Autoassociative_Pattern_completion.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Autoassociative - Pattern completion",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNaKlNSZHyrDab1w8Ac4/qN",
      "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/NinaRag/Paul-Miller---Python-converted/blob/main/Autoassociative_Pattern_completion.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "ib1r-so7vZLv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7dcb2d4a-87ab-4a05-d779-934e8fb0451b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[-0.00103806 -0.00103806 -0.00103806 ... -0.00103806 -0.00103806\n",
            "  -0.00103806]\n",
            " [-0.00103806 -0.00103806 -0.00103806 ... -0.00103806 -0.00103806\n",
            "  -0.00103806]\n",
            " [-0.00103806 -0.00103806 -0.00103806 ... -0.00103806 -0.00103806\n",
            "  -0.00103806]\n",
            " ...\n",
            " [-0.00103806 -0.00103806 -0.00103806 ... -0.00103806 -0.00103806\n",
            "  -0.00103806]\n",
            " [-0.00103806 -0.00103806 -0.00103806 ... -0.00103806 -0.00103806\n",
            "  -0.00103806]\n",
            " [-0.00103806 -0.00103806 -0.00103806 ... -0.00103806 -0.00103806\n",
            "  -0.00103806]]\n"
          ]
        }
      ],
      "source": [
        "import numpy as np \n",
        "rule=4;\n",
        "np.random.seed(7)\n",
        "N1 = 17; \n",
        "viewmatrix= np.zeros([N1, N1])\n",
        "Ncells = viewmatrix.size; \n",
        "Ntrials = 404; \n",
        "inputstrength = 50; \n",
        "Wmean = -0.3/Ncells; \n",
        "W =  np.zeros([Ncells, Ncells])+Wmean\n",
        "print(W)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(W.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pBsvUHHeLrB4",
        "outputId": "64c5b6bb-7b66-4dbb-d482-c6221897ea14"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(289, 289)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Npatterns = 4; \n",
        "pattern1 = np.zeros([N1, N1]);\n",
        "pattern2 = np.zeros([N1, N1]);\n",
        "pattern3 = np.zeros([N1, N1]);\n",
        "pattern4 = np.zeros([N1, N1]);\n",
        "print(len(pattern1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hULvV-f_DJTs",
        "outputId": "9d0e9be6-baf9-4341-e982-6a2dd2f65f99"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "17\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pattern3 = np.zeros([N1, N1]);"
      ],
      "metadata": {
        "id": "SsUOXpiFM8JY"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(0, len(pattern1)):\n",
        "  pattern1[i,i] = 1;\n",
        "  pattern1[i,N1-1-i] = 1;    \n",
        "\n",
        "print(pattern1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mi_gtX0OEhlS",
        "outputId": "b482039a-ccda-4d3c-cdf8-9298d6692843"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
            " [0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]\n",
            " [0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 1. 0. 1. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 1. 0. 1. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]\n",
            " [0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]\n",
            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
            " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import math\n",
        "leng =len(pattern2)\n",
        "for i in range(int(leng/2)-1):\n",
        "  \n",
        "  pattern2[i,i+2] = 1;               \n",
        "  pattern2[i,i+3] = 1;                \n",
        "  pattern2[i,N1-3-i] = 1;             \n",
        "  pattern2[i,N1-4-i] = 1;\n",
        "\n",
        "ceiling = math.ceil((N1+2)/2)-3\n",
        "floor = math.floor((N1+2)/2)\n",
        "\n",
        "for i in range(ceiling, N1):\n",
        "    pattern2[i,floor-1] = 1;\n",
        "\n",
        "print(pattern2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aM2JjP2-I-2E",
