course-exercises/week6-Neural-Networks/Retrieving-Images-exercise.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import turicreate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_data = turicreate.SFrame('../../data/image_train_data/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_test = turicreate.SFrame('../../data/image_test_data/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
       "    <tr>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">id</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">image</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">label</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">deep_features</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">image_array</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">24</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Height: 32 Width: 32</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">bird</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[0.24287176132202148,<br>1.0954537391662598, 0.0, ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[73.0, 77.0, 58.0, 71.0,<br>68.0, 50.0, 77.0, 69.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">33</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Height: 32 Width: 32</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">cat</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[0.5250879526138306, 0.0,<br>0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[7.0, 5.0, 8.0, 7.0, 5.0,<br>8.0, 5.0, 4.0, 6.0, 7.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">36</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Height: 32 Width: 32</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">cat</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[0.5660159587860107, 0.0,<br>0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[169.0, 122.0, 65.0,<br>131.0, 108.0, 75.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">70</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Height: 32 Width: 32</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">dog</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[1.129795789718628, 0.0,<br>0.0, 0.7781944870948792, ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[154.0, 179.0, 152.0,<br>159.0, 183.0, 157.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">90</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Height: 32 Width: 32</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">bird</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[1.7178692817687988, 0.0,<br>0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[216.0, 195.0, 180.0,<br>201.0, 178.0, 160.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">97</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Height: 32 Width: 32</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">automobile</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[1.5781855583190918, 0.0,<br>0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[33.0, 44.0, 27.0, 29.0,<br>44.0, 31.0, 32.0, 45.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">107</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Height: 32 Width: 32</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">dog</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[0.0, 0.0,<br>0.22067785263061523,  ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[97.0, 51.0, 31.0, 104.0,<br>58.0, 38.0, 107.0, 61.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">121</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Height: 32 Width: 32</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">bird</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[0.0,<br>0.23753464221954346,  ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[93.0, 96.0, 88.0, 102.0,<br>106.0, 97.0, 117.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">136</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Height: 32 Width: 32</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">automobile</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[0.0, 0.0, 0.0, 0.0, 0.0,<br>0.0, 7.57378625869751, ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[35.0, 59.0, 53.0, 36.0,<br>56.0, 56.0, 42.0, 62.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">138</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Height: 32 Width: 32</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">bird</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[0.6589357256889343, 0.0,<br>0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[205.0, 193.0, 195.0,<br>200.0, 187.0, 193.0, ...</td>\n",
       "    </tr>\n",
       "</table>\n",
       "[2005 rows x 5 columns]<br/>Note: Only the head of the SFrame is printed.<br/>You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.\n",
       "</div>"
      ],
      "text/plain": [
       "Columns:\n",
       "\tid\tint\n",
       "\timage\tImage\n",
       "\tlabel\tstr\n",
       "\tdeep_features\tarray\n",
       "\timage_array\tarray\n",
       "\n",
       "Rows: 2005\n",
       "\n",
       "Data:\n",
       "+-----+----------------------+------------+-------------------------------+\n",
       "|  id |        image         |   label    |         deep_features         |\n",
       "+-----+----------------------+------------+-------------------------------+\n",
       "|  24 | Height: 32 Width: 32 |    bird    | [0.24287176132202148, 1.09... |\n",
       "|  33 | Height: 32 Width: 32 |    cat     | [0.5250879526138306, 0.0, ... |\n",
       "|  36 | Height: 32 Width: 32 |    cat     | [0.5660159587860107, 0.0, ... |\n",
       "|  70 | Height: 32 Width: 32 |    dog     | [1.129795789718628, 0.0, 0... |\n",
       "|  90 | Height: 32 Width: 32 |    bird    | [1.7178692817687988, 0.0, ... |\n",
       "|  97 | Height: 32 Width: 32 | automobile | [1.5781855583190918, 0.0, ... |\n",
       "| 107 | Height: 32 Width: 32 |    dog     | [0.0, 0.0, 0.2206778526306... |\n",
       "| 121 | Height: 32 Width: 32 |    bird    | [0.0, 0.23753464221954346,... |\n",
       "| 136 | Height: 32 Width: 32 | automobile | [0.0, 0.0, 0.0, 0.0, 0.0, ... |\n",
       "| 138 | Height: 32 Width: 32 |    bird    | [0.6589357256889343, 0.0, ... |\n",
       "+-----+----------------------+------------+-------------------------------+\n",
       "+-------------------------------+\n",
       "|          image_array          |\n",
       "+-------------------------------+\n",
       "| [73.0, 77.0, 58.0, 71.0, 6... |\n",
       "| [7.0, 5.0, 8.0, 7.0, 5.0, ... |\n",
       "| [169.0, 122.0, 65.0, 131.0... |\n",
       "| [154.0, 179.0, 152.0, 159.... |\n",
       "| [216.0, 195.0, 180.0, 201.... |\n",
       "| [33.0, 44.0, 27.0, 29.0, 4... |\n",
       "| [97.0, 51.0, 31.0, 104.0, ... |\n",
       "| [93.0, 96.0, 88.0, 102.0, ... |\n",
       "| [35.0, 59.0, 53.0, 36.0, 5... |\n",
       "| [205.0, 193.0, 195.0, 200.... |\n",
       "+-------------------------------+\n",
       "[2005 rows x 5 columns]\n",
       "Note: Only the head of the SFrame is printed.\n",
       "You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns."
