{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-02-13T01:26:13.770837",
     "start_time": "2017-02-13T01:26:11.888927"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as ply\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-02-13T01:27:00.127991",
     "start_time": "2017-02-13T01:27:00.101627"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "iris = sns.load_dataset(\"iris\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-02-13T01:29:06.374402",
     "start_time": "2017-02-13T01:29:05.824826"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x10fc2acf8>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jk+MzcgBBWXySDuGuvzMJfpBPhF/PrMNuwDUEP7fXAN8G9jezGwmuEPxo2NCK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\nRczSpcBZ4e0MwaVUUs3dfwi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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fc2aeb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(\"petal_length\", \"petal_width\", data=iris, hue=\"species\", fit_reg=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-02-13T01:35:07.187431",
     "start_time": "2017-02-13T01:35:07.167241"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=1, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import neighbors\n",
    "knn = neighbors.KNeighborsClassifier(n_neighbors=1)\n",
    "X = iris[[\"petal_length\", \"petal_width\"]]\n",
    "y = iris.species.astype(\"category\").cat.codes\n",
    "knn.fit(X, y)\n",
    "knn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-02-13T03:46:28.959867",
     "start_time": "2017-02-13T03:46:28.952899"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'algorithm': 'auto',\n",
       " 'leaf_size': 30,\n",
       " 'metric': 'minkowski',\n",
       " 'metric_params': None,\n",
       " 'n_jobs': 1,\n",
       " 'n_neighbors': 1,\n",
       " 'p': 2,\n",
       " 'weights': 'uniform'}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "hide_input": false,
  "kernelspec": {
   "display_name": "Python [default]",
   "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"
  },
  "toc": {
   "toc_cell": false,
   "toc_number_sections": true,
   "toc_threshold": 6,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}