        "outputId": "46b042a6-6e75-43d2-a9f9-c658dd51bf8f"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0. 0. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 1. 0. 0.]\n",
            " [0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 1. 1. 0. 1. 1. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 1. 1. 1. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 1. 1. 1. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for j in range(0, N1):\n",
        "    pattern3[0,j] = 1;                  \n",
        "    pattern3[N1-1,j] = 1;                 \n",
        "    if ( j > 1 ):\n",
        "        pattern3[N1-j,j] = 1;          \n",
        "    \n",
        "print(pattern3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vJNeEmlUPVlx",
        "outputId": "e3c40d9d-82a0-49bc-a230-1c379183c0b1"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pattern3 = np.zeros([N1, N1]);"
      ],
      "metadata": {
        "id": "1a51QSzgQB17"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(1,N1-1):\n",
        "    pattern4[i,1] = 1;                  \n",
        "    pattern4[i,N1-2] = 1;               \n",
        "\n",
        "for j in range(1, N1-1):\n",
        "    pattern4[1,j] = 1;                  \n",
        "    pattern4[N1-2,j] = 1;               \n",
        "\n",
        "\n",
        "print(pattern4)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uNoLLQHoQeaK",
        "outputId": "42921375-d6a1-476b-8925-4c0cd02bdeba"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]\n",
            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
            " [0. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "all_patterns = np.zeros([N1,N1,Npatterns])\n",
        "all_patterns[:,:,0]=pattern1\n",
        "all_patterns[:,:,1]=pattern2\n",
        "all_patterns[:,:,2]=pattern3\n",
        "all_patterns[:,:,3]=pattern4"
      ],
      "metadata": {
        "id": "_DNCgw46RbzY"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dt = 0.001;         \n",
        "tau = 0.010;       \n",
        "tmax = 1;     \n",
        "t=[]     \n",
        "for i in np.arange(0, 1.001, 0.001):\n",
        "\tt.append(i)\n",
        "\n",
        "Nt=len(t)\n",
        "print(Nt)\n",
        "rmax = 50;              \n",
        "gain = 1;               \n",
        "threshold = 10;"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ne4jJ8A5TP79",
        "outputId": "7c056c66-31eb-4039-ade7-605e5fa58089"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1001\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "if rule==1:\n",
        "  rate_thresh = rmax/2;\n",
        "  epsilon = 0.1/Ncells;\n",
        "elif rule==2:\n",
        "  rate_thresh = rmax/2;\n",
        "  epsilon = 0.1/Ncells;    \n",
        "  LTD_factor = 0.15;\n",
        "elif rule==3:\n",
        "  rate_thresh = rmax/2; \n",
        "  epsilon = 0.1/Ncells;      \n",
        "  LTD_factor = 0.4; \n",
        "elif rule==4:\n",
        "  rate_thresh = rmax/2;\n",
        "  epsilon = 0.1/Ncells;     \n",
        "  LTD_factor = 0.25;  \n",
        "elif rule==5:\n",
        "  rate_thresh = 0.9*rmax; \n",
        "  epsilon = 0.005/Ncells;"
      ],
      "metadata": {
        "id": "44dJuZVrWzEQ"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Wmax = 8/Ncells;     \n",
        "Wmin = -8/Ncells;\n",
        "errorprob_train = 0.1;"
      ],
      "metadata": {
        "id": "FRxRgRkwXugQ"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(np.random.randint(1, Npatterns))\n",
        "print(np.random.randint(1, Npatterns))\n",
        "\n",
        "print(np.random.randint(1, Npatterns))\n",
        "\n",
        "print(np.random.randint(1, Npatterns))\n",
        "\n",
        "selectpattern =np.random.randint(1, Npatterns)\n",
        "input_pattern = all_patterns[:,:,selectpattern];\n",
        "print(input_pattern)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i54J6CH-ZNLX",
        "outputId": "b7365731-1ecb-4431-e10f-85601b10b011"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1\n",
            "2\n",
            "3\n",
