perceptrons-nand-gate.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Perceptron\n",
    "import numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "perceptron = Perceptron()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "xy = numpy.array([[1,1],[1,0],[0,1],[0,0]])\n",
    "out = numpy.array([0,1,1,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 1.0\n"
     ]
    }
   ],
   "source": [
    "perceptron.fit(xy, out)\n",
    "\n",
    "print('accuracy:', perceptron.score(xy,out))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 0])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "perceptron.predict([[1,0],[0,1],[0,0],[1,1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from matplotlib import pyplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAD8CAYAAACYebj1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nH/3fZB0e3bfsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f691008a6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pyplot.plot(xy[1:,0],xy[1:,1], 'ro', color=\"green\")\n",
    "pyplot.plot(xy[0:1,0],xy[0:1,1], 'ro')\n",
    "pyplot.plot([1.5,0.5])\n",
    "pyplot.ylabel('NAND Gate')\n",
    "pyplot.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}