{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.loadtxt(\"housing_data.txt\")\n",
    "train_data = data[:450,:]\n",
    "test_data = data[450:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_x = train_data[:, :-1]\n",
    "train_y = train_data[:, -1]\n",
    "test_x = test_data[:, :-1]\n",
    "test_y = test_data[:, -1]\n",
    "bias1 = [1 for i in range(450)]\n",
    "train_x = np.c_[train_x, bias1]\n",
    "bias2 = [1 for i in range(56)]\n",
    "test_x = np.c_[test_x, bias2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "w1 = (np.linalg.inv(train_x.T.dot(train_x)).dot(train_x.T)).dot(train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "err = 0\n",
    "for i, v in enumerate(test_x):\n",
    "    err += (v.T.dot(w1) - test_y[i])**2\n",
    "err /= len(test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11.40700326882932\n"
     ]
    }
   ],
   "source": [
    "print(err)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w:\n",
      "[-1.12706397e-01  4.84679322e-02  3.51619982e-02  2.43036682e+00\n",
      " -1.71555914e+01  3.92937664e+00  1.05952560e-02 -1.40598405e+00\n",
      "  3.72316544e-01 -1.54340774e-02 -9.03360601e-01  9.74047349e-03\n",
      " -5.51630479e-01]\n",
      "bias:\n",
      "34.26122511497033\n"
     ]
    }
   ],
   "source": [
    "print(\"w:\")\n",
    "print(w1[:-1])\n",
    "print(\"bias:\")\n",
    "print(w1[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "file_extension": ".py",
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