Multiple_linear_regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0348c757",
   "metadata": {},
   "outputs": [
    {
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      "1.1736755183167556e-16\n",
      "1.173560358231876e-16\n",
      "a\n",
      "460.0000000138093\n",
      "b\n"
     ]
    }
   ],
   "source": [
    "#features -> parámetros\n",
    "#parameters -> cost function\n",
    "#cost function -> minimización\n",
    "\n",
    "import copy, math\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "X_train = np.array([[2104, 5, 1, 45], [1416, 3, 2, 40], [852, 2, 1, 35]])\n",
    "y_train = np.array([460, 232, 178])\n",
    "def f(w,b,x):\n",
    "    return b + np.dot(w,x)\n",
    "\n",
    "def dJ(w,b,nd):\n",
    "    m=len(y_train)\n",
    "    t1=np.array(list(map(lambda x: f(w,b,x),X_train)))-y_train\n",
    "    t2=np.array(list(map(lambda x: x[nd] if nd<len(x) else 1, X_train)))\n",
    "    return np.sum(np.multiply(t1,t2))/m\n",
    "\n",
    "def sqerror(w,b):\n",
    "    return np.sum(np.array(list(map(lambda xy: pow((f(w,b,xy[0])-xy[1]),2),zip(X_train,y_train)))))/(2*len(X_train))\n",
    "\n",
    "def grad_descent(w,b,a,n):\n",
    "    for i in range(n):\n",
    "        new_b = b - a*dJ(w,b,len(w))\n",
    "        new_w = np.array(list(map(lambda wi: wi[1] - a*dJ(w,b,wi[0]), enumerate(w))))\n",
    "        w=new_w\n",
    "        b=new_b\n",
    "        print(sqerror(w,b))\n",
    "    return [w,b]\n",
    "b_init=785.1811367994083\n",
    "w_init = np.array([ 0.39133535, 18.75376741, -53.36032453, -26.42131618])\n",
    "\n",
    "ww, bb = grad_descent(w_init,b_init,0.0000006,10000)\n",
    "print(\"a\")\n",
    "print(f(ww,bb,X_train[0]))\n",
    "print(\"b\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ff8fce8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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