            "3\n",
            "[[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]\n",
            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
            " [0. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pylab import *\n",
        "\n",
        "for trial in range(1,Ntrials+1):                      \n",
        "    rate = np.zeros([Nt,Ncells]);               \n",
        "    selectpattern = np.random.randint(1, Npatterns);   \n",
        "    \n",
        "    input_pattern = all_patterns[:,:,selectpattern];\n",
        "\n",
        "    values = np.random.rand(Ncells, 1)\n",
        "    flipinputs = []\n",
        "    for i in range(len(values)):\n",
        "      if(values[i] < errorprob_train):\n",
        "        flipinputs.append(i);\n",
        "\n",
        "    X= input_pattern\n",
        "\n",
        "\n",
        "    X = X.flatten('F')\n",
        "\n",
        "    for i in range(len(X)):\n",
        "      if i in flipinputs[0:]:\n",
        "        X[flipinputs] = 1-X[flipinputs]\n",
        "    \n",
        "    input_pattern = np.reshape(X, (17, 17), order='F')\n",
        "\n",
        "    for i in range(1,Nt):                \n",
        "      if ( i < Nt / 2 ):           \n",
        "        \n",
        "        Y= input_pattern\n",
        "        Y = Y.flatten('F')\n",
        "        Y= Y.reshape(1,-1)\n",
        "        Temp1 = np.multiply(Y,inputstrength)\n",
        "        \n",
        "        Temp2 = np.dot(rate[i-1].reshape(1,-1),W)\n",
        "        \n",
        "        current = Temp1 + Temp2;\n",
        "      else:\n",
        "        current = np.multiply(rate[i-1].reshape(1,-1),W)   \n",
        "      \n",
        "\n",
        "      Temp3 = -gain*(current-threshold);  \n",
        "      Temp4 = 1 + np.exp(Temp3)\n",
        "      rss = np.divide(rmax, Temp4);\n",
        "      \n",
        "      Temp5 = rss - rate[i-1].reshape(1,-1)\n",
        "      Temp6 = np.multiply (Temp5, (dt/tau))\n",
        "      qi = np.add(rate[i-1].reshape(1,-1), Temp6)\n",
        "      #print(qi.shape)\n",
        "      #qi=qi.reshape(289,)\n",
        "      #rate[i] = qi\n",
        "        \n",
        "    k= viewmatrix\n",
        "    k = k.flatten('F')\n",
        "    k = k.transpose()\n",
        "    k = rate[len(rate)-1];\n",
        "    viewmatrix = np.reshape(k, (17, 17), order='F')\n",
        "    \n",
        "    rate1 = rate.transpose();\n",
        "    if rule == 1:\n",
        "      rate1 = rate.transpose();\n",
        "      dW=np.dot(np.where(rate1>rate_thresh,1,0), np.where(rate>rate_thresh,1,0))\n",
        "    elif rule == 2:\n",
        "      dW=np.dot(np.where(rate1>rate_thresh,1,0), np.where(rate>rate_thresh,1,0))\n",
        "      dW= dW - np.multiply(LTD_factor, np.dot(np.where(rate1>rate_thresh,1,0), np.where(rate<rate_thresh,1,0)))\n",
        "      dW= dW - np.multiply(LTD_factor, np.dot(np.where(rate1<rate_thresh,1,0), np.where(rate>rate_thresh,1,0)))\n",
        "\n",
        "    elif rule == 3:\n",
        "      dW=np.dot(np.where(rate1>rate_thresh,1,0), np.where(rate>rate_thresh,1,0))\n",
        "      dW= dW - np.multiply(LTD_factor, np.dot(np.where(rate1>rate_thresh,1,0), np.where(rate<rate_thresh,1,0)))\n",
        "    elif rule == 4:\n",
        "      dW = np.dot(np.where(rate1>rate_thresh,1,0), np.where(rate>rate_thresh,1,0))\n",
        "      dW= dW - np.multiply(LTD_factor, np.dot(np.where(rate1<rate_thresh,1,0), np.where(rate>rate_thresh,1,0)))\n",
        "    elif rule == 5:\n",
        "       temp = np.multiply(rate, np.subtract(rate, rate_thresh));\n",
        "       dW = np.dot(rate1, temp)\n",
        "\n",
        "    W = np.add(W,np.multiply(W, epsilon*dt))\n",
        "    W[W>Wmax] = Wmax\n",
        "    W[W<Wmin] = Wmin\n",
        "\n",
        "    if rule==1:\n",
        "      m1 = W.mean(1)\n",
        "      m2 = m1.mean()\n",
        "      W = np.subtract (W, m2+Wmean)\n",
        "  \n",
