = pd.DataFrame(test_df)\n",
        "test_df = test_df[(test_df.iloc[:,-1].isin(g))]\n",
        "#test_df = test_df.drop(test_df[test_df.columns[41] in g].index, inplace=True)"
      ],
      "metadata": {
        "id": "fWXIsyzeJmCj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ytest_df = test_df.iloc[:,-1]\n",
        "test_df = test_df.drop(test_df.columns[-1], axis=1)"
      ],
      "metadata": {
        "id": "18pFwQ_07oGt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#ct = make_column_transformer((OneHotEncoder(handle_unknown = 'ignore'),[1,2,3]),remainder = 'passthrough')\n",
        "#train_df = ct.fit_transform(train_df)\n",
        "#test_df = ct.fit_transform(test_df)\n",
        "D_train = np.asarray(train_df)\n",
        "D_test = np.asarray(test_df)\n",
        "#print(D_train.shape)\n",
        "#print(test_df.shape)"
      ],
      "metadata": {
        "id": "EFGfKPg0v2gl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_train = ytrain_df.to_numpy()\n",
        "y_test = ytest_df.to_numpy()\n",
        "\n",
        "le = LabelEncoder()\n",
        "le.fit(y_train)\n",
        "y_train = le.transform(y_train)\n",
        "y_test = le.transform(y_test)\n",
        "\n",
        "X = np.concatenate((D_train,D_test))\n",
        "\n",
        "le1 = LabelEncoder()\n",
        "le1.fit(X[:,1])\n",
        "D_train[:, 1] = le1.transform(D_train[:, 1])\n",
        "D_test[:, 1] = le1.transform(D_test[:, 1])\n",
        "\n",
        "le2 = LabelEncoder()\n",
        "le2.fit(X[:,2])\n",
        "D_train[:, 2] = le2.transform(D_train[:, 2])\n",
        "D_test[:, 2] = le2.transform(D_test[:, 2])\n",
        "\n",
        "le3 = LabelEncoder()\n",
        "le3.fit(X[:,3])\n",
        "D_train[:, 3] = le3.transform(D_train[:, 3])\n",
        "D_test[:, 3] = le3.transform(D_test[:, 3])\n"
      ],
      "metadata": {
        "id": "sMptbATgN4og"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Scaling the training data"
      ],
      "metadata": {
        "id": "n-P8kpKHLprh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"data before scaling: \",D_train)\n",
        "scaler1 = MinMaxScaler()\n",
        "scaler1.fit(D_train)\n",
        "D_train_s = scaler1.transform(D_train)\n",
        "print(\"Data after scaling\",D_train_s)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FNZ6rw62fOJv",
        "outputId": "721ee1e6-5e78-4a83-dc26-6ec3a7eb9cce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "data before scaling:  [[0 1 22 ... 0.0 0.0 0.0]\n",
            " [0 1 22 ... 0.0 0.0 0.0]\n",
            " [0 1 22 ... 0.0 0.0 0.0]\n",
            " ...\n",
            " [0 1 22 ... 0.01 0.0 0.0]\n",
            " [0 1 22 ... 0.01 0.0 0.0]\n",
            " [0 1 22 ... 0.01 0.0 0.0]]\n",
            "Data after scaling [[0.         0.5        0.33333333 ... 0.         0.         0.        ]\n",
            " [0.         0.5        0.33333333 ... 0.         0.         0.        ]\n",
            " [0.         0.5        0.33333333 ... 0.         0.         0.        ]\n",
            " ...\n",
            " [0.         0.5        0.33333333 ... 0.01       0.         0.        ]\n",
            " [0.         0.5        0.33333333 ... 0.01       0.         0.        ]\n",
            " [0.         0.5        0.33333333 ... 0.01       0.         0.        ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Scaling the testing data"
      ],
      "metadata": {
        "id": "Gwu-IjcZMvqL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"data before scaling: \",D_test) \n",
        "scaler2 = MinMaxScaler()\n",
        "scaler2.fit(D_test)\n",
        "D_test_s = scaler2.transform(D_test)\n",
        "print(\"Data after scaling\",D_test_s)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RdsqU6GCfNob",
        "outputId": "1712be88-480f-4ecd-ac46-74ac5067d1af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "data before scaling:  [[0 2 46 ... 0.0 0.0 0.0]\n",
            " [0 2 46 ... 0.0 0.0 0.0]\n",
            " [0 2 11 ... 0.0 0.0 0.0]\n",
            " ...\n",
            " [0 2 46 ... 0.0 0.0 0.0]\n",
            " [0 2 46 ... 0.0 0.0 0.0]\n",
            " [0 2 46 ... 0.0 0.0 0.0]]\n",
            "Data after scaling [[0.         1.         0.6969697  ... 0.         0.         0.        ]\n",
            " [0.         1.         0.6969697  ... 0.         0.         0.        ]\n",
            " [0.         1.         0.16666667 ... 0.         0.         0.        ]\n",
            " ...\n",
            " [0.         1.         0.6969697  ... 0.         0.         0.        ]\n",
            " [0.         1.         0.6969697  ... 0.         0.         0.        ]\n",
            " [0.         1.         0.6969697  ... 0.         0.         0.        ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Accuracy Measures"
      ],
      "metadata": {
        "id": "CIo0u1wCTOEM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def evaluate(y_prd,y_trn,act_lbl):  \n",
        "  x = set(y_prd)\n",
        "  dct_p = {}\n",
        "  dct_t = {}\n",
        "  dct_e = {}\n",
        "  for i in x:\n",
        "    dct_p['clst_%s' % i] = []\n",
        "    dct_t['clst_%s' % i] = []\n",
        "    dct_e['clst_%s' % i] = []\n",
        "    for j in range (len(y_prd)):\n",
        "      if y_prd[j] == i:\n",
        "        dct_p['clst_%s' % i].append(y_prd[j])\n",
        "        dct_t['clst_%s' % i].append(y_trn[j])\n",
        "  \n",
        "  #print(dct_p['clst_0'])\n",
        "\n",
        "  for i in (set(y_prd)):\n",
        "    for j in range (len(dct_p['clst_%s' % i])):\n",
        "      dct_p['clst_%s' % i][j] = act_lbl[i]\n",
        "\n",
        "  #print(dct_p['clst_0'])\n",
        "  #print(dct_p['clst_6'])         \n",
        "\n",
        "  for i in (set(y_prd)):\n",
        "    cnt = 0.0\n",
        "    prec = 0.0\n",
        "    rec = 0.0\n",
        "    f1 = 0.0\n",
        "    for j in range (len(dct_p['clst_%s' % i])):\n",
        "      if dct_t['clst_%s' % i][j] == dct_p['clst_%s' % i][j]:\n",
        "        cnt += 1\n",
        "    prec = float(cnt / (len(dct_p['clst_%s' % i])))\n",
        "    dct_e['clst_%s' % i].append(prec)\n",
        "    #print(prec)\n",
        "    rec = float(cnt / (list(y_trn).count(act_lbl[i])))\n",
        "    dct_e['clst_%s' % i].append(rec)\n",
        "    #print(rec)\n",
        "    if rec == 0.0 and prec == 0.0:\n",
        "      f1 = 0.0\n",
        "    else:  \n",
        "      f1 = float((2*prec*rec))/float((prec+rec))\n",
        "    dct_e['clst_%s' % i].append(f1)\n",
        "  #print(dct_e) \n",
        "  \n",
        "  for i in (set(y_prd)):\n",
        "    ent_c = 0\n",
        "    for j in set(dct_t['clst_%s' % i]):\n",
        "      prob = ((dct_t['clst_%s' % i]).count(j)) /(len(dct_t['clst_%s' % i]))\n",
        "      ent_e = -(prob * math.log2(prob))\n",
        "      ent_c += ent_e\n",
        "    dct_e['clst_%s' % i].append(ent_c)\n",
        "    ent_cp = ent_c * (len(dct_t['clst_%s' % i])/len(y_trn))\n",
        "    dct_e['clst_%s' % i].append(ent_cp)\n",
        "\n",
        "  p = 0.0\n",
        "  r = 0.0\n",
        "  f = 0.0\n",
        "  e = 0.0\n",
        "  for i in (set(y_prd)):\n",
        "    p += dct_e['clst_%s' % i][0] \n",
        "    r += dct_e['clst_%s' % i][1] \n",
        "    f += dct_e['clst_%s' % i][2]\n",
        "    e += float(dct_e['clst_%s' % i][4])\n",
        "  total_prec = float(p / len(set(y_prd))) \n",
        "  total_rec = float(r / len(set(y_prd)))\n",
        "  total_f1 = float(f / len(set(y_prd)))\n",
        "  total_cent = float(e) \n",
        "\n",
        "  for i in (set(y_prd)):\n",
        "    print(\"For cluster :\", i)\n",
        "    print(\" Precision = \", dct_e['clst_%s' % i][0] )\n",
        "    print(\" Recall = \", dct_e['clst_%s' % i][1] )\n",
        "    print(\" F1 Measure = \", dct_e['clst_%s' % i][2] )\n",
        "    print(\" Conditional Entropy = \", dct_e['clst_%s' % i][3] )\n",
        "    print(\"\\n\")\n",
        "\n",
        "  print(\"Total Meaures:\\n\")\n",
        "  print(\"Precision =\",total_prec,\"\\n\")\n",
        "  print(\"Recall =\",total_rec,\"\\n\")\n",
        "  print(\"F1 Measure =\",total_f1,\"\\n\")\n",
        "  print(\"Conditional Entropy =\",total_cent,\"\\n\")\n",
        "  return \n"
      ],
      "metadata": {
        "id": "329qtgC4TWn7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## KMeans Clustering"
      ],
      "metadata": {
        "id": "QyB6dUikTZUu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### KMeans Clustering Algorithm"
      ],
      "metadata": {
        "id": "3z4XVX00dASj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#majority label function to detect the majority in each cluster\n",
        "def majority_label(arr):\n",
        "  label_counts = {}\n",
        "  for label in arr:\n",
        "    if label in label_counts:\n",
        "      label_counts[label] += 1\n",
        "    else:\n",
        "      label_counts[label] = 1  \n",
        "  majority = None\n",
        "  max = 0\n",
        "  for label, cnt in label_counts.items():\n",
        "    if cnt > max:\n",
        "      majority = label\n",
        "      max = cnt\n",
        "  return majority\n",
        "\n",
        "def actual_clusters(k,ypred,y_truth):\n",
        "  act_clusters=[]\n",
        "  for j in range(k):\n",
        "    temp=[]\n",
        "    for i in range(len(ypred)):\n",
        "      if ypred[i]==j:\n",
        "        temp.append(y_truth[i])        \n",
        "    act_clusters.append(majority_label(temp))        \n",
        "  return act_clusters\n",
        "\n",
        "def actual_labels(ypred,act_clusters): \n",
        "  act_ypred = []\n",
        "  for i in ypred:\n",
        "    act_ypred.append(act_clusters[i])\n",
        "  return act_ypred"
      ],
      "metadata": {
        "id": "vKGhnbKgu_Id"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def kmeans(data, num_clusters, max_itr,seed):\n",
        "  k =num_clusters\n",
        "  data = np.array(data, dtype = float)\n",
        "  n = data.shape[0]\n",
        "\n",
        "  m = data.shape[1]\n",
        "  cent = np.zeros((k,data.shape[1]), dtype = float)\n",
        "  noc = num_clusters\n",
        "\n",
        "  if(seed is None):\n",
        "    l = list(range(0,data.shape[0]-1))\n",
        "    for i in range (k):\n",
        "      x = np.random.choice(l,size = 1, replace = False)\n",
        "      while any(np.array_equal(z,np.array(data[x].flatten())) for z in cent):\n",
        "        x = np.random.choice(l,size = 1, replace = False)\n",
        "      cent[i]= np.array(data[x], dtype = float)\n",
        "\n",
        "  else:\n",
        "    cent =seed\n",
        "# old centroids\n",
        "  old_centroids = np.zeros(cent.shape)\n",
        "#new centroids\n",
        "  new_centroids = np.zeros(cent.shape)\n",
        "#create error vector r\n",
        "  maxit = max_itr\n",
        "  distance = np.zeros((n,k))\n",
        "  r = 0 \n",
        "#create new clusters\n",
        "#After an update,if the estimate of that center stays the same, exit loop\n",
        "  while r < maxit:\n",
        "    r +=1\n",
        "    clusters = np.zeros(n)\n",
        "# Measure the distance to every center\n",
        "    for i in range(num_clusters):\n",
        "      distance[:,i] = np.linalg.norm(data - cent[i], axis=1)\n",
        "# Assign all training data to closest center(labeling the points)\n",
        "    clusters = np.argmin(distance, axis = 1)\n",
        "    old_centroids = deepcopy(cent)\n",
        "    cent = new_centroids\n",
        "    j = 0\n",
        "# Calculate mean for every cluster and update the centroids\n",
        "    for i in range(num_clusters):\n",
        "      if i in np.unique(clusters):\n",
        "        new_centroids[i] = np.mean(data[clusters == i], axis=0)\n",
        "        j+=1\n",
        "  return old_centroids,new_centroids,clusters"
      ],
      "metadata": {
        "id": "3PQgw9gjEihW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### K = 7"
      ],
      "metadata": {
        "id": "kgdfKOFLZkin"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "KMeans for k = 7 on training set\n",
        "\n",
        "Evaluation on K=7 (Training set)"
      ],
      "metadata": {
        "id": "hDlrCgBzX9AY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "k=7\n",
        "c_old_7,c_new_7,y_predt_7=kmeans(D_train_s,k, 30,None)\n",
        "act_lbl_7 = actual_clusters(7,y_predt_7,y_train)\n",
        "act_y_predt_7 =  actual_labels(y_predt_7,act_lbl_7)\n",
        "evaluate(y_predt_7,y_train,act_lbl_7)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M488OB0iZlGs",
        "outputId": "44a46bb3-6f87-4b9d-9cd6-9f1c83d19098"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For cluster : 0\n",
            " Precision =  0.9537099504077454\n",
            " Recall =  0.8283355777830319\n",
            " F1 Measure =  0.8866124577753814\n",
            " Conditional Entropy =  0.3427786893080907\n",
            "\n",
            "\n",
            "For cluster : 1\n",
            " Precision =  0.9956748306409589\n",
            " Recall =  0.356470555311984\n",
            " F1 Measure =  0.5249860902178168\n",
            " Conditional Entropy =  0.04774855409798316\n",
            "\n",
            "\n",
            "For cluster : 2\n",
            " Precision =  0.9963864079663279\n",
            " Recall =  0.45269167265230736\n",
            " F1 Measure =  0.6225417880004618\n",
            " Conditional Entropy =  0.03519199795294428\n",
            "\n",
            "\n",
            "For cluster : 3\n",
            " Precision =  0.8385315624533651\n",
            " Recall =  0.11552576662520431\n",
            " F1 Measure =  0.20307375382863957\n",
            " Conditional Entropy =  1.0084958524602687\n",
            "\n",
            "\n",
            "For cluster : 4\n",
            " Precision =  0.9943568038989354\n",
            " Recall =  0.055222764343459525\n",
            " F1 Measure =  0.10463452817965882\n",
            " Conditional Entropy =  0.050268961409191334\n",
            "\n",
            "\n",
            "For cluster : 5\n",
            " Precision =  0.7257343550446999\n",
            " Recall =  0.19082844376451713\n",
            " F1 Measure =  0.3021958947920437\n",
            " Conditional Entropy =  1.2034998739049851\n",
            "\n",
            "\n",
            "For cluster : 6\n",
            " Precision =  0.9999924602845489\n",
            " Recall =  0.9446917625271555\n",
            " F1 Measure =  0.9715558225223971\n",
            " Conditional Entropy =  0.00013918124793015372\n",
            "\n",
            "\n",
            "Total Meaures:\n",
            "\n",
            "Precision = 0.9291980529566545 \n",
            "\n",
            "Recall = 0.4205380775725228 \n",
            "\n",
            "F1 Measure = 0.5165143336166286 \n",
            "\n",
            "Conditional Entropy = 0.16349251849771193 \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "KMeans for k = 7 on test set\n",
        "\n",
        "Evaluation on K=7 (Testset)"
      ],
      "metadata": {
        "id": "PbubJUNLYcsW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "k=7\n",
        "c_old1,c_new1,y_pred_7=kmeans(D_test_s,k, 30,c_new_7)\n",
        "act_y_pred_7 =  actual_labels(y_pred_7,act_lbl_7)\n",
        "evaluate(y_pred_7,y_test,act_lbl_7)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GQnqEe6WbP-t",
        "outputId": "eb72cc7b-4b01-4ffe-8cef-fbc76859a302"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For cluster : 0\n",
            " Precision =  0.9142787246187719\n",
            " Recall =  0.9251881436493267\n",
            " F1 Measure =  0.9197010836129181\n",
            " Conditional Entropy =  0.500076759336662\n",
            "\n",
            "\n",
            "For cluster : 1\n",
            " Precision =  0.9919882275997384\n",
            " Recall =  0.2092032895984552\n",
            " F1 Measure =  0.34553557444506144\n",
            " Conditional Entropy =  0.07349022616694748\n",
            "\n",
            "\n",
            "For cluster : 2\n",
            " Precision =  0.9853893101535047\n",
            " Recall =  0.09186048516404889\n",
            " F1 Measure =  0.16805450416351247\n",
            " Conditional Entropy =  0.11000294645250495\n",
            "\n",
            "\n",
            "For cluster : 3\n",
            " Precision =  0.5816457730633237\n",
            " Recall =  0.05972735674676525\n",
            " F1 Measure =  0.10833058939743168\n",
            " Conditional Entropy =  1.8129339637501196\n",
            "\n",
            "\n",
            "For cluster : 4\n",
            " Precision =  0.9923069933670533\n",
            " Recall =  0.19966360129440372\n",
            " F1 Measure =  0.33243703959250764\n",
            " Conditional Entropy =  0.07328511762209243\n",
            "\n",
            "\n",
            "For cluster : 5\n",
            " Precision =  0.961664373487688\n",
            " Recall =  0.6989189841554456\n",
            " F1 Measure =  0.8095052668364034\n",
            " Conditional Entropy =  0.27967299860941097\n",
            "\n",
            "\n",
            "For cluster : 6\n",
            " Precision =  0.9951497514247605\n",
            " Recall =  0.800238891834409\n",
            " F1 Measure =  0.8871143718986769\n",
            " Conditional Entropy =  0.044476440100065975\n",
            "\n",
            "\n",
            "Total Meaures:\n",
            "\n",
            "Precision = 0.9174890219592629 \n",
            "\n",
            "Recall = 0.4264001074918364 \n",
            "\n",
            "F1 Measure = 0.5100969185637874 \n",
            "\n",
            "Conditional Entropy = 0.21729032783251778 \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "jXgdduN0YtFw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import precision_score,recall_score, accuracy_score,f1_score\n",
        "print(\"acc:\",accuracy_score(list(y_test),act_y_pred_7))\n",
        "print(\"f1 macro:\",f1_score(list(y_test),act_y_pred_7,average = 'macro'))\n",
        "print(\"f1 weighted:\",f1_score(list(y_test),act_y_pred_7,average = 'weighted'))\n",
        "print(\"rec macro:\",recall_score(list(y_test),act_y_pred_7,average = 'macro'))\n",
        "print(\"rec weighted:\",recall_score(list(y_test),act_y_pred_7,average = 'weighted'))\n",
        "print(\"prec macro:\",precision_score(list(y_test),act_y_pred_7,average = 'macro'))\n",
        "print(\"prec weighted:\",precision_score(list(y_test),act_y_pred_7,average = 'weighted'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d6Gq01-lZmAJ",
        "outputId": "9c20f27b-6c62-4705-b886-c13606114168"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "acc: 0.9640026137619355\n",
            "f1 macro: 0.1387529846851516\n",
            "f1 weighted: 0.9484198374279479\n",
            "rec macro: 0.14215223068267493\n",
            "rec weighted: 0.9640026137619355\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "prec macro: 0.13563925774220184\n",
            "prec weighted: 0.9340807105393717\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### K = 15"
      ],
      "metadata": {
        "id": "0IS89m4yafem"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "KMeans for k = 15 on training set\n",
        "\n",
        "Evaluation on K=15 (Training set)"
      ],
      "metadata": {
        "id": "7KWDKiPzZlnq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "k=15\n",
        "c_old_15,c_new_15,y_predt_15=kmeans(D_train_s,k, 30,None)\n",
        "act_lbl_15 = actual_clusters(k,y_predt_15,y_train)\n",
        "act_y_predt_15 =  actual_labels(y_predt_15,act_lbl_15)\n",
        "evaluate(y_predt_15,y_train,act_lbl_15)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mp-QTVR0bYJL",
        "outputId": "1ea4b02a-129f-4271-ff76-cdb8feff078c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For cluster : 0\n",
            " Precision =  0.9722703639514731\n",
            " Recall =  0.005767036401204807\n",
            " F1 Measure =  0.011466061683732908\n",
            " Conditional Entropy =  0.19222800459754388\n",
            "\n",
            "\n",
            "For cluster : 1\n",
            " Precision =  1.0\n",
            " Recall =  0.0004344884077068272\n",
            " F1 Measure =  0.0008685994190351428\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 2\n",
            " Precision =  0.9985879861151968\n",
            " Recall =  0.060447309377114566\n",
            " F1 Measure =  0.11399423077568865\n",
            " Conditional Entropy =  0.015404656376415634\n",
            "\n",
            "\n",
            "For cluster : 3\n",
            " Precision =  0.9185286677908938\n",
            " Recall =  0.08958952270320836\n",
            " F1 Measure =  0.16325574860675315\n",
            " Conditional Entropy =  0.520387698458607\n",
            "\n",
            "\n",
            "For cluster : 4\n",
            " Precision =  0.8991253131730473\n",
            " Recall =  0.19081911549332561\n",
            " F1 Measure =  0.31482393499138145\n",
            " Conditional Entropy =  0.5893384629803557\n",
            "\n",
            "\n",
            "For cluster : 5\n",
            " Precision =  0.987658869036655\n",
            " Recall =  0.05512094328566876\n",
            " F1 Measure =  0.10441454248047825\n",
            " Conditional Entropy =  0.1000727924094658\n",
            "\n",
            "\n",
            "For cluster : 6\n",
            " Precision =  0.9963473466574776\n",
            " Recall =  0.8091528996930999\n",
            " F1 Measure =  0.8930459536396909\n",
            " Conditional Entropy =  0.04074520364214199\n",
            "\n",
            "\n",
            "For cluster : 7\n",
            " Precision =  0.9831717578196452\n",
            " Recall =  0.055254582275358\n",
            " F1 Measure =  0.10462898076773339\n",
            " Conditional Entropy =  0.1484303021279315\n",
            "\n",
            "\n",
            "For cluster : 8\n",
            " Precision =  1.0\n",
            " Recall =  0.21208507663682064\n",
            " F1 Measure =  0.34995080910540427\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 9\n",
            " Precision =  0.984038718978478\n",
            " Recall =  0.09823493734387367\n",
            " F1 Measure =  0.17863685646988445\n",
            " Conditional Entropy =  0.11811985457503958\n",
            "\n",
            "\n",
            "For cluster : 10\n",
            " Precision =  0.9978231748158071\n",
            " Recall =  0.12251611377817984\n",
            " F1 Measure =  0.21823641973613134\n",
            " Conditional Entropy =  0.025819984404535687\n",
            "\n",
            "\n",
            "For cluster : 11\n",
            " Precision =  0.9999924148184107\n",
            " Recall =  0.9390291677054026\n",
            " F1 Measure =  0.9685524425947082\n",
            " Conditional Entropy =  0.0001399547499897432\n",
            "\n",
            "\n",
            "For cluster : 12\n",
            " Precision =  0.8700828157349897\n",
            " Recall =  0.08640274679523423\n",
            " F1 Measure =  0.15719535801453194\n",
            " Conditional Entropy =  0.7039618683291057\n",
            "\n",
            "\n",
            "For cluster : 13\n",
            " Precision =  0.5332064297800339\n",
            " Recall =  0.025915684077428374\n",
            " F1 Measure =  0.04942894956129601\n",
            " Conditional Entropy =  1.8765686812328872\n",
            "\n",
            "\n",
            "For cluster : 14\n",
            " Precision =  0.9380987472365512\n",
            " Recall =  0.2486404802779691\n",
            " F1 Measure =  0.39309280026003574\n",
            " Conditional Entropy =  0.3366362589884996\n",
            "\n",
            "\n",
            "Total Meaures:\n",
            "\n",
            "Precision = 0.9385955070605774 \n",
            "\n",
            "Recall = 0.199960673616773 \n",
            "\n",
            "F1 Measure = 0.2681061125404324 \n",
            "\n",
            "Conditional Entropy = 0.10012713336890681 \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "k=15\n",
        "c_old1,c_new1,y_pred_15=kmeans(D_test_s,k, 30,c_new_15)\n",
        "act_y_pred_15 =  actual_labels(y_pred_15,act_lbl_15)\n",
        "evaluate(y_pred_15,y_test,act_lbl_15)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5nKerwh4c15p",
        "outputId": "68f4dca7-4564-49c1-cfe9-e2caec470623"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For cluster : 0\n",
            " Precision =  0.15333333333333332\n",
            " Recall =  0.0018979403221547398\n",
            " F1 Measure =  0.003749470183561019\n",
            " Conditional Entropy =  1.1964763794695354\n",
            "\n",
            "\n",
            "For cluster : 1\n",
            " Precision =  1.0\n",
            " Recall =  0.0005850412271239739\n",
            " F1 Measure =  0.0011693983080268231\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 2\n",
            " Precision =  0.9986892641589953\n",
            " Recall =  0.19966360129440372\n",
            " F1 Measure =  0.332793287861166\n",
            " Conditional Entropy =  0.014440581831073633\n",
            "\n",
            "\n",
            "For cluster : 3\n",
            " Precision =  0.8665120900960583\n",
            " Recall =  0.043174016371798254\n",
            " F1 Measure =  0.08224992532738049\n",
            " Conditional Entropy =  0.6413430556707663\n",
            "\n",
            "\n",
            "For cluster : 4\n",
            " Precision =  0.9723434876469177\n",
            " Recall =  0.6989017430733953\n",
            " F1 Measure =  0.8132529516205074\n",
            " Conditional Entropy =  0.185842415745335\n",
            "\n",
            "\n",
            "For cluster : 5\n",
            " Precision =  0.994751715785224\n",
            " Recall =  0.04066543438077634\n",
            " F1 Measure =  0.07813664399308695\n",
            " Conditional Entropy =  0.04935530856291907\n",
            "\n",
            "\n",
            "For cluster : 6\n",
            " Precision =  0.9902984227845228\n",
            " Recall =  0.30094308718815194\n",
            " F1 Measure =  0.46160762690573476\n",
            " Conditional Entropy =  0.09112724325680735\n",
            "\n",
            "\n",
            "For cluster : 7\n",
            " Precision =  0.23354564755838642\n",
            " Recall =  0.0018154211777132296\n",
            " F1 Measure =  0.003602836414850237\n",
            " Conditional Entropy =  1.1612367214845358\n",
            "\n",
            "\n",
            "For cluster : 8\n",
            " Precision =  1.0\n",
            " Recall =  0.22842949564298917\n",
            " F1 Measure =  0.37190493463920576\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 9\n",
            " Precision =  1.0\n",
            " Recall =  0.1194547134935305\n",
            " F1 Measure =  0.21341589267285863\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 10\n",
            " Precision =  0.9685484407207926\n",
            " Recall =  0.2403947715870082\n",
            " F1 Measure =  0.3851859687165316\n",
            " Conditional Entropy =  0.20161992252863387\n",
            "\n",
            "\n",
            "For cluster : 11\n",
            " Precision =  0.99517648307977\n",
            " Recall =  0.7996660389661834\n",
            " F1 Measure =  0.8867728800959638\n",
            " Conditional Entropy =  0.04427096376783117\n",
            "\n",
            "\n",
            "For cluster : 12\n",
            " Precision =  0.7299009900990099\n",
            " Recall =  0.030416556641140746\n",
            " F1 Measure =  0.058399480330180456\n",
            " Conditional Entropy =  1.316801668243766\n",
            "\n",
            "\n",
            "For cluster : 13\n",
            " Precision =  0.3647220946256316\n",
            " Recall =  0.013104040137311857\n",
            " F1 Measure =  0.02529911261928659\n",
            " Conditional Entropy =  1.7148907762594643\n",
            "\n",
            "\n",
            "For cluster : 14\n",
            " Precision =  0.7889318511358144\n",
            " Recall =  0.26939199894375493\n",
            " F1 Measure =  0.40163873920425186\n",
            " Conditional Entropy =  0.9157894940041689\n",
            "\n",
            "\n",
            "Total Meaures:\n",
            "\n",
            "Precision = 0.8037835880682973 \n",
            "\n",
            "Recall = 0.19923359336316246 \n",
            "\n",
            "F1 Measure = 0.2746119432595062 \n",
            "\n",
            "Conditional Entropy = 0.1649197435815717 \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import precision_score,recall_score, accuracy_score,f1_score\n",
        "print(\"acc:\",accuracy_score(list(y_test),act_y_pred_15))\n",
        "print(\"f1 macro:\",f1_score(list(y_test),act_y_pred_15,average = 'macro'))\n",
        "print(\"f1 weighted:\",f1_score(list(y_test),act_y_pred_15,average = 'weighted'))\n",
        "print(\"rec macro:\",recall_score(list(y_test),act_y_pred_15,average = 'macro'))\n",
        "print(\"rec weighted:\",recall_score(list(y_test),act_y_pred_15,average = 'weighted'))\n",
        "print(\"prec macro:\",precision_score(list(y_test),act_y_pred_15,average = 'macro'))\n",
        "print(\"prec weighted:\",precision_score(list(y_test),act_y_pred_15,average = 'weighted'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w7iH2bchc12N",
        "outputId": "6450467c-0b7e-44a2-fe78-f127bee9d550"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "acc: 0.963509967533245\n",
            "f1 macro: 0.1424672981527595\n",
            "f1 weighted: 0.9487196514627407\n",
            "rec macro: 0.144270537777097\n",
            "rec weighted: 0.963509967533245\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "prec macro: 0.146309239941931\n",
            "prec weighted: 0.9358109487804293\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### K = 23"
      ],
      "metadata": {
        "id": "nCWEo5TuZeuQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Best performance according to total precision in training set and test set is at k = 23.\n",
        "\n",
        "In addition to it is better than Normalized cut at k = 23."
      ],
      "metadata": {
        "id": "eW7Y5E1lvvhb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "k=23\n",
        "c_old_23,c_new_23,y_predt_23=kmeans(D_train_s,k, 30,None)\n",
        "act_lbl_23 = actual_clusters(k,y_predt_23,y_train)\n",
        "act_y_predt_23 =  actual_labels(y_predt_23,act_lbl_23) \n",
        "evaluate(y_predt_23,y_train,act_lbl_23)\n"
      ],
      "metadata": {
        "id": "InHcHlaCFmJt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1c362f18-35f2-4911-e074-f235df9b8106"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For cluster : 0\n",
            " Precision =  0.9407796634239863\n",
            " Recall =  0.2723973806758021\n",
            " F1 Measure =  0.42247076361375285\n",
            " Conditional Entropy =  0.3258980794649029\n",
            "\n",
            "\n",
            "For cluster : 1\n",
            " Precision =  0.9999924576971086\n",
            " Recall =  0.944367676911571\n",
            " F1 Measure =  0.9713844029152265\n",
            " Conditional Entropy =  0.0001392252778829243\n",
            "\n",
            "\n",
            "For cluster : 2\n",
            " Precision =  0.9972707889125799\n",
            " Recall =  0.12020313126432765\n",
            " F1 Measure =  0.21454652208216365\n",
            " Conditional Entropy =  0.03125594758336288\n",
            "\n",
            "\n",
            "For cluster : 3\n",
            " Precision =  1.0\n",
            " Recall =  0.00038462908223227323\n",
            " F1 Measure =  0.0007689623991626854\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 4\n",
            " Precision =  0.9958860962910587\n",
            " Recall =  0.05517646639837601\n",
            " F1 Measure =  0.1045598571944376\n",
            " Conditional Entropy =  0.03852670686567091\n",
            "\n",
            "\n",
            "For cluster : 5\n",
            " Precision =  0.9990140497904856\n",
            " Recall =  0.07561496627829964\n",
            " F1 Measure =  0.14058882192255995\n",
            " Conditional Entropy =  0.011267617630269566\n",
            "\n",
            "\n",
            "For cluster : 6\n",
            " Precision =  0.9996060776589758\n",
            " Recall =  0.3313961623492318\n",
            " F1 Measure =  0.49776868594167056\n",
            " Conditional Entropy =  0.005169617906536709\n",
            "\n",
            "\n",
            "For cluster : 7\n",
            " Precision =  0.9091613569829516\n",
            " Recall =  0.09849721551104934\n",
            " F1 Measure =  0.1777385010310146\n",
            " Conditional Entropy =  0.4866214886806719\n",
            "\n",
            "\n",
            "For cluster : 8\n",
            " Precision =  1.0\n",
            " Recall =  0.07355341834497812\n",
            " F1 Measure =  0.1370279616982083\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 9\n",
            " Precision =  0.8620361560418649\n",
            " Recall =  0.008451413699499071\n",
            " F1 Measure =  0.01673872076266489\n",
            " Conditional Entropy =  0.7716841522606426\n",
            "\n",
            "\n",
            "For cluster : 10\n",
            " Precision =  0.9833333333333333\n",
            " Recall =  0.11139081183134046\n",
            " F1 Measure =  0.20011305822498587\n",
            " Conditional Entropy =  0.1222915970693747\n",
            "\n",
            "\n",
            "For cluster : 11\n",
            " Precision =  1.0\n",
            " Recall =  0.07496198729489463\n",
            " F1 Measure =  0.1394690941277541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 12\n",
            " Precision =  0.9972997299729973\n",
            " Recall =  0.020671448960364176\n",
            " F1 Measure =  0.040503367664933336\n",
            " Conditional Entropy =  0.030871036822411137\n",
            "\n",
            "\n",
            "For cluster : 13\n",
            " Precision =  1.0\n",
            " Recall =  0.2907778817192142\n",
            " F1 Measure =  0.45054673749432556\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 14\n",
            " Precision =  0.712388689267302\n",
            " Recall =  0.04687644561407116\n",
            " F1 Measure =  0.08796465981211057\n",
            " Conditional Entropy =  1.5242237210238596\n",
            "\n",
            "\n",
            "For cluster : 15\n",
            " Precision =  1.0\n",
            " Recall =  0.07864665441553717\n",
            " F1 Measure =  0.14582468520824687\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 16\n",
            " Precision =  0.9862536656891495\n",
            " Recall =  0.05531626180906073\n",
            " F1 Measure =  0.10475699142437192\n",
            " Conditional Entropy =  0.12532481714747246\n",
            "\n",
            "\n",
            "For cluster : 17\n",
            " Precision =  1.0\n",
            " Recall =  0.07782576655068515\n",
            " F1 Measure =  0.1444125181748944\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 18\n",
            " Precision =  1.0\n",
            " Recall =  0.06804973834199309\n",
            " F1 Measure =  0.12742803242034656\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 19\n",
            " Precision =  0.9012464046021093\n",
            " Recall =  0.057978761680561694\n",
            " F1 Measure =  0.10894866470275752\n",
            " Conditional Entropy =  0.5471186715074673\n",
            "\n",
            "\n",
            "For cluster : 20\n",
            " Precision =  0.9152822306421841\n",
            " Recall =  0.09684714783556236\n",
            " F1 Measure =  0.1751603606953612\n",
            " Conditional Entropy =  0.5375014310458361\n",
            "\n",
            "\n",
            "For cluster : 21\n",
            " Precision =  0.8871459304410183\n",
            " Recall =  0.09232189998227629\n",
            " F1 Measure =  0.16723979147832407\n",
            " Conditional Entropy =  0.530953314047375\n",
            "\n",
            "\n",
            "For cluster : 22\n",
            " Precision =  0.8469994058229352\n",
            " Recall =  0.05861611686215652\n",
            " F1 Measure =  0.10964435769981444\n",
            " Conditional Entropy =  0.7229437913151796\n",
            "\n",
            "\n",
            "Total Meaures:\n",
            "\n",
            "Precision = 0.9536389581117409 \n",
            "\n",
            "Recall = 0.13523145145274282 \n",
            "\n",
            "F1 Measure = 0.20372197907343864 \n",
            "\n",
            "Conditional Entropy = 0.09553721566439567 \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "k=23\n",
        "c_old1,c_new1,y_pred_23=kmeans(D_test_s,k, 30,c_new_23)\n",
        "act_y_pred_23 =  actual_labels(y_pred_23,act_lbl_23)\n",
        "evaluate(y_pred_23,y_test,act_lbl_23)"
      ],
      "metadata": {
        "id": "bCZOrkUQBZh-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2ed226b5-0ae0-4ba5-cf94-42d532326221"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For cluster : 0\n",
            " Precision =  0.8030957523398128\n",
            " Recall =  0.29456033799841563\n",
            " F1 Measure =  0.43102782071097373\n",
            " Conditional Entropy =  0.8762413410079519\n",
            "\n",
            "\n",
            "For cluster : 1\n",
            " Precision =  0.9951765928240442\n",
            " Recall =  0.7996843215045311\n",
            " F1 Measure =  0.8867841647857055\n",
            " Conditional Entropy =  0.044270115227167096\n",
            "\n",
            "\n",
            "For cluster : 2\n",
            " Precision =  0.9652856287823369\n",
            " Recall =  0.2395530763137048\n",
            " F1 Measure =  0.383847465919158\n",
            " Conditional Entropy =  0.2175092166406746\n",
            "\n",
            "\n",
            "For cluster : 3\n",
            " Precision =  1.0\n",
            " Recall =  0.0005728528682255577\n",
            " F1 Measure =  0.0011450497913938543\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 4\n",
            " Precision =  0.9958055986140238\n",
            " Recall =  0.19966360129440372\n",
            " F1 Measure =  0.3326327972709552\n",
            " Conditional Entropy =  0.044203347425323926\n",
            "\n",
            "\n",
            "For cluster : 5\n",
            " Precision =  1.0\n",
            " Recall =  0.08042964776469372\n",
            " F1 Measure =  0.1488845626017298\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 6\n",
            " Precision =  1.0\n",
            " Recall =  0.01549973276322822\n",
            " F1 Measure =  0.030526315789473683\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 7\n",
            " Precision =  0.9994185201337403\n",
            " Recall =  0.355597317287633\n",
            " F1 Measure =  0.5245555583814441\n",
            " Conditional Entropy =  0.007858857311604265\n",
            "\n",
            "\n",
            "For cluster : 8\n",
            " Precision =  1.0\n",
            " Recall =  0.05399906898156928\n",
            " F1 Measure =  0.10246511703989662\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 9\n",
            " Precision =  0.5345622119815668\n",
            " Recall =  0.0019999655178358994\n",
            " F1 Measure =  0.003985021814559071\n",
            " Conditional Entropy =  1.3790176658030275\n",
            "\n",
            "\n",
            "For cluster : 10\n",
            " Precision =  0.5222929936305732\n",
            " Recall =  0.05021432945499082\n",
            " F1 Measure =  0.09162011173184358\n",
            " Conditional Entropy =  0.998565550382876\n",
            "\n",
            "\n",
            "For cluster : 11\n",
            " Precision =  0.8744588744588745\n",
            " Recall =  0.0034826985741625146\n",
            " F1 Measure =  0.00693776617667262\n",
            " Conditional Entropy =  0.687035769260157\n",
            "\n",
            "\n",
            "For cluster : 12\n",
            " Precision =  1.0\n",
            " Recall =  0.008223996137997621\n",
            " F1 Measure =  0.016313827422278465\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 13\n",
            " Precision =  1.0\n",
            " Recall =  0.3282281489305519\n",
            " F1 Measure =  0.4942345924453281\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 14\n",
            " Precision =  0.600450337753315\n",
            " Recall =  0.039609189331925004\n",
            " F1 Measure =  0.07431606001021844\n",
            " Conditional Entropy =  1.6138630777590488\n",
            "\n",
            "\n",
            "For cluster : 15\n",
            " Precision =  1.0\n",
            " Recall =  0.051706005068878125\n",
            " F1 Measure =  0.09832786885245903\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 16\n",
            " Precision =  0.2349137931034483\n",
            " Recall =  0.0017989173488249274\n",
            " F1 Measure =  0.0035704926624737948\n",
            " Conditional Entropy =  1.1677444577040386\n",
            "\n",
            "\n",
            "For cluster : 17\n",
            " Precision =  0.9970149253731343\n",
            " Recall =  0.005758521404803365\n",
            " F1 Measure =  0.011450905101481076\n",
            " Conditional Entropy =  0.029338975833691713\n",
            "\n",
            "\n",
            "For cluster : 18\n",
            " Precision =  1.0\n",
            " Recall =  0.08282615816968673\n",
            " F1 Measure =  0.15298145052145531\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 19\n",