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
       "    <tr>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">id</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">image</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">label</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">deep_features</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">image_array</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Height: 32 Width: 32</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">cat</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[1.1346900463104248, 0.0,<br>0.0, 0.0, ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[158.0, 112.0, 49.0,<br>159.0, 111.0, 47.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">6</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Height: 32 Width: 32</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">automobile</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[0.2313588261604309, 0.0,<br>0.0, 0.0, 0.0, ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[160.0, 37.0, 13.0,<br>185.0, 49.0, 11.0, 20 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">8</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Height: 32 Width: 32</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">cat</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[0.0, 0.0,<br>0.034419238567352295, ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[23.0, 19.0, 23.0, 19.0,<br>21.0, 28.0, 21.0, 16.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">9</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Height: 32 Width: 32</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">automobile</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[0.0, 0.0, 0.0, 0.0, 0.0,<br>0.0, 11.6065092086792, ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[217.0, 215.0, 209.0,<br>210.0, 208.0, 202.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">12</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Height: 32 Width: 32</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">dog</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[0.3223174810409546, 0.0,<br>1.2493335008621216, 0.0, ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[91.0, 64.0, 30.0, 82.0,<br>58.0, 30.0, 87.0, 73.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">16</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Height: 32 Width: 32</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">dog</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[0.0, 0.0,<br>0.34735703468322754,  ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[95.0, 76.0, 78.0, 92.0,<br>77.0, 78.0, 89.0, 77.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">24</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Height: 32 Width: 32</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">dog</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[1.3155765533447266, 0.0,<br>0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[136.0, 134.0, 118.0,<br>142.0, 141.0, 126.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">25</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Height: 32 Width: 32</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">bird</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[0.0,<br>0.31728875637054443,  ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[100.0, 103.0, 74.0,<br>68.0, 91.0, 65.0, 116.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">31</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Height: 32 Width: 32</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">dog</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[0.0, 0.0, 0.0, 0.0, 0.0,<br>0.0, 9.260188102722168, ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[127.0, 130.0, 81.0,<br>130.0, 133.0, 88.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">33</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Height: 32 Width: 32</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">dog</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[0.1307867169380188,<br>0.7276672124862671, 0.0, ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[118.0, 113.0, 81.0,<br>122.0, 117.0, 83.0, ...</td>\n",
       "    </tr>\n",
       "</table>\n",
       "[4000 rows x 5 columns]<br/>Note: Only the head of the SFrame is printed.<br/>You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.\n",
       "</div>"
      ],
      "text/plain": [
       "Columns:\n",
       "\tid\tint\n",
       "\timage\tImage\n",
       "\tlabel\tstr\n",
       "\tdeep_features\tarray\n",
       "\timage_array\tarray\n",
       "\n",
       "Rows: 4000\n",
       "\n",
       "Data:\n",
       "+----+----------------------+------------+-------------------------------+\n",
       "| id |        image         |   label    |         deep_features         |\n",
       "+----+----------------------+------------+-------------------------------+\n",
       "| 0  | Height: 32 Width: 32 |    cat     | [1.1346900463104248, 0.0, ... |\n",
       "| 6  | Height: 32 Width: 32 | automobile | [0.2313588261604309, 0.0, ... |\n",
       "| 8  | Height: 32 Width: 32 |    cat     | [0.0, 0.0, 0.0344192385673... |\n",
       "| 9  | Height: 32 Width: 32 | automobile | [0.0, 0.0, 0.0, 0.0, 0.0, ... |\n",
       "| 12 | Height: 32 Width: 32 |    dog     | [0.3223174810409546, 0.0, ... |\n",
       "| 16 | Height: 32 Width: 32 |    dog     | [0.0, 0.0, 0.3473570346832... |\n",
       "| 24 | Height: 32 Width: 32 |    dog     | [1.3155765533447266, 0.0, ... |\n",
       "| 25 | Height: 32 Width: 32 |    bird    | [0.0, 0.31728875637054443,... |\n",
       "| 31 | Height: 32 Width: 32 |    dog     | [0.0, 0.0, 0.0, 0.0, 0.0, ... |\n",
       "| 33 | Height: 32 Width: 32 |    dog     | [0.1307867169380188, 0.727... |\n",
       "+----+----------------------+------------+-------------------------------+\n",
       "+-------------------------------+\n",
       "|          image_array          |\n",
       "+-------------------------------+\n",
       "| [158.0, 112.0, 49.0, 159.0... |\n",
       "| [160.0, 37.0, 13.0, 185.0,... |\n",
       "| [23.0, 19.0, 23.0, 19.0, 2... |\n",
       "| [217.0, 215.0, 209.0, 210.... |\n",
       "| [91.0, 64.0, 30.0, 82.0, 5... |\n",
       "| [95.0, 76.0, 78.0, 92.0, 7... |\n",
       "| [136.0, 134.0, 118.0, 142.... |\n",
       "| [100.0, 103.0, 74.0, 68.0,... |\n",
       "| [127.0, 130.0, 81.0, 130.0... |\n",
       "| [118.0, 113.0, 81.0, 122.0... |\n",
       "+-------------------------------+\n",
       "[4000 rows x 5 columns]\n",
       "Note: Only the head of the SFrame is printed.\n",
       "You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns."
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task 1: Compute summary statistics of the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using the sketch method for computing summary statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "sketch = turicreate.Sketch(image_data['label'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "+------------------+-------+----------+\n",
