K-Means Clustering.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# K Means Clustering Applications\n",
    "\n",
    "In this notebook, I give you several basic applications of K Means Clustering algorithm (clustering in general)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "\n",
    "from k_means import KMeans\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import cv2\n",
    "from skimage import io\n",
    "import time\n",
    "\n",
    "from sklearn.cluster import KMeans as KMeans_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simple Customer Segmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Annual Income (k$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Female</td>\n",
       "      <td>23</td>\n",
       "      <td>16</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Female</td>\n",
       "      <td>31</td>\n",
       "      <td>17</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>196</td>\n",
       "      <td>Female</td>\n",
       "      <td>35</td>\n",
       "      <td>120</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>197</td>\n",
       "      <td>Female</td>\n",
       "      <td>45</td>\n",
       "      <td>126</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>198</td>\n",
       "      <td>Male</td>\n",
       "      <td>32</td>\n",
       "      <td>126</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>199</td>\n",
       "      <td>Male</td>\n",
       "      <td>32</td>\n",
       "      <td>137</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>200</td>\n",
       "      <td>Male</td>\n",
       "      <td>30</td>\n",
       "      <td>137</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     CustomerID  Gender  Age  Annual Income (k$)  Spending Score (1-100)\n",
       "0             1    Male   19                  15                      39\n",
       "1             2    Male   21                  15                      81\n",
       "2             3  Female   20                  16                       6\n",
       "3             4  Female   23                  16                      77\n",
       "4             5  Female   31                  17                      40\n",
       "..          ...     ...  ...                 ...                     ...\n",
       "195         196  Female   35                 120                      79\n",
       "196         197  Female   45                 126                      28\n",
       "197         198    Male   32                 126                      74\n",
       "198         199    Male   32                 137                      18\n",
       "199         200    Male   30                 137                      83\n",
       "\n",
       "[200 rows x 5 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('example_datasets/Mall_Customers.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[['Annual Income (k$)', 'Spending Score (1-100)']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
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       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"800\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Spending Score (1-100)')"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.close()\n",
    "plt.style.use('seaborn')\n",
    "plt.scatter(X['Annual Income (k$)'], X['Spending Score (1-100)'])\n",
    "plt.xlabel('Annual Income (k$)')\n",
    "plt.ylabel('Spending Score (1-100)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "model = KMeans(max_iter = 500, tolerance = 0.001, n_clusters = 5, runs = 100)\n",
    "(clusters, data_with_clusters) = model.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2602.972578838644"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.cost_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    },
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\" 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      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.close()\n",
    "for i, cluster_mean in enumerate(clusters):\n",
    "    data_cluster_i = data_with_clusters[ data_with_clusters[:, -1] == i ]\n",
    "    plt.scatter(data_cluster_i[:, 0], data_cluster_i[:, 1], label = 'Cluster ' + str(i))\n",
    "    plt.plot(cluster_mean[0], cluster_mean[1], label = 'Centroid ' + str(i), marker='*', markersize=15, markeredgecolor=\"k\", markeredgewidth=1)\n",
    "    plt.xlabel('Annual Income (k$)')\n",
    "    plt.ylabel('Spending Score (1-100)')\n",
    "    plt.style.use('seaborn')\n",