        "    if (trial%100) < 5 :\n",
        "      \n",
        "      imshow(input_pattern)        \n",
        "      \n",
        "             \n",
        "       \n",
        "      plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "wy9L8pKkYDIR",
        "outputId": "819c0e8b-f3d6-4f99-9536-16b870a23891"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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rz8Fu4GMF299Nb3/46Gfg6Lc/ZwEPHmtZFWr/76plu51egM8c3/6wrDT98emyZkmkO0BnlpXDbpaEw26WhMNuloTDbpaEw26WhMNulsT/A/6sggB22P+5AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JrTLsUiMMu9QIwy41wrBLjTDsUiMMu9SI/wPArEKS9sy6bQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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OLX9v+wK+VK6fZ4DRxOv/dIrwntXzXGPLT/Gfyj7gCMXnzo9RnINZD+wA/gUYKecdBe7vee1t5b+DncBHE7a/k+Lz8LF/A8e+/bkAePx42ypR+39XbtutFAE+f2L7g7JS98eXy5plIrsTdGa5ctjNMuGwm2XCYTfLhMNulgmH3SwTDrtZJv4fUdqpLcACDpQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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bbXQCfObk9vtlpemPT5c1SyLdATqzrBx2syQcdrMkHHazJBx2syQcdrMkHHazJP4PmjdEFoTXprQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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6d7AT+EjB9nfSOR4+/m/g+Lc/5wOPnmhdFWr/76p1u4VOgM8b336/rDT98eWyZkmkO0FnlpXDbpaEw26WhMNuloTDbpaEw26WhMNulsT/A3kPQpEYJUHdAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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vAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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iz/8sivCe2/FcY8tP8Z/KPuAoxXHnxyjOwawHdgDfBobKeUeABzpee0f572An8NGE7e+kOB4+8W/gxLc/FwNPnGxdJWr/q+W63UIR4Ismtt8rK3V/fLmsWSayO0FnliuH3SwTDrtZJhx2s0w47GaZcNjNMuGwm2Xi/wCR4YQ8VbFcFwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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2ADuAfwHmldOOAff0vPaW8u9gJ/CxhO3vpPg8fPRv4Oi3P+cAjxxrXSVq/x/KdbuVIsBnT25/UFbq/vh0WbNMZHeAzixXDrtZJhx2s0w47GaZcNjNMuGwm2XCYTfLxP8Bq7V4FxNWpgYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD4CAYAAAAq5pAIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAANSElEQVR4nO3de8xkdX3H8fenLJeCVKC0yC0FDJKgqUJWq9ZSLC0CNa5N/ANSWhCTjW2x0NgYLEk1/ctb7dVoKNDSloCtQiUGClvUNk3K6rJd7ioLpbi4gK0NWE251G//mLPN48M8e5nzm2dHf+9XMnnOzPnN/L4zZz97LnPm/FJVSPrB90N7uwBJq8OwS50w7FInDLvUCcMudWLNana2X/avAzhoNbuUuvI/fJtn65lMm7eqYT+Ag/ipnLGaXUpd2Vi3rzjPzXipE4Zd6sSosCc5K8lXkmxNclmroiS1N3PYk+wDfAw4GzgZOC/Jya0Kk9TWmDX7a4CtVfVwVT0LXA+sa1OWpNbGhP1o4GtL7m8bHvseSdYn2ZRk03M8M6I7SWPM/QBdVV1RVWurau2+7D/v7iStYEzYHwOOXXL/mOExSQtoTNi/BJyY5Pgk+wHnAje1KUtSazOfQVdVzye5GLgV2Ae4uqrua1aZpKZGnS5bVTcDNzeqRdIcreq58bO49etb9nYJ0l7zpqNe1ey1PF1W6oRhlzph2KVOGHapE4Zd6oRhlzph2KVOGHapE4Zd6oRhlzph2KVOGHapEwv/Q5hZtPzxgNTK3v5Rl2t2qROGXerEmOvGH5vk80nuT3JfkktaFiaprTH77M8D766qzUkOBu5MsqGq7m9Um6SGZl6zV9X2qto8TH8LeIAp142XtBiaHI1PchxwCrBxyrz1wHqAAziwRXeSZjD6AF2SFwGfBi6tqqeXz3eQCGkxjB3FdV8mQb+2qm5oU5KkeRhzND7AVcADVfXRdiVJmocxa/afBn4F+LkkW4bbOY3qktTYmBFh/hlIw1okzZFn0EmdMOxSJwy71AnDLnXCsEudMOxSJwy71AnDLnXCsEudMOxSJwy71AnDLnXCsEudMOxSJwy71AnDLnXCsEudaHF12X2S/GuSz7YoSNJ8tFizX8JkgAhJC2zspaSPAX4RuLJNOZLmZeya/Q+B9wDfXalBkvVJNiXZ9BzPjOxO0qzGXDf+zcCTVXXnzto5Ioy0GMZeN/4tSR4Brmdy/fi/blKVpObGjOL63qo6pqqOA84FPldV5zerTFJTfs8udaLJkM1V9QXgCy1eS9J8uGaXOmHYpU4YdqkThl3qhGGXOmHYpU4YdqkThl3qhGGXOmHYpU4YdqkThl3qhGGXOmHYpU4YdqkThl3qxNhLSR+S5FNJvpzkgSSva1WYpLbGXqnmj4C/r6q3JdkPOLBBTZLmYOawJ3kxcBpwIUBVPQs826YsSa2N2Yw/HvgG8OfDWG9XJjloeSMHiZAWw5iwrwFOBT5eVacA3wYuW97IQSKkxTAm7NuAbVW1cbj/KSbhl7SAxgwS8TjwtSQnDQ+dAdzfpCpJzY09Gv8u4NrhSPzDwNvHlyRpHkaFvaq2AGsb1SJpjjyDTuqEYZc6YdilThh2qROGXeqEYZc6YdilThh2qROGXeqEYZc6YdilThh2qROGXeqEYZc6YdilThh2qROGXerE2BFhfivJfUnuTXJdkgNaFSaprZnDnuRo4DeBtVX1CmAf4NxWhUlqa+xm/Brgh5OsYTL009fHlyRpHsZcSvox4CPAo8B24Kmqum15O0eEkRbDmM34Q4F1TIaBOgo4KMn5y9s5Ioy0GMZsxv888G9V9Y2qeg64AXh9m7IktTYm7I8Cr01yYJIwGRHmgTZlSWptzD77Ribju20G7hle64pGdUlqbOyIMO8D3teoFklz5Bl0UicMu9QJwy51wrBLnTDsUicMu9QJwy51wrBLnTDsUicMu9QJwy51wrBLnTDsUicMu9QJwy51wrBLnTDsUid2GfYkVyd5Msm9Sx47LMmGJA8Ofw+db5mSxtqdNftfAGcte+wy4PaqOhG4fbgvaYHtMuxV9U/AN5c9vA64Zpi+Bnhr47okNTbrBSePqKrtw/TjwBErNUyyHlgPcAAHztidpLFGH6CrqgJqJ/MdEUZaALOG/YkkRwIMf59sV5KkeZg17DcBFwzTFwCfaVOOpHnZna/ergP+BTgpybYk7wA+APxCkgeZjPn2gfmWKWmsXR6gq6rzVph1RuNaJM2RZ9BJnTDsUicMu9QJwy51wrBLnTDsUicMu9QJwy51wrBLnTDsUicMu9QJwy51wrBLnTDsUicMu9QJwy51YtZBIj6c5MtJ7k5yY5JD5lumpLFmHSRiA/CKqvpJ4KvAexvXJamxmQaJqKrbqur54e4dwDFzqE1SQy322S8CbllpZpL1STYl2fQczzToTtIsRoU9yeXA88C1K7VxkAhpMcw6/BNJLgTeDJwxjAojaYHNFPYkZwHvAX62qr7TtiRJ8zDrIBF/ChwMbEiyJckn5lynpJFmHSTiqjnUImmOPINO6oRhlzph2KVOGHapE4Zd6oRhlzph2KVOGHapE4Zd6oRhlzph2KVOGHapE4Zd6oRhlzph2KVOGHapE4Zd6sRMI8IsmffuJJXk8PmUJ6mVWUeEIcmxwJnAo41rkjQHM40IM/gDJleY9TLS0veBmfbZk6wDHququ3ajrSPCSAtgj68bn+RA4HeYbMLvUlVdAVwB8CM5zK0AaS+ZZc3+UuB44K4kjzAZ1HFzkpe0LExSW3u8Zq+qe4Af33F/CPzaqvqPhnVJamzWEWEkfZ+ZdUSYpfOPa1aNpLmZeRTXRXbr17fs7RKkhePpslInDLvUCcMudcKwS50w7FInDLvUCcMudcKwS50w7FInDLvUCcMudcKwS51Y+B/CvOmoV+3tEqQfCK7ZpU4YdqkTMw8SkeRdSb6c5L4kH5pfiZJamGmQiCRvBNYBr6yqlwMfaV+apJZmHSTi14APVNUzQ5sn51CbpIZm3Wd/GfAzSTYm+cckr16poYNESIth1q/e1gCHAa8FXg38TZITquoFg0A4SIS0GGZds28DbqiJLwLfBRzJVVpgs4b974A3AiR5GbAf4CAR0gLb5Wb8MEjE6cDhSbYB7wOuBq4evo57Frhg2ia8pMUxZpCI8xvXImmOPINO6kRWc+s7yTeAf58y63D27j6//dv/D0r/P1FVPzZtxqqGfSVJNlXVWvu3f/ufHzfjpU4YdqkTixL2K+zf/u1/vhZin13S/C3Kml3SnBl2qROrGvYkZyX5SpKtSS6bMn//JJ8c5m9MclzDvo9N8vkk9w9X17lkSpvTkzyVZMtw+91W/Q+v/0iSe4bX3jRlfpL88fD+705yasO+T1ryvrYkeTrJpcvaNH3/065ylOSwJBuSPDj8PXSF514wtHkwyQUN+//wcIWlu5PcmOSQFZ6702U1ov/3J3lsyWd8zgrP3WlWZlJVq3ID9gEeAk5g8sOZu4CTl7X5deATw/S5wCcb9n8kcOowfTDw1Sn9nw58do6fwSPA4TuZfw5wCxAmPx/eOMdl8TiTEzDm9v6B04BTgXuXPPYh4LJh+jLgg1Oedxjw8PD30GH60Eb9nwmsGaY/OK3/3VlWI/p/P/Dbu7F8dpqVWW6ruWZ/DbC1qh6uqmeB65lc2mqpdcA1w/SngDOSpEXnVbW9qjYP098CHgCObvHaDa0D/rIm7gAOSXLkHPo5A3ioqqadzdhMTb/K0dJlfA3w1ilPfROwoaq+WVX/BWxg2aXRZu2/qm6rqueHu3cAx+zp647pfzftTlb22GqG/Wjga0vub+OFYfv/NsMCeQr40daFDLsHpwAbp8x+XZK7ktyS5OWNuy7gtiR3Jlk/Zf7ufEYtnAtct8K8eb5/gCOqavsw/ThwxJQ2q/U5XMRkS2qaXS2rMS4ediOuXmE3Zi7vv7sDdEleBHwauLSqnl42ezOTTdtXAn/C5Hf7Lb2hqk4FzgZ+I8lpjV9/l5LsB7wF+Nsps+f9/r9HTbZZ98p3v0kuB54Hrl2hybyW1ceBlwKvArYDv9/odXdpNcP+GHDskvvHDI9NbZNkDfBi4D9bFZBkXyZBv7aqblg+v6qerqr/HqZvBvZN0uwKPFX12PD3SeBGJptrS+3OZzTW2cDmqnpiSn1zff+DJ3bsmgx/p12sdK6fQ5ILgTcDvzz8h/MCu7GsZlJVT1TV/1bVd4E/W+F15/L+VzPsXwJOTHL8sHY5F7hpWZubgB1HXt8GfG6lhbGnhn3/q4AHquqjK7R5yY5jBElew+TzafKfTZKDkhy8Y5rJgaJ7lzW7CfjV4aj8a4GnlmzytnIeK2zCz/P9L7F0GV8AfGZKm1uBM5McOmzmnjk8NlqSs4D3AG+pqu+s0GZ3ltWs/S89BvNLK7zu7mRlz409wreHRyfPYXIU/CHg8uGx32PywQMcwGTzcivwReCEhn2/gckm493AluF2DvBO4J1Dm4uB+5gc/bwDeH3D/k8YXveuoY8d739p/wE+Nnw+9wBrG3/+BzEJ74uXPDa398/kP5XtwHNM9jvfweQYzO3Ag8A/AIcNbdcCVy557kXDv4OtwNsb9r+Vyf7wjn8DO779OQq4eWfLqlH/fzUs27uZBPjI5f2vlJWxN0+XlTrR3QE6qVeGXeqEYZc6YdilThh2qROGXeqEYZc68X/eR9XNTsM/FAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PXV7+SlkZ+vByWakRzZ2gk1pl2KVGGHapEYZdaoRhlxph2KVGGHapEf8HM+xTaOh4HUoAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "errorprob = 0.2; \n",
        "\n",
        "inputstrength = inputstrength*0.5; \n",
        "\n",
        "\n",
        "\n",
        "\n",
        "for trial in range(Npatterns):\n",
        "    rate = np.zeros([Nt,Ncells]);\n",
        "    selectpattern = trial;\n",
        "    \n",
        "   \n",
        "    input_pattern = all_patterns[:,:,selectpattern];\n",
        "    \n",
        "    values = np.random.rand(Ncells, 1)\n",
        "    flipinputs = []\n",
        "    for i in range(len(values)):\n",
        "      if(values[i] < errorprob_train):\n",
        "        flipinputs.append(i);\n",
        "    X= input_pattern\n",
        "\n",
        "\n",
        "    X = X.flatten('F')\n",
        "\n",
        "    for i in range(len(X)):\n",
        "      if i in flipinputs[0:]:\n",
        "        X[flipinputs] = 1-X[flipinputs]\n",
        "    \n",
        "    input_pattern = np.reshape(X, (17, 17), order='F')\n",
        "    \n",
        "    \n",
        "      \n",
        "    imshow(input_pattern);    \n",
        "       \n",
        "    plt.show()\n",
        "\n",
        "    for i in range(1,Nt):\n",
        "      if ( i < Nt / 2 ):\n",
        "        Y= input_pattern\n",
        "        Y = Y.flatten('F')\n",