       "|       item       | value | is exact |\n",
       "+------------------+-------+----------+\n",
       "|      Length      |  2005 |   Yes    |\n",
       "| # Missing Values |   0   |   Yes    |\n",
       "| # unique values  |   4   |    No    |\n",
       "+------------------+-------+----------+\n",
       "\n",
       "Most frequent items:\n",
       "+------------+-------+\n",
       "|   value    | count |\n",
       "+------------+-------+\n",
       "|    cat     |  509  |\n",
       "|    dog     |  509  |\n",
       "| automobile |  509  |\n",
       "|    bird    |  478  |\n",
       "+------------+-------+\n"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sketch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task 2: Create category-specific image retrieval models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Split the training data into 4 different SFrame data structures\n",
    "### Each will contain data for one of the four categories image categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_data_cat = image_data.filter_by('cat', 'label')\n",
    "training_data_dog = image_data.filter_by('dog', 'label')\n",
    "training_data_automobile = image_data.filter_by('automobile', 'label')\n",
    "training_data_bird = image_data.filter_by('bird', 'label')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create a nearest neighbor model using deep features (one such model for each category)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Starting brute force nearest neighbors model training.</pre>"
      ],
      "text/plain": [
       "Starting brute force nearest neighbors model training."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Starting brute force nearest neighbors model training.</pre>"
      ],
      "text/plain": [
       "Starting brute force nearest neighbors model training."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Starting brute force nearest neighbors model training.</pre>"
      ],
      "text/plain": [
       "Starting brute force nearest neighbors model training."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Starting brute force nearest neighbors model training.</pre>"
      ],
      "text/plain": [
       "Starting brute force nearest neighbors model training."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cat_model = turicreate.nearest_neighbors.create(training_data_cat,\n",
    "                                               features = ['deep_features'],\n",
    "                                               label = 'id')\n",
    "dog_model = turicreate.nearest_neighbors.create(training_data_dog,\n",
    "                                               features = ['deep_features'],\n",
    "                                               label = 'id')\n",
    "automobile_model = turicreate.nearest_neighbors.create(training_data_automobile,\n",
    "                                               features = ['deep_features'],\n",
    "                                               label = 'id')\n",
    "bird_model = turicreate.nearest_neighbors.create(training_data_bird,\n",
    "                                               features = ['deep_features'],\n",
    "                                               label = 'id')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What is the nearest cat-labeled image in the training data to the cat image that is the first image in the test data ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat = image_test[0:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<html lang=\"en\">                                                     <head>                                                               <style>                                                              .sframe {                                                            font-size: 12px;                                                   font-family: HelveticaNeue;                                        border: 1px solid silver;                                        }                                                                  .sframe thead th {                                                   background: #F7F7F7;                                               font-family: HelveticaNeue-Medium;                                 font-size: 14px;                                                   line-height: 16.8px;                                               padding-top: 16px;                                                 padding-bottom: 16px;                                              padding-left: 10px;                                                padding-right: 38px;                                               border-top: 1px solid #E9E9E9;                                     border-bottom: 1px solid #E9E9E9;                                  white-space: nowrap;                                               overflow: hidden;                                                  text-overflow:ellipsis;                                            text-align:center;                                                 font-weight:normal;                                              }                                                                  .sframe tbody th {                                                   background: #FFFFFF;                                               text-align:left;                                                   font-weight:normal;                                                border-right: 1px solid #E9E9E9;                                 }                                                                  .sframe td {                                                         background: #FFFFFF;                                               padding-left: 10px;                                                padding-right: 38px;                                               padding-top: 14px;                                                 padding-bottom: 14px;                                              border-bottom: 1px solid #E9E9E9;                                  max-height: 0px;                                                   transition: max-height 5s ease-out;                                vertical-align: middle;                                            font-family: HelveticaNeue;                                        font-size: 12px;                                                   line-height: 16.8px;                                               background: #FFFFFF;                                             }                                                                  .sframe tr {                                                         padding-left: 