started to share with others

Chapter2/.Rapp.history

@@ -0,0 +1,3 @@
+load("/Users/lasiadhi/Dropbox-2/Dropbox/Statistical_Learning/Chapter2/ESL.mixture.rda")
+ESL.mixture
+load("/Users/lasiadhi/Dropbox-2/Dropbox/Statistical_Learning/Chapter2/mixture.rda")

Chapter2/.ipynb_checkpoints/Fig_2_1_linear_reg-checkpoint.ipynb

@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 0
+}

Chapter2/.ipynb_checkpoints/Fig_2_2_KNN-checkpoint.ipynb

@@ -0,0 +1,141 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'DataFrame' object has no attribute 'ravel'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-8-90b4bbfc116b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     33\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     34\u001b[0m \u001b[0mknn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mneighbors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mKNeighborsClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_neighbors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 35\u001b[0;31m \u001b[0mknn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     36\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m//anaconda/lib/python3.5/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   2742\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2743\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2744\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2745\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2746\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'ravel'"
+     ]
+    }
+   ],
+   "source": [
+    "import rpy2.robjects as robjects\n",
+    "import pandas.rpy.common as com\n",
+    "import pandas as pd\n",
+    "from rpy2.robjects import pandas2ri\n",
+    "%matplotlib notebook\n",
+    "import matplotlib.pyplot as plt  # for scatter plot\n",
+    "from ggplot import *\n",
+    "from sklearn import neighbors\n",
+    "pandas2ri.activate()\n",
+    "\n",
+    "## load .RData and converts to pd.DataFrame\n",
+    "robj    = robjects.r.load(\"/Users/lasiadhi/Dropbox-2/Dropbox/Statistical_Learning/Chapter2/ESL.mixture.rda\")\n",
+    "#myRData = com.load_data(robj)\n",
+    "\n",
+    "for sets in robj:\n",
+    "    myRData = com.load_data(sets)\n",
+    "\n",
+    "# load each table to seperate dataframes\n",
+    "marginal= pd.DataFrame(myRData['marginal'])\n",
+    "means   = pd.DataFrame(myRData['means'])\n",
+    "px1     = pd.DataFrame(myRData['px1'])\n",
+    "px2     = pd.DataFrame(myRData['px2'])\n",
+    "xnew    = pd.DataFrame(myRData['xnew'])\n",
+    "x       = pd.DataFrame(myRData['x'])\n",
+    "x.columns = ['x1', 'x2']\n",
+    "x.index = x.index - 1\n",
+    "\n",
+    "y       = pd.DataFrame(myRData['y'])\n",
+    "y.columns = ['y']\n",
+    "\n",
+    "mydata = x.join(y)\n",
+    "\n",
+    "#KNN\n",
+    "knn = neighbors.KNeighborsClassifier(n_neighbors=5)\n",
+    "knn.fit(x, y) \n",
+    "\n",
+    "# Plot the decision boundary. For that, we will asign a color to each\n",
+    "# point in the mesh [x_min, m_max]x[y_min, y_max].\n",
+    "h = 0.05 #step size in the mesh\n",
+    "x_min, x_max = x['x1'].min() - .5, x['x1'].max() + .5\n",
+    "y_min, y_max = x['x2'].min() - .5, x['x2'].max() + .5\n",
+    "xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
+    "Z = knn.predict(np.c_[xx.ravel(), yy.ravel()])\n",
+    "\n",
+    "# Put the result into a color plot\n",
+    "Z = Z.reshape(xx.shape)\n",
+    "plt.figure(1, figsize=(4, 3))\n",
+    "plt.set_cmap(plt.cm.Paired)\n",
+    "plt.pcolormesh(xx, yy, Z)\n",
+    "\n",
+    "# Plot also the training points\n",
+    "plt.figure(figsize=(9,11))         # initialize figure\n",
+    "use_colours = {0: \"blue\", 1: \"orange\"}\n",
+    "plt.scatter(mydata['x1'],mydata['x2'], c=[use_colours[i] for i in y['y']], s=40)\n",
+    "plt.xlabel(\"X1\")\n",
+    "plt.ylabel(\"X2\")\n",
+    "plt.title(\"Fig. 2.1: A classification example in 2D (linear regression)\")\n",
+    "#pl.scatter(x['x1'], x['x2'],c=y )\n",
+    "#pl.xlabel('Sepal length')\n",
+    "#pl.ylabel('Sepal width')\n",
+    "\n",
+    "plt.xlim(xx.min(), xx.max())\n",
+    "plt.ylim(yy.min(), yy.max())\n",
+    "plt.xticks(())\n",
+    "plt.yticks(())\n",
+    "\n",
+    "plt.show()\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-2.5208196765959814"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x['x1'].min()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.5.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}

Chapter2/.ipynb_checkpoints/test-checkpoint.ipynb

@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 0
+}