Pred.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#prediction_value/\n",
    "\n",
    "import matplotlib\n",
    "matplotlib.use('Agg')\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn import linear_model\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Carrega os seus dados\n",
    "segundos_dia = np.array([[50700], [51000], [52500], [52800], [53100]]) \n",
    "temperatura  = np.array([27,   26,   25,   25.5,   24])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Cria o modelo linear\n",
    "regr = linear_model.LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Treina o modelo com os dados de exemplo\n",
    "regr.fit(segundos_dia, temperatura)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Previsto:\n",
      "[ 22.5018797]\n"
     ]
    }
   ],
   "source": [
    "# Dados para previsao (isto eh, os segundos do dia)\n",
    "segundos_prev = np.array([[55320]])\n",
    "temp_prev = regr.predict(segundos_prev)\n",
    "print('Previsto:')\n",
    "print(temp_prev)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x9707ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Dados usados no treinamento\n",
    "plt.scatter(segundos_dia, temperatura,  color='black')\n",
    "plt.xlabel('Segundos do dia')\n",
    "plt.ylabel('Temperatura')\n",
    "plt.show()\n",
    "#plt.savefig('pred.png')\n",
    "#ref: https://pt.stackoverflow.com/questions/38292/como-fazer-previs%C3%A3o-de-valores-de-uma-vari%C3%A1vel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}