10px;                                                padding-right: 38px;                                               padding-top: 14px;                                                 padding-bottom: 14px;                                              border-bottom: 1px solid #E9E9E9;                                  max-height: 0px;                                                   transition: max-height 5s ease-out;                                vertical-align: middle;                                            font-family: HelveticaNeue;                                        font-size: 12px;                                                   line-height: 16.8px;                                               background: #FFFFFF;                                             }                                                                  .sframe tr:hover {                                                   background: silver;                                              },                                                               </style>                                                         </head>                                                            <body>                                                               <h1>  </h1>                                             <table border=\"1\" class=\"dataframe sframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SArray</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td><img src=\"data:image/png;base64,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\"/></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>                          </body>                                                          </html>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cat['image'].explore()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This function will match the 'id' in image_train with 'reference_lebel' in query_result\n",
    "def get_images_from_ids(query_result):\n",
    "    return image_data.filter_by(query_result['reference_label'],'id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Starting pairwise querying.</pre>"
      ],
      "text/plain": [
       "Starting pairwise querying."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------+---------+-------------+--------------+</pre>"
      ],
      "text/plain": [
       "+--------------+---------+-------------+--------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Query points | # Pairs | % Complete. | Elapsed Time |</pre>"
      ],
      "text/plain": [
       "| Query points | # Pairs | % Complete. | Elapsed Time |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------+---------+-------------+--------------+</pre>"
      ],
      "text/plain": [
       "+--------------+---------+-------------+--------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 0            | 1       | 0.196464    | 3.252ms      |</pre>"
      ],
      "text/plain": [
       "| 0            | 1       | 0.196464    | 3.252ms      |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Done         |         | 100         | 27.538ms     |</pre>"
      ],
      "text/plain": [
       "| Done         |         | 100         | 27.538ms     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------+---------+-------------+--------------+</pre>"
      ],
      "text/plain": [
       "+--------------+---------+-------------+--------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cat_neighbors = get_images_from_ids(cat_model.query(cat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
       "    <tr>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">id</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">image</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">label</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">deep_features</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">image_array</th>\n",
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       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">cat</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[0.0, 0.0,<br>0.5109639167785645, 0.0, ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[45.0, 65.0, 92.0, 72.0,<br>95.0, 110.0, 106.0, ...</td>\n",
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       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">cat</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[0.964287519454956, 0.0,<br>0.0, 0.0, ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[215.0, 219.0, 231.0,<br>215.0, 219.0, 232.0, ...</td>\n",
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       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">25713</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Height: 32 Width: 32</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">cat</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[0.5369712710380554, 0.0,<br>0.0, 0.08944588899612 ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[228.0, 222.0, 236.0,<br>224.0, 213.0, 222.0, ...</td>\n",
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       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">cat</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[1.2940946817398071, 0.0,<br>0.0, 0.5138002634048462, ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[217.0, 220.0, 205.0,<br>221.0, 227.0, 218.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">45646</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Height: 32 Width: 32</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">cat</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[0.9836775064468384, 0.0,<br>0.0, 0.0, 0.0, ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[51.0, 42.0, 26.0, 56.0,<br>47.0, 31.0, 59.0, 50.0, ...</td>\n",
       "    </tr>\n",
       "</table>\n",
       "[5 rows x 5 columns]<br/>\n",
       "</div>"
      ],
      "text/plain": [
       "Columns:\n",
       "\tid\tint\n",
       "\timage\tImage\n",
       "\tlabel\tstr\n",
       "\tdeep_features\tarray\n",
       "\timage_array\tarray\n",
       "\n",
       "Rows: 5\n",
       "\n",
       "Data:\n",
       "+-------+----------------------+-------+-------------------------------+\n",
       "|   id  |        image         | label |         deep_features         |\n",
       "+-------+----------------------+-------+-------------------------------+\n",
       "|  331  | Height: 32 Width: 32 |  cat  | [0.0, 0.0, 0.5109639167785... |\n",
       "| 16289 | Height: 32 Width: 32 |  cat  | [0.964287519454956, 0.0, 0... |\n",
       "| 25713 | Height: 32 Width: 32 |  cat  | [0.5369712710380554, 0.0, ... |\n",
       "| 32139 | Height: 32 Width: 32 |  cat  | [1.2940946817398071, 0.0, ... |\n",
       "| 45646 | Height: 32 Width: 32 |  cat  | [0.9836775064468384, 0.0, ... |\n",
       "+-------+----------------------+-------+-------------------------------+\n",
       "+-------------------------------+\n",
