T1-Ex7.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import scipy.io as spio\n",
    "import matplotlib.pyplot as plt\n",
    "from poly_features import poly_features\n",
    "from normalizacao import normalizar_caracteristica\n",
    "from linearRegCostFunction import RegCost, gdReg, minimize\n",
    "from learningCurve import learningCurve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ler arquivo de dados\n",
    "data = spio.loadmat('data/ex5data1.mat')\n",
    "#armazena os dados de cada array identificado em [''] na sua respectiva variável a ser usada\n",
    "X = data['X']\n",
    "y = data ['y']\n",
    "Xval = data['Xval']\n",
    "yval = data['yval']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12, 8)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_poly = poly_features(X, 8)\n",
    "X_poly.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_norm, y_norm = normalizar_caracteristica(X_poly, y)\n",
    "theta0 = np.zeros((X_norm.shape[1],1))\n",
    "custo, theta = minimize(theta0, X_norm, y_norm, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#def plot(X, y, xx1, h, theta):\n",
    "#    t = np.arange(-80, 80, 14)\n",
    "#    plt.axis ([-80, 80, -60, 40])\n",
    "#    plt.scatter(X.T, y, color='red', marker='x')\n",
    "#    plt.plot(t, h, color = 'blue')\n",
    "#    plt.xlabel('Change in water level (x)')\n",
    "#    plt.ylabel('Water flowing out of the dam (y)')\n",
    "#    plt.title('Learning Curve for Linear Regression')\n",
    "    #define o nome do arquivo que guardará a imagem do gráfico, e cria o caminho, caso não exista\n",
    "#    filename = 'target/plot7.1.png'\n",
    "#    if not os.path.exists(os.path.dirname(filename)):\n",
    "#      os.makedirs(os.path.dirname(filename))\n",
    "\n",
    "    #salva imagem do gráfico e o exibe\n",
    "#    plt.savefig(filename)\n",
    "#    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ploterro(erro_val, erro):\n",
    "    t = np.arange(1,13)\n",
    "    plt.axis ([0, 13, 0, 100])\n",
    "    plt.plot (t, erro_val,color = 'green', label = 'Cross Validation')\n",
    "    plt.plot (t, erro, color = \"blue\", label = 'Train')\n",
    "    plt.legend()\n",
    "    plt.xlabel('Change in water level (x)')\n",
    "    plt.ylabel('Water flowing out of the dam (y)')\n",
    "    plt.title('Learning Curve for Linear Regression')\n",
    "    #define o nome do arquivo que guardará a imagem do gráfico, e cria o caminho, caso não exista\n",
    "    filename = 'target/plot7.2.png'\n",
    "    if not os.path.exists(os.path.dirname(filename)):\n",
    "      os.makedirs(os.path.dirname(filename))\n",
    "\n",
    "    #salva imagem do gráfico e o exibe\n",
    "    plt.savefig(filename)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Xval_poly = poly_features(Xval, 8)\n",
    "Xval_norm, yval_norm = normalizar_caracteristica(Xval_poly, yval)\n",
    "erro, erro_val = learningCurve(X_norm, y_norm, Xval_norm, yval_norm, 0)\n",
    "ploterro(erro_val, erro)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}