    "    plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Searching for optimum K by Elbow Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "def elbow_method(X, max_k = 10):\n",
    "    costs = []\n",
    "    for k in range(2, max_k):\n",
    "        model = KMeans(n_clusters=k, init_method='var_part')\n",
    "        model.fit(X)\n",
    "        costs.append(model.cost_)\n",
    "    plt.close()\n",
    "    plt.plot(list(range(2, max_k)), costs)\n",
    "    plt.xlabel(\"# of clusters (K)\")\n",
    "    plt.ylabel(\"Cost\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    },
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\" width=\"800\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "elbow_method(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Image Compression by K Means Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### With OpenCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(423, 640, 3)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "cat_img_orig = cv2.imread('example_datasets/sleeping_cats.jpg')\n",
    "\n",
    "cat_img_orig = cv2.cvtColor(cat_img_orig, cv2.COLOR_BGR2RGB)\n",
    "cat_img_shape = cat_img_orig.shape\n",
    "# Convert 3d image array to 2d array\n",
    "cat_img_orig = cat_img_orig / 255\n",
    "cat_img = cat_img_orig.reshape(-1, 3)\n",
    "\n",
    "cat_img_shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = KMeans(n_clusters=16, init_method = 'var_part') # When var_part is used, the algorithm is a lot faster.\n",
    "cluster_means, image_data_with_clusters = model.fit(cat_img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14484.706305129"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.cost_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "compressed_image = np.zeros(cat_img.shape)\n",
    "\n",
    "## Assigning each pixel color to its corresponding cluster centroid\n",
    "for i, cluster in enumerate(image_data_with_clusters[:, -1]):\n",
    "    compressed_image[i, :] = cluster_means[ int(cluster) ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(270720, 3)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compressed_image.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "compressed_image_reshaped = compressed_image.reshape(cat_img_shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(423, 640, 3)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compressed_image_reshaped.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f1eb6181cd0>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.close()\n",
    "plt.axis('off')\n",
    "plt.imshow(cat_img_orig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f1eb72cb760>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/zFylyR5cV8BSTD0hRQCrhK8Ey0vStNSkRRKSOLWIAxmZYIV1cdBc+Xzj6bQL0IqXyCaHLmg/KD5bsvZ6WIQrx1rjuGkhCsfDpph6LUce/2IhTajZDadcXemJlaZgKwXaFyGtqraa81lwqUELnACIWCzHWQiy2IF9SNtHLKTDQygomqAI0HlI2crzcmLvalwV+64tV2IFEHOy+bDkDi0+ycVSrxqJLmXB68VCXsXA254AiKXi8kr7UpVRpSdOrFzhaLUISPVpNj1JsFbwu0Qa+6im+rS4Xhe0H4IoDli8Vr7apFr3hZDi2PKefa8BvZYhJBwyhua9B8Cr8+p1uSvXUCyZci1npZpy9OJabBRhEiAU8aNB7xd/fcg8oPW2orAczQnjUp8rJwZMtSnjYvl+6b3msMK7aB+BwrO0EC3NEUdL/tiyX9hu5Xgg2aRRA7dVUyHyjRtbY2FersI19GOjzY3Pi7+WtF37QUsTpqXZvq81d6Ct545lS6x1tzSn5qolNwu48trzQLaoDulhVSnEWgSKJGcJ/jpRGBHvwEogkpWQX1ReZYtncvFkA6v4Nm0PUa7yeCvRQHNESbh+IPi5fYQkXyNyFNJj7bpWnQUOIkjX65Y3gSAE/MdqW/eOql8dciw5uZYbkmTL0X21UsEJtqiMLevHj4jVZVPUFJsWA3r65PFYsRLtj/YRtCUqV6CShEJIkx5r9xRCTJLIXMdwte+DlXghVaiVeOGyJYfWwvVB+xFov/ky9uzYXlUpri5UJLnmUa8uwiSilYRbaSin6gSyEyx/X1wq3yJYqRZ5jVRflS0NkiC18CLX0jk0Q8taxoeQpqsNNjdD0By4OcW6Zl5o96uZOOQ+CVc4Fz+WQPUmKvm7J5Hnh6AiyTUveOyqFscq1Ss3GVTCG19kARZNvWaF64NGDpkzw6Ne5wyRiyRWuu+QYHkJ/iWnmFdeck97DviX1YSQModWny9X/y+5jXL1uYp1pb6GKFJXHQUfcWrkKbdbY6Xdl+65UksU0pzIvprXPlyx5Jr1prSmcVZXVjluqUFEWLRyLco8IMNQKGqAyuZxUuURA1KVWopR3jc5cWhJGeqlt5xbkgTSdCaQ8a4uEwWfP5C8T5dJgnL1OaQd2rpeCLSMNQmLJDW4IgasfVyVVxrJyvm4HFk+zqiY9FcLaV98GTRfLtIsR70C3mnAV3YuC4pMi5WtYCQmRU1SK4xJSwvd1r3DGQ8b0q5bgxVAH2o/5am8vjReV5dbDleHVYJsp5NGDdPc5Zjm5g3OhAJJqFbCQZq4WZkaO3Li3SWPHMhL7if/9heV0+YlDbLc+GI5rMpxbiK/ctVl5aSdp6A2EG9y2Dttt5UGksSqhUpRsWkrbEurG0Ckw7Ol0ub8a5BkSNe81tyBbewjSercVTsha+NACXlemSThmr82dmJi3Fn7lbZxApVzzZKQQD8MXY2rcHH+M7OUBJsnJNTHARVrFihiuVCu0CqXPbccuDh2tjDiLdLkIB1c9Dc4ORebM4dcjvJlsAyU16Dt0+oChMA1NoTwXB5+GYrl6pJgefgJnFg1EwN3iFl2ZnlNAInMKl/8sHUfWeJfrzV3VETdgSzxqwTfd79iybWSkccBRn2VpLpLQ3pEXHmJtojwLK3OpvzQJduflGDZ8rjzxoqJBeKkoyUKuJIHNEgykrVf5R8R/bXmjmgu3Lyhxbpqc5TQ1DnNQ5unBpcnPyQGVxJtkSFahG3dO5bcuSUzRNNg2SrXIrDUv4oSRKg7606jcUML9uzYjj07tgOAtz0xUJy5YDHjX4GFZfPq6YFYFpBWC4AgveOrpwfMuqh0PF/+aqmhMjOJgytqn9OLtl9p6IiRLClYGacr50RjCJJgXZlhvkphHLL3GEeaGNysJhYrooCvVmTdgcUG/9wXTfJV59CSqBSC7cFJAEDX1obY9s6ONgyu2IMzw6M4fPyUWfA6BGmX/OVwcAHJ15zXItXsrpo9lUDEZYUi+Zw4LoeXVkA6xJGjnUdzzFkhZdJBxeFzVml1aUNr0oY6wax6tnIft9lK51ZIhEFvby+A0srm1UNvLbrttQhuOfzGX5sOrYom19Cb18rqVQqpAgvEStAIFgAGV+zBCwfezHSNvIW185Cr9j6FECyHJKfQwtC+QiUEyzsuCdZXko8gnUFEsFpLcd4ZVnN+bdzYGqlyq8OA9SORtli3PJ+GEJNDXgyMXYq9ZoePn8LoqWO5z5sG5SbXijYLhH7RLcNyJRTClsSqoX9gBP0DIwCAR/bfnek6edVuXvNA68dn0frxWXyu4QP1h+3i2NmoF5RcIlt20ZUTAzh98njiD0iX828FyfN9VtESFzTlKH8QeBSE9WOxenpA7bvFYRVd4fu15owaQok1JAOrCOQtpZgV5coEJVQ0uRZhA6kkBQskVStH+/XDuHfrbOZzl7u1tg9bbkvGu8ofNyJYQCdV6ujqy7WX9lcXCWjqztViWqtP6vOiu8DJg+6HmwQ0UnURYJq6BvJxaCKGtJdmgSvES4I7toqyffJMqzTJASEIEW0VHeeaB5VQnzVEtWp4ZP/dqc0DeUKsyM6btWtB68dnncRK7wUlSPRNXQWm9O4FElE1e7HUHvxwLTZCr+9KkHVZfYRIc3EtvUPiUl0JBjJW1mWDJvBODzTeFYFgbfeZFkK9/rJ+rpaAwItpa8e7UITdP8v+EKThlKol16VGe9MaYDK5vW9o2lSvZBq4t7MHLzjOrWVyFdkaJm9OdQ9O4m+m4xla8seOCmhzaE0OAWBwnpDIJKAlG7hauLiC9aWzxyJYF6wMLJdtWZuH65pZCtv4QCRI8w9NaACypb2GgBOg7zPIP6eVlkYLVLhZIC8Ww95qkdmqSX9oFVByZpFDi/4D6WyvRRCqy/baWX8d3/r1Deisv64eO3rTphiZnkB3Ygydt+mjD2LOKisGlkKdrjQs1Ind1r1DNRNoQfjastgqyZdl2axBK7Qtg/ldlbhoji64SFaLxbVgLfU100hoqnBoXKycM2B3wQ1Ro+UkVq2SXiivVDS5FhGasVhOLU5wLnOAplr7B0ZixDrSf8Jpe5VqtRy21s81fID7t6/A/dtXYMtta9A3NI1vfe3zJsECwPD5OfzN9K1m5EZ705qoLTeRqubM4FlGEteaOxKOMSswP0TtWemj1nxCentpYVMh3WktQiI7NP1psM7pqvplKcwstmVXyJYFPsYqRVjJytSHiiZXoLi846JJdn3LJnVZThg+Pxd0HjIF8IgBQtbIgbz4XMMH0WP5Y6DZVuk9Gr3JVtAyIy3UQ8yD9TmorbdWVBsorj4sn4fcpyUchGRP0Xz5f2t/FljdCrQuBFpRbV54m+BTpfKaLuVqteKWKzDLRLDURBtqd10WNldJsEV4+fI6u2TVKnreg5MYPj+nkpALXLkCJfW6+8E/wJ/98AXvXPMkHhCaPvoAm5R43L6h6eh5W2cPXv3XurnDai7Jq2aRapXQbKih6Ju6GnMQcYQ0FJTEI+Nd01Z+0hDqSHPN1aomJiHnr3UtCHGKuWzVrh8Pa9XhKm04MTGOjRtbsXJiABcdTlX5+XIJr0qINFoW5CqhvahpX0xOslkjCxL1R+edWC5iJbLiilASK2Hd0PPYdfv2qG6qBU6saR1b1AOL24g5qdI8+4amcWnro6i75Wfmh1pTFzPTY1iFD3C16Q4AwOYtrTgzPBoRKilPrcIVB335LRuthEasriiAtLZWV8fZPBW5XMH+PCbXVRWL5ue6hlb8m6IS5HX5/biy2azrhnQrmJgYLxVzmbqKGdYlljutQlaxRaravBFHy5JcNWQlXB4uBKT7tZIe+1WT7wapVrnUljZXjl137sagJywri3LVSJXPjc+xb2gaDz+wE0cPHgg201hKViuorTXi4+DEqmU/AUjUf5WkwzO0eM49d2pxpeaKgy0CIZEE1g+Dqz5smowtOQffDxI/t0WYWvqw64fIWhnQZ8f1vZZtupfaXCBRNeSqIcuLnvbXSpKaRqyuxAEC2Vslyd67ddYZlqXNwQUfqWro2tqAts4eHHwjmx3wBLrRjlLHAmka4GTps5NKYqVwLldhbQ38C+0iWA65hNZqyMpxaeHLOnPVX9XIku9zXUtTwaGZbj5Yc7a2dzWuQtzzYEOGDMofdOsHfrFQ1eQKLO6vmaVaXbGtgG0WGOk/gbbOHuzZsR2Hj58CEFep1mMNklTl0t81R5ofzSEUvKD2IEq2V2oJwzsV0ArABUm8FM5FxCrTWCXRpFV0vnYvvnO7ogp8QfvWtWX7Gs2eCiwUfwnpWMDP42rUKEnQpUatOYdsB0qrx9FTx8w+Vq7VExdUeb77RSQgVS25Zn1hi8zqoogBCmXyJQ8AOtE+cU89DjNu0Yjo4tjZuEd+nkzJXruJVeWSpEqPLbR19uDZN2YyRVtIggUWIgfyJD5YDjDXkllDmrEudWhBy2RyOXt4UD+fo69gC7eXAn7HmM/xpd2HRbB5HX4a8hBjpZgHqpJcl+LF7cFJDCvbuZLlnneNzDixkh2W1CtQIqVBYeflhLqz7nR0HTr/zjpbOfsUNc3p0tZHsXko3N5qgUwuJcJPOumodxZQyrfnrbepAErf1NVEdtfq6QFYM5MtVXxjLEhizVKdKoSIrDEuJ58kVj5XS73StQhpf5QIRRPrteYOIOUKiaOSSpRWDblWQuiFBq5eAbvcoERnR1vM0fX/PPXf4dk3ZtBI3+nGUpHt2eM/XTiobsEZxYlcQjqrtHkR2jp78IuxS3jl6FDufluAbdPWCplcaehAO6XAzve8x9RCCixQSo31tX6h5y4nUhpFatkji3R4EULm51OsLoLVIPtkaYRvqeksdtms8yw3bvhogUolVaBEqGSHpf+WWiQiHVyxB+3XD6sRBPtazwAoEd5I/4nS43sei9Qt/X/twEt4+IGdePGVhfqYmmrmYVb0X5vbzHtvRkq5CILliOq8iu2RDZU2zH/puhpXRY4tSpkc/HAt2ufL9rlgfXFdihSIhz5xuJbzWfL0LUebBUlGVtgWH+uzKfNj6DyaOs2q3AnynPx+Sz6G7OnreRxZnFBvyDjXIlEEsVK6KydRgraNk5jMygIQmQH4/rbOnohAiVgBxMwG9P++/Q8BAJ6cf/7agZcSSlZ7LmNv2zp7cGnroxh8743Y2LyFXQjtTWtitW2kAuVf2PabLyfUK1XX6mpchcHmjoiANWhxnRIhXnmCdObIIiiEkBRTuq6Vw+8KjdLUnlb424p2kCTM99P2tKms8jhXAgGBXjsAOAdEpgEZbuVDkRmdebmh4tNffaAGeVlf1DS/jpTyCpSIoQcnE3UEOIlyk0BIOmz/wAjarx9eUKWdPZF5gLbxdFlOuABipMtx3/6H8PADO9G1tcFrMiAQUR89eCDy1NOHLW+3AnrNqUss1Qog+yrvtUWw2rxYBT84fB1TObQ6phq5cCLkefpWyiiHLBgTak6wMqW07q8aoWn3q6W/apEWco5a+q82t9BasHKc5uQMjQDIu5otqlzpsifXxYTVcdUiTm5nDUmHJTMAV7OcYGkMPbbIlCD30/kt+6pGurv37ffOeynQ1bgqiFg5tLx/SSShPbNorKvUYRr7oUWwWg3WxeoQkAYa2VbaHNMiby2SqjIL8CVEuSIGqHbAKthkyc0ARLxSMWqOJE6qVMiFlyOUJoCR/hN47cBL0XOJmG1WHEdz0MKxuK1Xkkce9Wqh6aMP0Dd1a2m5T86Zc6ORKUBiW/eOhI2Wg9cq0MhJWwpzWDGcMvLAZ3f0OclczROteFg5Ly1sK+SaaeakqVYf8hIrJROk/R4X5YMpQr1WdIPCIpCmyaEPZALQlKpmDpDbrbhSLWJA2mJpDJEkJ00iWLKTcgcXnYvssBJ0LJ8XnQcA/vOlXTh3bhSnTx6PNTNMQ67yB4/XxeSve3vTGkx++taYInUVdNEys1wOHun1tojIIs3Quq++sC15baujqgZXk0XXddNEMoTYWNOQJ3fY8ecaJiYWOun2vvaSqh6tz15RgioNqS7bBoVFIA0JaG/kzPQY6s6/i7rzpboBMrTKB34MKUWttoAsOciLaPMx/DlQItnOjjbct/8hvPjKsZj9ta2zJxYvK0tpdy4AACAASURBVDHSf0IlXa6CiViBuB0szQfZtZKQtR0ujp3F4eOncPj4qaisIG9MSNi4sTV17ykLslxh2h5Vru2ypqu041qqVDqjuCmC/5fXlfeh2Ut5aURpKtHmk0eFWrZpF6wShBqKXqEWGTlU9eQKINjhJV/YmekxtH6sp5RK9WqpVWkeAEokq9k3uT0V0NUrkeR9+x+K7R/pP4GHH9iJ/oGRKASLiJeHZNF2/pg7vKSK1mJIs3wA+esva+uOnjqG2QsT6H3/VLRvZnoMIyfejdm5tYpYGkFYFaNCvthp+1FJ+BQrh2+/JDVJsHybREhEhGZikNe2iFUjTUmerkgJy9nH57Ctewfaeu5IjJGNDIvqRsBXVPzzmbUWdFXZXH0IUVCE1o/Pok6M0bz+/TMrEtX5pbrlBDt8fg4P3lkiMLkUJ5VJZMhz/mksJz9JxqR279v/UMxU8ORTj+GZp59DlzAP8NAuijyQ4FWsyoW6W5qx6/bt+PnhdxLvD39f+rAJXSgR7GpHsRbZ4kUWWXHVMbXAQ55ctlIrJpbDysLyNf6j88tru8wVviQEHkIWAn79LHUD6NjQEK+uxlVoun07et8/Ffv+LkYWZs2hVQZoajXEDECOLCv0is6h1UolEu0fGMHDD+wEUFKurtApSYbcBEDES+RLSQWdwh5L55HPAeD1oToAM7HAfZ56W0S8Kx3f+777C0Mf9KmPFlrFrJ4ewExg8kBo11SNhIgMLFXKbYncWWYRiJbtROdMk68fUo/WF26WFVaMbChJW+Pk3M4BaARQ76lpXASKbgdV9Q4tCd8vnmUG0NA/swIAEsqVh2D1DU0niHbLbWvUSlQyi0qSqvTmc+cWEav8DyTrxdJz6fwiIubqdqT/BA6ObgZQKhk4+elbcXHsbOyDmNa5xY+pu6U5VgXJBdmL6xpLfZW1CMhG62pzLcEVqctxpI3xjdUQUk1Ls5cSsYVW49Lm4ro/l4PNZY/OU5JQYmDsUsxRd/rkcYycKFV0W6pSgpo5zOXQuqGUaznfEE2tuoL1ObESGYbUfSVwYiRYRbfp/PftfyiysT7z9HN4GAvRBtycIJ1fT9xTP1/XoAWNKC3PAf2X3qdmtfJxu27fjpnp+HvT+vHZRE+uKPkALQDGMDl1FSvnIwuufbgWwFq1oHYaaAQhs69CSSSkPqxc0odEMLjKGVrbQ8oY8rn5ssZ817SQVTVTaNZSEGtWJ9cNpVyLVK2AW7nyJf+rp66rY7j9FdCTCFyhW5JgpYKlsVyZ8ucEuV+Cn//g6GZMjY9hcHIus3rl0OKSeYNE3rJbgvpz8QLcRKxUIzZUufoynEJCo/i4PBEHfD58TnQ9i/BCVKk8n9xnhXNpClqC5qZdPwupSvVqhWaVGy5yveGVa8gvXVZipceSPLlqvX/7CgyfX9gvC7kAJXutqyShVlRFFmwBkg4vGif3AcmYWVK2Evz8T9xTj+8+P1Yqfwi7mVwodt2+Hb3vlx63fnw2YduWClZW2IpU7KfjJExhPJRQ4KvSH1qrNaSoSRZilTUCtISFopomumoKcIQoZGk20Gou5MVSVcq64WsL+FAOYg0BEaEWG0vEyslSK0noq/+qJQwQuLqV6bT8Px8js7k4+Pkf2XUdjRtKqpE+gCHhMJqn9+eH34kej960KbgluQR1WiCQ0y1NV1lqKQ0kQ40suJbKViwpEbmscWDVO0gLy2FG16Zt2vaNG1uDl/pWXG6euVrbliNqZgFkI1euXIG4aUCaBYB4SuzVpjuiwtbR8fNL+dA6rFYd2BBw8rTIWY7XogmefWMGh4+fipxRod05SX1a4VekYIloSbnKnmhW225ekpC3ggHi9kfuACP4wqyAMJUb6sQKvY5VsYsymkLnDbgTLVzE6qp0FdL2JQtpyowtoHivvoZQ1XpDZ2i50Prx2cJUKydbKwxr+PwcXj11HT8//A6+/1/GY0RK6lLL4CLI8bxClgapWgmybKF6P+w4Wb+Ajn/inno8sv9u/Nqez2PX7duDYhD5sn5wck4l5dGbNuFvpm/F6E2bEk4tfm6NWIGFSllpVCtlgVGGE1d2/HHaVi9WZpZ8rI3RrmNV4NKysuS8NWjFbLQ5yflqDQddca8hpRctpMnYKgJFZWndMOQqu0KWCxTnSkh0HphXuPT/5SMjsYwtHvNqtbkG4umxXMVyVUpRAATNPAAkq2dRJS4ObjagMSP9J3Dv1tlYJ1mfgqX34f69X8DoqWPmOB+0L8DmLQtkZFXM0r7MWtdZmQrLC00DemYYwVJ3nDx9aa++kCztPjQCldW55Py1eVtzcoGn5YaaVOQ1s+yvZFQ9ucq4ytkLE+isv55bsUoHFkFTrbHKV/MKt39mRWLsy0dGYqT68pER9Rx9Q9OxegQyDZZX1OLqlhOmVi1LqlzNSSZttQDw5FOP4ZFdpde0vqHFa3+dvTCBVw+9pbZGdiHEfn6tuSP6I1i1YMlUsK17B7oaVyUiC7izC7BNBi7154OPuDgx+gjVys4qYk5WTK9U0iHlGEOuryl5/oNZToFUFKqeXLmdrsg3RNpcaRuRppUA4MKDd7bFlCwp4JePjERESw4yrnQBvfgLPX7xlWOqjVYSLK+GRfv5PlKvVnnDb33t8zEnl4bZCxP4rd/47YT9VHtvshRB1+oPWODkw0sUuoppc2gkkJZkNQJKZCkJgtVsqFbvLNfzkLoB2jZXAXBrfygstb9xYyuuNXdgz45S77hyxrry+hZ5UPXkSqAvqaU408I6T5rz98+siDluNGcWV7daZ4MQ0rZaykgbamxuhh1XQlbhWjX5brzFt4LDrIUHJ09OtGkULcFVByFExfnINK0SzKrc+HFpGyf6ioETtMIsWvWtLMiaqbXci2tL3BBxruWAplw5NPLbctsa9M8skG+JiFfEwrCGz8/F0mZdqbQvHxnBg3e2qdleVqYWB1eukiRljCw/N0GaCEb6T6BrawM+1b0bg5NzKin+2p7Px8KvNKRVJVPjYzGnFqlXnqm1rXtH6gaFRDQhXUlDi7HIc/N6Ahq0oH4+L23e8h4kwfrIj6ICLILV7MEh2WuhiRmVgrxFs28Ycq27pTlhZw3tbaWBFKqPZBON/+aPK137eoxAyRSgESv9f/Kpx/DagZdiZKqlt2rRABbpcsjuBTJUi5O3zA5bqKw1izM7tuNVpai2RaySUHn5NwuD88U82pvWYGq8NI6SCShDi3D65HFs694R/dfAHVFas8G0sEjKdc4QQgsteuK6prXM135E+OtgOfBcxGrNS8vo8qFkvnkreHwe1MwCgdAcWFmJleAjViCuXOUS3hVVILdbSpI/5upTW9b7MrRc22VKLjVI5Mdw88IT99Rj1+3bY+f9rd/4bQB6Pc7ZCxO4f+8XcP/eL2DX7duj5o+fa/gAu27fHoXN0fvIi2wPTs5FRMvrv8p+ZxQVYBXgDiVS6bSRzhurVqnvXECSiKyqVlkb/7kgM8TSFA/XwsqKaAej/dgUWdC6nLhhkgiKsrVa0FJgtawrIF4vIDRpANCX/FrjQmtsKKwwLavSlkbGtO2Zp58DANTt+DL+8j/9x0SyxfD5OXzxd/5nnDzwp+o9A8leX0D8h1HGwgLJL6ArRnJb9w6vacAKZ+IIbRtDY638fCDM0UXwqT9X1Su+3Wf6oOwt3/I+NMHAZxbgppaQ9i9FI4TEb+gkgs7664kvtIxFzQOKELCIVQOFW/mIT4sI4P+BZDsYGi+Va6iDiqClwspoAstBxsmWyHL2+E/xrV/fgC23rcGDd7ZFWWzf+trnncTKt9PrRt106U9LBpEe36aPPoj+gLjC5ckD1NbbFYBvKToiLavzq5VMIPfR87Rwefldy3pCmkgJjpB2MFnsq9Yx5U4mqG9oMYk1DalXvXItt2L1IWTp71KyVlNDCalc+bY06tVVV4Dbdfm1ZP1Xfh6yxVKWFz9GbsuKY7PbokQGqWhlA8RBo+gyfWEpZVaCmxAspauFSIVU2UpDSlkUIMFlF7WKuQDJ+7Hm4KqKpd0DJ/6QCAOa12TvIRw+fiq3evUpU3Jm0XXS1nOtkWsBcClXzTGlLXN9JgIJy2TAzy9LDxIk8fHHVhyrlS774ivHEh1j5Tks51pWWFlnHPy1vNq00IeJyFV+MfkXR1NG3EFGDrEQdWeRKxBejs9FsCHKNES1WtDUeQjBh3R6DZkfP49VPDsPQhQqjxqQ0QM3LLkutWoFdPNAFiLVSg66wCMIKA3WVaDFRayAHrZFsAiWxvPnHJbTzUe8LiVuHXtsdhsAYNXkuziBbqfqcSkaqhHrakToq7HKEWKv5OcM7TSg7ffNxZpfCMH6SDQLwWvXINvryomBQtQrEH+/fZ+LNOR6w4RilQOuylguhBCrJFIZNQC41Z/spwXoUQG+Ai4SmtOqS6Tfyv30mNc60EwY2mNtrLQ5W+fhY6kKWd8k0IOTGP54zusIa29aE7W1kVg937vLRRa0nLZiWX1xsaHLfV8olFUzIAQ0Xgvl0uapkWhImFaaewVKvbUwn4ySBVanVxdkG3ifWaFGrjngItO8DjOtiSE1LgQWyNOn8tLYXK1lvWscsGA/lREE2nmymAO0+YfelyRzWgVsuW0NZkWxb96ji9D00QdomifZLPARLOAvwO1TfZK8fDbPvNDid61ICV5ZzKWcQ8O2+DXXt2zKrFyzHOezv0pUbbTAYpkErFjXLDG0shIWEYFmEtAqYtFznlggba1EMjJZgMNFrHIc1Rsgp5bM4PIpYy3aIQRpj+Hju7Y24GrTHVEdBPprb1oTtY3hfxyy2lae2FjteOu5K47UpUzzppRKcg5JAU6TpZVnLtu6d6SKec0TH8uPDT1P1ZLrYkEjce7M4n8+cCLlZQgJL74SL9FnLa214i0AYiQoSc9Fgq6x0mlmpcha55fFZsoJTrCyWLlWG5ZKGDZuaEmQKi++zaGFb/EasdpYAtUGkKFgHBZ5uoL2Q5SslgyhQXPiuWy+rtKMIfDNJ01YVl77bNpU2Kp0aGmprksNzUwg279osEKxQttnW88ltLRXbR8955BRB9oYAN7QqzzdFdKCCJ0cXcACsfK6sECpIMy15o6oZgGvoEWQqbKhzQfpWAtcuRatQrN49GkueRBaH9aq18ATCk6fPJ5o9V40sjYorErlWi5iDUl3tWBVt5LE6upEEJsLs7lKkrXGWvuBeG0AXyiV1cQQcBfhtpDVNFAk5PKfg8j0WnMH+qauqmUNXcSapYwhIUT1hahOreYq367t02B1T9DuIc+Pga8JYxG9xkKQx5RQleRaLuS144YoVc2RJfcRNC+6FU0gSVg73oc0tlifU4xjMU0DBHodyDxAhV8srJwYSFTb8qlTSaY+kiyq4V+oEyvEIUXgJJ8mm8wXEyvrM4T8UPDXsatxVeEZW0SoeWsYVF20QCXEtroga7Jy+yxtDwnV4rbZrq0NCXJyVcbi+1xRB1KFatWy6PlrB15SzQXrHvwDrBt63ns/SwG67511p3F27V3RdqoLu3lLKwY/XKuWMCT4HDxWK5UQR5hUb74aAtwjn7bld95QqdAfA1ekgc8Rxo+JhWRNJdv0pIUk0iKKw9SU6xLBMhP44CNeUoEu5UhhUxxWrVfCQjlBHTwFlh8jiZVHM1hzrxS4im+HoBz9n0KD9V3IGvead6nv62Dgu+ZyK6hdI9clBJFp2kIyVraWJF5yIFlhW7RNIzVNhbpg7dc6HPDraaS8mKDXZNPlt7Hp8tuJ/VbhbcBuryKh2SA14nURp9afygrpcpGYRqwuu6kc65unhKY2tXnyKAntmi50Na7KrDRdRVryoqrItdJNAkCyKhepVleoltyn1Sig5oYcvJEhgROtfMyrblk1Xjl8hOoiZLomZZAV5dAihR1q7+VzoTlI+ysR67buHWpdARfJpo07dRGXFs4F6OFfPueUK6QrCzTC1+6Fm0Xosfzvg4zvTdNCnYMnBZQDVRWKVankqtlV6bm0vWrg5KoVgSGERBmEZjtZ9Vp9pOUiZR6KpYWJudJaQ+CrixCK/oGRKESLMrOoWpYr/ApIVxGLQoqs+rAEjSRleqnWDsaFPCQqz1OOLLCQHyFeJatv6qpayMUi0KLUaq22wBJDKk8iU24WkGM00uWPrYpasnyhRrghyQf0vG9oGsM/egdbbluDhx/YaRLspa2PYt3Q85GzK03aLY/ZzWpz9aXrpiVYCs2aVJxYQFJtaX2tCKH1ArRYUh+x+p6nzd1PC+mYCsnO8hUCzztXV92AxexiUDXkWqmqVUMomRIevLNEUpwsZYiWJNOXj4w4Iw+044Fk6cKHH9gZPf/+j96J5gIsEBoRKxCmEi3SlaFjaWsi+Makibs96/BlSUVK/bYA3fbp66OldTmQ+6W6lWSkFd+Wz32RB3lJLzTtVc7FVR9WAx1LK4drzR3A1HGVOJeyJUzVmAWWE7lq0Oq/hkBrf2I1XpTbtULeshaBVl4QQFREpq2zB5e2PgoAToKVVbEkXO1qfEhjXw0l2IOjm6PHfVNXY2YBi+AAvWA2ISSLi4+XpBtSeJvgii+1iqgs9vI+hEBdkK9172sls5MsrsLTVvN0c9Vww2VoLUfw+gOyJoGsTcDbdnPVSe1PAHe6Le2XBKzZWOUy/eEHdqJrawO+/6N3IsdXmjhWGS1A27TH5UAoEe9rPWPus4qwaOBVsfh/K4tLbgtB3mIoMurAVdsgZC5pM7SykrpUyjyZIIRYy92HqyrMAstNtUoHF4fW1YAgCZZUqku5us6XuDarAUvQlOR9+x/CfftLz595+jk8qcSz0rlCCrfkhauegQbfnDT0TV3FtuaFJbpmX9TIyCLIjRtbE+q2HHGxErwEoLymLw7Vp5zTFuHm/7V9IeA/Vl2Nq3B4ni+5euUkqpWYLBdqynWR4VvyExG6xlFTRCCueF8+MhJtc52PHpP9lJsW+gdG8OIrx5xOL56t9ff++AW1IIvVxpvvDyHWpU4qmBofi5oanj55HCsnBmJVrjSVZ5GmDCHiatVKLS2qmpRFWNr8eSWrvIQfmo7rWgVY1cEkmVNIVqg65U0s0xTMDh1bFcp1OUEqS+7I0pb+HDSGK9GFYjJzXpWqtfrmpgVX7QItkoAysCid1qUc03rrKwH7Ws/gIDZHBDs4OYc+bAKmjmNb9w51aS+3SWIlZ44W7+ly+vCxriaALican4+EKwW1CEXti5JwXUNzIErw1z7Pct/qlSX3haAqHFrLzSxAsBxPrv0uk4Icw6E5yjjJWvUGZISCDO2S9WF9TQ6zhlwV7dii+YTg4OjmKLGAGhxaxUIoyQDwd0uV8DmhyCQRGg+bhhStbgYh83R5/l2KmTv4fG1hXI6vcjQuJHB7rdwGVLlDaymINW8Ll9BrcEKUBbitlNnQuXGS7BuaxjNPPwcg2Z+Kt5Z5+IGdkemB8NqBl2JkSiYCi7hcqbguaI4wibTq2Mrkktv3tZ6J4l6pqPbFsbOxP8Lpk8djbbiBcCK14CuyTdukavYV3Q7pZpA2n1+Ge2nQTBAWlrqegDQDpHGILXvlulxVK0GSoVSktNTf0L0X4ycPJY7zKVIOmWDAY1hpv3UsD/Snbq+8hbeFcrTYLmd4lqumgqZgOaSapS6xLkdQmkwkLR5WyxLjSJMxRghV2hyWWYGr0LQFt0OLapdTufpw8m9/Ub3KdblBI1MZbiXH9M+siBErP44ULj/eqgGrFeIuef4fiqlUeq4VVekfGImFfvHCL9RTyyLMtMt2CyGEnEbF+rK7aL9UsLyRIYBEl9jTJ4/H6gFYnvHQ+qVajVirVYzmNCsHpDKWjzmJWk6yLOq0XBlnRaLm0FpEaDVcpXOLthNImVvOKl9RFxkLS49JtcqQKQBRsL/suUVdXum/NBFwQuNhXS7yyprymiaDKwRp4l/JyQXEO8UOTs4lCTbFHGRIk6uiFOC3saapHRsKaSsNGa/NjeBy4lm2XC2MK6Rx4mKjplwXETJJwDVGqtfO+utBbWYk4QHwLt1leJUsQyjjXuk5T4UF4gTFz+FadtPYpW7z4oOmYLXusBboy8/jTPMG7HOELLPzXqMIks7abSG0gWIloaZclwi+dFeNfDWC5WqWVDCvK6CFWNFzsp/SNu68ov3PPP1cwjYrPf70eKT/RMyWywlTdi7Qlu1pVaxPvaZpNRMKmrtsYljCWGSLpbCdi2Nn0Tt2FutbNmHlxECsPJ6mtqwQKkCvJyAJT7OnplGadIymFEPCpQi++rFFk6QWBgcAsxcmUHdLs3kc7feNy4Jl7dBa7s4sQCdZzakFxMnVIlqtAIxWf4Bfm0MWhtGqa3GnFt8mIw0k+UrzA6AX0w5FWrVbJNG+PlSX2HZmeBR9U1cTpgEOcnpt696B0yePJ0K3AJ3ELCeTJDFXeUPr3ISQMVkRktWlFf5Oo8i5Q2v2woQ5nkiUxhC5amN8qDm0yoCiwrG0lFaJzvrr0Q+J9YNC27Xi20SOWo0Cua1vaBp9Q9N48qnHIvJ8+IGdMbXLq2YRNBVJ5+JFuCW0zC0XaVaKCeHerbOJbdeaO6KGedofsOD0kuFahDTE1tGyLjiLyrK/amYJa0zIfCznlKVUteW+lZJrzUHev4tYaT8fo42XY7KgZhbwQKsk5bKZ+o7n23xFs331AbhSdcHK9tJUKRGmVJQ8MoCHYHGi5TGsFFUgFS05xDi0XH+ebmu1p0mLos0E926dTSjYa80d2GaInpWNqxLK9jTiiQe+bKWs4EkIeWyneYvE5MFiVfLiyKpogWVsFqgGkwCARLSAlgrLybV/ZgV+bc/nEzGvFsG6bLvD5+fw4J1tiXbeAEx7LRHm9+cLaHNnGbfFuurCauYBQrnTZIu2wRJeH6rDlQZ3uxGyCZ4+eRwX5+2wgJ7VJRFams+VFquVQfQVj5FL9KwJBRrSLv8JWjRFb29v9LqOnjoWfK4s4ATrMgvUyNWDEGWY95wa6UmiJYKl/xZpWu26gbjtVeu3xbdr7WPI1krXkDZXDZopwEWuwPIkWFKwRLBWPjyRHDcLaH256BxW7QGOIjrC8jlrx/vmYM3JR6Bp7bwasfLXdDHIFViw054ZGakum+tiqtY0y/8055SESH9Xm+5Qj+E2V03t8vNy0qP9PPBftoWxkg5oPG3/h//6XQyfn8O3vvb56ByyF5Y6d5HyysdRoW2O0AaDLlvuYoPbYK1CLGQj3bixNWp2yFUrHSvto2TzDG0R44KseCUD+7V6rKHE5yojqM3RSssN2Wah3DVaCSH22GVpc01To3SxkFbdWtWxxk8echa8BoBXT11