       "|          image_array          |\n",
       "+-------------------------------+\n",
       "| [45.0, 65.0, 92.0, 72.0, 9... |\n",
       "| [215.0, 219.0, 231.0, 215.... |\n",
       "| [228.0, 222.0, 236.0, 224.... |\n",
       "| [217.0, 220.0, 205.0, 221.... |\n",
       "| [51.0, 42.0, 26.0, 56.0, 4... |\n",
       "+-------------------------------+\n",
       "[5 rows x 5 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SArray</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td><img src=\"data:image/png;base64,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\"/></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td><img src=\"data:image/png;base64,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\"/></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td><img src=\"data:image/png;base64,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\"/></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td><img src=\"data:image/png;base64,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\"/></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td><img src=\"data:image/png;base64,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\"/></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>                          </body>                                                          </html>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "cat_neighbors['image'].explore()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task 3: Try a simple example of nearest-neighbors classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## For the first image in the test data, compute the mean distance between this image at its five nearest neighbors that are labeled ‘cat’ in the training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "first_image = image_test[0:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<html lang=\"en\">                                                     <head>                                                               <style>                                                              .sframe {                                                            font-size: 12px;                                                   font-family: HelveticaNeue;                                        border: 1px solid silver;                                        }                                                                  .sframe thead th {                                                   background: #F7F7F7;                                               font-family: HelveticaNeue-Medium;                                 font-size: 14px;                                                   line-height: 16.8px;                                               padding-top: 16px;                                                 padding-bottom: 16px;                                              padding-left: 10px;                                                padding-right: 38px;                                               border-top: 1px solid #E9E9E9;                                     border-bottom: 1px solid #E9E9E9;                                  white-space: nowrap;                                               overflow: hidden;                                                  text-overflow:ellipsis;                                            text-align:center;                                                 font-weight:normal;                                              }                                                                  .sframe tbody th {                                                   background: #FFFFFF;                                               text-align:left;                                                   font-weight:normal;                                                border-right: 1px solid #E9E9E9;                                 }                                                                  .sframe td {                                                         background: #FFFFFF;                                               padding-left: 10px;                                                padding-right: 38px;                                               padding-top: 14px;                                                 padding-bottom: 14px;                                              border-bottom: 1px solid #E9E9E9;                                  max-height: 0px;                                                   transition: max-height 5s ease-out;                                vertical-align: middle;                                            font-family: HelveticaNeue;                                        font-size: 12px;                                                   line-height: 16.8px;                                               background: #FFFFFF;                                             }                                                                  .sframe tr {                                                         padding-left: 10px;                                                padding-right: 38px;                                               padding-top: 14px;                                                 padding-bottom: 14px;                                              border-bottom: 1px solid #E9E9E9;                                  max-height: 0px;                                                   transition: max-height 5s ease-out;                                vertical-align: middle;                                            font-family: HelveticaNeue;                                        font-size: 12px;                                                   line-height: 16.8px;                                               background: #FFFFFF;                                             }                                                                  .sframe tr:hover {                                                   background: silver;                                              },                                                               </style>                                                         </head>                                                            <body>                                                               <h1>  </h1>                                             <table border=\"1\" class=\"dataframe sframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SArray</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "</table>                          </body>                                                          </html>"
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   "source": [
    "first_image['image'].explore()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Starting pairwise querying.</pre>"
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       "Starting pairwise querying."