            " Precision =  0.9944111776447105\n",
            " Recall =  0.0411110377607605\n",
            " F1 Measure =  0.07895779514081494\n",
            " Conditional Entropy =  0.05396804901886712\n",
            "\n",
            "\n",
            "For cluster : 20\n",
            " Precision =  0.71997585998793\n",
            " Recall =  0.03937813572748878\n",
            " F1 Measure =  0.07467217475667386\n",
            " Conditional Entropy =  1.2022801618980192\n",
            "\n",
            "\n",
            "For cluster : 21\n",
            " Precision =  0.946023957280993\n",
            " Recall =  0.3390458785193359\n",
            " F1 Measure =  0.49918769355739445\n",
            " Conditional Entropy =  0.3030526193115485\n",
            "\n",
            "\n",
            "For cluster : 22\n",
            " Precision =  0.3746312684365782\n",
            " Recall =  0.00419197253762873\n",
            " F1 Measure =  0.008291170230128939\n",
            " Conditional Entropy =  1.0535670769602978\n",
            "\n",
            "\n",
            "Total Meaures:\n",
            "\n",
            "Precision = 0.8503268474932644 \n",
            "\n",
            "Recall = 0.13204934396788157 \n",
            "\n",
            "F1 Measure = 0.19377025142237012 \n",
            "\n",
            "Conditional Entropy = 0.16746049455686182 \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import precision_score,recall_score, accuracy_score,f1_score\n",
        "print(\"acc:\",accuracy_score(list(y_test),act_y_pred_23))\n",
        "print(\"f1 macro:\",f1_score(list(y_test),act_y_pred_23,average = 'macro'))\n",
        "print(\"f1 weighted:\",f1_score(list(y_test),act_y_pred_23,average = 'weighted'))\n",
        "print(\"rec macro:\",recall_score(list(y_test),act_y_pred_23,average = 'macro'))\n",
        "print(\"rec weighted:\",recall_score(list(y_test),act_y_pred_23,average = 'weighted'))\n",
        "print(\"prec macro:\",precision_score(list(y_test),act_y_pred_23,average = 'macro'))\n",
        "print(\"prec weighted:\",precision_score(list(y_test),act_y_pred_23,average = 'weighted'))\n"
      ],
      "metadata": {
        "id": "kfytrfRhU5im",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7a052cbf-3148-4366-9d38-e0da5e209531"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "acc: 0.9643105176548671\n",
            "f1 macro: 0.18906507344094972\n",
            "f1 weighted: 0.9495163900187714\n",
            "rec macro: 0.19204084228377688\n",
            "rec weighted: 0.9643105176548671\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "prec macro: 0.19571424795621067\n",
            "prec weighted: 0.9370797812812731\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### K = 31"
      ],
      "metadata": {
        "id": "xlMw0KJvZmh2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "k=31\n",
        "c_old_31,c_new_31,y_predt_31=kmeans(D_train_s,k, 30,None)\n",
        "act_lbl_31 = actual_clusters(k,y_predt_31,y_train)\n",
        "act_y_predt_31 =  actual_labels(y_predt_31,act_lbl_31)\n",
        "evaluate(y_predt_31,y_train,act_lbl_31)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QUF6jgLlZnHK",
        "outputId": "60fe9457-bc9f-4526-f9fc-087a70947e4d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For cluster : 0\n",
            " Precision =  1.0\n",
            " Recall =  0.00039175184301435236\n",
            " F1 Measure =  0.0007831968672125312\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 1\n",
            " Precision =  0.9695740365111561\n",
            " Recall =  0.004458913629537038\n",
            " F1 Measure =  0.00887700336137575\n",
            " Conditional Entropy =  0.2423396849665499\n",
            "\n",
            "\n",
            "For cluster : 2\n",
            " Precision =  0.8600405679513184\n",
            " Recall =  0.013076061144977743\n",
            " F1 Measure =  0.025760460123941835\n",
            " Conditional Entropy =  0.727156494574264\n",
            "\n",
            "\n",
            "For cluster : 3\n",
            " Precision =  1.0\n",
            " Recall =  0.1043833546328859\n",
            " F1 Measure =  0.1890346394573912\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 4\n",
            " Precision =  0.9886561954624782\n",
            " Recall =  0.01056893125996959\n",
            " F1 Measure =  0.020914284659473727\n",
            " Conditional Entropy =  0.10491275438724953\n",
            "\n",
            "\n",
            "For cluster : 5\n",
            " Precision =  0.9990085266706326\n",
            " Recall =  0.0469958302627774\n",
            " F1 Measure =  0.0897687181497452\n",
            " Conditional Entropy =  0.011322742437683686\n",
            "\n",
            "\n",
            "For cluster : 6\n",
            " Precision =  0.9948257080610022\n",
            " Recall =  0.07510511220535172\n",
            " F1 Measure =  0.13966603263207195\n",
            " Conditional Entropy =  0.05472899628308278\n",
            "\n",
            "\n",
            "For cluster : 7\n",
            " Precision =  1.0\n",
            " Recall =  0.07594145577000215\n",
            " F1 Measure =  0.1411628027951657\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 8\n",
            " Precision =  0.9967159277504105\n",
            " Recall =  0.045298084905924386\n",
            " F1 Measure =  0.08665780339600082\n",
            " Conditional Entropy =  0.03182470247579214\n",
            "\n",
            "\n",
            "For cluster : 9\n",
            " Precision =  0.9999961761714299\n",
            " Recall =  0.9313615157234945\n",
            " F1 Measure =  0.9644593109450719\n",
            " Conditional Entropy =  7.433233278793482e-05\n",
            "\n",
            "\n",
            "For cluster : 10\n",
            " Precision =  0.44945054945054946\n",
            " Recall =  0.012613464642207304\n",
            " F1 Measure =  0.02453828232023758\n",
            " Conditional Entropy =  1.661553434892668\n",
            "\n",
            "\n",
            "For cluster : 11\n",
            " Precision =  0.8678104720419593\n",
            " Recall =  0.10035260133433391\n",
            " F1 Measure =  0.17990159040229992\n",
            " Conditional Entropy =  0.7222207452703381\n",
            "\n",
            "\n",
            "For cluster : 12\n",
            " Precision =  1.0\n",
            " Recall =  0.08401041035064971\n",
            " F1 Measure =  0.15499926854664522\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 13\n",
            " Precision =  1.0\n",
            " Recall =  0.868470736312146\n",
            " F1 Measure =  0.9296059279218593\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 14\n",
            " Precision =  0.9996532994337224\n",
            " Recall =  0.030805940382492254\n",
            " F1 Measure =  0.05976997198066632\n",
            " Conditional Entropy =  0.0044850804716258415\n",
            "\n",
            "\n",
            "For cluster : 15\n",
            " Precision =  1.0\n",
            " Recall =  0.16245184279997388\n",
            " F1 Measure =  0.2794986197599024\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 16\n",
            " Precision =  0.9913739987677141\n",
            " Recall =  0.045027565041370884\n",
            " F1 Measure =  0.08614258945302043\n",
            " Conditional Entropy =  0.07898542650551996\n",
            "\n",
            "\n",
            "For cluster : 17\n",
            " Precision =  0.9991214583790907\n",
            " Recall =  0.16973722260053545\n",
            " F1 Measure =  0.2901772543516222\n",
            " Conditional Entropy =  0.010482717269524763\n",
            "\n",
            "\n",
            "For cluster : 18\n",
            " Precision =  0.9992448759439051\n",
            " Recall =  0.08640777604686524\n",
            " F1 Measure =  0.15906105382455718\n",
            " Conditional Entropy =  0.008920392911352625\n",
            "\n",
            "\n",
            "For cluster : 19\n",
            " Precision =  0.7741077366437596\n",
            " Recall =  0.03589748861498607\n",
            " F1 Measure =  0.06861319605454473\n",
            " Conditional Entropy =  0.9680063086111468\n",
            "\n",
            "\n",
            "For cluster : 20\n",
            " Precision =  0.9884212460944679\n",
            " Recall =  0.05528542204220936\n",
            " F1 Measure =  0.10471387682781985\n",
            " Conditional Entropy =  0.10394609507021378\n",
            "\n",
            "\n",
            "For cluster : 21\n",
            " Precision =  0.9941376701966718\n",
            " Recall =  0.049038721653715915\n",
            " F1 Measure =  0.09346691676519484\n",
            " Conditional Entropy =  0.056317031938663636\n",
            "\n",
            "\n",
            "For cluster : 22\n",
            " Precision =  0.9691832483298614\n",
            " Recall =  0.5547868458114457\n",
            " F1 Measure =  0.7056439223069932\n",
            " Conditional Entropy =  0.19926140087076458\n",
            "\n",
            "\n",
            "For cluster : 23\n",
            " Precision =  0.988197251414713\n",
            " Recall =  0.06283088499850942\n",
            " F1 Measure =  0.11814965881193096\n",
            " Conditional Entropy =  0.09977878854511078\n",
            "\n",
            "\n",
            "For cluster : 24\n",
            " Precision =  1.0\n",
            " Recall =  0.04577649392970589\n",
            " F1 Measure =  0.08754546348176546\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 25\n",
            " Precision =  0.9978076446477934\n",
            " Recall =  0.03728052993340219\n",
            " F1 Measure =  0.07187561152289372\n",
            " Conditional Entropy =  0.022525168845796535\n",
            "\n",
            "\n",
            "For cluster : 26\n",
            " Precision =  0.9902075075775238\n",
            " Recall =  0.04365882993924566\n",
            " F1 Measure =  0.08363034873875114\n",
            " Conditional Entropy =  0.08577967464024439\n",
            "\n",
            "\n",
            "For cluster : 27\n",
            " Precision =  0.9994130881345985\n",
            " Recall =  0.0953069467635563\n",
            " F1 Measure =  0.1740189399100695\n",
            " Conditional Entropy =  0.007146731935013536\n",
            "\n",
            "\n",
            "For cluster : 28\n",
            " Precision =  0.9153386454183267\n",
            " Recall =  0.8836538461538461\n",
            " F1 Measure =  0.8992172211350293\n",
            " Conditional Entropy =  0.4320099693527467\n",
            "\n",
            "\n",
            "For cluster : 29\n",
            " Precision =  0.6959459459459459\n",
            " Recall =  0.4458874458874459\n",
            " F1 Measure =  0.5435356200527705\n",
            " Conditional Entropy =  1.5773284456682375\n",
            "\n",
            "\n",
            "For cluster : 30\n",
            " Precision =  0.9977095739807604\n",
            " Recall =  0.020316974655087174\n",
            " F1 Measure =  0.03982300884955753\n",
            " Conditional Entropy =  0.025611911359280877\n",
            "\n",
            "\n",
            "Total Meaures:\n",
            "\n",
            "Precision = 0.9492239145477354 \n",
            "\n",
            "Recall = 0.16765093552489244 \n",
            "\n",
            "F1 Measure = 0.22003266436792202 \n",
            "\n",
            "Conditional Entropy = 0.06811305727432114 \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "k=31\n",
        "c_old1,c_new1,y_pred_31=kmeans(D_test_s,k, 30,c_new_31)\n",
        "act_y_pred_31 =  actual_labels(y_pred_31,act_lbl_31)\n",
        "evaluate(y_pred_31,y_test,act_lbl_31)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2MYu5nGOfSQw",
        "outputId": "9a8eb58f-fdd8-42d0-b7b4-ffbb0ad5e03d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For cluster : 0\n",
            " Precision =  1.0\n",
            " Recall =  0.0005850412271239739\n",
            " F1 Measure =  0.0011693983080268231\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 1\n",
            " Precision =  0.9979529170931423\n",
            " Recall =  0.016810054999051742\n",
            " F1 Measure =  0.03306317609956255\n",
            " Conditional Entropy =  0.02123527559064859\n",
            "\n",
            "\n",
            "For cluster : 2\n",
            " Precision =  0.24358974358974358\n",
            " Recall =  0.0021950092421441775\n",
            " F1 Measure =  0.004350812915044653\n",
            " Conditional Entropy =  0.8009591077369054\n",
            "\n",
            "\n",
            "For cluster : 3\n",
            " Precision =  1.0\n",
            " Recall =  0.053654247340563094\n",
            " F1 Measure =  0.10184412481796018\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 4\n",
            " Precision =  0.956140350877193\n",
            " Recall =  0.0018792779434837331\n",
            " F1 Measure =  0.0037511829992256736\n",
            " Conditional Entropy =  0.3023024799292291\n",
            "\n",
            "\n",
            "For cluster : 5\n",
            " Precision =  1.0\n",
            " Recall =  0.16894536301098256\n",
            " F1 Measure =  0.2890560471976401\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 6\n",
            " Precision =  0.9635745507527926\n",
            " Recall =  0.19646157908634804\n",
            " F1 Measure =  0.3263784169111398\n",
            " Conditional Entropy =  0.22565580691260798\n",
            "\n",
            "\n",
            "For cluster : 7\n",
            " Precision =  1.0\n",
            " Recall =  0.00394820778952087\n",
            " F1 Measure =  0.007865361497509875\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 8\n",
            " Precision =  1.0\n",
            " Recall =  0.16570403958552438\n",
            " F1 Measure =  0.28429864521090925\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 9\n",
            " Precision =  0.9999771378056866\n",
            " Recall =  0.7996660389661834\n",
            " F1 Measure =  0.8886736739448448\n",
            " Conditional Entropy =  0.00038544185329043656\n",
            "\n",
            "\n",
            "For cluster : 10\n",
            " Precision =  0.3159739609414121\n",
            " Recall =  0.010413916028518617\n",
            " F1 Measure =  0.020163287478630432\n",
            " Conditional Entropy =  1.6883633123874247\n",
            "\n",
            "\n",
            "For cluster : 11\n",
            " Precision =  0.94880174291939\n",
            " Recall =  0.057499339846844466\n",
            " F1 Measure =  0.10842773559068841\n",
            " Conditional Entropy =  0.317933435506367\n",
            "\n",
            "\n",
            "For cluster : 12\n",
            " Precision =  1.0\n",
            " Recall =  0.08353304253374942\n",
            " F1 Measure =  0.15418642395697418\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 13\n",
            " Precision =  1.0\n",
            " Recall =  0.6938150642988365\n",
            " F1 Measure =  0.8192335502530731\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 14\n",
            " Precision =  0.0\n",
            " Recall =  0.0\n",
            " F1 Measure =  0.0\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 15\n",
            " Precision =  0.9993665540540541\n",
            " Recall =  0.08160204134411475\n",
            " F1 Measure =  0.15088384844669012\n",
            " Conditional Entropy =  0.008225311259719441\n",
            "\n",
            "\n",
            "For cluster : 16\n",
            " Precision =  0.9996170049789352\n",
            " Recall =  0.17999689660523094\n",
            " F1 Measure =  0.3050624589086127\n",
            " Conditional Entropy =  0.004899580802533915\n",
            "\n",
            "\n",
            "For cluster : 17\n",
            " Precision =  1.0\n",
            " Recall =  0.01551697384527853\n",
            " F1 Measure =  0.030559752805555083\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 18\n",
            " Precision =  0.7132616487455197\n",
            " Recall =  0.003430975328011586\n",
            " F1 Measure =  0.006829100892244338\n",
            " Conditional Entropy =  0.8922683419575846\n",
            "\n",
            "\n",
            "For cluster : 19\n",
            " Precision =  0.8301720335244817\n",
            " Recall =  0.031060205967784527\n",
            " F1 Measure =  0.059880048998552315\n",
            " Conditional Entropy =  0.9624101624660075\n",
            "\n",
            "\n",
            "For cluster : 20\n",
            " Precision =  0.3700787401574803\n",
            " Recall =  0.0007756799577501981\n",
            " F1 Measure =  0.0015481150875343798\n",
            " Conditional Entropy =  2.021622879694747\n",
            "\n",
            "\n",
            "For cluster : 21\n",
            " Precision =  0.9989461481715671\n",
            " Recall =  0.16342821675488353\n",
            " F1 Measure =  0.2809008742035857\n",
            " Conditional Entropy =  0.011942298268154903\n",
            "\n",
            "\n",
            "For cluster : 22\n",
            " Precision =  0.8006316620607975\n",
            " Recall =  0.3012278848692897\n",
            " F1 Measure =  0.43775557927304565\n",
            " Conditional Entropy =  0.8810458177609956\n",
            "\n",
            "\n",
            "For cluster : 23\n",
            " Precision =  1.0\n",
            " Recall =  0.34060602059677847\n",
            " F1 Measure =  0.5081373876646559\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 24\n",
            " Precision =  1.0\n",
            " Recall =  0.04345458146289939\n",
            " F1 Measure =  0.08328983788058521\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 25\n",
            " Precision =  0.9996948707777744\n",
            " Recall =  0.19966360129440372\n",
            " F1 Measure =  0.33284907347204157\n",
            " Conditional Entropy =  0.004003529411528723\n",
            "\n",
            "\n",
            "For cluster : 26\n",
            " Precision =  0.25617685305591675\n",
            " Recall =  0.003251254290995511\n",
            " F1 Measure =  0.0064210166066393974\n",
            " Conditional Entropy =  1.1951712183936953\n",
            "\n",
            "\n",
            "For cluster : 27\n",
            " Precision =  1.0\n",
            " Recall =  0.05317149704315443\n",
            " F1 Measure =  0.10097405254972579\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 28\n",
            " Precision =  0.7855297157622739\n",
            " Recall =  0.8587570621468926\n",
            " F1 Measure =  0.8205128205128205\n",
            " Conditional Entropy =  0.8203732764737586\n",
            "\n",
            "\n",
            "For cluster : 29\n",
            " Precision =  0.9438202247191011\n",
            " Recall =  1.0\n",
            " F1 Measure =  0.9710982658959537\n",
            " Conditional Entropy =  0.3120895659343452\n",
            "\n",
            "\n",
            "For cluster : 30\n",
            " Precision =  0.9917355371900827\n",
            " Recall =  0.008275719384148549\n",
            " F1 Measure =  0.01641446524749936\n",
            " Conditional Entropy =  0.07731884493556784\n",
            "\n",
            "\n",
            "Total Meaures:\n",
            "\n",
            "Precision = 0.842420690231527 \n",
            "\n",
            "Recall = 0.1786880268642094 \n",
            "\n",
            "F1 Measure = 0.2308251140524829 \n",
            "\n",
            "Conditional Entropy = 0.11043169563948206 \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import precision_score,recall_score, accuracy_score,f1_score\n",
        "print(\"acc:\",accuracy_score(list(y_test),act_y_pred_31))\n",
        "print(\"f1 macro:\",f1_score(list(y_test),act_y_pred_31,average = 'macro'))\n",
        "print(\"f1 weighted:\",f1_score(list(y_test),act_y_pred_31,average = 'weighted'))\n",
        "print(\"rec macro:\",recall_score(list(y_test),act_y_pred_31,average = 'macro'))\n",
        "print(\"rec weighted:\",recall_score(list(y_test),act_y_pred_31,average = 'weighted'))\n",
        "print(\"prec macro:\",precision_score(list(y_test),act_y_pred_31,average = 'macro'))\n",
        "print(\"prec weighted:\",precision_score(list(y_test),act_y_pred_31,average = 'weighted'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ooQ0ML3BfSMg",
        "outputId": "7a780de7-5b0e-4561-db02-1a4f6c98349c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "acc: 0.9605814593960295\n",
            "f1 macro: 0.18900911008351814\n",
            "f1 weighted: 0.9478053604065385\n",
            "rec macro: 0.1912416803974591\n",
            "rec weighted: 0.9605814593960295\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "prec macro: 0.20561430637392647\n",
            "prec weighted: 0.938181052635064\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### K = 45"
      ],
      "metadata": {
        "id": "vKqiqfo4a6xA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "k=45\n",
        "c_old_45,c_new_45,y_predt_45=kmeans(D_train_s,k, 30,None)\n",
        "act_lbl_45 = actual_clusters(k,y_predt_45,y_train)\n",
        "act_y_predt_45 =  actual_labels(y_predt_45,act_lbl_45) \n",
        "evaluate(y_predt_45,y_train,act_lbl_45)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c3JOCgIYa7Dk",
        "outputId": "0b6c12c3-d9c4-4912-e81c-6df24a0959d2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For cluster : 0\n",
            " Precision =  1.0\n",
            " Recall =  0.0003775063214501941\n",
            " F1 Measure =  0.000754727728411939\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 1\n",
            " Precision =  1.0\n",
            " Recall =  0.9018376722817765\n",
            " F1 Measure =  0.948385538288107\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 2\n",
            " Precision =  0.976256754543966\n",
            " Recall =  0.06128889665594128\n",
            " F1 Measure =  0.11533699605354794\n",
            " Conditional Entropy =  0.1619709539926678\n",
            "\n",
            "\n",
            "For cluster : 3\n",
            " Precision =  0.9993369579631348\n",
            " Recall =  0.1405957033982892\n",
            " F1 Measure =  0.24651014450087094\n",
            " Conditional Entropy =  0.00795705389925986\n",
            "\n",
            "\n",
            "For cluster : 4\n",
            " Precision =  0.9832371530640286\n",
            " Recall =  0.0367815619313918\n",
            " F1 Measure =  0.07091046018470809\n",
            " Conditional Entropy =  0.14055127800438608\n",
            "\n",
            "\n",
            "For cluster : 5\n",
            " Precision =  1.0\n",
            " Recall =  0.02900744328501727\n",
            " F1 Measure =  0.05637946250886877\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 6\n",
            " Precision =  0.7884615384615384\n",
            " Recall =  0.0010221161722283557\n",
            " F1 Measure =  0.0020415857501582765\n",
            " Conditional Entropy =  0.7444131797881748\n",
            "\n",
            "\n",
            "For cluster : 7\n",
            " Precision =  0.9983177120884402\n",
            " Recall =  0.04270279716685342\n",
            " F1 Measure =  0.08190224570673714\n",
            " Conditional Entropy =  0.017927873942843692\n",
            "\n",
            "\n",
            "For cluster : 8\n",
            " Precision =  0.9975031210986267\n",
            " Recall =  0.005691085864881228\n",
            " F1 Measure =  0.011317601065185982\n",
            " Conditional Entropy =  0.02518489601738248\n",
            "\n",
            "\n",
            "For cluster : 9\n",
            " Precision =  1.0\n",
            " Recall =  0.02966390238897025\n",
            " F1 Measure =  0.05761861189878693\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 10\n",
            " Precision =  0.8033924441017734\n",
            " Recall =  0.010711679019706611\n",
            " F1 Measure =  0.021141477468703714\n",
            " Conditional Entropy =  0.7223649737086495\n",
            "\n",
            "\n",
            "For cluster : 11\n",
            " Precision =  0.998165137614679\n",
            " Recall =  0.02029831811270417\n",
            " F1 Measure =  0.03978753165540633\n",
            " Conditional Entropy =  0.021158684544683788\n",
            "\n",
            "\n",
            "For cluster : 12\n",
            " Precision =  0.9809523809523809\n",
            " Recall =  0.4458874458874459\n",
            " F1 Measure =  0.6130952380952381\n",
            " Conditional Entropy =  0.13605928623877514\n",
            "\n",
            "\n",
            "For cluster : 13\n",
            " Precision =  0.7493932038834952\n",
            " Recall =  0.012695704020477604\n",
            " F1 Measure =  0.024968410411928224\n",
            " Conditional Entropy =  1.082745459262802\n",
            "\n",
            "\n",
            "For cluster : 14\n",
            " Precision =  0.9985258411377038\n",
            " Recall =  0.11837330509781346\n",
            " F1 Measure =  0.21165528586789695\n",
            " Conditional Entropy =  0.0177909911935022\n",
            "\n",
            "\n",
            "For cluster : 15\n",
            " Precision =  1.0\n",
            " Recall =  0.1639630227329969\n",
            " F1 Measure =  0.28173235666543783\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 16\n",
            " Precision =  1.0\n",
            " Recall =  0.19340645784717866\n",
            " F1 Measure =  0.3241250398394363\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 17\n",
            " Precision =  0.6828294352538505\n",
            " Recall =  0.02461013394738736\n",
            " F1 Measure =  0.047508012263973096\n",
            " Conditional Entropy =  1.2942195924009159\n",
            "\n",
            "\n",
            "For cluster : 18\n",
            " Precision =  0.9935483870967742\n",
            " Recall =  0.0063324321268131215\n",
            " F1 Measure =  0.0125846553009796\n",
            " Conditional Entropy =  0.06145441080613644\n",
            "\n",
            "\n",
            "For cluster : 19\n",
            " Precision =  0.9989462592202318\n",
            " Recall =  0.02923609897509175\n",
            " F1 Measure =  0.056809556150373544\n",
            " Conditional Entropy =  0.01290884591310606\n",
            "\n",
            "\n",
            "For cluster : 20\n",
            " Precision =  0.9831460674157303\n",
            " Recall =  0.11013215859030837\n",
            " F1 Measure =  0.1980758347481607\n",
            " Conditional Entropy =  0.12339167774830226\n",
            "\n",
            "\n",
            "For cluster : 21\n",
            " Precision =  0.9991944146079484\n",
            " Recall =  0.0694209942071436\n",
            " F1 Measure =  0.1298223272771677\n",
            " Conditional Entropy =  0.009441290521961498\n",
            "\n",
            "\n",
            "For cluster : 22\n",
            " Precision =  1.0\n",
            " Recall =  0.0708855327842091\n",
            " F1 Measure =  0.13238675958188154\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 23\n",
            " Precision =  1.0\n",
            " Recall =  0.020342604793618005\n",
            " F1 Measure =  0.03987406719673859\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 24\n",
            " Precision =  0.9947970863683663\n",
            " Recall =  0.008917827259074076\n",
            " F1 Measure =  0.017677187921820974\n",
            " Conditional Entropy =  0.05071450586262075\n",
            "\n",
            "\n",
            "For cluster : 25\n",
            " Precision =  1.0\n",
            " Recall =  0.014833927855505412\n",
            " F1 Measure =  0.029234197730956236\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 26\n",
            " Precision =  1.0\n",
            " Recall =  0.05949571365938751\n",
            " F1 Measure =  0.11230949383248665\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 27\n",
            " Precision =  0.9966382460414129\n",
            " Recall =  0.19081911549332561\n",
            " F1 Measure =  0.32031066501730265\n",
            " Conditional Entropy =  0.03439195684561991\n",
            "\n",
            "\n",
            "For cluster : 28\n",
            " Precision =  0.9829242262540021\n",
            " Recall =  0.009467808423368319\n",
            " F1 Measure =  0.018754963650803348\n",
            " Conditional Entropy =  0.1385441215879233\n",
            "\n",
            "\n",
            "For cluster : 29\n",
            " Precision =  0.3375\n",
            " Recall =  0.00025186332217050215\n",
            " F1 Measure =  0.0005033510127608803\n",
            " Conditional Entropy =  2.1606961022423263\n",
            "\n",
            "\n",
            "For cluster : 30\n",
            " Precision =  0.7288343558282209\n",
            " Recall =  0.030531369182848978\n",
            " F1 Measure =  0.05860762491120057\n",
            " Conditional Entropy =  1.039428770429257\n",
            "\n",
            "\n",
            "For cluster : 31\n",
            " Precision =  1.0\n",
            " Recall =  0.06077368681262302\n",
            " F1 Measure =  0.11458369974321995\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 32\n",
            " Precision =  0.99863434619324\n",
            " Recall =  0.04166815057516293\n",
            " F1 Measure =  0.07999835900802034\n",
            " Conditional Entropy =  0.014964700524458068\n",
            "\n",
            "\n",
            "For cluster : 33\n",
            " Precision =  0.9848280512474713\n",
            " Recall =  0.09008295897283017\n",
            " F1 Measure =  0.16506710619260653\n",
            " Conditional Entropy =  0.11339734198299657\n",
            "\n",
            "\n",
            "For cluster : 34\n",
            " Precision =  0.9727321333095705\n",
            " Recall =  0.056107815824912366\n",
            " F1 Measure =  0.10609594899307986\n",
            " Conditional Entropy =  0.1901077195363874\n",
            "\n",
            "\n",
            "For cluster : 35\n",
            " Precision =  0.9986518368722616\n",
            " Recall =  0.027639667540414734\n",
            " F1 Measure =  0.05379057439546873\n",
            " Conditional Entropy =  0.01479812783406556\n",
            "\n",
            "\n",
            "For cluster : 36\n",
            " Precision =  1.0\n",
            " Recall =  0.03894553222451283\n",
            " F1 Measure =  0.07497126849590575\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 37\n",
            " Precision =  0.6919148936170213\n",
            " Recall =  0.6519647153167603\n",
            " F1 Measure =  0.6713459950454171\n",
            " Conditional Entropy =  1.3758824218833963\n",
            "\n",
            "\n",
            "For cluster : 38\n",
            " Precision =  0.9333759386483463\n",
            " Recall =  0.18016591794566034\n",
            " F1 Measure =  0.3020318127768108\n",
            " Conditional Entropy =  0.3548179451818156\n",
            "\n",
            "\n",
            "For cluster : 39\n",
            " Precision =  0.5433186490455213\n",
            " Recall =  0.0038035712450013876\n",
            " F1 Measure =  0.007554257947283529\n",
            " Conditional Entropy =  1.045763179025756\n",
            "\n",
            "\n",
            "For cluster : 40\n",
            " Precision =  1.0\n",
            " Recall =  0.012202267750855804\n",
            " F1 Measure =  0.02411033474163146\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 41\n",
            " Precision =  0.5458593054318789\n",
            " Recall =  0.7715544367526747\n",
            " F1 Measure =  0.639374185136897\n",
            " Conditional Entropy =  1.184812604562128\n",
            "\n",
            "\n",
            "For cluster : 42\n",
            " Precision =  1.0\n",
            " Recall =  0.05679051501385248\n",
            " F1 Measure =  0.10747733672289453\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 43\n",
            " Precision =  0.9906542056074766\n",
            " Recall =  0.06429063396280724\n",
            " F1 Measure =  0.12074524568008495\n",
            " Conditional Entropy =  0.08771137948574834\n",
            "\n",
            "\n",
            "For cluster : 44\n",
            " Precision =  1.0\n",
            " Recall =  0.06152927677913452\n",
            " F1 Measure =  0.11592572739175902\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "Total Meaures:\n",
            "\n",
            "Precision = 0.9251526685104243 \n",
            "\n",
            "Recall = 0.11060225212272397 \n",
            "\n",
            "F1 Measure = 0.15255985027904703 \n",
            "\n",
            "Conditional Entropy = 0.05969804207053493 \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "k=45\n",
        "c_old1,c_new1,y_pred_45=kmeans(D_test_s,k, 30,c_new_45)\n",
        "act_y_pred_45 =  actual_labels(y_pred_45,act_lbl_45)\n",
        "evaluate(y_pred_45,y_test,act_lbl_45)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FFXwQKuYf4V8",
        "outputId": "8621fe9f-48c7-4a78-f2d4-519965a892bc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For cluster : 0\n",
            " Precision =  1.0\n",
            " Recall =  0.0005850412271239739\n",
            " F1 Measure =  0.0011693983080268231\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 1\n",
            " Precision =  0.9999752303576737\n",
            " Recall =  0.738084355631936\n",
            " F1 Measure =  0.8492989302506601\n",
            " Conditional Entropy =  0.0004147365652293264\n",
            "\n",
            "\n",
            "For cluster : 2\n",
            " Precision =  0.9698225833195159\n",
            " Recall =  0.0965308951676789\n",
            " F1 Measure =  0.17558500818035813\n",
            " Conditional Entropy =  0.19528081489811935\n",
            "\n",
            "\n",
            "For cluster : 3\n",
            " Precision =  1.0\n",
            " Recall =  0.05699901725832313\n",
            " F1 Measure =  0.10785065326961031\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 4\n",
            " Precision =  0.19746835443037974\n",
            " Recall =  0.0012872986532875628\n",
            " F1 Measure =  0.002557922180136751\n",
            " Conditional Entropy =  0.8396039743032697\n",
            "\n",
            "\n",
            "For cluster : 5\n",
            " Precision =  1.0\n",
            " Recall =  0.06065536805796783\n",
            " F1 Measure =  0.11437337684723403\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 6\n",
            " Precision =  0.9994787993990863\n",
            " Recall =  0.1986702500441828\n",
            " F1 Measure =  0.3314557618398845\n",
            " Conditional Entropy =  0.006435885424734857\n",
            "\n",
            "\n",
            "For cluster : 7\n",
            " Precision =  0.9986531986531987\n",
            " Recall =  0.024475178241351994\n",
            " F1 Measure =  0.047779370781448845\n",
            " Conditional Entropy =  0.01478514519639947\n",
            "\n",
            "\n",
            "For cluster : 8\n",
            " Precision =  0.0\n",
            " Recall =  0.0\n",
            " F1 Measure =  0.0\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 9\n",
            " Precision =  1.0\n",
            " Recall =  0.022689263978207273\n",
            " F1 Measure =  0.0443717652612236\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 10\n",
            " Precision =  0.9508379888268157\n",
            " Recall =  0.014044758383945075\n",
            " F1 Measure =  0.027680647941841365\n",
            " Conditional Entropy =  0.320839898625206\n",
            "\n",
            "\n",
            "For cluster : 11\n",
            " Precision =  1.0\n",
            " Recall =  0.008137790727746074\n",
            " F1 Measure =  0.01614420330750945\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 12\n",
            " Precision =  1.0\n",
            " Recall =  1.0\n",
            " F1 Measure =  1.0\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 13\n",
            " Precision =  0.27619559376679204\n",
            " Recall =  0.008482968048587272\n",
            " F1 Measure =  0.016460378204409713\n",
            " Conditional Entropy =  1.6624136426596323\n",
            "\n",
            "\n",
            "For cluster : 14\n",
            " Precision =  0.9701884253028263\n",
            " Recall =  0.23793570108265116\n",
            " F1 Measure =  0.3821502412129566\n",
            " Conditional Entropy =  0.1934460324076968\n",
            "\n",
            "\n",
            "For cluster : 15\n",
            " Precision =  1.0\n",
            " Recall =  0.07082636506267133\n",
            " F1 Measure =  0.1322835659888261\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 16\n",
            " Precision =  1.0\n",
            " Recall =  0.20514259308159494\n",
            " F1 Measure =  0.34044534523842135\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 17\n",
            " Precision =  0.934001178550383\n",
            " Recall =  0.026158568787958806\n",
            " F1 Measure =  0.05089181075310247\n",
            " Conditional Entropy =  0.3690176124176873\n",
            "\n",
            "\n",
            "For cluster : 18\n",
            " Precision =  1.0\n",
            " Recall =  1.6503828888302086e-05\n",
            " F1 Measure =  3.3007113032858576e-05\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 19\n",
            " Precision =  1.0\n",
            " Recall =  0.031934908898864536\n",
            " F1 Measure =  0.06189326211076815\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 20\n",
            " Precision =  1.0\n",
            " Recall =  0.05021432945499082\n",
            " F1 Measure =  0.09562682215743441\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 21\n",
            " Precision =  1.0\n",
            " Recall =  0.03246495750073274\n",
            " F1 Measure =  0.06288825061786119\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 22\n",
            " Precision =  1.0\n",
            " Recall =  0.03160290339821727\n",
            " F1 Measure =  0.06126951231741151\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 23\n",
            " Precision =  1.0\n",
            " Recall =  0.0009202210968304173\n",
            " F1 Measure =  0.001838750136992974\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 24\n",
            " Precision =  0.967032967032967\n",
            " Recall =  0.0015172152204272341\n",
            " F1 Measure =  0.0030296770639675003\n",
            " Conditional Entropy =  0.20905980566852453\n",
            "\n",
            "\n",
            "For cluster : 25\n",
            " Precision =  0.9939810834049871\n",
            " Recall =  0.01907842619487721\n",
            " F1 Measure =  0.037438264108169375\n",
            " Conditional Entropy =  0.06135327573737592\n",
            "\n",
            "\n",
            "For cluster : 26\n",
            " Precision =  1.0\n",
            " Recall =  0.024206479198634508\n",
            " F1 Measure =  0.04726874842185001\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 27\n",
            " Precision =  0.9996525278598198\n",
            " Recall =  0.6944190617403149\n",
            " F1 Measure =  0.8195377039840477\n",
            " Conditional Entropy =  0.004754410803342877\n",
            "\n",
            "\n",
            "For cluster : 28\n",
            " Precision =  0.8941605839416058\n",
            " Recall =  0.004043438077634011\n",
            " F1 Measure =  0.008050471527618046\n",
            " Conditional Entropy =  0.6328803299767738\n",
            "\n",
            "\n",
            "For cluster : 29\n",
            " Precision =  0.6382978723404256\n",
            " Recall =  0.0005172324615092843\n",
            " F1 Measure =  0.001033627342888644\n",
            " Conditional Entropy =  1.3974074190044545\n",
            "\n",
            "\n",
            "For cluster : 30\n",
            " Precision =  0.8449525934188511\n",
            " Recall =  0.02500330076577766\n",
            " F1 Measure =  0.04856936763645107\n",
            " Conditional Entropy =  0.9263738245396553\n",
            "\n",
            "\n",
            "For cluster : 31\n",
            " Precision =  1.0\n",
            " Recall =  0.0029137428665023017\n",
            " F1 Measure =  0.005810555269039024\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 32\n",
            " Precision =  1.0\n",
            " Recall =  0.000999445429670122\n",
            " F1 Measure =  0.001996895071687315\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 33\n",
            " Precision =  1.0\n",
            " Recall =  0.10034327964087668\n",
            " F1 Measure =  0.1823854091672666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 34\n",
            " Precision =  0.2430939226519337\n",
            " Recall =  0.0021785054132558756\n",
            " F1 Measure =  0.004318311932608163\n",
            " Conditional Entropy =  0.8001476186542926\n",
            "\n",
            "\n",
            "For cluster : 35\n",
            " Precision =  1.0\n",
            " Recall =  0.01548249168117791\n",
            " F1 Measure =  0.030492877637990454\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 36\n",
            " Precision =  1.0\n",
            " Recall =  0.0054654230099481045\n",
            " F1 Measure =  0.010871429061353272\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 37\n",
            " Precision =  0.0\n",
            " Recall =  0.0\n",
            " F1 Measure =  0.0\n",
            " Conditional Entropy =  0.3374471256068248\n",