HZ32yFxdQIsOza+8C8NNo+7e+9vkEAWnVsHiLGN5aBig5tR6eP1aLFKD//QMjscQDqWjT2Ep9dVfz9NwqF1ZPDwAeMuI1SKVa1QgnT4C8pRyz9tbSwrR4aJU8X5peYHJudD5XvQJ+XleUxlJgWZoFlpJYizATaCFXWqYWjdGu2T+zAvdvL4Vi8VoB3//RO9E5rjbdgVWT70Z20cEVe9B+/XCUI7+z7rSznoDsrSVJlUPr5sorZP3j//MHEWHLjgchKNpUUG7bq2sZrS2ZNbjawISqSVe8qZxPGi+/jFUtKowrrbOK25J7X3tp0UwChKoJxeIhSUUjdFlfhJlAVrbSlKrm5OLorL8enYdIsH9gJHa+VZMLBSwGV+wBUCo+suny29hZV0rMJGKW5gEOCqeKnFn/ZTxhErBABPtHv//NKHtMy9wKOU+RKJdd996ts1g9PZBoBUOwbJEypEoSqxW6xc9jLeldxMqP84FfK3Tpb8FlP5avWZHpuyGou6U5+pPPqzZaICuxhtRALfc5XCFZ1rXoelblLK3BIFAiw+79v4feI0fRuKEFmy6/jU91fwVAKdCd8uKJYDkkwZKNlRoRXnr5nwJATI1S9AA3P8hwK7Kt/l/f3B8d46r96kJRxFgu9QrEowd8dQGs/WmVq8/hFVKBy5eEYF2bj/Gp1zQmCd85pXKdmR6L2UOLzrqSqJpogTTk6ivlV25YEQGhxbE1Dz9B28ZJcKT/BAZX7MEnJ3+Cl4+M4I9+/5uRN5sTrmVzBeK21tAQKurwCiyQq+awevfpvx/rXpC2BxZdLy+KIlfrB0JGEEiEFL+2ygxypPHgZyknSHORCCVR33V4MRgrgcBH6K5yg+XsleUqlr1sHFppVetSk6kv7tQ3DkDMww/EawJIW2nX1ga8+MoxPIwSaRx8YxRTs9uwoXtb7JyNG1owNT6G2cnp2DnpMb8mmQOsTCyNUKRi5Y0LiWhlzQE6VxoVa9UnWGzQHKy5nxkexbUP1wJYIMdQB5VV5QpIH8xfVAA/4E90SDs3XvvAqroVmlzhIlKre0Qo0jrMlg25ZkVIkH7W83GEnk8u87VQKp4BJh1IZFuVGVO078VXjqF7xR5s3lKHzVta8cnJn+DZN1qweUtJRW26/DY21QGoW1CsWgiXvPbDD+xMeO+5SpWFXe7b/xBeH6rD7n37sW7o+ZiCXffgH2Bk3rxA4CRF8BFtli6u5YQk2Pbrh4EtewBcBgBc8RzvcubkIVYOqyhLmoD+LMq5iEwqy0Sg/VjlIdKuxlUASh1/85xzWZkFLCxFQoHLXpqGuH3FU6hSlSyCYoEqYtE5zq69K6rg9MnJn8TGai25eXQAV5OvD9VFsZyXtj6aaKVNeO3AS/hU91diYzWsG3o+mBRD1GxWgi3CNCCvLbsvkDORzANyqR/aIUAbE4pQcksbuhXaT8tXQEZudzmx5Bie+RYCIs9rzR1YOTEQ28erlhFojCRbAHj5P/zJ8jcLcCxVKJaV/STNAWmaCLoqVAFI2DtJLfI0U8KLrxyL0lnPrr0Lmy6/DaC0NAUArL0LADB7/KcxVUpzrtvxZdzxO9/EX/35D/CpFa1ow2zk2f9U91fwzNPP4cmnHjOJ9dLWR3HffgDwEyuwYT+sSgAAIABJREFUdMqznI4s9Vr9hzG4Yg9WTw94U2SzINRhFeJw0myfvq60vnmlQUhkgNbG3AUiUwlOrBqpyjFpsaxCsSoFrgaBtD8LtNRTCnmShMCJtX9gBC++cgxPPvVYZDfddPnt+WSCEva1nonIVi7/e/Y9iiefegybt7Ti333nEWze0op7t87i2TdmAAB3PPUn6D1yFE8+9RhG+k/gu8/3q4R49OCBVPcb6sgKId80ttpyw6VkV08vfFld7VesUCQXYVnFpGU9Vl+Vq6yxquUsonLu3KiXSIlEuxpXxf4AXXVKQvURqUXSFpadWWCpM7Ncefxpwq3kuegcViEVnv3EW1QDeuWowRV7cPLAn6J7/+/hzPBoqT3JfIwrgNjS/bvP92PV5Luo2/FlPHFPPZ55+jnU7fgyfnNexQLAE/fUR6FYZCb45ORPonnR+ch8QKr16MED2L1vfzQvS/XKyAGJLJEEWnPEcpGri1AJFD0wOO/gApIRAFZwPh+jjaOxi1mL1YIVWeBTpXwMz15Lk6FmEWTf1NUEOWrbLMKlsZKkXWaBZUWuS02sLoQSqytEi4dfAfFiKkSePEuKVCp56J95+rno+bNvzERECpRsr/taz8RaYB+b3Ra1KXninnp89/l+AMB//7/9Y/zVn/8As8d/iu79vxeRsFYu8NLWR3H04IFoDDmxLFjkCvgV6mIu59MihFzJ/npmeDT6EodUo3JVrgqtceqDy5kWmjXlmnMosqT6UocHl/LUlCsHV7hEoto2iaog10om1jTQyFWSJX+sleuTDi2pWrX9pAppH5kMuLOLPPwnD/wp6nZ8GcCCYn336b8fc44B8XqmpFZlZAAHEasr5MpFsJVKrlpjRotcD45ujp5LgpWwiEYbrym90FTZIpQpn0cItLKHGlZODMSIzrKN8vESPmLNgxq5MhRZQtBSpoDt8LLqCkjwflayTipHiHmg/frhBOFSxhYnVYomOLv2LjxxTz2AkjL9d995JKaISQFTZADBihDQ1KovpnU5kSzNlf8AAsl50jgi2L6pq2q5wSI7mcokBcshZUUDcFNDqCOJvPay4pcPRKQWXARrqddyEitQI1cviiZcglaXVY7h4wi8YpVGnLIYNeCupyrHcvIksuwfGIkiDPjS/9k3ZjA1Pob/5dHOyMFFDi8CV6+AO1SL21bThlhVGqkS+IqA4CJYKpwz+elbUxMQgKj3luzBpSFtoRcgTJlKAnWFQVlz5ESokWBaBxJHuUmVsOzJtdwmgTQxq0UlILjsq1alKv6F5SQlW6/w7Vq1KvkYKKkpbpMlIgVKpoFn35jB5i2tODM8iifuqY8R6utDdaj/zN2R+nHZVSV8jqZyO6KKgHwPgHTqFUiv8rRi2xbS2GoBv3IOjSeVsOyWoWPT2EIXC8ueXIHFTRQg5MnmCiFsXyUu+UWVpfwIWpdVPp6Ps2y2PFHgtQMvRSUKd+/bj7/68x/EbK1nhkdjz6UDKw25AuHqNK3qXQxwYiW4CNYiV4JGlFrbaE5wmhdcO2da00NWEr2RsOyTCIomVt6eWkISX2hSgHUe13bNHADAVK7AQrk+Au+G6iNWfgx/3tbZg2ffmMET98wr1rV34b7OerRhFs/OEysPs8LWerw+VFKtv7j5i+o8sxKg6zjaXknEmhb040ChcV2Nq0wnDSfDkLAkTqqUB7++ZVNkPgghVJ/ds4ZwLAtyLRKuLgZyX9pOr1nAY1ulaYAeh9hWgVKOv5bBdXB0MzZBb4nS2dGGZ55+Dt37fw+vHfgJMG+PfX2oDu3XD2Pzlj0Rse7etx+YV6W79+3HJQCSFiitNQ0BSiVeaaYAl4POp1qzwooIkLg4dhZ92JQoKiLbnSSLS9ZQblR8hlbR9RhdKpjvy0OqMnvLlc0FlOoB8GuRctUytlygtiqaLZX3zJIpsw8/sDOKFNjXegbPPP0cPjn5E/QPjJSKj6CkUikDS7aTJsi01jTQ+maN9J+omKIsGrTWOUCcWLXXgrZx00oa0HKdCPXi2NmITGemx2LESo8vjp01qzpVWnuUakHFK9fZCxNAfXHnyxsZ4DueRwdIQtU6DcjtvDOABplEoMXA8oIvmy6/jcEVX0Hdjs04Nj6GnVuB7//onVgmmKxoReeWuHeeFO7dOgvISlcp7awaKpFIrZhd+oHREj1CflzaOnswOBQ2B99ynhOrtZ9K8XFzASFvKb4adCwLh1beDgSATmaaw8rqV+Wag+ak4lWmLKLUbK68OSBBW3bK/fJYnoTAj6ExVD2Lk7MGTtREGj5SzetwCsl2Wixo5Lpu6PkoLvjY7DbsrDudqS8YUCJpKqzjC5DnDiYiSU6ovJYpJ1SJSiTTi2NnK3JePixptABf1lP7BWupP3thQt3X+rF/2RJCktoxGjTS1QjW5egK6cmltXDhkM0B5XaLQGUpQT4fTvYPP7AzUsKyxKAkyLSEWYRHX1OymumgnOTrIlcgHjnBY3/l8Zay5+RKjiTu/bfCjTi5hlbaX47kVenIRa51tzTHSFH26y5Xj5r6hpboVzmEXItCaKcAlyq2elxpDisfrKgBgqs9tlVkRhKyVF2hhBWSLJAHRVbDygN+n657to4lWCrfCstywWcnXd8Sd3JlIdblqiYXE5lDsXj3Q7mtHKBfYCJVTrChyNtIUBKlZULgZCWzsCSpcfumC1ZCAYEXzib7Krf18WOteyJHHTUTlC1X0pCVi2TyxKOGHLeYbV74fYbUTtC2Xdr6qKpi2zp7gNFSwkYIYYZCEisRpSRMboN1XV+z1dbghlO5dv/qZ5fc5hqqWi1StRRmKEJqtvrGaKTL1aMrrpUglSkvQUjhVzTOamAoOw3IflhZg/S14/J0dbWIs1JCswDb0QXYPzqWiYDU6+