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
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       "<pre>| Query points | # Pairs | % Complete. | Elapsed Time |</pre>"
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       "| Query points | # Pairs | % Complete. | Elapsed Time |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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     "data": {
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       "<pre>| 0            | 1       | 0.196464    | 7.715ms      |</pre>"
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       "| 0            | 1       | 0.196464    | 7.715ms      |"
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     "data": {
      "text/html": [
       "<pre>| Done         |         | 100         | 37.572ms     |</pre>"
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      "text/plain": [
       "| Done         |         | 100         | 37.572ms     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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     "data": {
      "text/html": [
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   ],
   "source": [
    "first_image_cat_neighbors = cat_model.query(first_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">distance</th>\n",
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       "    </tr>\n",
       "</table>\n",
       "[5 rows x 4 columns]<br/>\n",
       "</div>"
      ],
      "text/plain": [
       "Columns:\n",
       "\tquery_label\tint\n",
       "\treference_label\tint\n",
       "\tdistance\tfloat\n",
       "\trank\tint\n",
       "\n",
       "Rows: 5\n",
       "\n",
       "Data:\n",
       "+-------------+-----------------+--------------------+------+\n",
       "| query_label | reference_label |      distance      | rank |\n",
       "+-------------+-----------------+--------------------+------+\n",
       "|      0      |      16289      | 34.62371920804245  |  1   |\n",
       "|      0      |      45646      | 36.00687992842462  |  2   |\n",
       "|      0      |      32139      | 36.52008134363789  |  3   |\n",
       "|      0      |      25713      | 36.754850252057054 |  4   |\n",
       "|      0      |       331       | 36.87312281675268  |  5   |\n",
       "+-------------+-----------------+--------------------+------+\n",
       "[5 rows x 4 columns]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_image_cat_neighbors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## For the first image in the test data, compute the mean distance between this image at its five nearest neighbors that are labeled ‘dog’ in the training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Starting pairwise querying.</pre>"
      ],
      "text/plain": [
       "Starting pairwise querying."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------+---------+-------------+--------------+</pre>"
      ],
      "text/plain": [
       "+--------------+---------+-------------+--------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Query points | # Pairs | % Complete. | Elapsed Time |</pre>"
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       "| Query points | # Pairs | % Complete. | Elapsed Time |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------+---------+-------------+--------------+</pre>"
      ],
      "text/plain": [
       "+--------------+---------+-------------+--------------+"
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     "output_type": "display_data"
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     "data": {
      "text/html": [
       "<pre>| 0            | 1       | 0.196464    | 3.226ms      |</pre>"
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       "| 0            | 1       | 0.196464    | 3.226ms      |"
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     "data": {
      "text/html": [
       "<pre>| Done         |         | 100         | 34.142ms     |</pre>"
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       "| Done         |         | 100         | 34.142ms     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------+---------+-------------+--------------+</pre>"
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       "+--------------+---------+-------------+--------------+"
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   ],
   "source": [
    "first_image_dog_neighbors = dog_model.query(first_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
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       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">distance</th>\n",
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       "    <tr>\n",
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       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5</td>\n",
       "    </tr>\n",
       "</table>\n",
       "[5 rows x 4 columns]<br/>\n",
       "</div>"
      ],
      "text/plain": [
       "Columns:\n",
       "\tquery_label\tint\n",
       "\treference_label\tint\n",
       "\tdistance\tfloat\n",
       "\trank\tint\n",
       "\n",
       "Rows: 5\n",
       "\n",
       "Data:\n",
       "+-------------+-----------------+--------------------+------+\n",
       "| query_label | reference_label |      distance      | rank |\n",
       "+-------------+-----------------+--------------------+------+\n",
       "|      0      |      16976      | 37.464262878423774 |  1   |\n",
       "|      0      |      13387      | 37.56668321685285  |  2   |\n",
       "|      0      |      35867      | 37.60472670789396  |  3   |\n",
       "|      0      |      44603      | 37.70655851529755  |  4   |\n",
       "|      0      |       6094      | 38.511325490739715 |  5   |\n",
       "+-------------+-----------------+--------------------+------+\n",
       "[5 rows x 4 columns]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_image_dog_neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Starting pairwise querying.</pre>"
      ],
      "text/plain": [
       "Starting pairwise querying."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------+---------+-------------+--------------+</pre>"
      ],
      "text/plain": [
       "+--------------+---------+-------------+--------------+"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Query points | # Pairs | % Complete. | Elapsed Time |</pre>"
      ],
      "text/plain": [
       "| Query points | # Pairs | % Complete. | Elapsed Time |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------+---------+-------------+--------------+</pre>"
      ],
      "text/plain": [
       "+--------------+---------+-------------+--------------+"
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 0            | 1       | 0.196464    | 5.519ms      |</pre>"
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       "| 0            | 1       | 0.196464    | 5.519ms      |"
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     "data": {
      "text/html": [
       "<pre>| Done         |         | 100         | 33.817ms     |</pre>"
      ],
      "text/plain": [
       "| Done         |         | 100         | 33.817ms     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------+---------+-------------+--------------+</pre>"
      ],