        "        Y= Y.reshape(1,-1)\n",
        "        Temp1 = np.multiply(Y,inputstrength)\n",
        "        \n",
        "        Temp2 = np.dot(rate[i-1].reshape(1,-1),W)\n",
        "        \n",
        "        current = Temp1 + Temp2;\n",
        "      else:\n",
        "        current = np.multiply(rate[i-1].reshape(1,-1),W)\n",
        "        \n",
        "       \n",
        "      Temp3 = -gain*(current-threshold);  \n",
        "      Temp4 = 1 + np.exp(Temp3)\n",
        "      rss = np.divide(rmax, Temp4);\n",
        "\n",
        "      Temp5 = rss - rate[i-1].reshape(1,-1)\n",
        "      Temp6 = np.multiply (Temp5, (dt/tau))\n",
        "      qi = np.add(rate[i-1].reshape(1,-1), Temp6)\n",
        "      \n",
        "      rate[i] =qi.reshape(289,)\n",
        "    \n",
        "   \n",
        "    k= viewmatrix\n",
        "    k = k.flatten('F')\n",
        "    k = k.transpose()\n",
        "    k = rate[len(rate)-1];\n",
        "    viewmatrix = np.reshape(k, (17, 17), order='F')\n",
        "    \n",
        "    \n",
        "    \n",
        "   \n",
        "    imshow(viewmatrix);\n",
        "    plt.show()\n",
        "\n",
        "\n",
        "\n",
        "    "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 482
        },
        "id": "qRQyV-Z4XQdg",
        "outputId": "988b1dd6-93e2-4eaa-89db-dbc6fa8011c2"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD4CAYAAAAq5pAIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAOIUlEQVR4nO3da6xlZ13H8e/PmZbaUmmHYintxLYESIBwacYGEBGstqUSBhNelIiWSzJBBcGgpEgixFfcxCuBVKhWbSgKRRpSnI5cNCZ2yjBOpzdoh1rplOmFYlqUSC/8fbHXmMNh75k5e621u2ee7yc5OevstfZ+/nuv+c267PWsJ1WFpCPfjz3WBUhaDMMuNcKwS40w7FIjDLvUiPWLbOykDevq9I1Hrek5t+4+dqRqpOX39Od8b03L33Hnw3z7O49m2ryFhv30jUdx3daNa3rOeU953kjVSMtv69Zda1r+7PPunDnP3XipEYZdakSvsCc5P8nXk+xJcvFQRUka3txhT7IO+DDwcuCZwGuSPHOowiQNq8+W/WxgT1XdXlUPAVcAm4cpS9LQ+oT9VGDlqb+93WM/JMmWJDuS7Ljv/kd7NCepj9FP0FXVJVW1qao2PemJ68ZuTtIMfcJ+F7DyS/PTusckLaE+Yf8K8LQkZyQ5GrgQuGqYsiQNbe4r6KrqkSRvBrYC64BLq+qmwSqTNKhel8tW1dXA1QPVImlEC702/tbdx675Wvet31rbtcHg9fRaTov4t3xr3T9znpfLSo0w7FIjDLvUCMMuNcKwS40w7FIjDLvUCMMuNcKwS40w7FIjDLvUCMMuNWKhHWHmMU+nlrV2OLDjjNbqcOyg5ZZdaoRhlxrR577xG5N8KcnNSW5K8tYhC5M0rD7H7I8Ab6+qnUmOB76aZFtV3TxQbZIGNPeWvar2VdXObvq7wC1MuW+8pOUwyNn4JKcDzwe2T5m3BdgCcAyOtS49VnqfoEvyeODTwNuq6sHV81cOEnEUj+vbnKQ59R3F9SgmQb+8qq4cpiRJY+hzNj7Ax4FbqupDw5UkaQx9tuw/A/wq8PNJdnU/FwxUl6SB9RkR5l+BDFiLpBEt/bXx83AgCq1VC/0pvFxWaoRhlxph2KVGGHapEYZdaoRhlxph2KVGGHapEYZdaoRhlxph2KVGGHapEUdkR5i1WsRAFPO2o7Vz3Uznll1qhGGXGmHYpUYMcXfZdUn+PcnnhihI0jiG2LK/lckAEZKWWN9bSZ8G/BLwsWHKkTSWvlv2PwbeAfxg1gJJtiTZkWTHw3y/Z3OS5tXnvvGvAO6tqq8eaDlHhJGWQ9/7xr8yyR3AFUzuH/+3g1QlaXB9RnF9Z1WdVlWnAxcCX6yq1w5WmaRB+T271IhBro2vqi8DXx7itSSNw44wc7LzzGL4mQ3H3XipEYZdaoRhlxph2KVGGHapEYZdaoRhlxph2KVGGHapEYZdaoRhlxph2KVG2BFmgVrvPHMkvZfDkVt2qRGGXWpE31tJn5DkU0m+luSWJC8cqjBJw+p7zP4nwD9W1auTHA0cO0BNkkYwd9iTPAF4CfA6gKp6CHhomLIkDa3PbvwZwH3AX3ZjvX0syXGrF3KQCGk59An7euAs4CNV9Xzgf4CLVy/kIBHScugT9r3A3qra3v39KSbhl7SE+gwScTdwZ5JndA+dA9w8SFWSBtf3bPxbgMu7M/G3A6/vX5KkMfQKe1XtAjYNVIukEXkFndQIO8IsuWXtPGOnlsOPW3apEYZdaoRhlxph2KVGGHapEYZdaoRhlxph2KVGGHapEYZdaoRhlxph2KVG2BHmCLSIzjN2ajn8uGWXGmHYpUb0HRHmt5PclOTGJJ9IcsxQhUka1txhT3Iq8FvApqp6NrAOuHCowiQNq+9u/Hrgx5OsZzL007f6lyRpDH1uJX0X8EHgm8A+4IGqumb1co4IIy2HPrvxJwKbmQwD9RTguCSvXb2cI8JIy6HPbvwvAP9RVfdV1cPAlcCLhilL0tD6hP2bwAuSHJskTEaEuWWYsiQNrc8x+3Ym47vtBG7oXuuSgeqSNLC+I8K8G3j3QLVIGpHXxh+BHCRC03i5rNQIwy41wrBLjTDsUiMMu9QIwy41wrBLjTDsUiMMu9QIwy41wrBLjTDsUiPsCLPklrXDySIGopi3HU3nll1qhGGXGmHYpUYcNOxJLk1yb5IbVzy2Icm2JLd1v08ct0xJfR3Klv2vgPNXPXYx8IWqehrwhe5vSUvsoGGvqn8BvrPq4c3AZd30ZcCrBq5L0sDm/ert5Kra103fDZw8a8EkW4AtAMdw7JzNSeqr9wm6qiqgDjDfEWGkJTBv2O9JcgpA9/ve4UqSNIZ5w34VcFE3fRHw2WHKkTSWQ/nq7RPAvwHPSLI3yRuB9wK/mOQ2JmO+vXfcMiX1ddATdFX1mhmzzhm4FkkjsiPMArXeEcTOM48tL5eVGmHYpUYYdqkRhl1qhGGXGmHYpUYYdqkRhl1qhGGXGmHYpUYYdqkRhl1qhB1h5mQHjcWw88xw3LJLjTDsUiPmHSTiA0m+lmR3ks8kOWHcMiX1Ne8gEduAZ1fVc4BbgXcOXJekgc01SERVXVNVj3R/XgucNkJtkgY0xDH7G4DPz5qZZEuSHUl2PMz3B2hO0jx6hT3Ju4BHgMtnLeMgEdJymPt79iSvA14BnNONCiNpic0V9iTnA+8Afq6qvjdsSZLGMO8gEX8OHA9sS7IryUdHrlNST/MOEvHxEWqRNCKvoJMaYUcY7DhxpFlE55nDcf27ZZcaYdilRhh2qRGGXWqEYZcaYdilRhh2qRGGXWqEYZcaYdilRhh2qRGGXWrEEdkRpoVODRrWWv8NHI6dp9yyS40w7FIj5hoRZsW8tyepJCeNU56kocw7IgxJNgLnAt8cuCZJI5hrRJjOHzG5w6y3kZYOA3MdsyfZDNxVVdcfwrKOCCMtgTV/9ZbkWOD3mOzCH1RVXQJcAvAT2eBegPQYmWfL/lTgDOD6JHcwGdRxZ5InD1mYpGGtecteVTcAP7n/7y7wm6rq2wPWJWlg844II+kwM++IMCvnnz5YNZJGs/TXxh+O1yDryLeIgSjmbWcWL5eVGmHYpUYYdqkRhl1qhGGXGmHYpUYYdqkRhl1qhGGXGmHYpUYYdqkRhl1qRKoWd/OYTc89pq7bunFNz7FTi1q21s4zZ593Jzuu/99Mm+eWXWqEYZcaMfcgEUnekuRrSW5K8v7xSpQ0hLkGiUjyMmAz8NyqehbwweFLkzSkeQeJ+HXgvVX1/W6Ze0eoTdKA5j1mfzrws0m2J/nnJD89a8GVg0Tcd/+jczYnqa95w74e2AC8APhd4O+STD3dX1WXVNWmqtr0pCeum7M5SX3NG/a9wJU1cR3wA8CRXKUlNm/Y/wF4GUCSpwNHAw4SIS2xg95Kuhsk4qXASUn2Au8GLgUu7b6Oewi4qBZ5KZ6kNeszSMRrB65F0oi8gk5qxEI7wiS5D/jPKbNO4rE95rd92z9S2v+pqnrStBkLDfssSXZU1Sbbt33bH4+78VIjDLvUiGUJ+yW2b/u2P66lOGaXNL5l2bJLGplhlxqx0LAnOT/J15PsSXLxlPmPS/LJbv72JKcP2PbGJF9KcnN3d523TlnmpUkeSLKr+/n9odrvXv+OJDd0r71jyvwk+dPu/e9OctaAbT9jxfvaleTBJG9btcyg73/aXY6SbEiyLclt3e8TZzz3om6Z25JcNGD7H+jusLQ7yWeSnDDjuQdcVz3af0+Su1Z8xhfMeO4BszKXqlrID7AO+AZwJpOOM9cDz1y1zG8AH+2mLwQ+OWD7pwBnddPHA7dOaf+lwOdG/AzuAE46wPwLgM8DYdJ9ePuI6+JuJhdgjPb+gZcAZwE3rnjs/cDF3fTFwPumPG8DcHv3+8Ru+sSB2j8XWN9Nv29a+4eyrnq0/x7gdw5h/RwwK/P8LHLLfjawp6pur6qHgCuY3Npqpc3AZd30p4BzZvWTX6uq2ldVO7vp7wK3AKcO8doD2gz8dU1cC5yQ5JQR2jkH+EZVTbuacTA1/S5HK9fxZcCrpjz1PGBbVX2nqv4L2MaqW6PN235VXVNVj3R/XgucttbX7dP+ITqUrKzZIsN+KnDnir/38qNh+/9luhXyAPDEoQvpDg+eD2yfMvuFSa5P8vkkzxq46QKuSfLVJFumzD+Uz2gIFwKfmDFvzPcPcHJV7eum7wZOnrLMoj6HNzDZk5rmYOuqjzd3hxGXzjiMGeX9N3eCLsnjgU8Db6uqB1fN3slk1/a5wJ8x6bc/pBdX1VnAy4HfTPKSgV//oJIcDbwS+Psps8d+/z+kJvusj8l3v0neBTwCXD5jkbHW1UeApwLPA/YBfzjQ6x7UIsN+F7ByOJjTusemLpNkPfAE4P6hCkhyFJOgX15VV66eX1UPVtV/d9NXA0clGewOPFV1V/f7XuAzTHbXVjqUz6ivlwM7q+qeKfWN+v479+w/NOl+T7tZ6aifQ5LXAa8AfqX7D+dHHMK6mktV3VNVj1bVD4C/mPG6o7z/RYb9K8DTkpzRbV0uBK5atcxVwP4zr68GvjhrZaxVd+z/ceCWqvrQjGWevP8cQZKzmXw+g/xnk+S4JMfvn2ZyoujGVYtdBfxad1b+BcADK3Z5h/IaZuzCj/n+V1i5ji8CPjtlma3AuUlO7HZzz+0e6y3J+cA7gFdW1fdmLHMo62re9leeg/nlGa97KFlZu75n+NZ4dvICJmfBvwG8q3vsD5h88ADHMNm93ANcB5w5YNsvZrLLuBvY1f1cALwJeFO3zJuBm5ic/bwWeNGA7Z/Zve71XRv73//K9gN8uPt8bgA2Dfz5H8ckvE9Y8dho75/Jfyr7gIeZHHe+kck5mC8AtwH/BGzolt0EfGzFc9/Q/TvYA7x+wPb3MDke3v9vYP+3P08Brj7Quhqo/b/p1u1uJgE+ZXX7s7LS98fLZaVGNHeCTmqVYZcaYdilRhh2qRGGXWqEYZcaYdilRvwfdWRHLzgro/QAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-16-cd91a75202a4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     59\u001b[0m       \u001b[0mqi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrate\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTemp6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 61\u001b[0;31m       \u001b[0mrate\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0mqi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m289\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: cannot reshape array of size 83521 into shape (289,)"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pylab import *"
      ],
      "metadata": {
        "id": "aMjs9TYfGHn8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Y= input_pattern\n",
        "Y = Y.flatten('F')\n",
        "Y= Y.reshape(1,-1)\n",
        "Temp1 = np.multiply(Y,inputstrength)\n",
        "\n",
        "Temp2 = np.dot(rate[i-1].reshape(1,-1),W)\n",
        "\n",
        "current = Temp1 + Temp2;\n",
        "\n",
        "\n",
        "\n",
        "Temp3 = -gain*(current-threshold);  \n",
        "Temp4 = 1 + np.exp(Temp3)\n",
        "rss = np.divide(rmax, Temp4);\n",
        "\n",
        "Temp5 = rss - rate[i-1].reshape(1,-1)\n",
        "Temp6 = np.multiply (Temp5, (dt/tau))\n",
        "qi = np.add(rate[i-1].reshape(1,-1), Temp6)"
      ],
      "metadata": {
        "id": "5lwIqsIRMPgT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(qi.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4aJrmHv6jkhA",
        "outputId": "85f130a9-d093-4803-9769-16014100463c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1, 289)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "values1 = np.random.rand(Ncells, 1)\n",