            "\n",
            "\n",
            "For cluster : 38\n",
            " Precision =  0.7042314647377939\n",
            " Recall =  0.1606812780565091\n",
            " F1 Measure =  0.2616606420575944\n",
            " Conditional Entropy =  1.0978650977042377\n",
            "\n",
            "\n",
            "For cluster : 39\n",
            " Precision =  0.1537433155080214\n",
            " Recall =  0.0018979403221547398\n",
            " F1 Measure =  0.0037495924356048256\n",
            " Conditional Entropy =  1.1951010868113499\n",
            "\n",
            "\n",
            "For cluster : 40\n",
            " Precision =  0.46\n",
            " Recall =  0.000759176128861896\n",
            " F1 Measure =  0.001515850523957029\n",
            " Conditional Entropy =  1.8685622017306682\n",
            "\n",
            "\n",
            "For cluster : 41\n",
            " Precision =  1.0\n",
            " Recall =  0.6919779546846295\n",
            " F1 Measure =  0.8179515019905899\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 42\n",
            " Precision =  0.9039301310043668\n",
            " Recall =  0.003568903984414062\n",
            " F1 Measure =  0.007109737248840804\n",
            " Conditional Entropy =  0.4986345212842471\n",
            "\n",
            "\n",
            "For cluster : 43\n",
            " Precision =  0.8028083028083028\n",
            " Recall =  0.021702534988117243\n",
            " F1 Measure =  0.042262574321067004\n",
            " Conditional Entropy =  0.8372267478177623\n",
            "\n",
            "\n",
            "For cluster : 44\n",
            " Precision =  1.0\n",
            " Recall =  0.029068464336821778\n",
            " F1 Measure =  0.056494714091845796\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "Total Meaures:\n",
            "\n",
            "Precision = 0.8422779137181277 \n",
            "\n",
            "Recall = 0.1049707451514634 \n",
            "\n",
            "F1 Measure = 0.14034657633141304 \n",
            "\n",
            "Conditional Entropy = 0.09978485313918131 \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import precision_score,recall_score, accuracy_score,f1_score\n",
        "print(\"acc:\",accuracy_score(list(y_test),act_y_pred_45))\n",
        "print(\"f1 macro:\",f1_score(list(y_test),act_y_pred_45,average = 'macro'))\n",
        "print(\"f1 weighted:\",f1_score(list(y_test),act_y_pred_45,average = 'weighted'))\n",
        "print(\"rec macro:\",recall_score(list(y_test),act_y_pred_45,average = 'macro'))\n",
        "print(\"rec weighted:\",recall_score(list(y_test),act_y_pred_45,average = 'weighted'))\n",
        "print(\"prec macro:\",precision_score(list(y_test),act_y_pred_45,average = 'macro'))\n",
        "print(\"prec weighted:\",precision_score(list(y_test),act_y_pred_45,average = 'weighted'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t_GOZLXrf4KW",
        "outputId": "d54c9726-3ba3-443a-81fa-d213be83363d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "acc: 0.9706738647754526\n",
            "f1 macro: 0.3060213351870163\n",
            "f1 weighted: 0.9589881317611819\n",
            "rec macro: 0.3110353258487041\n",
            "rec weighted: 0.9706738647754526\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "prec macro: 0.30427988157151487\n",
            "prec weighted: 0.9486558574026664\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Spectral Clustering"
      ],
      "metadata": {
        "id": "odIqkfjDF_bm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Preprocessing Data"
      ],
      "metadata": {
        "id": "Rfe0wWXAJq5c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "D_train_SC, D_test_SC, y_train_sc, y_test_sc = train_test_split(D_train_s, y_train, train_size=0.0025, random_state=42, stratify = y_train)"
      ],
      "metadata": {
        "id": "t461SyJQGEJg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Spectral Clustering Algorithm"
      ],
      "metadata": {
        "id": "Nasi3uJP9GHs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "K = len(set(y_train_sc))\n",
        "gamma = 100\n",
        "\n",
        "A = metrics.pairwise.rbf_kernel(D_train_SC, gamma = gamma)\n",
        "\n",
        "Delta = np.sum(A,axis = 1)\n",
        "Delta = np.diag(Delta)\n",
        "\n",
        "L = Delta - A\n",
        "\n",
        "Delta_inv = np.linalg.inv(Delta)\n",
        "La = np.matmul(Delta_inv,L)\n",
        "\n",
        "eigvals, eigvecs = np.linalg.eig(La)\n",
        "idxs = eigvals.argsort()\n",
        "eigvals = eigvals[idxs]\n",
        "eigvecs = eigvecs[:,idxs]\n",
        "U = (eigvecs[:,:K])\n",
        "\n",
        "Y = normalize(U.real, norm=\"l2\", axis = 1)\n",
        "\n",
        "c_old_sc,c_new_sc,y_pred_sc=kmeans(Y,K,30,None)\n",
        "act_lbl_sc = actual_clusters(K,y_pred_sc,y_train_sc)\n",
        "act_y_pred_sc =  actual_labels(y_pred_sc,act_lbl_sc)"
      ],
      "metadata": {
        "id": "BuPFS6TZJCEu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Spectral Clustering Evaluation"
      ],
      "metadata": {
        "id": "qQ-ExMU8uMFi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "evaluate(y_pred_sc,y_train_sc,act_lbl_sc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WfGLmA9ksBP-",
        "outputId": "fd2ae0dc-acb5-496d-dc2e-b375046ec5e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For cluster : 0\n",
            " Precision =  0.9330357142857143\n",
            " Recall =  0.7798507462686567\n",
            " F1 Measure =  0.8495934959349594\n",
            " Conditional Entropy =  0.35449145090121004\n",
            "\n",
            "\n",
            "For cluster : 1\n",
            " Precision =  0.8676470588235294\n",
            " Recall =  0.22014925373134328\n",
            " F1 Measure =  0.3511904761904762\n",
            " Conditional Entropy =  0.8141435042404045\n",
            "\n",
            "\n",
            "For cluster : 2\n",
            " Precision =  0.8571428571428571\n",
            " Recall =  0.04938271604938271\n",
            " F1 Measure =  0.0933852140077821\n",
            " Conditional Entropy =  0.7345299214394703\n",
            "\n",
            "\n",
            "For cluster : 3\n",
            " Precision =  1.0\n",
            " Recall =  0.6566951566951567\n",
            " F1 Measure =  0.7927773000859845\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 4\n",
            " Precision =  0.9484536082474226\n",
            " Recall =  0.757201646090535\n",
            " F1 Measure =  0.8421052631578948\n",
            " Conditional Entropy =  0.36950084274851613\n",
            "\n",
            "\n",
            "For cluster : 6\n",
            " Precision =  0.8\n",
            " Recall =  0.04938271604938271\n",
            " F1 Measure =  0.09302325581395349\n",
            " Conditional Entropy =  1.0389205950315936\n",
            "\n",
            "\n",
            "For cluster : 7\n",
            " Precision =  1.0\n",
            " Recall =  0.06584362139917696\n",
            " F1 Measure =  0.12355212355212357\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 8\n",
            " Precision =  1.0\n",
            " Recall =  0.04700854700854701\n",
            " F1 Measure =  0.08979591836734693\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 9\n",
            " Precision =  1.0\n",
            " Recall =  0.2962962962962963\n",
            " F1 Measure =  0.45714285714285713\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 10\n",
            " Precision =  1.0\n",
            " Recall =  0.00823045267489712\n",
            " F1 Measure =  0.0163265306122449\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "Total Meaures:\n",
            "\n",
            "Precision = 0.9406279238499524 \n",
            "\n",
            "Recall = 0.2930041152263375 \n",
            "\n",
            "F1 Measure = 0.3708892434865624 \n",
            "\n",
            "Conditional Entropy = 0.18811193085753616 \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import precision_score,recall_score, accuracy_score,f1_score\n",
        "print(\"acc:\",accuracy_score(list(y_train_sc),act_y_pred_sc))\n",
        "print(\"f1 macro:\",f1_score(list(y_train_sc),act_y_pred_sc,average = 'macro'))\n",
        "print(\"f1 weighted:\",f1_score(list(y_train_sc),act_y_pred_sc,average = 'weighted'))\n",
        "print(\"rec macro:\",recall_score(list(y_train_sc),act_y_pred_sc,average = 'macro'))\n",
        "print(\"rec weighted:\",recall_score(list(y_train_sc),act_y_pred_sc,average = 'weighted'))\n",
        "print(\"prec macro:\",precision_score(list(y_train_sc),act_y_pred_sc,average = 'macro'))\n",
        "print(\"prec weighted:\",precision_score(list(y_train_sc),act_y_pred_sc,average = 'weighted'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QhtsKZNZt8Oj",
        "outputId": "aff8963e-40e5-48f2-89e5-f6331547ed25"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "acc: 0.968421052631579\n",
            "f1 macro: 0.2628206504239562\n",
            "f1 weighted: 0.9598770607383047\n",
            "rec macro: 0.2663673774784886\n",
            "rec weighted: 0.968421052631579\n",
            "prec macro: 0.2595970504797879\n",
            "prec weighted: 0.9521037420339475\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## DBSCAN Clustering"
      ],
      "metadata": {
        "id": "T-eZOJJoUhyE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "DBSCAN is Denisty-Based Clustering Algorithm. It is based on the threshold distance between each point and its neighbour points and the minimumm number of points in this neighbourhood.\n",
        "\n",
        "There are three types of points anr here:\n",
        "\n",
        "1.   Core point : if it satisfies that there are minimum number of points or more around it and the raduis of the neighbourhood area is equal to threshold distance which we call epsilon.\n",
        "2.   Branch point : if it doesnot have the min number of points around it.\n",
        "3. Noise point : if it does not satisfy minmum number of points or epsilon distance.  \n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "0gXVm8PJrTLt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "P7Ji1TJft0YV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### DBSCAN Algorithm: "
      ],
      "metadata": {
        "id": "6zI2AH_psXuz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![Screenshot 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2WFSEbStc0bzXR1oqNyEm3K6gQKP/05N8tiTyj4fNfyuF9XNbU1KOy9qtfCqfIcjija1dzG/V1uNRq8cRGWdlObPg4LU7pVp7FHzPSJDwC4aUsGd0nvVpDbtm1AZDyuy8WxCC25KXfMGejitqjysSUs1ZvuJ43QklXNzIlu1ue4qrJ3Izc2D2v4dQg/m1DLXIYUVlZ94I8HQqwja1SMraTJ3pC2p3dnRnaTB+c1DQjTletCQul7kmdOD0onPUqgzUY2xh+iZpS69vERLv6tEWSxDueRts87ehOZft5SngQJ2DaEK7unJq2Ybat+kDHlY2ZBnkwVk2rw0laVgeEFHxtekTNI2KxSk0dqQe6UO1CivnNeiI+hiCodW0Mom5GQ6rsRyX/1Gd9NyxyNiLbU09fIqKX3bjeakda3ku4gaKvTLWbmHNJoO/pjT3Ost3b3aIR1FqWxz9BkaWc6NnGzV8nnJmpzc3Mij7d+mtZEhkI7/3oTy2KvIPl9D+q5XXcptrSQ7r1a08KpcGrGXaGRx83GhKT+Rzvz7M5UWdUvl+R3tjX61vkiiNrUnF9P5QEvVRq+kQWWdSDRoTOtQqN2p8tijcXcso2+uoK7F0pNKmY6KfzuSRrQpTHZiWYXWk6p16EmDlqehdzj2DA0tpCHnZkto/XfZzeWjdKO2r1xMImhtS0dTWSjTtyXLiLw050Gc449Nb0eFHdXkVKgJfd/nWyqXUU1q5+LUadFlCjOtTSqykVTcdM7SiOPiDP37c2lyUqrI87u9Yh0PaVpV+RytrUGz/QypHGMvxSEymPKrzWVZcegC+qm0s+mu7atl+ZL+bCifr61q04jZ7SmfneVYEfWqkth3T0/ShGquCd6fgSqMPEipXHIZ+6JwUJ6iCPq3VzbSeHShbWE6Ojk4P6mlk4fSiRou9ReXmeRJFyrzBU8EsD8dSX28cMQ6auUkn5RSHGP4GtG7qYccEEOVlXrtTS2UTHCbWOQv36ATcrosQX7UufqQNIIh1m8/DSkrgjWFCGZL9qedAclsefR++t7SaHH5ljanFiy+SVAeV5YK0jp7klfefJS/QEEqWKggFcifj/Lm9aLMDnJgp85Pg08lU9r6WzSpvC2VHfkn9c1rHp6i0BSgQcfjMxnyT1fytC5CP2+cQBVNFyoF2TVZmfwwomTEB+UqymIZg5uQKNfWmevRr5sX0++jf6EhQ0fTHysO0N03DdITBOWqLD3p37S2GtIkgta1cpLLMk8KQbmero8vTRppGbGt2XvvN6Ua/NdQGw+VeK+GCg89bWrMROztRdlFA0dhU5rGXpL2cTTt7y0HI1LgVagZjV26nGb0LEvpTcGRghy+WUwBBh0d6edF6gR1Q39/MTXO247WyxHFy2UNzMORFDbUaLmcmKAOWjVYFr/vXqymlq7yd0KKjyRTViThO6irh4byDTyeeuNLDqak9So0manJorsi+NHTvfnfUAYp36pM1PJv8/lAf2cKNanWh+b+c44eBDykEwvbkpfpuwZKcmm5xhxY6I5QPy81aUqOIfPm6en+4saUt916U8CUtrI0kN/fLSmT1IBUaKhA750UoAui4yPLkYMczGhKjKLLlm1NREeHLEGzVF977E59+y0i1lMb07nBPJRMErWlA7ma9p2GvvrtrtzgiaULw4ua64hoKA84LurRa7bZVDe+l+uGVQNaFrfzDOS/pg15SOPeNYVp6GlTadDeXtKyCrIpPda8PxN8t0RTujk1zWkO8NV5B5L08cnVl+j9vU1lamo0ORWiZmOX0vIZPamsPAxP4fANLZbOdWLdM2uYOz1U2b+n/VIWwjbRt27ScqL8umyj1Np0FhH/9qJsGg/qsi0sQRArzqkNl1Lir2dE0Po25uNQ6dqBtkinqTTnQUeXJ1c2DUtSpv+GFsrjDIO3i/ObVH80uajHDvMgdb3PFKokB+WlmzelnKZgW015xfEgBc/LGpiDZ4VNI7IcYikdY+bOK0tZFk65LE0Nc/NQTYXSitIXb0+Tly2lMQ3FdVbaP0p7ypKjGDUYsZiWz/yeyjvL77erRXMS9aIw9uWS74WzVwRtw5KNfsjRshNq22tRqn1rFJXu0RlDsW/lOtNt0eSJZWTxz5JnDH2K55HSeUdQuMDN/S12hyEEwaHyrUmlI9I7iktbKpRx97cJYcGh8vN4llcNT9ahR8k8yJKnNn67nB3fzt6Hi8emom4KeYzb1tdt9BuJX5nGKQtyeeVFvgIFUahAfuT1yo1cObLBzc6yjGXoRRJ6H9x7ZIVsBZuif/9acJSuCLE3sWTyapgmLzHcxKLJaxH+9U/4sVZ2uJvGhBBi/B7DN7XxKylQKZMpH91LhMXcxZEdN/HM7wI2zxqL/h1qoFCRpph3JfkZY15LpXz/UyelYd/F3VoXYmOkG8sG3F81B5v9DCJicEf5KkUhjYSwLV0ORbTiSht1CRs3XBUpCvNwJOmNCgfUG7UEIzp3QJ+ZU9AptzSAhxDpfQneuhe4fPkB9Pqr+HP4FOx/pIMq+7dYuPd31JNnkFOJ9ZiH/GhhZW0phWjoYqRjSXyOjW389I0uDdG9maf4mxB7dT1WnTPfDA8/uBG7Q8qiS7eypvymynKIOlZFu1a5xGerkLNVW1ROJ7bGEIBtC9eYZqJQ5e6Ldf/OwHfflEQ2d0+U7zIGXUpKOTXipfdl3NGLZy8u4/IDPfRX/8TwKfvxSKdC9m8XYu/v9WCf1rI0+GDV3K0IEJ+psCqPrv1rw12bHhX69EZNqYKnSo/nL6ThchIlHF2c48vqNeRiiPtXYW0NK/MOha2Dg1x9pP0sj19TWMHaSvmabTaL1sWY1quwsoGtJUOG+1g1ZzP8DKLt414eVYqaSgOlyxWB1NaJurQRG66KQpWGh8njJ/QXd+BKoRFYtXsvNk5uihyi+JOrLwrTUD3TMzjUG4UlIzqjQ5+ZmNIpt2lZivTGJe9YUckv4vTFCFPeVDnyIa+0adY5kF20iKS6H3D8MK6/dnxFELYt2Qi/HC3RqbY9tKXao3VRjfhkI0L3rcS6pBeTpAWd1jwEbcaEKccQYlTApnxjNMxq3lananXwlagXFCvqzYw1kEYtKqwtM6bocXHHFRQasQq7927E5KY5RJoKVlpziUEr9mFclUrhGBPMWZXKcmTKZSlYiXKXkMINLSYswOBOnTFsYjcUlRY0RiAoT08sGN0VHX6chqldvMzvjxbvv25+P2Nfutedwf9PGfFo7TLsDC2E1h3Lmy6M6gIt0aykdCIVF4PjK7DS+5VRhzLLmTThs+Qp1RqozdcSwYDYFM47ugMjUadadVSvXhX1Rh82jZmMo0qHdA7ybqQYcdJMMnYwkVgEBr6UL8gKOGVwNj1LyJJnlUcVVMkchOcRIkCIeSIC2KzImsqcx3Hb+rqNliRYJvVZ8uNyg0wNJ2HLti3YtH4t1qxdj01btmPHri0YWN4SUlGyH218dgt3g92RzdMKnu0H4dtcprM/gndNxYzT0Qje+TtmnvZAxwFtkNnKAx5u5kuN0f8xnqS0i5N63VT/6Vth8dXz2L3od/y2YAdOrOoITxUh2mcLBvScjutp/ZwUtvG9ee3KDQgUAZ15MSVcM2US/+pw4dxV6EyJCvgdXYDZs2dj9rJTeGoqSgOePHggPYFaI1/oVTlQsJAcjqlzI7ccPFB0FKIUzsiZwxUqUZfvbx2KWvnyoGrPBbihchXhmEwlhcWCQgWNxlL3ddCZDgwRkNjaJDix2aJqt3YoKEUg+ttYt/wwIhGC3at3ILp2d3TII63JiJDbJ3Bg/37stzwOHMfNIHGkGGMRE2veYlUW0RC0MT0Vq82CLC7Se0UD7toFXDIdu9L8+9K/RkT6XcQ/8yZh3XVzwEUGAwxidUrnnMjhKpoIMfexdWgt5MtTFT0X3IDKVdq6NJal7jzOXjEHscqMhVHcNM5fcMyH/Fnk5+LDkj8TKEUeVWLtEoIuMiqF5ZKSflPA/MygN1dYEo0yU4wm1qbRaOLKXPoNAjOVtKtes80SkQ/zzoNCBHs2lhXpLuDcVZ25vin8cHSBKAtRHstOPTXvf8MTPBDBvvQ5YpNMFPZ1MXrZSLT5uiYaNipv/q5HcvVFOveanqiQo2AhuXGgRu7cWc3bQdGIkr48o8yETK7m91CsqGNSZozhCDd1piigdM0Id/mzU2J8tBbLdoaiUOuOMJ2u1AXQsllJU1BMUcexYqV3gnHXoqTjC9qcnsY8RJ/ciyOB0t5Uwi1nbvP3oCQad7hL3yUSJRl16QwuSAG8pUzE++3rjsaykW3wdc2GaFReOqall+VXVRpYiiylY8wYGwPzIfKaspTqiaXuqbxQpKj5gqLKmgUZTZ1FSqT3zCZ/f0kr3i81pgXx/uho0wcw9sWzHFcsIb03li89hPDYa/jtKyfY29vD3rEUxp43R80Ucxmrlp1MHBxbiMjaEluT8TWXu3TiIp9RPqMbfHDrdgpRebQfrh45iIMHj+FmYJKTk9oL+XLKJzrDUzyM+7ZMMmKu48qNSPNFTumCkmULmJLjibzLWSByRYuxfVDCRqzZGIIDv/6CNSnOiy1OypasGw2vuciLZeM6hkTgkNrCcWVJ4vokGgem5wmJhkxcugh64tYbL/bmDdwxZkIWD3EJtq2Evj9Ugr10MYy9gSUTx2Pcr2vwskof9KkgLhAqd2R0NZUkjM+f4EmU6enriSDFclE1Jhug2yNjRkufoBIuX7dB3czSfheB+YV92O//mnoSR3xO/AclUx7vJjZuJ6awZmMgLl99LEpaULmhdPm84okOL19Gye/QIyI4EC9evMCLEHfUGTASo0aNxKDG0nJKKJVKcz1V2MHOcodDpCnlQ0AhgjmNwhpfD/0VzXKYe9Up6jEOL+yL2lV6YqOlnCzrEZdsVVxkIBqkpshAXPitrOXXzdTFOqFjBRuRZsDjzX9ix90tWLXbGs16NIP5xo8eV+Z2FEFJTdS0PGq3xYwLUnmIoFwvl4eDY3ygIz7bEgiSLho6KWt6Pxye9QPqF8+KzCW7Y9XTkqhSxBz6KaQfzJLea/01hv7aDDlM3bQiSHp8GAv71kaVnhvhb0xjWerCEGaO3KF0coGznA+pXEWsZCaOw4SBXjwpWMouB2QG+N+5jZR+tyARspwbxLEYK69ZRGjmoFzkQ4q+ZfF3U8R+kjYz1W2WljMiRmf+krVCYxX/BWbdS7y0fKtcH4HgQFEWojxC3OtgwMhRGDVyEBrnlaJ+6cvj5jcp3bIhm+VQs0imvkh10fwWhaiLdvLrUh2Nq4wibhep2jLo+mMNuKoU0F/eiS13RT27dQDHHhqg0OZA+37tkcOyucnSw3v5UhwKj8W1376Ck3QtsXdEqbHnzec20fi8vGoZTsadtinuOCRDLPRS+aQxD9FPAyC1I6XtcHROL+9jSfwXtykqHBFiGaVIMG+/COCzZYu7Y2ERd0dV7Mu4Kp/SMSaCcvMh8pqyFKRyNxF11d7SmSQdG/LKFJbXBXVcPRINA/nmC2NfuvgjgMWJPr4YKy+nQ8Pph3HqxEmcOn0KJ08cx4m941BViujEifbe2sXY9eroDxhixInUdIISFy8RJcqXlORZlUfTelnNJ0+DH/ZsPJL8zC62tvKFSjoZJpnRRZUNdesWN99GpnCcOXg8xdlhYi5swvbb0gVVAXWuluhYM8mp2CACZksAIk60xqI/YXzHnKYTpvHpZgwbvh3Pk7uAiwuLpTeRpGAguWUsTL35llIRgXTykYNJfFlK2UmusSHdTpWfkh4xceu1MOLZjdt4pnGBm6mnSIVcHfujlak30YjA7b9i2jl3tBnQwfxz5FJQnsHcwCERYD1KY1e5MSoKlo4cgyi311JlgKu5O0iqJaYe1DQxRiEq/oPe86wEBrEP9XH1Nbm2heHhBmw4Hi2WEfUwX3t0qSb1dNkgQwYH+USiQd4mv2D06NEJHqMwpJnU+DMmOB6Mop6YnoinCRpT8o/zqHJ1xN+nj2JR31rIaScFVATdvZWYsvyOeTnxHvN6RAMwruxEXZHbFIqkU8eocqJd59qmgNr47B9M7TEbB7N+ix417eQFxKHonAXZc+RAjrhHVmSQDrq4YFQQdVCOhcWKQhAqz2gh9VRmVEfjxIi6qNt3DnbeyY1hB05g9bg2KCgNcZGIAMPSM5ur4984fXQR+tbKCTsp+CEd7q2cguV3tGkrSysnOEqNZUGaDUkKskwMwQgONWeQpAZtsvVKjXwNvkEhS4B8Yhu2J/uLYEmIbY+RK0WsXNBGsRMtxWHpPZfWGRz6Un5u2c+pbbN551vWKUWPcomJqpUBGSyBmyYvmvySsCzEY9QQNCsgSlVUAqNopJpIz83P4iVTX4yi0plnZBLP4yujqY6aSbPuSP+qkK/3JhxZOwhVHc5gUuOKqNxpK6zr98PCwyewsGnG1C+i0cexeOVlpGs4HYdPncDJU6dx6uQJHD+xF+Oq2pu2VX9vLRbHXUzM50dT1sTxaC6VtOVBa28nXyfE8RIdHV8ORtG4iaurmSD1AxmlcoorsldKTJSP/GKiOy7JH2OmumZ5Lpd08mUptjUuTazX8lSUv6UzI2FHlt7yXPoRJfM4Kca+eKmeT/4/BWHb/NW471ofXbqUR9EihVGoYCEULlIUxSt1RdvK5hOpwX8rFq31TXDCMouWb8NKDK+NtmxRfehYNM4kBYIGPFz5M8YfTWact1X8r+nF3xq2UKPgd8PRPrs0tMYA3/VTsORWMsFk9FXM+mUhboqXFNrc6PjbL6KBIb9mES2CDvkpieAyBo6oNXwEGmQQ1UQEvY/+GoiR/wbLSyQg9aBYAmLTxdv8NHmWW6CCdDs0leE28WVJpvHLr6zWaBnjKNFBXIeSiMVN77sgJ3Fxt3S6ONbBTz1LxjVy7Cp8j5/iGifWcHNPb9q/0q3x+/fTFpTHTxNJIrC1XJ4sRLA2dw6OJBw6HvMYj6UBweKTNF5foaKHfBgafbFjZDPUbNAT0w8FmNMSoihEyj2HJBoscjso3uvenypRlnFVV1xak65bdxvL+k/C4QgRklvlQ48pg1DGNHJIizIVy8DU8S0alof+vZD8HSQhNkYuTxINN8tFX7o1Le9DqZdMqTAi6Op53Hcog67T/8X1yxvQp5QjlCJDwYGBpuWkKN50PRcXc72lBUgieDftY7EPdJbeZgsl3Jt2RRNpDC69xOlDN1Gic2cUizuUtCg78iDu+PjAx/K4cwzjKkpzdohysTQ4XwTgqRxIGJ/dg0+QlK5EhgrVUAKXsXWrt6keqLJXQJU84r0iGAqzfGdErRKhlWAMwtXz9+FQpium/3sdlzf0QSlpvC8FIzBQlbaytCqCYvml410sFnAXd+WfujUGXsU1+YsQJMogrgGRhLroDxjZztM0fMIYuhsThm2Re6yT0sHv8TPTM6M4uMzrIxjliqK0tYet+WDB/Vs3zY1E4zOcF9tn3tNyozvVbZbyS2K/mw9QEucPS+e41ENcsYy559Xgdwj/XkipZsU37Ei6a5V0u5OrL1LvrulJwrtwJM4h8nlGIfX0mjZOHK/3sHfJXziZoR92XT2D0+fP48jGqehWzt28T1MRtG0+Vt93Rf0uXVC+aBEULlQQhQoXQdHildC1bWXTXTtxMcHWRWthmqFWnNOi5R1HRun8aHqapjxYlSqHYqbg1Yinjx/HNdqNAbdw13SHVQX3yjVRUjpuLWUitlYvyuzVIpNfNYrytNSNFI4xEtcNy2nYGHfNS64spbsAlrIW65X3mXT7Ly4v4nPNrxvFLpKnqBX5TvBVFsa+aByUJ2G4/xfmbw+E69dNUeOVe3puqN+0ChxM55eX2LdoaZLxwEZEREg9iRJCxK1/sXzxQsyfMwO/jR2OQSNXvTJ+WJn1WyzZPAl1soiLeORF/N7ka/RefBy+CYK4yMAgiFjIhJLrwkxfH9M3TkYdDw0o5AB+btQe0w8+kucB1uHZtY0Y+nVN/HwoBGTrhRazNmJ241d7eIwREXE9vtJFPVqcX5Ue7TCmX1nTxZdi72HJTyOxL8S8TByjCErlgJiMUYhI7buLIrAU52r5eXRckPmqxGWpEx9gOd3HEeuK6zmOG7eYgPEFbtx+CrJzMF/8TNQo0L2vuaGhyozm/bsib9wJX4l0DvbmcjEG44GPOSB5HQqPiAvK9eJCkpDRdzV+GzUGv/11X77w6OHz9xLsem6EwiY/ekzuB9N3AYWYs7MweOIm7P9nIQb3nWNOTIjCESFvI+ljXukJfe37U2OMQERcWRoQExUlLsYGRAc/wsV/ZqF3rWr4fqsfjPYF0XHRJkyNmwtfiUxtBqF7Ael2th6353+Pfmu8ESzquT7kJrb/NgZ/mX5PXOq9k8cHizoZ394Kw0t5/nApoIqlWJye2RYdxh2G9Ove1rnqom1NT3FR9kCVGkVMy8WK9ZhLWTTELJGnJgdymsZWi+Dd5x6eJq0sDrXRrU0esfdFjl3qoXtbyxCO15CCJLlcDI+OYO9VKeMGPNi2HRdEJKJMVw59B3wDB5Ur3OS7LIbHp3H0VhieHVuA1WfNOSVxbIVLeYo9jZltO2Dc4ediLdbIVbctanqKoMOjCmoUsU5bWapyoWW7KkgndgC9PIDFiy4j0hiCYzPX4oaNOVhHjDgepH+To3RF4xlrMb5mZmhESfosb4fy9Udh2+0w8zEmjueA63sx7/taaDv3nuktJI4/S+AlBc4SbZGSKGzqsRd5XdQb3cZOx2992+O3E9KvLgii0R0ljetJdZvFeU/kIkdOD9P+oGAf3LPsPGUmtBnUHQWkFrT+NuZ/3w9rvIOlcA4hN7fjtzF/mX+qPkHDThpKlPQ0kFx9MQ05kpezjGcXqQiLr4zQy40x3YGZmPyvH3Q3luKn7oMxevwETJo8BTMWrsEhS4soOYb7+Gv+dgS6fo2mr15M4Fa/KaqYLyZ4uW8RlkoXB9P5Uc6YdFdROgkLacmDKmcnDO6UG1rRsA09sgW7n0nvNeDuZlFXxbYonati8KB6pt/BINHIMn+MdFwmbcTGinKSz2MJyimlYyxhgy1aPDcvnnxZxr0u1Y0o8wqMcUF9wvO8KBPxfvO7YqVsMPb/wTwJCzOLpXPSdF5KF2q1NvmJrgwBS6hBOmn6KHG+UOWg7/clnNdOTzcmlJWnjHv1kdpPXxuenaLFA5pQcXcrUkg/lW+Tnjyy56RsmRxJq1SQ2j4zFazUhPqvTuZn7mV6v0M0s2dN8nJSi3WoyNbFgzycrU3z7yq0LlSw/gBadja5X/s009+YQGVNU1uJ5RP+YmTYAfrRy/JrlxrK1/dA4mnAXiykOvIvgUJbmf545Sc/E9CdoIH5zHMpi4sudfknpfkTE5al+ZcyX5lBMPYKjS5hngpN+hXO5quTTJAevZd6ZVWRpuhwSjzbZDSdHFyA7EuNTTJtnNj/w4rETfmXtddeOT11EX83k+dTV5Bd01Vyqlms90rqWcmT7Kw9qEyT9tSuYQnKqNFQhqKtaOJ+v0T70vBoKTV2VZn2nVuzlXJqAhF/UzN5HnmFXVNalWRzX/v+1Ohv0ISyclkmfSiUZO1WgKp3Gk+bb1jmR0ss+s5a6lMps/yLnwrS2NiSlcqWcjScJZdxFG1sJ/9ybcLpK6MP0o855ek03aS510NoR99C5KRSU7oshahk0Wzk5JSXmkw+QoGmamWgB39Ujpt3uvos87zTUn3xnl2H3KWp3ZR25FGkLLWccVV+zUx/XdRvKzVl/24vpTaVfiIRq6mFmxc1+3037fqtLnl5VaM27b8mr3Ti+MrdkMbt9ZX3oYFe7BpIJdNL0xmK/aNQk2uVUbT6jwbmaQOV6alQ+8VEITuobyEnUqnTUZZCJaloNidyytuEJh8JjDsuX1+WQuxdWtW5gHnufYWWHJ0zkFermTTya/NUdsoMr5maVBL7iPZO6UqVs9mbfw1YaUVOmbNSZidxrIvjPH2+b+i3k+YTlv7mBConnxu0lf8wpUlT5x2fUIM8NPL5ULwnU7URtGhwGfMxJI7vrtLxnYZt1nvPpjru0lSGSrLzKEJlW86QX4mmO2v7UKXM4rwof4aNrRWpbHNQw1mXzXNzx16kkcXkaSvtm1Pi00Dy9SVqYztylsoOaso/+JQ8dW2COdaVbtRJ/iEA/c0ZVMPJPD1f0ofC2ou6bw6I246EYs8Np6IaJbm0Wpv8tImGAFrSIJ15u8T5Jsf3+yhcf5MmlJOPQ+lcet+85jTnIfoWbfy5HuW2V5NLiWbUtUNNyuOgonSm8o4/98deHEnF5GkM7ZuvTnw8GB7QH5Ut85RXp1l+cXsp2WNM+j0J81ScryvLEFpaX96PmlI0Vv6dfv2tSVRerltW9RbLc5Lr6PjAvPI0xM7UzjJhO2NfOIX0H3EwMBOjaJHHQq0wQKeyhW3SkSImeoSHREHrYIPYl0GIVDvD1SF+QUNkOKK19rCLSzLCoI9FbLS5B9nOzQmpTGJi6qXyv34el+4FIDiSYO+WGR4e2ZArV1Y4SZ1KaaEPxr0LF+D9+Ble6jRI55YVBUqWRK70yW5QPEMkwqLU0hB2RIVEinOhU9yMF7rQYMTYOMJOGYXQUD1sXRzjp5IzRiM8QgEbOxWiQ8OBdE4QT1MQg4iwWGhsrUGRoaKsnOEYt6LEDJFhiFKL/YAohEQq4ewUN/+GTC/WFS3WZSMyHGraF06JClfqaRLriY0V+8vO1EsaRxeKoBgbODskLlRp/+ms7MVnRiNEbIqTY6p7yyw6DKHiHXaGUATqrOHunDSfRkQ/98G1qzfwOFwLd68SKJnPNdmp+AyBV3HgRChy16iIHElXI/IUFgrY2hkQGqiDtbtz3P6xSP39qTEgMiwKarHzlXqprkaabj/rjQpo7DMgg6NW7hlPTTQCrp7CaW9fhCMdshatgAr5XOLKXRcRbppdA1FhiNE4wt5U9KI+hBOs7UWrLiICBjs7US5i+x5ewYVLdxFslVnU3bLI55rwGIsQ+8gGVrFS77MtHOPm0ItBwJUjOHHzJWw9i6Bc6dxwSlAPjb6L0LDYHBTbdQ7jS73mWLAwBuOxrxIeWR3F9hsR7nMSB875QyPWX6q4F1yT7ERdwFUcP3UDQXb5UaVaYbiqo/HwxC6ceuqEIlUqI7/0rUxRTx5euYBLd4NhlbkASpbNJ5aTVxAn9bI0i8Ezsb1HvYNglb0MqpXzhCoyxnxsxZVlWujw9PpZXLzrjyDp2EznjmwFiqFYbhfEHR3i3BCu04pzgwLRETpRBy2Vy4Dgu6dx8pIvDJlLoVqFHLCOEMe1xh7amJfi+E0P0yGUhm2OCbiCIydu4qWtJ4qUK43cCXdedACunjoNb1/p/JIVRStUQD4Xywr0iAyPgdZGC314FODokOgcm2x90YnnChvTuSUsRgNHc2VEjKijZG0PK4pAhMEOdqIAjU+P4LeuP2J7xlZoU8YNVvpQPLm0A8uWH8LjGHFOr78Ej7Z3RtK5rIzi5BOrVsCgU4lyS76+6cNDEKV1gE3sSwRFquHsaouYcB204jhUREv5tjNNEfmmedA9u45Tp67BN0KF9DlKoELpnEg0W65e7M8YLWy04npmKrJEJYZIsY+tbKwQK8rDaOsYP01lcseY3lyW9upYRERJX/ZMqSzFeVB8mFrUT+k8q7cW1xOpWKS6Fa2GjbURkZFG2DmIc1Ci94cj3GAPcYpg7IvHQTlj7P9ADM4OK41axzrg3IEByJ0wQGEsJfrr+K1qOYyI/RmXTgxD/rh6E4Fd3fOhweInUNWai0e7v5Nn8vkAPoU8MMb+E3wIM8a+eMZnmzBl6WNU6dQOOTkgZ2lkfHEYe89GQJkho2nWknga2NlaQaHQIG/p0jBN7vSBfAp5YIz9N/gwZox94fS4Mm8KtlM9dGjyminsGEtA6VQABTxViDm+HHPOxn+pM+bBJizc9hCavF3wa98SSYYVvV+fQh4YY/8NHr7CGPuiGZ+vRbsi3+JEw+3wXvA14mcnZ+z1Qs8vxtBBE/DXqWjkrlQBeeyCcOPKc3jW742hP3fHV4m7rz+ITyEPjLEPj4NyxtiXTReG4KgYKDTp4WTH/eTsbRgREeCDe/4R0LplQw4PpzR+ifZ9+hTywBj7kDgoZ4wxxhhj7CPjbiPGGGOMMcY+Mg7KGWOMMcYY+8g4KGeMMcYYY+wj46CcMcYYY4yxj4yDcsYYY4wxxj4ynn1FplAo5GeMMcYYY4z9t7innDHGGGOMsY+Mg3LGGGOMMcY+Mh6+whhjjDHG2EfGPeWMMcYYY4x9ZByUM8YYY4wx9pFxUM4YY4wxxthHxkE5Y4wxxhhjHxkH5YwxxhhjjH1kHJQzxhhjjDH2kXFQzhhjjDHG2EfGQTljjDHGGGMfGQfljDHGGGOMfWQclDPGGGOMMfaRcVDOGGOMMcbYR8ZBOWOMMcYYYx8ZB+WMMcYYY4x9ZByUM8YYY4wx9pFxUM4YY4wxxthHxkE5Y4wxxhhjHxkH5YwxxhhjjH1kHJQzxhhjjDH2kXFQzhhjjDHG2EemIEF+ztiXTx+AU8efIn9RB9zx9ocuae1XKKFSa2Blkw7untmR2VErv8D+O3rcPXkWtqXLI7NaTmKMMca+cNxTzv5v6O5txICmvbEXmWEX+xJ+1zbil8aVUbFiRfOjcn30nrYeO7atxcLxXVE+sz3S56mCtkOX41ywUV7L5y4at7bOxOyd90Xo+6lSI6P2PIa1G4adjz/dXDLGGGPvE/eUs/8LEZdno02DX6EYdQqbu2aTW6MRWN08E9ptDIN0ECjsmmDl001oZ2d6EeGXZqJlvf7Y5W+Edfa6GLViJQZXcv68W7K6ffjOqw4WW/2IA9emodIneyPAiIB13+Kr4YRR//yJDl58x4IxxtiXjYPyT53+FrZPW46994MRK+8phUIFtVYNja0LPHIXR436tVHULXHQYvQ9hEULt+Lys2hTwCneBaVKBaVSAzvnLMhfuRGaV88FOf6U6fBgx1SMX3kF4THhiLTKjiLZwhFRYAKmdsicJBg14NmZvzF30SYce6BH+vQa6JUZUaJGdRTWn8T17L9iWF1redkEjIHY+ENnXGm3DmO+SuZ1Ieb6OkxYfAhPo02hMqzztcQvP1aFqykDRgSdWY35G0/hyUsDNK4V0fWXtiiS/KpMjE+3oUel5lhu7IW912aiatyy0dja0RNNVzwXaxWf5NQG6/z/RvO41414vKgBivTaiRCxgNK1FqYf3IkfC37OYyoicWPzAuxRfoPvGnnBSk79oIzPcO5cGIqXyQWVnJQm+osYUaospuh7Ycvh6fjahW/sMcYY+4JJQTn7xOmv07hSGilCJagyUbv1L8igD6P7h2dR27w2pLTJQfVG7qQHsfLyMsPTpdQgncL0PoVjM1oV/JKuz29CmVViPQoHKvnLEXopLyuWpierWlGW9DVp5h1pRQZ6cXwy1clsTeUm3iS9eSEzwzM6MKoaZVQryaX6RDoTYk6OfrCTRtb0II3CnpqvjjAnJqG/NYUq2SrJueES8jXIia8wUMC2rpRbbWpPEJQuVG3yWQqXX5Xo78+kmpnr0YInKa5EFkI7e+Qg0RyhTF22U5ScahZFWzq4kgj1zGXk1IbWJ15AvH0ttXJRmvMh1pGhyQrye91HsniGF3RoZFWqOfYqJameaaCjE4PykVqhppw9dlKwnGoSG0g+d/yT7E/GGGPs88VdT58DVVZkySTvKoUzsno6QKmyR/bKP2DpqkEoZniAneOaom7fHXiWYOiz0jEzMqUzv0+hsYWttQMKdOyAGk5SGBqGi0uX4XC06WXAcAcrZ22GnzoDMmaQeoKVcKkwEH/+0QLOOp0pajUz4MHyHmgz7iACkBddf+2P0o7mV6yy1cWYTWsxqIStOeEVOpxcsBAnI40I3jUNs8/q5PSklHCvWhEF0jnAXsq+MRCHhjdD5xX34sZBqzIVRqGC+ZDP3H2eIuPDlZi66oHItRp5ChbCGw+CcCiHcoUtPeNGBO5bh10v3nZ8eQwCzm/G3NF90blVM7QYswfh8itpZgjD4yuHsHn1AdwzSAnReHxiLRavPh237w2B17Bj+WJsvRZmTrDQh8HP+wR2rt2IkwFJtsEQDt+rR7B19T7clgrZGA6fw6uwYPlBPEiym2JCHuH6qX3YtPIvHHhggD7gPLYtm41Zy3bjZoKPDL+3C5NbVkC9Cbfh7BaKS/eCTHck4hie49KWxZgxfR5W7TqJ0/uO4VaiIeRaFCiSH9Zir99fMR4Lb5o2WIjAzu+LwStvATRf9CjxOhljjLHPVOoRDftExe82q2Kd0K6cBgqKxs1Fg/HHmRj5FYkSCvlZPBXUcoypTO+KDJZVGQLg/9wI44uN6Nf8F2zwlqIrERw3HoyB1TLEf6LuGKb9+g+eikhImaEcKhVLEuY6fIWfJ/dGcSnwTypwC+avuQ+DyBTFemPxlA2m9aREU3YIFv5UAvam5R9hw3eN0W/nU3MQplCKhokime1LyAjf7VtwIlI0KRRauGZye/MKr8wgGkS2cZ9DUbdx/c6bf/kw8qZorNTKh+Kd1yIwT2MMmLESa0d9DXv59TQJuYjV47uiTrnqaDPvEhS6u1g3pCt++vlnfN+xC/44G4PQ03PxQ+9hGDu0F1p2nYkbljjWGIBT62ejb+MqaNBrLR7aJCiJkPP4a2Q7VCtTFS3nXIQy2ht/DeqBoRMmYGCXhujz93N5QUmkaBQcwapf2qBlz6U4fHgKBk/ZhusPTmJx3/oo1/AP82cafHH24FXc93+GmPR5kTH0FM7dDU3QuAvHgQGVUW9xFGq0/gZZr45Fsx82QVTBRKwzZzLVUYo+i7VrveVUFewcHaBV2SKdA481Z4wx9mV44xiFfWJUmVEwnznYpNg72LPLErgkx4CAPVtwOJCgca2A/lP6oYwlplHnQaG8IvikWPjun4iWxfOiXOcp2OOfB9Uqx48nj7m0C/semoNSpYs73JIZXu1QYyR+qZN0kLcBd1euh7LfSFR3kEJcI15s+wPzrqQS4CocUXXyJixunwtaKTCPvIa57Vti4ukkPcApisHFc1fN0x4qrGGbMBBNM6VoxKjjg3/R+ImMig8t0yL60h9oWKUD1tgOwO4TazCiXTUUymj75gefU3G0+aEucqrUKFStGK7NWgNVz2VY0rMEbJXOcArbgBlHcmHEij/QPKdYu60dbC0ZV2ZEuRa14GWtgHXZGqjiIKdLnEqi/cBmKKjRoGjtkrg6cxPsf1yBlcNrI6P4LBtbG3lBiS1yVW6MEhmNUNhHIcS2OX6bMgZDxyzCmMbOCDu+AdukLnyVB6p1boCs+kjYV+6K0YMHoNfXOeLHlOvOYtO2e1C6ZUf2jJ6o/NNodK2bH7mT1CeFrT1sTNugx41TJ01pIlRHld8v43mID/5unZFPYowxxr4IfD377Kng7OIk70gjnvn6mZ4lRREX8Gef9mjZdy18Xb9C9zFj0aNygoBGmRntxo9CrYzmAJRi/HH6z8H4pmQ1DD8YP+zA6PsY/vIfCjt7mOLrtNCdwqID2dD1+x/Rv2VWU3BGMZexcMoWBJqXSJ4yG1ot3IKpdTJCJT7LGHwUo5t3xBLTGIvXicTTp6Fy3tXQJNOAeD09Xr6MiO/hFcGvW4Y3+Lqi/jwmdRuOoxm+x4oVvVH0jbrGXxVx7DDO6rIjV8w5hNUfhGY5CWePnkVU7qx4eUGLb/t/jcxhx3D8KpC3QkV4JDjCDY8O4cgtoGi1anBLcuRHnjiCMzov5I8+ihffDETj7Er4n7+Ix6oSqFghyXCkmPM4euYlrAq1Qp+muUTJSkTjRSOVix56edcYn4nPu6ZCyapVIY9wiqfJhUJeNvBfNx5TzkQC2pLo80vzV/KlVGugNtUxgv5Zwrqthq0t95Izxhj7ciS5BLLPjxHRUfEzrNg7vhL+mCjsSqDTzNUiSNqHQZnPY973tVCkdDds9I0fL2BbvD+2n9mFiW2KwtkUCREMQacwudd4nJDHFStsbON7X6OjIY0MSYugbUtxp2I3VLR1RO2+PVHKWlqJAQGbpmLh9dcE2NaF8MPf6zC0jKOosCI4e7IFPzQbjmMvX/fhBiScWyiNWU3M4Af/Z/q496rcS6N8vrRH97pjy7Hykk6UmTfmdm+Jli3Nj9bd5+JcwpFGaRKDc/uPikaMeGOBFmhd0ErEwFdw8HgAVDotirZrihwiLo48eRinozKjYvXCcsAsMeL5/gO4SHlRrUbO+B5rkxhcOngUz7VRCM/aHJ2LSnc5QnDkyCUYC1VDjcyJTxP6W4dw/JENqrRpZfo8E8MTeN8MhMKtCIplNyeGHTmEs/qCqFItmd5spSc6jf0JxY3nMLnnOByPUMPFNf2ryyleN0SJMcYY+zJwUP7Zi4Gf3wsRfgpKJ5QsX8iUmiL70ujSppQI1giRN5dj5Kyz5nSDDy5eDoQ6a00M+fs8vA/PQediUg+8CILvH8Nh87cKoSlaHAWksSSCMeQFgi1jllNjuIcVCw7g8ckJ6NyhA7pMPgejk9wjH3UWc6fuEiHgazhVwphNf6JLPmvxPkL07ZO4kHQA8isc4O6eTq7k8T24b0Q0Sk7fkN+osEKRzt1RzTIyJ/oxzu7ZjeM+lt74V0XevQtfyoAqnfuhV8+e6Ck/enSpmXioRhrWBf117D/yGMjRDD+2NA8FMTw4iMO3gdwtf0ATU7e4CLAPn0CgU0XULJtwwsMIHNl/GrrMlVE97ourMv1t7Dt0H5SxLn7oUNAcyEeexKHTkchdTeQzUQRvhP+hI7iuKYf637jHnUAM97fin4uAV5vOqGHqWNfhzMGTCM8uPi9P4iaAhXWZIZgzsDiUV6aj75RLYg+9yqiPhd7UIlKYhksxxhhjXyoOyj9LCcK2iGM4cEr68RsFbIp/j34Nku8pj6eAtZXGFBBLfceR4ZGmZ9IY35nj1stDU1Rwr/AdFu/4HXUdFVAo7eEgfdtSUGZsho71zF/8NPpfxvmHr0blxqf7sSdBN7DuzCLs9pqHQ5tXYsWKFVixchNO7PoFxU3BvQG+637H4luvj+6VmRtjzuaZaOSRYIx3qrQoXa4YrEzRvzQW/DWfQUbp/wkYcHvVn9gXKkWFCjiU6I/pA0qb5/bWe2NavSIoV7cuKhWuiMEHk59HxTZnTmRShsPoUhY1atSIe1Qv74W478KmcV2GB/tw4AZQtG0nlDNlwojnBw7isqIU2nUobp5ZxnAfR47dh02FmqhiE4qgYDnU1Z3GwRMv4VSxBspqIhEh73aJ8ck+HLwm1tu+ByrJE9fHXDqIY88zonKt4lCFhiAsrlxCcfjQBRiL1UQtd8sGhOHgtAU4n7Urfh9cHqY2i/4qDh1/igxfVUVxZRACpYneZaHb/8R6U0WzRdkhU9Atlx5Xdu+WZ5NJIjoSpunqRZ3MVaKEKclMh9CQiJQbMIwxxthnhoPyz0I0YuJiXBFcWqYxFMHQ6SljsfqxETZ52mDemmEonbBzlHSIsfzikNFoDmCMgTh87JqpV1JhWwitW5eVUkVNUCPm8G8YutHX3OsuJTk4wVGjhFP1VmhoGZysdEfrqdPROrsIAfXnsHT6PgTFRUZGhF5dge/bzsA18T5z0lNsmLYZrjUrJ/qhInWR7uhezc4UXFPECcz+fXei3nJjeDgiwsOQdISKNl93rNg4ChXTp6XqKpGxcSvUEA0LqSye+j2L2zazKBG0JhgvHv0MAYGWjYmB765h6DTmsChlLbLUGolN28ejsqXNE3EWh06FwijeTJHe+GvlIfmFxKyq/IShddLh6J+LcCVuvyWRpnVZhp8UQcNm+eVhKWE4vP80YgvXRX1Ld3bICdN48uJVSuP2/D+wxde8xYZ7p3AuwArlqpfEnTXLcei5pSSMeHFgP84bi6JRc8t6DXhw5Dge2JVF5TynMH/xUYRYCin6hMhrBDxLl4On6SONCNg2AP33FsKU9VPxjTxFpcFnPw7etkbZKvlxZulGeMfVET0enl+B1buemeujXREUzqOBXSYPJDdUPzbgKUy7RJ0H9ZsUNyciEgf7FUVG1zxos8rfvB7GGGPscyfPV84+Vfo7tGNKNyrjJP+AjUJF7mVb03c/9KKODStQ4WLVqN0vy+lsYOJftDH4HqFFg2qRh/RDQeJ9CnVWqtb1e+rSqAzlzVuKarYdQotPPqW4d0VtpU7Z3MnDIy/V7Dmaps2aSD/U8KI8X4+g3cn8Wk7sw500rkUxcrVyoFzV2lL3Hp2oea0yVLJmb1pyIdi83tjrtLpvNfLQqClLrZ9o+i4f848QGYLp3Opx1DK/NUnhsjl/malqn7/oajSR7vp6+qVhbrLSZKM6A/+gzVfCpHclYKCAHT9QsSL96IhOTkpRBB0fUpi0CiW5d9wq/jLT39tGk76vQ7lszD+uZC5bJTnmqkBf169L1csVoBzZC1D5hj1p/KozFJDo15MEgy9t+qEMeaSzIrV4X4YOW+QXkvHyEi39rioVK9eEeg0dRSP6d6dRWwLiyz5N6wqhNS3Tk6bYSLpo+RWeqJ3UzUNDXv2PkaUY9LcmUwUrFWUs05GmHHwW9xmxV8dSKa2GslTqSjOOJvwZnpe0vo1L4vVSLF0cWZw06oxU8tsZdDpB8euOD6C8ai3laDmD/j2ym1ZM+I7adZ9M+54k/mmg6J3dRN3TkEflH+jPywl/SCqKtnXLQ+75GtDotXtp5+Leoqzb00Jpx78ili6NLEYaKMmpzjy6H7cPwmlXr+ykUbpQwyWP4suRMcYY+4zxz+wzM+Mz3LqjQJ7c1nh86TxuBGuQJX8R5PdwSPKlwMT0wfdw6cpdPI+2QcZ8RVE0m/RlzP+CHn437sE6b144v+4Dw45jePU6mPy8PXZ6z0OtlH7b6G0Y7uH3quVwpNMVbO+aSU5MQUwwHnjfhJ/SE0ULecAuab7fZF0pikHgQ38gUza4JJqcJAbP7t6DLmNeZDX9ItNrxATiwRM93LK7wzZucQNuTKiAohPdMf/WElQOfAZt1jzwdEpuFpRI+N18BIVnPmRKVN4GPPV9BhcPZ7z0uYVHMW7I45Xx1bKQGO7i9yqF8PPNSph2ZAf6FEjwOYZQ+PnqkcHT5c1/EIoxxhj7BHFQzv4v6B+sR69v+sG/z1Fs65lgvux3FH56NJqN0mL8pl9Q+h2D/fe5rg/CGIAF9XKhT9goXD8yOMkXQN+/0AM/omzzE6i7agem1uX5yBljjH3Z+DrH/i+os7fAgn1/ovT+YZhyIvg9jEOOwM31w9BrgT1G/v3zOwbR73NdH1DEURw6F4ssJcvK48k/nOibKzBg8nP0+OdfDsgZY4z9X+Cecvb/xeCP44cCUKRGcST8Ucs3ZvDFudNRyF8hd6IvsL6V97muD8TofwyLxw/Hr2uvQ1OuO4YP7oH2lbMnmAf9fdLDe98eoOw3KPBOO4kxxhj7fHBQzhhjjDHG2EfGd4UZY4wxxhj7yDgoZ4wxxhhj7CPjoJwxxhhjjLGPjINyxhhjjDHGPjIOyhljjDHGGPvIOChnjDHGGGPsI+MpET9rEXh46gCOXvGBX6AO1m65ULxSdZT3fA7vBx4oku9TnfX6Q4uG3/WrCHEuggKZrOQ0xhhjjLFPFwflnyPdYxxaMA5j5h5GbNnW+LZ+eRTwsEWYz3Gsnz4N625aodXfd7C0obX8hi+X/tFuzF9xDaosbrAzRuDpC2uUK/UQA1r+i8YHj+CXwh/m520YY4wxxt4nDso/M/qH2/Bzmy6Y9bgMxq5ejkEVXRONQTI+XYVWJZeh8ul/8aPHlz06yei3AT067kODv+aikbt5W42+S9C4yPe41X43zs+oBntTKmOMMcbYp43HlH9GjL4b0aNWC0y7WRCjN63HkCQBuUTpWhlVvymNEq5f+q4Nw57Rw3C7Vn/UlwNyUUIIPHUIF0KN4rmCKzdjjDHGPhsct/yHDIFXsGn2KpyJlBPehOEmZnXohuX3bFBlxAIMKp3CeHFlJlTt3hrFtPLfX6rgf7B0izUqV88JlZyEyBOYPGo38rRpgNgNS/FvuJzOGGOMMfaJ46D8g9Pj+aUNmNitOrxyFEfzgStxKUJ+Kc2M8F89HBMOhUCZqxNGf5cPKY+UVqNgqaJIHLJHw2fXdPzcbwhGjRqGPh1boftve/FQJ78ccQ0bJw1AtxZ18FX5PtgWEY3rf/XF12XbYPEDg7yQyEXQOawc0w/9hw1Fh2pl8M3wXQiQOqXTyhiEA+OG4a/Hb/Km5OnOHMLJCE/kymMpCQNuzB2GP7U9MWlCbeQOOYnDF2Pk1xhjjDHGPm0clH9gxuBruODviWqFrBAUYcRbDeA3PsaGZbvw3KhB4TZdUOFNvr8ZfRMrupRD3bkKtBo7GWPG/IqZM9pCP7MhavffgxBpGbtCaNa9GIL27cU5RSY4bu+HH8evw8HrT2Hp1A87Nx1NK/fB5Qq/YMqvY9C1cCD+nbkUh6LkBV7H4Ic9IzpgtnULtMj6rtXOiKC79/DCKh3SyZOrGP3XYOS0x2g8bgDKujgjvcYX9+69zS0JxhhjjLH/HgflH5gyfTF8XbcMSpQuALe3Le3Avdh9Kgqk9kSlGqn1kidhfIS13eqix/5imLTkRxR3kNPTlUbJvEbcXbMC+y1xq1EHXYwK+QtFYMOpylh66TK8L63B99lVMNz/Ex0bj0Fol6WYXMsVysjz2HXoCexKV0KJNDQQwr3XYUid2hgT+R3mDCiGd5+kkBD6Mlw0Juxhr5D+DsPBX8fiaMlhGFk3vSh0W9hZE8JfhpmWZowxxhj71HFQ/l9RKmGKH9+C/uEDPI4lsQ5P5MqR1pBcGvIyED+teYZyfYejYcIWgTEQgaEEio2GTh5JEnH8KM7FZoDST49aI1ohu7Ubcud2g9roh78HDsY/dh0wulc+6B8dwG/tOmFthn7468/e8Iob0J2UAcHXt2P6j7VRtExXbEnfDn2qE87v/Af//JPWxw7suRQgry8xK61WfIQBBlEsMVdmYNgaZ/w4viM8TZupQ7QO0Gh5jnLGGGOMfR54SsT/SMzJQShSeQpuqb7Ggse70cNVfiEN9FfHoFzJ0TivqIHZD/5F70xpaEsZbmJixaIYdq0G5t/+Bz0SvMf4dDEa5O6BfYV+xeVjQ5FPpcOhH/Oj5txg1Jx1ATu+zxH35UnDzUmoWHQYbhRuha6V0yMsJgPKNvkWLWvkhqXjPTnG4FNYMHQEpq89hDuhVsherhLyp3/TNqAC9lV/wdpBFeS/44X93RxZe6ux6PFvCG1bBr9n+xvnZlU35yl0GRpkGgLn1Y+wvNGXP1c7Y4wxxr4AUlDOPjzdiYGUVw2C1de04JmcmFYv11EbFyVBlY2+3xctJ75G0FKqb6MgTbmJdFMvp5kY6OHc2uSgykCNlj4SfwmxF2h4EQ2pPDrTtpemheIEL61PNgprqjP/qXnZN2QIukjL+1ahHHka0h+nQ+TUd6e/Pp5K21WgEXO7UM7MLWiVf3zu9DcnUDmHajT9wdvkmDHGGGPsv/emXZfsY3CojXaNs0JleIKty3fgeQqTlxj992DSr5vgK1436qIQLf7VZvZE1oRDTMKPYfrMQ7CtMx6Tv81qGr9keLgfh24ScrXqitpJur9jYmJghAo29raJxzpFByM4Dd+jlMbUd5h+AOdWVMeJvt9i+pVo+ZV3o/JqhIYFvTFjyCZ4DhiLVhnjcxd67jwel2mMBlm4ejPGGGPs88BRy3+EoiIRLQ0UoljExL7plICOqDt+Jrrm08BvdR90nnYKgQlXYQyB98bR6Dr0PMr0aAzphzyVGSqhShErxD7zx1PLrIbGp9g55EdsyDQM61Z0R17T8HQjXhw4gIvGnKjftPQrX8J0KlgQ2VTRuHDgoPyZBoReX4MRA+bhXFhat0MJ53J9sWZdZ/hMmYH3MlOhOh/q1MqKaKcWGNwzX/xc5YYHWLfeH62GdUbOFMe7M8YYY4x9WnhM+YcWcRN7Nx7AoTWT8Nvux9ArHFGq83D0qlsF9ZuURtyPUaaB8elhTB88BFPXXUCUZ1lULOYJW90LvIi0Ra46P2JUn+rInOB7oJGX56N7p9kIrPk92hbU487xk/DL3h7DB36DHHHRdyjWtc6J9mfb4V/vmaiaNCo3+mJLnwbovOAW7IqURm5bQvoy3TB29LconNqg8hRE3zyOK07lUSZBz/bbMD5bj29L/ISAmg2RN19d/NizDnLob2LnggU4lf0HjG2fHzyanDHGGGOfCw7KP0PGsEe4duUunsXYwNUzF/LkcoOt/Nqr9Aj1vYvH4emQLVdmOKR5PsWEjAj3uwmfYFtkyeUJZ+uPfYPFiICtIzHgUAGMm9IW2cIf4sr5y3gQ7YLCFcojtxPfAGKMMcbY54WDcsYYY4wxxj4y7lJkjDHGGGPsI+OgnDHGGGOMsY+Mg3LGGGOMMcY+Mg7KGWOMMcYY+8g4KGeMMcYYY+wj46CcMcYYY4yxj4yDcsYYY4wxxj4yDsoZY4wxxhj7yPjHgz4bMfA9vgEbDl6HX5Q98lRuhlZfe+Etfun+4zIG4taZW3hhMFc7hTYj8pfMhfSfXfPQiBCfC/D21yHpAaRQqqDWWMEmnTs8s2eGo1Z+4T3RB1zH2XshIgcyZXrkLlMA7ir57w8g7O55PHIshoKur/mQsLs4/8gRxQq64gNmhzHGGPvicE/558D4FDv6lkWByu0xYOlTlK4UghkNSqLqiBOIlhf5bFAk/PeMRpPKFVGxYkVUajwRJ2Pl1z4zsS/9cG3jL2gsb4v0qFy/N6at34FtaxdifNfyyGyfHnmqtMXQ5ecQHBdFvxtjdDB8DkxGmyry51YbiO0v5Rc/hMgjGFW/IlpMOSsnpCQSR0bVR8UWU3A2Rk5ijDHGWJpwUP4Z0F+bh5HzLuOlkaDOWxr5757D7dhwXN21D3f18kKfC1VWVOrwNfJ+9t2oSrgWa4ge43qhip1CTlPAptog/PnHOIz/fR7WHr2O41PKI+L4akzqXAklGkzC0aB3j8y12Sui3YB2KB/3uR+SEX5rpmL5o8Jo276UnJY8o98aTF3+CIXbtkepNN8dMCJoz1i0blgf33zzTbKP+o06YMZpnby8zPgYO8Z0Q6/px95bY0cSsv9XtBF5qd+gBYZvD4i/G/HehOLQxLZoWL8+GrQYjm0B7/8T2LsxPt6BMd16Yfqx4A+w/xljLGUclH8GYm7fgo9eHiShsYJHvV74qW079B/SDvnU5uTPiUKtgea/iCf/Cypb2FnHb4zG2irBsA17FPthAcZ87QQlRePBzmFo1noOrr+PhpRSBdV/cfTGnMecGXugbtAfPQqmVtlicH7ODOxRN0D/HgWR9mqphLVHQZQS676z9wieOpdE6dKl4x8lneC7dx12XgyVl5fFemP74uVYtmIvHrzHyMk6UwGUKumJyHPbsPn4Q7zVrtJdxJKfJ2KHb3IZs0bG/KVQMnsULmzbjBMPP7dW9Zcv1ns7Fi9fhhV7H7x7UJ5qXWCMscQ4KP+PRDw4iS0rlmD5ljPwe9Nb+wYDDPJTiSpnC0xe9RcmtcgVH/wYwuF/1xve9wIQnnDhFOnw/LE/wpNcKwxh/rjrfQuPQ1LJpD4MAT7euH7rAQLCUlnOEIGn9+Q8vS72iH4On5v3EBCRpsx/IEaE3T+Jrcvn4o9Jv+LX8eMxXjwmrjiFEHmJN6bMipYdv5bHzBvxfP9YjFjt/5YX+xiEPL6NewGRMKZhBYZwaV96i+XDE9WftBP53TwVS255oeuApnBL5WxhfL4ZU5fcglfXAWia2oLJsC3UDP1/+hrZRQOncItfMHr06PjHL61Q1EEp2iBJbq1YfY05l33gc3gUir/Hhql1gSYYMKwrKri+/anRGHAYK2fMxuYryY3LskK+xv0xrHtFuPPZ95Nk9fUcXPbxweFRxd+gcZm81OsCY4wlxpeFD86AB393RsnCX6FJx27o1LQCCteehPNpGgwugqJNA9B4wlFEySn601PRsGYTTDgqB8P6x9gzvgVKZCmINtM2YcOkpsjnWQqtJ+6DnxyJGXzWoEfpLMjg4gIXFzeUGrodW74vgWzZc6Hq2POmZYzPDmNKm1LI4tUIv679G0Or50ShJmOxxz9B9Bd6Hgu/q4XSNXti+t/rMLV9CXg4u8Kr3ijsSXgb3vgcx6Z3QJlsJdBx7lpMbZ0fbllLof3sswiTF4lDMXiw5juUzuKB3PlzI7N7AbRa7J18D6UxANt+LIXMGaTtkB6uyF1rGHbcPI457cogi2sWFG88FBvuvnkIavDbjwktiiF/9b5YvO8qHgeFIzwiAhHSIyoW9HZRtIlDuXIobLm6GwOxb90uvHjD9YVfWohOpbLALVte5MmaGcV7bMXjFDZT/3gPxrcogSwF22Dapg2Y1DQfPEu1xsR9fm8WnOu9sfCPbYip1Re9Ux2Poof3wj+wLaYW+vYuhff6vVbrmhi1bS+mtUhv/jviIc4e2IPdu3dj71lv3PZ5KpoqiRlD7uLEvj3Ys/cIvJ+LBm3QDRzatBprth7DvVcqoJk++B5O79yADbvP4lHY23333fD8Oo7sFZ977A5CjDr4X9pryufu3Xuw9+gNJD9yyYigG4ewafUabD1279XjwyImELeOb8Py+fOwfNsZ+L7zl0kMCLl1COuXzcWM6bOxZO1+eAcnUzuiA3DlwDosXbIW+y77IVJONguDz6n92CO2cc+eg7jsL+0JI8J9r+DQ5r+xevvZZDogYhB46zi2LZ+Pecu34cxbbEjC/Xtd7N+Y5zdwbPsGbDl0DQEpri4aAVcOYN3SJVi77zL8Em+IScTDsziwR9pfe3HW+zZ8nr5SsxBy9wT27RH784g3nhsMKe67N64LugBc3r0GS+bMwPTZy7DtzGUcXLsVV8Pl1xlj/x+k2VfYh2N4tIx6d5pImw7so7+H16RMKhCUbtRpW5S8ROpiX9yjs1PrkJ3CNMkHaav9SqcuX6OHIQaxcn/a0s2LrBQK0laaQvf04g36azS2lIagsKZ8vbbTU7GYJGjJN2RtWoeKslSsRLk0CvFcQU6t1hFFnKKRZexJATXlG3SSdGL54OWNyF6s167sOLooJdBL+qdrVlIp1OT102GKEClR2ztTRqV5ndm+20vmLYqgM2PLUzqlgqyq/EE+egPdn1qZRKBGCuuqNM1HL8pkBlXTmrdHofGgGkOW0z+relJBU55AyoxdaHtKxRNxkoaVsBF5ld6vJNcmy8nXoCefOfWoSOuV5BMrL/cGDP5bqWfh3FRn3D56YtrWNxC1hTq4Kk35NpVnm/VyOSQQsZ7aOJm3TXqovfrTsTf4HMOLLdQlu9q0zaqsrWnVgygK2NaVcqvN64NVXVoUJC/rv4W6eVmRQqGlSlPukblKjKVSGqn881Gv7U9JrhKvFbytM2XR5KE+h6S9nYrgbdQ5i4by9DlkqhdvwxAwl2pZi+Niq1x6UZdo1aSldDLY/KdF1K4fqYC7Czk7O5OTnYbU2b6jfdHyi7IXq7+lnK5OZKtWkUfZalTQSUsOmbNT1vRWZOvVk7a9SFgCIXRmRnPysleSNr0HZXGxIVvPopTPRUMFhpwyHQtpo6cb0+tQlgwib052pFGoyDqdsymfzs4ulMGzCS18EP+5sZdGUnGtO5WsXJCctA6UOXtWSm9lS149t1Gi7Alhl+ZRm/zpSGOfmfIXK0yejhqyz9eBlt96i8ouibhKSzsVISe1Dbl7FacyZYqRp4PY/rwD6EiCsnx5biY1zWVHWidPKlAwu8inDeVoNJVOhcgLhG2h7nncyMXZiew04rzSeQ4t6FqCXDRWlN4jEzmqNOTV74i8sBB2iea1yU/pNPaUOX8xKuzpKLYpH3VYfoveZEsS7t9Mpaqa9q9j5qzkZqsirUcNGrU/SR1/eY5mNs1Fdlon8ixQkLKL5W1yNKKpcRsiiaJdPxYgdxdpf0nboxbntH2UuGq9oNXf5iRXJ1tSqzyobLWU9t0b1oUbS6hVHltRztmoUKmyVKZYLnLWivOF2ov6HX3TExJj7HPGQfkHFutzha5bzv2xZ2hoIREwKzNR13/SFpRLIta0IAc5KLdqsEyEx2bRR/uTlymQFcFH913yBSSKtnRwJaVYVqHJT4NOmE/qCdehzt2F1h5aRj82bUPj9gWQ76JvyFF6TWFHjZebMxu9uwdlMTUgMpvzqvemX8uIvEvvzzeQpNXqjvxEeeTAUPPVb3RXRICGJwuorqM5T54iUJfyFH3sZypkrSCVW1Na7mtIFJQr3TqRKQ6L2kad3OTgVluNZj5JEpkkEHZkIBWSLlpiWYU6D/WcN5Ea1RxKhxNeY9MsjHb39KLig4+JZ28hLUF51Gb6NoNlGRFYe/aivUkCyZTp6d4fVchGblDF7eeINdTCQQ7044LyaDra30sEASJNBA3dd8kfEpdHBWnyDzLtu9fS36FpVe3JofoMc2MvRXq6M60q2TtUpxmpL5gqU1Bu5UBlu02i33//nX4b1ohyaPPSwBQza6CH4nOtPbqTZTMTib1EI4trRP3IRs3nXxaht3iH31JqlN6Gqs14KAdtBnq2qSN5ajJRrV8PUYCpBeNHeweXEceK+g2D8niGB39QZWtx3OxIeSebgnJx7KqzNaf5l025I7+ljSi9TTWa8TBB3Q/aQT1yWZF77Yl07Km5fPVP99HAkvbkVGuOaPSakt5ACO39QTTkM9aiXw8HyMFwLJ2b0pAqiID7nCU6DtlN3+W2oiwNZ9JZuWH08tJ8aprNirJ33UaB5iQzwwP6o7KWVBoNORTqRIvOviC9/gntmtiPxm1/KC8URDt65CIr99o08dhTU2NRbAjtG1iS7J1q0Zw33RDL/rUpRN3X3TI3Bl9epoUtcopta05/ifOMWQjt/i43WWVpSDPjN4TmN81GVtm70rZEGyIzPBR13zrBOTWxNO874fV1IZr2985BTtUm05VwOUmsL3BbF8qmyU19D79NDWSMfa54+MoHps5RGAUc5T8MkYiIImhyNkXrKtZy4tuKwaXtu+ATK8VlgJW1ZX0KWFtbif+KaC32Lnb9c8WcnIAqfyXUrdIJMzf+jeE1HHB8/3G8NK1GiWdnV2L+/PlYvPcORFYhDUW5cum+eJMXWvf/HvWq1kOPoZ1Rgp7i6uWHCDd/vMiODtL8GKH7d+GYaWUKaK3M+bD6aiLOPnkM33vr0CFzkiqnVkEtLaRQQx337U+ShtGnyL7ScEzrkcf0ZVHS38XCvhtQ9PfRqGwpZ4uw6zhw6Lb8RwqiD2P9v57o/EN52MtJ753+JV5GWApKlLKzGzKkefaZcJw4egHR8v7xyJbNPM5Vmgs96TpiLmH7Lh+Yq4SVqAemVFG21rC2MtUIxN7dhX+uvP7LhRGHp2P2cWe07N8ROVPLa8RhTJ99HM4t+6NjqgumhQ6PzuzEP//8gx17r0G0M/Cuv6KQrt5ozOleBFLVULoWRP7MRjzzl2dVMTzEXzPWIaLOeCz5uYp5nndVJtQcNQItMlnq4oeUDvVGz0H3IqbcwbVgfmQ2PoN/3FAwIx7/PQ2rXtTEuGWD8ZWbuXxVbjUwckgDqI6ux7Y3/AKh0X8dpq14iqqjFuPnyu7ymGk1Sg7YiuNb+qOkKUGU0cbZWBVYG6Pn9kYpJ9NCcCjaHXPG1UP42llY6/fq5xq1ZTF87SJ0K+UClcoDdX7+A8Pre5pfe/w3pq16gZrjlmHwV27mL0Or3FBj5BA0UB3F+m2+puXejAIZW/2KP1p4wVb606EIus6ZgCb4B7NXmI9747ONmL0qELVHz0Xv+A1B9znjUC98LWat9TOnvbHX7bu0UsElgzNi7x/Fhi2HcNU3DAaxPufaAzBt6mi0K/Kef+SAMfZJ46D8PyMudFsXYOPLChi5YiJqvnMEaIS//3NzcGGiMAXAEmVcPGHAMz9/+XlKIhAUFCV1wwp6Eb+5IEOGDHAv9z3mr12P9evXYGKTzOI1FXK2mo6tfw9HKe8R+KpUS8y6kHTKMAMCnvjJAaQRQc9fxL1u7eIBd/t3DdosHFF9zFR0zK4W2ywCt1hv7NxyxdQoSCjm8ir8MnyV/FcKIv3g638Yg/Ong729fbKP9LVm4MEbDcZOzCD2wTPL7DmiHN1Ll0/7rDnGELwIiv+BIqmhkyKjP/yfJ9gjirgaEVc3YHgGP//XBOXGR1g1ZRX8S/ZCvzpJWzoJGfFo1RSs8i+JXv3qmALfd6Jwwtfj9uDQoUM4sHMy2pYri4KZ3qXOqJDRKy+c4s5y0ow1osaISN9USrGXcfG6EUVr14VHwjOhNhMyubyvupoKVUZ45XWKPwmrVOLYlRoiln0Yg6sXryNafwnTvimJ4sWLxz0qjziISIM/njx5s9lbYi+fxWVdflSt5pHKyV+PG5dvICbfV6iUMeFSIvis+BXy62/g0tVXv7ho/VU7dEihYsdcvYjr0XpcmvYNSibYjuKVR+BgpAH+T57IS74JJdK7uyf6DoPStTZqlgBuXr5m+lt/4zJuxOTDV5UyJtpepWtFfJVfbOelq3LKG3rtvksrNYr+/BeWtbPFvpGNUTJreqRzy4MyzWbgpkcx5JPbEYyx/w8pn5fZe2X024BB08LQ759tGF7+ffwOp7hAujon2IHigiA/M1qeiFedXV3l5ymxg4uLjXk9JILyjOXQpHlzNI97NEUNU1e/Ec8PjkLNYpXR9Y87qLloFxZ0LoZ0iToUFXBMZy83CowIu3Ie194sZkgzpV1W5MzpDNNshBSF89MGYJZ34g/TVpyAU8fGyH+lwDYzPDJVwe83XiI8PDzZR/Devsj+DjFa0KnTuCFnTWFVBJ27V0Oa75Mo7WFnGx9Ux8a+8s25eEpXuDonOKTjupmNcXUDSmdRb1I/7HVn5mDmfms07t8DBVPbbt0ZzJm5H9aNpekS328Qq3RphrlHVqBzjndbryrVeSM10GpEmeqi48tHYoxAeNSbBlepiwl6DP+kUx2ZGgny02QpYGsrjs30JdH6x77o2zfB4+dJmL9yPnoVe7OeVIWNjah7OkSZW84psrLWgl6GICRJY5SCghFK1rCxSXTgC+LYz5IlQQMoMYWtLWyU6VGy9Y+Jt6Pvz5g0fyXm9yomL/kmCLqoJN/sNIYjIkrk30Y+wqysoaWXCHl1QxAcSrAW5fF23n5K0qR1IfzRM7i0no+j9wIR7HcV+/4ciVZe/ljeuTIaTU/hS++MsS/SW55W2BuJuYllk0+gxtL1GFBGBLg6b8wdtwwP36b3Ne5aqkXxOtXhIccsMTGWnitxoYqJMS+myooadV53sbNG2UplYGu6xupxbvWfuJjgOqe7sQrztgeIJ4cxtuuvOPxMBO7OpfBVMVsojEZzj2McJdwqVURB03gUab7f1Vi0N7nJBBMEBNLTV+KDVxKSCMXhkf1xrtkGzGnsZqrE9PIYJg5YDB9TmRoRdHUr5o4eiCl7nksJKbOugha1H2HJrBN4HxMdkGgRJSoTw22s+nOfCGTEc4UDSvSfjgGlU+ntfoUDChbwFCGAxIinT/zMF+noMIQlvTWgLY461T3kZWMQXyV0on6Yy1SVtQbqpBbIGZ9i/ZSluFugG/o3cpETkyPysn4Klt4tgG79GyG1JT9Z2lKoWMYGFzasjms0SXQXd+PAw3cIyhVKUSfFsWFpHRsDsKR1fpQffPCVuzmps0KpOtXg/swbj1zrokOnTuhkeTTOiaDjR3Ej5M3yqS1RA5UyeGP9smPiKEog/DTm/jQSG023hLQoXLkCMtxejz+PJJxTJBxHl6/HjfQVULV40jqkEI3xBHdkkrAqVQfV3J/B+5Er6nZIsB2dGiNn0HEcvfE2k44a8fjYwUT7Tn9rDTacs0H5KmVMf2sLV0aFDLex/s8jiWZHCT+6HOtvpEeFqsXllA/ktXUhGvvGN0PdVhNxKloFu4zitXrfYsAfm7C+fw6c3rwHPMU5Y/8/OCj/0IxBOPBLa/SdtwDfl85gGg5hm64YZgTnQVrvzMdGRsESXxkDn+KpHMzbVh+CsY09oFYY8fzOLfM0e4YnuHk7GEaFGlmbjcMgeew66fXx0+HF6BL8PL8SWdsMQicvrbigEqIvTETjOt9h4ryFmDm6K2p33AmHwq7Ay9u4I48jNQYexZrFa7Bo6X48saxUF20ag64u3BMDmmSGSro6G+5jadcm6D9/E7ZvmI+f23XD/BsGUFS0PMRFkMeiS4GjTicniufRKfbkGfB4/Y/44UIDTO5RCR0mDUNVR6kai0D839EYsOKBeUxmXhfcXbsTAQ6vG1Rhj1qjfkOZXR3RfNx++KbSEZ2sqCAEJxgvHv0sAIGWi2iML3YN64Qxh0U4oM2CWiNFOYyv/IbDPLQo1aolCmpNBYon+zfjeIgeDzfuwEXLx1IkwsOkD7VF9SFj0dhDLRpMz3Hnlnn4kOHJTdwONkKhzopm4wYhta8z6K/Ox/QdhLp9v8crcVdC+quYP30HqG5ffJ/qgmkQ/RS3b/ghnPQIfngJV+4FvjLNYZwIP9y4dBHnz1+At384jPpgPLh0HucvXoFPkPyu6Ge4ffU2nkUZoXt+F1duBiAyJhj3r93BM5057eqtp4hWuqPlz32Q/9oktO42B/uv38P1fTPRpeN0XBMHnOm9twISHCtpo8yQCzmdA3F88xZc8/OD97bfsey0CrkL5DSN4Y5+dhtXbz9DlFGH53ev4GZAJGKC7+PanWfQmdKu4tZT86fafz0Ck5vFYmWHr9FpylacvecPvxt7MKVLBwzZcA1hb3oGd/gGw0fXQcjcFvj6+/nYe/kB/O+fwKLenTFo5QUEyWPf7OoMwrCaYVjSqTlGbTiLu/fOY/OYVui4MBBVhg5BfdPNPpHXe1dw8YI3/COMiA1+gEvnz+Pi5ZsQm5SY/dcYMbkZYld2wNedpmDr2Xvw97uBPVO6oMOQDbj2xhtiFnNpGrr0WY4Tt+7h8vbJaN14DK4WHoBhrdzMC9jVwaBhNRG2pBOaj9qAs3fv4fzmMWjVcSECqwzFEPOGmESI/Fy6KOqStD3hRugt23PFB/FVK+37TvK6uiDRi3Nz7I156NlrJv45fRcBzx7j2r75+G3tLbgXLIx3mDKfMfa5MX/fk30YBvL9uxV5qORZMiwPpTO1WZ+2uT5enpxPnUo5mWZTkd6rUGeirzqPpHXe8jQJEddp5Q9VKKtDdmowcj7NHVqbPByyUbWf/qYb8vx0+jvr6YdyLvHrsMtHjYasoesJ5iGLvrGKvv8qM1mZZvkQD4WW3Ep3pcWX5HzqfWhZ86wkYkPxmpJsPGvQwGULqVcB8/SEClsvarbginnZ4JM0rVk+SmfZboUVZSzdgWYcf0GG2Mu0tG3huCkeocpC38y7QBfn16es0mwvpuXtqfC3y83rSsgQQLtG1qHcdkqyztuYRm65SXe3DqSKCWY2UTqVpBFHIkh/cyJVyN6VdqRxkhu9714a37QQeWQvTfW//Y76Df6Zfv7Z/Bg2/wgFvTIZjJ7ubZtE39fJJc+MYn4olI6Uq8LXVL9udSpXIAdlL1CeGvYcT6vOBJhnnHgr4XR2eiPKaSOVp4K0zl5Ub9RKGvGVNBuOgpRaFyrZdZW8rFQlVtIPVbKSQ/YGNHL+XBpa24McslWjn/6+8ZopC4No07eZSZO3Hx15TbkFbfqWMmvyUr/XLfhaBnoyv258XRHbo87YibYkm1E93ZpSheykwbtyecc9FGrK2GmzafueLW9CTgnWp0xXj2Yu7Uoe8pSbpjTHerTANEOHnp7sGkWNi2YkW/EehXVmqtBtNPUqa0dKhYJUTt/QwriZPNJKTz7r+1AlDytxbIh12Oeg6v3W0C3TRBrPaHkTJxKNVjnfSkpXbyYt7ephnjVHTnOst4DiPlZ3n7aPbkFlcjiTldh2hcqWspTvQnNPB4vSexvhdG1VP6pbWNQLqUzEse5SqAmN3euXeH0h52hBtyqU29lKlIWWnHJWpC5zz1DcLJVh66htBvNUneZyNT8USgeqNetBMnnT0f3to6lFmRzkbKUkhUJFtlnKi3WepuA33RDT7CtWlL1xH3F+zCDKTkEax2xUrv0fdPRZ0pWF0LkF3ahKbnP5aZ1yUsUuc+lMwuk29bdoShVpnyfeFtP2qDNSp82mmvXm+y7VuiCJph1ds1C2mp2pTTkPedpacQzYe1L5TrNJ7GLG2P8RhfQfcTJhnzlD6ENcvXofIYr0yFmkMDwd3qZ7RY+ge5dw/XEkrDLmRZF87knGPofj0aXL8FXnQvFCGc2vhT/E+ctP4ZCvGLxcEvaYGhH68DKu+LyENlN+FMvnhjcZtPFujPCbVxdlDnbG9XWt36BnWuTZ5yQOHL4In6ehiNabx+mrPGviuw7l8LG/c6UPvI3z1wNEfoqiRHY17p48j6hMhZDP0xlWr+xugyj/q7h6PwSK9DlRpLAnXlclDPemoUbRMdBOvoRdvbPLw2CSYbiHaTWKYox2Mi7t6v1O4+0/JTEhzxGudYaz7XvaIKMOIS9eQu3sCvu0frH3NWJCAhCscIa74zvenZAZwp/jeYwdXJ1tU97fxmiEvDQinZPt+7u1GhOCgGAFnN0d3+7HpvSXMapMOWyofQgXJ5WGPjgU5Jgedqlm0IjokJcwpnOC7XvbkDRKrS5IQ9GUDnDQGhDxPABBsdZwdnOB3XuqM4yxzwcH5ewLFI51rfPhz6p7Mau2G3Ll/CxHPP/njM8PY87se6g4pAuKm+aYS4HxOQ7PmY17FYegS6oLMvaBJArKy75dYM8YY5+Y/7q/gLEPz/AcTwLEdfvBBTy1k3+enb2W0rUKfhzzmoBconRFlR/HcEDOGGOMvUfcU86+SDEhwYhJlx723Oxk7IsSuKYjyvbZBv/gUMRoHOHk/jWmnPgbHTPxwc4Y+7xxUM4YY+yzYQy5jZPn7iPM9INcCqjss6FkhXxIOEU/Y4x9jjgoZ4wxxhhj7CPjvgXGGGOMMcY+Mg7KvxAKhcL0YIwxxhhjnx8OyhljjDHGGPvIOChnjDHGGGPsI+OgnDHGGGOMsY+MZ1/5QljGk3/I3akPuoXj+w7h/B0/BEWr4JglH0pVroGv8qdH2KXLCC1cHDm/kJ9cf++MIbh3wRsBOvM0bhr3/CiVO32SVnEEHolyfBwh70OlA7IXLQIP/o0exhhj7IvHPeXstYwh57Dkh+rInbs2Ru72h23u0qhSsQjcY65heZdiyJqzMMrUG4F9QfIb2KuUKqgpCGdnd0GNSl/hq7ojcThCfi2OAioKxIGxTVG10TDs8FNA+wa/Hx5xYz1+m7EX/kY54a1E4OyySVh5MVT+mzHGGGP/Be4p/0J8qJ5y4+ON+K5eZywJKIVRW9Zj2FcuiVtyxiCcmNwSTcaEoe+lE/gl3xfQVW70xeZFJ1GyW3N4vufN0V8bi3IlRuG8wQnfLLiCbd2yJmkZ63F+eFn0VC3BqTHFoJZTU2dE0NEJ+P5PN4ya0wP5reXkt2V8jr2jBuJghd8wvq47t9wZY4yx/wBfb1nKYq5gSrtuWHzDHs1mrno1IJconVFhyApMaaRFcKBBTvycGeG/aSiG/HkT4XLK+2PEi5NnEZXLCzYUin9nzsO5GPklC9HIuXRNg9r1C6UxIBdh/M2F6DzkAdpM6PbuAblE6Ypaw/vAakYX/HExUk5kjDHG2IfEQfl/JPLBcWxevhCLVu3HrbB3Gl/wHxHB6ZrR+P14CFQFOmNQi0wpVxZlZrT8oRHcTeOlEzIg3PcGrno/RHDS4DOOEWFBIdBLTw1h8L15HfeeR5tesTCG++LmTT+EJ1tsMQgOChdrEctFBODWVW88CjGtLZ4xBmHPfeFz8zb842JMPSJDnuLxvRu46WsJv/Xw3TkETbqvwoNU2hf6MJGf67fxNPJN92MYDh0MQv1Zk9AmixKx15di2pZAU97jRBzGwSelUbNIGkNyw23M+3E8ItsMR33393g4W5XED53sMb37JJzVyWmMMcYY+2A4KP/gdLi+uDPqNv8e/ft+j57ta6F4+UE4GCa//KkyPsOOtXsRaFQje406eF2MaFVpIAZWtzL/YXyO4zM6o0HT7zFxxXZsXdQfNfLkRe1+f+O6PI7a6H8cf07oh7bVvJCj+VJc3DsOjfJ7wLNAIeTxzIemC24g8tFOjGpaHFncPVGggCc8yw/DoRD5/WF3sW/BELQskxPF+m/G4aktUChLFuQvUhDZM3qi4o9rcVdqCBh9sW9mXzQolg25i7TE3FtSwG7A4z0zMbBpKeTKUxjfTD4vQnuRen87Fm+7DSnWNorP/rX3d+g9dBWuW2L8iGtY0bcNOgxdgC1bF+G78rlQqvNiXHllbHgKIo/jwP1iqFWpAfp/VwY29Axbpy/EzQQNgOjj+3GzcA2UlYvydSL2/YE/TuVH46aesIy0MQZsw4+lMiODiwtcpIdrbtQatgM3j89BuzJZ4JqlOBoP3YC7r7mx4VK3KSo+nIffNgbKKYwxxhj7YKQx5ewDenmE/lrjTdHiadTFkVRSoyBoStN4b7359fdE2pXvdXdG/0s9s6jEOrVUa95TMsjJr/eSjg0vTS7Ff6bjYXKSEHTgJyqkVVPmpn/SfWnTw33pytafqLgoD1XGMtR5zAo6fC+Inp6eSnXdlaR0yks1u4ymlUfu0TO/kzS9QWZSKRyowdIXpvWFnFtDk7qWJielgqyzVaMf5h4in6AQ8jv3N/Ur70xKhTUVGXqCokxLR9O+77ORSlOUhl+INaVIdKd/poJqFeX88RDp5DSK3k+9s6tIU3ocXU+4iwz+tK5DcWow9w5Z1qD3mUnV7bWUr98hipDTUqM70p8qdNxEpmIJ3EDtM4vyVWWj7jtfml4XS9DxQWWo+YpA+e/XCaetnTKRJv9gOhm3AWYRJ4dRCRtR16R6oXSlJst9yaD3oTn1ilDrlT5x25Aqw32aWllLDg2X0TM5iTHGGGMfBveUf2gOldCuVX5IHZ/WOXIjq5UWWev3QAuvT/wLkYZABIZKAysUsLKySvMtFf21WRgw9TIKftsL5ezlRCF9lcHoW8sW/tvGY/pxHWCXGYVrlkUuUTDqYl3x+8hvUTlneriV6YmOVRxAEc6oOWQ42lfKCddM5dDru7pwV0bD96GfaaiLY8lWGPhLSxRSKWBdticmfFcFOdI7IlPJNpiyZiLqOMbg2pJ52GUamaKARqMR/01MqdVCkzQxBTFnp2PsnpxoVE2Fx/fv4754PDLkQqGsRtxeswIHE4+4SYYeN/adR6bqlWEqFudGGNijGKyMj7B22grzcBn9bew/5YJq1Z2kJV5P741TZ19A4ZEdOZLcybAt9zOm9y4IrbR9xhfYPn4sFv7eD5uLzsL89jnSNl5dmRE5PB0Qdf44zvMQFsYYY+yD4qD8P2II8cb6YdOwV5cBObPZwhArv/CpUjkhfTqpehgREhSEtH2F04A7W7fggs4ROXIlmbVD6YZqVQpDrX+IowduyYkyhTLBsirY29uIAFr8L8EKFPYOsBMBZqw+fnC6Qq2GWqRpbW2gkdMkSs/maFHZFhR8DZd8kowvfysioN65B7cVgTi3ejmWL5cff51B+pbDMOL7asj4uuHlhkfYd9IO1WqklxPUKPzdADRyVSDs0GzMPhUNg+9+HFVVRo2MaTws9U/wOMAAlb097F95iz0qDZ+GHnmkxghBf3ch+m4oit9HV4ajvIRF2PUDOHQ7uTE4CqRLZw968RiP3v+3XhljjDGWQBqv/uzdRODqtuXYdvkF9HpfHJ7RGe0nXjT1+H6yNAVQJK9WhGUG3LpyBWnrKNXj4QM/EcYn1/2sQkYPd1gpjAh68UJOS0kau69TZI8c2d3FJyqgfNdVmejx4IEvyK4I2gwbjdGjkzyGt0fJ1/zAj/H5fhwxVELNBAG30q0ZBnYvJBoqt7H8j3W4te8wwr+qiVxvchOFzOOWkuVYHWOmdkR2qeVChFjvndhyJemejMHlVb9g+Kpr8t8JSfOkizpgjEDYy7Q1yxhjjDH2djgo/0/YoViHyVh5+Aw29JCGssTg+umzCJZf/SQps6BBiypIJwXR+7ZgX5q+mKpAeud0IogLhs8d/1d611VWVlArlMiQMaOc8qEQYnQxQLp8KJR0XMdbkYbwaGF8fBYnk5uWxRiCgKepj18JO3QIIeWTBtxalOzdH984A4E7fkePJQ9RumbhNE+FCKULXJyVoOjoFBpNSthlzYmcztamZg5Fnce0AbPgnag1qEXFCadwbExZ+e+ECNFR0SCVAxzTfeLDrRhjjLHPHAfl/yVlRtQb1RsVNUo4e3jAQU7+NCmRrfNY9CttD6P/evz6xzmkHHYaEXxsOsav9UOhOjXgqdLj4q4deJJoSIcB97zvIEJTBN/U95LTPhDDE1y+/gLujVqjZoJx7aBIhCecV1GvT+ZuhRKm32EyGmCI64LWoFiZYrCNPYN54zfDL9F2heHczOFY5J3aeKRIHNv/AEVrvBpwKzO1wqAueaGOuYaT94ugRulXp12J9NmD2cMHY9i4SVhyJEBOFbSFUKyANYzP/eGX3G2X0MMY2f8cmm2Yg8Zu0qFOeHlsIgYs9jE3mIxBuLp1LkYPnII9z5MbfxOLFy9CocxWEAU+7crKGGOMffY4KP/AIo+MRo2SVdFrhbcIzQQR6Clcq2LQT7XwPn7n5YOyKo3hG5bju6IanJvQEu3nnENw0tjNGIrLfw7CoAMF0aNFdthVGYxfW3lCd3gqRmyShrLIRIA4f9UdFOzzO34qJoemMdHQieiQoiNN0xCaGREVFSX+jUJEZPzADAoPQwQRdJFR8es0IUQ8uIdHcUGpEYF7fsPiR/Xw64h68vhpFTJmcoXScB+bZy3HxWeheHppPUb9uglPDEa8uHYClwLkCcxV6eHspITh0VVceqFH8PldOPKIkLFFf3TJp8ajvzqhaqOBmL5iLVYvmYL+jathgG9jfF8llag1dD82HkyPIkWTm+fQGuV++Am1HFVIV6EmKtnJySZG+P/TDzWaL4GyyVCMG/EzulZOeJfBCdW/Lg/re9dxPWmLyfAY63/8ARcaTEaPSh0waVhVOEpHuwjE/x09ACukHn+lM/K63MXanQFwML2YhP4OvO/qkLX2Nyj5Bj/3zxhjjLG3IM/Cwj4Q3dmJVNVdSwqVCxVr2oN6f/8zLTkXIr/6/ki78oPtzohbtGVcB6qY05WylGhI3QaPoYkTx9CQHzpTi8bt6Je13uZp/iyi79KmX+pTAY88VLPHSJo0aSR916Q+dZh6mJ7Kcyvqb/1Dv3UtQ+mVIIVdIWo1+i868/Q6bZ7Qjcq5KAkKLeWsL8rqmC/d3T6ROpd1ISUUpM5amwbOPUCP9ESGh9OoqlaaErE4VanVin4YOZHG9WtF1b/uSUsvJ8oRUdB+Glk5E2mlbz0qNOReoS+tOzyJKju4UcHqbajvlF3ygjo6O7YMpVMqSGWflaoO2UUBcp6j766nAdWzk51KmmpQ5CVdXmoyfi/5pji7pZ4e7JtFfWtkIY3Knar0nUGbLwUnM71kBB3sU5TqzfNN/FrQJvo2a17qsdGbfHx8yOd+AIXLL1kYXmyg9lmyUNd/4l8xBOyikXVyk53SmvI2Hklbbt6lrQMrUgZR1uZ6oiSnkiPoSISebk6sQNm77pCnjkxMf2syfeVUgSYmmhuSMcYYYx+CQvqPFJyzD8gQikf3g2CT2ROuth9mbK7CNObCFJWb/v1QYoIf4pb3Ddx7TnDOVRxlCmdMucc/JhgP7z5BlFNOeGW2e++3ZYyPpqNGngHwbrsZPrPLwt/nKWKdPJEnq1MK47J1CHpwD376DMid2w3WMUF4HpkOrk5Jl47B87u38MImB/J62CfJtxERAXfhE6hFlrzZkT7NA8BTZwy8j4fKbMghWikWuv29kb+DN77uVgXuYvcqbAqjxYBmKJDoMw3wWdoCjXY0wp71HZH5TQrZ6Id5dcvgYOfrWNc66ZwsETg6oDL6W83CoQkVkKgDnzHGGGPvHQflX4j/Kij/lCQMyh8ua/jpDwd6Qwafmfi6+gl0v7AGrZzlxOQYg3FoZA9sKDEDM5tmTnvjJ3wdWuf7E1X3zkJttxzI6WJ5pxFBewei9eJ8mLmyB/Lx0BXGGGPsg3uTfjXGPjlfchNElbMn5kzKjM39fsaMpX9iyY4byU+jqUyPqmOmo77PLEzd+TDNU20anj9BgDTd44WnsIvroTfi2eEZGHOwJGYu54CcMcYY+69wT/kX4v+xpzxs3/coXnc+fCtOwbW9/ZHrPQ0l+eQYIhDwIACKTLngnup86DF4fN0HdvnzwTlNze0YhATHIF36hEN0InHvuh8yFszNQ1YYY4yx/xAH5V+I/7egPPTSeizbfRvheml7FdA45EK19i1RJm4IBmOMMcbY54OD8i/E/2NPOWOMMcbYl4K7FRljjDHGGPvIOChnjDHGGGPsI+OgnDHGGGOMsY+Mg3LGGGOMMcY+Mg7KGWOMMcYY+8g4KGdpYITBKD9Nge7xaWxdOReTfumNH+efR4yc/n8v2g/Xz3rDXyf//QbC7p7H9ecG+a9UhN3F+evPkYYlGWOMMfaJ4qCcpc5wH4saZ4FL3u+wPVROS4bKzg0Zn/+DyZOX4HqsG179HZ8I3Fj/G2bs9Rch/juIOItlk1biYip5+Tj0eLR7NsZPmYc/V6zA0nm/47dlR3FgUmNU6bsFL1TyYmkVeQSj6ldEiyln5YSUROLIqPqo2GIKznJLiDHGGPtscVDOUqewhVuOXMidNwfcrOS0ZKidsyGHAxCjyoevKmZKXLGMQTg6viumBDdAz1pJXntTdqXRsWtGbOrzC3Y9fafw/j0ywm9DL3TdkhXd+3+HTh06oMt3A9DO+DtaTAxFu1/7oPAb/dqoWN+aqVj+qDDati8lpyXP6LcGU5c/QuG27VHqQ/wkvuEB1g1shgb1v8E334hH/UboNPsc3wlJyPgYO8Z0Q6/pxxCchioZsv9XtGlYH/UbtMDw7QHv1Eg1Bp7D8l++xTdVyqF8zW8xcf+7rY8xxtjHw0E5S53SHY2mHcW5fwajrLWclqxonDp+ATFuZfBV/oQRqB43F3bGkAdtMKFbfqS6ijRSutbC8D5WmNHlD1yMlBM/prA9GD3sNmr1rw93yxFlDMSpQxcQKkVIijc8zGLOY86MPVA36I8eBVOL5mNwfs4M7FE3QP8eBZO5O/EeKNIhW5HSKF1aepSEw6M9WLP7Cv67Ytfh4pKfMXGH76cbbMZ6Y/vi5Vi2Yi8epCGT1pkKoFRJT0Se24bNxx+KI+Qt6a9gcoMa+GFzBIrUrw7nm2vwx58nxZHIGGPssyT9oif777y8uIFmzVxHF17KCe+JtCs/6u7UnaIhBTSUrvFyCpKTJPpbM6mmR02aeV8vp7wvL2h1Sw8qOeIMRcspH0vQ383JtcgwOhsrJwgRRwdQwQxVqX2THJT5280UJqe/noGerWlF7laFaehpnZyWPMOzNdTK3YoKDz1NqS/5vkTQmhYOZP3NEgqWUz44wwOaVsWaMnfd+dH3c2r0gY/oyUuD/FcaxJ6jYYWtqcCQU2+97/Te46m0VRbqsTvK9HdUwEMKMD9ljDH2GeKe8v9SyD4Ma9MWfQbMwsHAT/0mcygur/8dv/T+Fg0qf4U+W8PldJkxGJfXjMaPvQdjxIj++K7/ZOy5o0CRryoinbyINI583x9/4FT+xmjqKQ+qNgZg24+lkDmDC1xcpIcrctcahh03j2NOuzLI4poFxRsPxYa7r/vaogvqNq2Ih/N+w8ZAOekNGIMOYNywv/D4nXeDDmcOnUSEZy7ksXRVG25g7rA/oe05CRNq50bIycO4mNbxHnpvLPxjG2Jq9UXvVMej6OG98A9si6mFvr1L4Z1GrhhCcOvQeiybOwPTZy/B2v3eCH6jb40aEXL3BPbt2YO9R7xh/m6qSLsdn/Zqddch4PJurFkyBzOmz8aybWdw+eBabL0q1zPDc1w/shd79hzDnRAjdP6XsHf3buwWjz17j+JGUHI7LgaBt45j2/L5mLd8G874Ju4zDvM5hf17xDpEng5e9jcNwTGG++LKoc34e/V2nPV7w0E5EQ9x9sAeU572nvXGbZ+nqQ7r0Qffw+mdG7Bh91k8CpPa0G/DiEDvI9grtmPP4VsIIhVC7+wXediDw95BMCjkxRKKDsCVA+uwdMla7Lvs98pdjvdXLkYEbPsRpTJnkI9tF7jmroVhO27i+Jx2KJPFFVmKN8bQDXf5S8mMMZYcOThnH5ohkHYPKE5OSgVBU4mm3n+DXrU0kHbl+9+dQbSiSTpS2taiOU8S5Df6Bv35bREq8u1KumPqmYum3T2ykEqdjwadTNDvF76VOmXSUP7BJxP3BkacpGElbEjEDyLPSnJtspx8DXrymVOPirReST4JepxTY7g/lSprHajhsmdyStrofXfTL980od8uvoe+V4Mfza5hRenbbiBzJ6WB/P5qTplzdKV/gsSmrm1JjjYNaFkau5aDt3WmLJo81OdQhJySguBt1DmLhvL0OUSvWTJVEVeXUqciTqS2cSev4mWoTDFPclBqKe+AI8n0TKfUU/6CVn+bk1ydbEmtKUzDzokdaAiglW1zUAYnG5FWxJxmEXuDlrTKQ7ZaJ8pWqBSVLVOMcjlrRX1Qk1e/o6a6or8xnepkyUAuzk5kp1GQyjodOTs7mx4uGTypycIHoqQTCLtE89rkp3Qae8qcvxgV9nQkjX0+6rD8Fpk/OYy2dM9Dbi7O5GSnIVWWzjRnQVcq4aIhq/QelMlRRRqvfqYl0ypq149UwN3FlCdpneps39G+ZKtUCJ2Z0Zy87JWkFZ+VxcWGbD2LUj7x2W/cU66/R3MbZqUMYjucpbJVqMnGSSoXF3LJVIFGJbm78vLcTGqay460Tp5UoGB2ctLaUI5GU+lUiLzAey+XCDo5rATZKMznI6VrE1ruayC9zxyqV6Q1rUzrwc0YY/+HOCj/Txjo2T8DqMvEkdTA5jMKynUnaGBeNVlVnko+ltEnhmf0T6+8lK7wIDoaLqdROG1s50Jq9460JUGEGHtmKBXUaKnm3IDEAZQQdmQgFdKKshB5VqjzUM95E6lRzaF0OC5YSIOoTdTeRU0e3XalcWhDGF1fO5hqFixPP/3j90qe3or+Bk0oa0VZeuw25+HlPurt5U4NFj80rT9qe2dyt65GMx6l4dP0d2haVXtyqD6D7qU62kdPd6ZVJXuH6jQj9QVTF7KXfvCyooy1fqXDAXKwFHuOpjSsQI2mnpOD2YRSH74Se2kkFbcuQENOxQeGseeGUeEkadH7e1MOp2o0+UpcBRJt1m3UJZuGcvc9nDhINTygPypLw1d2pLKPg2hHj1xk5V6bJh57KkpH0D+lfQNLkr2TaFDGVV6JgR78UZm0Kg1pHApRp0Vn6YVeT092TaR+47bLy7wpAz0U+8PaozvteiWT4tjf1JE8NZmo1q+HKEDKit6P9g4uQw4ioH6X4SuxF4ZTUev8iRvCCYXspu9yi7rZcCadlXfYy0vzqWk2K8redRsFmpNk77Fcwo7QwEJSI0uck8Q25uk5jyY2qklD3+jgZoyx/z88fOU/YHy6DePWuKNvrzwf5st4H4j+9l4c9FGgaO26sIw+Cdn1C35YEoy6vwxCBTtzGnTnceR0KKxKVkT5BN/k1D95jACDCvb29q98o9i+0nBM65EHGunKrb+LhX03oOjvo1HZUV7AIuw6Dhy6Lf+RhCId0tkTXjx+hCSDaxIzBOP69un4sXZRlOm6Benb9UF1Oo+d//yDf9L62LEHlwKSGzJhBa1WfITBICKQGFyZMQxrnH/E+I6e5m3WRUMHDbRWrz/UIg5Px+zjzmjZvyNypjaFYsRhTJ99HM4t+6Njqgumxgj/ddOw4mlVjFr8Myq7yzVTXRIDth7Hlv4lP1hdVblkgHPsfRzdsAWHrvoizAAonWtjwLSpGN2uyBsPxTE+/hvTVr1AzXHLMPgrN5hKROWGGiOHoIHqKNZv8zUtl4hRi7LD12JRt1JwUangUedn/DG8vvzie2R4iL9mrENEnfFY8nMVuEuZU2VCzVEj0CJTcmNN3hcjnm2cjVWBtTF6bm+UcjKnOhTtjjnj6iF87Sys9UumPr+PcrGvhOHTeiCP+eDG3YV9saHo7xj96sGN6wcO4XaE/CdjjP2f46D8QzMGYPOo1cjUvw+KfIgp6z4YAx7vPYBrKICv6+UxBzqGW1g8+W88ydQUXRu7xlWeyKNrsO2+AgUqfAWXJDWKTB34yXFE9TFT0TG7GtKdbor1xs4tV0QAm1jM5VX4Zfgq+a8kFFoRECtgjAjDyxQHqRoRfHYNZs2ch78O3kckxeLJkZWYP28e5r3RYyn2+iQzT4YqAzK5WSMqPAL6RyswfHYw2vwq9rUc0epehkHnkBGZ4gfaJ8/4CKumrIJ/yV7oVydp8JKQEY9WTcEq/5Lo1a+OKMW3FYvLZy9Dl78qqnn8t6cBddGf8deydrDdNxKNS2ZF+nRuyFOmGWbc9ECxfHL0+AZirl7E9Wg9Lk37BiWLF0dxy6PyCByMNMD/yRN5yQSsv0K7Dvk+fCM59jIuXjeaGraJilmbCZlc3rZBlRZ63Lh8AzH5vkKljAk/WAnXil8hv/4GLl2NldMSeE/l4lh9DKZ2zA61FJeLY8575xZcefXgxqpfhmPVtTccy88YY1+o//Zq/H8ofPcIjLhoi9g90zDpj124K8V15Isjf+3EvU/5207GF9i/7zyMOWuhjjwtn+HeVmw+HQ2XanXxlaVH3OiLzUv/gZ/CE+Ur5jIH7zKliwuclYTo6OR/zlJplxU5czrDWuowpCicnzYAs7wTB77aihNw6tgY+a8kKBpR0QSVgyPSpRjfKJG+3HeYv/cGfM7+iT6lgBfamhj19w7s2PEGj3/WYlCF5FpVtihW3Auxj69i5ZiJOF95FIZVdZBfMyAg4AXUhUqg2GsaZLozczBzvzUa9++BgqnFarozmDNzP6wbS9MlvktQp4CNjdiJuiiIIvxvhT/CM5fWmH/0HgKD/XB1358Y2coL/ss7o3Kj6UhSBV4VE4TH/uGieWKmsLWFjTI9Srb+EX379k3w+BmT5osGWK9i8pLxFI5ZkMXpvzj9aaDViNhcF524eWqMQHhUcnde3h8ray3oZQhCkpxnKCgYoWQt9v+rPfXvrVyUdsiaMyeczQc3os5Pw4BZ3omnf9RWxIRTxzCm7GfVW8EYYx8MB+UfWNSTx/A58ydGDR2KoSP+xtVYcWnW+2D77E24kUxH1Scj7BD2noyGR826KKkNx6OHz6G7fhW39CrkLlgQ5t8RMsJ3wxzseWYFY7rSqFhCi2h/v7iZNrSFiqGAtRHPRdqrcVYoDo/sj3PNNmBOYzdTRaSXxzBxwGL4mIIII4KubsXc0QMxZc9zKeFVsS/wIlSJbAULwBIGp0wE58U6YPqBc1hR/QT6fjsdV97LhM4qeDVqiILeMzBkkycGjG2F+I7JUJw7/xhlGjdAltSONONTrJ+yFHcLdEP/Ri5yYnKMeLp+CpbeLYBu/RshtSVfT4sSNSohg/d6LDuW+OdRw0/PxU8jN+LBmzYaTTFe4nsj+ufPXvlBneh949GsbitMPBUNlV1G5C9fD98O+AOb1vdHjtObscc34RsUUIqyMxqN8nqNCFjSWrxnMA7KbT2rUnVQzf0ZvB+5om6HTujUyfJojJxBx3H0Roh5wQQUSrHeV2PS909bChXL2ODChtW4keAg0F3cjQMPP2RQrkXhyhWQ4fZ6/HkkTE6ThOPo8vW4kb4CqhZ/NRh+X+USengk+p9r9j/27gIuivSNA/hvd1kaRRAMwgARO7BFEbvbsxPzzjhbz/7b3aKe3V1nnkHYoqIIKkojLaE07O7zn10GpFkU897vfdZj33134p13Zp539p13cGprdxim7dy4s3wqdqXt3JBFvcD5bQsxbc01RHzdtgnDMMxPI79QgSkC+kOOwj8kBCHyl+82dJRfORI3woIra9GmKJ6k85UkuzjhwUcxalhVwZt9q3DCWwiBuhrUBISo9xGKIDvuxWHsCasJs5QACGo1Rv2Ic9h0xksRRCnotkS7xurw9vDI9kATKQJPTsD4p12wcnQzDFkxBy2Ky7/EBeL/LsTUA35cDiH0KuvD6/hlhOrk3klD8vYlvJJN0LaTlfL9kIV6aDTpGE4M98Gaja75DmGnLBXL9mhjkgTdPjMwxvLT1Wup3wmcDOmLOcMrZvkFITvJi+3YcInQYdLvyCVO+kTyAts3XAJ1mITf882oHJ1Oc7GwfQy29WmH37dfx3O/EPje+xt/DJ+Og0+jMoKzuCAPPHN9gidPniPggwyy2EC8eMK9d3WFe8AHbqulERqUgaEoGC7XH8EvPAxed3ZhwuxjCJIlI8LLDZ6hfC2QSCBJfQX7MWOx6eJDeIWGI9D9BravOg7PUtVQwyDTYUlYEmYV9RB59yzOuQcj+OUFrN77ECLzqqiY3sdCux3mreyF1IND0G7YGpx38UZI8CtcWzMCQ2aegnts2vSSI7zh5voUL0PiIUuNht8z+To8x+vQz3wUUnwwXj1z5cpFPs04yCTp03SDTxRfs4Sl8NusiajivgL9Rm7FTQ9veNzYhBFDN8Cda5QnR3jBzTO0UA/8iQt6qdgeT9+EI5Er2/dez7h5PsPLoKx3Vmi1n445rWOxe1hvLDjlAi/vJzi7qC+G7oyEzeyZ6My3ZIu6XKSBJzFh/FN0WTkazYaswJwWxRUnGlnUv1g49YCisSfUqwx9r+O4HKoDxa7PMAzDgI2+8i3FH6QeP8noK0n/jiFjkYC0KnWi+RcD0ka0iL9HixrpkkjdiOp1HkbTt9+m0GQPWtpQjYS61anbzGP0OuvQGfT+1CAyNraji+kDbUhD6cr89mSuJST1yt1p/rnX5HV+GlmXFGasg1DXiuY5x5Pk9XJqUt6OLuX6QBQJea5sSrpNlpNH5sE1lJZIr+48pJAv3gxSCjsxgIzKtqShY8fR8vMvKSo5kcKfn6bFv0+ngy8LeppLFJ0ZXJbElSeTcwFZo84MprLiyjS5oIyFEedOhyd3oBpGOiQWgASq+lS9x//oejBfMKnP6H/1NYgL0DO2T8ZLICC1ajPpbsY2j6Hby7uQRXERCSAgoWY5sunbkapqCkkgEJFup50UxE026ZIdGZdrTcP7NyIj9fQReLTJtPEw2vIw57guEp+TNLGZEanJl0+kTRVaTqZjntlHHEkm338WUp8GFUhPLW1+msaNacS2hxStWJVYOjGgJKlkXw+BkHTabCa/QtcDrv6tsSEt+RCnmaenmKYKlR52NtNQlfJRTBZQ91qlSZPbpwTqZanJyIU0tqEWV64CEul2op3yglGGxINWNJV/L/s8haRRbxE9yz5kTsxj2jHShsz1uH1UoEq6Fa25MnmUafScoiwXKYVemU/tzbnlU69M3eefo9de52madUn5iAJp0xXqktU8Z4qXvKblTcqTXe47N8MwzH+SQP4Pd7BkfnICQdplzaLbnAkI8Q6DmkkF6GW+KCv9gADvKGiX/5QuifSFT7IhzMtq5fzpReqDPX264VK3azg5tGwhfpqRIdi+Axo4DIfHiX45b2iMv42pzadAbbMjlmUMA/MdyEJxfv5UOFZdjDUDyiHO3w1PnvshSb8GmjQ2R0Hdc6Xe69Gq1iKornyGK3+Uz/uKutQb61vVwiLVlXh25Q+U/5Lu5LmSIi4iAilaBtDT/MKJS+IQ8T4RmgYG0Mp1UkmIjRVCR0cV0vgIhEalQl3PEPpa+d1eKENyzHt8VNGDgXYBtyGmxCA0WgC9UsW/7KFKX0FKTATiVPW+vIwLS5aEmI8yFNPV/CF+HpUF26NDAwcM9ziBfp9/tzLDMMwvhQXlv4iiD8qLjizaEfNHn0LdjZvQs6yyIUEcTvSzxL4W17G5rSHMKmbqQS2LwvVp/bDLchMOjrb84QKvwpBFOGHrFm9YzxyBOpp8Ym5kEXDaugXe1jMxIt+MDPPji+Ma2pb7WuD65rYwrFAxx6hNDMMw/0XsUMh8dcISLbBoQ2f4bF6Ly/4FDa3Bk0bgXSgg8XuKMK0SfCJHFg6njYvgYLUJ+3/ygFxOaGCDCYsKCMjlhAawmbCIBeTML0CKiLSdG0/DtFCCnYUYhmEU2JXyX8SPfKU8Q0ogPHy0UMVST6nWYEpMNFKKlYB25swJ3vAILo1q5t+xywrDMF8mJQbRKcVQIsvOzTAM89/GgvJfxE8RlDMMwzAMwzC5YpcpGIZhGIZhGOY7Y0E5wzAMwzAMw3xnLChnGIZhGIZhmO+MBeUMwzAMwzAM852xoJxhGIZhGIZhvjMWlDMMwzAMwzDMd8aGRPxFfI0hEVMCbmLXHgfEVOyKUYMawCC9CZcShLunTsHBIxiJ2pXQvFdftLPQ4T/MRhYD76cvEZosXy4BxKWqoJ55iWytwXgEPHuOwHh+2YU6KF+rJozYc3L+s2SRnnjk+R5SRZUQQLV0FViZZa83P654/we4ddsNPsGRSFY3hFmdZmjZ2BQRL/1gUdOSz/UfE++PB7duw80nGJHJ6jA0q4NmLRvDNOIl/IxqwpI9eoBhmP84dqWcyZ0sCHtGd8f4RUsxZ0RvzLuZnJYcdgmTGlZF80FTsSesPprFbEQXqxaYdy9J8XkOQhFUKAouW0agVbOmaNphPpzi+c8yCCCiSNz6X0+06DYHl4K5IKwwj+qMf4WTqzbieoiMT/g88S57seKgKz7w738mSZ7nsWnLZfgq+cDUH48MUV5eCOc3ISWE4NrCHmhubQ1r62bovvw+UtM++oElI9BxE0bbWqLuQHs8ji+Byo1t0MhcDW/2DEDl4iXRasMbPu9/R3KgIzaNtoVl3YGwfxyPEpUbw6aROdTe7MGAysVRstUGvBHxmX9KcXi8dTCaNOyJ+UceZ9ThL/HB4zxWDGqGhj2XwTGyCCbIMMzPQX6lnPn5yTdlkW5OaRDt7FiMBNw0BSpmNNExmUtMpecL6pJYIJ+XGrXf7k6bW6pyf4vJatEL7tO8pb5YRFZi7ntCXer0dwBJ+fRPUunxnLpkNd813+lkJ410psV9R9COl4l8ypeQUvi/c2nI7MsUmnMBf2BJdH2sKYlUKtGfzvLt9PNJfrWF2pfvQ0fj+QSSkM8aaxIr6rWQytpd4tbyB5bqR+enNiZ9VWPqsOI2hWevP9JQOtTbiFptescn/Bekkt/5qdRYX5WMO6yg2zkLhUIP9SajVpvo3XfZ35LpzrJBtNipgJqV+pK2282kCzH8+2ykAVuotVYx6rgzOJfj2heIOU2DSqlSnfnPCnVMZBjm58WulDO5E5bF8N1XcXDjOuy4cBFLbeSXrlPwxtMHEnn4zxGrGaHj2D8xYOAUzBxoCZW05FzI8P6+CxLNLKBBH/DvJns8TuE/SieLwjN3Mdp2rp7PdLKRvMbO4TPh138ZRlZR5xO/hBAGbeZiotpGjFjnigQ+NS+xLmdw9G5Agfm+PjU0mbgBq1evxrgGhfmJ4UvIEP74Ebyl/NsvIAu7jKm/zcC/7/mKpSCAiljM/fsTkAXh9Og26LP+NaotPIOTM60/dfVKJzRA8xadUL+uAZ/wq5Mh6PRotOmzHq+rLcSZkzNhnbNQYNC8BTrVr5uzvL4J7rjk+QieWepdLmQf4fvUHSHZj1k8ie8b+KYaolJl/aL96Vm7MixMCL5v3nBHXoZh/gtYUP5NSBHq5ohbN2/iJv9ycA1EWoeQH5dK6cYYOHEyRnWwhDafJpVmjsJEqNhnJQ4fWoE+ZvmF0rFwdIhC580r0N9YiFSPPVh/LpI7JWYS7wSHd/XRuqayIbkUb+wnYElCf8ztXKoIK7IarMYPg/aGUVjhkv8WEiIcNxd0RM3aHTFuxXE8Cvl+W1SzSg9M/rMbLNT4hK9KhkinReg7+xoSC4hnCpLicwJj2/TF1hcJip96CpIU4YPX3qGIz6MxII0NgddLTwTGKBfGJEf5400+0yuYFK83D8HI/d7QsJmHHdPrI/eu0UKUaTEK/Wp/q0bT9yV9vRlDRu6Ht4YN5u2Yjvp59BcXlmmBUf1q42cuFZJIuVoghIrSVxOUpQIVERf0c9NnGOa/gQXl30LKI6zr2watWrdGa8WrPcYdCfyBrwKmwPXvwWhoUhL6+vrQN6iGSf++w5mp3bHsdiKfR4KHa7uidY9luF1Q/JNwF7d8a6NNsy6YMq4BNCgc5zfsxOtM55qkuzfxukYrNFQ2qIy/gXXrHqBK954w5fujykIvYEK9sigpX2bFcpujzZxLeH13KwY2MIaBcR10n30KXgWd4/Q7oKe1P+xXnUYkn5QbrfpjseuGG56dmYH6iZcxs1UN1Os+CevOPkdEofp2SxEb6AbHs0dxi7/0nBR4D8d3HcXD9A6q0ki4X9qPXefduSZOZhLEBr/EvcvHcfp+aNaGDkcaF4QXzudx9MYbLidXRnE+cDq8A/sd/LI2ClNiEODxADfOHMShW36QSkLx5MJebNm8F1dfZ5pjnDeurPwNTTouwxs9Q3x45o2oLDOVIuLZOezauAH2h6/g/sMbuOOZR2F8uIQJbQZj14u4tIA86Q6Wd26DARtdFR9nRil+ODauPoyNzFHFvCxKVe2LXS8/TVcW7oQ1/evB2KIblh4/gtktK6J6j//hGn+fgeT5bgyqb8zXjVJoMGM/7IfVRtlSFVA5l+kpSxZyFHOXOSJGaIZhC8fBMp/ATKVaPdTKEpwmwefKBsyaPBMLFszBxKF9MWrVdfhnbJh4uJ9egakj+6B908aYeCEeSR6HMKldQ/TfxW0jPpf8V6bHBxdh8pQ5mD3EFg06zcWV0Ow1IR/c928tnoNDgYX4Tn5kITg6dxkcY4QwG7YQ4/IvFNTLWihI8rmCDbMmY+aCBZgzcSj6jlqF658KBfHup7Fi6kj0ad8UjSdeQHySBw5NaoeG/XfB71OhIOrxQSyaPAVzZg+BbYNOmHsllP/sK5OF4sKEeihbkj8O6RvAvM0cXHp9F1sHNoCxgTHqdJ+NUwUeiBiG+c/hu7EwX1Gq22Lq1Wks/fnnn2mvyfPpqEfR9hKUb8oi3ZxSX1rbXN5fXN4PvCzZXYqj994utLa9lqKfOaBKtksf0HN3f4opoCNlsvMUajL0DMXK30SeokFlRQRRORp1+aPic3nfzrvTG1DvA5H8+4LFnR9GZcRVaMb9rH2o4+/PoboaAn65DajH/iCSSnxoa8ea1O+gj5J9M6Xku7Y5qep0pb3hfJIypDHkeX0H/TWgGVlaNKTeUzfTRfdIkvAf5y6anh5ZSH2qapBArRmt9Y6nt8dn0ICezai8WExVZz6g5JgHtHVsX+raoAyJVBvQkpfpU5RSyP0jtKx3JVIR6lKfo1k7vUY/Pkh/da1E6gJVarrqLcV6HKQp/X+jtlW1SajdhfZkWrd4Lyc6ONuWSoo0yHb+PloxeSrNXzafBtTUIVHxFrRWMU8Jvbv1N60c04R0VQzIduJKWmN/lXwyrWDszUlkWaYTbXoRQv5OK6m9UWWafDuPfu6pkeTzaBnZqqbVXYFmO1rzyI1eBclripQCNtqSqqKuCUhs1Ipm7r9Ih8dU4+9pEFLpEf+Q4k6C+Ac0v4E2Vy9VyHL6fa42ceu+vxtpCwSk1XAxuSpmL6FXyxryfdQFpFmhI806kMf0lCYlv00tSZP7vrjOfHpemF068RXtH16LLDpvoKfpu0H0ORpmpE4Wv1/lakW693Sghy4JVRvTMsejNNa2MpUWa5HtRt+0evXRhdZ3q0aNp/6r6Mee7DiBKqroUO+jcYpvF0gSRFf/6kQ9VrkWWZ99qd8maqnJ7YPiOjS/cIVCr/YPp1oWnWnDp0Khc8OMSN3id7r6qVDo/YEepCtUpcbLHOnoWFuqXFpMWrYbyTetUMhlfTeq1ngq/ZtWKDShogrp9D6q+O4niXRuqAUNOl3AVk9+QDPrdKIdeRwLkm7+QeVVLWjq3Uz1PP4+zanL7dOK+iYkgx77KUgqIZ+tHalmv4Pko0yxSF7TskZi0uaWO+NWC4ZhfmksKP/qJOS9tiu1GLOSth91oLcfv84dTUUflAfQRtvMQbn8lB1Px/ro8CcaNeqyN/3EmZ9Uejbfhnrtf//p/UIrUuMCpmJttqSdRFNf0P+adaCtSt/tlUqPZnPBlGpr2pbjjsxYcp5WnVQVgZaAVCqNIfvl3aj1bCfK4z6tXCWeGUT6KkY08srnhSqSqJd0eetkaltRn8o1HUDzjrtT3mHSe9rTWZvEVvPp5vkVtPiUNyVHH6KexdXIevl1OrxoFV0NSiavVU25dW5B6/0yr3MqufxVg8RcQLs9OGf5Re3vTjqq9WnRzTO0dPFZ8k1NJuc/zUms+xsdU7SSPok92Z/0uGC70cQT5MUHDfFnB5OBUEyNV3imJXD1+eWSBqSq24sORfFJGZLo1h8VuAB6OF1QTDuZHsz/k3aG5LNd4/ZTV7W0uivQ7p3pRs/MQbmQDIedVwTMiReGkaEwLb+qrfwGQSkF/d2Jisu3t0CLuu9P28pJV0eTsYhLk9fdi/JvctPboMz0FF9XUjj93VFT0Rgwm+ikaAwoRepPxwaWJzXToXQmLNMMpUGKG6eFegPoVEY5pM1DXGsUzRk/iY74JlLY27cUJt8+XGNzbw8j0m2xll4pgtF4ujezBqkWb0kbPTO1lPIQ63GcZrTmAvo/L1IuVeezhf/dUdFQUTGbSE7KFwr5HxtI5dVMaeiZMO5dOm77bm5JqkI9GvCpUNLmIa5Fo+aMp0lHfCkx7C29TSsU8tnbg4x0uYZkWqFQ/L2ZVEO1OLXcmF6H0xVNUJ54YTiVUq1Hiz2ylnms8zSqrpp2gUCgUonG2C+nbq1nk5OyByJpCG1ro07qnXZTjl2NYZhfEuu+8rXJwvHvvzfgtGMmxvZviSqm1fHbJpdsXRB+YdIA3LivBdtWJfgEFdQYNxXdDASIddyCLQ+SIA26idui5mhVWtnqKMG7wFBIRdrQ1s7+HW00m7seoyvJbxIkSLx2YtKpWli9sDmK8znSxXrcguObHOMzKgiKFYM2vUdgQByfojxplAeuHNqF3fsvIcC4PQYNHYEhHark0deYE38HTi7JKG+WgsexnTG9V0WQy224JJrD5ONTqA6egnZlY3Hn7gugchNYG2VaZ658HZ09gVq2sOWiy6wScM/5EZItqiDp9nt0mtYd5YUheOIaCFFdazTJMg58Cp7cfoSPatXRd2JPpN8iIFQRQ947SCLhu3Zw9dnR2R0iqxZokb1AIYZZdQtohJzAkjWPuLmrwmriX+idY7kKT0WkoujuJVBRgTi93xfJ+/Im4e7Nu/goD33kvfxdDmL79u3Ydf1tWn93WQTcnvnKP+S+nPY/ubynVwgSf/gFpsojf5iaVVDyBmUZQo5Ow5/HwtFo0lx0zVw2skhEfiBQahKS03uSxN/F7cepKCkMhqTNPPQtrw5Dc3MYqsgQfGQaZlzUwpCFY2EpCcCtVQMx7HhJTD60D39Y5DXGoBTRHv9gw4S2qNXADudKDMTEloQnly/i4kVlX5dw7VleXUEk8PcLRKq86WNqhgpK9rOWdwOa9ucxhDeahLldDTP1q5QhMvIDuMYnkj4VCu7efozUkkIES9pgXt/yUDc0h7mhCmTBRzBtxkVoDVmIsZYSBNxahYHDjqPk5EPY94cF//1PhNyMpNKCuu3IIJUJuX2Bf5tZig9OHnKESssh6JGtzLWbzcX60ZUU9YskXtg56RRqrV6I5jkPRLjlmMtQmUIDdBzSE2WfHcNet9yPUwzD/Fq+/GzJ5I9ioVevH/p0bIxKJVQgjXmFU9OGYvG973dT4Lcki7gJZ2kztM4UcAsNe2HaqOpQkbzB/nUn4HnDCXFNW8MsrzgiV9xZXxGI5aJ4SyxaOxTlVeRnQ0Lqy8s455a9vFPw/PBfmHvYnX+flUBVFaoCGeJjPyoXqMli4XXzb8wZ1Bw1mozGwXdmsNv/EB5Oh7BkVCuY6+S9q6U85holkfIlqoo+/apBjQts3BzuIlSUDNVaA9GzAlcwCffh9DARZa1bokam4EBevrdcCZVtW6Fi9vJLeQaH2xFQTYyDSe/hqCUfoCbGGc7PZKjO5S+beZEknnC8GwANm/7oK5+fghTvXr5GpMAQNWuXT0uKdYajiwTVbGyRsw3FBafD/oc/68jweOUYLL4bDxV9A5T4qkeZeERFJfJVQQKBuj5KliyJUo1+x/bjJ3Hy5DEs71FW8WnRU4OYv0NRyAX5SpG+wb4t5xGmYYMB/SsqGjzpZJEP8chTAnG1eqirkZaW7CKvG1IkGnbEmE6ZRvfgpnNg20VEauog4sJc/DFhFZz0x+OS2y2s6GySZbqZyaJdcGzzJtgfcoBvArdvvHPGwe32sLcv3GvPdR9+ijmpfSoUJRsqUrzZtwXnwzRgM6B/1nrMNVQePvKERFwN9T4VCm5yO4w00RAdx3SC/qdCwZsD23AxUhM6ERcw948JWOWkj/GX3HBrRWeY5CgUIXSLixETWUDDm1uGqEQd6Oa430WCwFv7cczHDP1GdoB5jpUtjpaL1mJoeXkDUN7YeonL59xy3OCf8vww/pp7mH+XmQjGNoMxuE4ULu65DM/vP8wTwzBfG3/FnPkWkoLIcXU3MhWLqd7/3NP6hBYR+aYs0s1ZRN1XYo4OoOZzXHL05ZYG76Pu+kISqFWnpo3r0kSHwnQTSSanSeakotaedubRDT3p2TJqZZjep1NAxZqvpsJ040/6dwyZiNSpw9/p3W5yF+9/m/YvHEGtalhSgx6TacN5N3pfqA2bSk/n1SKxigVNuc2XgeQtrWyiSuIaf5EL//N/8t1pVFlsQANPZ+1z8vF4X9JTKUe/38hZfmljw6uQ+fhbGV1n4i+OoLLiKjQ9W198acAmaqmuSW22BX3qOsAtx5pmGiSuMo3u8L/wK8pFzC3rnbz7JcQ/mEdWmkJSt1pIrgWVuZLdV9LHKU+6PJKM5N1SuHTVFuvJXxpPJ/vryn/y49LEZL3GJ4/9StnppeVWzkc60V+fm7eIyv1+Q7k+2VF7qLOGgMSNltPrLAsqJf9tbUlHVJK67Ukfx5+rG3NrklhkRMMvZNvXotOmo95+O2XuAaMsaZQr7Z9kQxUqdaV1DwvTsatgH0/0Jy5QJlG53ymXapmLKNrTmdtXxY1oedZCIan/NmqrI6KS3fZQAL+eqU/nUk2xiIyGX+C2QGbRadNRb0/blSyU93u6kdngM/l2bUt+OItqN12WbXulk1Lork6kU7wz7c7t4QZJz2hZK0PSUHSn4+p4sea0WukDUSydG1Ka1Bou47snMQzzq/uq17CYbNTKwmbKTizqUAyqqmI+8eclP8vkLwF3bvqhVqsaOa6YCcv0xfQRlaGS4o77vjXRqn7OYVcSfK5hy9wZmLN4BXY7Z/65XBXVa1eFuiwCIcG5jJjxwQnzpzxGr1Nb0V3RPYDw8c5yTN3lk3bVWxaFF+e3YeG0NbgWkftP16nv3+ODsByqVdXhU7KTIeLoMDTqtRou2h2x+vpzPDyzDpO61oB+XpcpcyP1w41br4BaAzCsUVoZyCJuweG5APUGDkEdxUVHKXyd78BXowla22jgQ1Q00tY6GQ8d7uGjrjVaNRQjIT7zpTQZ3t1wgDtqYdDoZnzXmRQ8c7iDiNLN0aaOCB9iPnWi+uDkiKey2mjd5tPwkrEO67HjiQnsVs9AY8Uw8BK8cLyLsJJN0aKOEFGRMdxceB/+wb6TIYr3mg1nYs1IM0jcruLqlwxknqmCKSKatD8/USSoo2GzBtBUdEGR4PHRfXDN9HDZ5FeHYf8PX3eUml5h6KDtwO4wEUnx7vx+XMqjLslHI7m2YinOBMkgS05EEpdNtaxp1iu3cXewYZMjNNsvwcrBJmnbQOqPm46vQWZ9Ydc2Wz1MSUEKNx2RhjY0sxzFkxAdXfAlVWGJ2hiy4RYeH2iJe5MGY4NbHk/k/Qw6bQeiO7dy0nfnsf9SxKc6koUMIddWYOmZIMhkyUhMKxSYZi0U3NmwCY6a7bFk5WCYpBUK/G864jWZoa9dW24LZMaVSVqhQDtroSApOjrX5wmU6PQb6t5ehfWP8ugeIvHCviUnoNe7D8xz3a+F0C1nihKJPnjjl/1Y9AFO86fgca9T2No9rUsOfbyD5VN3wSftQISoF+exbeE0rLkWIU/IShqGN95RUDEqlzHCFMMwv7asRy6myCXcXow2ta3QeeYpvJGf94QqUNNrhC6dsv50/eOR4tPwuFwwIZNHLKlISEx/2LkMkWFhaUFuXj7cxGmHEqhZK7dxDtXRaPyfaFNchGJNWqNZlg7X3An74mS06r0bwh6zsXjeLNg1L81/lka3ZTsuUPSGh0e2YEIaiJMTxuNpl5UY3WwIVsxpgeLyWs4F4v8unIoD8jHThHqorO+F45dDoaP4MDsJ3r70QrJJW3Sy4n+Kz0EI/Z7b8djlPDZP7YU6pfLKl7/07ic1u/ZCFb7lEut0Ew9Ta6BDZ3O+jsTgnrw/eR0b1H+zHevOBaWVu9QbDx6HQq1RS1i9PYb9jhGftofsPW7dfAJZrW7onT5hrgHgfNcPWg2bo9KD7dh1OyYtnQvk7jk+QLxpfTTiz/7y4SWnTrmO6mtOYm0nAz5I9MFNhzdQb2iDKo/24PTLT+GWxP8JDhy9wj9iXAs1a1SCWKsMjEoWUMsFKlCRdzOSkyTiw9sz2HiQW1cuQk5MSsqIk1OS0370J+7/yXwiJSchiYQw6T8dwyxUFV0Ekp4uR/f247Dcfic2LbRD26GXoVND/sAeZaeX9reyindYgk12lhAHH8XE4evxIMsj0WWIeXkaC+1m40mD0ehuJISwZDPY1FRDangIwtI3liwMl2dOwKkyc3DiwChU5jeX7P0trm7IULFzT+Ros+pWQ7VyIm59uQYcP0/pBw8cmzcV9o9juTkrQwi9RpNw7MRw+KzZCNeiekJN8Q5YsskOluJgHJ04HOsfZH0mgSzmJU5z22b2kwYY3d0IQmFJNLOpCbXUcIR8KhSEXZ6JCafKYM6JAxj1qVBw65YrZBU7o2fOQkG1auUgSnqKWw78PKUf4HFsHqbaP0ZsLoUiNOyLVUvMcLRXa4zZ9A9cvMMQL5UhKcofbjf3Ykandlgm/RMbxqXvi7kQctuVO2ZkHU5cisCTEzD+aRes5BrFQ1bMQYu0AxGi/l2IqQfkQ1py5V9ZH17HLyNUJ8cNGhxumhKCQN7xnWGY/wb+ijnzlcQ7z6V6JVRIIBBS8Uq21G/sVFpxzlv5kRqUJN+URbc5k8l1V3+qqSNMm65Ahcp1Xk8Htg6jerp8mnxkkzJNafj8E/Qyx6+xEvK7sZkmtTImsagU2UzaSGefRfM/yWcWTw4Ta1FH+0xdJuSiztBgk8o0+vRL8vHxIR/f0Jwjl0jf06lBxmRsdzHjM2noFZrf3py0hOpUuft8Ovfai85Ps6aS/Oga3NmTdK3mkXO8hF4vb0Ll7S4pRuDIQeJJK5vqUpPlHkXaxSg3Mcd+oxLi2jQ/o59HIl0eaURiiymU0UNEvjxN1EhUugENXePw6RHu8lFr6qmS2LgZ2W28nWkYPc7Hk9RfX0y157t+6jqU6krz64hJpbQVDd74MG2ISrnkuzS1sgqpVviNNv7rTFcPLKNxA0fRyhvvsnY7SrpMI41EJDZqTuP3Pc8yTFvihZFUqZQldVl4nK5f3kV/tGxEg3a+KLhLhzSADg8olzYsIbePFKtuR8d9JZT6fA8NqJE+/CZIZNyJ7J+60vbOJsQFR4o0gXYNGrzfXTGZpFeH6femZUmN7yYAgSoZ1rejXc/S1rKw0ysUaSg5rh1CDcuqk7iEBVl3+Y0G9O1GbVu1oe6j19LNoKw7SPwzexpQuxq1m7aV9u/dSHNH9qMRSy6ST7bCipF3TRJXpAm5du2S0ruzf1AdXRXSNKpLNjbW1Lz7FDrgpsyISDklvrpDD/MbJafQpBTquJaGNCxL6uISZGHdhX4b0Je6tW1FbbqPprU3g7LWrfhnZD+gNlVrN4227t9LG+eOpH4jltDFnIVCffXEVHGCQ651S/ruLP1RR5dUNI2oro0NWTfvTlMOuGXr5pKdhELv7qApvZpS5TLFSVNdjTR0DMmsXmcat+46+RVwsM4xJCJXH67Mb0/mWkJSr9yd5p97TV7np5F1yfRjJ0ioa0XznONJ8no5NSlvR5dyOxDJh0RsyIZEZJj/EoH8H+4gwXxN0jgEe79DUjEjmJbWUfLmp8IRCNKuNv5sm1MW6Qt/YTlUyHQ3YPLNP1BlyEu0G2mDUtxqCTRqoM/UXqiareCkPnvQp9sldLt2EkOz3LVYAFkw7Ds0gMNwD5zol/MKVfztqWg+RQ2bHZehSZ5DpnxbKZH+CEEZlNPPekU+JdwL3smlUdlEO+1qdgFSIv3wTmKI8qU0M/JLX3HrWWs5Sm33xO7mkQhXNUElU91cn7KYEPwaAQJTWJbJMnQLpGFBCNc3gt5HH3gGpMCwkgVKaym7TVIQ+fopXn0siap1zaH32TuIBFHez+ARmAC10pVR07IUFL1uvhVZLALc3eAVngINA1OYVTKDYdZiykSCD0FeCIwrhnJmZaHzmessiwvGa59oaBqbwVRPXak68G3JEBvgDjevcKRoGMDUrBLM8i4USD4EwSswDsXKmaHs5xcKgl/7IFrTGGamelD/yoWS7DAelu2vo+etF1jbtDC/mMkQbN8BDRyGw+NEvxyjQ0H6Gsub1sRykwMIPdkPeZcawzC/ChaU/yJ+1qA8N1KfTWjX8h5GPT2Gvnp8Yq5kiHacj9Gn6mLjpp5ZRxPJT9wJ9LPchxbXN6OtYQVU/DR8A2RR1zGt3y5YbjqI0Zb5n2Dj3c5gz62A/Lvw8IQGDdG/f2MY/HBRkwyhOzrCbGIsFng4Y0buHWcZhsmDxHUe6jXag3pnfbCrY25d9fIShxP9LLGvxXVsbmsIs4r6fDov5SFm1m6Gw00vwPvv9ijMlBmG+Tn9eBdWmP88UcUx2LqiLM5OnoWNe/Zh96VXyP0B6EKUaLEIGzr7YPPay/DPPVMO0oh3COWm6Pc0DFqZrtDLH9W+cZEDrDbtLzAgl5PEBMHH2xveSrx8AqNQVF12i1Y8bjs+RqqxFRqyu8kYptBUqnVDz9rxuLbnKLwLs5NLI/AulDuO+D1FmFb6cxzSSRF8dRfOBVRAj96NWUDOMP8R7Er5L+JXulKeQRqPUL9QCMqYoVQBv92mBHrAR6sKLPWUaWemICY6BcVKZO3ykeDtgeDS1WD+g3RZ+epkIbizawnmLj0OD3EjjJo7A6MHNUf5r9G/imF+ZbFvcPPCLXir1kP3XvWg7POyUmKikVKsBLI+A02GDx6Xcco5EqWbdUGH6nrs6hnD/EewoPwX8UsG5QzDMAzDMP8RrAHOMAzDMAzDMN8ZC8oZhmEYhmEY5jtj3Vd+EendV5TBNjnDMAzDMMyPhV0pZxiGYRiGYZjvjF0pZxiGYRiGYZjvjF0pZxiGYRiGYZjvjAXlDMMwDMMwDPOdsaCcYRiGYRiGYb4zFpQzDMMwDMMwzHfGgnKGYRiGYRiG+c5YUM4wDMMwDMMw3xkLyhmGYRiGYRjmO2NBOcMwDMMwDMN8Zywo/+nIIJXK+L9/FDJEv3bEmQN7cfjCffjF88mfJRZeTzwQIeXf5knZfAzDMAzDMD8+FpT/FKQIdLDH9D6NUKlUGfTaH8mn/wBkobg6qxXazX8MjVoNYZF8EWNtumD1gw98hsJJcF6AztZ9sMYlhU/JnbL5GIZhGIZhfgYsKP8piGBiOwbTWhZDYIwFrFvo8enfmxS+u0diyCEjzNg+DR1qVUX9Pv+D/ZAELO0/Bf9EFvKKviwYx9buR0CNARhUT5VPzIWy+b6A1O8EpvXqgs6dOqET9+rcbRi2PGYNgMxkgZewaORYbLgTjYK3dAxuLu2Prp07o0ufufgn9Ef7tYdhGIZhvi8WlP80kvDo0QtQZRvYmor4tO8s+QG2rPkX1KIH2me0E0Qo16s76oYcwaq9XlzYrryUJ1ux8ZoKukwZjWoqfGIulM33JQTFyqFm/fqoL39Z6SDg2jFcdUvgP/0Gkl2xe9ZyXAr6cYPX1Jf/YNf+vThw3U+JoFwdZarWg5VpAh5fOIu7/hI+nWEYhmEYORaUf2OyD95wPr0Xe6/7FCpgRcpTONyNRGlrW1T/SoFoYaU8PYdLPoBZtWrQ4NPkhCWro2qZVDz65xIClY0pZRE4u3Y3PC3sMLWnYd4VU9l8X0io1xBDZs3HgoULsXBWD1iqCvhPvg1ZqBMObtyCs26pfMqPR63dVjz38YHTgjoouEqqo2qPqZhj1wQG7KjDMAzDMDmw0+O3kvAKp+b3gXWnebin1hg9WlZEYa53S9864La/DprYNoA4yguPHz3Ew4fc65EbAuL4TEqRIerWYsw5FKjE1c38yBD51BW+UiH0DUtnXReRAQz1hZC4P8HTJD6tAJKXO7HuQgraTPoD+fVIUTafMqQxnnA8uRfbNm7Alt3HcfNldOEaSrIYeN27gWvXrsP5ZUTad7m0NxlpkTnLODkUz68ew+6tG7Fhy15cePQcDsfP4wW/DaURHnC+fg3X7rxFjCwZIc+u4+rVq9zrGq7ffoWo3DZaSiQ8717A/u322H/hEYKylHksfB7cxDVuGteuOeB5iLwLjgxxQW5wPHsER/9xQXAhe+XE+7vg1jX5Ml2Hy8s38AnLbwISRHs/xOVTp3DVJQCxxCczDMMwDJMVMV9ftBMtsC5J4gpD6VSQlE8sDCm929yKNLTa044Q7vux7+jOAmsqadmTFh17QAHxfLYCSSjo6l/Uqccqck3ikz5bKj2aXZ3EAg3qcTDbAkje0orGYoJqS9qs1PpG04XhxiSuNJEc810XZfMVJJ5e7BlGNXVVSKOUBdVp0IBqm+qQULUyTXXOpWDij1EfHXXqtDuaT+C9P0qDKxqQrqYKiWvMocep3JYKPUgDKpQkXQ0urWZaWrrUV7upbyVNUtUtR9XrNaQGtc1IT1VAULGgybeTuRwSerWhPRmX1Cc9XS2ubEWkXkyP9PTkL30qadqDdvplLc/YZ/bUv0oxEmuXpSq1a5BpcTFpWw6h/Z78jGPP0ahKhqSvp0taYhEZD99KO+zqkr5YjUoYlaHiIjFZTHZOy6uURLoyoSqV0pcvk3yaKlRu3A3KtTrFPKKNvS1IW6jKzcuY9DU0ybSWJTfvqjTzgXx9GYZhGIZJx4Lyr036ni7YVSAVoQH12B/EhdefI5oO99Il1cYryFPCxVmuO+nPSfbkEsN/rIxYDzo+ozVVa/wnXQz+vKXIKpnuTrUgFYEW9TycPSj3ptVN5UF5c1rrW/C8JG/XUwttHWq50ZsLS/OmbL6CxFwfTxZqpanNUicK5WPX1MdrqGuTbrQ2cxSdLq+gXCGVns2vQ+pVZ9KnODOVHs+pkS0tiW7+UYF0bVeSWxyfxNWGyAsjqJzYnCY5ZQ1SpX7rqLl6WbK7lE/rKeoSjTZTo1Jtl9OdsLQSkYTdoGlW2qTbZiv5ZC4kqR+ta65KIrGYdKoPo79d3pNE8o6uLJ9Mi//x5zMVktSf1rdQJ6NRV3IG5dJwOjPUlMRl2tBSx1DF9pIEX6cZDXRIoMKCcoZhGIbJjnVf+cpkAUew8ZgfpOrlUNx9Fto0aYUBCy7AtzBdBpLuw+FBAsybW0N4aQnmXTHGtLVjUa84/3k+pNEe+GfDBLSt1QB250pg4MSWoCeXcfHiRaVfl649Q87BMoTQ0tKCAFJIJdn7JKQiVd6XQ6ABba2Cqlg8nDZswV293zBlaH5depTNVwBZCE6sP4CwFguwa1ZzlOI7Q6tYTcX5u+cwxeprddgXQb+kHlJ9b+PUOUe8CIrlSk4IvbZTsX7tQgysWdi+ODIEHlmPw+9bY/HeGWhqmFYiIsNWmD+zC0S3T+JCLjeJylQbYu7xvzGynj5EIiO0n7UOczub8p8WHan/IWw8EY/2S3Zjlk0pxfYSlWmNBfP6oMy37Z7PMAzDMD8FFpR/ZYkPnOGSQFCp3Qdzl23EH+avcWLxYIzeqfyNninPHXE3XA1qrnPQYsgtlO/XFkbKRKWyaLgc24xN9ofg4JsASn0H54PbYW9vX7jXnuvwyTFYhgqMTUpz/xKSkrJ1HKckJCXLb5Y0hpEOn5YHWcBhrDkcAquxk9E+n0aGsvkKlPocLs+TUaWFLYy+ae1XQa1Zh7B3oCZuzO8OK5MSKGZYCQ16bcRro9qw1OWzKS0FL1w9kCR5hvWdrFCnTp2MV/N5DkiQhuDdu5wjnKg3HYghll//TuHU567wkNVC2w5GWQ4yqmXKQP+zW1QMwzAM8+tiQflXlpqQiGQCBOqa0BAWR9PG1SCiWNy7fhuxfJ78SeHr4Axv1SpoO7YH6gnvYs9uVy4kU4KwBBqN247rr3zgsm8i6uE9VFsvwJFLl3CpEK+Lx6ejSS4XcovXrgtzFRnehwQjS/jHBYQh4TKoVuOCxHwvACfj0dZNuKneHVNGc+XCp+akbD4lCDSgoc5NMTGJa058W3EB4dDvtx23vSMRHfwCN/bNR1+LEOwf3hzdNrzMWoa5SIkKREhc+tVvATQ1NbhNbIV+EyZh0qRMr1krsJ1rfI2tnb3wBShubAzdb7HXi1UhRiqSk7KWsiw+Dom53azKMAzDMP9xLCj/yjRMjGHARZEUH4uPJETxUgZQ9OiQSZUb/UQWASenFxDW749xXe0wpndpvNq3FVdi+I+VmQgXnNcesgG3Hh9Ay3uTMHiDG5QcFCVfKjW7opMF4O32PEsDQ/rODR4R6mjStRNK51PDZGEnsWaPF6qOnIJu+nxiLpTNpxTVumjVrCRentyLO1keOhqHh9v+xPzTfvx75Sl6Y1Dm4FOCiPDsD9RJwo0lvdCh73I8SBJBq3QVNO44GFPXncHJKRXw8Ow1ZOltIhByO6eM2778dGWh2N2P+84MB66JIqeGeu1tUSr8JQIMOmDIsGEYlv7qXhFRd2/jVUz2yiGAkJvut+g9olrPGg00nuLU0VeZGhvJcL16C/5KVXyGYRiG+W9hQflXpta4OzoaiyAL9IaPPJpSVYOaoBisO7aEUr0w4m/D0SUVVW1awUikjTbjR6NO9Cms3uGOBM/DsL8QolxwzxHqNcKkYycw3GcNNroWchy83KjWw4S/ekDL+RhOZURaKXA/dBLuFUdizrDy+VQwCV5s34BL1AGTfq+DvC+oK5tPWTroNHch2sdsQ592v2P79efwC/HFvb//wPDpB/E0Kn2J4xDk8QyuT57gyfMAfOBaP7GBL/CEe+/q6o6AD+nrK4RBGUOIgl1w/ZEfwsO8cGfXBMw+FgRZcgS83DwRyreAJBIJUl/ZY8zYTbj40Auh4YFwv7Edq457olS1GlnG7xaWNENFvUjcPXsO7sHBeHlhNfY+FMG8asWMMcG1283Dyl6pODikHYatOQ8X7xAEv7qGNSOGYOYpd8QqppeMCG83uD59iZB4GVKj/fBMvg7PXyP0M5+FFB/8Cs9cuXKRTzNOBkn6NN18EMVXK2Gp3zBrYhW4r+iHkVtvwsPbAzc2jcDQDe7y6+eI8HKDZ3rBMAzDMAzDhkT8+qQU9u8camFUlmz/Okg7R7cg2ylnyTeXQT5yk3Tj92yjc8SS88xapKVWiuoM30tvlZxOFomv6M7DkM8cCSa7GHq4eRC1aGNHy3btp+3z+lGL1n/Q4dcFjLkYdYYGlxVT5cnOlMgn5UrZfIUU536YJneoQUY6YhJAQKr61anH/65T+sA0qc/+R/U1hNxnil4uWV4CgRpVm3k3LaNczG1a3sWCiosEBIGQNMvZUN+OVUlTKCCBSJc67QziMiXRJTtjKtd6OPVvZETqAvm0BKSibUqNh22hhzkGdpGQz8mJ1MxITbF8Iu0K1HLyMfLMPmhJsi/9s7APNaigR2qK+WmSceMRtI2boGJVYk/QgJIqOdZDINShNpv9Cl8HJJ60xkaLhIrlzzZNldI07GymkXjko7ss6E61SmuSSCAg9bJNaOTCsdRQiytXgYh0O+2kzxohlGEYhmF+QQL5P9wJlfnaUqLh7xMJVZOKKFPgiCQFSUKIdxg0ypeD7o9y01xyNAJ8gpBYvBzMyuoU8IRHKbzXt0KtRapY+ewK/iif10oom+8LSOMQEZECLQM9aH7h5CVxEXifqAkDA63c+70nxSJWqAMdVSniI0IRlaoOPUN9aOVXWLJkxLz/CBU9A2gXcH9mSkwoogV6KFX8y39PKFopiImIg6rel5cxwzAMw/yqWFDOfAcyRDhtxRZva8wcUQeafGpOyuZjGIZhGIb5ubGgnGEYhmEYhmG+sy/tR8EwDMMwDMMwzBdiQTnDMAzDMAzDfGcsKGcYhmEYhmGY74wF5QzDMAzDMAzznbGgnGEYhmEYhmG+MxaUMwzDMAzDMMx3xoJyhmEYhmEYhvnOWFDOMAzDMAzDMN8Ze3jQTyMFQXdP4ZSDB4ITtVGpeS/0bWcBHf7Tn4YsEp6PPPFemlbtBKqlUcXKDCV+teZhvD8e3LoNN59gRCarw9CsDpq1bAzTiJfwM6oJSy0+33+RLBwvbt2Hu48nnj+NRu2ZS9Cvwpc9f18W/gK37rvDx/M5nkbXxswl/fCFkywcmRQyoSjfqxzJgQ9x1fEJXr3yQJDpCKwdawVV/rP/gqRgD7yI0UPNqmWgxqflTYJQDxd4x8j494CwhDkaVC2Fb7lZGYZhviV2pfxnIAvDpUkNUbX5IEzdE4b6zWKwsYsVWsy7hyQ+y0+DEhBybSF6NLeGtbU1mnVfjvup/Ge/gORAR2wabQvLugNh/zgeJSo3hk0jc6i92YMBlYujZKsNePOfjypUoVMsHOcX/4XVZ4OgblgEBaKqg2Lh57H4r9U4G8Q1gr5hGUt9/0Z3Y31UHvcPn5I7kZYhSkdcxMqVu+GRaggVPj1D/CucXLUR10M+BaKfI95lL1YcdMUH/v03JwnA1S1LsMZ+Hw4c2AP71auw9/YtrOhug0nn3isZVMuQFO2DWyv7w4Y7TsiPFbbT/sFH/lOGYZhfEQvKfwISd3vMt3+OjzKCSuX6qOL1GG9S4/Diyg14SfhMPwuRCZoNaYfKv1xgKoH/hWmwrdMOq9+1x647zti/aAz6tG+GJs07YtDs3bi0viN0K9eB1X/p8mhuhLooX7UEhAkE7cat0KwIfjUQ6pZH1RJCJJA2Grdqhm/5Q4RA0xAVzMxRuYIhn5I7Fb1yqKADpIgs0dS6TJaDryzqNpbYrUF0lzFoU+bLDsta9YfCrvQZTPzrCsK+LL4vPFkwTo21wzmTUZgybhiGDBmBcVMHQra6D5Z/GIilE2vkbIzkShXlrQdi6sDG0BLwSQzDML84FpT/BFLeeMJHwvcyEqvBqONY/DlgIKbMHAhL5c5wPxSBihjiX+pEK0PQ6dFo02c9XldbiDMnZ8LaIPuuJYRB8xboVL8ucnz0H5T88BbufRDDytYWJfi0Qom+humT9yM8I+hMxsNb9/BBbAVb28+aYq6ir03H5P3h3BbOm7BUN6y//RgXZzTkU/KShAd3nyLFsAGaVsm040peY+fwmfDrvwwjq6jziV+Cq2tt5mKi2kaMWOeKBD71W4i9thBz3rTBlM6lMk4ussgHcHz6QVGGgkLWfaEo/y5BDMMwvxJ2vPvaZNHwvHsLN2/ezPpyeI4QKZ+nIFIpMmcVVeyDlYcPYUUfs09XnaRxCPF6iZfeoYhTarrJiAgMQVy2aEMaGwKvl54IjEnhU3IhiUWoz0t4ePohNDaffNJ4hHnzy1TQFf2kCPi89kZovLKF8jXIEOt7H+f3b8O6FUuxdMkSLOFeyw88QAyfIzfS15sxZOR+eGvYYN6O6aifx2VaYZkWGNWv9n+qH3HuUvDsuhOChNXRolXZQh+EZNEPsWbAKNwzaAj99C+nPMN1pyAIq7dAq7JFcViTIfrhGgwYdQ8GDfWL5kCZ8gL3XKKgWd8aDTNibyne2E/AkoT+mJspkP1yarAaPwzaG0ZhhUsyn/a1RePinnNQb94SFTN+CUvAvZULcLVSf3RJPYU9/8bx6flIiUHgG+5YkCDLtzGURoq4EC+8fMnlV+7AxzAM88MqunMAk7uEq5jTsTVat876aj+WC+IKPIfIEHFmKrovu41EPkXycC26tu6BZbf5YFgSiGtL+qCucTX0X38Gp1b0hKVpPfRbfgPB/PSlPscwur4xSurrQ1/fEPVm/4Nzv9dFufJmaPG/J4o8snAnrOlfD8YW3bD0+BHMblkR1Xv8D9cy92/98AQ7x7VB/dZjsOHICawdVBdGegaw6LgA10Iz5ZNF4M6GIWhQri6GbjuOtf2qwNCkHgZtcUEsnyUDpcDv2DjUNzaCeRVzlC1VFX13vUSuMbwsFBcm1EPZkvL1kL8MYN5mDi69voutAxvA2MAYdbrPximvwp+cpcE3saxPbVRpOQm7brxAYFQc4uLjES9/JaaC8ooOZCE4OncZHGOEMBu2EOPy++lCpRrq1coWsSf54MqGWZg8cwEWzJmIoX1HYdV1f67JxIt3x+kVUzGyT3s0bTwRF+KT4HFoEto17I9dfp/WUxb1GAcXTcaUObMxxLYBOs29gsybpGBxeLZ3IvoN+AOz50zD72PGYujojXgmkSHS5TBWzP4dg7o0R9OJ57mc6VLweMNAdOy9AneyxX3J/jexZdZETJ23EPMnDcHgBZcRnL48Ui/cuPUWqGCDVhZceSV54dwSO7S3skS9kUcQkGdZh+Pujsno2qgVZlyLxnuHhRix4paiTkm9biBtkq2QNslzWGLXHlaW9TDySECO4C4l0AGbpv2BqXNmYtz4ddi5rB86j9mHN1yRysLvYsfkrmjUagauRb+Hw8IRWHErR83l9ofnOLn6L/wxuAuaN52I89njTa5B/vzYQkz4YwbmzZuCcVNW4tpbAWo2tUYxPgvib2Ddugeo0r0nTPlAVhZ6ARPqleX3V+5lYI42cy7h9d2tGNjAGAbGddB99ikUWM31O6CntT/sV51GJJ/0VSU/guP9eJiaVcq4WCB9tQ1z9qlizIplaGseg/tOrlytyQtXB3cOQz1jQ5SrXAkmZetg9PnALBckPpEg8NoS9KlrjGr91+PMqRXoaWmKev2W40b6gY9hGOZnIx99hfl6Up8voAalLKiBjS21bNmSWraoQaVUVMhi6l1K5vPkJ/W9N7msbU9aAsj7r5Cq7VJ68Nyd/GOkRNIQOjfSgtQEAlJttoa8JdwXJO70v3pigkCdLMf+Q2FcNrmo3Z1IXTENERlbNyMzsYD7W0C6fU8QxT+g+Q20SQAVspx+X7Fc0fu7kTY3Xa2Gi8lVsaAf6aKdCYkE3LL/6UTxXEriP8OptDBtmuXGXadEeTbuk0f/a0zFhAJSs1lHPhIp+a5tTqrcsgvUW9B6HwlJAzaSrWra+gjERtRq5n66eHgMVVMsE0hYegT9kzaxnOLv05y6Gtyyyr8vJIMe+ylIKiGfrR2pZr+D5JPK5ysEach5GlPDnNovvkHvlNkomUj9NlFLTW65xXVo/vPCzTzx1X4aXsuCOm94ypVumuhzw8hI3YJ+vxrNp3DeH6AeukJSbbyMHI+OJdvKpUmsZUsbfeUbnNsyLuupW7XGNPXfcJJyW89xQkVS0elNR+MUHysl8eZ4qqhmQVPuyAtASuFnh5FFxx0UnvYxRR3sQcWF6tRyUwD3KS/VlebVEpNGq80UkJEooYDzk6ihRWtadi+Ky5tKrgusSF3ckJa9SlteqT+3/dVFZGR3iWJ9T9PcUVNo/alj9GcdMYkqTiCHJEW2PETTkd66JDafRM4Z20pK/httSV1kRHaXYsn39FwaNWU9nTr2J9URi6jiBAfKPMn4Z1u48mpOc50jFesSc6wf6YtEVGb4BUW9Vog+Qr11xWQ+yTn//TSK2zbFhKTZZiu9yygDTtIr2je4JtUcfJDeKupyEl0dbUwiFUuafv/TFOPOD6My4io0I1OaXPz9OVRXI21/gNCAeuwPIqnEh7Z2rEn9DvpwpaoMft/T6Up70zfkVyQN3kKt1ErQgFP8zisNpkO9y1IFu4sUxZXs8d+Kk0aXvdwWzI2U3p8bQeVV5OssIpN+h8kvMZQu2JkTF+ArykGtw9/cdNLyhpwbSRZqAhKoNqM1aQc+cv9fPRJzxzR1y7H0T/qBj2EY5ifCgvKvLMnBnuxdPkWYyXemcieTrCfmgsQf60M6fFCuxp3U0gO4pNtTyEIRyHIBzqgrfOCRSOeGGJCQyyvgTvbT76XNJ/M0VMxH0HHHvTShZ39afCOUgv7uRMXlnwm0qPv+GEX+pKujyVgkDwjKkt1FbvklL2lpAy7Yl3/fchrJJ5vs/CdVUkmbprjpKvLizo3SdzuoQ/G0ZTLlAnX5MiXdmUXV1QUkMuxJ+4OkWYJyoeEwOi8vnsQLNMxQqEiDqi1tyhLhZBXrPI2qq6YFLAKVSjTGfjl1az2bnNIWvZBi6eoYC6oz4w73V+GF/92RNLmyUzGbSE7Kb1IuMD1GA8urkenQMxkNJzlp0GZqqSokvQGnPgWI4X9TR00x1Ro1h8ZPOkK+iWH09m2YIjCT+OylHka61GLtKy4s4cTfo5k1VKl4y43kmRYDKyWSa7RpCLSpxbrXadNJuk7LlzvydSqZHsysSipiK/qf+6dwMPnhLKqmIqYGS/l5cz7emUNWxcpQr4PvFAGvPICKerCblq6/RN78V8P3dCUdoS712nSM5k3YQA/l2y31Ec2uJiatTrsyGgK5SvqXxpiokMGgM/SpzRFOe7rqkFC3F206No8mbHhIaZOczTX0tKjTrk9TlL6/Qr9blqCmK9wzAtvIvV1Ii1uenofSQj65pH/HkImKAQ06k3/LJvneNKqsokbN1/pklAFJw+ni2MpUrMZ0up3x9Tg6PVCfVEoNpXMZGzaVHs2uRmLV1rQtNHt9jyXnadVJVbHPCkil0hiyX96NWs92UqybshLPDCJ9FSMaeSWPlk7iPzSynA5paWkp/Spedw49zKWuS14to4ZqxjT6atq8Pt74gyxKdaFd/vJ1S6R/hpcidduNmRpwmUi8aZ0N39jmGlej+OXNcuxLD8qTbtMUC7Eir8hoFKWvWuK5IWQgv0ggEFOV6feUuujBMAzzI2HdV74ytRZjMbZeegfSZNw/cQ5+Zt3Qq+6X9ixOwbN/rsAnVX6+4uajnj4PAdTV1bh/uTNWqheuXHRLS85EVKUZOtgMw6bTRzC3lQ7u3ryLj4rJCBHuchDbt2/HrutvkShPk0XA7Zkv9yUL9JvyOzq26IjRs4ejLoXhxXN/xKXNnlucZEWXiw83r+COYmICqKqlLYda0+VweReIIO8TGJK9v6+KCCryTAIVqGTc/UnybvR50m42F+tHV1LcLEoSL+ycdAq1Vi9E8+J8hnSxHrjl+IZ/k4ckJ5z81xTDxzeGNp+kPAn8/QIh3wRCUzNUyKfnShbyLi/T/sSx8EaYNLcrDDMViSwyEh+4xnJqUnJGl4v4u7fxOLUkhMEStJnXF+XVDWFubggVWTCOTJuBi1pDsHCsJSQBt7Bq4DAcLzkZh/b9AYtCjHCj26432uknwHmRHVY8SeA2WmvMmmWTNp601BcOTmndTVpyrbA0H/DvjsN4DQu0bF0xbZg7iRs2TlkHt/IjMOs3I75vnBAlGo7AX392REXFV2PhdOMe4lWNEf3oGerPmYAG3HaT+jvC+a0AtZrbQE/xvdxJ3J1wL1QN9W2aQoNPQ6wTbtyLh6pxNB49q485ExqgOKTwd3TGW0EtNLdJn2I87i6bgt0fu2L62Gp8F4s43HZ0QZJaPbRqkV6BJHB3uodQtfqwaZoxl1xI8Oa6A3y4ebTtYJox1F/Mlb8wfnc0Ovw1HU3SeyslP4Hzww9Qs7JG44z+5BK8CwyFVKQNbe3sh2JtNJu7HqMribl9iCDx2olJp2ph9cLm3LplFetxC45v4vl3WQmKFYM2vUdgQB59udVtseTWUzx9qvzr8ZnJqJXb4UtNFapcuUvlzyBI4erCnGPQm7AEQ03T1i2Zq9MQq0It+6rKxd3D7adJiugbQiOUK5e2dYQilRxDKKY8+wdXfFLT8qqpI+PIxx0D1dIOfPC6chFuP9vIVAzD/Ofldnhkvpak2zh+PhAW3XoVwbB4MoSERGTqKytQBMBywvQ/uBNkeHAI/3de4hEVlZh2guOCBIG6PkqWLIlSjX7H9uMncfLkMSzvUZb7TISKfTfg/JG5qPdyHprW+w2bn0Zn66srRei7YCQpJiZDVMT7jM/V9Y1QSrsQUWK+iqPlorUYWl5FEbBQ6ktcPuf2qR82L+X5Yfw19zD/Lg8JwQgKccKMKlzwoi0PjnK+SrTZiEzdt7NQ44IMBSHXuEj7q0DSN/uw5XwYNGwGoP+nO+I4MkQ+fARPiRjV6tXlg85kuNy8jUhpIgw7jkGnjDsb5dM5gG0XI6GpE4ELc//AhFVO0B9/CW63VqCzSeHKWmg0GBvX90QZLjhaMmIhnDN1oZaFO8LRTQaDpraow6+u9PV+bDwXDJRtjlZ8hJbCNegOPZWgcqduqJ1X/U66h+vO0SBpAlefpqFTKfn6yPD+lgOeURW0bFM+n3GsZXjnfAeeqIFmzT/dfJl07zqco7mGXEJF9J3WCWmTfI9bDs9AVVqiTfm0KcpCT2Hd3rco1WUg2qVHttFXceJqOIQ1bdGyND9F2Ts43/EEajRD80zlnYM0ENdvuQNV26FjJX6ppZ7YtfII3pXpCbvuBhnLmHD7GC74ClC1SdNPN6cqcDtL2s6XU/GWWLR2KMrLW63yhtrLyzjnlqOW4/nhvzD3MLccuRCocoGyQIb42I959M3WQqmK5rCwsFD6ZV5OP9eH/4hKloGheiLi4iUIODAXW6L7Y+nEmvx+kYyPscnQKV3mU3/6TGQx7xGVnF4QXOCez9OFZCEhiMh04BHkPPBBGh6MEBaUMwzzk8nnjMMUtXiH4/gnxBLdetVROoDLmxAGBnqZNiAXnPJ/yTJO8kLoGRjwf+dFC/r6GmnTIS4oL90IPXr3Ru+MV0+0qiqPYGSIcFiA1rWbw27dW7T++wp2DK+NYp/OgxwBihfT5s+NMsS6PYH7VzoxCrVMULGiHtTl86JEPFk/FZtfZp2ZqvUyPLiziH+XB82yMCpjg9WvPiIuLi7XV/T1SeDjumxUUL6qBYpxhSeVj3yj5EOQPt6/h+epKqje3CbLVXJ5MHjm5G0k6nfA6EEW/NXnl7jp/A4o0wN/DK6QJWCNvX+Xm44qGo/agNXrtmLnlkWwa2X+mU95FcF0wDbssKsE6YvN+MveMyOIi3W6iUdJ2mjUsik05QlSPxw/9BQCbsX1mrVGIz6AinnuBl+pGJWqVsmzfqc8vQGnUBl0Wk7D3I7pgXU8nG89QopJc7Sult+eEYPbt11BZs1gk7FBUvD0hhNCZTpoOW0uOqZHvPHOuPUoBSbNWyNtklzgf+kkbnwoDus2Tfirq1J4HduLG5EiVGrR6tOIITG3cduVYNbMJo/tnkb2/iZuPJGhYpv2/Dy4KXqfx9mHSdC37YCm6ZdwZUE4u+ciggWmaGxtlmkbCrl9j9uHKQnyi8g5CaFlUhEV9dQVDW5KfIL1UzcjazVXhfWyB7izKPfhGCkpkWski6BTvFgejZ1kfAgPQ2hoqNKvsMg4rvmeC83aqGORisAXB7Fo+RM0XzAHLdIro5T77nuuztfNffQhobYWNDOC6lSk5jOok5A7pull2m8ynkn96cAHoZ4BG3qUYZifDjtsfTNxuHniMsKrdEPvWl8Qkmecd1RRp31LGPFn2pSU9IiQkJySkpZNZIJW7WsrUvOmjobNGkBTcT6U4PHRfXDN9JjQ5FeHYf9PKPeHE/5ntxRO4VzgrlcPTWtrQiDLPmSZEIbNrLkAJe3kmvryKP6+nttggp9Onoo/M71NkyMhmw9wmj8Fj3udwtbuhopKTB/vYPnUXfBRRJIyRL04j20Lp2HNtQh5Qt7UbdCnbQB2b76XaVQR5em0HYjuJiJI353H/kuZf7nITIaQayuw9EwQ95cMyYlJ3L+qKGtqkiVQiruzAZscNdF+yUoMNknbNaX+N+H4mgsQ+9qhbbZoO4XbzjJuChramll35KRoRPODU0u5PPn0BOI2uTs2rT6Ttu5CA3T430y000mBx7MXXGgkl4wHDvfxUbUubG3kjbN4PN6xGzFVSyMiSB2NW9tAUxIC/+Ak+f0pXPAoRVJitggzJQJh0fKSkeDNDQf4yAzRZdQAlEtf6OSHuHXvI/Sbt0E9eo2HrlH8B9kk3YfTwwSUbNwctVQlkMgjQ8kb3HDwgcywC0YNKJdRDopx0D/qo3mbeqDXD+EalQzXR8+RKKqAKvxY4LKg8zjukgyxoDSataoFSVy8oqyS7jvhYUJJNG5eC6rcTPJqV8Y6Xsf9JCO07mAF1bgA+EfIkOrxAp4SEcyrVeOvJssQdGorroWrQVasPqzrqiIpJBiRioqiiuq1q0JdFoGQ4Fzm8sEJ86c8Ri/u+90VrTfCxzvLMXWXT9o2lUXhxfltWDhtDa5lvnScSer79/ggLIdqVfNoqiVdw+RaJjAxUf5VrsUSPMktaBZZoFvXani5cSbOmE7F//qW/lQvPzzGk8AG6N7FOGtdTadTDVUzhp8Jwzu+PJJiY3P8AqZapz1afjrw8fWUK53kFKSkHfhg0qp93r/WMAzD/KByPT4yX8GHf3HyyntU794b1QsZk6cmJGaceGSRYQjjoyzNljPxv+5GUBHIEPHWE+/l52XpO7x+Ew2ZQAUmvRZjuk1aAEJccJERnKUkZ3o8vxAm/adjmIWqoitI0tPl6N5+HJbb78SmhVwgOPQydGoYAB/f4C0/pp0s8jaO7TqGv/fcxLv0iSYnKfqgq9QYg6k9ykIkj8ulvthj1wNTtp/BP6e2Y9bAkdj+SgrigtK0Li4cvi86d0ZFcvrP19zfSRkZspMi8OQEjH/aBStHN8OQFXPQori8GnOB+L8LMfWAH5dDCL3K+vA6fhmhOtl74GanjTYLVqHBlaHovfgmgvK5Qper4h2wZJMdLMXBODpxONY/iOSW5BNZzEuc5spx9pMGGM1tKyH3X8lmNqiplorwkLCMbSILu4yZE06hzJwTODCqMn+lWd6t4xZcZRXRuWf9HF0GdLnAr5woCU9vOfBBnhQfPI5h3lR7PI6VIclhKqrrFoPZ0BN5D48oDYD78Uu4xT+TXahTEvoa6rCsURVieYI0GO6vwoBSVVDN4COeHVoLB9NRaB92Hy9RETWrxeHqqu24n6gC/caNUVUlFQ/On4Y/v2KSUAesGL8Ct+RBueydoruHzLA9+mT0H+HyvHLG/RABqtQxw5MtR+CplVsHB3m+h3gcIYB51fLwOrgCf7vEc5O8jlvuMhi27/OpSwoXRr9yvo8QQRXUMXuCLUc8oVVMwNUvrrFKcfgQwwXawTexbsd7VDCIQDjX4GhQ+iq2HHDlGifcdx8+RoTAHFXLe+Hgir/BzSYXyXBxeoCP4hqwqvIG+1adgDdXDQXqalATEKLeRyiC+bgXh7EnrCbMUgIgqNUY9SPOYdMZLwj5I69uy3ZorO4ND49MLWE5aSBOThiPp11WYnSzIVgxpwXSqnkU/l04FQf8uAIW6qGyvheOXw6FjuLD7CR4+9ILySZt0Smv/nLqXbEnhAtsU1OVfiW9WIGGuU5OBZbt28AkSRd9ZoyBZUaLUwq/EycR0ncOhmfprpWJaj30/a0aVBXHjXe4efYuYiT+OH3JNaOJTglx4Kq1/MCHmf/rDiOu8S+LeAvPtAMf3r1+g2iZAComvbB4uk1GX3OGYZifhuJ2T+arizrWl0qqWtFCN+UGM0v38f52GlZPVzGainxzCVTKUNPh8+nES3468R50cLwNmeiUpy7zt9O22W3JSKcc2f55hF7xozxI3p6k8Y30P01Dy5K6zTxGHpkWJenVYfq9aVlS40c6gECVDOvb0a5n/JgkEh/a29skbTQIgZA0TFvRtL07aWzVtBETBJoW1GuHW1re6Pu0vpclFROljZACgRqVrj+ENt59T9LU57RnQI2MIR4hMqZO9k/JdXtnMpGP9qLIr001Bu9Pm1Zm0lC6Mr89mWsJSb1yd5p/7jV5nZ9G1iX5UVu4l1DXiuY5x5Pk9XJqUt6OLn0a+CZfkqDrtKRndTIqX586Dx5Hk2fMolmz0l5ztjtTVG4jRmSQUqjjWhrSsCypi0uQhXUX+m1AX+rWthW16T6a1t4MyjaEXTw9sx9Atau1o2lb99PejXNpZL8RtOSiT5ah+4hi6HhfPRLnNUyg9B2d/aMO6apoklFdG7Kxbk7dpxwgN354nkSnmVRDS0Aik7F0Pbfvy73/m3oalaHKHWfTzsP7afXIVmQzdDs9zRg1JIpODSxDIqE2VWgygJY5hHFrK6HXK5uSukCFDBuM5OoIn1kaRU7/a0VGampkWKMFtW3bgbrbLaOr79LWXhq2izppC0nvt2NZhsVLvj+TqooFpF6xIy25KZ9+7lIf/UU1uHyqxi1o+hlfrkylFLarE2kL9ei3Y1mmSPdnViWxQJ0qdlxCNxXD28iH3LOjiqoCEuuWpwb919H9qES6PtaURCIDajj+GL1VDNeRSo/+qsF9V5WMW0ynM7557a9J9O8YYxIJtKhSp/l0MYAfeyX+Hi1qpEsidSOq13kYTd9+m0KTPWhpQzWublZX7HevMw8LIn1PpwYZk7HdxYzRZKShV2h+e3PSEqpT5e7z6dxrLzo/zZpKKoYflb+EpGs1j5zjue2wvAmVt7vED0eajcSTVjbVpSbLPT6NDPM1ScPoxAAjKttyKI0dt5zOv4yi5MRwen56Mf0+/SC9LGhfjHOhDd0qkobiGKNKehYdacHBedRULH8vJFV9K7I7/JrPHE8eB8eTjYkOle8yn7Zvm01tjXSonO2fdCT9wMcwDPOTEcj/4Q70zE9O+sEfL174IkZQAhVr1oCpTm5XzgoiQZT3M3gEJkCtdGXUtCyV7WpTHAKePUeQihnqVC+d9lmcP548D4OOZW1Y6Ge+fCbDB//ncPP5CNUyVVDb0jDXm8O+DhmC7TuggcNweJzol2O0irxxy+xzH7ecXOET9gFJkrR++iLT1hg3pBF00zLlQ4bYAHe4eYUjRcNA8RAVM0NFL+zcST4gyCsQccXKwaysTp79sPMnQ1zwa/hEa8LYzBR66pm3uwyBm1qjzpW+cL00BnyPmKySIxGZUgI6kkB4BcRCw9gc5fTVs/6ElhIBn3cEo4qZtqE0Gn6+iTCoWBZa2aabEuULz3fJ0CtXEUbFlelDkIxwnyAIjCrCIN9KIs/3DihrBsOCLoMmh8MnSMAts0GmeidDfIgPwlVNUYGvq7LYAHjFlIC5ic6ndZZ/VzEbw/yvtiaEwDtMDSYV9LL2k5Z+QIB3FLTLV4Ae/4Ek0hc+yYYwL6uVtWw5Up896NPtErpdO4mhhXkaqSwY9h0awGG4B070y1nL429PRfMpatjsuOzTKDBfkSz0POZPdUTVxWswoFwc/N2e4LlfEvRrNEFjc90c6507CSLfPIFHqAimteqivIoX7j9JRJnqljDVU8s5Da6s/V+8gG+MACUq1kQN00zbkWEY5ifDgnLmFxTHBSmW2NfiOja3NYRZRX0+/T9GFoRj46bAb+RezKqfT+OA+c5kiHacj9Gn6mLjpp5QOi6PO4F+lvvQ4vpmtDWsgIqZhnWRRV3HtH67YLnpIEZbKtMwYhiGYb43dlGB+fVII/AuFJD4PUWYVgk+8b9FFuqAzbO24KPdDsxgAfkPTogSLRZhQ2cfbF57Gf553VmajTTiHUIhgd/TMGiVyBSQhzth4yIHWG3azwJyhmGYnwi7Us78klJiopFSrARyPJPlvyIuBjHqutD9goF+mG8vJdADPlpVYJl5zL88pSAmOgXFSmhnubqS4O2B4NLVYP4NuqwwDMMwRYcF5QzDMAzDMAzznbHuKwzDMAzDMAzznbGgnGEYhmEYhmG+MxaU/yIEAoHixTAMwzAMw/x8WFDOMAzDMAzDMN8ZC8oZhmEYhmEY5jtjQTnDMAzDMAzDfGcsKGeUI5NCKuP//sHIpFJ8j0VLCbyFQxc9oeSzXhiGYRiGYfLEgnImb9JAONhPR59GlVCqTC/sj+TTfwDSQAfYT++DRpVKoUyv/fimiyYNw71tI9GkTnvMuRQIKZ/MMAzDMAzzuVhQzuRNZALbMdPQslggYiys0UKPT/8BiExsMWZaSxQLjIGFdQt81qLJouH3KhCxSl9mj8ebi5sw/69NuPrKF8ExP+hPBwzDMAzD/HRYUM7kL+kRHr0gVLaxhamIT/tBJD16hBdUGTa2pvisRYs9h4lt5+JWCv++QAT1ar9h7sql+N+SgajLHmHPMAzDMEwRYUH5N5GCkMcXcWT3Duw+eg3ukT9Ph4eUpw64G1ka1rbV8WPFoCl46nAXkaWtYVv9Wy2ZNkwrlIaq/E+x+AcrD4ZhGIZhfmYsKP/aZBG4MqUR6k13R/Ve3WF6fwqsKrXAiifJfIYfmRRvHW7DX6cJbBuIEeX1GI8ePsRD7vXILQBxfC5lyKJuYfGcQwgsqh4f0rdwuO0PnSa2aCCOgtfjR4rlevjwEdwCCrNkDMMwDMMw3x8Lyr8yWegJrLN/jkR9ExjrlkLzrs1R+uNDHDv/6scftUMWCgcnD4gb2KKZlhCqGsm4PK0zhq26Bt9EgdKVRxp8DfOGbIF6nz4wKaIaJwt1gJOHGA1sm0FLqAqN5MuY1nkYVl3zRaKAVWuGYRiGYX4uLHr5yigpEYlSGWIuzMXo7c8QFf0BCSrl0bRphR+/+8NHJzg+kaJmixYwEMbB6/IpxAy8ggen56NvQxNo8tnyFoeXJ2aifdtFSBi3FVNrq/HpX+6jkyOeSGuiRQsDCOO8cPlUDAZeeYDT8/uioUnBS8YwDMMwDPMjERCH/5v5GqT+2P9bE4w8GwyJQBMljWui/5ZTWN/F6PNuTsyDQCBQ/L8oN2fSlVEw734Pg26dRxOHrfCwmoYZHZRYbmk0PC7vx/bNm7H3vgo6/rUIg2toI20JlSQQw9iqDWqXzq3dmIQro8zR/d4g3DrfBA5bPWA1bQY6GOWxZLIwPL/5GIHJ2comyRFLx7mh/d8TYZWthSTQMUPTZlWgm1ezNeEguusNh+vwq3hj3xpF19xgGIZhGOa/iAXl34As9BL+7DQI2599QKoMUC3fBztvHMFQs6ILy4s+KE/Bw5m10cxeHbZ1w/FQOB2u1yehQoGLLEP0gx2YPW8Djju+xQe18mjEBbclCvubjEAbLf46iOlNFLdVZpXyEDNrN4O9ui3qhj+EcLorrk+qkHdjIeUB1g1bipsfsnVol4bgqfN7mNjUgEG25ROZ9MKqrSNgmddEWVDOMAzDMEwRYkH5VyaLvISJzfvhWJXNuD7ADeNGbsTDaAGMRv4Dr787QJ3P96WKPCiXemKFdU0skU3GwQGvYTfDF7/fdcGSerkEybnigvNnh7Doz4W4XHwCDh2YjAbF+Y++kNRzBaxrLoFs8kEMeG2HGb6/467LEii9aOk+7EXX6o6we7sf3Qq7IVhQzjAMwzBMEWJ9yr+yhH/34eCrZJSrXhc1eq7B6Q3dYCiUIsLfHx9/4GfPyCKc4PRCiPr9x6Gr3Rj0Lv0K+7ZeQUzap0o81l6IErWHYMOtxzjQ8h4mDd4AtyT+oy8iQ4STE14I66P/uK6wG9MbpV/tw9YraUsG2bctVNaiZRiGYRimKLCg/CsTlygBHSHhY0wMF04KUbphA1QUiVG5vhX0fuDSj7/tCJfUqrBpZQSRdhuMH10H0adWY4d7AjwP2+NCiJLBr1APjSYdw4nhPliz0RVKP6cnT/G47eiC1Ko2aGUkgnab8RhdJxqnVu+Ae4InDttfgLKL9iVkMR8QSzLEvo9AkbQ1GIZhGIb5T2NB+Vem1uYvbJ7UFEnnlmPZ0TPYve4iEjouxfbp9X/g0VeS8cjxHj6aNkPLyvKlVEHNSWswwewZFrS2xsrUduhcpjBVRwSTHqvwl3Uqor40YE5+BMd7H2HarCXSFq0mJq2ZALNnC9DaeiVS23VGoRatUGQIvXsYWzeuwtzxm3EvlfDhwmz0mbQU67ddgucPP8YlwzAMwzA/Ktan/BtJ+RAMv3fREBlUgJlh0Q/Z9zVGX8khKQTeYRooX063SEeOKQpJId4I0yiPcrqFWLIv6VPOMAzDMAxThFhQ/ov4JkH5rybpDjbOeQ2blSNR+4cfNJ5hGIZhmF8ZC8p/ESwoZxiGYRiG+XmxPuUMwzAMwzAM852xoJxhGIZhGIZhvjMWlDMMwzAMwzDMd8aCcoZhGIZhGIb5zlhQzjAMwzAMwzDfGQvKGYZhGIZhGOY7Y0E5wzAMwzAMw3xnLChnGIZhGIZhmO+MBeWMEmSQyvg/85Ac+BDnD27Dir/+wITtT5DCp//3cGVVUGEpIdbrCTwipPy7fMR64YlHBJTIyTDMr0AmLfB4zDDMz4kF5Uz+pL74u7sx9CuPwz8f+LRciLQMUTriIlau3A2PVEPkfGp9PF6dXIWN10O4sPULxLtg74qDcM1nWb49KQId7DG9TyNUKlUGvfZH8umfKcEZCzpbo88aFz4hLwlwXtAZ1n3WwOW/2wpimF+fNBAO9tPRp1EllCrTC196iGEY5sfEgnImfwJNGFYwg3nlCjBU49NyoaJXDhV0gBSRJZpal8lasWRRuL3EDmuiu2BMm2yfFZZWfQy1K40zE//ClbAf5XKRCCa2YzCtZTEExljAuoUen/45ZAg+thb7A2pgwKB6fFruZMHHsHZ/AGoMGIR6qnziz0IWiEuLRmLshjuIZlf9fihSvxOY1qsLOnfqhE7cq3O3YdjymLX6sihU/Y3BzaX90bVzZ3TpMxf/hH5GhReZwHbMNLQsFogYC2t80SGGYZgfFgvKmfwJS6Hb+tt4fHEGGqrzablKwoO7T5Fi2ABNq2S+Ti7B653DMdOvP5aNrIJ8J6EkoUEbzJ2oho0j1sE1gU/87pLw6NELUGUb2JqK+LTPkPIEWzdeg0qXKRhdLefvDZ+k4MnWjbim0gVTRlfL5ZeJH1zqS/yzaz/2HrgOv/9kUJ4M192zsPxS0Jf9cvQVCIqVQ8369VFf/rLSQcC1Y7jq9g13tGRX7J61HJeCfuCKUaj6q44yVevByjQBjy+cxV1/CZ9eSEmP8OgFobKNLb7kEMMwzI+LBeXfRDz87pzC7u1/4/A1D0T9ih2AU17gnksUNOtbZwnepW/sMWFJAvrP7YxSRVjb1KzGY5j2Boxa4cKFNz+AlKdwuBuJ0ta2qP7ZEbIMEWfXYrenBeym9oRhPuUliziLtbs9YWE3FT3zy/ijUmuHrc994OO0AHV+uhZFEZCFwungRmw564ZUPulHIdRriCGz5mPBwoVYOKsHLFUF/CffhizUCQc3bsFZtx+tZDIpVP1VR9UeUzHHrgkMvmBXTXnqgLuRpWFtW/3na4QzDKMUAXH4v5mvQRaGixNbY/D9Lrh6oT1OdGiPY0bLcfnsJNQqisvGPIEg7cRZdJvzA56f3Injjm548cIHFaZew6Zu2vxnHFk0np/YiF23E6CrJ8H7aD882HkJ2stewXFaRaRdyInHtbHV0dt7Gl5f+wNG8hMSF4xcmNQZY4/6IlmxqEKUqDsaGzd3hN/iyVj+bzAMmg7GnDVL0Ns8/8tBH473g/l4KTa+PokB+nyikmRRt7B0bTCGLR4EkyKIaaUei9Go7gaYH/LD4VZheOoVCal8/QQaKFO1JkwzFV2eJO5Y2rQB1hpuxovzdmnllSsJ3Jc2RYO1htj84jzs8s6Yt+QQPLl+Ay7ekUhRLQHTWjZo3aQ8cl3M5FA8d3DEY+8wxFIxVGxQFzq+fijZqRtqZP6CMvni/eHy8DUiU7jC4eqsWumaaFqrDPLqfZMQ4oYH9z3wXrMKmttWhcDtAk47B0LTqif61k6F62Nvbl4aMKndFJVFb3Db8RlCRSao39IaZjr8RJRRhPUyX9IIeNx9hnfx4bgwewSOGy3Evgl1FEGWQKQFU6umqKKXbXsmhcLtnjMe+xJM6zVDk1plocl/9LmkMZ64ff0eXgZ/gEy7DKo0bosWVUvw+20mCcfxW+lhSNgQgosjdPlEjiwGXg8ewzuWoGFSG02rGkDEpb3h0nwVaXVhXVU/61WfAuqHNMIDd5+9Q3z4BcwecRxGC/dhgiLiFUCkZQqrplWQvWiQEglPl7t48CIIKGuF1m0bwCjjuBoLnweP8DaGC+4FaihdsylqlVFBXJA7Hj9yR4hKJdi0q4+yhen6Vaj6K0G09xPcdw2EsFwDNBTshE2zs+jo6IoVDQvb30wKj8WNUHeDOQ75HUarsKfwipQi7RBTBlVrmua+7zIM83ORB+XM15PqOp9qi4Wk2/8kJVIS3fy9HIkExaj99gCS8nmKgnxTFv3mjKIDPYqRULMNbX2XaWmTXtG+wTWp5uCD9DZRkUBXRxuTSMWSpt9PVmRRiDtPw8qIqcqM+5QplSj+Ps2pq0FcM4JbZiEZ9NhPQVIJ+WztSDX7HSSfVD5fAaS+a6m5qg513RvOpyhHEnSV/urUg1a5JvEpX0pK7za3Ig2t9rQjhCun2Hd0Z4E1lbTsSYuOPaCAeD5bAaIvDCdjcSWa6FjAF6Iv0HBjMVWa6EhKTjqbSDo5sAypqOiTRf1GVK9GeSqhqkplbefTrfdZa2Xqq93Ut5ImqeqWo+r1GlKD2makpyogqFjQ5Nuftqqy+RKvTKCqpfRJT0+PdLXEpFJuHN3IdTMk0ou/B1CVYuqkX6ESGRcXk1ZJA9LRNqJqFgakot2dtu4aTBUNdElTRURGDW2pmq4q6ZQtTyYl1EjTYgxdyLYuBSqiepkfyasN1N64JOnr6ZKWWEAi9WKKspC/9EuaUo+dflmOCx8fb6KeZlpcuZpS1WrlSVdVgyp0W0sPYvgMhRZPL/YMo5q6KqRRyoLqNGhAtU11SKhamaY657Ih4o9RHx116rQ7mk/gvT9KgysakK6mColrzKHHXNlIQw/SgAolSVeDS6uZlpau4PohoVcb2pNxSa5u6GqRWCAi9WJp5aKnp08lTXvQTr+s2zP2mT31r1KMxNplqUrtGmTK1RFtyyG035Ofcew5GlXJkC9rERkP30o77OqSvliNShiVoeIiMVlMdk7LqySl62/MI9rY24K0harcvIxJX0OTTGtZcvOuSjMfZDkaKkf6jja30iCt9jso7RBzhxZYlyTLnovo2IOAzzwOMAzzo2FB+Vf2cV9XUhcISL3Tbi7E5U7wa5qRGAIq1uswfeTzFIWvEpQn36NplVVIrfla8pHwadJwuji2MhWrMZ1ux/FpFEenB+qTSqmhdC7T2SH10WyqJlal1ttCczRAYp2nUXX5SZlbZoFKJRpjv5y6tZ5NToUJNhLP0CB9FTIaeYVrFigjljyOz6DW1RrTnxeDi7BRFE2He+mSauMV5MmVU6zrTvpzkj25FGZdJG9pfQtt0mm5kbzTyzpXEnq7vgVp67SkjflnzEc0/ftXK2oydF/GvGLdd1APIzGZT3LKVJZcI/KPCqRru5LcMra1lCIvjKByYnOa5JQeXCibLzMp+XProW40iq7ksvEkr9dQc21darHMheTFKA27Tn/W1qSSvQ5RaLQ73bjhoUin1Gc0v46Yq0PlqPf252l5g/dQtxIaZLvRv9DbuEjqpTKkfrSuuTqVtbuUd92NuUrjzNXIuOsmcuFj4o/PtlPPcmpU3u4C17QqvJjr48lCrTS1WepEoXzsmvp4DXVt0o3WZo6i0+UVlCuk0rP5dUi96kz6FGem0uM5NbKlFa5+SP3WUXP1smR3KZ+9OuoSjTZTo1Jtl9OdsLRKLAm7QdOstEm3zdZPxys5RVmrkkgsJp3qw+hvl/ckkbyjK8sn0+J//PlMhZVP/eWOkWeGmpK4TBta6hjK7bHcsgVfpxkNdLg69ZlBefRh6sU1Ohuv8OSmF0uuO/+kSfZp+wbDML+Oz/jdmykMcfHi0OLO8dJ3fvCXiKCtrQkhFz8nfYhB/A9+g5vkzXU4+AhQq22HjBuLYq78hfG7o9Hhr+loopWWhuQncH74AWpW1micqUuO5F0gQqXyddbOcfOCdrO5WD+6EsTyy5ISL+ycdAq1Vi9E8+J8hnSxHrjl+IZ/k42gGIppE94HBiCOT8qVNBoe/2zAhLa10MDuHEoMnIiW9ASXL17ERWVfl67hWV6jJiTdh8ODBJg3t4bw0hLMu2KMaWvHol72dclHvNMGbLmrh9+mDEXF/HpHxDthw5a70PttCobmmzE/umiz9Abu7uPmJYhH2JsncA0phTo1dBD4+CHeZdzzIIJ+ST2k+t7GqXOOeBEUCym3JfXaTsX6tQsxsGb6T/DK5lNewgNnuEitMWRsPciLUWhoi/ED6iDu4W24aVRDq1ZVFenpinVciK2jaqblNaiGKmVlCA8JLfRNlEVSL4sEt/ynt+BwZFss3PYH6vE9R3RqjcLWxR0Rd3wzjgcXcu1kITix/gDCWizArlnNUYrvmKxiNRXn757DFKuv1VO5qOuHDIFH1uPw+9ZYvHcGmhqm7Qciw1aYP7MLRLdP4kIuN4nKVBti7vG/MbKePkQiI7SftQ5zO5vynxYdqf8hbDwRj/ZLdmOWTSlFlyBRmdZYMK8PyqT1Miy0pPsOeJBgjubWQlxaMg9XjKdhLb9vMAzz62BB+Vem3uI3dDdWgeT1JRy9GwDf4EjulCKEdqnS0P2hS1+KwOu34I6qaNexUlpfU6kndq08gndlesKuu0FG5Um4fQwXfAWo2qQp9LOtEyku4OemOFouWouh5VUgkOdKfYnL59xy3LSZ8vww/pp7mH+XjUAVqqoCyOJj8THPm2dliHY5hs2b7HHIwRcJlIp3zgex3d4e9oV67cF1n9xHTUh57oi74WpQc52DFkNuoXy/tjAqTLwsC8DhNYcRYjUWk9vnd5qVIeDwGhwOscLYye2/6ISc4n8Vq+xawtJQD2WrWqPrkInY8TAOMokEn9ZSBbVmHcLegZq4Mb87rExKoJhhJTTotRGvjWrDMqOLsbL5lKdaujT0pL547ZnEp8TilWcAqGQpGOQoWxFKW1TOtD+JIBJytYqIK7HCKoJ6WSQkePX8FVIsm6JZ6cw7lRAG1k1RRfIKz14U8kbI1OdweZ6MKi1s87lf4Wso6vqRgheuHkiSPMP6TlaoU6dOxqv5PAckSEPw7l3OfVW96UAMsfz6t0imPneFh6wW2nYwynKCVS1TBvqFOS5kSMFzx7sIV1OD65wWGHKrPPq1NUo7JjMM80v5pofm/yTdzth05QgWjaiKwIMbsOGSByQCHVi3ts7z5rYfguw9bt54AlnFNmjPD80n9T6Psw+ToG/bAU3Tr4jLgnB2z0UEC0zR2Nosy4lCqK8PPSEhKSn38VGEWiaoWFEP6vKrR5SIJ+unYvPLrCdTVetleHBnEf8uG0pCYhJBpFMcxfI8QwlRotE4bL/+Cj4u+zCxHvBetTUWHLmES5cK8bp4HNOb5LbFpPB1cIa3ahW0HdsD9YR3sWe3a6GeaJr8aCs23VRH9ymjUS2/M23yI2zddBPq3eXDJX7+KVkWeR6/23bHag9zjNl1B74xcYgOfosjQ0xznOjjAsKh3287bntHcnle4Ma++ehrEYL9w5uj24aXGQG8svmUpdaiH3oYeWJjvy4Yv2QVFo7shFGHZOgyeQRq5BJXiURFdyj74nr5uVKiEBjCNYz4t2rqqqCPMYjJ1uCkqGh8IHVoaBTysqtAAxrcfpucmJRnU/lr+dL6kRIViJC49JIRQFNTA8ISVug3YRImTcr0mrUC2w9ux9ja2fdVAYobG3+bCyFiVYiRimTu2JSZLD4OiYVvJXKHGF84OHtDtUpbjO1RD8K7e7DblY0bzzC/IhaUfwOaVftg3raDOLTECqmBKVCrMQ4zfzP8sQs/1hHX7yfBqHUHWKnGIcA/AskeL+ApEcG8WjWkPUdIhqBTW3EtXA2yYvVhXVcVSSHBiORPPKrVa6OqugwRXFrOk+4HOM2fgse9TmFr97SyoI93sHzqLvgoghAZol6cx7aF07DmWoQ8IafU93j/QYhy1aqi4IE2uOC89hBsuPUYB1rew6TBG+CWfhH2S8gi4OT0AsL6/TGuqx3G9C6NV/u24koM/3FBJ2FZGE6u2QOvqiMxpVt+Q8jIEHZyDfZ4VcXIKd1QyMFmskh5+A8uB9fC5H32mNy9Pky15aE4QSrJ/nNDEm4s6YUOfZfjQZIIWqWroHHHwZi67gxOTqmAh2evIa2XgLL5lCcLcYVbRA306FEK3v9ewr0PtTH94n0cHlLuK18hLIJ6qRQBhNzEZVwFSXczvxoAAOMnSURBVAvdZAjd3Q9VGs+Ag6INq4oazZug5JuT2Occq8iRJg6395/EqxJN0KJOIZv1qnXRqllJvDy5F3eyPBE3Dg+3/Yn5p/3498pTNAuyjPgkQUR4NLc2mRWyfgiEXLnLuLLhpysLxe5+3HdmOPC/WKihXntblAp/iQCDDhgybBiGpb+6V0TU3dt4FZO9wnHlzU33M3uPFIpqPWs00HiKU0dfZTruJcP16i34F3I/kJNFOMHphRD1+49DV7sx6F36FfZtvYK0QwxXTor/MwzzK2BB+beS9Ao7R0/HlWJ9sPXYQjT50jHNvrJkFyc8+ChGDasqeLNvFU54cyc0dTWoCQhR7yMUJ5u4F4exJ6wmzFICIKjVGPUjzmHTGS9FsKGg2xLtGqvD28ODOy1nJkXgyQkY/7QLVo5uhiEr5qBFcfmXuIDn34WYesAvrc9pZX14Hb+MUJ3cO2pI3r6EV7IJ2nayUv5XB6EeGk06hhPDfbBmY+GuaOcq/jYcXVJR1aYVjETaaDN+NOpEn8LqHe5I8DwM+wsh+Z40JS+2Y8MlQodJvyPfGEvyAts3XAJ1mITfCxuMZSOuaoWaGu44vHwXnDxDEBHsAceDi7HiXCAkMT54/OA5fKLSSkYikSD1lT3GjN2Eiw+9EBoeCPcb27HquCdKVauRMe6ysvkQH4xXz1zx5MlTvJRfFZZEw+/ZEzxxdcuYp5ywuCH0VeOhXnMUFq5bh5V/jUH7CipIzty6SwrHmxdvEJ4oQ3KEF9xehyIhJRq+7m8RnpyW9sIzLFvdy0/R1EulCEvCrKIeIu+exTn3YAS/vIDVex9CZF4VFflfArTaT8ec1rHYPaw3FpxygZf3E5xd1BdDd0bCZvZMdC7MkI8KOug0dyHax2xDn3a/Y/v15/AL8cW9v//A8OkH8TQqfSPFIcjjGVyfcNvleQA+cA2H2MAX3DZ7AldXdwR8SK/RQhiUMYQo2AXXH/khPMwLd3ZNwOxjQZAlR8DLzROhfOErXT84wpJmqKgXibtnz8E9OBgvL6zG3ocimFetmDE+t3a7eVjZKxUHh7TDsDXn4eIdguBX17BmxBDMPOWOWMX0khHh7QbXpy8REi9DarQfnsnX4flrhH7us5CUqL/CUr9h1sQqcF/RDyO33oSHtwdubBqBoRvc5dfPEeHlBs/0glFC/G1HuKRWhU0rI4i022D86DqIPrUaO9wT4HnYHhdCWFjOML+MtPs9ma9FGuVBV/cvp3Fd21Cv6XvJpbDDtClJvimLcnMm/TuGjEUC0qrUieZfDFCMIEDx92hRI10SqRtRvc7DaPr22xSa7EFLG6qRULc6dZt5jF5nGVhASu9PDSJjYzu6mD7qgjSUrsxvT+ZaQlKv3J3mn3tNXuenkXVJYcY6CHWtaJ5zPEleL6cm5e3okmLYxewk5LmyKek2WU4emUdaUFoivbrzUDG82JdIuvF7thEkYsl5Zi3SUitFdYbvpbe5DGjxSRSdGVyWxJUnk3Ou6/hJ1JnBVFZcmSYXlFEpEvI7P4vaWeiSWMC1sgQqVLxSSxo2cRi1qKBHmmI1Mhp5kcuXRJfsjKlc6+HUv5ERqcs7WUNAKtqm1HjYFnqYMSCHsvm4bbbGhrSEaaObZHlxy1B62NmMod1i7i+nloYiEqnpkG6JEqRbXIfUxdw0dcqTzbi99IKrT+H7e5AuV0fTpiEgYbGOtGmPHRlx+TLSinekHUFKbOQiq5fKk/icpInNjEiNKy+BSJsqtJxMxzyzjcwR85h2jLQhcz1uHxOokm5Faxqx7RHlNhaKsuLcD9PkDjXISEdMAq6MVPWrU4//XadgvphSn/2P6msI+aEhs74EAjWqNvNuWka5mNu0vIsFFZdvB4GQNMvZUN+OVUmT28YCkS512hnEZVK2fqSTkM/JidTMSE2xfCLtCtRy8jHKXjSU7Ev/LOxDDbg6q6aYnyYZNx5B27gJKlYl9gQNKKmSYz0EQh1qsznr0JPKUb7+knx0lwXdqVZpTRLJR98q24RGLhxLDbn6JRCISLfTTlKmWsrL7sbv5UhsPok+HWKcaWYtLVIrVYeG731L+R5iGIb5qbCg/CuTxrwj73fRWcfp/grSTw5FJ56CvXwoMvuCS2LI3zNreup7H/IMisv9JCfxpt09qlPPfUGFPAlKKWhbWzLqczT3Yb/inGlK3Xo0+27GGGs/kESu7PwouoDGgsRrHdloFac2W3y5030+JF60zkaLirfZQr6f1QDJW1J0MAWFx+U9/8SP9FGxrSUUF/6OAoLeU1xuUYCy+ZSRfI+mVylB9adcIJ/MAzAnR5PP/QP0u5U+VZ12hwtXvocC6mWhSbltEEbhsQUUljSRoqPjC7kPFUASS+EhkRRfBHUqNTacQoqiHmUmTaLosHAqqGjkkqNDKDQm+8HqR5BM0eFFU8ZZJAaTl1903uXNMMxPiT3R8xdR9E/0LDqyaEfMH30KdTduQs+ymX6nzlccTvSzxL4W17G5rSHMKmbqRS2LwvVp/bDLchMOjrb8sW+YzYe8r+jWLd6wnjkCdfLrziTvt751C7ytZ2JEvhl/DTL/DWhZ4wr6eV3DWEM+MZOoXR1Rfo8NHt+eCYuv28E8F/nUS4ZhGIb5AspGSAzz2YQlWmDRhs7w2bwWl/0LGmeBJ43Au1BA4vcUYVol+ESOLBxOGxfBwWoT9v/EAbmc0MAGExYVEJDLCQ1gM2HRfyIglxOWbojG5R9h89StcPLP3Pk3AQG37TFl432YNGmSMXb+N5VXvWQYhmGYL8SulP8ifuQr5RlSAuHho4UqlnpKtQZTYqKRUqwEtDNnTvCGR3BpVDNPf3IR8ytK8jyGWWOmY+fdGBQzNUWZ4gLEhvgj4EMJNB21EvYr+8My04OqvqVc6yXDMAzDfCEWlP8ifoqgnGEKRYIorydw9fDFuxiCrrEZqtetC7MSX/8BMAzDMAzzrbGg/BfBgnKGYRiGYZifF/sBlmEYhmEYhmG+MxaUMwzDMAzDMMx3xoJyhmEYhmEYhvnOWFDOMAzDMAzDMN8ZC8oZhmGKgiwKr18GQ8q//b5kiH7zCkFKPhaAYRiG+f7Y6Cu/iKIdfUWGGJ+neBmSrHh2fxbcfMTahjA2NUFpXTXWqiskSZQn7t5wxJO3wYhKEqG4sSXqNW+FplVKIPbZc3yoUQcVv8dDcZgvE++O/Qt34mPPRfijcYlvu1/IpFxDQARRtpkmvT6MBfbv0XX+BDTVZ3sqwzDMj44dqZlcCSDBh7enMKNLc1hbW8OmzyzsOnUe508fxa5VU9C7vhGKlzBBnQ4jsfS0Bz7w32NyJ4t5jN3jW8LcvC3mXw2Bpnl92FjXRKkULpgbURsmFWugQcd5uBHFf4H5ecQ9wso+E+DW/hsG5NJAONhPR59GlVCqTC/sj+TTM1G3HIhFI1Kxou8s3IiQ8akMwzDMj4pdKf9FfJ1xyuNxqr8J+h77COOxV/HGvjXU+E+4SASe/6zD9EnLcdGPYNp1FU4dnIh6OvzHTAZZ4GmM6zgcu0PrYcG5k5jTVD9r4CaLwr2Vv6HHolhMenYPf1myS+U/DVkoTg+3xtxiO+GyuSW0+eRvQhYG+/bl8GfiErxynJbHLywSuC9vgS73huDGudEwY1WLYRjmh8WC8l/E1wnKk3Hz98poZx8Es6lOeLGmCVT5T9JJA05guO0gHPKVwXjQcbjs64VSXMQp9XPCwZNX8dg3BtI8Fklk1AEzZneFKRcofHA9hPUH7iEsWQijdtMxu1s5ZMQPshDcP34Ul+57IQolYNaoMwb0a4wy8sg22R/Xt6zGYfdEUGI8hMa1UMXAFG2nDkRt+YMfuYDX9fxRXLjtgdBEgkCohTLVbdB9QBdUerkNMw68QKpiJnkQ6qLp74sxyPgFjm4+grvBcYr1EWiUQtVm3TCgWx3oyZdD6oPr9vtx+WUEUjSN0HrkdPSw5EorxQ2rWttg9j0N9D74BEf7l8n9SqosGAf794XbxJtY3TRTKSf44sb+v3Hsjj+kGhoQJidCVqoeeo4ciS6WmVpAH1xxaP0B3AtLhtCoHabP7oZynwoQIfeP4+il+/CKAkqYNULnAf3QWPN54b9jGACngydx9bEvYvLesOgwYza6yjdsFlJEPD6JXQcuwjVCBcV0uPWUqcPIqhnqyKKh8ttIdNZ5iG0zDuBF/hsFuk1/x/L+WoVbFqkfnA6dwr9PfBCVmp5fAKGaDgzKGMG4Yk00b9MMFrrZlztvUeeHo9Ygf0xzu45JFZT/XpFI+AfDzXrjid09PF1ihTyfcxpzCgOrjEPskqc4Z2fCfh5lGIb5UcmDcqYISd7T89Ob6dDDeD4hTaL/HTqxezcdd/alrJ8UDfmmLPrNmUQO4yuQCCpUedo9SuZTs5LS+9ODyUgEEoir0swHmXLF36bJlVVIoNWW1rsHUUhISNrrnQ89v7qWereeRs4Z2aUUdWcONdAWkECnHv3lFM2np0sl9/81Iqu/XD4thzSczg43I/MhJ+mdRJ4QS8939aNKJXrQoVhFDp6Uwg/1Ij2hkEoNOElRfFrY9i5UZ+QRcguNo1RJIr1Y3JDEguLUfW8YpUqTKcb/Lm3oXpOGnU1UfIMkAXT+95qkKRCSbre9FCpNS04nDdpGHSx60a5XfH5uHsH7e1BJIUhc4y9ySeWT85DkvJpW30zi33F1xn0PDapaimqPOkgvuKgzTTx5nZ1OTctWpK7rHlAMnyonjbpDcxpok0CgQ/X+cqIcJej+P2pkxS1Hpk30Od+RL8PtyZVJRaBFbde7U1D6dg15Rz7Pr9La3q1p2qcNm4YruwvTmlJpkza04KI3xfHJFPeWzs9tRWXUrGihWypJw7ZTlzoj6YhbKMWlSijxxWJqKBZQ8e57KSxVSskx/nR3Q3eqOewsP4HCLksiOU6qxOVXJ9v13pQkSaaPIa/p7sk1NNq6LKkVq0zdl97MsW1zJfGgZY01SKPVFnqnTP4ilnx7MlUSm9K4G5/qTO7i6OyQUiSuPJXuFJSVYRiG+W5YUF5EUsNd6eQyO7KtqENCgRq12xHOf8KFivcWUhOjVrT+1Vva1sGYGsx1yhJMFYXvF5Rzkm7Q7+VE3PzFVG32Iy585kmDaUsrVRLqD6b0uPYTCXmfO0MPM0809Rkt4AIjAy0hiYy609+eWecYf6gv9dj5nn/HidpP3XSKUa8jmSPwj3RzyjBa81YRpWdIvjeNLFXEVGveU375pOS3YzntCU6PpqQUsrUVqQoNaMi5Twub+nQ5LT6ZqRmV9JxW2eiSUMWEBh4P4r7Fk76nC2Na0shzYZnSQujvjlzAy5VfpT+d8y6/3MRcpwmW6lS85QbyzBHMSynoQE8yFJvQgOPBn+bHrdmzBdZU1kCLhCIj6v63Z9Z5xh+ivj12UqYS5HzOd7jGxhZ5WenT4JwbliTe5+hMlg2bSA8WNKBiajW4APkjn5ZZFN2c3ImmOCSR1G8HLd/zaZ2kIVuplaqQDIac46bCS31Kyxef5N8Udlm4stvcksuvRwPPZMufGkCn7SqTmlCb6s1/UGDjOfX5AqorFlOd+c8+1Xlu+iHnx5NVGX3S09NTvEqataa/Lr6iO1sGUH2jkmRUuxvNOvmW2wO+hIRrpNYjVb1+dOKjlCLfutDDBw/oAfd6+Nyfa55mFb6zA2mqlOcC+JxlxDAMw/wY2C+ZRUEWDfenITC1rQ61qHjI5OFxOukrbJm6Eo/Uq6FORVPUqqaFp6unYOPzX2isMrW6qFtNrLg51MfdA/F8srybgVBew9J61mSR5OMNUeceaJCtP4zQsDfW2/eHafh5TOozE9cjM9+gJlD8l4FkXCskDje3roJzRj4d2EwajxZa2WYqSPtmejcf+bIZ9f8DfRV9YPKmUusPTGinzr/jqNXE5J3L0U43CMf+HI+D/vIB8GSIvDwHG/EnlnUx/NQ9IPUFHrslcq0lIcpXrpx394IcpHizazF2v9FGh7HDYJHji0KU7TMOvcoG4+TizXicwifLCQ3Re709+puG4/ykPph5PZJbuk/kq5+tZD7rO8K8Nyy8RZ3RI9OGlfnvw8INj4E2kzC1aW43HZSAzZ9j0ExdAKFRf/zRN48uPulUauGPCe34N4VbFjmRSN7NhKub2b+iYoKea9djqGkinqxfhMMh+d0cKUOYkzM8JBowq2KWadsKUbrrSmwZUQ6J0VGIiuJeH3VQuY4FGnVsCgP91lhx5hSW9zb/1D3rc8hC4eDkAXEDWzTTEkJVIxmXp3XGsFXX4JvIlSOfLV3xKpYwRhDuOnrwKQzDMMyPJt9zH6MkYQnUbtcBDerWR1XDrEUq9TyDM48TuRO+GGKuuMViLnhNdsOZ0+5cCPurUIOOjvwWUII0IR4J+cUyctIQ/LNuN57m2m9YBKPfduD4omZQc9+EwYO2wSOZ/yg7ve748/dakNxbgnb1O2P+WU9Fg0BkagWrAoJtORUdHWjyf+dJqIPiOlmnpWIxGts39EJpruEwbexOeIZdxtyNwKTFnWCQOas0EpEf5IUhgJpaIYaPlIXhxtVHSFSphgYN8rhzVt0KDWqqQeL5L654Zq1JIqPfsOP4IjRTc8emwYOwLc8C/ORzvpOTFCH/rMPuLBtWhvBr5+H8UYQazVtkLZ9MRKZd0L0RFzyr6ECn4I0CneIF3VGc27IooXhL9GlvBGHcXVxzTOATc5MKN7c3kHD7voGBmE9Lp4lGszbgj2qqiqaC7P0/WPK/nVg9+Sxqbd6OQRUyt7Ji4XHLEW8+tWSV89EJjk+kqNlCXqZx8Lp8CjEDr+DB6fno29AkR70WGRhATyCB1/MXfArDMAzzo1E6TmCUkfPqW/ILN7xRxEzpV2rlf0vw9vnzTFeUf3KyWERGya8IC6BeQg/ZYlhQ7B2sG/QbfvtN/uqDbrbNMfpoIBc25UUL9Wcew57h5oi5Nh29J/2D0FwDfV3YLr+Oq6u6wSj0Khb3skKdHktxM+RrP75FCNP+m7FlaHl8+HcWurZaBZq0FJ2yR5wiXZQoJk+TISYqKp/1zUYSCH/5U1+EutAvmdcuqgE9PS0IpMEICMjZvNOqPxPH9gyHecw1TO89Cf/kXoBZFPo7FIs76wbx2/U39Olmi+ajjyIwy4pK4O8TgBSusaVvWOrLrg7nR6llUYYKTMuVhZASER4cwaflRoLIyGjIBJrQyf6rjJx2M8xdPxqVxNxnxAXDOyfhVK3VWNi8OJ+Bl/Ich/+ai8PumX/uKFjSfQc8SDBHc2shLi2ZhyvG07B2bD1km3oGgbwBKiCkRuW3TgzDMMz3xILyryw5Lg5JmbuzKBAkcbGILzhO+jkkPsBDNy4wFKiiRoP6XLiYlUDHGlMOncCJE/LXSZx3foaz4y3y784hLIPuW05hZRsdvPl7OAasdUUi/1EWQn00nXYaLs72GFpdBO9z89C5+XAcVXQr+YqEpdB91VL00IuDr05HTO6QbZhDOXFV1Kwsv1oqhaebG5S+9izUgLq6ACRLREKelUSKhIRkEFfm6hq5BIXc0pTpvgWnVraBzpu/MXzAWrjmWoCZFfI7Ah1YTznEb9cTOHneGc/Ojs/W3UYIDTU1CEiGuNjYLN1iipRSy6IMQnx8Ajc9AVTVM3VbyiGBK38pV/4a0NDM/TBavOUirOUabiryuJxS8fLyObhlrwSq1lj24A4WNczWjytfKXjueBfhXLm6zmmBIbfKo19bo3wbPEItLS4o5/5IiEtLYBiGYX44LCj/yjRKlsxx5Zg740ND3wDFf4nSl8H/yN+4ECHj4uMOGDmgohJXQ7Vg/cc4NCsoDlGvgQmHDmB8tQQ4zvkNf1wOzxLUSbmGTVoHAyFK1BuDfXdvYUPnspB4H8GsFQ7KB8GfS7sk9LhIR6CqBi6GzklojC59bFBMIEPUjXO4EcunF0TFDFY1uSBf5gPPbF1TMkh88caHi5g1a8CqevbuE+nUUWPCIRwYXw0JjnPw2x+XEV5gVPw53/lEy/oPjMuyYVVQoWZV6ArlDZPnXDj57eRcFiXIwvHgoRckKlXRtKkBn5gbLehop91HIcmrh4xQCyYVK0JPUTkIiU/WY+rmlxnd1mRRL3B+20JMW3MNhXq2j9QXDs7eUK3SFmN71IPw7h7sdi2gZFNS04b+1M7rWjrDMAzzvbGg/CtTrdsAtdQUl8q407L8f9y/AlXUbFifC39+Jjku9yvEPl4LuzlXECMqjwFbNmGIkXJVSsWwFPSFyXh20ylT1xTFaEBZ5iQ0aI/VJzagi4EvThy5iyQ+XS7pxiosvZ7pWaI6VpiwZyW6lSCEBwbkuLKe+xp8ohjDpkgJUW74/zC5vjZkISexdN3jLMuflQzRdzZgyXE/SLmAr/XIQbAU+OHaP09ybVxI31zAZXcZjHuPRPdM9zEoyi9rAaL96hPY0MUAvieO4G4uC/A538mTiiFK6QuR/OwmnPgNq9NuBPqaCxF66TAuZblxNxNpKDz/z95dwEWR9nEA/23QoAhioRggtqKCYiACFibW2d3dHad3nnm2nnF299kNgq0YGKBylKiAQUktsLP/d3Z36BAU697n+3n3XveZ2dmZ53l2+c+z/3nGN5vbmX5Jo2SzL58S5bEU69wTUaLdBAyqmdswuyYsK5eHVBGJDxHZ/yoT7TEXE+91xpH1LlA1EX3E9UWTsCVAvb7YqBKM/Q7ibJhBvk7QFe894PFEDJseI9B+0DB0KfEMO9bzn0H10mx/jeDev0eEQgzjipWEEoZhGOZHw4LygkQJiFflqhCSk5JVfxzFpp0xoLUJRIkJSCAFZPF8iGXUEv1/Kff18msLjAxv30UpQ2XEx8RlCGoTQz1xYH5X2DrOxB39Zph+1B1bu2W+MUkiEpP42pAnISlLpKBAxLVFWHNTU33zHVVRDKLffkRMpjhMs/JQ7Ng3BXX0Mw5Ha1fVx/Xxk/BPSFpQpCA5ONJDfcfGSH8pIMXGIp4P8OJj47INWpRtFh+XwMeAMsQn5DH1heKRwLc3JcQjLqfYUcsGs4/sxIhaGri38Bf0Xn8PkZl3QBGNRzumYIpbNQztqu4Xuo3nYstsW7z9eyqW3s00xC7zwaapa/Gs8mhsWtwORkKxsk5jot/iY9YKxNAd+zCljr7quoaMPuc1fMuqGxZJWRsWiohrWLTmJjRTGla/KeZvmoK68QcxZfQ++GUe1JX54fjKvfDVLCQUpKH4OP5zQ/znJiHHnPx87QtPtT6/NS79Bvm+53N4Ktp334jopvOwf2NvmOX67ShFNftGKIn3CPCLyNKnuFeHMWb0A7RbMhR2fRdjVtPCqs+GIuIi5k3ahSDle3OBuH5HhoaOdTLdlCseN35vDivbXtiYJd8FiLvmDs/kqrB3MoVEvzlGD62NyCPLsOlpPF7s3YCT2cwak+znh2BFITSwryeUMAzDMD8cYgpALD27uJPWz3SmMlIRH92IqbD1IFq25QjdDeNI/vIIjazfmIZu+ptG2tvQkAMBXzhHcVbKpiy45pTTS9cNNHdoUyqtoTweZeqsKVnZO5Fjk4ZkXd2cypSrRo06jqEVx59QeDYHIw90o01zO5KlNv96cWGq5NiRunbtKjw6URv7qmSiXZHGCzd2kQe60sbpLfn3K0lOUzbQ6aeZ57OWU8DuvjRsW7jwnJdwhoZUKk3mNVvQkF9X0YY1c6lfg6rUePQBSp3inAunh/+spYkOJUkCEUlMW9C0v07Rk/QTxScHkNuW36iLpRaJ+LYzsh1OK/Zco5c5NhJH4Q+P0eqxTaiY8qZJmhWp46+b6OK/udyZJe4FHf+9LzWuYEKl67SnwVPn06JF82na6AHU1aUXzTzok2VuaWW/erpvCrWr34A6jv6d1mz+m9YvnkTd7Gyo+YhNdCcibYZyvgLJdeN0allag0o6TaENp59SlhoM2E19h22j1Br8zNe4bZpLHS21VXVVuJIjdUxt167UqY09VTXRznZe9pgne2lq+9pUyaYTjftjHf3993pa+usUmjB9NZ0PzFx3yRTgtoV+62JJWiKQ2MiWhq/YQ9fSN0p+90W5/t/zqWtlHXU7W3WkgYMHUp9f2lOzhvXIvusU+uviC/qYrlpzJbtOEy01qFjvY+najqOwc3OplYUeibUrkcvc4/Tc7wRNblxUOf+s+nMqNqS6c65SzJu/qIVpV9qf+aYFXBht72DIr69BVnO9hMIUMro8sixpWIwjj5QKjrlK02rpkVbx2jRg+7/p5kxPkUh3plUljeI96bD6zlkMwzDMD4jdZv9b4WIR4h8CKmkOU4OCHyP/OrfZ/9ElIjqaQ2EDQsizp/ALF6NE5RqwKKb9Q/8ElBT5Ei98nsH/PcHIvDbq1SjxiVSmJET4P8GzVx+h0CsJyxqVUfznyn3KSB6DkH994PceMKlkhSrFldNp/owUCN7SAXXnFsIG773oUkQozqPYQ91ReUdTXFrbAsXKV4Bx+k6bFIGAozMw78NM7BpTVijMhSwU/m91UK6sYdZf4BJvY2qdFnDvdhXX51plGpVnGIZhfhQsKP+P+P8MyhnmO+NeYH2b5jjocB5u06rmPqNQBhwCVzjB7rwtlsybgB4Ni2c8kVSE4/LidYgeMAed8zDnfs4UCNn9Cxqtrox9HgvQQE8oZhiGYX44LCj/j2BBOcN8H4qQ4xjeeQuqbjmG8dXyMQ6dFIXIpEIoop816I73PIQTopboYf1ls6Uo3hxE/y4HYbf9AIZUZmPkDMMwPzIWlP9HsKCcYb4feeBhTJ7lCac/F6FdqR/jEm5F5A0sHbcdhpNXY3hNNkTOMAzzo2NB+X8EC8oZ5juL8sKpe/po3cziB5hZSYG318/A16I17Er8GCcJDMMwTO5YUP4fwYJyhmEYhmGYn9ePPEkFwzAMwzAMw/xfYCPl/xEpI+V5wZqcYRiGYRjmx8KCcoZhGIZhGIb5zlj6CsMwDMMwDMN8ZywoZxiGYRiGYZjvjAXlDMMwDMMwDPOdsaCcYRiGYRiGYb4zFpQzDMMwDMMwzHfGgnKGYRiGYRiG+c5YUM4wDMMwDMMw3xkLypn/DMW7+9j7+zD0/PUMooQyhmEYhmGYnwELypn/DHGxGrCIvY1/UQb6QhnDMAzDMMzPgN3R82cVG4S71+7g8YsgRGpXQatubVGjyM91jhX38jbcrj1GQEg4ErWLwby2HRwbmOG9TxAsa1YW1soHLgDLndrg5TwvrGmqJRT+4ORRCLjngUsXn8GkzxR0Ki8RFvw8Crwd/w8oogLwwCcUiZm/fUViSKQa0NIphOJm5VCqsKaw4AfFceAkEvx8vZZhGObHw0bKfzKKKC/sntEXHXuMx58HzuHsnpWYObIL+qx4JKzx/cTeW48+Deuj09x9uPdOIZRmlohX7msw1KEy6vTagHtxRVCpgT1sLbTgu60nKhUuCqdVvsK6WUV7n8Di3nao32kh3MMzvoci/Ao8gm3gUOsdbu3+E/NXHMD99zntxw+AC8aZRaPRo2MXjFhwAA+jfqbz4y9rx+8nFvfW90HD+p0wd9895NhNv7bkjwh5ehQzXZqgcePG6keTthi18jDOnDyIzQsGoUEpfRSpaI+eM3biXmRedlSB0L3D0GmJp/A8H2QvcGLNOpwNlAsFueD7rdvacWhXqzSKmPbE4WihPIM4PDu8FKsvhfJ7lTdxntuxePdDZLu5b0D24gTWrDuL9FWQ2/cNwzBMgVOOlDM/h9hHm6i3YxdacjWMkoUy+dPfyFpDl5qteymUfCdcMK1rpkeFWm+mEE4oyyw5iE5MakDGmqXJefE1epd5PS6M9nQxJac1r4WCHEQdpd7FNan2XK/UelCKOdqbyrffRm8jH9DuxX+RW4hcWPIjk9GV0eVJolGLZj9IfzQ/sIJqx++AC15HzfQKUevNIZRTN/12YmlfZwMS8V/Dyq9ikV5H2hMrLOLFPFxNziUl/DIRaZdrTYuuhn9inzkKXN6ULEe5Cs/zTnZpOJlJpFRx/FWh5BPk3vS7tQZpO62j11naP5yu/t6NBm7yoQShKG84endxNvWdcZbCvnnjyOjScDOSSCvS+KuJQpkgh+8bhmGYgsZGyn8SXNBuDOi+GxUX78BUu+KQqkoViHjwEIGGzujftbSq5LuRB8I3MBnFKlaCcXa9SvEGR4c2R9eVz1Ft3jEcntYYJpnXE5ugSdM2sKljIhTkQL8SLMsQAn19kSQUKUdu77l7omT5GGzbHIwGE0bAoeTP8KO6CGKJhP/vT6Ig2/E7kAf6IjC5GCpWMv4BfiaUQFdPO63tNbShla7L6luNxqb5LWEoJsiCzmJW5+5Y752HkezPoNVwLFYtW4ZlI+oJJblThN/E7edAdfumKJGhIuV4vnkApgX1wMLBVaAtlOaNGCbNZ2Os1moMXPEQ8ULpt6GFhmNXYRlfByPqZUoZyvb7hmEYpuB9/79L/zVcOB4fW4e9d7P+SeHCH+PYur3IZlHuOD9sGj4Vvh2WYaqNnlDIkz/Dlg03UWn8LHQt9p2bkuTK9FKIperThYw4PF/bF4N3+kPHfg42TbFBuqNIR4ySTYegu9Wn8milkPLBi1zOv2EK+XNcuZmIQuGnsOXyK/Bx7k9LEfsGfkGRuQQAcsS8eQ5v37eIz/EXdRnev4tWpQ4kRbxEcETmYC4v28isoNvx2yO+z3D8/mXbTb+QIiYQt07sxF8rFuOPPxZgwQL+sWgXbn/2VEBilPmlH1oK14oo3rvitzn7Efo1sih0q6DjhPHoYJm3azHir13BncTyaOJYMUM+Oee7AWMWxKPH7LYo/llfSVqoO7o/9FcNwWLPRKHs29Ct0hETxndA1irI5vuGYRjmK2BBeQGRv/fCkUWD4WhZHrW7TMZur7iUJXjvdQSLBjvCsnxtdJm8G6mL8ijixHwsvFMHo8bUyzjylEBouOQWLs6sjXyHP4oIuP0+C3teff08SUXofsxe6I4osTn6zxuByrkERNJq1qiVfaSXK8Ubd1yPbomJf/2Gzm+3YfP9RESFhCJWWP5VcUE4OaEhTDQ1oGncCKOPPkfiy0tY1rMGCinLCtdBv6Vn4Mf/TVeEXcW6vlYwLNYYs93eZ8i3pahbWN3PFmVLlIVlheKo0Gol7suEhYK4p7swrkdfzNh0HCf+HoEG5tYYsOUx1F1KgdDrWzF/ZBfYVSyJSqP/gefGbqhhVh7lKrTHpmD1u+W+jZx9i3bMSIEIt98xa8+rDPVUoBRhODnGGqWKGsPYWPkwgUXzWTjz/AbW96qH0ialUdtlBo4oGy8nXAhcF3aFVRVHjNtyGU9eRSA2Ng5xccpHApLpC/bewBa2NVIqWoHwy4dw7kPB1oY8JgQ+N8/i4NFbCMtl0/FB13Fo83psveAND9cbiDaxg2Pt9N88cbi8YgVuV3FBJ7OUUF2BsJNjYF2qqFC/xjCxaI5ZZ57jxvpeqFfaBKVru2DGET/+ZElg7IxOjV9iw9KjCBeKvi7+BDXEBzfPHsTRW2Ffr68xDMN8ipDGwnwJLoIenj9Ld26tpFbK35qhRS03vRMWPaTzZ+/QrZWtyFAMglZLEhblDRdEa530qXCHHZSfl+VK/obOz2xDHZc+JJlQ9MVkrjSqnCZZTrpBGTMyOQpa40i6IpBG7bn0qCCSMuXPaaGtBul32U9xQlH4zo5UttsBiuLf7/XuX6iSdR9adv4lfbOsctktmlpFg0T6zWn9SyEhNvYcDTGTkMiwI+2OUBep8ma3daTGcx4I+amJ5DHOgqRiQ6re9Vc64PmGIt950d9dy6rKXHa85V+hxoUeor6129Ff/6ZUopwC+LrV16xME9zVNRH39gVdX9eRTCUi0ijdkIauPEPnt0ynYZN20CN+lbxsI3tfoR1zJac352dSm45L6WGBdVJlNx1F5TQtadKNdL007hbNqqMj5HaLyaTjTnrD8fWyvjXV7L6bAnI7Vi6UTgyrQRatfqfLrzPlIn9SAh3va6LMIVTnlBv2oMNZkrDj6HAPw9S8c0gtaeL1nN4n/znlXOgt2rewC1WUismw637+85MNLpJur+hOLfuupMt+YfRi63CyMpWSYZe9FCmsohJ7gvqX1KAqU29l+g6Io1uz6pAO33eUxyA26Ug733AkD1hPrWt2p91ZKlh5HE1I06A9bS+wL72ccBR6ax8t7FJR9Xnruj+bGsjm+4ZhGOZrYCPlBUFcBFYtnVGvjg2qZkojERexQkvneqhjUxWfk2GiCD2DE7eSUNOuCYyEsi8R63MI01q1wPz4EVg/yQoFNnEgxSE+AShkaJhperRwXDp/GwkkhZmdU66jq3kmKgxDAwnkCfFI+YHbqO8xBB3ohsIQw7T3Pty7shGTW5p9u6natGwwoJ81NOOuYd+Bf9WjfnqO6NOpAiQfr+DoqXfqETjFO5w8k4D2vWoK1wUIJGboMG02ulmXgqFJLfQZ2Q5lRLHwefwv1IknSfBc9RsuVOgAB8krBAYG8o9gcObVUUbhiwO7rkA5qK5bzBINOjrAkj9wjRr98fvY1mg5aBE2/tkPNXXzto3sfYV2zEmsDw5Na4UW8+MxYv0kWBXg7JYUF48EFIKhYbqeoWuL6atGoZqmMrtbgQ+nFuC3zcsw4Z9aWLuxN8rncqyxl+Zhyl1n7Dg8G06mXyNdR5lqI03LOycZ/zlTxrYFQ1zCFl2bW0JbpI36TvYwEMpTKV7hxLhm6OPuiHVbx8PJvDjKVdRG3FsN2Dg25WsyjdznNjw/iGBarnzGvg1d2E5fhVHVNFXHofhwCgt+24xlE/5BrbUb0TtLBYtRorwZDBLu48b97FJYZDg9pBwK6etDP68Pw7qYfTe7hDD+vWy7ormlNkTa9eFkn6UGsv2+YRiG+RpYUF6gxBCn/vXMRCz+rIv5kjxvwyupCMwtS35BgMkh0vsUVo1pgVr1BuF4kV4Y60i4f/Y0Tp/O6+MMLniFCdvLLAkBh/fAXeqIvh0tM+6n/CWCXiWD+LoxM8/8xzp3Md5ucPfNJqlCbILWfTuhlNcBbH+cXdKFhP9DrCv8+1uRwLJ3fzgaJOLugX14qoykuWDc85ehsCQal3YcwEtl+krocZzh2qJbxcytKVJOUZ1KYmiEwmKCPFnIBZc/w9kLvhCF38P+nTuxU3jsuVsEv8yag5EOJdJ+dpdIVfUs0S8E/fSf8PxsI7Ov0Y6ZcJHeOLVqDFrUqodBx4ug11hH0P2z2fTFnB9nLnjlnIKRFIDDe9whdeyLjsqzlnT07WZj5dCK0OA/pCT3w+ZxR1Br2Tw0KSyskC0ZPA5fhNmA0Wjw1e5WJcfHj3GqIWYVsRGKFf38b4KsOAS7X8UL1IKDQ7FMfxDk8F7THwP3GGP8qoGwEBo9/uVLvBXXgqNj8Qzry1+/Qhin/OzpZ9oOT98Os1cORUV1BcNv8zgcqbUM83KoYFEhvu/SB7wKzi4BTRsOC9zw4MGDvD/uHcOEWjmcNPGfU/erL4BaDnDIbuTkk983DMMwBUQYMWcKQuJNmlxJyv/9TEtfSZF4czJVkvJ/W/OVvsLRm7WOpCkpT6OvfP5v+FzELfprWDOyNJSSSKRH5Ru0otatW+fz0YZ+WXpD2GJGycHnaG5ra2o26Qj9m3k3kx/TvLoafJ1oktO6/ExDl0jXZtSnRnNvC88zkvPv+WvbuuQw7hA9L8DflJOTvyQvI4IO9ShGYmklVXqE7PpUajv+KP3d0YjEGnVp3mMZBa7pSN22haarByF9JdOUiMkP55KVhoQqjHFXpwIkHKe+JhpkPtYjU2pAVlzYX9RcU0QGXQ9k/Lk9H9vI4iu1Yyougm79NYyaWRqSVCQivfINqFW2/TD3R5tfllL6zJQ0yRR8bi61tm5Gk478m23aFhd+igaX5z8j/NciRDpk/avnJ9K7wmlzK23S0NYjPb0cHobNaFVgTklUeUhfkb+gxQ2U9a5eR1J6CJ3LcZ7Bz5gSkQulzc56pFF9Jnlm6vrc6+3kUlSDyg67SGkzNcbQyQGlSLPadLqTaf2EY73JSKxNLrvSzeuYHhdOpwaX59tXeSwi0rH+lTxzqODEq+OpolSDGv8Z8NVT0LjQzeSsp0HVZ3rmOOXh1/q+YRiGSY8F5QXpKwTlL1fak6aoMHXel222J09OMRFReQqylPntO8fZU/mK7WnFnZy293m4sC3UxqAwtd0alilg+8gHqsZ84CGhsiMvF1AOewwfzJQgrfoL6Vmmv9gfn7rSlRc5BAW5kYfR9bX9qV7HtRSc94gzi7jLI6i8REJmQw/Szn5taMGTZIo9N0Q1B7TluD20qGM/OpiaX66Ux6Bcdo6GmEpIs8FiepFNlMJFhlKYEKzlGJTnYxtZfY12zIby+oyd48i+fEVqv+JO9jnOn42jsC1tyKBwW9qa3UTYMi9a6FQsNfdZVKgJLfPOKUxTSqDTg8pTs7XB+ThJSS8PQfm7ndTRUKRaDpEW1Z5zN5e6/4yg/ONB6mYkzaZN5eT3ZxPSlpanUW5pS7iQHdTRWEpmwy9l2Y9Ej3FkIdWiVpvDhZLMZOS10ImK6aQcTyFqssw720BYdnEYlZFok/PfH4SSjGRRbyksNJRC8/oI+0AxOTTlx4PdyEhalkZezqlmc/6+YRiGKUjZ/FbH/DjEMDYrjUKiGNw8exmRQmkqRRhOj7dDq4UP8pTrqMxv77vKDfd2OeLmuD5Y9TjnDOL8EhuWhVmRBAT4Bgk50CkM0KKXC8pIOLw+sRNncrrDpiIUFxb/gWNv+OWKCDw58RfmTf4TF7Jbn3sLX/8ISE3598zwS34SHu2didl7nwrP80YR7oXTh9zx7Ol9PI3KMYEjT3TtB6FnNTFeHxyDeVHt0LeqFHrNhqJPNRH8do7HHuMuaF1EWDk/NKxQz0oXyXc3YME/IRnTTGLuYc3sv+GTLDznKaOeLPK5jYy+QjtmR3l9Rt9VcLu3C443x6HPqse55LnnlxiGZc1QJCEAvkGZp4iMhsfcibjX+QjWu6jTOOjjdSyatAUBOU68og37ri0QvHUtbhbEND+kUP4vHQ6+e3fgcrSyNUUwqDMRqybZFNx1ILzEO1dw86MhGjvVh0Z8XLq5wRPx8N5jJGlUh3Vt4R0Vb3F2/mKcjSyMRo71oSVPyvBZ16xuharaCrwPDcn0HaAW7TEXE+91xpH1Lurra+gjri+ahC3ZVHDyhw+IFpdFtarZ5HjzPeLChFooU6ZM3h9lm2LB/exyyhNx58pNfDRsDKf6GoiPy2a+2hy/bxiGYQoWC8oLEiUgXqYeBEpOSs4Q9FBCPNSLkpGUnMcAhafn0BGtigNhB6dhxJbHiBHKoYjE7WXD8WfMaOz4wyHrBVo5EsPIdhwOHBqAgD9X42GB3Q1DrEybz3Yu38LOC7BmUGVohOzH2AErcTvD7aoViPI5inmDZuB+vaFwMeU3IjZCJWM/HDwbBoPC2XVROf8+BJHyDTPQROOFt3F9fn3hed6Ija3Qtkc39GxsDg2hLL24a7+iqXl5WA/cBd8cAzSBlD/x6WsLrVgxGvb5BWWUuyitjYEDG0MnthAcejZDxvRjQkKCDEQyyGRp9aJIlCGR7y8yPlBSBdjiEug6cSAqS4Oxp39TdJi8CrsO7sfWPyfCxWES3riMRMo1ahQfr3ptMv/aDM2bj21kp+DbMWdiI1uMO3AIAwL+xOqC66T8hvl+quw/GdqRw6vDYzD6QTssGWqHvotnoalqfxWIuDgPk3YFqS/czYZ+81+xtN459OvyO1zf5Hc/ExARmS5fXPYOYal1moQ352ah/3wP/jOvidLN5+LYqQWfyHHPLw7+t+8hTMsWjnX/xYGd7nifeqBiSKR8HXCR+BChLIzBo+3r4Skvopx+B41sXuHQuiN4ln40wNARLRtow9/bO8uJFPfqMMaMfoB2S4bCru9izGqqvCSbr+GIi5g3aReCMlSwHP/6+CGxTAu0qZtdHrg22m8L5b9Hk5Gc14fsCRbXz2ZbnD9u3wuDlq0j6v57ADvd32fT1jl93zAMwxQwYcSc+SKx9OziTlo/05nKSJU/zYqpsPUgWrblCN3196aLO9fTTOcy6lxKcWGyHrSMthy5m8dbScvp5ZGhVF2f365Ij0rXaUYdf+lM7dp0ofFb7lHkF6RaJDy7TndCv2AD6eU4JaKACyP35X2pfilt0ihiSY3b/UI9u3WgFk7NyWXocnJ9k/63ZTk9X9SQyg06k/1tupVTlNXPNEUZF06Pj6+nXycto/NZ7vueN3G7O1Jhh9WZ0lc4Ct3RkYwlfP1ndwvubHAhW8ml7njySLfz3Lu91L3JdLqd/uXy1+S2bgw1Vd5KXSQlU8dxtM4tiPzOraTxzdX9RVK8MQ3/65rwAhn5HZ5EjuX0SKLsS/xrClXqSAsuvRHybvm+4voXzehYWZWCIS5Ui3rN/YsuBqT/zf1T2/iEgmzHvEh4RtfvpM/B/zJZpkTkj+fc3FZkoScm7UouNPf4c/I7MZkaF1VObaqKl0lsWJfmXM0lkVj+hi4t6ETVTcuRTds+NGLCVJo+fbr6MWsjXY3Iuvdy/5O0eGQrMk9J5VA+RPz3hnlDatnWmRxtq1L5clWpQfthtGAv/12Rt8bJZ/pKMj35zZo0NUqT3aDVdC3D/IZEUZcnUnW+XvTM6pJDq5706+lA8l5oSxriIlSr22JyzfIFxtGHI72pdOlBdDo1g4yjsHNzqZWFHom1K5HL3OP03O8ETW5cNDVtB2JDqjvnatpnWf6CljQypIaLvPPWJ79E8hP6zVqTNErb0aDV1zJO8Zgiu+8bhmGYr0Ck/A//xcj84GTBN3Hy7G28ppKoUa8hbK3KwuBH+ik18QpGV26FS53c8GR5o5xvZqSIQfDTx/B7lwQdEzOYVzRHscwTpShCsMG5Hq4M8Mah7tkMDXLPsahRTSwqswthh7sj5eVJ1yei1lAxtnr9iYb8DiTdWY2hSz3wMaceLioE+6mbMU4YQYvf0wmltjXFk8tj1SPcqThEvfHDhXmjcX/AGSxVbjxXiYgIT4KhsYFqNFBNjqjIBBQqkr7sMyniEOYXgHDN0qhUrki+ZkJJ9aXbKIh2/A4Sr4xG5VaX0MntCZY3+lQ75o8iOgC33DzwMOAtomVyqL5ZJWZoNqIvbA3V63xdCgStcELLgDl4sc5RKPuEpHfw809EiUplMs7UI0h8+xxPXwOlq1VGceWdy+KD4eWdhHJ1LWCYXUfmArCtawec6XABh/uV+qy+HndtEppM1MJa94Vo+MU3oPq0pHd+8E8sgUplspk1RimH7xuGYZiCxoJypmDIH2KOtS22Wf+DgC2tvyzvNfYQulfegaaX1qJFsfKoYJzpT2XSHUyzssPeRifh/3cr4b04vFjcBK38ZuEZ//6qO58mxSIyNhGKHINyMbT0i0BfiM1yDsp5svtYOvUqWq+YgOqfFQX/H/pUO34H8odzYG27Ddb/BGBL6y/qpT+gzwjKvwJFpDvmDj2COqvXoFOp/LW5IuISJnffgsprdmNo5YI9afps2X7fMAzDFLzv/1eS+W+QVkOHTlaIu7AN+/2/LAeYe/8aYZAj6MFb6BXJ3EU5hJzfguPB5dGxS4O0P5CKt3C78hI2zRqpA/KCFOWJbQsvofKkMSwgz4fc2/H7kFbrgE5WcbiwbT++sJsyORAXaYr5q9oiYO1ynH2Z3SWf2VO888Dq+VdQd83OHycgz+n7hmEY5itgI+VMAYqBr+tJuPlrwtqlM6w/5xamKkmIikxCoSKZf05WINr7LI5cDUcJu3Zwrm6UtjybUdn8pq/E7XJBqa1N8OjKRJRL/8byeMQrdKH7o8QJP42c2vE7i/GF60k3+Gtaw6Wz9WfdafdHFXVrJ3ZHNsGY1uWFku8o6RW8A/RQpXK6z2ku4v29EVKiGiy+QcpK3uTyfcMwDPMVsKCc+U/gAlfAye48bJfMw4QeDVE8P389FaF4eOkW3LdOxrTLphi1dAK6O7dHfVM2LM4wDMMwzLfBgnLmP+IHHZVlGIZhGIbJAxaUMwzDMAzDMMx3xgYVGYZhGIZhGOY7Y0E5wzAMwzAMw3xnLChnGIZhGIZhmO+MBeUMwzAMwzAM852xCz1/Fooo+D/wQViisrlE0CheBdYWRTKdVcUh2OsRXsUJTSo2QLlaNWGa7/tCK8ApxJCwU7Yvx7dbAN9uoap2S08EsUQKDS0dFCpuhnKlCuPbTYOeh/ZNfIU7591x/9kzeL8xw8Dlw1H3vzhPuywE3k+iYFSzKkqyO8MwDMMw3xELu34WYgmkFAHPdQPhZNcIjZznwiNOWJZKBAmFw+23TmjaYRbOhIigmd9AigvE3y6lYVxpBE5FC2U/pVjcW98HDet3wtx99/BOIRR/c8n4GPIUR2e6oEnjxmisejRB21ErcfjMSRzcvACDGpSCfpGKsO85AzvvRfIh86cpQvdiWKclwrN8yGv7SvRQrMR7nF6yBFu9k1Esy5TtcXh2eClWXwrN0/4q1/fcvhi7H36vTiVH8Pl1WPDnBuzYtQvbNizD0u3X4LbYBfbjjuODRLlONLxPLEZvu/rotNAd4d+tzzAMwzD/l5Qj5czPI/nJfKqrAYLYkNr8HUycUJ4mme7NqkN15z7k//UZuDA6Pr4x1W2zhG4nCGU/IS54HTXTK0StN4dkU0ffXuy+zmQg4tuN/8hBpEcd98QKS3gxD2m1c0ni40ISaZej1ouuUvgndpoLXE5NLUcJz/IhH+3Lvd1MzroaVGv2g4x9iQunq793o4GbfChfXYR7Rxdn96UZZ8O+cZtw9ObwIGo27DiFpb4xR6+3tCMjTUsa6xYjlKlFHe1NxTVr01yvz/oEMQzDMMxnYSPlPxUFPtzyRIK5JXQoGhfXbMC9JGFRCkUEvJ5qoEXb6vis+1GKi6PDymu4d3oq6msLZT8heaAvApOLoWIl4x/i5yCJrh60RcITaEBbSzU0q6ZvhdGb5qOloRgkC8LZWZ3Rfb035MLiApWP9pXdvoEHScVQr1GVdH1JjuebB2BaUA8sHFwF+eoiYhM0nz0WWqsHYsXDeKHwG4i5gHmzfNF8Ytu0O70qwnHb/QGilaPhoow9RL+SJcpQIHx9M3+4GIZhGObrYUF5QePC8fjYOuy9mzHoiAu6heO7tmLn8bsI+ey/9TFwvxKBtmsXo0dpMZK9t2Hl8fCM6QNxHrjy2gbNav5/3yKe5Bw4vntLv0I1KGICcevETvy1YjH++GMBFizgH4t24XaUsMJnEJf5Bf1aCtcIKN7D9bc52B/6PfMnkvDkpicidG3QOF30zvluwJgF8egxO12Amx9adTG6vz5WDVkMz0Sh7CuLPL0Nx7WbwLFC2olQ/M0l+PV8RfRol4wj2y4iVihX4TuNhD/54LsQwzAMw3wzLCgvIPL3XjiyaDAcLcujdpfJ2O2VkvDNIWjfANSt0Qgd+w1G/04NUaPFYtyXCYvzI/4G3AKt0NyuHSaOqAcdeocTqzbjebrgQXbDFc9rOKF+lovWZAg4twrTJ0zDr7/Owth+3TBk6SW8TA2MovHo8DLMHNUH7Zo0wtgTaWFKrNd2jO3eE6NmzMLkkcMwvN9QrPZKP46rQMS93Zg/YSJmzegLh3ptMPtcmLAsbxQRbvh91h68+mpxqAJhJ8fAulRRGBsbqx4mFs0x68xz3FjfC/VKm6B0bRfMOOLHt1gOuBC4LuwKqyqOGLflMp68ikBsbBzi4pSPBCTTl+y8AWxta6SOSCvCL+PQuQ/Cs4KQc/uqKRD56ADmjRmFqXPm8P1rIpZc+Beimo3QuJCwCuJwecUK3K7igk5mQoCrCMPJMdYoVVRdp8bGJrBoPgtnnt/A+l71UNqkNGq7zMARv7RaNXbuhMYvN2Dp0XCh5GtKxF33W4gzM0fFlMrlnuGvWTugOWwxFrawQNQtDzxkg+IMwzDM9yaksTBfgough+fP0p1bK6mVoZgALWq56Z16UfB2GtV/ER1zu0z7ZjejkhJlPngx6n8y/wnbiVcnUsN+x0iVARt+hHqXkhAkZWnI2Y+q5fwadGNKPeqyK1x4Lkh4RjsH1CLLtqvoQcqqkcepv6k2WY48T5FCEVEE7epYiMS6zWn9ayH5NsGVRlfQIsuJ1/mtK9OC/6H+lq1JODzeR/Jc2YGqNZhEF9/xr0l0pzEVpGTQZb+w/NPkb87TzDYdaelDmVDy5WSuo6icpiVNuqHc6xRxdGtWHdIRcrvFJh1p5xuO5AHrqXXN7rQ7IJccYi6UTgyrQRatfqfLr9NvM28SjvclE3FKTrkh9Tictf3jDvcgw5S8c0hVdZ6Tz8spz6Z9VWT0bEcfqlmzD+3+V71fsvNDqbRESpWn3FK1u0rsCepfUoOqTE1XphR3i2bV0SGRar/FZNJxJ73h5BSwvjXV7L6bslQrF0jLm2iSQfvtlNqNvhYuhNY5aVGRnkeE/HeOQvZ0oVLlB9HpCH7XD/5ChXXa0fa0DwHJny8kWw196rI/TihhGIZhmK+PjZQXBHERWLV0Rr06NqhaLGOVKuR1MXLVdHR0cEKPXxeifxUNvlTyGdMNyvHs8n2UdGwCfeVTow6YPNQKWopgHFy5C0HKgUi5L1xvG8PB0VC5hppy+WBnDHW1wuKtY1DbQCgvZIO6lRTwO7ALrimZNknP8dgnHhrWreBcQtjB+CAEhiYh5MFdBPLvITZxRq8BdqikGj3lELijH1zmR2PgtiVozked8ffPwf21Hmzs6qhenrtY+ByahlYt5iN+xHpMsiq4OekoLh4JKARDw3S529CF7fRVGFVNE8r0bsWHU1jw22Ysm/APaq3diN7lc851ib00D1PuOmPH4dlwMv06cwOKpVJIU/POCbL4BOHfBSS79oUC78+Mh8tYL7RcvwG9LdSpKvK4OCSIjFG/Uc3UqRrlPrfh+UEE03LlM16voGuL6atGoZqmqlbx4dQC/LZ5GSb8UwtrN/ZGlmoVl0B5MwMk3L+B+zmksMhOD0G5QvrQ18/rwxB1Z98VXp0OReNjLKDHr6Oq2pgr+OO3a6g7ay6ci/C7osz1p1h8jEn7lUNU2BAGEjkS4r9Rfg3DMAzD8FhQXqDEEKcGVWrS8jVQtbDwhItHXAJBo0IndLfP51WUXDAu39KDgxMfSahIUWPEJHQwESHGfR3W3ZaBe+OKa5ImcEoXcIXun4zxB97BdtxstE9/wqAIR3g0gZJlSBTiEbnvJVwJEKFWC2ekZCfAsCW6tDRG/NX5GLT4PuKhhWbTp8Oej58VIfsweepp6PWdh+GV5Qh2W4pe/Q+i6IQ92DHKUthANrhIeJ9ahTEtaqHeoOMo0mssHOk+zp4+jdN5fZy5AK+wHNJFkgJweI87pI590dEyfVDO07fD7JVDUVGDbyiSw2/zOByptQzzmqQ0UnZk8Dh8EWYDRqOB6ozo65B//IiUKeaVfcmoWFHh3wUj2/aNOoeZo7ci0nkmpjTUEwoTcf/qHURr1UXjBmn9VP76FcI4iSoIzvzFoW83GyuHVoS6Wv2wedwR1Fo2D9lXqwiF+ICbPrxCcOYsGoG2wwK4PXiAB3l+3MOxCbWEV6enpZoWlOM4/jQnCY9Xz8IBozFY0M9MfQyJfP+HBjS10o5IbNIafTuVgteB7XicZdpRhmEYhvk6Mv9tZb4aBd6d2ISjHxti7q5FaJbP4E7x3hVXOTs0Sw24+cYr1hmTh1SHVO6LnSsO4cVlD8Q2agbzlICL88WOdSfwVscePXtUQPrwVBF+B3dfyKFRzRp1dJQlHF5dcsNTVEXL1hXT1hWbos/qlehUMhY3FwzEvKsxwgIOvrv+wulwXRi8P4nZo8ZgqYcxRp95DLfFbVEmUyycRoFIzwNYu2YD9lwJRDwl4/XV3di4YQM25OuxDZcCspufRI5XbjtxIMAc3Qc7wyKbwe/CjvOxvF851ag08e/vc/Y4Huc6KBqPkDeh8JhaBYUyjM6mexRpjtWqnys+F4eQ0HeQpwTlkuKwaVBZeFIQsmtfDi+2LMG+1yXRaZALTFK6Vvw1HDgZCFHVhmhknPErgj+NE/6VWWE4zl+OfuWkfMitPNnzwdnjj/mANzvK+fM1IVLEIeZjDnWmVxwVLCxhaZnXhwXKGmfzS4ukKEoW00ZCbBzkwbswe10kevwxFinXQSd+jEGiQQmUTM2b50lKw75PH9SOOI1tZ1/wrc8wDMMw34CQxsIUhMSbNLmSlI9a0nLKU3BvDlJf27b0550ooSR/ovb3pCazPLPMPc6F7CAXYzGJtKpTowZ1aOyVdHnZEduorY6INGwX0XO5UKbC0cu/WpCBpCh12CbMdc6F0d+t9Umj0mTKkIatwtG700PJUkNE2o2WCNuKpG1tdUik3Yo2vk2fn5xXHEU83Enj7MtTxfYr6DOrJQcchW1pQwaF29LWtImp05GR10InKsbXjfIjAFEharLMO5d53RPo9KDy1GxtdvPC582nc8rf0c6OhkJetoi0as+hu7mk2Oc7pzy79pW/oCUNNUlcvB+dSE2f5uj1nh5URktK5mPdM+SOJ3qMIwupFrXanOmahRQyL1roVCw1Z19UqAkt886uVmV0cVgZkmg7098fhKLMZFH0NiyUQkPz+gijDzHZvZecvBfYkF7DOfTXwApUquteCk1tRDk9X2hLBg6rKCh9w8Ycp74ltKj+wmf8GgzDMAzzbbCR8m8h6Tm2L7kJp22HMaleYSDRB3/9vh0v8zywGo/rrkGo5ZQ2O0cKcclumDKwEqRJT3ErsCacbNJGCxWJCZApAM1SZhlHrmOvY9Uad+i2WoAlfcqofy6JccelWzKYNnNGXc1YBL8MxeM1y3BMlV4ghonzb5jW0gBJ3l54kqwsS0JSEr9xiQ70dTN2I1lkZB5GF8UoYtUXq9zuYZfjTYzrswqPP2dGmmyJYVjWDEUSAuAblHU0PdpjLibe64wj612gyuihj7i+aBK2BOTUINqw79oCwVvX4mYO6Rb5Q1BkmqmF892LHZejVdGsyKAOJq6ahHRN+eWytO97KJK98eSFHBKLaqgmvJfizRGsv/AOWopCsGlcB5qyUIQIt7bUrG6FqtoKvA8NyWYO9Wh4zJ2Ie53517sUU/Up+ngdiyZtQdZqTcaHD9EQl62GqinXOGQiuzABtcqUQZk8P8qi6YL7wqvTk8CyQ3tU81mNacfMMOm3bkj7sSka9+6/Qj2Xdiidrgtzb33hHyGFKd+HcvzBh2EYhmEKWMZoivkylIB4mXowNDkpGapQRhEBt5ndMW7DJoy0KapKddAtZIXVkRVRMq9/8aNdcfRKEdSslV2Upg3b0ePRvLAEhRo2g11KWjBPXNQO9jW1kPwuFG9TAiPFW5ydNgZHSs7CoV1DUCnlZ3xPD9z+qIEadavAd8dSHPInBD89iDNuwm3RxQYoaqwD7co1UFV5rSoMUa1aWUhkD+B2RZgrnYuG94E5mLThHtJdN5c7sRFsxx3AoQEB+HP1Qz7ULyBiMd+5s841zb06jDGjH6DdkqGw67sYs5oWVn0IFBEXMW+ScMFsNvSb/4ql9c6hX5ff4fom/3uZEBGZLl9chndhafPLJ705h1n958Mjhg98SzfH3GOnsCDXHPf8y9q+/FGLtKGlJQJFfMB7ZZQd+wR7t71FTfMkBItqoYHNexxfcwx+fF2qGDqiZQNt+Ht780eQHodXh8dg9IN2WDLUDn0Xz0LTwqpaRcTFeZi0K4hfIx35v/DxS0SZFm34EwShLBPt9tsQyn+GkpPz+pDhyeL6wqszklZuheZlZDDsOhXDKqd96LigQzgc2g2zBmRM7YKc7zckUnYhhmEYhvl2hBFz5ovE0rOLO2n9TGcqI1WmRIipsPUgWrblEJ3Z8AuZSoQ0iZSH2Ih6HM54a+/sySno8loa51SaNCTFyX7cavrHKzKbFIo4ujK2FrXe8CbLsjivDdTTqhq1nLyedm5fTbMHd6eBC05TQKbUCNnFYVSa30+9im1o7ulg/p0/0N+dTKlkpdY0Y/Ne2rlsMDnZ96OND9JuD8+9/odG1TYkqa4p1bG3p8ZNXGjirseUMuti/iTQs+t30qUWfJmsUyJyFHZuLrWy0COxdiVymXucnvudoMmNiyp/LhLaxZDqzrnK12YO5G/o0oJOVN20HNm07UMjJkyl6dOnqx+zNtLViGx2Xu5PJxePpFbmKVMGKh8iEhc2p4Yt25Kzoy1VLV+OqjZoT8MW7KW7YXlLmMhv+krW9lWKo5vzbclQok2m1m2p/5SNdC0skbz/qE9afF1U7zCNDjxPn8DC0Ycjval06UF0OqUbcGF0bm4rstATk3YlF5p7/Dn5nZhMjYsqpwZVH6/YsC7NuZpWq/IXS6iRYUNa5P0tkkM4enuoJ5mWcqR+w0fQohM+FJGYQO8eHaXfR06h3T5Zp6ZUTolYn02JyDAMw3xjIuV/+D+czE9OER6Il+KyKF8ku+E9OaLf+OFVbCGUNS8Fg2wuflSmyIT6v4VWmfIwUo1eJiI8PAlFDOR45ReMGJ3SsChrDO3Mm1fEIuR5ACJ1S8PczCjr8u8k8cpoVG51CZ3cnmB5o4KdwlARHYBbbh54GPAW0TI5f2LLF0rM0GxEX9imm43ya1IErYBTywBcebFOKPmUzO2bgkN0sD8i9MuhfMoCeTgCAxJRzKIU9DK3JxeAbV074EyHCzjcr9Rn/NQWh2uTmmCi1lq4L2yIdD/sfB2KMJyYOwnuVX/Hnz3LIvblY9x/FASZcQ00bGABw2wOgHu+CI1qLkKZXWE43F1XKGUYhmGYr4sF5cx/kvzhHFjbboP1PwHY0rogk7N/DPkPyguOItIdc4ceQZ3Va9CpVH7CcgUiLk1G9y2VsWb3UFQu2HOlApN0Zxqs7Pai0Ul//N3qv9d3GIZhmB/TDzKuyTAFS1qtAzpZxeHCtv3wZ7dQL1DiIk0xf1VbBKxdjrMvs5uWMjsKvPNYjflX6mLNzh83IAcXgvNbjiO4fEd0acACcoZhGObbYSPlzH9XjC9cT7rBX9MaLp2t1TOt/FdE3cLO3ZHoN6a1UPAdJL2Cd4AeqlQ2ysPZfTz8vUNQoprF109Z+VyKaHifPYKr4SVg184Z1Y3YmAXDMAzz7bCgnGEYhmEYhmG+MzYUxDAMwzAMwzDfGQvKGYZhGIZhGOY7Y0E5wzAMwzAMw3xnLChnGIZhGIZhmO+MBeUMwzAMwzAM852xoJxhGIZhGIZhvjM2JeJPRPHuGe78GwGF8FxFJIaGbiGYmJqjrIn2Nz3LSnx1B+fd7+PZM2+8MRuI5cPr4ke9J0z2FIgKeACf0ERk/hCIxBJINbSgU6g4zMqVQuEf6cDiXuK22zU8DghBeKI2ipnXhp1jA5i990GQaU1U/mEnAs+dIsofD3zCkKhsDJEGilexhkWRTD06Lhhej14hTmgwsUE51KppinzfDF/BQcG3MRuVYBiGYX4U7G/ST4Q0dKAjD8DuUU6wa9wEbcZtwkUPd1w4sgGTmpdHyeptMfWwLxKF9b82iV4xlHh/GkuWbIV3cjFIhfKfSfLHEDw9OhMuTRqjcWP1o0nbUVh5+AxOHtyMBYMaoJR+EVS074kZO+8hMsMZUUac/xYM6LEGT/N6k8tUsbi3vg8a1u+Eufvu4V0O75H4yh1rhjqgcp1e2HAvDkUqNYC9rQW0fLehZ6XCKOq0Cr4SYeWfkFgiBUV4Yt1Avn83agTnuR6IE5alEklA4W74rVNTdJh1BiEizXyfCHKBf8OltDEqjTgllHyJaHifWIzedvXRaaE7wnPpHwzDMAyTK+VIOfMzCadtbfVIJDahnoejhDJe+AkaYCYhkVYd+vVBslD4tXH0drMz6WrUotnf7D2/gth91NlApBx75R8i0uu4h2KFRUQx9HC1M5XkY0GItKlc60V0NZwTlmWS/Jjm1bOiabcThYK84YLXUTO9QtR6cwhfo9lJpqATk6iBsSaVdl5M195lXoujsD1dyNRpDb3OYde+rkS6vrA3/e4hE57nINmHNg6aRifTdduskunJ/LqkwbeF2LAN/R2czQEl36NZderS3Ief1+e4sOM0vnFdarPktlDy5aKO9qbimrVprtdP/DlgGIZhvis2Uv6zibsOD88EQL8hWjgUFgp5Ro3QoKoUlOiN23cjhcKvTYbbNx4gqVg9NKryM46TCyS60NMWCU8ADW0tpA0468Nq9CbMb2kIMckQdHYWOndfD+/sRsOlVdGjcyGc2ncrX79WyAN9EZhcDBUrGWfz05UCb44ORfOuK/G82jwcOzwNjU0yryWGSZOmaGNTB1kWfRMKfHhxFy8+ZE4CykTxEYEPniI0SXieHcUH3OL7t7mlDij6ItZsuIfMqysivPBUowXaVv+8Picu3gErr93D6an1hZIvp1/JEmUoEL6+uR0cwzAMw+SMBeUFjQvH42PrsPduvFCgFh90A//s3Iy/97riRczn/8ad6HkZ1/jgR8vaCY5FhEKlJD/4B3N8gFkC5haFhMKvLOkJbnpGQNemMeprC2X/ReIy+KVfS6jTmxV47/ob5uwPzZjbryKBRbeuML2wF25Z8i5yRnIOHP9RlGYTY3LP16Lv4J3w17HHnE1TYJNDvri4ZFMM6W71k+X0ZyPGHVci2mLt4h4oLU6G97aVOJ4pJyTO4wpe2zRDzR/pPJBvPAnk4JuSYRiGYT4LC8oLiPy9F44sGgxHy/Ko3WUydnulRGWJ8N4yAM5dRmLiuJEY1rs5ajeYgisxwuJ8kePpZQ+8UkhR06kZSqW2Xizur5mFrS+0UW3gckx30BLKU8gQcG4Vpk+Yhl9/nYWx/bphyNJLeJkynBv3FEcXT8Lgrq3QqMFYnIyTwXvPOLSs3wNbgtJFGYpIPDowD2NGTcWcORMxYuISXPhXhJqNGiPjaYACEfd2Y/6EiZg1oy8c6rXB7HNhqiVxT49i8aTB6NqqERqMPYk4mTf2jGuJ+j22IP1b5UoRAbffZ2HPq2+XwGtga4saKUGgIhyXD53Dh2zeXlymM7pZeuDAxWih5AsoQrF/9kK4R4lh3n8eRlTOJQqVVoN1rUwRuywA51ZNx4Rpv+LXWWPRr9sQLL30Mm0UP4/troi4h93zJ2DirBno61APbWafQ9hXqvr4G24ItGoOu3Z8/6qnA3p3Aqs2P+dPWlLIcMP1OWo41UfmXv7J441+hMPLZmJUn3Zo0mgsTsQK5fznx2v7WHTvOQozZk3GyGHD0W/oanil/zWE73P3ds/HhImzMKOvA+q1mY1zX6sSGIZhmP9PQhoL8yW4CHp4/izdubWSWhmKCdCilpveqZd9vEp7DviQMts24eFcqqshImjY0AIfuXp5fshf0JKGmiQSG5HDtC20c9smWv3HZOrtUIfqtR9FK876U5ywaqqEZ7RzQC2ybLuKHnwUyiKPU39TbbIceZ4ihSL6sIs68vuu2WAhue8fTg6VSpCGngOtDhT2U/aMdvSpSTX77KZ/E1QFdH5oaZJIK9OUW+lzqD+S58oOVK3BJLqozH1OdKcxFaRk0GW/sFz5Vh3JUKxJDRa60/7hDlSphAbpOaymlLfKlfwNnZ/Zhjoufaiq0wKRcJz6mijbTZ1TbtjjMKkOMb24w9TDMCXvHCS1nEjXc0gd/7C3K1Xsupc+CM8/ReY6isppWtKkGxk3yAWtIUddZX+pTXMf5S9XOeHZThpQy5LarnrAt4ha5PH+ZKptSSPPp7b6J9v9o+dK6lCtAU26+I44SiT3MRVIatCF9qcl3fMS6Hg/S+p9NEutZZR4m6bVbkMpH42sEunqxIbU71iM6ln4kd5USgKSlB1CZ1MOIvEGTanXhXaFC88FeT7eCP54C4lJt/n61Pz7BNfRVEHLkiaqGpSjd//0J8vWmyh1Nz960soO1ajBpIuk7tJjqILUgLqkqwT584Vkq6HPl2X5BDIMwzBMnrCR8oIgLgKrls6oV8cGVYtlqlIDO/TqVkU1qqdd3gJltDRRpu1QdLXM/zQZinducPVKhqhILdSrbAAtqQhcXBj+fe6DZ098EZYkzjgDiiIYBwc7Y6irFRZvHYPaBkJ5IRvUraSA34FdcE3JslEkIjFJgirV43DkdhNs83oEH68DGFmO30/Fe5wZ74KxXi2xfkNvWKhSVeSIi0uAyLg+GtVMSZrgELijH1zmR2PgtiVobiJG/P1zcH+tBxu7OsI6yrdKRJKkCqrHHcHtJtvg9cgHXgdGQvlWuYn1OYRprVpgfvwIrJ9klXWk9GsSSyHl6zsFyeKRoAzPs2HcpgcaPDqI43kcSaU4flsoBEPDjBUQfuk8bvNvIjWzg1Nuo+SZKIIPYrDzULhaLcbWMbWR1ux1UUnhhwO7XJGaXJVLu3OBO9DPZT6iB27DkuYmEMffxzn319CzsUOdr5GuJH+Gy/dLwrGJvuqpUYfJGGqlpTqelbuCVKPlcl9X3DZ2gKOhahWV/Bxv0vPH8InXgHUrZ5QQPqrxQYEITQrBg7uBqjQiE+deGGBXSf3rDxeIHf1cMD96ILYtaQ4TcTzun3PHaz0b2KWrBFFhQxhI5EiI/1ZzHzEMwzD/NSwoL1BiiNPitgy4KB8cnrUSlxKLokJZXXDJwoJ8+HjlMu4miGDSZhzm9P8F3foMwYQ/duPqhdmoFnoZy/oMwLrnKT/0KxC6fzLGH3gH23Gz0T79yYIiHOHRBEqWIVGIG+NuXMO95KIQh8jRfE43lNMuBgsL9TSHUedmYvTWSDjPnIKGKRkSifdx9U40tOo2RgMhNlGE7MPkqaeh13cehleWI9htKXr1P4iiE/ZgxyhL9UqIw41r95BcVIwQeXPM6VYO2sUsYFEsp6CTQ6T3Kawa0wK16g3C8SK9MNaRcP/saZw+ndfHGVzwUqfPfDb5R3xMmRybJzYqhqI5nUQUbokeTV/g0JFX2eSdZ5IUgMN73CF17IuOGU7U5HgZ9ArJ/FuKzcxRPq8xuTLlZfJ4HHhni3Gz2yNjs4cjmgjJssTU/cqx3RUh2Dd5Kk7r9cW84ZUhD3bD0l79cbDoBOzZMQqZzynF/Ptw3KeOVgFOwX9GcjgWLvgybuk5wCnlWglpDYyY1AEmohi4r1uH2zIOb1yvQdLEKTWgzt/xyuF76QoCRLXQwtks9WJew5Zd0NI4HlfnD8Li+3z4rtUM06fb8yd9CoTsm4ypp/XQd95wVJYHw21pL/Q/WBQT9uzAqHSVIDZpjb6dSvEnl9vxOB/XEzAMwzBMinR/wpivJw5PTu7EyUcfIJe/gcfqAei96CEfIuRHPK5fvomPMEDDZnZInz2sWaU1mlWSgmJv4qzrO3UAwvlix7oTeKtjj549KqSbTUQZrNzB3RdyaFSzRh0dZUkiPPlgJ5xLQLHWw9DGOF234F5gy5J9eF2yEwa5mKR2mPhrB3AyUISqDRtBvToH311/4XS4Lgzen8TsUWOw1MMYo888htvitiiTsgOJnnC9Fg4uoRhaD2sjvDZnikhPHFi7Bhv2XEFgPB9gvb6K3Rs3YMOG/D22XQoQtvh5uJBQvJOnBOUSFLdpgJwHr3Xh0KsV3hw5BL9c8+TleOW2EwcCzNF9sDMsMm1PS0P4BUJ5IyP1vz6J892BdSfeQse+J3pUyNDqCL9zFy/kGqhmXQeqZs+l3TnfXfjrdDh0Dd7j5OxRGLPUA8ajz+Cx22K0TW3MFGIYFtZAVHhqknb2+JPBiAQDGGb7E4fyAtqr4OyapQXc/HaLdZ6MIdWlkPvuxIpDL3DZIxaNmpmn9ud8HS/3CpfcngJVW6J1xbR1xaZ9sHplJ5TkPz8LBs7D1ZTrPfjP0K6/TiNc1wDvT87GqDFL4WE8Gmceu2Fx2zIZPlOQlIZ9nz6oHXEa286+SPslgmEYhmHySkhjYQpC4k2aXEnKR27pcsrT40Lp1LAqpCUCabVMl7OaF4keNNZcSiIdJ1qXeTJq/n2nVObfV6RH7bdHqMsitlFbHRFp2C6i5xlytTl6+VcLMpAUpQ7bgvlnvOQHNLumBklMB9DJlIRcgfzFEmqoKabi/U6k5atzr2lPjzKkJTWnse4pedCRtK2tDom0W9HGt5n2L53kB7OppoaETAecTM39zQsu4iHtHGdP5Su2pxV3cp3oOv/ykFP+bmdHMuTbTbmOSKs2zbn7qTm579D02vVp/pNP5YJzFLalDRkUbktbwzLW28dDPYiPk0lSdiRdzmMCfcS2tqQj0iDbRc8pY7O/pL9aGJCkaAfaljL3dy7tHqnajja12vhW3Uc+4cO2DmTe5xjl1jKJd6aTVaOFmfpjiija37MJzfLMXF8chexw4etBRFrVG1GDOmPpSrq6yM/xcmF/U2t9Dao0+QZluRyAe0enh1qSBn/MjZYI24pUf4a0W22kXLq0Wgzfh0poUf2FzzLuB8MwDMPk0SfGKZkCJS6B1r+OQmMNMYxMTVNzX/NC7nMZHsEcpDWd4JQ2lKgSf/8fnPXnIDZugU4t1HOXKxITIFMAmqXM0kaplWKvY9Uad+i2WoAlfcqoRr65l65wf04w7zYILTLtVLL3E7yQS2BRrZqQw63AmyPrceGdFhSFbNC4jiZkoSEIVyQhKYl/Q4kO9HUz7p8sMlIYOeTw0tUdz8kc3Qa1yNfxi4tYoe8qN9zb5Yib4/pg1WOZsKTgkYLUvzak4Hyxd8dlRCtDcpEB6kxchUk2n8hol9ZBzw6E4/s+9YuIGIZlzVAkIQC+QRnXNGjRCy5843GvT2DnmfcZ9ymVAqEXFuOPY2/4fymQmCDj/6uJUmYZR3Jjr6/CGnddtFqwBH3KqNsnt3ZPSkrityOBjr5u6q8jKrJIRGYzDFykzS+oc20pVt7NIXdD7ocdCw7BqEtXWGQYYhbEX4drUC04pU5xk0KMkt2mYGAlKZKe3kIg3//Tqj5/xxvjfgm3ZKZo5lwXmrHBeBn6GGuWHYNqfF9sAuffpqGlQRK8vZ5AlV3G14G6S+sjY5eWITJTJXBvfeEfIYUp35bZHR7DMAzDfErG6In5MpSAeJl6wDU5KZkPFvhY4+o8ONVtiuG7fNSBqXIs1qQppoxvjrxfKyfH81Pn4KMMju0dkfYrvQKRDzdjSP+1eKZTC8M3r0ZvYZ5EcVE72NfUQvK7ULxNTTN/i7PTxuBIyVk4tGsI+DhHWYgPbm54qKiAtp1sslw8KdLWgpaIEPHhvSq4jH2yF9ve1oR5UjBEtRrA5v1xrDnmB7HYENWqlYVE9gBuV8JVxw4uGt4H5mDShntQTc2u+AA3t4dQVGiLTp8KarPFn8zYjsOBQwMQ8OdqPCyo+7QkRCAyXb647F1Y2u3Sk97g3Kz+mO8Rw5/hlEbzucdwakETpLttUw6kqNqjCwxO7cOtT137JxbzR5bNHNeFnbFgzSBU1gjB/rEDsPK2UK8CRZQPjs4bhBn362Goiym/DTGK2tmjplYy3oW+VV0YqVrv7VlMG3MEJWcdwq4hlYRUmNzb3ZA/CSsrkeGB2xWhLjhEex/AnEkbcC+befbFxbph6QJz7O/cDMPWnIKn/1vEcQrIIl7iset2TG3TEgu58Vg1wiLboDXa9SiuFKmJWtl1C21bjOY/L4UlhTKlbuXneBPh6XEbHzVqoG4VX+xYegj+FIynB8/ATZi9UmxQFMY62qhcoyo0lAWG1VCtrASyB264InQILtobB+ZMwoZ7MRnaAnK+/Uikyq1nGIZhmM8ijJgzXySWnl3cSetnOlMZqXLaPDEVth5Ey7YcoRvn/6CmxTVJJDEmq05DadTI6bT1Xt7TL7jQG7T91x5Uq7AyvUJE+hb21Kl7D+rW2YWc7etQ1VpNqMu4VXTeP+t0dHFeG6inVTVqOXk97dy+mmYP7k4DF5ymgAypEFF0sJsRaVQYkyEtIFXcTZpva0gSbVOybtufpmy8RmGJ3vRHfS0SG1anDtMO0HMhF4B7/Q+Nqm1IUl1TqmNvT42buNDEXY/T0lSiDlI3Iw2qMObKF09nmPDsOt0JzUtiRW7k5H9yMY1sZU46QmqK8iESFybzhi2prbMj2VYtT+WqNqD2wxbQ3rth+UtN4IJobYvKNOx87tPk5TQlohpHYe7LqW/9UqStUYQsG7ejX3p2ow4tnKi5y1Ba7vqGMiZ8xJHXhp5kVa0lTV6/k7avnk2Duw+kBacDMtX5J9qde03/jKpNhlJdMq1jT/aNm5DLxF30ONecIzmF3dhEEzs3okolC5OuthbpGBQjc+u2NGLFJQrK7vDkQXR57ThyKq1BkuL2NG71P+QVmU27xl2hsbVa04Y3mZfl9XhldHFYaZKI9Khim7l0OphvyQ9/UyfTklSp9QzavHcnLRvsRPb9NtKD1JkOOXr9zyiqbSglXdM6ZG/fmJq4TKRd2VSCckrE+mxKRIZhGOYLiJT/UUXnzNfDRSM4MAI6pcxgovutf9yWI/qNH17FFkJZ81IwyOsVg+kp998/AvrlysNIuPZQHh6IgMRisCilhwyDg4pYhDwPQKRuaZibGUH7/3rkUIGwrR1hf30g7m3vkGO6TuKV0ajc6hI6uT3B8kY53ZNTgZjgp3js9w5JOiYwM68I82K6wrJsyKPxxu8VYguVhXkpgzxfKJqRArEhzxEQqYvS5mYw+q6NqUB44EuIy5YX7qyaSV6ONz4U/m+1UKa8kfrOp4nhCE8qAgP5K/gFx0CntAXKGmtn7M88RWwIngdEQre0OcyMsi5X4p4vQqOai1BmVxgOd8+lXRiGYRgmBywoZ5ivKXwfujU8iY63DqC7kVCWifzhHFjbboP1PwHY0vpz0nqY7y3pzjRY2e1Fo5P++LsVa0OGYRgm//6vxzEZ5qszboPutg9