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  1. {
  2.  "cells": [
  3.   {
  4.    "cell_type": "code",
  5.    "execution_count": 4,
  6.    "metadata": {},
  7.    "outputs": [
  8.     {
  9.      "name": "stdout",
  10.      "output_type": "stream",
  11.      "text": [
  12.       "Feature :  [1. 0. 0. 1. 0.]  Target :  [0. 1.]\n",
  13.       "Feature :  [1. 1. 0. 0. 1.]  Target :  [1. 0.]\n",
  14.       "Feature :  [1. 1. 1. 0. 1.]  Target :  [1. 0.]\n",
  15.       "Feature :  [1. 1. 0. 1. 1.]  Target :  [1. 0.]\n",
  16.       "Feature :  [0. 0. 1. 1. 1.]  Target :  [0. 1.]\n",
  17.       "Feature :  [0. 0. 0. 1. 1.]  Target :  [0. 1.]\n",
  18.       "Feature :  [1. 1. 1. 1. 1.]  Target :  [1. 0.]\n",
  19.       "Feature :  [0. 0. 0. 0. 0.]  Target :  [0. 1.]\n",
  20.       "Feature :  [1. 0. 1. 1. 0.]  Target :  [0. 1.]\n",
  21.       "Feature :  [1. 0. 1. 1. 1.]  Target :  [1. 0.]\n",
  22.       "Feature :  [1. 0. 0. 0. 0.]  Target :  [0. 1.]\n",
  23.       "Feature :  [0. 1. 0. 1. 1.]  Target :  [0. 1.]\n",
  24.       "Feature :  [0. 0. 0. 0. 1.]  Target :  [0. 1.]\n",
  25.       "Feature :  [1. 1. 1. 1. 0.]  Target :  [0. 1.]\n",
  26.       "Feature :  [1. 0. 0. 0. 1.]  Target :  [1. 0.]\n",
  27.       "Feature :  [0. 1. 1. 1. 1.]  Target :  [0. 1.]\n",
  28.       "Feature :  [1. 1. 0. 0. 0.]  Target :  [0. 1.]\n",
  29.       "Feature :  [1. 0. 0. 1. 1.]  Target :  [1. 0.]\n",
  30.       "Feature :  [1. 1. 0. 1. 0.]  Target :  [0. 1.]\n",
  31.       "Feature :  [1. 1. 1. 0. 0.]  Target :  [0. 1.]\n",
  32.       "Feature :  [0. 0. 1. 0. 1.]  Target :  [0. 1.]\n",
  33.       "Feature :  [1. 0. 1. 0. 1.]  Target :  [1. 0.]\n"
  34.      ]
  35.     }
  36.    ],
  37.    "source": [
  38.     "import numpy as np\n",
  39.     "\n",
  40.     "# You should write your target function in this space\n",
  41.     "#====================================================\n",
  42.     "\n",
  43.     "\n",
  44.     "\n",
  45.     "\n",
  46.     "\n",
  47.     "\n",
  48.     "\n",
  49.     "#=====================================================\n",
  50.     "    \n",
  51.     "\n",
  52.     "## Set up training data\n",
  53.     "## Each row is a case\n",
  54.     "## Columns 0-4 are features\n",
  55.     "## Columns 5 & 6 are targets\n",
  56.     "\n",
  57.     "features_and_targets = np.array( \n",
  58.     "                                   [ [0, 0, 0, 0, 0, 0, 1],\n",
  59.     "                                     [0, 0, 0, 0, 1, 0, 1],\n",
  60.     "                                     [0, 0, 0, 1, 1, 0, 1],\n",
  61.     "                                     [0, 0, 1, 1, 1, 0, 1],\n",
  62.     "                                     [0, 1, 1, 1, 1, 0, 1],\n",
  63.     "                                     [1, 1, 1, 1, 0, 0, 1],\n",
  64.     "                                     [1, 1, 1, 0, 0, 0, 1],\n",
  65.     "                                     [1, 1, 0, 0, 0, 0, 1],\n",
  66.     "                                     [1, 0, 0, 0, 0, 0, 1],\n",
  67.     "                                     [1, 0, 0, 1, 0, 0, 1],\n",
  68.     "                                     [1, 0, 1, 1, 0, 0, 1],\n",
  69.     "                                     [1, 1, 0, 1, 0, 0, 1],\n",
  70.     "                                     [0, 1, 0, 1, 1, 0, 1],\n",
  71.     "                                     [0, 0, 1, 0, 1, 0, 1],\n",
  72.     "                                     [1, 0, 1, 1, 1, 1, 0],\n",
  73.     "                                     [1, 1, 0, 1, 1, 1, 0],\n",
  74.     "                                     [1, 0, 1, 0, 1, 1, 0],\n",
  75.     "                                     [1, 0, 0, 0, 1, 1, 0],\n",
  76.     "                                     [1, 1, 0, 0, 1, 1, 0],\n",
  77.     "                                     [1, 1, 1, 0, 1, 1, 0],\n",
  78.     "                                     [1, 1, 1, 1, 1, 1, 0],\n",
  79.     "                                     [1, 0, 0, 1, 1, 1, 0]  ]\n",
  80.     "                           , dtype=float)\n",
  81.     "\n",
  82.     "# shuffle our cases\n",
  83.     "np.random.shuffle(features_and_targets)\n",
  84.     "\n",
  85.     "for i in range(22):\n",
  86.     "    features = features_and_targets[i,0:5]\n",
  87.     "    targets = features_and_targets[i,5:7]\n",
  88.     "    \n",
  89.     "    # Call your target function here based on x\n",
  90.     "    # to compute your target values based on random \n",
  91.     "    # values for all weights and biases then you can add\n",
  92.     "    # your predicted y's to the print statement\n",
  93.     "    \n",
  94.     "    print('Feature : ',features, ' Target : ', targets)\n",
  95.     "    \n",
  96.     "    "
  97.    ]
  98.   },
  99.   {
  100.    "cell_type": "code",
  101.    "execution_count": null,
  102.    "metadata": {},
  103.    "outputs": [],
  104.    "source": []
  105.   }
  106.  ],
  107.  "metadata": {
  108.   "kernelspec": {
  109.    "display_name": "Python 3",
  110.    "language": "python",
  111.    "name": "python3"
  112.   },
  113.   "language_info": {
  114.    "codemirror_mode": {
  115.     "name": "ipython",
  116.     "version": 3
  117.    },
  118.    "file_extension": ".py",
  119.    "mimetype": "text/x-python",
  120.    "name": "python",
  121.    "nbconvert_exporter": "python",
  122.    "pygments_lexer": "ipython3",
  123.    "version": "3.6.5"
  124.   }
  125.  },
  126.  "nbformat": 4,
  127.  "nbformat_minor": 2
  128. }
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