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July 6, 2015 16:30
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This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters. Learn more about bidirectional Unicode charactersOriginal file line number Diff line number Diff line change @@ -0,0 +1,157 @@ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import scipy as sp\n", "import statsmodels.api as sm\n", "import matplotlib.pyplot as plt\n", "from sklearn.linear_model import LinearRegression\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Data from R ISLR package - write.csv(Boston, \"Boston.csv\", col.names = FALSE)\n", "boston_df = pd.read_csv(\"../../r/Boston.csv\")" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(47.117263854857882,\n", " array([ -3.05335819e+09, 3.05335819e+09, 9.31299461e-02,\n", " -3.29341722e+00]))" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# fitting medv ~ lstat + I(lstat^2)\n", "boston_df[\"lstat^2\"] = boston_df[\"lstat\"] ** 2\n", "# fitting medv ~ poly(lstat,4). We already have lstat^2 and lstat from previous\n", "boston_df[\"lstat^4\"] = np.power(boston_df[\"lstat\"], 4)\n", "boston_df[\"lstat^3\"] = np.power(boston_df[\"lstat\"], 4)\n", "X = boston_df[[\"lstat^4\", \"lstat^3\", \"lstat^2\", \"lstat\"]]\n", "y = boston_df[\"medv\"]\n", "reg7 = LinearRegression()\n", "reg7.fit(X, y)\n", "(reg7.intercept_, reg7.coef_)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# X = boston_df[[\"lstat^4\", \"lstat^3\", \"lstat^2\", \"lstat\"]]\n", "X = sm.add_constant(X)\n", "# X = boston_df[[1., \"lstat^4\", \"lstat^3\", \"lstat^2\", \"lstat\"]]\n", "ols = sm.OLS(y,X).fit()\n", "# ols.summary()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "eps = 0.0000000001\n", "np.all(np.abs(ols.params.values[1:] - reg7.coef_) < eps)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ -1.17513710e-05, -1.17509020e-05, 9.23027375e-02,\n", " -3.27115207e+00])" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ols.params.values[1:]" ] }, { "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": "2.7.9" } }, "nbformat": 4, "nbformat_minor": 0 }