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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Usando scikit-learn\n",
    "\n",
    "Aqui mostramos como usar alguns classificadores disponíveis no scikit-learn\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Gerar os dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from funcoes import geraDados2DfronteiraNonLinear\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "N = 100\n",
    "X, y, x, fx = geraDados2DfronteiraNonLinear(N)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Usar regressão logística do scikit-learn\n",
    "\n",
    "http://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "clf = LogisticRegression(max_iter=100)  # default max_iter=100\n",
    "clf.fit(X,y)\n",
    "\n",
    "z = clf.predict(X)\n",
    "\n",
    "print(z)\n",
    "\n",
    "for i in range(N):\n",
    "    if z[i] > 0.5:\n",
    "        plt.plot(X[i,1], X[i,2], 'bx')\n",
    "    else:\n",
    "        plt.plot(X[i,1], X[i,2], 'ro')\n",
    "\n",
    "plt.plot(x, fx, lw=2)\n",
    "plt.xlabel('x1')\n",
    "plt.ylabel('x2')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Usar SVM do scikit-learn\n",
    "\n",
    "http://scikit-learn.org/stable/modules/svm.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from sklearn import svm\n",
    "\n",
    "clf = svm.SVC(gamma=0.3, C=100.)\n",
    "clf.fit(X,y)\n",
    "\n",
    "z = clf.predict(X)\n",
    "\n",
    "print(z)\n",
    "\n",
    "for i in range(N):\n",
    "    if z[i] > 0.5:\n",
    "        plt.plot(X[i,1], X[i,2], 'bx')\n",
    "    else:\n",
    "        plt.plot(X[i,1], X[i,2], 'ro')\n",
    "        \n",
    "plt.plot(x, fx, lw=2)\n",
    "plt.xlabel('x1')\n",
    "plt.ylabel('x2')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Redes neurais multicamadas\n",
    "\n",
    "http://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html#sklearn.neural_network.MLPClassifier\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "clf = MLPClassifier(solver='sgd', learning_rate_init=0.01, max_iter=500, hidden_layer_sizes=(10), random_state=1)\n",
    "\n",
    "clf.fit(X, y)\n",
    "\n",
    "z = clf.predict(X)\n",
    "print(z)\n",
    "\n",
    "for i in range(N):\n",
    "    if z[i] > 0.5:\n",
    "        plt.plot(X[i,1], X[i,2], 'bx')\n",
    "    else:\n",
    "        plt.plot(X[i,1], X[i,2], 'ro')\n",
    "        \n",
    "plt.plot(x, fx, lw=2)\n",
    "plt.xlabel('x1')\n",
    "plt.ylabel('x2')\n",
    "plt.show()"
   ]
  }
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