      "text/plain": [
       "+--------------+---------+-------------+--------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html lang=\"en\">                                                     <head>                                                               <style>                                                              .sframe {                                                            font-size: 12px;                                                   font-family: HelveticaNeue;                                        border: 1px solid silver;                                        }                                                                  .sframe thead th {                                                   background: #F7F7F7;                                               font-family: HelveticaNeue-Medium;                                 font-size: 14px;                                                   line-height: 16.8px;                                               padding-top: 16px;                                                 padding-bottom: 16px;                                              padding-left: 10px;                                                padding-right: 38px;                                               border-top: 1px solid #E9E9E9;                                     border-bottom: 1px solid #E9E9E9;                                  white-space: nowrap;                                               overflow: hidden;                                                  text-overflow:ellipsis;                                            text-align:center;                                                 font-weight:normal;                                              }                                                                  .sframe tbody th {                                                   background: #FFFFFF;                                               text-align:left;                                                   font-weight:normal;                                                border-right: 1px solid #E9E9E9;                                 }                                                                  .sframe td {                                                         background: #FFFFFF;                                               padding-left: 10px;                                                padding-right: 38px;                                               padding-top: 14px;                                                 padding-bottom: 14px;                                              border-bottom: 1px solid #E9E9E9;                                  max-height: 0px;                                                   transition: max-height 5s ease-out;                                vertical-align: middle;                                            font-family: HelveticaNeue;                                        font-size: 12px;                                                   line-height: 16.8px;                                               background: #FFFFFF;                                             }                                                                  .sframe tr {                                                         padding-left: 10px;                                                padding-right: 38px;                                               padding-top: 14px;                                                 padding-bottom: 14px;                                              border-bottom: 1px solid #E9E9E9;                                  max-height: 0px;                                                   transition: max-height 5s ease-out;                                vertical-align: middle;                                            font-family: HelveticaNeue;                                        font-size: 12px;                                                   line-height: 16.8px;                                               background: #FFFFFF;                                             }                                                                  .sframe tr:hover {                                                   background: silver;                                              },                                                               </style>                                                         </head>                                                            <body>                                                               <h1>  </h1>                                             <table border=\"1\" class=\"dataframe sframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SArray</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td><img src=\"data:image/png;base64,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\"/></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td><img src=\"data:image/png;base64,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\"/></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td><img src=\"data:image/png;base64,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\"/></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td><img src=\"data:image/png;base64,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\"/></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td><img src=\"data:image/png;base64,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\"/></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>                          </body>                                                          </html>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_images_from_ids(dog_model.query(first_image))['image'].explore()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task 4: Compute nearest neighbors accuracy\n",
    "### In this task, you will measure the accuracy of a 1-nearest-neighbor classifier, i.e., predict the output as the label of the nearest neighbor in the training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_test_cat = image_test.filter_by('cat', 'label')\n",
    "image_test_dog = image_test.filter_by('dog', 'label')\n",
    "image_test_automobile = image_test.filter_by('automobile', 'label')\n",
    "image_test_bird = image_test.filter_by('bird', 'label')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Next you'll find nearest neighbors in the training set for each part of the test set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find the closest neighbor to the dog test data using each of the trained models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Starting blockwise querying.</pre>"
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      "text/plain": [
       "Starting blockwise querying."
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       "| Query points | # Pairs | % Complete. | Elapsed Time |"
      ]
     },
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    },
    {
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      "text/html": [
       "<pre>| Done         | 509000  | 100         | 223.715ms    |</pre>"
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       "| Done         | 509000  | 100         | 223.715ms    |"
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       "Starting blockwise querying."
