sklearn_mlp.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "from sklearn import datasets\n",
    "from sklearn.neural_network import MLPClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def normalize_data(x):\n",
    "    data = zeros(shape(x))\n",
    "    for i in range(len(x[0])):\n",
    "        feature = [example[i] for example in x]\n",
    "        max_val = np.max(feature)\n",
    "        min_val = np.min(feature)\n",
    "        val_range = max_val - min_val\n",
    "        for j in range(len(x)):\n",
    "            data[j][i] = (x[j][i] - min_val) / val_range\n",
    "    return data\n",
    "\n",
    "def accuracy_score(pred, true):\n",
    "    correct = np.sum(pred==true)\n",
    "    accuracy = correct/len(pred)\n",
    "    return accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.93\n",
      "[1 2 1 2 1 1 2 0 0 1 2 2 1 2 2 0 1 0 1 0 1 2 1 2 2 1 0 0 1 0 1 2 2 1 2 0 0\n",
      " 1 1 0 2 0 2 1 2 0 2 2 1 0 0 1 1 0 1 2 0 1 1 0 2 0 1 2 1 2 2 0 0 1 2 0 2 2\n",
      " 1 0 0 0 0 1 1 0 0 0 1 1 0 0 0 2 2 0 1 2 0 1 1 0 0 1]\n"
     ]
    }
   ],
   "source": [
    "iris = datasets.load_iris()\n",
    "X = iris.data  # we only take the first two features.\n",
    "y = iris.target\n",
    "\n",
    "normX = normalize_data(X)\n",
    "\n",
    "indices = np.random.permutation(len(normX))\n",
    "\n",
    "train, test = indices[100:], indices[:100]\n",
    "\n",
    "net = MLPClassifier(activation='logistic', hidden_layer_sizes=(5), learning_rate_init=0.01)\n",
    "\n",
    "net.fit(normX[train], y[train])\n",
    "\n",
    "preds = net.predict(normX[test])\n",
    "\n",
    "accuracy = accuracy_score(preds, y[test])\n",
    "print(accuracy)\n",
    "print(preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}