x8MRboSArabUO6GQVhwvb9sO/oPLkmW+HC8H5LccRXL4jujRgATnDMAzzedhIOcN8ZfGXxsFhax3cOdBPKMlGjC9cT7rBX9MaLp2tM9wEh/mBKaLhffYIroaXgF07Z1Q3Yg3HMAzDfB4WlDMMwzAMwzDMd8aGdRiGYRiGYRjmO2NBOcMwDMMwDMN8ZywoZxiGYRiGYZjvjAXlDMMwDMMwDPOdsaCcYRiGYRiGYb4zFpQzDMMwDMMwzHfGpkRkflyxQbh77Q4evwhCpHYVtOrWFjWyvcd6PsS9xG23a3gcEILwRG0UM68NO8cGMHvvgyDTmqisJ6z3o1C8w7M7/yJCITxXEUGsoYtCJqYwL2uCb3r3+8RXuHPeHfefPYP3GzMMXD4cdVX3rP/aFIjyfwCfsEQov7BEGsVRxdoiyy3344K98OhVnGod5ZiDQblaqGma39veK8ApxJCwIQuGYRjmG2J/dv7fKCLg5/eODzt+XIooL+ye0Rcde4zHnwfO4eyelZg5sgv6rHgkrJF/ia/csWaoAyrX6YUN9+JQpFID2NtaQMt3G3pWKoyiTqvgKxFW/pGQBnR05AjYPQpOdo3RpM04bLroAfcLR7BhUnOUL1kdbacehm+isP7XJtFDsRLvcXrJEmz1TkYxqVD+1fFBspQQ4bkOA53s0KiRM+Z6xAnL0ogkhHC339CpaQfMOhMCkWY+zxi4QPztUhrGlUbgVLRQxjAMwzDfgnKknPl/kUjP1rWicl33U5xQ8qOJfbSJejt2oSVXwyhZKJM//Y2sNXSp2bqXQkl+JFPQiUnUwFiTSjsvpmvvOKE8BUdhe7qQqdMaep150Q8kfFtb0hOJyaTnYYoSyvhSOjHAjCQiLarz6wOh7Ovj3m4mZ10NqjX7QWobfTPJT2h+XQ3ig3QybPM3BWfTZsn3ZlGdunPp4efsHBdGx8c3prptltDtBKGMYRiGYb4BNlL+f0OBt2cn4ZepF/HhB01Y4oJ2Y0D33ai4eAem2hWHehBWgYgHDxFo6Iz+XUurSvJOgTdHh6J515V4Xm0ejh2ehsYmmbu8GCZNmqKNTR1kWfTDiMN1D08kQB8NWzigsFAKGKFRg6qQUiK8b98Vyr4+2e0beJBUDPUaVRHa6NtRfLgFzwRzWOoQoi+uwYZ7ScKSFHx/8XoKjRZtUf1zdk5cHB1WXsO901NRX1soYxiGYZhvgAXlBY0Lx+Nj67D3brxQkFGM11GsW3sYD2OEgs+SiPevQhGbKQeFiwmFn88LvIrKHKgkIeDQcDTvth5P4gsyIs9+P9IkIuKlL/zD4sAJJTni/LBp+FT4dliGqTbpErvlz7Blw01UGj8LXYvlr7tyz9ei7+Cd8Nexx5xNU5B+s+mJSzbFkO5W+Cap0Z8j0ROXr30AaVnDybGIUKiUBD//YL5uJShhbiGUfW1JeHLTExG6Nmj8HaLWGPcriGi7Fot7lIY42RvbVh5HeIb+FwePK69h06zmNz9hYBiGYZgvIoyYM18o+d1DOrxwEDlUMCCxSItabnonLEkn8hKNqaxJIg07Wh6Yt1wJuf9+GmJtSsZGRmRkZEJ1p5+kf0ZUJR2xDtX99Z5qHe6tOy3rXpdKlLCh/nPnUq/aplTNZT6dD1G/R9TpoVRBU6SMxvmHiKQlapJDsx606kECee/sR9aljPlt89svWpXGXoqjlyem8sdRVF1mVILabXhNSbnux23y2tKLrE2F7RSzoSk7/qJ+tYxIKuLfU6RHlr/8Td65pBOEH+1NpoatafObTPXy8Qm5Xw2gWOFpnnEhtLtzMRJDShXHeVB+MxES/M/SymnjaSpfnzPH9KVfBi+hi0EyYSlR7JMjtGjiIOrSsiHZjjlBsQlPaffYFlSv+98UKBdWIo7CPXfRvPETaOb0PtTUpjXNOhsqLMs7ZTpGTQ0RadT7g3xSt00Uc28ZORWVkH61IXQkmzyO3I8hlp4cWUQTB3Whlg1tacyJWEp4upvGtqhH3f8OpLS34SjCaz/9OnokTZk9myYMH0UdrTRIs/Ey8k+3L0pcuCftmjeeJsycTn2a2lDrWWcpVLlbsU/oyKKJNKhLS2poO4ZOxCbQ091jqUW97vR3WmXlQRydGWJLIy/JKPnpQmqgIyKRTgNa6J1uGwnnaFjd/vRPlg6TQP5nV9K08VNp7tyZNKbvLzR4yUVKa9Io8jq0lGaM7E1t7RrSmOMxQjlfzw+30ZhuPWjk9Jk0acRQGtZ3CK3KkBtTMO3MMAzD/H9jQXlB4CLo4fmzdOfWSmplKOYD32yCci6czk+qTYZiPjjOR1CuFLG1DWkrg1tIqHRjOzLnAzRlcG3Y7RAfp9ymufX0ScQHn5Wn3KJEfv3InR1IXyQivfq/00O+IDk8gO4udCBNISjXbfkn3X38jN4o4w75M/qjvjJHl18mLkWDziijlFg61N2Q36ZyfU1quvIlH3Z8Yj/47SxM2Y5Il8q3nk67Tu+lYdU01NsRl6CBp3IIjbkgWuukT4U77KBsTmU+Cxe0hhx1lXVdm+Y+yk9ycQI92zmAalm25U9aPgplkXS8vylpW46k85FCEe/Dro58e2pSg4XutH+4A1UqoUF6DquFoPwjea7sQNUaTKKLyjz2RHcaU0FKBl32q16bd3J6saQhaYrEZOQwjbbs3EabVv9Bk3s7UJ167WnUirPkn+UCgbwewwfa1dGQxJp8YOu+n4Y7VKISGnrksDolKJfRsx19qGbNPrT7X3Xbyc4PpdKStL6W4qPnSupQrQFNuviO7yuJ5D6mAkkNutD+lOD4wy7qyH82NBssJPf9w8mhUgnS0HOg1fkJyhOv0sSG/eiYKl4OpyO9S5GE74tlh5zla1st8cYUqtdlF780nYRntHNALbJsu4rSquM49TfVJsuR5/maSRHB10chEus2p/UpFxgkuNLoClpkOfG66ni5d/9Qf8vWlPbxLqh2ZhiGYf7fsfSVgiAuAquWzqhXxwZVs02xUOD9uYU4VLQd7LSEonzQ0tOFhupfHMLCKmLhpW0Y06k7Jg1pgpB9v2O1ZyxIpIXK1auoUjB0iheH8twgznMD1l2SQWpUHlVNDcAHxypiA1NUq1EZpfT5JyI96OmkLEkhhT7/npmPJOf9aKrajq6usB2RPuyH/Yo+bTqhjU0R9fsqIhAYGM7XRFaK0DM4cSsJNe2awEgo+1Lhl87jdgJBamYHp8p5TWRQIPjgYDgPdYXV4q0YU9tAKC8Em7qVoPA7gF2uaWlJisREJEmqoHrcEdxusg1ej3zgdWAkykk4BO7oB5f50Ri4bQmam4gRf/8c3F/rwcaujvDqPFK8g5urF5JFRVCrXmUYaEkh4uIQ9u9z+Dx7At+wJIgzHF5+jkGBxMQkSKpUR9yR22iyzQuPfLxwYGQ5SJR99sx4uIz1Qsv1G9DbQp2qIo+LQ4LIGPUb1UxN9+ECd6Cfy3xED9yGJc1NII6/j3Pur6FnY4c6KRkuikQkJklQpXocjtxugm1ej+DjdQAjy+V9yhv5s8u4X9IRTZT9lu8pHSYPhZWW8nhXYleQMkFKDl/X2zB2cIShchUlRTAODnbGUFcrLN46BmnVYYO6lRTwO7ALqdWR9ByPfeKhYd0KziWE3h8fhMDQJIQ8uItA/i3EJs7oNcAOlQopFxZgOzMMwzD/91hQXqDEEGeOb3mKtyfx+4HiGDe84hfnuUqq2MHZvj/WHN2H2U4GuOF6Ax+V49P8e7/z3I2NGzdiy6V/wcej/Bu/x2OvQOXCXImy2efsC9Nk3A8TVVnaK6SQSJXPRJBKNVLLics+szzJ8za8korA3LIkHwwWBDleBr1CMl8HYjNzlM9jpStC92Py+AN4ZzsOs9sXS/fhUCA8PBpEyZAlppxWxOHGtXtILipGiLw55nQrB+1iFrAoJoUiZB8mTz0Nvb7zMLyyHMFuS9Gr/0EUnbAHO0ZZCq/Po49XcPluAkQmbTBuTn/80q0Phkz4A7uvXsDsaqG4vKwPBqx7npqzn69jiLuBa/eSUVQcAnnzOehWThvFLCzU0xxGncPM0VsR6TwTUxqmJOMn4v7VO4jWqovGDYRoWxGCfZOn4rReX8wbXhnyYDcs7dUfB4tOwJ4do2ApNGjcjWu4l1wU4hA5ms/phnLaxWBhUSwfnwcOwZdvQc/BCSlZ9dIaIzCpgwlEMe5Yt+42ZNwbuF6ToIlTCeG4FQjdPxnjD7yD7bjZaJ/+hFkRjvBoAiXLkFIdct9LuBIgQq0WzjBL6YiGLdGlpTHir87HoMX3EQ8tNJs+Hfb8yXWBtjPDMAzzf48F5V+bIgz//LofJSeORc0Cv5IwDhERCcp8Ep4cIm1jFC1aFMVtR2LjwcM4fPgAFnUspVr641Ig4vUb/sRCF/oGuZ8I5IeWhlDZYkkeAz8OvjvW4cRbHdj37IEK6c8O+ADuzt0XkGtUg3UdHXVZoicfAIaDSyiG1sPawDj1k8RvZ9dfOB2uC4P3JzF71Bgs9TDG6DOP4ba4Lcrk86wj/vpl3PwIGDRsBrv0F6pqVkHrZpUgpVjcPOuKd6rAMn/HkOjpimvhHBKKtcawNsbpvgw4vNiyBPtel0SnQS5ps9LEX8OBk4EQVW2IRsIBc7678NfpcOgavMfJ2aMwZqkHjEefwWO3xWiberCJ8HS9hnAuAcVaD0ObtMrKO/4E0/UqB7tmKQE3T1wMnScPQXWpHL47V+DQi8vwiG2EZubC+3K+2LHuBN7q2KNnjwoZTvgU4Xdw94UcGtWsoa4ODq8uueEpqqJl64pp64pN0Wf1SnQqydfzgoGYdzXlCu2CbWeGYRiG+Yy/jkx+xJ6fgzkPdZF8YSUWrzgHPzlfSG9wdc9Z+H9ySpJP0YOxsY66EYkPykvYomOXLuiS+ugEp6ppE+j9qORyviIUEQgNlQklmXGIjYxG5jllciZFuaqWKMRXDOfnA59koThXH3Hr5iMkS6ujiX36EWZ+114fw+FrCTB2HorewtCv3McVV18DJTuOQp/y6SOwGNy6wW9HswGGrFqGFes3Y938QXCySMmbyI8k3Lt0Fe9JG9ZO6VIyVBSQJarvbqmhpyfc1TM/xyCHj+tVvEZJdBzVBxkOgfPHiX/uQGbsAOdGqfknePPPNpwOEcGsQWOkxL0xt27gUbImGgxZhWUr1mPzuvkY5GSBDEcr9+EDalVlYVSf8p/3a0iMO9yjGqQF3ALNuqMwsY0REH4Gy4ZuxUubZqiRchb28RZuPkqGtHoT2GdIK1Pg9bHDuJZgDOehvdWj+YoPcL18H4oKzdGqWsbTOIlZT/y1aRAqck+wduYGvFB9bguynRmGYRiGBeVfXcLrVwi4uwO/zpiBGXP24Ykyp0IegFPrjuFZnoLF3Gijvl09qFO55bi3fwcepotrE5/txYZTYcKznKTvAnJwcvW4++dIe6XyX1m3k/2WxTA2K41CohjcPHsZkUJpKkUYTo+3Q6uFD5Cfm1YatOgFlzIScK9PYOeZ93wYlh0FQi8sxh/H3kChSESCjF9LsxTMMgxzxuL6qjVw122FBUv6oIyquji8dHXHczJHt0EtMgagfCCdlMRvR6IDfd2MHy9ZZCSynygzB3wwe9kjGJy0JpxSUzIE8ffxz1l/cGJjtOjUQj13eX6OgXsJV/fnIPNuGNQiUyCZ7I0nL+SQWFRDNeEaCMWbI1h/4R20FIVg07gONGWhCAlX8MeaxNeiBDr6ma5BkEUiUjhY7qUr3J8TzLsNQua3yqv4664IquWUFnCnEJdEtykDUUmahKe3AlHTyQYpl20oEhOgrg6zjCPXsdexao07dFstwJI+ZdT7zQf9l27JYNrMGXU1YxH8MhSP1yzDsVjlQjFMnH/DtJYGSPL24j/DyrICbGeGYRiG4WX8a8J8GUpAvEwdkCYnJasCQeO++/EyNBShykfgX2itzUfQGrb49dxyNE8ZhPwEkvPBsvBvJCUiLe4Wo0yPKehvqQnlpCiyB4vg0moEFm3YjDXz+ACo31kY1BDyvaVSqNK8efKEaPx7bDV2P5HzmyiCUiX01B2BouDj5YuY8Kd4GhQnBNGED+/Vt+XPeT94/LHLVMeulIREVQRN/P+rR3NV/5bJhH9npOfQEa2KA2EHp2HElsdIncJdEYnby4bjz5jR2PGHQ7rgNx43fm8OK9te2Pg4h1C9sDMWrBmEyhoh2D92AFbezniRqSLKB0f5Oppxvx6GuphCLC4KO/ua0Ep+h9C3KUepvOHSNIw5UhKzDu3CkEpCRKj4ADe3h1BUaItONpmv3DVEtWplIZE9gNsV4T25aHgfmINJG+4hJmUn4m/g9+ZWsO21ETkdgvz5KZzzUQbH9nBMl4uiiHyIzUP6Y+0zHdQavhmre5dSt18+jkHxwQ1uDxWo0LYTshyCSBtaWnyPiviA98pfdmKfYO+2t6hpnoRgUS00sHmP42uOwU8shmG1aigrkeGB2xVhvnAO0d4HMGfSBtxTHawCH9zc8FBRAW07pQXMGXyyLqLhevQKitSsle3rtW1HY3zzwpAUaohm6XJ8xEXtYF9TC8nvQpFWHW9xdtoYHCk5C4d2DeGDeXVxoqcHbn/UQI26VeC7YykO+ROCnx7EGTfhXvtiAxQ11oF25RqoqrraOY/tzDAMwzB5JczCwnyRWHp2cSetn+lMZaTKaQLFVNh6EC3bcoTuhqWb+jBuN3XUyd+UiPJ/D9NoW2PlNDnKeJZEepWpw7QDGeb8lj3bSyMblSJlHKVcByJNKmYziLZ4pc21zAXvpZ5l1dMTisSFqPqgg6nzacden0cNjaXCMn0yd55IY1qYkFgkIV2T8lSj8S+0/Mi+XPYjmR5t60k19JTHrlwuodJtNtCDhxupbRmJUCYi/Rp9aOfT7KYnlNPLI0Opuj7/epEela7TjDr+0pnatelC47fco8jMVcWF0fYOhvy+aJDVXC+hMDschbkvp771S5G2RhGybNyOfunZjTq0cKLmLkNpueubjLeJj/OiDT2tqFrLybR+53ZaPXswdR+4gE4HpM1PrhJ1kLoZaVCFMVco0xIV7vU/NKq2IUl1TamOvT01buJCE3c9Tp22T4kL204dlFPkaFjRXK9MdcKF0o3tv1KPWoVV9S3StyD7Tt2pR7fO5OJsT3Wq1qImXcbRqvP+Wedfz+MxRB3sRkYaFWjMleyOII5uzrclQ4k2mVq3pf5TNtK1sETy/qM+aYkNqXqHaXTguTAhIvea/hlVmwylumRax57sGzchl4m76HHqwUbRwW5GpFFhDGX7Vrzc6kIedJnWjnOi0hoSKm4/jlb/45W1P/DiroylWq03UOZp7uO8NlBPq2rUcvJ62rl9Nc0e3J0GLjhNmZtUdnEYlZaISK9iG5p7OpjvkR/o706mVLJSa5qxeS/tXDaYnOz70cYHaROg56WdGYZhGCavRMr/8EET89OTI8LfC96v4qFVohJqVi6OLAPxSeF4/uAZPhatijoWRhkugJRHBuKRTxi0zWuhSgltvH14Da+LWKF2ucL5mCHjy8iCb+Lk2dt4TSVRo15D2FqVhUH6tIP0kiIQcHQG5n2YiV1jygqFOVEgJvgpHvu9Q5KOCczMK8K8mK6wLCt59Bv4vYpFobLmKGXwmUeviEXI8wBE6paGuZmRkPOdUVJEAI7OmIcPM3dgTNlsVvgCX34MHKKD/RGhXw7ljYSLZuXhCAxIRDGLUtDLsLsKxIY8R0CkLkqbm8Eou4P9hC+uCwW/by/FKFu+SDY//8kR/cYPr2ILoax5KWRfHfEI9X8LrTLloT7cRISHJ6GIgRyv/IIRo1MaFmWNs7ZjHtqZYRiGYfKCBeXMz4kPwi4vXofoAXPQueTPGQkpwi9j8bpoDJjTGT/pIRQYVhcMwzDM/zv254/5KcXfd8X7FuN/2oBcOTJ73/U9WoxnQSirC4ZhGIZhI+UMwzAMwzAM892xcSmGYRiGYRiG+c5YUM4wDMMwDMMw3xkLyhmGYRiGYRjmO2NBOcMwDMMwDMN8ZywoZxiGYRiGYZjvjAXlDMMwDMMwDPOdsaCcYRiGYRiGYb4zFpQzDMMwDMMwzHfGgnKGYRiGYRiG+c5YUM4wDMMwDMMw3xkLyhmGYRiGYRjmO2NBOcMwDMMwDMN8ZywoZxiGYRiGYZjvjAXlDMMwDMMwDPOdsaCcYRiGYRiGYb4zEfGEfzM/IEVUAB74hCIxcyuJxJBINaClUwjFzcqhVGFNYQHzaQrEBnvC9cJVeAW9Ryzpo0T5GmjUsgVszfQQe/co3Axd0N5SIqzP/GzkYbdx42012NcyEEq+AgUHDhJIMgxtyOF3yxO6Ng1QSioUMQzDMEwesJHyH13yR4Q8PYqZLk3QuHFj9aNJW4xaeRhnTh7E5gWD0KCUPopUtEfPGTtxL1IhvJDJjiL8FtYNtIWFRUP0WXUXivINYG9jBvgfxMg6JVGiYhVUbz0VF17JhVcwP5dE+B+dhE6jLgGl9ISyAsS9wpUNU9DVtiKKl+yMneFCeSopSmjex6xes3CW9SGGYRgmP5Qj5cyPLpb2dTYgEd9cyiYT6XWkPbHCIl7Mw9XkXFLCLxORdrnWtOhqOHHCMiYNF3qCRlbXV9WjxLQb7XuTsZYS/j1Ag6pok0hSlkZclgmlzM8jlrzWtqMyJdrTlqCv+Angwuiv5lqk2XgZ+cuFsgw4Cj3YkypU7EE7XyQKZQzDMAyTO5a+8lOQ4UQ/M3Ta9R7KcXCRYQ8cCt2HLtrqpcp0jFd/t0PN4WcRxa8gNmmOVVfOYkw1Kb8oDNe3b8Sx+2+RkGNLa6BGn6UY2ZDfYJI3Di3cAve3MtUZgEi7Mn6ZOQZNTdQ/qigi7mL/xqO4/fojOA0TNB40Ez1rqnckMegMli/YjcexSYiN10K5mmURG1cVC5f3RSlxHB4dWo29V18jhlPviEhaCGUc+mJCp3K4v24G9ngnq8pzIirUACP+6IjEPUuw+244klKOR6SNYtXbYuAwJ5QVReLu3rU4fCcMsQoNmNgNwawe1aHFBWFrp3oYelJZh1JUmXodXkvqI3PSj+zRErRsuhHVjjzHX05aQqlSHPzObsGGfe4IFBdFUXEE3slM0ajbcAztWBWFhbWUkrwPYeEWd7yVqWoQ2pV/wcwxTaGuQgUi7u7HxqO38fojBw2Txhg0sycq++f3Nd1R7N52bDx2H29zblho1OiDpSMbIrWrqHB4d3cf/vr7GK4HyVGkiAbk4hKo4+SIGvJb8C73B2Y5c7iR5zbpDZPb+dkXOV6cWomdlwIRmSysLxJBItWEVEMXxqYWqO3UFi1qFcvSPjlT4O3JobDrshOK4ZfwdE3TTMdcgOJPYYB5F9wfdBMPFtTle1M25A8xx7o+/pQPx3GPVWhpzH6UZBiGYT5BFZozP7gEOt7XhPg/68oIhvignA4nCItSRB2kbsZi1XI+LKeiHXdRSOpgYQTt7VJE/XqRFjks86EPUVEU8TaIvM6soK4VC1GbrZHCusqBwJM0yEKaui1jhyXkmW5knuSBtKZZKWq96XXqiDz3ei91K12Emq35l5KVzz/coCWtSpG27SJ6njqamEBuoysQH8Tw25VS5ck3STUezYXQWqdCZNFpCZ14EED+G9qQrki5joTMBh+noBc3aO90Bypp3IOOKI+bC6UTgy1JQ7UOSGI2nC5lqI84Oj3IjMz7HqGUwfCEK2PIXKpeH+KSNPB05gpMEUceE5vTmPQj5QnetK13ZdIT61O9WdcoQrXNGHqw2IEMxVpU3mUNeX5UrSngKOzkILJIfT9jcljiSRmrcA01K9WaNr1OrcHPeA3fsnu7UBGx8jUi0nJYRj4foigq4i0FeZ2hFV0rUqE2WymtZXncO3L71YFKSPl2dVxEd6PUxbKgszS3mSlfp/rUZX9c/tuEl799kZP379akoeoLEirZ6zB94OQUE+hBa3tWIh2xDpVvPZfOBil7Ux5EnaWh5SXqtj2VU9sWjMRrE6iihtknfk1JpJtTKpNUJKUKQ89mbIPkcAr4N5T/NDAMwzBMGhaU/xTyEJRzL2llU03VctU6Bm1p69uU4C2RPMaaq4NhkQF1PcAHXTzuYziFyziKOjGMhm5PHzZ8pO3tjchAXwjyRRpk1nUn+aXGRzK6Mr45TfRI+WleTs8X2ZKm2IS6H0of3B+gPq3n0aPU16nXUwdimmS/8qU6qJf708pBU8lDiEDj9nchfVUAKKWK46/ye8/j3tLevkNpn3rXicLP0FALDXVKDx/AttkcyG9dEOtBk5oNpKPpjt99TAWSqN6Xf2hY0+/e2eYdCLjUkw1lXbiOr0Ja/P6ITXpQusPjd/QSjSjLB4J8/ZQb8A+lvp3Sx+3U3siA9FVBKt8efBDXdaef6oRFRXaFxjefSKlVqPQZr0n0GCucbIjIoOsB/pSCx32k8HAZcVEnaNjQ7RkC4cBtLlRcuX1pFZp6K1NqxcfrNLNuMXVQ/hltkr99UR5uO9JStYmUqk2/o96mkuweza2jybetiLSrjKTTGSo2OxwFrXUiPeX+aTah5YHC+sqTt9F1+RMHIzIyUj6KknmzmXT62XVa19OGTIuaklWH6XT439z6QmZyevqbNWkadadDHzkK/9eT7ty+Tbf5x51HL/lTtTRRuzuq6kyk3ZCWPEt5j1g6M6QMScVF+D4r9H+GYRiG4bHfVP8rxEVRuqQu+CBVhRJ84f1vugvNRClLUijwavcSbPUnFHYciSH1dYRygUZ9TNs8HnX4qAKUjOAjI+Ay4Szeqq4jFUEslqTbJIew0PdQKD7g6IQumHnEBzF8qbi4C6ZOdkDRdL0s/V6k/ltignZTx6FRbtfliYvBZe5ENErJZzBqhd+XdIepcoIURTjO/zoVB0OUO5eI+yuWIazPPLgUE96Y368n3q/5vRSICsGwSOb6SE+cegU0F7ANC/5+rpr9RlqzERoXEhYo6dZHozp8nfP183LvAmx4kvHCPo3607B5fB2oqzAYR0a4YMLZt3zN80T8e0j4sFO1Zpr8vybrNhSvdmPJVn9QYUeMHFIfqS2beB0r/zitakNxUVvYWWVKDjFohOlLRqG2IX/0n9Mm+dmXbKR2Ey0r9O9lCw0RQfb8b0xdcRdJwqJsKd7g1PGbiFeeDmiaoGRKu4tLoP2SdRhYNgGRERGI4B8fDSqhtqUtWjcygXGzxTh2ZBG6WORjlh1FGK54eEOjngPs9MTQ1EnE2clt0X/pBQQm8J8LYTUl7VIlVX2fZJ44eNBHKJVAr7ABNCW6KGSQ9+QchmEY5r8v/d8Q5qcmhlQqTQuKSIb4XPJ7k16ewIKNN/gQlqdfE9ZV0udPK4lQuOkSHNvSG+aayggxHk//6o1fFt1RBdwZSVGxeiXo8kFU8htXLPqlNirZDsCfF0JR0aEJSn2ylxnA3LIUH67kTte8EsxSE3jFKOayAL+3M1F1Yi70GGbMPIFQ7/X43asV5vUqk9a5KQpRMenrIvM0djlR4MPl87ijjPb4remXKIUiGV6niVKliqreh5If4+KFIHVxClFhNF1yDFt6m0NdhU/xV+9fsOhO1hpM9TmvSS/pJU4s2Igb6oZFTesqSGnZJK9zuPxSfeIgNi6OYtkkQxs4zcXMVsps7M9pk0xy2ZfcSVCqWmWoYmv+hOffC+fgk9tEJkkPce9JonLIHSJtXeikbyNdW0xfNQrVlJWpbM9TC/Db5mWY8E8trN3YG+Vz2vecfPSA+30ONZsq8/1j4Xf2CKJ6ncPto3PRrX4Z6AqrKYl09aGj+kDK8ez2LVUZH6rDftkjvI8KwL7uJdL6KMMwDPN/j/1N+M+Q4+PHOFVgoiI2QrGi2YVUiXi0fTBa2PfE1ue5jj/yxCjbbTOOL2+FEhI+ulBE4tq8Lui31Zd/t/TEKNVrAX5tXgJSZRBCSQi9swNT29SFw+wriFAN834FYjP0WTwLTZUju+Dwau9YNO93GQ3mDUGGwU+RMYwLq6IjQSJkMuGfuVIg9E0YUq5F1NbWyjQSLIKWjqa6jPh1X71WlWYgLotum49jeasSUFfhNczr0g9bfXOJMj/nNbzER9sxuIU9em59nu3IsuLNK4QKbSHS04dBxoMpUJ/al0+RGBlD1aw8xbs3CEn9mSMb8W/xNlo4MKlGlgsv9e1mY+XQitDgj5fkftg87ghqLZuHJumvzs0j2a0ruB1vgSaNxTizYA7OlZ6M5cOtM1zom0Ks3Bd154D8XYiqTE0KXV02Ss4wDMNkxILy/wouBKHv5KlBuaS4DRpUzm4YUAu1BmyBm+dpjK2ZOcjMjjaqj96HQzPqoTDfW0j+GsdHd8bs6x/TTgCUdGtj4qm7OLeoB2oZqUfsiYvA7SXDseCmaqj0q5BUGoE/x9WGtirgCsG7yoMxqkamgEdcBLVrm6cFa4oPeBuWe4CrJuKDp7SEC4XyGgzh3ylIkVLCr6uffpw0He3qGL3vEGbUK8x/4PgA7fVxjO48G9c/Zt5aOp/xGq1aA7DFzROnx9aEVjYNK9LRhW5KOX9WovoB4Cv51L58ikKWANVENDyRfmFkOKfKjEvfLtkdVGE4zl+OfuWU/ZJfN9kHZ48/Vv9KlC9JeOR+A++0tPBwVlP0dSuH7i1Mc/41QZQ1nYdhGIZhcsKC8v+KiNu480wINEVaqDlgCBxymRNObOKAiYMapaU2vAlCSI5xqiHs5h/DjoGV1cGvzBe3HqinZ1TjEPDwEcKlZdBs2j7c9/HA+gFWqpFOkgfiuoe/sN7XoIkqdWqgkCr6EUFTRwcaqvL0NFGniwuqKIdKlbiXePo0Wv3v7CSG4JUqeV6Csja11XnrfDCXEBuX7piVCLF8mSoMFBuilnUVVWm2DO0w/9gODKysrQoMZb638OB9xq1l8TmvEZvAYeIgNEprWAQJDatRqzaqqtI4+KA36gMicxt9Lgi57MunJIWE4INq/8QwrNsA1XMbWDYojuKFhK8yuTzTrzhqYr0yqFDBSNV/QQm4v3IS1mbIiZHhlecFnL8RgJRB9yy4QFy56g/NKi0wvKM1xDe2YevDnH8HUMiTIVd1DpEqXYhhGIZhcsOC8p8RKZT/S4eD794duBytjABEMKgzEasm2Xwif1cMs4FzMEJ5K3lFMPbO+wteucVL4lJwWf8P1nQwFX6STy8Rnmt+x2EhN0JSvCFGbDmDZc6FIRKJoW+gryr/njStx2JudzMhvSYBN06cSk3lyCApAAdH9sXyJ+r5ubUaD0ZfK3VQHB/gi6D0gawiHH7+4XygLoKGRS8MaZX7Ld3FpVyw/p816GCaLvf/Ez7rNWYDMWeEJX9KoUDw3nn4S2hYcYnO6NdanQOvCH2E+y+zRuWKt664cO9zEk6yl9O+ZCetOeJw3e02lJcBiHRqY+SEdtmmh6TStIGtlfpXH5LFIyHLYUXDY+5E3Ot8BOtdiqmOnz5ex6JJWxCgWlcOn5WtUdPWGc52NdB46hVlYRaK9x7weCKGTY8RaD9oGLqUeIYd688hSr003f4L+H1Rj/ZLYF6njqpILRHRUZlP8BiGYZj/dywo/ykkICIyXb647B3CwlP+pCfhzblZ6D/fAzHQROnmc3Hs1IJM+bKEpMQk4fUckpKEqEVLD3riCNxY1B9z/EuhWspopCIWsXGxiMmcKqFZGUN2HcWvjYtk6jhiSJM8sHTGUbxJCYjEBjAsrAGxoSO6tTcVCvn9SEq5GY3y3yn7lFGSLFGYKYWQkKC+iVGukhKFGwkRkpOTs19fXAJd1uzDfAdl3rsC0efmY+zO50hLLU9C6O0dmNjSEfOSB2JsU+GURrM2pm2ch6bGEsgfH8fBx2lJD5zfARy+nQhRYWtM2cyvk26mEkVsLOJiY5C1Codg19Ff0TjjFaMqn/MaSj12fn/4+lTXmxb09MSIuLEI/ef4o1RKw4qLo/vyVehejn8uv4dtqy6ny/fn6+TJLozsuRpPNbK+T17aJF/7wpPx66SQxae1RMydP/Hb/ldQ6FREjw0HMMsm99NLZdu6dHNSpbhQ4luEvEvphEocXh0eg9EP2mHJUDv0VV6DoMzD4o834uI8TNoVxK8RB0/324hWpiJRPHz27Fa/NJO4a+7wTK4KeydTSPSbY/TQ2og8sgybnsbjxd4NOJnpLC857C1UH1NpRbTtWFtdiHhcmVALJUz4Y9sbygJzhmEYJo0wNSLzg5L7n6TFI1uRuY5IGe6oHyIxFTZvSC3bOpOjbVUqX64qNWg/jBbsvUthmadc5sLoxvaZ1LqsVLhNv4j0LByoa+/e1OuX9tS0alHSEEmo7MjL6hv5JHrT4ZntyUJLg8q2mkwr/nmcYe5lJS7sDI22qkkTrqbMLJ1AJ/qXpeKmplSp2TCat3ItLRrtRJYVW9Kc8yHCXMyx9PjoAupeVSdtP2r1ocXHvdXvq8S9Jo8tc6l7DQNhHZDYuD4NWrCX7oRnN6OznIKvbKAJDiVS5yCXmDanqVuupbtxUiayADq7qA/ZmuqSWKRDprXsqXmzxlTbogQV5+tlxMY79CGb18Y+3UuTWlpS8UouNHvjPtq/ZT51q1GCyjmMoL8908++razCwzSzvQVpaZSlVpNX0D+Ps9QghZ0ZTVY1J1BKFeb/NfzzG9tpZuuyJFUO4/PHLtKzIIeuval3r1+ofdOqVFRDRJKyIynzPW6SX56l37takYmWAZk79KQhQ/tTl+b1qG6zUbT1QWTGubPz1Cb53Rc5/XvmTxpcz1CYe59fVrw+dR8xmob3a08Na1iRQ6+ZtNMzPOO+5CbuBk2roUkicXHqd0KYOJ3v++fmtiILPTFp8+029/hz8jsxmRoXTbnJFn8shnVpztUYenNsNNUzLURaUhGJi/ZVvz4DGV0eWZY0LMalzRMfc5Wm1dIjreK1acB29U2z0iST11wr4k9LybDVBgpMm0Sfzg0vRxpiY2q/NTjvx8cwDMP857Hb7DOfRR7yDP7alVDJSD3q+O7FvxBVtID2Ky/cfxYJjdJVULOKKQxyvAruO1PEIviRJ54EvMVHMkBJi5qwsSqD3KblVpKFeuOBTzCi+deYVqmNmqafekVO5Ah55g/tSpWgqsI8+ZzX5Ewe6Q+vx354L9NBicq1UKus8qLSn1fMjdlwbLUE73ufhc+G5hmmJ8wbDv7LmsL2an+8PzVIKPsEWSj83+qgXFnDjBd8cn5YZl8d05/bYeXVMxhbNV1SPBeNkDdyFDUzRrpShmEY5v8cC8oZhvmPkCPo8HC0mRCKsddOYlj5fJ4Rxt7BvM6/QnPBMcy0yX9In1602xjU73ITznvPYLkzm4+cYRiG+TT2t4JhmP8IKcp13YTLO2zgOutP3IzMe8Z23PPDmDV8E/Tn7sP0LwzIZc93YdKS9xh6+iILyBmGYZg8YyPlDMP853ChN+AeVhNOtXOfEUeNw5t7d5BQpSEsPjcbKZUcPpcvAPXboGpe3pphGIZhBCwoZxiGYRiGYZjvjP2yyjAMwzAMwzDfGQvKGYZhGIZhGOY7Y0E5wzAMwzAMw3xnLChnGIZhGIZhmO+MBeUMwzAMwzAM852xoJxhGIZhGIZhvjM2JeJPIwlvbhzBkSveCEnQR8UmndGtpSV+uqmQFeF4cfcFPnDqbifSLIEqdc1R5Gc4PVREwf+BD8ISlfsugkbxKrC2KJLpzDYOwV6P8CpO+FiJDVCuVk2Yftn9aL4iOcK8PeEflXajHXERC9SrWjzjbeMZhmEYhvmq2Ej5z0DxFmfG1UfVJr0xadtb2NhFYXW7umg65yZkwio/DYpH6IV56NikMRo3bgw7l0W4lSws+9GJJZBSBDzXDYSTXSM0cp4LjzhhWSoRJBQOt986oWmHWTgTIoKmprAoD+KeHcbS1ZcQmtebUcZ5Yvvi3XgYLTzPNwVkkQFwW9ID9nx7KNvEYfIpfBSWMgzDMAzzbbCg/Ccgf7oBczc8wkcFQVrJBlX87sE3ORZPzl2Gn1xY6WchKQO7vi1R6acchjVAWZu2GD2rF6pL+XYJ2IPl+1/xYW16ujCt7YzWNqVRa9Qq/Na5Bkz4dT9NgYhrCzDoz0i0G9YcJfP6ydSzQb9BJXBs7Eyce5v328qn0US5xr0wqVcD6ImEIoZhGIZhvjkWlP8EknxfIEAupENoaMG09XCM79kLE6f1QuU8BXw/FpFUAxo/bQCowIdbnkgwt4QORePimg24lyQsSqGIgNdTDbRoWx15bR75880YMC0IPRYORhVtoTCPxCbNMXusFlYPXIGH8UJhPoklEvZlwDAMwzDfEfs7XNC4cDw+tg5776aPjjiEPXaHm6srXIXHlYevkCgs/SSO47eQRlKhK5bs3YPFXc3Tgj4uFqF+PvDxD0Ns+pVzlIj3r0IRm2lwlYsJhZ/PC7yKyhxppiOPQViAD7xfBCEsJpf1uDi89Rf26VMj+rL3CHjuj7C4PO38F1IgJvAWTuz8CysW/4E/FizAAv6xaNdtRAlr5CwG7lci0HbtYvQoLUay9zasPB6ecbQ8zgNXXtugWc08huScLzaMWYD4HrPRtvjnfSS16o5Gf/1VGLLYM+/9KikKr3z5Oo9XZBrtzw6H2FA/+Pjw6+etgzEMwzAMkw8sKC8g8vdeOLJoMBwty6N2l8nY7ZUu2TjpLlZ0aw6nZs3QTPVohRH7XuHTg8UKvD82CS4LryFBKJHfWY72zTpi4TUhGJa/woUFXVGndDX0WHkMRxZ3QmUza3RfdBkhQuzEBRzAUJvSKGpsDGPjYrCecQrHR9ZB2XLmaPrbfdU6ince+LOHNUpbdsAfB/dhhmMFVO/4Gy6kT26Ovo/NI5rDptkwrNp3CMt714GpkQksW/+KC2Hp1lO8x/VVfVGvbB30++sglnevgmJlrNF7nScf0mZCSQg6MAI2pU1hUcUCpYpXRbctPsg2hleEw3WOA8qbKI9D+TBBBYdh2P0kCdE316NfQzOUrPkL/n6S8xkAF+KKhV2tUMVxHLZcfoJXEbGIjYtDnPKRkAz6VHQafwNugVZobtcOE0fUgw69w4lVm/E8XZwqu+GK5zWcUF9LKPiEuMsrsOJ2Fbh0Mku9uFIRdhJjrEsJbcY/TCzQfNYZPL+xHr3qlYZJ6dpwmXEEfqnvawznTo3xcsNSHA0XinIUC6/N/WFduhjKVqqIMqVqY+iJVxlO/NLI8erCAnStUxrVeqzEsSOL0amyGay7L8LllA7GMAzDMMyXU86+wnwhLoIenj9Ld26tpFaGYgK0qOWmd8JCouTHv1PnNsNp/Pjx6seEubTfO1lYmrvkD/7kubwV6YmgzF8hTYc/6Pajp/QyiuPfN5SOD7YkLZGINO3+JH85/wL5U/rNWoMg0qbKw0/RW341pYitbUhbtQ0JlW5sR+YaIv7fIjLsdogo7jbNradPIkip8pRblMivH7mzA+nz29Wr/zs9VBbQRzo9qAxJRFKyHO9BcXxJwqkBVEKs3mbZEZcoQbkav+Tubw2okFhEWvYrKEDOUeDyJqTJ77tIuymtDJATF7yaHDTVxyPSMCWnaTvp9N5hVE21TyBxiYF0Sr2xrJK9aF5dLX5fla+XUpWpt1X7yy8gz1mNqcPm1yQcchZc6AkaVsOCWv1+mV6rX5RviVcnUsN+xyhG+ST8CPUuJSFIytKQsx9Vy/k16MaUetRlV7jw/FNi6UT/kqRRZSrdyrRPcbdmUR0ddZ1AbEIdd74hTh5A61vXpO67A/gjzogLXE5NNA2o/fa0vpcVRx+OD6RyUuV2JVSm+14KSgijk4MsSKqqU5CW898UIawbenwwWWqJSKRpR3+qOxg9/c2aNPi+o115OJ1K6WAMwzAMw3wRNlJeEMRFYNXSGfXq2KBqscxVyiH4kifCS5dH5fodMOq35Vi5Yj66V81baoPUuAKqmhqk/qQh0i+FqjWrwaywGIk3l2Hqzn+RyDejSeVqMFUOs0rMUbOqIcQkw4utU/HnHfWIupaeLjRU/+IQFlYRCy9tw5hO3TFpSBOE7Psdqz1j+QhZC5WrV4FyshCd4sWhPL+I89yAdZdk/Mte49GTMHAkR8D5U3jEb1ZcuDAMVDvGIeTxQ7zhAMWbPfh1+W18VIhRvGoNlJKIUbJ+Q1jyZwTiQkYw0sr4+4CoSHOMndcXbTq1gU0R9TJFRCACw3MYspbWwJCRLcAfPk8OvzMn8Eg5MM4F45p3OXTrbJrDzz+xuDRvCu4678Dh2U4wzceMKGnkeHb5Pko6NoG+8qlRB0weagUtRTAOrtyFIOXAsdwXrreN4eBoqFzj0+Q+uO35ASLTciifqUvo2k7HqlHVoKmsFsUHnFrwGzYvm4B/aq3Fxt7ls+Sri0uUh5lBAu7fuJ9zCgsXhF0r9+Ol8hoFSQm06tcZZbWLw6l5behk/ukm8SaWTd2JfxOV5wSVUU3dwWBes6qqb8hebMXUP+8glwQmhmEYhmHyKPv4hflMYogzBzaKd7h48TI8Nk3D8B6OqGJWHb+sySaNI9+S4HXqHAKSlYObfNCtnXJ1oAja2lqq1BhK9sO504/VxelIqtjB2b4/1hzdh9lOBrjhegMfVZsR453nbmzcuBFbLv2LBGWZ4j0eewXyL7JE94kj0bppawydMQB16C2ePHqJWPXb87uTqAoEo13P4bpqYyJoaqn3Q6vRIni+foU3/ofQt1SmLieVQKpcSSSFNPXqT1Km0edAjFJdh6FzKXWiR/Lzg9h1TQbu5QlcK9wOrYxUxVnJPHD4ohkGjG6gDqg/Bx/4X76lBwenIkKBFDVGTEIHExFi3Ndh3W1+P9644pqkCZxK5PGjJX+NV2EcJPr60M/yEn3YzV6JoRU1+HokyP02Y9yRWlg2rwkKC2tkICqEQvqED6+C+VOQHMTexLUHMtWQOMSmKFtWHdqLJdIs85IneZ3CuYBk9bpa2kjtYXxfU51bUTL8zp3G459tBiCGYRiG+QGxoPxroxgYWXdH19YNULGIFFzUMxyZ3A+/38zz5Xg5UCA09H26C/REqgBYKe3EgMO7kFDh3zmJQ0REgjrwgpwPuIxRtGhRFLcdiY0HD+Pw4QNY1LEUv0yCCt1W4cS+2bD2mYNG1r9g7YPITBcIcgh7HQKZamMKRLz/kLpc29gUxfUzh32fyaA5hverpp7BhXuJY7vOw/vEdRRp2xIp4XIW8SF4E+qBqVUKQV8ZAGfzKNJ8tXq0OweK9664ytmhWbqAW1ysMyYPqQ6p3Bc7VxzCi8seiG3UDOb5OVS+vtT1n43Cjpi/vB/KKc9ciJDscxbHH+fQd0Sa0NQUQREXg485HIci6gMiVDc/UtIEf96UI0VoKN6na2BR1g4G7l0IQllQzjAMwzBfjAXlX5vEEr8s2IqDZ27CNzQIV5Z1QBn44YqrHx/CfgkxTEyM0jUgpQZ2itQITwwjExPh3znRg7Gxjno7xAflJWzRsUsXdEl9dIJTVeW4rALvr/yKZlZNMGjFv2j29zlsGmCFQmnxGU+EwoX0hZhNgZjH9/H0qwRsmqg9aDDsVBNr8ycCJ5Zh/PkiaNsy2/FjNd1SMC1pj2XPPiI2NjbbR+SlcSiXSzAd4+6OqAaZA25N1B01EW2MgPAzyzB060vYNKuR56kQITaGsZEYJJPlkHIihl6ZCqhgpK066aKE+1g5aS18sqtXkiGBPyOSGBRGoRyOQ6yvB93UoDoZybnknoj5vsPvWqrUe/+mdTCIjUxgwr5FGIZhGOaLsT+n35JWKdhP3Iz5zoWgqanO8M631HiID0xbOarzyHlJSSm3xSQkJiWpV5OUgVMrK1VpzrRR364edFVxmhz39u/Aw3S3CU18thcbToXx//DAb4P+gMc7PnA3skYjK12IFJmn0hOjmF1jVFPlo/Ahn89+/H0pu0kG04I61T/TPVXLUpCFpHwvDG9XTNWBFZG38bxUezQ3UC/LlrY9urYIxta1N3NO7chVPK67BqGWU9aAW1yyG6YMrARp0lPcCqwJJ5usw8/xARewbvZUzPp9MbZe5eszhWZ1WFXVhuJ9KEKyC7SjPTB34j10PrIeLqrrFQgfry/CpC0BWU/qkj/gQ7QYZatVRY5VYVANVc2ETqN4i9fCm8piYrKcFGjWbgXHtA7Gh/BqlJiEJHUHQxmnVrD6rPx8hmEYhmHSY0F5QaIExKtyNwjJfJCsDFjjr/2O5lZ10XbaEfgqg12xFFpGtmjXpkKWHN6cJMcnpAZEivC3eCtEY7qO0/CbiymkIgXe//sCH5RvyL3Gc99IKERSlOn8O6bYqzOBSS5PC+KSEtPdnl+MMj2moL+lpipvWfZgEVxajcCiDZuxZt4gtOh3FgY1TICPvvg3RB2CK8Kv4cCWA/h7mytep2w0UabKQZfWGIZJHUtBohrEDsS2QR0xceMxnDqyEdN7DcbGZxxfTTIhxYUn5KLzkR4SU9Iq+H/LUlfIiRHajegBC2WELCmJ1l2cPpErro/mvy5FvXP90OV3V7zJ79WJ0a44eqUIatbKLt9DG7ajx6N5YQkKNWwGOz2hWEWB0NMT4NRlK8QdZ+D3OdMxqEkJYZmSIRxbNoC2vze8050MqXCvcHjMaDxotwRD7fpi8aym6gtcFRG4OG8SdmXKtZH/6wO/xDJo0aau6mLdbGlao9svwsWjfF9x/ecGouQvcfTMw9RTIYqPRYyyqXUdMe03F5jyJ1mK9//ihbqD4fVzX0QqRJCW6Yzfp9in5pozDMMwDPMFhFlYmC8SS88u7qT1M52pjGqqOTEVth5Ey7YcoavHZ5F1ESmJRHxZRQfqPnwSLT7uL0zj92kfb22k/taGymlylDETiaQlqdGAuXTIR5gQL86bdo+2pzIG5ajd3I3014wWZGpQlhzG76NnynkLefJ/D9NoW+O0behVpg7TDlD6WRllz/bSyEalSEuYehEiTSpmM4i2eKkm/+M3EkDbu5QhPpjjl4lJx8yJJm/fTMOr6qimJxTpWlLnTY/V60beopWdK1MhiTCdn0iLStj0pdU3PhCX/Ii29ayROsUjJKWpzYYH9HBjWyojEcpE+lSjz071tnIj96FFDbRJUnoInROO9VPkby7Rgk7VybScDbXtM4ImTJ1O06erH7M2XqWILDP8ySno8loa97/27gMuimOPA/jvCr2DdMSOiIIFu9jFjoIl9oo1tthNQ40aE2Nv2LvGFruJNYpYsAIiIipVqUpROtzd/+0VpAh4KAb0zfd97kX25vZ2Z2Z3/zM7N9vRilQEptR22ho67pdUxLSLaXRlan3q7hlV8L3EYzSscm0a99djCg0NpdCwWK62FCR+fZSGWlmR+5m8d8Sx/5BH15qkxVen2q4edOLJczo5y4kqyaaglL74pO/4M117t98iCv69Fem3XEqB0pkLS5J6l1b3rk4asrJUJUOb7jR/78/USkX6N59UjRzJff8TReI0Ctw7mdpW1qGqLh60aeP31NlSh6q0/44O5FYwhmEYhmE+GU/6f9xFnvmcxKmIDnmJTF1LWJvpKD/euBTEbyIQEBCGZJ4BqjvYw1o+V2EpiZAY4ofAF+lQM6sNB1vTQr2gqYj080eUsAYa1jOTv5cagfv+cdCxbQAbo/z9sxK8ifDHw9C3UDWvgwa2JlDyWTqlIEHMZhd08PsOvp7Opeix5bYt9Bb+9fJFaNwbZIrk4/EF1p0wcXhzKDmZ4XskCWGI4FdBNYO8vM+6PAl1hj9GlzFtYcq1Xnga9ug/sy8KzogpRuiO/uh9tjfOHxmBwhPUKCXNGzPbzIDauqv4tWWBrvpiiJDw9D4CYwWwrt8IVYXPcet+Bszr2cLaUO39W2jiN4gICEBYMg8G1R1gb503TSfDMAzDMJ+OBeXMl0XCNXDCEqFZxRr6wmzcnOuGo92OYWW7sg/5y4I4dC26dLiJsQ8OYkBx0zVKSZJw1WMcjjZag7V9LEoX8EoScXHWQGyzXYu942yLH7rCMAzDMEyFxTq7mC9INm57OKFGreqoO/oYktJv4tizxhjsVDEDcilB9fHY8JsFjk+fhzU7dmH72SAUOSEN3wDtFq5Gz9B1WPF3RNFpiiKJh9eahbjiuBa7WUDOMAzDMF8s1lPOfEGycG5cDfTcGgWYtESvRqowHnsInn3ks7BUaOI0xIbHgmdeA6aaimVFysaLwFBo1bEtMB1hsdJDEBhthro1lRmywjAMwzBMRcWCcuaLIonzxqblu+CToAt7t0mY5FITJca4DMMwDMMwXwAWlDMMwzAMwzBMOWNjyhmGYRiGYRimnLGg/CvB4/FkL4ZhGIZhGObLw4JyhmEYhmEYhilnLChnGIZhGIZhmHLGgnKGYRiGYRiGKWcsKGeUIIFYovhnMbJe3MbJvRvx2w+TMGXTfWQrln/NJGIxlzOfKOU57ge+gljxZ7GUTccwzBenTM4lDMN88VhQzpRMHIatrlYwqj0Rp98olhVBoGUCs1dn8Pvv2xGYYwKhYnmeNAQdWYY1F2OUvPik4e7O37DXt4QvLQfiF1fgObs/mtcyhXnf3UhQLP846bg2vyec+i/H3RJbMcqmYxjmS1G25xKGYb4GLChnSsbThEm1GqhZuxpMSniavdCwCqrpANkCW7RyMi9YsSSJ8F7sjuVJLhjvXOi9YmmhyQh3mB2bih/+iaswvUiCyu0xflYH6L5Iho1TOxgqln8MSfRBrNgdCfvBQ9G4hOfjK5uuopK8OIuFYyZg9fUk1htYLsQIPzwLfV16okePHtyrJ3qPXI97rIGXjwQvzi7EmAmrcT1JiVqafBlLBvVCz54u6P/TacR+RMUuy3MJwzBfBxaUMyXjm6L3Km/cOzMHzdQVy4qUCZ8bD5Bt0hSt6uTvJxfhyZZRmBs+CL+OqYMSV1EY3xjOP02F2prRWOmbrlhY/jLv3EEA1Ubb9tYQKJaVXjbub1iD80IXzBhXt4g7C7mUTVdx5Tw+jW27d2LPxfCvOyjP8sX2eUtxNqqi7SUPulUc0KRJE9nLUScS5w+ew8P/8JDK8t2OeUvPosJlzTs5eHx6G3bv3IOL4UpspLo57Bo7wjr9Hk4dv4EIkWJ5KZXNuYRhmK8FC8rLmjgBD4+tx/47RV/xJG9CcO2vndh5MfTrGh+cHYCbdxOh2cSpQPAufuqJKYvTMeinnjD9mNqm5ojJI7WxeuxvuJulWFausvHgyg0kmDmhfb2PD5Elr45jxfZg2LjPRB+T4jNG2XQVmVqXDfAPDYXX/IZfZKNCWZJYL+xdsx7HH+YollQUfBg2G455HvOxYMECzHOzhep/+kgDCWK99mLN+uOocFnzjhq6bPBHaKgX5jdUopaq28Ft5o9wb2n8CRfRsjmXMAzz9WCP2S8jold+OLFtPTZuOwyvsGw4b3qBc+OMFe9y0oNw9DcPrPxXBb3meWBCN1vol2HXSO6Dg8quON/A/8gWHLr6EAEBoag28zzW9tZWvMeRJMH/8Bps806HvqEIr5PC4bPlLLR/DcLVWdUVvT5pOD+hHvqFzMKT85NgKb16SRJweX4/jNn0EG9lHVJ86Dn0wcK169ArZSumzvodF1Kb45f9BzDWXnGhenMIA2tOhnjNExwZbCRfpixJIv5dsgLRIxdhaOUyCGrFgVjUvBFW19yH8P0dEffgORLE0jznQcPcDg7W+fKoWCI8WtIKTVeYYF3ASbjLMqYoyqYrWVbMfVy8dBchCdlQNbBG/bad0LJqUduZhVj/K7h6LwRxKQTd6k3RSCcM4ZV6oLd9/vTKpUuLuIvbTxKQzWUPj6cGM4dWqG9ezPib9Bg89LmFwNeaqNOmPex4D3Hqr2t4oemIPoPaQO+FD+6FpIA0KqNBq9oQPPXGVb9YCCo3QQenGtBRrObDJEi4PB/9xmzCQ3kFBF/PAX0WrsW6XinYOnUWfr+Qiua/7Mfhsfay9z9E/CoQN/xeIi3+FL4ffQiWC3Zhiiyw40GgZQ3HVnVgWLjoMmPx8OY13AsjWDdujZb1LaCpeOvjiJEc7I2LNx8j+o0E2uZ10KJzO9gZvH+SST/0DcxGpmN1zBmM1lcslJIk47nPPYRwZapRuQFa2RlDwC17yi0Lky1rBCc7owJBaFasP65c5T4Tx5WNbnU0baSDsPBK6NHbHtrcNr0KvAG/l2mIP/U9Rh+yxIJdUyDPGgG0rB3Rqo7he0FtdkIw7t7wQUAUYOHYCZ2bWubdYUsJhc+dZ0jmgnuemhkcWtWHuTAVUY/u4c6jGAhrtUWXJhZQfpRXGiLu3sYT7tggrrzerbO4FYiSEHL/Fnxf8FGlaTPwtrRF6+PdcdX3NzQr7dCyMjmXMAzzVZEG5cwnEieS77m/6fatVdRVn8+dVdWoy+Z4xZucJC+a71SJVKqNoKNRYsXCsiUtyrIvzkTa46ZLfE1n2vAy33ZnBtGuYQ7kMGwvPcuQLaBz46xIILSl2beyZElkUk/SSHMVqjPnFuVbSpTjRwsc1YhrRnDbLOTe91G8n0N3f3Si3lteUoFcEofRijaqpNNrJ+XL1Q8TRdG5H3qQ2zJfbgvLhvjlOuqooUVdN8dw25hCL6/PJ6dKttRn4UHyiUxTpPqApFM0ykqFak29SiV+Qtl0JUk4QkPMhSQ0sqEmzRuTfVUDUlW1oPYe/9Lr/JmcE0TbB9QiTVV9qlKvMTVr2oBqGKpyZSQkm+neeeWnbDrKoH+m2JGpkSEZGuqTloqQqky8VGQ5ZARspcF1dEndqBrVstIjFa1KZKyjTZZ1bchYqE2ue0Loz2HVyVhfk4QCS2rWvi7pq+qQRdXKZKCmSTbjTxXclw/KIb8FjqTGkx8zwjpzyEex4Tl3fySn3lsof3UvmYiCVnclq0pGZKivRSo8AanrSvdZ+jKiStZutCW84Mre3ltLfWpokaq+NdnVrcrtiwZV672CfJIVCUorLYB2jHQgfaEGmdo0pKZcmVjr8Em19ky6VkSGpx3sTzrqPWh7kmJBrtd/0rDqxqSvKSQV+x/pXg5X32P30uBqlUhfg1vmIF8ml0NB2wdQLU1V0q9Sjxo3a0oNahiSKpenQpvp5C3NT1EQre5qRZW4OqCvpUI8gTrpyvKFexlVImu3LVQwa1LIz3MQ1dFVIW2LOtTA3pr0VLTJdvhuClZ8b8qJsVTLRJHXAisatWEzuTcyIhU1A7I01yOBig1Nv5ZXCz8o4x+aYmdKRtJtkq5TWIUmXir6bJF8Zw31s9Emvir3XVZGpKFpTfVtue+2m/uu/pRGmZxLGIb5qrCgvCxlXacZNsKCQbn4NZ1yr0ZCvjG57Y4qGGyWoc8SlGfdpFm1haTWZgWFihTLxPF0ZkJt0rWfTd6pimWUSn8NMSKh6Qg6ke9aknPne6qrokqdNsYW2m8xRW13IX2+fJtV6n5Pd6QXXVEIrXQdSgcS5KnyZNCxodz6LcfQP0pG1ymBh2hOp7rU4rszFF2GmZ60vy8XRLWg34K5DEnxpS3fTSPPu6WJpkT0bFU70tbpQGtCcjO1KMqm+4CkC/RDx5Y0YlcIt0apFHq02Y0sVWrSNK+8zMy8PImq6ben3x++K1QSJ5yi0VVUqOY0r3fBtrLpChBH0Kp26mQ59p/3g3LRE1reRpv02/1KsmwUx9HF7xqQZqW+tC82iR5dukSBiuzN8fOghio8ElbpR5v8ZYkpekdvMtBoT2siSlfI4qjt5CJrQHN1UKUufS+vgBSy0pWGvl8BlSIOX0lt1C3I/WwJlTT5HE2sqUZWvdbSXUVQ/NZvE/WpokZV3U9R6b85mS5OtiE1M2da4hXLhcpSOXRveS9qyQX6eUF0nmKDchmuweLRkNQLBJrc+n60L7gs8zJNqqZP7X9/yB39CuIEOjW6CqnUnEZeBSqCmMJXtiF1C3cqKWsSz46jGmqm1HnpdYqTVVYRxV2aRY7a+uS8IVRRf+Vkea0qIBUVHao3civdfS0i0ct/aOn0RXQ64uOOF3HEKmqnbkljizjJiOOP0QhrFTJ3XkJXY6XrF1H0xTnUVIerjx8ZlH/6uYRhmK/Nx90PZ4rBB7/QWE1J5AGsORgOsXoV6D2aB+eWHTF4/imEfQEzH4ieXsSVUB7qd+4Ga8Vd8OR/fsDk7Uno9sNstNSSL0PWfVy7/QZqjk5okW88uejlC8SKBdDW1i50i5oPi/7j0ddCvtKcJ4ewxzsT4oiT8NZzQdf3piHgQVdXG/T6BSJTFYuKJEZS4GmsntIZ9Zu644TBEEztQLj/9xmcOaPs6yzO+8Uq1ldYJm5d8UF6zTZw4p/F4p//gdWsFZjQWE/xvhLSvLB6/Q0YfjMDI6qXMH5J2XQfou+MJZduYNeI6uClxeHpfV/EmDaEvc4L3Lv98t3vGgRGlWCYEwbvoydwNSAKKdwbfMPOmLlqBRYMcXg3HEDZdEpL98G1u2I4DZ8AWTbyTdB+8mA0TL0N74caqNuxI+wKZK8uui/YgLEOssQwrlsHFpJ4xJRy+gu+RX+M72shH2aV8wSH9ngjUxyBk956cHm/ApYRCeL/Wo/9CZ2xYOMkNFYMHdGpPxYbFnVH6qF1OBRduv2QxBzGqj1xaDd/G+a1MVWM2RfCceZJ3DgxA46fa6iywAiVDHMQ5n0UJ64GIEpeEdB55iqsWDAEDqWtCJIXOLBqP153WoSdc1rBRFYwAph09MBcFwG8j5wq4keiEqg2+wmHto5BYyMBBJZdMW/lT+iZe7IqM2JE7FuDw2ldsXj7PLQ1la5fAPNO8/Fzf3Pu7PQxyuBcwjDMV4cF5Z9Zhs813E0nCBv0x0+/rsGkmk9weNEwjNtS0X/oKcaLi//iEezQpXstefAiDsa23w/gpXkfuLvm/cAp3fsgToXxYNeyFYwK1SiSdYYXQccZE0bUhYr0isYFQ8f2nEPgyesw6NkFBvIU+fCgqqoKniQNKW+LzzVJ0l0cXLcWnvuuIIzL85yX17B3kyc8PUv32nExVLHGQrL9cfVGPNTUfPFju+H4t+pAdLYsTQAgQeT+5dgf44gJ07ui+MuvsumUkY2Ic8vg3sEWJoYWsHPqheFTN+N2qgQiUd6UEcL687Bv5xBoXvKAq2NlGOiaoFbTvljzxBINbPMGHiubTmmqZjAzFCPsSTAXpsilBAUjkirB1LiIvBWYwaa2ft6JSyDgGsLSO36lC2a5CgjnCSNQV14BEXFsD84FnsR1g57o8n4FLCMiBPkHIdu2FVqb5T9QuMaFUyvUEQXBL6B0v4TM8b8L/6w6aNfe8r89mQvrY96+nRiieQkero6obKALk1pN0XfNE1g2sEWpa0J2AHwDMyHyW4Uejg3RsGHuqw1+vpIOccxLvHxvhhN1tBoyHLafq+HxTg78fQMhqd8Z3Qr8rkMV5uZGit/PlNInn0sYhvka/afn8f9HOekZyOLiUp66JjT4emjVoi4ElIKbF72RokhTIUle4/Kl+5BUd0bXuvKrnjjkJI7fzoRR+25oldsjLonC8R1nEM2zRgunGgUuUHwjIxjyCZmZRU2booqG7mPQWkseFMWe/APfnTNAzy5FhaDcOjIyQQId6OkWf+HiGzTHxE0XERR6F7umNgZeq6LT/AM4e/ZsKV5ncGh2S8UaCxKHXcG1EFXU6TwBbo35uLFjO3xLc8cj6w42rL0MddcZGFe3hAuwsuk+SIKEk9+ivesfCKw5HtuuhyE5NQnRzw5geOHexNRIxBsNxCbvECQkRSPg0i54DLBBzO5RaNN7NR7nBkTKplOWWjsMdLNE8JqBcJm8GMsWjEGPsfsgcZmO0bk/9C1AAEEZnbVUG7pjTGstWU+nOPYk/vjunKxRWJZ9ldmJLxDDNYByqamrgt4mI7lQ25ISk/CG1KGhUbp+V56GBheaZiEjs5jG72eTish4Iwzc5I2QBK5OBVzCLo8BsInZjVFtemP1BytCNhJfxOBd1vA0oanBh4HjQEyZNg3T8r3m/baJa1xPQIPCve88PVhZ5WugfUYqqircyTwLBbNZgrTUDO7/S++TzyUMw3yVWFD+mWlUtoK0w4/SUvCW+NAzNYaWNNclFfyxyilXcfFWJiw7dYOjKncBjniFrMAABIsEqFm3LuTPEZIg6ugGnI9Xg0S3CZwaqSIzJhoJih1TrdcAduoSvOKWFXWJFlQbggkuJrJKKEnywROLXnAuchqNHLx+/Qb8KnVhp8Q0G3yDBhi++l/c29MBN6cNw+qHuX2wn4LbDy8vBPCbYNDEXnAf3w9mQbuw4Z9kxdsfKk0J4o4sx47ndhgzozeKn0NG2XTKyMbt038juv507PKcDtcm1tCWxuIkhqhQUJh5aTH6dhuApT6ZEGiZoU6L7hg2cyWOHZmBareP47xi7ICy6ZQmiYHvw1ewd3ODacgFnL35Bg1mn8Gt/cNR5VPaI8oQVMOQCS6QzTQpSYLPEwv0KroCKofH5+qyhKsKishNEovtA+ugxZwrXNgspQr7Ni1R6ekR7LqWv0meCu/dRxBk0BLtGpZu3Idqo45oXekxjuy8jgLPvk29jY3feeCv8NLfj5M1CwrM4iTCq/hCD37KvITFfbthwFIfZAq0YFanBboPm4mVx45gRrXbOH4+SpFQjsfnMpk7RvKyZjsGcp+Zc0XRYFdrjK7tTRH/OBLG3YZj5MiR716u1RNxwzsIyYWrFpffvMLjBT8LVTR2agqNB0fxZ1C+M1mWL879G1EwX5TyqecShmG+ViwoL0uUgXRZVwohJztHdrJWa+GK7lYCSF6EIFR6/VFVgxpPF07dO5Rpj1xZy7rrBZ+3KrB3rIOnu5bhcAh3AVSXbjsh8fUrWZCdGrAfO+IcUCM7Erz6LdDk1QmsPfYc0uuvjH4HdGmhjpDAwHdDEwoyhMvEQagp7RAVmKN7v44ochIw0TM8fp6Fyp17cA0ExbIP4sOw+TQcPDwKocvXlEEvVBq8r95Fjl1bdLQUQNt5MsY1TMLRPzbjUXow9nueQkxJ11JRADatPgvqNg3flhR4KZtOKSqwc3SAxqP9WLrNC8ExrxAdeBV7F/2GEy9ESA69Bx//UCRK80YkgignCJ7jJ2Dtmdt4HhuPF48uYdOyQwg2rQt7Y0WhKpuOkxYdBD/f+7j/4LGst1iUFA6/+/e5IFzxnVJ8PZgYqSJN3QFjF6zEyt9/wPiu1SDMKtiMy4x/ioCn8ciQZOHV84d4EpuO7KQwPHoWjyzZMq7BGFf6xpehy0QMkldAmHfvh46fMAsdv1INVDdMwI3jJ/AoOhqPT/2Bnbe5Rqxd9Xfzs2t1nY0fO6Vg+8h+mH/0Lp6H3MfxhQMwYksC2n4/Fz1L2ybQ6YGfFnRF8sb+6PLtJlz0D0dM2E1snTQKs/c+QGJu0JoahUA/X9zn8t8/8g0X96XgRQBXNvd94fsoEm/e1V0+jM1NIIi+i4t3whEf9xzXt03B9wejIMl6hecPgxEry2auHohyEOQ5HhPWnsHt57GIf/EIlzYtw6FgU9S1zzcdLLfOSjWqwzDhBo6feITo6Mc49cdO3BbUhF313JzRRpeff0ffnL0Y3mUklp+8ixCuMR90fjlGD5+Lo49S5BcrbhtCHvriweMYpElykBTuJ9sH/yex+LhnIaUhOsgPvly+SNeZKhG9W+fD0ESuWSvFh+k38zC1ziP8NnAMNlwO5M5pl7B29AisfpQj26a8fFHGJ55LGIb5eil+8Ml8klQKurCbNvzQjSoLuagVfNJr7E5/bDtKd2JzKO7Cj9TO0oLa/7CXtoxrR+1nHKewImZF+BTSoizL4sy8MJ6sBDzSqtWDPM5Eymc+SLtJC5vrk0Ddkhr3HEmzN3lTbFYgLWmmRnz9etR77kF6UmjWhddHh5KVlTudyZusoyDRY1raQp0EVmPpn2JmARMF/06t9FvS0sCPm1UhI+g63Y75xClYMi/Rt4VmGEm5Npfqa6mRacNRtPNZyQWaeGwYWajUpunXZHNIFkvZdEoThdPJeV3IRl+FeDwQT6hHtTqMpKkj21E1Q01SUbOkMWcyKPOsO1lV6USjBjUnS3VpHZam1SbrFiNp/e28aTqUTccVGi1vq0VcTPiubua+eEIzGnlcUdjJt2hpBxMSCNRIR9+ADPT1SEed21ahDlVtO5F2BkgrTjztduPqXe66eHzS7b6Wdrhbkkq+ZXrdN1PpZxwV0eOlLUhdYEVji6uAShNR6JGp1NpSOt0njwTa1ajD9IMUXHhmjuR7tHlMW6ppyB03PFXSr+5EozfeoSInQ1FKKj3aP5262VuSjgpXJtw6jeq50S8XoxWzHuWQ3y9NSEM6+D5fOchfPOKp1aW5N/JtZLI3LXWxIT3u+Jfmq2aVtjSgux1pcp/nCfSpx5YoaUUgd6sq1GnUIGpuqS6f3pQnJG3rFjRy/e3390UUSkemtiZLaaueJyDtah1o+sHg92bryQo7TQv6N+XqpjRvuDzUtKIWozfS7ST5nqQcHkyVZOfYgvvA13GmdYWmnlSGKHg5tdXiy7e/0DqFZiMpt5pKSWd3me9an8w0BdyxpE4WLcfQggnNSCt/vijjE88lDMN8vVhQ/l/JSqTwoGcUnfqJwWExci8mZSeNop+HUkLhq6YomSKCCy7PeR1KwVGpigCgEFEIbXerR312FTcdpJiiN3Un2wkXqOgwNJWuzWhEjb+/kTf1WgWSEf2cwpM+0FgQPaeVXICq57yewkpKqmy6j5JJSdFRFJ9a3Ioz6O1beaGKUuPpZWQUvU4tKjhQNp0ysujm7Dpk0GQGnQrNHxBnUVLoLdrzrSMZ2c2i6yVMo1cWxNGbqLvtBLpQRu0gEnN5HRdPKR/KFnEGJSWlFXNcfBxRSjzFJKQVmD7wY+VI1xWfWuy6Mt6+lQeVolSKfxlJUa9TFVMyFk+cmURx8SkfTCerAzGxlFz4/FMRZCVRfBnlcX5KnUsYhvmqsSd6fiXK/omeZUeSdBUe446i0Zq16GPB5xakIjosEZpVrKEvzMbNuW442u0YVraTj1TPI0HixVkYuM0Wa/eOg+2njuYoL5JX8NqwHiFOczG6YQnPbVQ23ddCEoHVHezxz8DnOD/BRLEwn8Rt6F51B9re88Zcm7IcYC5BanQYEjWrwFpfiOybc+F2tBuOrWyn+K0EwzAMw/z38gaAMsxnwjdoh4WreyJ03Qr8HZGO2x5OqFGrOuqOPoak9Js49qwxBju9H5DHe63BwiuOWLv7Cw7IpfjGaDtl4YcDbWXTfS34ZmjWoirurJuJDV4RBcYEp0d6w3PGGtyq3BIty3je6ezbHnCqUQvV647GsaR03Dz2DI0HO7GAnGEYhilXrKf8K1GRe8rfyX6BwFAhIlY2Qa+tUYBJS/RqpArjsYfg2Uc+C0uedIQERsOsbk3kPqOI+QplBuPgvPGYveUGknWtYW2uB15KDCIi38Cg1Vj87vk7BtnmeyJVGcg6Nw41em5FFEzQslcjqBqPxSHPPvJZWBiGYRimnLCg/CvxRQTlCpI4b2xavgs+Cbqwd5uESS418X/SN8wUQ5T4HPd9AxH2Mhmkb4Ua9RqhUQ2Dd7OWlClJHLw3LccunwTo2rth0iQX1GQVkGEYhilnLCj/SnxJQTnDMAzDMAxTELthyzAMwzAMwzDljAXlDMMwDMMwDFPOWFDOMAzDMAzDMOWMBeUMwzAMwzAMU85YUM4wDMMw/yVJIp48joZY8ed/RZL0FEFRIsVfDMNUNGz2la/E55h9JTvyMrbtuILk6r0wdmhTGOc24bKjcOPoUVwJjEaGdi206TsAXWx0FG9WYGkR8PnXGw9Do5GQpQ6TGg3RukMLWL96jHBLB9hWwAnRJQnBuBP8GmJZsfKgalYHjjUMKk5rWpKM0AePEZP1fr3j8VSgbWIF68pm0Fdj7f/SEiUG48alq7j/LBqJmQLoWdmicZuOaFXHACl+/nhj3xDVy/a5Ssx/Ie0Rdi/Ygrd9FmJSi891LEsg5iJ+gaDQ2jOfYP98T7zu5YEprYxYrxzDVDDsmGSKJonCjnGumLxwCX4c3Q8/X86SL447i2nN7NBm6EzsiGuC1slr4OLYDj/fzJS9XxFlvbiKtePaw7bREHjeS4NB7RZo27wm1J7uwODaeqjUcTWeVqDgRpL4HM/jJbJ/U3oMzi9wQxsnJzg5tYbr0lvIkb1TUXCNQdEbPDs6By5tpNvYFv3nbcPRkyfx15/bsGxGPzSx1INB5YboNmYJ/gp8o/gcUxxJ8j1sn9wBNWt2hse5GGjWbIK2Tg4wzeaCudENULm6PZp2/xmXEhUfYL4cqXfwe/8peNj1cwTkYry44onZ/Zujlqk5+u5OUCzPR90WQxaORs5vAzDv0isudGcYpkKR9pQzXz5pUZZpcYqjaEt3XeJCLuIJa9DUq1ncwhzyn9+IVHjS71Kjrpse0boOqty/VchxYQD3bkWTQ+EnZ1ILI1Wy6vYbeceLFctziSl2Xz+y7LiWXhZ+q7xkBdH6rlWp/59pigUiCl3uRCqy8uWThftZylS8U6GkHqGBBnyCwJomXCy0hSlP6NRCF6qmziOeWhXqveYuvVW8xRQkjjxK4+rpkKBSe/rl+muuhhYiTqAbv3YkE7WmtCRIpFjIfBHEMXR0eA2ynXyZUhSLyh53TtvoTGqqTvRHSPH1IyfgV2pVtSdtfs7qEMNUJGz4ylficwxfEcXewqHDPkiv1Q2DutlCG+k4OsgS3xxM5iJENbjsjMVqraX4+SQPrgsXo3+Nz/L8xY8kQdRfY9B+8G68br4Y5//+Hk2KGJ4iebEBEzc2wLqlraCqWFZuJHH4e5oz+m8MQ/f9cTgyUPqYSQlerO2EmtOuIBt8WLifRui27lzuVzBZl/Ft7S7wjKqBmV4BWN6ycG6KEXl4FNoP3YcwiRWGHrqLXX1NuT0SI9xrL46cu4ewZLGsZfk+ASy7zcH3vay5f72B775V2HMzDll8S3SZ/T16V8m7zSGJ4ersn2dx63kiYFADzXsOxsAW5vIeyawIXFz/B/Y/ygBlpIFvVR91jK3ReeYQNOCqriTRFyf/PAXvwFhkcM1RvpY56rV1xWAXe6jd2og5ewJKvkvB10erbxfBJfsI1h24gehU6f7woGFqh9a9B6N3Q0PZdohDL8Jz9994/CobmpadMGa2G2yl2ZX9EMs6tcX3NzXQb+99/DlIsd2FSaKxd9AAPJx6GX+0ysvn9LBL2L31IK5HiKGhwUdWhgSmjftgzBgX2OYfXfbGF/tW7cHNuCzwLbtg9ve9kZeFEsTcOoQ/z96CPAubo+fggWih6V/6z5hEwmvvEZy7F4Zk+fir9wks0W3O9+hlXfhWlRiv7h3Btj1n4PtKCF0dbj8l6rB0bI2GkiQIvxmDnjq3sXHOHgSUXCjQb/Utlg7SKt22iMPhte8oLtwPRWJObnquTqjpwNjcElbVHdDGuTVs9Atvd/EST45C/aERmPXwIqZVU/5zpZOO06NqoN99d9x8sBiOxZ6Sk3F0SB1MTFmMByfcUZndM2eYikEalDNlSPSa/P9aR/tuK3o6xYn05PplunTpUsHXv34UXYadFNKi/PzFmUYH++vIes+lPeUuOz9/f6f4bSjdPLGLNqxYSosXL6JFi7jXr7vpVpIiQTFEQaupgz6f+HrtaWVQCX34OY/orl+q4o9ylBVCh8bak7b0LgRPm/q96ykXU+Sa9sSFJFye5/WUZ8SHUNDzGEotrg6J3lL0s0B6EplE0nscH5SZQOHBzymm2BV+QOYVmlxNQBDWplk3i/lG8Wv6a5glCcAjFbu55JMvWZr3dKot5JFW51X0KCqGYmLkr5eh/nRuRT/qNOta3n5wx9T1H5tyecUjncY/kFfhusCV6S/NHemHu/m+QBxPx0fVoJrDj9BL2S6mkP+2gVTLwI325e+25NLt62tIfL4pDT6SmLuQ4ja5UMMxB+hhbCrliDIoYFEzUuHpkevOOMoRZ1FyxA1a7epAI49nyD4hijxJ3zpoEo+vT713xhbq8RZT1MZuZNN3GwXJk3PEFL3bjSrxQSr2P9DdD9x2yrz2B/1xOfeORAY92jGU7Ewb0Ni9AcQFnXJpz+n47FZkUb0XrfRJViyUEydepx+bahOPp0ONf/Ci97PwF2ruyG1H/iz8iM9Izxne02uTkKdFnVc9oihFucbEvKRQ/3O0ol8nmnWtwAekmUenZrUis8rONP9MCL07OlOf0cmfOpK5miMteJhD4rhN5NJwDB14GEupOSLKCFhEzVR4pOe6k+JyxJSVHEE3VruSw8jjihWUdlsy6Oq0Wlx6dWq/KoQyRVn0NuYJ3TiynMY5WZCabm1yXXKZYpW5yyYKpF9baJBGx/Wf965cljdNr6VC1hMvffCOWurx4WSqUptmXq+Q994Y5v8SC8rLSE68Lx351Z3aV9chPk+NumyOl7+RcoD66vLeBc25L6HNdPIudC36FLnrLRtZ9GDLUGpqZUSGhoZkWMmOpp5/QX/NcCYHM6EiKBeQSb121NF1CRW+pkoDjNeXfqJ2VSvJP8+9KlVrR+P2POQulDdo/fAWVNnMnvpveahIXwRRFF1a0o/sLatSk55DacJ3s2nevHny14+b6FpiCVc2cTTt7WtCfAip1jQv7tJaWmkUees47Vi/mtbtOksBCfm/S8wFxM/o7qWjtPdCMIm4/yUEnKa9h+9QXJGbJKaU5150eMtaWrvrEj3PjbULSKYz46qTqmxYEPfiCcnMoT11GrSaHnDBRf6g3HzYBto1oTEZccEHuOBWy+Yb2hqYL4ITx9HVPwaSo5kZNRnpQR5DGpJlXVdaeC6a2xJOjh9tG9KYLI3k5WLSZDbt2jiC6hvKy5WnZUPfbA0s/VAkZYJyTualb6mKgNsXlbr0/Z28bxFHr6eOqnwyGnb8/fIShdCJY7cLNC5y/OaTk4UxafEFZOm6lYILfGUa7RvgRlteK/6UStxNvXV0qe+B/BH4W7o8YyQtf5a/IZJFN2fZklClPv38IHf7xBS+eSntiM4tYDHFbOhIqnxjGn4ib2tzHiylRUfyCjjTfxm15RqGwspD6FBUXuUQvz5F4zuMoRP5K4w4hrZ25wJeaZ39Ll8DRAnJF6eQrboedVgd/H65iaNoTx8TUqk8mA69236pHPKb70QWxlrEF1iS69bggt+Zto8GuG2h/Fn4cZ/hGhvrpXllRMMUDZb8RCEn6Njt/GvJIJ/5TUlXzZ4LkItq9CfS5ek9aMaVTBKHb6alOxT1miOO2SCrQ8bDT+TVoZwHtHTREcUfpd0WrvG0rgOX3pCGHCuUPieS/nKvTWp8bWrs4cPVuJLl+M+nRioq1NDDL18ZlcF5shAR1zBqrGpIAw+/JXECd5667UM+Ptzrtj9FFB4zE7+FumkKqSoXwL+fGwzDlAcWlJcFcSL5nvubbt9aRV25i7C0Fzk3KJeejJua2lDTtu2pQ4cO1KGdPZkKhWQz80apLrwfUrZBOUccRivaSMeLc+vlW5D72VR6HXKXVnTVUgTlqtR+iQ/5P4rI65krgLuAL3AkNUWgKawz513PaM7dH8mp95bie4y4AOXkeHuq2XURXXpZ+lwSh6+lDppc0KrSkDz8Sxdepj7aR1Oc29PIDd70MiWR7v7anoyrDqR94fLATRR6hfYuH0Q2Quk4ej96dnACNTZWIZ5KC/otuFAvc9I92jzGmbrP2Ee+kXfIw1GTuyj7Kt7ML4cSQu/Qr+0V+c3TpC7L79DDoChK4S7ceUE5j1QsO9Lc3Wdo//i6irH9fDIbfVpxUU0jHw9pDzKX37az6ZY065K4YFSbxwXbzWiRrzwvRUG/UjMVebnwNKtR93l76Mz+8VRXFuiD+Gaj6XRpr9JKBuWUuI26q3Pfw9Mgl515vbfi2I3kzAVUld4LyjMoJDica/oUlOO3kPpM3Ud7hlTj8kGTHL67QK/f1ScuKB/Yh7YkKP6USthJvbT5pN/qZ/LKS0iiiHt0r0CwmkW3ZtfhgvIG5OGbV3dy3r7NF3gVHZST+C0lv82/rhwK9uxGlbiGg7nbLpJVIfFrOj2+I40/Fc+tJZ/MCzTeiss/7rhy9owr+F5JRMG0vI0m8SsNpMMFO8Pfybg4kWsIqVC97/M3bLjjc2EfmrpvDw2pxtVfTQf67kK+MexcgD2wzxbKn4Uf9xn5GGdVfqX3A+GMEApWHFe5xOGe1FWPT7o9t1FMMZkgijhFx6WVO+ctvc0XDRcZlHPf/zY5N7gv3baUmF4q+W8aV1VAPJ1utKVAHSpMTC/XtufOhbrU78/CkfEnnCffw33Puo6kodWVNkszL+UlXecaUZVs+9DCgz4UWbjlIO1VtxGSisNPdK90p0mGYT4TNpKsLPAN0KBLNzRt1AR2JgWzVJxohlFn/HH76r+4fPky/lncGXqCmujdr3H5j2EukQDCAsMehTCqbgdLndz940Hbwg4Oda2hV2QtEsJ+7LforHhT9PwsTvpL58cVI9I7EFUH9IVlMbUv9eICzL7TDbuO/ISOlqXPpYSL5+CTwcWH1q3R0Vb5ce7p91fA1Xk2wvtvxdZvnWCpbYBGo/ujQfRfWLXrsSyNoFo7DOpmB12BJRwrXcW2mFH4+/JeLF32I77JNz+dJOkqfurWHSv4U7HzjyFoYPgGr5IADS0NRYr8hDCsJs1b+e8CpONgdSzrwt7WAtqKJXI8GDhPxYLhPdCnRxMYyJJLkBgWhgQJ96/oA1i05i5SuWaTmm091JFmnYYpTPX5oLS78Fx/EdI5cnhamtBUfBVPuy3Gzx+GHn16oIl8hZAkhiFMusLPQU0HOtIB8SRGelq6fFkJxDGnsXL7g6LHcnNl8M3mQ1jYWg2P1g7D0I2BkM8RVARDV3z3bX2Ibi5GlyY94XE8GGncYoG1IxzNi6mI+Qh1dCAd4V8ivg703h0fUkLYjNuE1X3NEH9yFiZsCUbc3z9hDaZhUQ/jguPFxQlIeCPNc67s1NSKHkteBEncJZy7kwFh3aZoWsyspOqOTeGgJkLwhX8QXGiKaoHlN9h8aCFaqz3C2mFDsTGw2Bx852M+8z4xYk6vxPYH+UtWgvjzJ3HtrQD2bdrlTcFaiMDaBa7Nucot5OrShwsFOnofmq61qG1Rgl4H9O9qCX7qDZy/WlJdzsHDh08h4q4TxsYqimW5Pv48+b638Lp6H2KHdmjHZV7q879xNHkI/vH5Cx4DmqFy4bwSGMPYkMd9pz8CUhTLGIYpV0of7owy+ODnxlUKau0mYEJjdcVfWbh1+ATCa/RG30YVOyQvC3yL/hjf14IL7zk5T3BojzcyxRE46a0Hl66GsjTvy4TXkQuwHjUZLQpGpEoSISL8BaS/zeJb10A1ZWPyVC/8PPxH+NjMwvLRNbhLpUJWFndJlSAuOlqxAEj0uY1gDTPEPhNgyLdNYWw/AHO/65H3XZJoHJo4FMtedMPvS3vCRBSB4zNn4KTJTCwdV1uR6OMIBUIubONCN6EQKrl1jQtwpQ8hybxxGTfeSjvcuH2Pv4u9mzZh07aLeMY1UKQBz6uHfgiTPa0kXyXlWl5C+QohzFuhbI7jz0GSkoBE6fbw1GFgWDhYIqRcX4mh33yDb6Sv/r3Rvs04/PmihI3RaoK5B3dgVM1knJ/dD9NOx3J7WhR9tF96EeeW9YZl7Dks6uuIhm5LcDnmM+1oLr41Bq1bjxFV3+DCvF7ouIwwbUmP9wNOgT4MdKULJUhOTJSVpzJELyIgfRYMX98IlYo7m2sYwlCLB3F0JCKLeG6MVpO5OLhjFGomn8fsftNwOvbDDbJSf4ZScH3lUHm5cq/+vdujzbg/UbBouWM3NBLZ3BnDyMRUft74HJTaFmUIYV3FAnzKQHz0K8WyooiQkJAECU8TOlw5FPZx58kiZN7CFZ901GzjBP7Zxfj5HyvMWjEBjfUU7xfGkzZqeKCcRLxK/MzHAcMwSmFB+X8p0xuHTr6ATe++cPz6Y3KODpwnjEBdWbAnRsSxPTgXeBLXDXqii4E8xfvSER0VA685daCrrQ3tol4GzlgTXvxFRE1Fkbl8LuCU/+sDxAjftQhbgnXQY+Io2OSLBjL9/fFELET12jaKJSnwuuyD9JxU1HIZCfsiyjHT6zf8/Fc87Aa6QufMAox2G4dDxj/j4sVf0EZfkegzSOOCOVn8zRFxQa9RpUqoZNoc3246hCNHjuDgUjdYvB8T/KcyuAbNQy4w5Knao2mTwncNeNBxmoF9hw/jsPR15CSu+R3HZJuSS5Fv7or1R3+Hs85TbB01GCt8MxTvFMI3QqtZf+HuNU+MqCdAyImf0bPNKPwZ8XkDEr6pK5YtcYNhahh0uk9HN6MiTrsqdnCorcrlgBjBDx8W3+NfCF9DHdLRQJKMdKQVFxeL05GeRVyeq0OjyPLnw9x1PY7+7gydp1sxavAKFJeFeUr5GS4AdJqxT16u3OvIyWvwOz4ZBYuWDw01NfBIgtSUlGIaV2VAqW1RBiFNereHx4Oqem7HS1G4/E8Xg3ga0NAs6pL7MefJ92X7X8WNeDWo+f6IdsP/RdWBnWFZUsuGrwUt2S2zdKSmKU4cDMOUKxaU/4fSrhzC6Rhb9O7bUMlg8cun2tAdY1pryfpmxbEn8cd352DQswuK67wBNGFhaY62fwThbWoqUot6JV3EtKrFXW2EqGpnA2mno/j5YzxW5o605DXOn7mJNK3W6NE5/1UwDV6nL+O1TkcM6VdFvijzJi56J0Gl/jCMbVtUV34WvA+dQDiqopZxKjJrDcWKk+dxcFF/2H1Uz7/ytIyMoCE7ookLys3Q3K0f+vXLe/XpaFfMUKP/iCQCB7aewisJH0bdxmCwMo+j1HLCpImtPzjUS91+CvbtmYy66Vfx4zeT8Lfi4Uu5xKkpXOghxYdB4/HYdeNfrO5pAVHIAcz77YrSQfDH0q5kCE1p8Kamnv8+RR6+FVz6t4UuT4LESydwScnhBMIajnDggnxJaPB7Q1NyicKeIpQLmDXtHVGv8OiJd9RhP2Uf9kyui/SrP+KbSX+jUBYW4WM+k0fLaRImts5fskJUc7CDPl/aMPFHtmLpf+H9bVGCJB4+t59DJLRDq1bGioVF0YKOtgpX7iKIijkflf48WZgYYVeuIUS1DjpPcENj/g3s2O77gTzMRo5se7ShJ7tLwzBMeWNH4n8mFZcPcxetOr3Rr/7XEZIr1bciqIYhE1wgG2ovSYLPEwv0ci5pjKc62vbvjMjt63AzVbGolHQ6D4FrZQHEL09i99ninlonQcz537DkWBQkkhhEx4kgrGIHu3ybJnlxEJ7HUtB87hKMsJYfKtm+l+AVqwanwQNRq8iYMh2RL+IhUXdAnylD0aNFTRgoilsi+ci+v3wZLf3ne/muWKDerDWaKgaLi+79iV2++Z6ymhWE/Z6nIR9loOQKP1aRH0/BvRXu+PGfZAiqDsb6tcOVHCsrhImpEfhZfrjslW9oivxH6oo/pPgw7voHDq92gXHYYRy4UfAJs5mXlmHJxXxPE9VxxJQdv6O3ASH+RSQKdvJ+eP8LfHWZ4KPKqF8wvYk2JDFHsGTlPdn4/6JJkHR9NRYfCodYqxPGDLUFL/w8Tt8vqmkhxtNTf+ORxAr9xrjKj0MFaf4VzEJjdP3jMFa7GCPs8AEUykKZj/lMsYQmMOUaFFl+l7ljSl6yOl1GY0BNPmLP7sfZ4n7XII5F8NMiHmf6KYVSxLZ8SLLXMqy/mgUzl+lwdyjpnK4KG9tqEHLnv9fFDRMp9XmyEMkreHkFgN9kECb2csf4fmYI2rUB3OEmf7uoXRK/wqtECfhGtVDbTKmDkWGYz4wdiWWJMpCeKb0wEHKypSOR83lzAUf+eY16rv1Q74uIycUQvbt+SLiTunS/cpCekdvVI0FCXByX6sMMXSZiUE3pTgtg3r0fOn6gx1jbeT6WNf0HI/otwuWoj+gv0+uGxWvdYasSjT+njsIqn4QCZSFJfoy/Frjj+/tNMc7VEnxhZdSspsVF3JnIyk0ojsShOcsQ5roFe2c3UDysR4SnF68gVNgCvV2sijl4NFGtmjn4mQG4flNxReQaZIF//gKPfY9K6JHlQSiUjxeXfk/Gm2c4tmYvAkSEjMzMd2FidpZ8DcT9N0uxkLIyIa12/MqDMHukDVS5lVDmAyx17YqJSz2xZe0CuHcegb917GVjmSlDnl4mm1uP9L/E/Tdvhch8l0BJmXGIT+Y+Q+lIKXArPAsxdw9iYf/m6PDDbWh3moe/rm7HgMJPK+H2J5vbS1F2dsHjRkqSCO+la3FTVf7wHdmilDeIe5tSKHxWhe24XTgwuxG0C3VHq9tp4/p3M3E8Oq/GSkgEMWmhWQcn5IU/hNTUdC6+k97SLyqSkSKkp2VwaTK540G5oS+UnsHlOVeW6WmFtjkftSb46ehuTKyvgnu/foOhG+4hqfAmSN7Af9dszP63Lsb1r8odUZpw8tiGn5rHYStXX+8U6mHPfLwZc9YFwXbyZvzmkn98sgQpb+LwNqXQ1qjaYtyuA5jdSLuIHv2P+Yy0aLljmMvr7Oz381OS6I2la29C1VBRstrtsHDzbDimH8LsyQfwvPDhn/kcJ1btx1NVXcWCPMTlbQaXx5lcXhdXKqXaFo4sPbe2Ar+xkKTg8ZE56DVwE960W4A/Nw2Fos1eDCHqtm0Fc7xC6PPE9+u3wofOk+k3FsG5QXMM2VTE8KY0b1y9mwO7th1hKdCG8+RxaJh0FH9sfoT04P3wPBXz/vfmPMfzSAl0W7RF01LeJGAY5jORTsHCfKpUCrqwmzb80I0qC6XTyvFJr7E7/bHtKN1RPFki8eAAqqQqf+jF5yAtyrIrzizy3TaIHHSk0zty6+UJqUrPVbRnw0hqLJvyUfpdPBKat6JRHofp8Qd3SUSPl7YgdYEVjf3nQzP6Koii6OLiPlRPNk/5MJo4fY7y85TLiCn26goa3syC1FUMyMbJhb4ZPIB6d+5Izq7jaMXlKMq/2RkPPamfTQ1y/vlPOvf3AVo+3Z2mrL9JBZ7MLw6jVW3VSL3tSgotPHtaPqLQ/TSyjg7xVQzJpkUH6tJ7LP32d9gHHuYhpsj9g6mKYv5xvm49cj/EfcZ/Bw22z52GEiSw6kGeD3xpU8/KJFAs42nb07Ddj+SryQyi/d+2Igs1+fSG0nWpmjQh921+8kd75/jTjsH2pKWYgg1cmfTwfEC+m3pSZen84dJlPG2yH7ZbtroPEkXQZU8PGtfOSjFFI480LBtQ244dqE3LxlSvRmWqWrcVuU1ZSScCEt6b2lBaN8L+3Uwebjakzn2er1ebOrj1p/79Fa8+PaitnTGp1/pOMR8+l/7yJprXhfs+84402/MMPSo8nbUolPYOH0878s/Nl3GWxta2ohoOnWns/NXkudaDRrSwI6fJB9/NcS5O8KXj62ZQe3MBlwcCsuw8lzaeDqACMw3mhNK/237h6ooaVyZ8Mmw+gVbu86aI4uqDOIF8j62hqW1MZA9NUq3lRvM3X6BnJVWGtGA6sWg4OVU3JqtGvWjMnIW0dOlCmjt5FPV3HUI/HHr8/mPaUx/Rgdku1KyFG01etJa2bN1Av80cQK2bONPEzbepwOEiCqPLm+ZRFysVMu84mzzPPKL3s3AvDR+/I296w4/8zL+bPcjNRl2WV3q1O5Bbbrlyrz492pKdsXqR87KnBOynOb0aUu0mfWjakvW0ldufZfNn0/R5a+hcWOHMy6HQf7fRL/1sZFML8g2b04SV+8g7f6GUdluk6bcupP62GvJybuBGo8eMpmHf9KJOLZtS2/6zaeOFYCowA2ZJMq/TDBsVMhl6rIRH7Jd0nuTOZzt7k3QiJZUGHuRX6Jwrm/9fpSZN88rNyRS6Nrc+aamZUsNRO+lZEeforNtzyU4l/0OyGIYpb+wx+1+Jz/GY/bIkidkMlw5++M7XE84l/SaqEMmbUNz61wu+oXF4kyni9o9bKLBGp4nD0VypH01KkBL5CA+fxyNbwxjWNWqhhkkx86iJ3yAy6Ble8c1hY2MJnU+5oyFORnhgGNIr2cDWQkvpW1LZCU/wIOgtKtk1Qk3Dj98AUWII/AJfIF3NDLUdbGFaijz/OmXhzRsx9HQI0UGP8DyBDzNbe9Q0Ua/QtwuzkyIQ/DgIIa8IhjUaoqm9GUosyuxEhAQE4cVbCbTMbWBva1py+opOlILoZ4/x/BVgXLsB6pjK71l9eSSI3NYbjh668Azcj37F/ICz5PNkNhJD/8L3C17jh11TUOWDFTcTMSFx0KhaBfrvDbXLgs+cRuh8dQCuXfdAA9ZTzjAVAgvKvxIVLyiXIDU6DImaVWCtL0T2zblwO9oNx1a2UwwFYRiG+T8iDsaGHs441P4c/p1rB3mTuzTnSQkSLv2G9W9G4ee+5p/UmJRE78U3rdbA9oAXFrfQUixlGKa8VeROIuYLln3bA041aqF63dE4lpSOm8eeofFgJxaQMwzz/0lQGxN3rIXtiTlYHygfLF+q82T6fVx+1RnffWJADkkUjszbCMkP++DBAnKGqVBYUM58FpQUj9c5BHHceaxw74uFKUMxqdGnjAdhGIb5svEtXLHxwAiEL/kJp6PFpTtPajbBN4Mal2KaxCJIknBj2fe42HIz9o61reBPlWaY/z9s+MpXosINX5HEwXvTcuzySYCuvRsmTXJBzQ8+EpthGOb/QLIfTt/TRvcOOrj5H54nJXHXcfZpTXRvbfb5npjKMMxHY0H5V6Ki/9CTYRiGYRiGKR4bvsIwDMMwDMMw5Yz1lH8lcnvKlcGKnGEYhmEYpmJhPeUMwzAMwzAMU85YTznDMAzDMAzDlDPWU84wDMMwDMMw5YwF5QzDMAzDMAxTzlhQzjAMwzAMwzDljAXlDMMwDMMwDFPOWFDOMAzDMAzDMOWMBeUMwzAMwzAMU85YUM4wDMMwDMMw5YwF5QzDMAzDMAxTzlhQzjAMwzAMwzDljAXlDMMwDMMwDFPOWFDOMAzDMAzDMOUK+B/x1MpOZC+gXAAAAABJRU5ErkJggg==)"