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       "+--------------+---------+-------------+--------------+"
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     "data": {
      "text/html": [
       "<pre>| Query points | # Pairs | % Complete. | Elapsed Time |</pre>"
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       "| Query points | # Pairs | % Complete. | Elapsed Time |"
      ]
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     "output_type": "display_data"
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      "text/html": [
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       "<pre>| 1000         | 59000   | 12.3431     | 235.27ms     |</pre>"
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       "| 1000         | 59000   | 12.3431     | 235.27ms     |"
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       "<pre>| Done         | 478000  | 100         | 317.094ms    |</pre>"
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       "| Done         | 478000  | 100         | 317.094ms    |"
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   "source": [
    "dog_cat_neighbors = cat_model.query(image_test_dog, k=1)\n",
    "dog_dog_neighbors = dog_model.query(image_test_dog, k=1)\n",
    "dog_automobile_neighbors = automobile_model.query(image_test_dog, k=1)\n",
    "dog_bird_neighbors = bird_model.query(image_test_dog, k=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The distance column in dog_cat_neighbors contains the distance between each dog-labeled image in the test set and its nearest cat-labeled image in the training set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create an SFrame with the distances from ‘dog’ test examples to the respective nearest neighbors in each class in the training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "dog_distances = turicreate.SFrame({'dog-automobile': dog_automobile_neighbors['distance'],\n",
    "                              'dog-bird': dog_bird_neighbors['distance'],\n",
    "                              'dog-cat': dog_cat_neighbors['distance'],\n",
    "                              'dog-dog': dog_dog_neighbors['distance']\n",
    "                             })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
       "    <tr>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">dog-automobile</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">dog-bird</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">dog-cat</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">dog-dog</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">41.95797614571203</td>\n",
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       "    </tr>\n",
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       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">34.94516534398124</td>\n",
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       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">45.10673529610854</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">40.523040105962316</td>\n",
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       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">39.095727834463545</td>\n",
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       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">41.32211409739762</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">38.19479183926956</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">39.90368673062214</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">37.76961310322034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">41.82446549950164</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">40.156713166131446</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">38.067470016821176</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">35.10891446032838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">45.497692940110376</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">45.55979626027668</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">42.72587329506032</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">43.242283258453455</td>\n",
       "    </tr>\n",
       "</table>\n",
       "[1000 rows x 4 columns]<br/>Note: Only the head of the SFrame is printed.<br/>You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.\n",
       "</div>"
      ],
      "text/plain": [
       "Columns:\n",
       "\tdog-automobile\tfloat\n",
       "\tdog-bird\tfloat\n",
       "\tdog-cat\tfloat\n",
       "\tdog-dog\tfloat\n",
       "\n",
       "Rows: 1000\n",
       "\n",
       "Data:\n",
       "+--------------------+--------------------+--------------------+\n",
       "|   dog-automobile   |      dog-bird      |      dog-cat       |\n",
       "+--------------------+--------------------+--------------------+\n",
       "| 41.95797614571203  | 41.75386473035126  | 36.419607706754384 |\n",
       "| 46.00213318067788  |  41.3382958924861  | 38.83532688735542  |\n",
       "| 42.946229069238804 | 38.615759085289056 | 36.97634108541546  |\n",
       "| 41.68660600484793  | 37.08922699538214  | 34.575007291446106 |\n",
       "| 39.22696649347584  | 38.27228869398105  | 34.77882479101661  |\n",
       "| 40.58451176980721  | 39.146208923590486 | 35.11715782924591  |\n",
       "| 45.10673529610854  | 40.523040105962316 | 40.60958309132649  |\n",
       "| 41.32211409739762  | 38.19479183926956  | 39.90368673062214  |\n",
       "| 41.82446549950164  | 40.156713166131446 | 38.067470016821176 |\n",
       "| 45.497692940110376 | 45.55979626027668  | 42.72587329506032  |\n",
       "+--------------------+--------------------+--------------------+\n",
       "+--------------------+\n",
       "|      dog-dog       |\n",
       "+--------------------+\n",
       "| 33.47735903726335  |\n",
       "| 32.84584956840554  |\n",
       "| 35.03970731890584  |\n",
       "| 33.90103276968193  |\n",
       "| 37.484925090925636 |\n",
       "| 34.94516534398124  |\n",
       "| 39.095727834463545 |\n",
       "| 37.76961310322034  |\n",
       "| 35.10891446032838  |\n",
       "| 43.242283258453455 |\n",
       "+--------------------+\n",
       "[1000 rows x 4 columns]\n",
       "Note: Only the head of the SFrame is printed.\n",
       "You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns."
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dog_distances"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Computing the number of correct predictions using 1-nearest neighbors for the dog class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a function which returns 1 if the value for row[‘dog-dog’] is lower than that of the other columns, and 0 otherwise. That is, returns 1 if this row is correctly classified by 1-nearest neighbors, and 0 otherwise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "def is_dog_correct(row):  \n",
    "    if row['dog-dog'] <= min(row.values()):     \n",
    "        return 1    \n",
    "    else:        \n",
    "        return 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Computing the number of correct predictions for ‘dog’"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "678"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dog_distances.apply(is_dog_correct).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Accuracy of predicting dog in the test data: Using the work you did in this question, what is the accuracy of the 1-nearest neighbor classifier at classifying ‘dog’ images from the test set?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.678"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dog_distances.apply(is_dog_correct).sum()/float(len(dog_distances))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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