diff --git a/AutoRegression.ipynb b/AutoRegression.ipynb
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+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Year | \n",
+ " All | \n",
+ " Car | \n",
+ " Minibus | \n",
+ " Bus | \n",
+ " SmallTruck | \n",
+ " Truck | \n",
+ " Motorcycle | \n",
+ " SpecialVehicles | \n",
+ " Machinery | \n",
+ " Tractor | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 45 | \n",
+ " 2011 | \n",
+ " 16089528 | \n",
+ " 8113111 | \n",
+ " 389435 | \n",
+ " 219906 | \n",
+ " 2611104 | \n",
+ " 728458 | \n",
+ " 2527190 | \n",
+ " 34116 | \n",
+ " NaN | \n",
+ " 1466208 | \n",
+ "
\n",
+ " \n",
+ " 46 | \n",
+ " 2012 | \n",
+ " 17033413 | \n",
+ " 8648875 | \n",
+ " 396119 | \n",
+ " 235949 | \n",
+ " 2794606 | \n",
+ " 751650 | \n",
+ " 2657722 | \n",
+ " 33071 | \n",
+ " NaN | \n",
+ " 1515421 | \n",
+ "
\n",
+ " \n",
+ " 47 | \n",
+ " 2013 | \n",
+ " 17939447 | \n",
+ " 9283923 | \n",
+ " 421848 | \n",
+ " 219885 | \n",
+ " 2933050 | \n",
+ " 755950 | \n",
+ " 2722826 | \n",
+ " 36148 | \n",
+ " NaN | \n",
+ " 1565817 | \n",
+ "
\n",
+ " \n",
+ " 48 | \n",
+ " 2014 | \n",
+ " 18828721 | \n",
+ " 9857915 | \n",
+ " 427264 | \n",
+ " 211200 | \n",
+ " 3062479 | \n",
+ " 773728 | \n",
+ " 2828466 | \n",
+ " 40731 | \n",
+ " NaN | \n",
+ " 1626938 | \n",
+ "
\n",
+ " \n",
+ " 49 | \n",
+ " 2015 | \n",
+ " 19882069 | \n",
+ " 10509258 | \n",
+ " 446822 | \n",
+ " 216566 | \n",
+ " 3235304 | \n",
+ " 802615 | \n",
+ " 2938821 | \n",
+ " 45138 | \n",
+ " NaN | \n",
+ " 1687545 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Year All Car Minibus Bus SmallTruck Truck Motorcycle \\\n",
+ "45 2011 16089528 8113111 389435 219906 2611104 728458 2527190 \n",
+ "46 2012 17033413 8648875 396119 235949 2794606 751650 2657722 \n",
+ "47 2013 17939447 9283923 421848 219885 2933050 755950 2722826 \n",
+ "48 2014 18828721 9857915 427264 211200 3062479 773728 2828466 \n",
+ "49 2015 19882069 10509258 446822 216566 3235304 802615 2938821 \n",
+ "\n",
+ " SpecialVehicles Machinery Tractor \n",
+ "45 34116 NaN 1466208 \n",
+ "46 33071 NaN 1515421 \n",
+ "47 36148 NaN 1565817 \n",
+ "48 40731 NaN 1626938 \n",
+ "49 45138 NaN 1687545 "
+ ]
+ },
+ "execution_count": 1,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "from matplotlib import pyplot as plt\n",
+ "%matplotlib inline\n",
+ "\n",
+ "df = pd.read_csv(\"data/cars.csv\", delimiter=\";\")\n",
+ "df[-5:]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 61,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "matrix([[ 8113111, 8648875],\n",
+ " [ 8648875, 9283923],\n",
+ " [ 9283923, 9857915],\n",
+ " [ 9857915, 10509258]])"
+ ]
+ },
+ "execution_count": 61,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "y = np.matrix(df.Car[1:]).T\n",
+ "x = np.matrix(df.Car[:-1]).T\n",
+ "# displaying X, Y pairs\n",
+ "np.hstack([x[-4:], y[-4:]])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 62,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def linreg_via_np(X, Y, **kwargs):\n",
+ " theta, e, r, s = np.linalg.lstsq(X, Y)\n",
+ " return theta\n",
+ "\n",
+ "def linreg_via_syseq(X, Y, **kwargs):\n",
+ " theta = (X.T * X).I * X.T * Y\n",
+ " return theta"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 63,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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AN4c9L6/EXPb16+Haa6FLF/j3v+GzzxTgRSR6CvKVQeiaMcG8esHtwxg/ZgfN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\nAAAASUVORK5CYII=\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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JW3Lmiy8gnV73mD8fDjoIDj00JOwdd0w4QJEyouQtWXGHmTPhjTfg9ddDsv7s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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "def prepareX(X, order=1, **kwargs):\n",
+ " return np.hstack([ np.power(X, i) for i in range(order + 1) ])\n",
+ "\n",
+ "def test(X, Y, trainer, **kwargs):\n",
+ " X = prepareX(X, **kwargs)\n",
+ " \n",
+ " # plotting the original data\n",
+ " plt.plot(X[:,1], Y, \"rx\")\n",
+ " \n",
+ " model = trainer(X, Y, **kwargs)\n",
+ " \n",
+ " # plotting the model\n",
+ " xTest = np.linspace(\n",
+ " int(X[:,1][0] - 1000), \n",
+ " int(X[:,1][-1] + 2000), \n",
+ " int(X[:,1][-1] - X[:,1][0]) * 2)\n",
+ " \n",
+ " xTest = prepareX(np.matrix(xTest).T, **kwargs)\n",
+ " plt.plot(xTest[:,1], xTest * model)\n",
+ " plt.title(kwargs.get(\"title\", \"\"))\n",
+ " plt.show()\n",
+ "\n",
+ "test(X, Y, linreg_via_np, order=1, title=\"linreg_via_np order 1\")\n",
+ "test(X, Y, linreg_via_syseq, order=1, title=\"linreg_via_syseq order 1\")\n",
+ "\n",
+ "test(X, Y, linreg_via_np, order=3, title=\"linreg_via_np order 3\")\n",
+ "test(X, Y, linreg_via_syseq, order=3, title=\"linreg_via_syseq order 3\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 2",
+ "language": "python",
+ "name": "python2"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}