{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import dautil as dl\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = dl.data.Weather.load().dropna()\n",
    "X = df.values\n",
    "y = df['WIND_DIR'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "lda = LinearDiscriminantAnalysis(n_components=2)\n",
    "X_r = lda.fit(X, y).transform(X).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "dl.options.mimic_seaborn()\n",
    "plt.scatter(X_r[0], X_r[1])\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.title('Dimension Reduction with LDA')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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