Linear-Regression_numpy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "93e5e4fb",
   "metadata": {},
   "source": [
    "# <big><font color='#D35400'>REGRESION LINEAL CON NUMPY</font><big>\n",
    "https://numpy.org/doc/stable/reference/generated/numpy.linalg.lstsq.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6c08a332",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a5d0447",
   "metadata": {},
   "source": [
    "# <font color='#8E44AD'>Creamos el conjunto de datos para trabajar y lo graficamos</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9d631f99",
   "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": [
    "# Creamos el conjunto de datos para trabajar\n",
    "x_data = np.linspace(0.0,10.0,500)      # Creamos 500 valores de x equidistantes entre 0 y 10.\n",
    "\n",
    "np.random.seed(652)                     # seed obtiene números al azar, pero siempre elegirá los mismos en el mismo orden.\n",
    "noise = np.random.randn(len(x_data))    # obtengo 500 números aleatorios ya predeterminados por seed.\n",
    "\n",
    "y_data =  (0.5 * x_data ) + 5 + noise   # Obtenemos 500 valores de y que seguirán la tendencia de y=0.5x+5\n",
    "\n",
    "\n",
    "# Graficamos los datos\n",
    "plt.plot(x_data,y_data,'.')\n",
    "plt.xlabel('x', fontsize=14)\n",
    "plt.ylabel('y', fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a6312b6",
   "metadata": {},
   "source": [
    "# <font color='#8E44AD'>Creamos la matriz A = (x 1)</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6d39625a",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.        ,  1.        ],\n",
       "       [ 0.02004008,  1.        ],\n",
       "       [ 0.04008016,  1.        ],\n",
       "       [ 0.06012024,  1.        ],\n",
       "       [ 0.08016032,  1.        ],\n",
       "       [ 0.1002004 ,  1.        ],\n",
       "       [ 0.12024048,  1.        ],\n",
       "       [ 0.14028056,  1.        ],\n",
       "       [ 0.16032064,  1.        ],\n",
       "       [ 0.18036072,  1.        ],\n",
       "       [ 0.2004008 ,  1.        ],\n",
       "       [ 0.22044088,  1.        ],\n",
       "       [ 0.24048096,  1.        ],\n",
       "       [ 0.26052104,  1.        ],\n",
       "       [ 0.28056112,  1.        ],\n",
       "       [ 0.3006012 ,  1.        ],\n",
       "       [ 0.32064128,  1.        ],\n",
       "       [ 0.34068136,  1.        ],\n",
       "       [ 0.36072144,  1.        ],\n",
       "       [ 0.38076152,  1.        ],\n",
       "       [ 0.4008016 ,  1.        ],\n",
       "       [ 0.42084168,  1.        ],\n",
       "       [ 0.44088176,  1.        ],\n",
       "       [ 0.46092184,  1.        ],\n",
       "       [ 0.48096192,  1.        ],\n",
       "       [ 0.501002  ,  1.        ],\n",
       "       [ 0.52104208,  1.        ],\n",
       "       [ 0.54108216,  1.        ],\n",
       "       [ 0.56112224,  1.        ],\n",
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       "       [ 1.60320641,  1.        ],\n",
       "       [ 1.62324649,  1.        ],\n",
       "       [ 1.64328657,  1.        ],\n",
       "       [ 1.66332665,  1.        ],\n",
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       "       [ 1.78356713,  1.        ],\n",
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       "       [ 1.84368737,  1.        ],\n",
       "       [ 1.86372745,  1.        ],\n",
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       "       [ 1.90380762,  1.        ],\n",
       "       [ 1.9238477 ,  1.        ],\n",
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       "       [ 1.98396794,  1.        ],\n",
       "       [ 2.00400802,  1.        ],\n",
       "       [ 2.0240481 ,  1.        ],\n",
       "       [ 2.04408818,  1.        ],\n",
       "       [ 2.06412826,  1.        ],\n",
       "       [ 2.08416834,  1.        ],\n",
       "       [ 2.10420842,  1.        ],\n",
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       "       [ 9.43887776,  1.        ],\n",
       "       [ 9.45891784,  1.        ],\n",
       "       [ 9.47895792,  1.        ],\n",
       "       [ 9.498998  ,  1.        ],\n",
       "       [ 9.51903808,  1.        ],\n",
       "       [ 9.53907816,  1.        ],\n",
       "       [ 9.55911824,  1.        ],\n",
       "       [ 9.57915832,  1.        ],\n",
       "       [ 9.5991984 ,  1.        ],\n",
       "       [ 9.61923848,  1.        ],\n",
       "       [ 9.63927856,  1.        ],\n",
       "       [ 9.65931864,  1.        ],\n",
       "       [ 9.67935872,  1.        ],\n",
       "       [ 9.6993988 ,  1.        ],\n",
       "       [ 9.71943888,  1.        ],\n",
       "       [ 9.73947896,  1.        ],\n",
       "       [ 9.75951904,  1.        ],\n",
       "       [ 9.77955912,  1.        ],\n",
       "       [ 9.7995992 ,  1.        ],\n",
       "       [ 9.81963928,  1.        ],\n",
       "       [ 9.83967936,  1.        ],\n",
       "       [ 9.85971944,  1.        ],\n",
       "       [ 9.87975952,  1.        ],\n",
       "       [ 9.8997996 ,  1.        ],\n",
       "       [ 9.91983968,  1.        ],\n",
       "       [ 9.93987976,  1.        ],\n",
       "       [ 9.95991984,  1.        ],\n",
       "       [ 9.97995992,  1.        ],\n",
       "       [10.        ,  1.        ]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creamos la matriz A = (x 1)\n",
    "A = np.vstack([x_data, np.ones(len(x_data))]).T\n",
    "A"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b02a6134",
   "metadata": {},
   "source": [
    "# <font color='#8E44AD'>Calculamos \"m\" y \"b\"</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0fa0653d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "m = 0.507\n",
      "b = 4.953\n"
     ]
    }
   ],
   "source": [
    "# Calculamos \"m\" y \"b\"\n",
    "m, b = np.linalg.lstsq(A, y_data, rcond=None)[0]\n",
    "print(f'm = {m:.3f}')\n",
    "print(f'b = {b:.3f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df4777cf",
   "metadata": {},
   "source": [
    "# <font color='#8E44AD'>Graficamos los datos originales junto con la recta de regresión lineal</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "5590c132",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Gráfico con los datos originales\n",
    "plt.figure(figsize=(9, 6))\n",
    "plt.plot(x_data,y_data,'.')\n",
    "plt.xlabel('x', fontsize=14,fontweight=\"bold\")\n",
    "plt.ylabel('y', fontsize=14,fontweight=\"bold\")\n",
    "\n",
    "# Gráfico recta regresión lineal\n",
    "plt.plot(x_data, m*x_data + b,'r', linewidth= 3,\n",
    "         label=f'y={m:.3f}x+{b:.3f}')\n",
    "plt.legend(fontsize=14)\n",
    "plt.title('DATOS Y RECTA DE REGRESIÓN LINEAL',fontsize=18,fontweight=\"bold\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d0825f23",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}