        "print(len(values1))\n",
        "flipinputs = []\n",
        "for i in range(len(values1)):\n",
        "  if(values1[i] < 0.01):\n",
        "    flipinputs.append(i);\n",
        "\n",
        "print(flipinputs)\n",
        "values1[0]\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3BR-_FBzb2dT",
        "outputId": "1582a5a2-40c6-4173-b874-5a595a179f33"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "289\n",
            "[23, 33, 49, 136]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.04352828])"
            ]
          },
          "metadata": {},
          "execution_count": 181
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "X= input_pattern\n",
        "\n",
        "X = X.flatten('F')\n",
        "print(X)\n",
        "for i in range(len(X)):\n",
        "  if i in flipinputs[0:]:\n",
        "    print(i)\n",
        "    X[flipinputs] = 1-X[flipinputs]\n",
        "arr_2d = np.reshape(X, (17, 17), order='F')\n",
        "print(arr_2d)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R9RypNkHj2R6",
        "outputId": "0fc8912d-3c27-4422-b5dc-8a3b112556d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 1. 1. 1. 1. 1.\n",
            " 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 1. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 1. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 1. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0.\n",
            " 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 1. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 1. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 1. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 1. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 1.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 1. 1. 1. 1. 1. 1. 1. 1.\n",
            " 1. 1. 1. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0.]\n",
            "23\n",
            "33\n",
            "49\n",
            "136\n",
            "[[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]\n",
            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
            " [0. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Temp1 = np.multiply(Y,inputstrength)\n",
        "Temp2 = np.multiply(rate[i-1],W)\n",
        "Temp1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 200
        },
        "id": "BUzvkwZZlr2c",
        "outputId": "761dd7cb-f350-4815-bd41-05375ac1cc24"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-205-fa53bfe76043>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mTemp1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmultiply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mY\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0minputstrength\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mTemp2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmultiply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrate\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mW\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mTemp1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'dim'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.seterr(divide='ignore', invalid='ignore')\n",
        "A = np.random.rand(3, 3)\n",
        "print(A)\n",
        "print(A[2])\n",
        "\n",
        "A[A>0.2] =0.2\n",
        "A\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5_6T1AVHv4et",
        "outputId": "6ebe3be3-b743-4020-8188-4ddb5f07bc66"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.75322299 0.66783585 0.73878038]\n",
            " [0.70374564 0.56677883 0.60792115]\n",
            " [0.53876744 0.1940833  0.39802986]]\n",
            "[0.53876744 0.1940833  0.39802986]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.2      , 0.2      , 0.2      ],\n",
              "       [0.2      , 0.2      , 0.2      ],\n",
              "       [0.2      , 0.1940833, 0.2      ]])"
            ]
          },
          "metadata": {},
          "execution_count": 243
        }
      ]
    }
  ]
}