      ],
      "metadata": {
        "id": "3uwa61q_t765"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Taking only 0.025% from the training set"
      ],
      "metadata": {
        "id": "V1irDzuvumnY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "D_train_db, D_test_db, y_train_db, y_test_db = train_test_split(D_train_s, y_train, train_size=0.0025, random_state=42, stratify = y_train)"
      ],
      "metadata": {
        "id": "gQjAMyPoV92l"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "-1 indicates the points which not belong to any cluster (noise).\n",
        "\n",
        "0 indicates the points which is not determined yet.\n",
        "\n",
        "P is each point we check the epsilon and minimum points around it to determine if it is core or border point.\n",
        "\n"
      ],
      "metadata": {
        "id": "ZCtKRaE2uBWM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def DBSCAN(data, eps, Minpoints):  \n",
        "    labels = [0]*len(data)\n",
        "# C is the ID of the current cluster.    \n",
        "    C = 0\n",
        "    for P in range(0, len(data)):\n",
        "        if not (labels[P] == 0):\n",
        "           continue\n",
        "        NeighborPts = regionQuery(data, P, eps)\n",
        "        if len(NeighborPts) < Minpoints:\n",
        "            labels[P] = -1    \n",
        "        else: \n",
        "           C += 1\n",
        "           growCluster(data, labels, P, NeighborPts, C, eps, Minpoints)\n",
        "    return labels\n",
        "\n",
        "# Assign the cluster label to the point\n",
        "def growCluster(data, labels, P, NeighborPts, C, eps, MinPts):\n",
        "    labels[P] = C\n",
        "    i = 0\n",
        "    while i < len(NeighborPts):    \n",
        "        # Get the next point from the queue.        \n",
        "        Pn = NeighborPts[i]\n",
        "        if labels[Pn] == -1:\n",
        "           labels[Pn] = C\n",
        "        elif labels[Pn] == 0:\n",
        "            # Add Pn to cluster C (Assign cluster label C)\n",
        "            labels[Pn] = C\n",
        "            # Find all the neighbors of the checked point pn\n",
        "            PnNeighborPts = regionQuery(data, Pn, eps) \n",
        "            if len(PnNeighborPts) >= MinPts:\n",
        "                NeighborPts = NeighborPts + PnNeighborPts\n",
        "            #else:\n",
        "                # continue              \n",
        "        i += 1        \n",
        "\n",
        "#cluster the labels which is near to the core point together\n",
        "def regionQuery(data, P, eps):\n",
        "    neighbors = []\n",
        "    for Pn in range(0, len(data)):\n",
        "        # If the distance is less than epsilon, add it to same neighbourhood.\n",
        "        if np.linalg.norm(data[P] - data[Pn]) < eps:\n",
        "           neighbors.append(Pn)\n",
        "    return neighbors"
      ],
      "metadata": {
        "id": "Ro-0vuovUpmV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "labels=DBSCAN(D_train_db,0.99,7)\n",
        "print(labels)\n",
        "print(np.unique(labels))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WbNuxAKiVbUj",
        "outputId": "f11bc8a1-94c8-4d1f-d6ca-e9a701f07607"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1, -1, 2, 3, 3, 3, 3, -1, 3, 4, 3, 2, 1, 4, 3, 2, 1, 3, 3, 3, 4, 3, 3, 4, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3, 3, 5, 4, 4, 3, 2, 3, 2, 2, 4, 3, 4, 3, 3, 3, 2, 2, 3, 1, 3, 3, 3, 3, 3, 3, 3, 4, 3, -1, 3, 3, 3, 2, 3, 3, 5, 3, -1, 5, 2, 4, 3, 4, 5, 3, 3, 3, 3, 3, 3, 3, 4, 2, 2, 3, 3, 3, 2, 3, 3, 3, 3, 3, -1, 3, 4, 4, 3, 4, 3, 3, 4, 3, 3, 2, 5, 3, 5, 3, 3, 3, 2, 2, 6, 5, 3, 3, 6, 4, 2, 3, 3, 2, 3, 5, 3, 3, 3, 3, 3, 3, 3, 5, 3, 4, 3, 2, 3, 3, 3, 1, 3, 3, 2, 3, 3, 3, 5, 2, -1, 4, 3, 4, 3, 3, 1, 3, 4, 3, 3, 2, 3, 3, 3, 5, 6, 2, 2, 3, 3, 3, 3, 3, 2, 1, 3, 5, 4, 5, 3, 3, 3, 3, 5, 2, 3, 3, 3, 3, 3, 3, 4, 3, 5, 2, 4, 5, 2, 3, 2, 3, 3, 4, 4, 2, 3, -1, 4, 3, 3, 2, 3, 4, 3, 3, 4, 3, 3, 4, 3, 3, 3, 4, 6, 3, 4, 3, 5, 4, 2, 3, -1, -1, 3, 2, 2, 2, 4, 3, 2, 3, 2, 2, 3, 2, -1, 3, 5, 4, 3, 3, 3, 3, 2, 3, 5, 3, 3, 3, 3, 2, 3, 1, 2, 3, -1, 3, 3, 2, 3, 2, 4, 3, 4, 2, 5, 2, 2, 4, 3, 3, 4, 3, 4, 3, 4, 3, 3, 3, 2, 2, 3, 3, 3, 3, 2, 3, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 3, 3, 5, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 2, 3, 3, 3, 4, 4, -1, 3, 5, 2, 3, 3, 5, 4, 4, 3, 1, 3, 4, 3, 4, 3, 3, 4, 3, 4, 2, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 2, 3, 2, 3, 2, 3, 3, 3, 3, 2, 3, 3, 3, 3, 1, 4, 3, 3, 4, 3, 4, 3, 3, 4, 3, 3, 3, 3, 3, 4, 2, 3, 4, 2, 3, 4, 3, 3, 4, 3, 3, 2, 3, 4, 3, 3, 5, 5, 4, 3, 3, 2, 3, 3, 2, 4, 4, 4, 3, 2, 4, 3, 4, 5, 3, 3, 2, 4, 4, 3, 2, 3, 3, 3, 3, 2, 2, 3, 3, 4, 3, 4, 3, 3, 3, 3, -1, 3, 3, -1, 2, 3, 3, 3, 3, 2, 4, 3, 3, 3, 3, 4, 3, 3, 3, 4, 4, 5, 3, 3, 3, 3, 3, 3, 3, 3, 4, 3, 3, 2, 3, 4, 3, 2, 4, 1, 4, 2, 3, 3, 2, 4, 3, 3, 3, 2, 3, 4, 2, 3, 3, 3, 1, 3, 2, 4, 4, 3, 3, 2, 3, 3, 4, 4, 3, 3, 3, 1, 2, 3, 4, 4, 5, 3, 3, 2, 3, 3, 3, 4, 4, 3, 3, 4, 3, 3, 2, 3, 2, 3, 2, 4, 2, 1, 3, 4, 3, 3, 3, 1, 3, 2, 4, 3, 2, 4, 3, 3, 3, 3, 5, 4, 2, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 3, 2, 4, 2, 4, 3, 2, 1, 4, 4, 3, 4, 4, 3, 3, 3, 2, 3, 3, 5, 3, 1, 2, 2, 3, 3, 4, 3, 2, 3, 3, 2, -1, 3, 2, 2, 4, 3, 4, 2, 3, 3, 3, 3, 3, 3, 3, 4, 3, 3, 2, 3, 2, 3, 3, -1, 3, 4, 3, 4, 3, 5, 4, 3, 3, 3, 3, 2, 4, 3, 3, 3, 4, 4, 3, 1, 3, 4, 3, 3, 2, 2, 4, 5, 3, 3, 3, 3, 4, 3, 5, 3, 3, 3, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 2, 3, 3, 3, 3, 2, 2, 3, 3, 4, 3, 3, 5, 3, 3, 2, 2, 3, 3, 4, 4, 3, 3, 4, 3, 4, 3, 3, 4, 2, 3, 3, 2, 5, 3, 2, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, -1, 5, 1, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 4, 3, 3, 4, 3, 5, 3, 4, 3, 3, 3, 4, 3, 2, 3, 4, -1, 3, 3, 4, 2, 3, 3, 2, 4, 3, 3, 3, 3, 3, 1, 3, 3, 5, 4, 2, 2, 2, 3, 5, 2, 3, 3, 3, 3, 3, -1, 3, 4, 4, 3, 3, 2, 3, 3, 4, 2, 4, 4, 3, 3, 2, 4, 3, 1, 3, 4, 3, 3, 3, 3, 1, 3, 3, 2, 4, 4, 3, 3, -1, 3, 3, 1, 3, 3, 1, 3, 4, 2, 3, 3, 3, 3, 3, 3, 1, 3, 3, 4, 3, 2, 2, 1, 2, 4, 3, 2, 3, 6, 4, 2, 3, 3, 2, 3, 2, 2, 3, 3, 3, 3, 3, -1, 3, 5, 3, -1, 3, 3, 3, 1, 3, 3, 3, 3, 2, 4, 3, 3, 3, 2, 3, 4, 4, 3, 2, 3, 3, 3, 3, 1, 3, 4, 4, 2, 4, 4, 3, 3, 3, 5, 5, 4, 2, 4, 5, 2, 3, 3, 4, 3, 3, 4, 3, -1, 4, 3, 3, 3, 4, 3, 3, 4, 3, 3, -1, 3, 3, 3, 3, 3, 5, 3, 3, 5, 3, 3, 4, 4, 3, 3, 2, 3, 4, 2, 3, 3, 3, 1, 3, 3, 3, 4, 3, 3, 2, 3, 5, 3, 3, 3, 4, 2, 2, 2, 4, 4, 3, 3, 4, 3, 3, 3, 3, 3, -1, 1, 4, 2, 3, 4, 3, 3, 2, 3, 1, 3, 3, 3, 6, 3, 3, 4, 3, 3, 3, 4, 5, 2, 3, 3, 3, 6, 3, 4, 5, 1, 4, 4, 3, 3, 4, 4, 3, 4, 4, 3, 3, 2, 4, 3, 1, 4, 4, 2, 3, 3, 3, 5, -1, 2, 3, 3, 2, 3, 2, 3, 2, 3, 3, 4, 3, 2, 2, 3, 3, 3, 3, 4, 4, 3, 3, 2, 5, 3, 4, 3, 3, 3, 5, 3, 3, 1, 3, 2, 4, 4, 3, 1, 4, 3, 4, 4, 3, 3, 4, 2, 3, 3, 3, 3, 5, 2, 2, 3, 3, 1, 3, 3, 5, 3, 3, 3, 2, 4, 4, 2, 4, 3, 3, 3, 5, 2, 5, 2, 3, 3, 3, 4, 3, 4, 3, 3, 3, 3, 3, 3, 5, 4, 1, 3, 3, 1, 3, 3, 3, -1, 3, 3, 1, 3, 3, 2, 3, 3, 3, 5, 2, 3, 3, 3, 3, 3, 3, 4, 3, 2, 3, 3, 3, 3, 3, 5, 3, 3, 4, 3, 3, 3, 1, 3, 3, 4, 3, 4, 3, 3, 4, 3, 2, 3, 4, 3, 3, 3, 3, 4, 4, 3, 1, 2, 3, 2, 3, 3, 2, 2, 3, 4, 3, 3, 3, 3, 2, 2, 3, 3, 4, 4, 3, 5, 3, 4, 1, 3, 4, 3, 3, 3, 2, 3, 3, 3, 2, 4, 2, 6, 4, 3, 4, 4, 1, 2]\n",
            "[-1  1  2  3  4  5  6]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "y_pred_db = labels\n",
        "\n",
        "act_lbl_db = actual_clusters(len(list(np.unique(labels))),y_pred_db,y_train_db)\n",
        "act_y_pred_db =  actual_labels(y_pred_db,act_lbl_db)\n",
        "evaluate(y_pred_db,y_train_db,act_lbl_db)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CLW2TmWGXS4d",
        "outputId": "ce7d0616-f212-4716-a8ae-26ae9c7c3f56"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For cluster : 1\n",
            " Precision =  0.9545454545454546\n",
            " Recall =  0.1728395061728395\n",
            " F1 Measure =  0.29268292682926833\n",
            " Conditional Entropy =  0.31221953325757146\n",
            "\n",
            "\n",
            "For cluster : 2\n",
            " Precision =  0.9731182795698925\n",
            " Recall =  0.7448559670781894\n",
            " F1 Measure =  0.8438228438228439\n",
            " Conditional Entropy =  0.17850426252471427\n",
            "\n",
            "\n",
            "For cluster : 3\n",
            " Precision =  1.0\n",
            " Recall =  1.0\n",
            " F1 Measure =  1.0\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 4\n",
            " Precision =  1.0\n",
            " Recall =  0.7798507462686567\n",
            " F1 Measure =  0.8763102725366876\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 5\n",
            " Precision =  1.0\n",
            " Recall =  0.22014925373134328\n",
            " F1 Measure =  0.3608562691131499\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 6\n",
            " Precision =  1.0\n",
            " Recall =  0.03292181069958848\n",
            " F1 Measure =  0.06374501992031872\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : -1\n",
            " Precision =  0.4444444444444444\n",
            " Recall =  0.04938271604938271\n",
            " F1 Measure =  0.08888888888888889\n",
            " Conditional Entropy =  2.383068983003922\n",
            "\n",
            "\n",
            "Total Meaures:\n",
            "\n",
            "Precision = 0.9103011683656845 \n",
            "\n",
            "Recall = 0.42857142857142855 \n",
            "\n",
            "F1 Measure = 0.5037580315873083 \n",
            "\n",
            "Conditional Entropy = 0.09010713751743796 \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import precision_score,recall_score, accuracy_score,f1_score\n",
        "print(\"acc:\",accuracy_score(list(y_train_db),act_y_pred_db))\n",
        "print(\"f1 macro:\",f1_score(list(y_train_db),act_y_pred_db,average = 'macro'))\n",
        "print(\"f1 weighted:\",f1_score(list(y_train_db),act_y_pred_db,average = 'weighted'))\n",
        "print(\"rec macro:\",recall_score(list(y_train_db),act_y_pred_db,average = 'macro'))\n",
        "print(\"rec weighted:\",recall_score(list(y_train_db),act_y_pred_db,average = 'weighted'))\n",
        "print(\"prec macro:\",precision_score(list(y_train_db),act_y_pred_db,average = 'macro'))\n",
        "print(\"prec weighted:\",precision_score(list(y_train_db),act_y_pred_db,average = 'weighted'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XXuQ9GJvCU5u",
        "outputId": "8ad7a0d6-989d-4dea-8cdc-a87b8317e115"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "acc: 0.9821862348178138\n",
            "f1 macro: 0.26879026485325697\n",
            "f1 weighted: 0.9736650833625554\n",
            "rec macro: 0.2727272727272727\n",
            "rec weighted: 0.9821862348178138\n",
            "prec macro: 0.26518010291595195\n",
            "prec weighted: 0.965851348254526\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "w6Pl4B5QfBoa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Agglomerative Hiearchical Clustering"
      ],
      "metadata": {
        "id": "J8SGsuRFgfgo"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Hiearchical Clustering is an algorithm that groups similar objects into groups called clusters. \n",
        "\n",
        "![Screenshot 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)"
      ],
      "metadata": {
        "id": "6O42RLZSwpDj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics.pairwise import euclidean_distances"
      ],
      "metadata": {
        "id": "XCnNGIOLgxQ6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "There are two ways for merging:\n",
        "\n",
        "1.   Single linkage: It is based on grouping clusters in bottom-up fashion (agglomerative clustering), at each step combining two clusters that contain the closest pair of elements not yet belonging to the same cluster as each other. we seek for minimum single linkage.\n",
        "2.   Complete linkage:each element is in a cluster of its own. The clusters are then sequentially combined into larger clusters until all elements end up being in the same cluster. The method is also known as farthest neighbour clustering. we seek for maximum complete linkage.\n",
        "\n"
      ],
      "metadata": {
        "id": "86AIbMNoxtgu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This implementation is bottom up approach of dynamic programming with single linkage as the type of merging."
      ],
      "metadata": {
        "id": "H231DMzIxiDy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def Heirarchical(data, cutoff):\n",
        "  dist= euclidean_distances(data, data)\n",
        "  dist = np.tril(dist)\n",
        "  dist[dist==0]= np.inf\n",
        "  #print(dist.min())\n",
        "  df = pd.DataFrame(data=np.ones(data.shape[0])*np.inf)\n",
        "  if cutoff > dist.shape[0]: #If user provides impractical cut-off, cluster everthing into one cluster and not listen to user \n",
        "    cutoff = dist.shape[0]\n",
        "    print(cutoff)\n",
        "  d = {} #This dictionary keeps record of which data points or cluster are merging, hence can be used to make a dendogram\n",
        "  for i in range(0,cutoff):\n",
        "    # np.unravel_index gives us the position of minimum distance.\n",
        "    ij_min = np.unravel_index(dist.argmin(), dist.shape)\n",
        "    #print(ij_min)\n",
        "    if i == 0:\n",
        "      df.iloc[ij_min[0]]=0\n",
        "      df.iloc[ij_min[1]]=0\n",
        "    else:\n",
        "      try:\n",
        "        a = int(df.iloc[int(ij_min[0])])\n",
        "        #print(\"a try= \", a)\n",
        "      except:\n",
        "        df.iloc[int(ij_min[0])] = i\n",
        "        a = i\n",
        "        #print(\"a except= \", a)\n",
        "      try:\n",
        "        b = int(df.iloc[int(ij_min[1])])\n",
        "        #print(\"b try= \", b)\n",
        "      except:\n",
        "        df.iloc[int(ij_min[1])] = i\n",
        "        b = i\n",
        "        df[(df[0]==a) | (df[0]==b)] = i\n",
        "    d[i] = ij_min\n",
        "    #print(\"d[i]= \",d)\n",
        "    for j in range(0, ij_min[0]):\n",
        "                #we want to ignore the diagonal, and diagonal is 0. We replaced 0 by infinte. \n",
        "                #So this if condition will skip diagonals\n",
        "        if np.isfinite(dist[ij_min[0]][j]) and np.isfinite(dist[ij_min[1]][j]):\n",
        "                    #after two points/cluster are linked, to calculate new distance we take minimum distance for single linkage\n",
        "          dist[ij_min[1]][j] = min(dist[ij_min[0]][j], dist[ij_min[1]][j])\n",
        "        #print(dist)\n",
        "  # To avoid the combined data points/cluster in further calculations, we make them infinte.\n",
        "  #Our if loop above this, will therefore skip the infinite record entries.\n",
        "    dist[ij_min[0]] = np.inf\n",
        "  return d, df[0].to_numpy()"
      ],
      "metadata": {
        "id": "EbEGsXC8gklQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Heirarchical(D_train_SC,10000)\n",
        "d,y=Heirarchical(D_train_db,10000)\n",
        "print(y)\n",
        "print(d)\n",
        "print(len(np.unique(y)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xxgmp1AAgmGB",
        "outputId": "e377a03c-dd3f-48cc-8684-a5f8829811f1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1235\n",
            "[ 795. 1208. 1020. ...  732. 1170.  576.]\n",
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1206: (837, 589), 1207: (798, 173), 1208: (1, 0), 1209: (11, 2), 1210: (75, 7), 1211: (493, 56), 1212: (2, 0), 1213: (253, 242), 1214: (937, 337), 1215: (240, 56), 1216: (214, 140), 1217: (66, 46), 1218: (637, 528), 1219: (454, 56), 1220: (157, 113), 1221: (16, 0), 1222: (768, 125), 1223: (457, 9), 1224: (121, 113), 1225: (7, 2), 1226: (8, 7), 1227: (10, 7), 1228: (3, 0), 1229: (4, 0), 1230: (5, 0), 1231: (6, 0), 1232: (9, 7), 1233: (39, 0), 1234: (0, 0)}\n",
            "1023\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "g = 0\n",
        "for i in np.unique(y):\n",
        "  y[y == i] = g\n",
        "  g+=1"
      ],
      "metadata": {
        "id": "vZGC7Jhd9AEW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(sorted(np.unique(y)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pR8gWqq1BN9J",
        "outputId": "12d42ba0-2061-4e6f-bd4b-1befb8837771"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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585.0, 586.0, 587.0, 588.0, 589.0, 590.0, 591.0, 592.0, 593.0, 594.0, 595.0, 596.0, 597.0, 598.0, 599.0, 600.0, 601.0, 602.0, 603.0, 604.0, 605.0, 606.0, 607.0, 608.0, 609.0, 610.0, 611.0, 612.0, 613.0, 614.0, 615.0, 616.0, 617.0, 618.0, 619.0, 620.0, 621.0, 622.0, 623.0, 624.0, 625.0, 626.0, 627.0, 628.0, 629.0, 630.0, 631.0, 632.0, 633.0, 634.0, 635.0, 636.0, 637.0, 638.0, 639.0, 640.0, 641.0, 642.0, 643.0, 644.0, 645.0, 646.0, 647.0, 648.0, 649.0, 650.0, 651.0, 652.0, 653.0, 654.0, 655.0, 656.0, 657.0, 658.0, 659.0, 660.0, 661.0, 662.0, 663.0, 664.0, 665.0, 666.0, 667.0, 668.0, 669.0, 670.0, 671.0, 672.0, 673.0, 674.0, 675.0, 676.0, 677.0, 678.0, 679.0, 680.0, 681.0, 682.0, 683.0, 684.0, 685.0, 686.0, 687.0, 688.0, 689.0, 690.0, 691.0, 692.0, 693.0, 694.0, 695.0, 696.0, 697.0, 698.0, 699.0, 700.0, 701.0, 702.0, 703.0, 704.0, 705.0, 706.0, 707.0, 708.0, 709.0, 710.0, 711.0, 712.0, 713.0, 714.0, 715.0, 716.0, 717.0, 718.0, 719.0, 720.0, 721.0, 722.0, 723.0, 724.0, 725.0, 726.0, 727.0, 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871.0, 872.0, 873.0, 874.0, 875.0, 876.0, 877.0, 878.0, 879.0, 880.0, 881.0, 882.0, 883.0, 884.0, 885.0, 886.0, 887.0, 888.0, 889.0, 890.0, 891.0, 892.0, 893.0, 894.0, 895.0, 896.0, 897.0, 898.0, 899.0, 900.0, 901.0, 902.0, 903.0, 904.0, 905.0, 906.0, 907.0, 908.0, 909.0, 910.0, 911.0, 912.0, 913.0, 914.0, 915.0, 916.0, 917.0, 918.0, 919.0, 920.0, 921.0, 922.0, 923.0, 924.0, 925.0, 926.0, 927.0, 928.0, 929.0, 930.0, 931.0, 932.0, 933.0, 934.0, 935.0, 936.0, 937.0, 938.0, 939.0, 940.0, 941.0, 942.0, 943.0, 944.0, 945.0, 946.0, 947.0, 948.0, 949.0, 950.0, 951.0, 952.0, 953.0, 954.0, 955.0, 956.0, 957.0, 958.0, 959.0, 960.0, 961.0, 962.0, 963.0, 964.0, 965.0, 966.0, 967.0, 968.0, 969.0, 970.0, 971.0, 972.0, 973.0, 974.0, 975.0, 976.0, 977.0, 978.0, 979.0, 980.0, 981.0, 982.0, 983.0, 984.0, 985.0, 986.0, 987.0, 988.0, 989.0, 990.0, 991.0, 992.0, 993.0, 994.0, 995.0, 996.0, 997.0, 998.0, 999.0, 1000.0, 1001.0, 1002.0, 1003.0, 1004.0, 1005.0, 1006.0, 1007.0, 1008.0, 1009.0, 1010.0, 1011.0, 1012.0, 1013.0, 1014.0, 1015.0, 1016.0, 1017.0, 1018.0, 1019.0, 1020.0, 1021.0, 1022.0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#for i in np.unique(y):\n",
        "#  print(list(y).count(i))"
      ],
      "metadata": {
        "id": "BHeaoGADBc1e"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_hc = np.array(y, dtype = int)\n",
        "\n",
        "act_lbl_hc = actual_clusters(len(list(np.unique(y))),y_pred_hc,y_train_db)\n",
        "act_y_pred_hc =  actual_labels(y_pred_hc,act_lbl_hc)\n",
        "evaluate(y_pred_hc,y_train_db,act_lbl_hc)"
      ],
      "metadata": {
        "id": "Qkx6Bcrs8YSe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9f1a9fa1-9d85-48d8-ef26-d21f58d7cf80"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 311\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 312\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 313\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 314\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 315\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 316\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 317\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 318\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 319\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 320\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 321\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 322\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 323\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 324\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 325\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 326\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 327\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 328\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 329\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 330\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 331\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 332\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 333\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 334\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 335\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 336\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 337\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 338\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 339\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 340\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 341\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 342\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 343\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 344\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 345\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 346\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 347\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 348\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 349\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 350\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 351\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 352\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 353\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 354\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 355\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 356\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 357\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 358\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 359\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 360\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 361\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 362\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 363\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 364\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 365\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 366\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 367\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 368\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 369\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 370\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 371\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 372\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 373\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 374\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 375\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 376\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 377\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 378\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 379\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 380\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 381\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 382\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 383\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 384\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 385\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 386\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 387\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 388\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 389\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 390\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 391\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 392\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 393\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 394\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 395\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 396\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 397\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 398\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 399\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 400\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 401\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 402\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 403\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 404\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 405\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 406\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 407\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 408\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 409\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 410\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 411\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 412\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 413\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 414\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 415\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 416\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 417\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 418\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 419\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 420\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 421\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 422\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 423\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 424\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 425\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 426\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 427\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 428\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 429\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 430\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 431\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 432\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 433\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 434\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 435\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 436\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 437\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 438\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 439\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 440\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 441\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 442\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 443\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 444\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 445\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 446\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 447\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 448\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 449\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 450\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 451\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 452\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 453\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 454\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 455\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 456\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 457\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 458\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 459\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 460\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 461\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 462\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 463\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 464\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 465\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 466\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 467\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 468\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 469\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 470\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 471\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 472\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 473\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 474\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 475\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 476\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 477\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 478\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 479\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 480\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 481\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 482\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 483\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 484\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 485\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 486\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 487\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 488\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 489\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 490\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 491\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 492\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 493\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 494\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 495\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 496\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 497\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 498\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 499\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 500\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 501\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 502\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 503\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 504\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 505\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 506\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 507\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 508\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 509\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 510\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 511\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 512\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 513\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 514\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 515\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 516\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 517\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 518\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 519\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 520\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 521\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 522\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 523\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 524\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 525\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 526\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 527\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 528\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 529\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 530\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 531\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 532\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 533\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 534\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 535\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 536\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 537\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 538\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 539\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 540\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 541\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 542\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 543\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 544\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 545\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 546\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 547\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 548\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 549\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 550\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 551\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 552\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 553\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 554\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 555\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 556\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 557\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 558\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 559\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 560\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 561\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 562\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 563\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 564\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 565\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 566\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 567\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 568\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 569\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 570\n",
            " Precision =  1.0\n",
            " Recall =  0.00823045267489712\n",
            " F1 Measure =  0.0163265306122449\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 571\n",
            " Precision =  1.0\n",
            " Recall =  0.00823045267489712\n",
            " F1 Measure =  0.0163265306122449\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 572\n",
            " Precision =  1.0\n",
            " Recall =  0.00823045267489712\n",
            " F1 Measure =  0.0163265306122449\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 573\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 574\n",
            " Precision =  1.0\n",
            " Recall =  0.012345679012345678\n",
            " F1 Measure =  0.024390243902439022\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 575\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 576\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 577\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 578\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 579\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 580\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 581\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 582\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 583\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 584\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 585\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 586\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 587\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 588\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 589\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 590\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 591\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 592\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 593\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 594\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 595\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 596\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 597\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 598\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 599\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 600\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 601\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 602\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 603\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 604\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 605\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 606\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 607\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 608\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 609\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 610\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 611\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 612\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 613\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 614\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 615\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 616\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 617\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 618\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 619\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 620\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 621\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 622\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 623\n",
            " Precision =  1.0\n",
            " Recall =  0.00823045267489712\n",
            " F1 Measure =  0.0163265306122449\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 624\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 625\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 626\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 627\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 628\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 629\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 630\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 631\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 632\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 633\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 634\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 635\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 636\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 637\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 638\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 639\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 640\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 641\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 642\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 643\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 644\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 645\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 646\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 647\n",
            " Precision =  1.0\n",
            " Recall =  0.002849002849002849\n",
            " F1 Measure =  0.005681818181818182\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 648\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 649\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 650\n",
            " Precision =  1.0\n",
            " Recall =  0.002849002849002849\n",
            " F1 Measure =  0.005681818181818182\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 651\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 652\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 653\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 654\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 655\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 656\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 657\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 658\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 659\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 660\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 661\n",
            " Precision =  1.0\n",
            " Recall =  0.002849002849002849\n",
            " F1 Measure =  0.005681818181818182\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 662\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 663\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 664\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 665\n",
            " Precision =  1.0\n",
            " Recall =  0.002849002849002849\n",
            " F1 Measure =  0.005681818181818182\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 666\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 667\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 668\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 669\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 670\n",
            " Precision =  1.0\n",
            " Recall =  0.002849002849002849\n",
            " F1 Measure =  0.005681818181818182\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 671\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 672\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 673\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 674\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 675\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 676\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 677\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 678\n",
            " Precision =  1.0\n",
            " Recall =  0.002849002849002849\n",
            " F1 Measure =  0.005681818181818182\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 679\n",
            " Precision =  1.0\n",
            " Recall =  0.00823045267489712\n",
            " F1 Measure =  0.0163265306122449\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 680\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 681\n",
            " Precision =  1.0\n",
            " Recall =  0.012345679012345678\n",
            " F1 Measure =  0.024390243902439022\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 682\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 683\n",
            " Precision =  1.0\n",
            " Recall =  0.00823045267489712\n",
            " F1 Measure =  0.0163265306122449\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 684\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 685\n",
            " Precision =  1.0\n",
            " Recall =  0.007462686567164179\n",
            " F1 Measure =  0.014814814814814815\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 686\n",
            " Precision =  1.0\n",
            " Recall =  0.00823045267489712\n",
            " F1 Measure =  0.0163265306122449\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 687\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 688\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 689\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 690\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 691\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 692\n",
            " Precision =  1.0\n",
            " Recall =  0.004273504273504274\n",
            " F1 Measure =  0.00851063829787234\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 693\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 694\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 695\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 696\n",
            " Precision =  1.0\n",
            " Recall =  0.007462686567164179\n",
            " F1 Measure =  0.014814814814814815\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 697\n",
            " Precision =  1.0\n",
            " Recall =  0.007462686567164179\n",
            " F1 Measure =  0.014814814814814815\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 698\n",
            " Precision =  1.0\n",
            " Recall =  0.007462686567164179\n",
            " F1 Measure =  0.014814814814814815\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 699\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 700\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 701\n",
            " Precision =  1.0\n",
            " Recall =  0.4\n",
            " F1 Measure =  0.5714285714285715\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 702\n",
            " Precision =  1.0\n",
            " Recall =  0.00823045267489712\n",
            " F1 Measure =  0.0163265306122449\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 703\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 704\n",
            " Precision =  1.0\n",
            " Recall =  0.007462686567164179\n",
            " F1 Measure =  0.014814814814814815\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 705\n",
            " Precision =  1.0\n",
            " Recall =  0.007462686567164179\n",
            " F1 Measure =  0.014814814814814815\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 706\n",
            " Precision =  1.0\n",
            " Recall =  0.007462686567164179\n",
            " F1 Measure =  0.014814814814814815\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 707\n",
            " Precision =  1.0\n",
            " Recall =  0.007462686567164179\n",
            " F1 Measure =  0.014814814814814815\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 708\n",
            " Precision =  1.0\n",
            " Recall =  0.00823045267489712\n",
            " F1 Measure =  0.0163265306122449\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 709\n",
            " Precision =  1.0\n",
            " Recall =  0.007462686567164179\n",
            " F1 Measure =  0.014814814814814815\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 710\n",
            " Precision =  1.0\n",
            " Recall =  0.00823045267489712\n",
            " F1 Measure =  0.0163265306122449\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 711\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 712\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 713\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 714\n",
            " Precision =  1.0\n",
            " Recall =  0.007462686567164179\n",
            " F1 Measure =  0.014814814814814815\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 715\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 716\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 717\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 718\n",
            " Precision =  1.0\n",
            " Recall =  0.007462686567164179\n",
            " F1 Measure =  0.014814814814814815\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 719\n",
            " Precision =  1.0\n",
            " Recall =  0.00823045267489712\n",
            " F1 Measure =  0.0163265306122449\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 720\n",
            " Precision =  1.0\n",
            " Recall =  0.007462686567164179\n",
            " F1 Measure =  0.014814814814814815\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 721\n",
            " Precision =  1.0\n",
            " Recall =  0.007462686567164179\n",
            " F1 Measure =  0.014814814814814815\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 722\n",
            " Precision =  1.0\n",
            " Recall =  0.007462686567164179\n",
            " F1 Measure =  0.014814814814814815\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 723\n",
            " Precision =  1.0\n",
            " Recall =  0.007462686567164179\n",
            " F1 Measure =  0.014814814814814815\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 724\n",
            " Precision =  1.0\n",
            " Recall =  0.00823045267489712\n",
            " F1 Measure =  0.0163265306122449\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 725\n",
            " Precision =  1.0\n",
            " Recall =  0.007462686567164179\n",
            " F1 Measure =  0.014814814814814815\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 726\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 727\n",
            " Precision =  1.0\n",
            " Recall =  0.007462686567164179\n",
            " F1 Measure =  0.014814814814814815\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 728\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 729\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 730\n",
            " Precision =  1.0\n",
            " Recall =  0.007462686567164179\n",
            " F1 Measure =  0.014814814814814815\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 731\n",
            " Precision =  1.0\n",
            " Recall =  0.007462686567164179\n",
            " F1 Measure =  0.014814814814814815\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 732\n",
            " Precision =  1.0\n",
            " Recall =  0.012345679012345678\n",
            " F1 Measure =  0.024390243902439022\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 733\n",
            " Precision =  1.0\n",
            " Recall =  0.007462686567164179\n",
            " F1 Measure =  0.014814814814814815\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 734\n",
            " Precision =  1.0\n",
            " Recall =  0.014925373134328358\n",
            " F1 Measure =  0.029411764705882353\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 735\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 736\n",
            " Precision =  1.0\n",
            " Recall =  0.007462686567164179\n",
            " F1 Measure =  0.014814814814814815\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 737\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 738\n",
            " Precision =  1.0\n",
            " Recall =  0.011194029850746268\n",
            " F1 Measure =  0.02214022140221402\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 739\n",
            " Precision =  1.0\n",
            " Recall =  0.007462686567164179\n",
            " F1 Measure =  0.014814814814814815\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 740\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 741\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 742\n",
            " Precision =  1.0\n",
            " Recall =  0.007462686567164179\n",
            " F1 Measure =  0.014814814814814815\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 743\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 744\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 745\n",
            " Precision =  1.0\n",
            " Recall =  0.007462686567164179\n",
            " F1 Measure =  0.014814814814814815\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 746\n",
            " Precision =  1.0\n",
            " Recall =  0.00823045267489712\n",
            " F1 Measure =  0.0163265306122449\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 747\n",
            " Precision =  1.0\n",
            " Recall =  0.007462686567164179\n",
            " F1 Measure =  0.014814814814814815\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 748\n",
            " Precision =  1.0\n",
            " Recall =  0.007462686567164179\n",
            " F1 Measure =  0.014814814814814815\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 749\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 750\n",
            " Precision =  1.0\n",
            " Recall =  0.2\n",
            " F1 Measure =  0.33333333333333337\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 751\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 752\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 753\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 754\n",
            " Precision =  1.0\n",
            " Recall =  0.007462686567164179\n",
            " F1 Measure =  0.014814814814814815\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 755\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 756\n",
            " Precision =  1.0\n",
            " Recall =  0.007462686567164179\n",
            " F1 Measure =  0.014814814814814815\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 757\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 758\n",
            " Precision =  1.0\n",
            " Recall =  0.007462686567164179\n",
            " F1 Measure =  0.014814814814814815\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 759\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 760\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 761\n",
            " Precision =  1.0\n",
            " Recall =  0.007462686567164179\n",
            " F1 Measure =  0.014814814814814815\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 762\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 763\n",
            " Precision =  1.0\n",
            " Recall =  0.007462686567164179\n",
            " F1 Measure =  0.014814814814814815\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 764\n",
            " Precision =  1.0\n",
            " Recall =  0.00823045267489712\n",
            " F1 Measure =  0.0163265306122449\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 765\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 766\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 767\n",
            " Precision =  1.0\n",
            " Recall =  0.007462686567164179\n",
            " F1 Measure =  0.014814814814814815\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 768\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 769\n",
            " Precision =  1.0\n",
            " Recall =  0.007462686567164179\n",
            " F1 Measure =  0.014814814814814815\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 770\n",
            " Precision =  1.0\n",
            " Recall =  0.007462686567164179\n",
            " F1 Measure =  0.014814814814814815\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 771\n",
            " Precision =  1.0\n",
            " Recall =  0.007462686567164179\n",
            " F1 Measure =  0.014814814814814815\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 772\n",
            " Precision =  1.0\n",
            " Recall =  0.007462686567164179\n",
            " F1 Measure =  0.014814814814814815\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 773\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 774\n",
            " Precision =  1.0\n",
            " Recall =  0.007462686567164179\n",
            " F1 Measure =  0.014814814814814815\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 775\n",
            " Precision =  1.0\n",
            " Recall =  0.00823045267489712\n",
            " F1 Measure =  0.0163265306122449\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 776\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 777\n",
            " Precision =  1.0\n",
            " Recall =  0.00823045267489712\n",
            " F1 Measure =  0.0163265306122449\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 778\n",
            " Precision =  1.0\n",
            " Recall =  0.007462686567164179\n",
            " F1 Measure =  0.014814814814814815\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 779\n",
            " Precision =  1.0\n",
            " Recall =  0.011194029850746268\n",
            " F1 Measure =  0.02214022140221402\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 780\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 781\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 782\n",
            " Precision =  1.0\n",
            " Recall =  0.007462686567164179\n",
            " F1 Measure =  0.014814814814814815\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 783\n",
            " Precision =  1.0\n",
            " Recall =  0.007462686567164179\n",
            " F1 Measure =  0.014814814814814815\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 784\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 785\n",
            " Precision =  1.0\n",
            " Recall =  0.011194029850746268\n",
            " F1 Measure =  0.02214022140221402\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 786\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 787\n",
            " Precision =  1.0\n",
            " Recall =  0.007462686567164179\n",
            " F1 Measure =  0.014814814814814815\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 788\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 789\n",
            " Precision =  1.0\n",
            " Recall =  0.007462686567164179\n",
            " F1 Measure =  0.014814814814814815\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 790\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 791\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 792\n",
            " Precision =  1.0\n",
            " Recall =  0.00823045267489712\n",
            " F1 Measure =  0.0163265306122449\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 793\n",
            " Precision =  1.0\n",
            " Recall =  0.007462686567164179\n",
            " F1 Measure =  0.014814814814814815\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 794\n",
            " Precision =  1.0\n",
            " Recall =  0.007462686567164179\n",
            " F1 Measure =  0.014814814814814815\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 795\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 796\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 797\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 798\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 799\n",
            " Precision =  1.0\n",
            " Recall =  0.00823045267489712\n",
            " F1 Measure =  0.0163265306122449\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 800\n",
            " Precision =  1.0\n",
            " Recall =  0.007462686567164179\n",
            " F1 Measure =  0.014814814814814815\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 801\n",
            " Precision =  1.0\n",
            " Recall =  0.014925373134328358\n",
            " F1 Measure =  0.029411764705882353\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 802\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 803\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 804\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 805\n",
            " Precision =  1.0\n",
            " Recall =  0.00823045267489712\n",
            " F1 Measure =  0.0163265306122449\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 806\n",
            " Precision =  1.0\n",
            " Recall =  0.007462686567164179\n",
            " F1 Measure =  0.014814814814814815\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 807\n",
            " Precision =  1.0\n",
            " Recall =  0.00823045267489712\n",
            " F1 Measure =  0.0163265306122449\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 808\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 809\n",
            " Precision =  1.0\n",
            " Recall =  0.007462686567164179\n",
            " F1 Measure =  0.014814814814814815\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 810\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 811\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 812\n",
            " Precision =  1.0\n",
            " Recall =  0.011194029850746268\n",
            " F1 Measure =  0.02214022140221402\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 813\n",
            " Precision =  1.0\n",
            " Recall =  0.007462686567164179\n",
            " F1 Measure =  0.014814814814814815\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 814\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 815\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 816\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 817\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 818\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 819\n",
            " Precision =  1.0\n",
            " Recall =  0.007462686567164179\n",
            " F1 Measure =  0.014814814814814815\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 820\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 821\n",
            " Precision =  1.0\n",
            " Recall =  0.00823045267489712\n",
            " F1 Measure =  0.0163265306122449\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 822\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 823\n",
            " Precision =  1.0\n",
            " Recall =  0.007462686567164179\n",
            " F1 Measure =  0.014814814814814815\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 824\n",
            " Precision =  1.0\n",
            " Recall =  0.00823045267489712\n",
            " F1 Measure =  0.0163265306122449\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 825\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 826\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 827\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 828\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 829\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 830\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 831\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 832\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 833\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 834\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 835\n",
            " Precision =  1.0\n",
            " Recall =  0.007462686567164179\n",
            " F1 Measure =  0.014814814814814815\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 836\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 837\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 838\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 839\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 840\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 841\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 842\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 843\n",
            " Precision =  1.0\n",
            " Recall =  0.007462686567164179\n",
            " F1 Measure =  0.014814814814814815\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 844\n",
            " Precision =  1.0\n",
            " Recall =  0.011194029850746268\n",
            " F1 Measure =  0.02214022140221402\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 845\n",
            " Precision =  1.0\n",
            " Recall =  0.007462686567164179\n",
            " F1 Measure =  0.014814814814814815\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 846\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 847\n",
            " Precision =  1.0\n",
            " Recall =  0.007462686567164179\n",
            " F1 Measure =  0.014814814814814815\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 848\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 849\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 850\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 851\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 852\n",
            " Precision =  1.0\n",
            " Recall =  0.012345679012345678\n",
            " F1 Measure =  0.024390243902439022\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 853\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 854\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 855\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 856\n",
            " Precision =  1.0\n",
            " Recall =  0.00823045267489712\n",
            " F1 Measure =  0.0163265306122449\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 857\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 858\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 859\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 860\n",
            " Precision =  1.0\n",
            " Recall =  0.2\n",
            " F1 Measure =  0.33333333333333337\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 861\n",
            " Precision =  1.0\n",
            " Recall =  0.00823045267489712\n",
            " F1 Measure =  0.0163265306122449\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 862\n",
            " Precision =  1.0\n",
            " Recall =  0.00823045267489712\n",
            " F1 Measure =  0.0163265306122449\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 863\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 864\n",
            " Precision =  1.0\n",
            " Recall =  0.012345679012345678\n",
            " F1 Measure =  0.024390243902439022\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 865\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 866\n",
            " Precision =  1.0\n",
            " Recall =  0.007462686567164179\n",
            " F1 Measure =  0.014814814814814815\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 867\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 868\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 869\n",
            " Precision =  1.0\n",
            " Recall =  0.014925373134328358\n",
            " F1 Measure =  0.029411764705882353\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 870\n",
            " Precision =  1.0\n",
            " Recall =  0.011194029850746268\n",
            " F1 Measure =  0.02214022140221402\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 871\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 872\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 873\n",
            " Precision =  1.0\n",
            " Recall =  0.00823045267489712\n",
            " F1 Measure =  0.0163265306122449\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 874\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 875\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 876\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 877\n",
            " Precision =  1.0\n",
            " Recall =  0.00823045267489712\n",
            " F1 Measure =  0.0163265306122449\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 878\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 879\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 880\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 881\n",
            " Precision =  1.0\n",
            " Recall =  0.011194029850746268\n",
            " F1 Measure =  0.02214022140221402\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 882\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 883\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 884\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 885\n",
            " Precision =  1.0\n",
            " Recall =  0.011194029850746268\n",
            " F1 Measure =  0.02214022140221402\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 886\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 887\n",
            " Precision =  1.0\n",
            " Recall =  0.012345679012345678\n",
            " F1 Measure =  0.024390243902439022\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 888\n",
            " Precision =  1.0\n",
            " Recall =  0.00823045267489712\n",
            " F1 Measure =  0.0163265306122449\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 889\n",
            " Precision =  1.0\n",
            " Recall =  0.007462686567164179\n",
            " F1 Measure =  0.014814814814814815\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 890\n",
            " Precision =  1.0\n",
            " Recall =  0.00823045267489712\n",
            " F1 Measure =  0.0163265306122449\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 891\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 892\n",
            " Precision =  1.0\n",
            " Recall =  0.00823045267489712\n",
            " F1 Measure =  0.0163265306122449\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 893\n",
            " Precision =  1.0\n",
            " Recall =  0.007462686567164179\n",
            " F1 Measure =  0.014814814814814815\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 894\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 895\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 896\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 897\n",
            " Precision =  1.0\n",
            " Recall =  0.007462686567164179\n",
            " F1 Measure =  0.014814814814814815\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 898\n",
            " Precision =  1.0\n",
            " Recall =  0.012345679012345678\n",
            " F1 Measure =  0.024390243902439022\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 899\n",
            " Precision =  1.0\n",
            " Recall =  0.00823045267489712\n",
            " F1 Measure =  0.0163265306122449\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 900\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 901\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 902\n",
            " Precision =  1.0\n",
            " Recall =  0.007462686567164179\n",
            " F1 Measure =  0.014814814814814815\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 903\n",
            " Precision =  1.0\n",
            " Recall =  0.00823045267489712\n",
            " F1 Measure =  0.0163265306122449\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 904\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 905\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 906\n",
            " Precision =  1.0\n",
            " Recall =  0.00823045267489712\n",
            " F1 Measure =  0.0163265306122449\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 907\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 908\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 909\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 910\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 911\n",
            " Precision =  1.0\n",
            " Recall =  0.00823045267489712\n",
            " F1 Measure =  0.0163265306122449\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 912\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 913\n",
            " Precision =  1.0\n",
            " Recall =  0.012345679012345678\n",
            " F1 Measure =  0.024390243902439022\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 914\n",
            " Precision =  1.0\n",
            " Recall =  0.00823045267489712\n",
            " F1 Measure =  0.0163265306122449\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 915\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 916\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 917\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 918\n",
            " Precision =  1.0\n",
            " Recall =  0.00823045267489712\n",
            " F1 Measure =  0.0163265306122449\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 919\n",
            " Precision =  1.0\n",
            " Recall =  0.00823045267489712\n",
            " F1 Measure =  0.0163265306122449\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 920\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 921\n",
            " Precision =  1.0\n",
            " Recall =  0.00823045267489712\n",
            " F1 Measure =  0.0163265306122449\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 922\n",
            " Precision =  1.0\n",
            " Recall =  0.012345679012345678\n",
            " F1 Measure =  0.024390243902439022\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 923\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 924\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 925\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 926\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 927\n",
            " Precision =  1.0\n",
            " Recall =  0.012345679012345678\n",
            " F1 Measure =  0.024390243902439022\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 928\n",
            " Precision =  1.0\n",
            " Recall =  0.007462686567164179\n",
            " F1 Measure =  0.014814814814814815\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 929\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 930\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 931\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 932\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 933\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 934\n",
            " Precision =  1.0\n",
            " Recall =  0.6666666666666666\n",
            " F1 Measure =  0.8\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 935\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 936\n",
            " Precision =  1.0\n",
            " Recall =  0.011194029850746268\n",
            " F1 Measure =  0.02214022140221402\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 937\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 938\n",
            " Precision =  1.0\n",
            " Recall =  0.007462686567164179\n",
            " F1 Measure =  0.014814814814814815\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 939\n",
            " Precision =  1.0\n",
            " Recall =  0.012345679012345678\n",
            " F1 Measure =  0.024390243902439022\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 940\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 941\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 942\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 943\n",
            " Precision =  1.0\n",
            " Recall =  0.018656716417910446\n",
            " F1 Measure =  0.03663003663003663\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 944\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 945\n",
            " Precision =  1.0\n",
            " Recall =  0.007462686567164179\n",
            " F1 Measure =  0.014814814814814815\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 946\n",
            " Precision =  1.0\n",
            " Recall =  0.007462686567164179\n",
            " F1 Measure =  0.014814814814814815\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 947\n",
            " Precision =  1.0\n",
            " Recall =  0.012345679012345678\n",
            " F1 Measure =  0.024390243902439022\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 948\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 949\n",
            " Precision =  1.0\n",
            " Recall =  0.012345679012345678\n",
            " F1 Measure =  0.024390243902439022\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 950\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 951\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 952\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 953\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 954\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 955\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 956\n",
            " Precision =  1.0\n",
            " Recall =  0.00823045267489712\n",
            " F1 Measure =  0.0163265306122449\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 957\n",
            " Precision =  1.0\n",
            " Recall =  0.00823045267489712\n",
            " F1 Measure =  0.0163265306122449\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 958\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 959\n",
            " Precision =  1.0\n",
            " Recall =  0.00823045267489712\n",
            " F1 Measure =  0.0163265306122449\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 960\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 961\n",
            " Precision =  1.0\n",
            " Recall =  0.007462686567164179\n",
            " F1 Measure =  0.014814814814814815\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 962\n",
            " Precision =  1.0\n",
            " Recall =  0.00823045267489712\n",
            " F1 Measure =  0.0163265306122449\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 963\n",
            " Precision =  1.0\n",
            " Recall =  0.00823045267489712\n",
            " F1 Measure =  0.0163265306122449\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 964\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 965\n",
            " Precision =  1.0\n",
            " Recall =  0.00823045267489712\n",
            " F1 Measure =  0.0163265306122449\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 966\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 967\n",
            " Precision =  1.0\n",
            " Recall =  0.00823045267489712\n",
            " F1 Measure =  0.0163265306122449\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 968\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 969\n",
            " Precision =  1.0\n",
            " Recall =  0.00823045267489712\n",
            " F1 Measure =  0.0163265306122449\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 970\n",
            " Precision =  1.0\n",
            " Recall =  0.75\n",
            " F1 Measure =  0.8571428571428571\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 971\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 972\n",
            " Precision =  1.0\n",
            " Recall =  0.012345679012345678\n",
            " F1 Measure =  0.024390243902439022\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 973\n",
            " Precision =  1.0\n",
            " Recall =  0.3333333333333333\n",
            " F1 Measure =  0.5\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 974\n",
            " Precision =  1.0\n",
            " Recall =  0.012345679012345678\n",
            " F1 Measure =  0.024390243902439022\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 975\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 976\n",
            " Precision =  1.0\n",
            " Recall =  0.0205761316872428\n",
            " F1 Measure =  0.04032258064516129\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 977\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 978\n",
            " Precision =  1.0\n",
            " Recall =  1.0\n",
            " F1 Measure =  1.0\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 979\n",
            " Precision =  1.0\n",
            " Recall =  0.00823045267489712\n",
            " F1 Measure =  0.0163265306122449\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 980\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 981\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 982\n",
            " Precision =  1.0\n",
            " Recall =  0.2\n",
            " F1 Measure =  0.33333333333333337\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 983\n",
            " Precision =  0.6666666666666666\n",
            " Recall =  0.6666666666666666\n",
            " F1 Measure =  0.6666666666666666\n",
            " Conditional Entropy =  0.9182958340544896\n",
            "\n",
            "\n",
            "For cluster : 984\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 985\n",
            " Precision =  1.0\n",
            " Recall =  0.012345679012345678\n",
            " F1 Measure =  0.024390243902439022\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 986\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 987\n",
            " Precision =  1.0\n",
            " Recall =  0.00823045267489712\n",
            " F1 Measure =  0.0163265306122449\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 988\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 989\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 990\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 991\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 992\n",
            " Precision =  1.0\n",
            " Recall =  0.00823045267489712\n",
            " F1 Measure =  0.0163265306122449\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 993\n",
            " Precision =  1.0\n",
            " Recall =  0.00823045267489712\n",
            " F1 Measure =  0.0163265306122449\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 994\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 995\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 996\n",
            " Precision =  1.0\n",
            " Recall =  0.0037313432835820895\n",
            " F1 Measure =  0.007434944237918215\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 997\n",
            " Precision =  1.0\n",
            " Recall =  0.00823045267489712\n",
            " F1 Measure =  0.0163265306122449\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 998\n",
            " Precision =  1.0\n",
            " Recall =  0.25\n",
            " F1 Measure =  0.4\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 999\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 1000\n",
            " Precision =  1.0\n",
            " Recall =  0.00823045267489712\n",
            " F1 Measure =  0.0163265306122449\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 1001\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 1002\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 1003\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 1004\n",
            " Precision =  1.0\n",
            " Recall =  0.00823045267489712\n",
            " F1 Measure =  0.0163265306122449\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 1005\n",
            " Precision =  1.0\n",
            " Recall =  1.0\n",
            " F1 Measure =  1.0\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 1006\n",
            " Precision =  1.0\n",
            " Recall =  0.00823045267489712\n",
            " F1 Measure =  0.0163265306122449\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 1007\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 1008\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 1009\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 1010\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 1011\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 1012\n",
            " Precision =  1.0\n",
            " Recall =  1.0\n",
            " F1 Measure =  1.0\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 1013\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 1014\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 1015\n",
            " Precision =  1.0\n",
            " Recall =  0.00411522633744856\n",
            " F1 Measure =  0.00819672131147541\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 1016\n",
            " Precision =  1.0\n",
            " Recall =  0.3333333333333333\n",
            " F1 Measure =  0.5\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 1017\n",
            " Precision =  1.0\n",
            " Recall =  1.0\n",
            " F1 Measure =  1.0\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 1018\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 1019\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 1020\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 1021\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "For cluster : 1022\n",
            " Precision =  1.0\n",
            " Recall =  0.0014245014245014246\n",
            " F1 Measure =  0.0028449502133712666\n",
            " Conditional Entropy =  0.0\n",
            "\n",
            "\n",
            "Total Meaures:\n",
            "\n",
            "Precision = 0.9996741609644835 \n",
            "\n",
            "Recall = 0.010748665467900858 \n",
            "\n",
            "F1 Measure = 0.014911236994748802 \n",
            "\n",
            "Conditional Entropy = 0.0022306781394036183 \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Built_in hiearchical clustering and dendrogram visualization."
      ],
      "metadata": {
        "id": "x8Q7HSFOMJtX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import scipy.cluster.hierarchy as sch # importing scipy.cluster.hierarchy for dendrogram\n",
        "import matplotlib.pyplot as plt\n",
        "#plt.figure(figsize=(15,15))\n",
        "dendrogram = sch.dendrogram(sch.linkage(D_train_db, method = 'ward')) \n",
        "plt.show()"
      ],
      "metadata": {
        "id": "9_DZ6J_LMQDG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        },
        "outputId": "3901af26-9bf4-4c6d-bace-c758b849095a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}