Hjp5zXtMjRRXicVK1z1BAG7bV0KdeKD8VKA05mUnXKfRxyPN8uCVku+TUzAv1ZhM/Vr4UQpdvZ0RaR0MtHFuItNQfYg3e2JUiXEysRNCfK0FAoOp6TX55wrKzEWinRBlylWvs1tDfZ9nciQ0mK8jmFXLlCr2pIh6yqveJDsUJheeutVFNLbVrjyBsv41ddx1uFW4ASsb18JJkcoAX59w+MRM6nvqHpBClTN1hugqBr0mPax89Jjyk+ViLN0pu3ygbyKVdtHiHIqmwtp1XRcJkUfAj9gmvLe6lUa6o1HDyW2NpnoWKV6+yFiYTzLARcpYbm8AOIBe3zbQTNQcSvox3rilj4/o/eAYCI/LSA9JePjERxq0SwBK5AKRGAyJTuWap1aX6QyQRAkqCyKsYsrVtIucoU3yyKNFR1W2THt3MVT+cMIV95jpDr+pBGja5v2RT91eCH9trmee2CbK4UIhUSShUCGZ6lhWtJ5KkvEEKuBJftU7ailh54UqKuOFVXMRcefyptsjweVZ5PtoZxmRT4/fGWMRxSxeaNWXVVvJJNCq36CFbx6ZBCLxpCyY0X+CZo8b6hx7d19iQiDngZwsHJML9AjTCLhbYyCPkhO/zGX2ezuVohWPSY/0+jMmm8PN51Diq2kga+tFMOSaqSoHhKKp2TSPThB3aib2gaD97ZFilEvuSX5+c2Xm677Ruajp7zxACqL0CprXSurq0NESGTetb6b9FzqVrpC08Eltc0kAWyjgDF9BLhWwo2TURBHtVIY/m1rPTfUEjVO7hiT3AqbBYlmtb2eqPYajXbNN+W9wfMaxawSE8SbLmRRbly5xI9tyA7rspt1jHD5+eiZblGyHKbrOPqAhE0T1PlZEPn58t+CY1UycxAJEbnbOvsUUsSWgSbNQ5VHscJkMwT/F76B0ZiZCqdZ+XuvaURbFZYcz0zPBpTrZYDy3JWuQhRO4f2n4+X11lOhKvNWzr6Qu4n7z1XrM01L7giDAnHou2kAjkpa6FSXMXSOE5kMiaWF2fhyvPhB3Ym4lD5PfC5yygAzU6sjaXn3DlGoK4DnR1tUcQAbfM5p0KIU0NbZw+eefq5yL4KLLQHl/dDap0I/pmnn4vCz0KqZxUVvpXVRspB92q9RjxaIITUpMrSyMMiFRf5aNepdDOEpUL5/8XGsqgtAKRTrmmLs/gKW5NNU2t1DSyQr9zGIW2yMh7V5Xzjnn4Onvfvqg8QAquoNm+2l8fGycdQfC51q9WyzGQGmVYoPMT26Ys5TQtKfaVatrK5n3ZtbtfWsuGkvdUX0L9UCCVYjYz5trRkLeNLKwkum2tVkmseWEQrnUZaDytJSlo9AG3ZLRWp5pySJCorYElzgXY9LdxLc15JMpCZXKEOKhfRErHSD472oyLrJdB9dna0Yd2DfxAjzsUiWHkui2Clw0q+BwQiVgAYnJyrWBKpQYeLXKsmzjUPrBx8Dh6KZTmAOJHx1FQXUWmwCi5rGVla9wJOxJzwpdKmMXQtq9K/ZYeVSFtUhV5TmXQhwX8UouaJBcSh5ok7JfX62Q9/hl+MxQlWI3OpWMkpNjV+FADUKIEa0VYmqFiOLzV/2ShXoHzq1dXSRVOyD97ZZpoFAKgq0tX+msCVKDnJQojZZxLQyNF3TgJlX2VNEtBCmOjY1w68FCNYIG6KkZBz4JDFVSQ0wova1ijH0HhSpvx4Oo4f7wJ1dRhcsScaT9WwuGq14CPZGgmXD5JAJame/NtfLH+zAKEogrUyp6xx2nYiTVr+u5bn0fw9qpWbFGShFmusq/GgJGdtP493lT8GAFQ7J0HbbhGg9PgDyeQMIBmra1X4l9dJaxrgIVX3bp31VrCS42X/sM9++LPEOB9kucFQErUIdamJNk0JxCKvSZDFnziIGLX5uUjUNb5qyHWx7K4cGrkSGZDCBGA6u+Q2F7lKkvR1cZXqltslNSebVNZWA0OtPuzDD+xMXcwFSJKg9JTzHlQucwhtp8eReUBBiHLVCJDagw+MXQIAzLz3pnkNGk+YmBjH6ukB53gXBj9cm2gOuJzUaOhSuVpA91o15AoUS7C+UoGauYCDk5fl3ebFqH2NBjUFSqpS7pdE6VPJLvOCnKtrjjLm1SJcas+txaNyQpWhZ08+9ZhKwNp9u7K3XE0FCbQ0Dwng18ZdaSh1DshCqnQsQZ5j8MO1AOLdWKlo9lKQrlRxNwqJ+lAjVwZXYRW+XY7l0MjWKiFoee7lc5dd1ILPLKCdx1LHtI/bNckm+ke//83YuUJrCTzz9HOJFjHccy7D1Xg0AD8vmRC0ljMWQtUrLcuzNgvMiysNHRGx+ggXKJH8YqrbGom6cUOTq+Ys8UUH8PFW+xQJK5g/lPxoLKlHuaynfVLZhS75Zd1WuV1Tgrz+qM+rrrXU1hSohAw1k3OU26x6syH21xDTQKVBEi6wQLpkSiCE2G1DUSPVMFQNueZpVOjqc2XVXrVsq1aaqQSvD0CV//l2bSmuBdRrMbOaEpUqlzvFaCxXf0BY9SqXd95Vlf/ZN2bwxD31kYlA2nUlpCPNuj8rYkGbq3RS8aiC5UCuFjSTxOCHa9F+82UAJYXrK76toUaq6eAi1xsizjU0W0sbR0VTtO1aRhE3A9DzLbetMbO5eJNAqlUgibp/YCQWnsRrFvD58Wvw8USqw+fnEkv8EGikJYlKkuylrY9i89ABjPQfBlAqsWjVVaC5WiYQqwh3SHlE/gOQtv9VJWP19EBEsGRaIGIFSmaO5eocq1RYDVQt3BDKlUOaBkIUrc/GSrDSOKV6dJkIrGythx/Yie//6J1Y/Cedh9pla0qYxvngS2v1jeHQQppG+k/E1Degd6a1zBny+r5ygq7sLU25ksOK/ydo21022lAnWShC7cJEspqq5SFfFtH6YjprSOKGNgukaYkd4tSyiFZCLuuBuJec9vGuAb6asFpiAz+uf2YF7t++IjEny2zg87gD6YmVIPtFvXbgpdi9aokZVn1ZugeCK+7WmoPVv0prkS3JbCmdXmmuzZ1j/Ln8z8O+Lo6dTYRR3YikmlaVEqqGXIFiogVcapVghV9ZalZGBIQ0H9SKu8hC2CEKm85PCpbPQ8KKwQ0pyJK3XTZPHCD0z6zAt359Q1Bsq8tJ54IrLEuqVxeWKqLApYTTqmTuICMi5vbZG41U86JGrvNwJQQAet8tjfCsfTKAX0KGaPFryh5d8jrHZrdh/OQh8x60AHxXxhbfxlHODqtkGhg+PxetKPiPAcEXVUHjZTywBl8b7F/c/MUoWUASlY9sAVvdaorTUqEh211j8pC+VLu9R46i9/30jrAbFVVBruXIznJFCGhwxcNK0tRCuLjC5NeWTQWt5b/LLACEE5SLZEPtmhKu2FcZ+kUEK0PdrAgIl9LmkQhSgfsiGo4ePBA91ohU2l5d8Nlg+RjtufZYe66dO814ifrP3B39uHCiJdPBq4feCj6XRNal9nKCi1yXTbFsavOSpd2LBBWh5tWY+mdWxLbJgtkcsvEfKVitVKBs98IVKxXbJkVLKo4T+Jbb1kRtsR+8s019DOgJChZ86pU6A1htUjSEkDDNUXtNtaI39Lyzo+Sso9fuxVeO4ZmnnwNQUqzcUUZzdUUGrBt6HuuGno+KqFiExAurbN7SGv1x8G1nhkdjZGwRsxzPz6kp3ZBzuvbR9hcOvBn97z1yFGeGR3HoL74HIKlgN25sRdOuvfjG449krhVQ7cTqw7JRroTFri9gKUZ6frXpjmi5DthJCVKdErjitZIXAJsstVRYC75xedWrdoxMeeXmj5Cas77MNZn5pSVCWOFiXLkStGgBCblM1yILAGBqfAyNG1oS2wBE2+W1CfxYuU+bD1Ba0jduaFHnCJRU6omXfhyLfqBz0/X4eDqG11hwmQ3IEVaEYl0uqrcqzAIc5SbYkIpZvu4FFtny50TMPKLBigUNVaIWgYZUyCL4sqF84MfKubiqXWmpwa5aCNq8tJoGBM2x9fpQXURKLlKdGh/Drjt3O733XOkCJQKVBOsiY+2xRq4aQct59R4p1YnddefuaH/Trr2JlFpNtdNYALHUXB5xULPNllB15AqUt7ZrmhYx2nFW5MHw+Tls6N6LVZPvxpw6vir8BKvYC98fUl8gBKEFWiQs1UqwutPKeVpKlra50mDlfC2b68DYpZgzC0BMwUlwUgMWCJCIV4Nly5X7LUeaRrKSQGlOGoHTnPd+/duRag2BnANH/WfuxsTEOACg72fPY3ByLgrnGj11LJPi5Ep1uahWoEpsrhxFEatmv81CrATZUFA73/jJQxHZ8vvgWV0WsYYWdiEbJd9vHd8/MKJuzxN2xe20ZA7QOuFa4POh+XGbK9/PuzIAJVLlPwwu2yvVX+Ug4iRSkkpUW3bTNs1GOjU+FpET2WdpP5EjbZM2W36cPG/9Z+5G44YWdH3xUez9+rfR89BX8cKBNzE1PobeI0eja9HcOLHya8lrcmze0oqeh74KoKRcrzR04MzwKGbeezNStLvu3I1v/MP/Hd94/BEAcTsr7wzt6xLNj1suxOrDsiTXIpxaQL5aBdZz2cp7y21rsKF7L149VboWOc4kXPGwgLuDAZGpNcZFrC6kbaftqtXKf2RcqpzPX5oCeKYbXcuVMhviiCNMjY/F/mhJT/sOHz8V/dfMB71HjsaImBxGQJx0ifjODI/GrkHXAZIOL34OApHlv/9Xf4yZ997EzHtv4kuP/y66vvhoTEVv3tKKri8+ip6HvhpTupK4AUTkSduvNHREyv4LOzuj7VQOcbL3EM4Mj0Yq9pH9d8ccYCGE6SPd5YwbwiygZWmVE5bNVnsO6MVgtJKFgF5IOq2zyzIv8OW26/y+bC4AMQ++vGfXjwgH7xdmVe/i3WQ1uKp5SYeW5lDikKTLbbAE17K798hRp/lAs5+eGR7Fq4fewv17v5BY7stzcefTmeHRSHVOTIxjsvcQAKDnoa9i5r03E2P5/Vn2Xv5cs98CcdvsZO8h/OV/+o9Vo0Q1VJ1ZAAhXryHEWkSYlysZgc5L26X5gAiUmwZouzlnxSYp//h2XwysRrhtnT2R6g0J7D84ujk6P92Dq/GgD888/VzMxEEkWlSiQ/1n7k4sw4lAOLHSdiIz2ifV6uYtrZE67D1yNPrj5gMtXEvaUcmUQOeSxMqX+1zZzrz3Zux8ExPjOPHSj9HcvCE6PxHqiZd+jCsNHaj/zN0JE4JUy3SPvUeO4kpDBzZvaY3ImkD3uXp6IDbPb/9PT+Ebjz+CXbdvT5gJ6Plit4VZLCxb5Qr41WsosS6FquXPQyBTaSkYX/bt0uDL2qLtVgqqHGuRGzXic6lWuhcXeJNGWdPWdX0Oq54r307K1VKs0nE0fvIQNnSXvOhaKBVQIuxDf/G9mJeezkXOr8HJOezZsT1xrAypsiIJSLXSOHpOanHzltYo7IqrV62Qizw3Pb/S0IHm5g048dKPI5XKz08E3nvkKLq++GhEqlLRuqIMtBoGeZxiEovRNaEqowWApemp5QNXv9r8XDUMXKm2GqwEAgsy/ZbbMAG90La1XeuNNbhiDz45+ZNYzC5Hmspi2ty1ho2htWa1TC2fWQCIO7i4mj18/BTamxbuhxMyD4MiEnrhwJux8QRJwvwcjRtacPj4KezZsR1Nu/ZGnvk9O7ZH22luRLbSVKCFZPHlPyUW7NmxPSJ3IlYyH5x46cfRvGi/RtQ0bx6ZwAmXXosrDR346x/+W5VEiyTXxUBVmgUAexlflMMrDfpnVpjE6lKn0jzgg6wVC4Tl4vNj6XH/wEiMnPgSPPS8I/0n8PpQHQZX7Im+ZJxY5evimpP2nObD69bSfacxD8iogXVDz2P3vv3qWDIBcDIdnJyLObz27NgeIzJydgElouX76j9zd0SEFHHAIw8A4Dd/Z6HOLjm7OIETmdF5HtlfWs4fPn4quhb9J8fUmeFR7P36t7H369+Oztvz0Fdj192zY3t0DXr/JnsPReYD+s/trX0/ez7RIYGIdNeduyMypu00d25C+dLjv4vf+o3fRn1DS8w8MDM9tmyI1YdlrVwJRGR8ib+Yy31JHtZ10yQYhELWj7VgKUJfppNWH5acR1y5Uj+q2eM/jWJ4AfePTKhjS5svIbS1tgQlEADx4H9SdKT4pO1VqjRpQpDKUDM50LUGJ+fQ3rQGg5NzeGR/qZMsqU+6PjchvHroLXz3T/4U/+af/1FCKTduaMG15hLhrZwYSMTFuuJWrUQCzWkmTRNXGjrw7//VH0ehWC5nlzYfenz4+KllWZGrapUrwaeO0p5Le2w95yRCf4A/XEt7zJ9r9Q20egdS7dJzvl1begN6gD7PipJtYvgxFBmgEZQPvh8BOX/L2SYR0mWA6grwUoPcWcXDpGSoFL9HUmmEP/vhC5EzSjqGZLjV4OQcGje0RKS5Z8d2vHDgzYQjqXFDC9qb1kTE943HH8Ff/fkPACAKDfuzH74QEXn7zZfRfvPl6B6kw61p195oWU7OPC0qQDMlcDLkr9df//Df4u/+g++oEQUu9Dz01Zjt9kuP/y7+7j/4TkzJAoieL8eQrZv+8A//0Nz5L//Fv7B3Viimr92E6Ws3AQAaV30CoESC1uPpazfFnnfWX4+202PaT/9prLwu339xLqnYrBoFF+euY/3aFdEx/PGW29bg4tz1xPP1a5M/JlMXrkTE1XTLavQNTaPpltWYunAl9rzpltWlOU9fQmPDOjQ2rMOl6fNY13gbtnW2493DR9HZ0YZf+eQ6pqcvRecn0l2BUvuQwRV7AAD/7cJKXLo4i6H/70A0T/ke0H3JeQKI5kX7pi5cie6BO7NovgCwrvE2XJo+j6Mz29Cw67ew6kKJ7K/esiPxunC80zuM/3ZhJc4Mj+LSxVmcfn8AHxz7KW7r/CzGzo3h+selcZu3tGL9+vro+dT4GK5cno3ZYMfOjaGrpxNtn/s76NlYh+sfA109nTj9/gDW1NVF43bduRun3x/Arjt34/rHwM2/vIo1dXV49dBbaGm8FWvq6rCp+daImHse+ioujw9Hz+l869fX49LFWXT1dKKncwuufwzcu3cPLl2cxfr19TgzPIq2z/0dXPxkFRpXXkfn/V9Fw6r5z9T6eqy4UvrBGus7jvODJ6L7jN6f9fW40tCBni/8On5Z34iPVjdgxZVpXLpYKnBz+v0BtGxaqFNw8ZNV2NlWF41ZuW03PlrdgNXTA1i/vh5AiWTp8ZWGDjSuLM1n8BeHsX59fXTNc+dGUV9fj51tdbj/d/5X9GysQ9e+38Qbf/kXqLulGdc+vBz9rbx5LWYvTGDlzWud73U5QNel/9/+1v/4T62xVaFcNRAxyjAruZ3v0x7X3dKcOI+mkl0mCK42tWW/q/UM387/uwpw05+0z2rxtC++cixmEhjpP4H79j9kJh1oKvLM8ChOHHw+em3odQNs+7eVtWXdF4WFyW3t1w+r4y2cGR5F/WdKS3DppOLE+b3/92ns3rcfm7e0qs6tvV//dqTeuI2RlvVEWoOTczFFyoP3v/H4I6pdd+a9N2OhUY0bWtC0a2/MQ8/vh7KyAODQX3wPk72H0HvkaGJeXF2SipVhYWRvnew9hMneQ1GoFhCP66UxBArP4gW4abulYmn7uXOjaL/5cuT0mnnvzchp9j/8H/83dt2+Ha3bSyso+lyVwy5LCplUsnye9rpVYXOVIALNO77ulubohaUx2tgstt00NQx8tlqC1ZlWG6PZUcnj3379sLfX16e6vxJ9sVdNvgsA+JvpWxPLN8tM4pq71aLm4OhmbLr8dixh4PWhOuzetz/y/FMZQcCugsU9+LQ0l2FQMu1VgtsjuX2S22rlEnlqfAy975/Crtu3J9JsCYOTc/hnf/zP8G/++R/Fog3+7IcvxI5zhWlZ87fmLMH3ycgAIBktwF9Tvo+HbklYLWks0H66T5mcQPUIstQlCE3NpXGcF86MjFRnKNZSIC+5Wm1bQhxwWnsZlxNMK29oOb9IFVI4FQCcXXsXNl1+G2fX3oWp8THsrDsdbacv9ImDJTvn6E2b1A9pKLmGzJFfm0hU64F179YIbz2NAAANaklEQVTZiFiPHjwQjf3u8/0A9MIr5FTihEveel7dikKgZJEU7b8W86qNo32kiGfeezMK3ZIZYb1HjsacXNI55gMRNkU20DGcZLky5gTP429pHH9tCNKZJh8Ddn8vuR+ItwynfUCpaEzv+6dixAogRrJyX9GokWtO+JRwEVEJPnKVKbWhhKrt05QhsKBegbiT69jsthixAqUv9fjJQ5i97Q6Mnop3deWwmkOGgM/z7Nq7MHv8p7jadAd23bkb926djSIUAODEwefRs69EqC7vOKV90n+ZHeUrD8gjBHisKUGWJuTHSrWoOZP42BDnkCuCgZQygEj1upbp/HWSUQ5Sccv75cfwHwN+PpofJ2xAV7Hnzo1GUQ+usfy8sgTiYhBsjVwLgkWyecg1TciYRbBWgW4XkUlVSCr1U91ficwCfUPTqNvx5YhIe/Y9itnjP43Ob6lViTT3J+fetbUhUs5kfiCC7T1yFKsm38XVpjvw88PvoHX7zlgwPEGS5quH3orm/Wt7Pp/4ssslNj3mIOLgZMHV7SP7744UIilQfk6uOL/0+O9GS16tbqycjyRUDk0tWvZPTviAbsrg1wgJM5Pz5PPikMdb9WMJ/EfjWnNHTMny8b1HjkafhfamNfj54XdqynU5g5OHRpYWgXIbblbTghWJ4CoSk5j/vHo9NrstEedJtWelKSJr6Jv8gZKvndzG6+D+/PA70Xb68mjLUYIVHkRfbCpioo2z7JhW3Kt8Lou7aLZVSbq975+KUjblvfmuaRWaIUg7LN8uXz9L2fLUXau2rTyPRu6bt7TGlvr8B4Fvp+U/mQ3oMVXlar/5ciyuVtplqf9XTblWCSRZaoTBt/NjtHFFg4p1k22TlCmAqF3N1aY7ACBSikSmWT6gaY/hy7j6hhZViRIsm55PJQHJav6uZTk/J5GYTOmU17UIncAVLdk/ibQsBxrNl59fzk9en+/Xzm2ZCbR74vPUYL1PEkSglhNLvvYaJJFazrRyE2zVk+tiGrDzQpIuYBOQT+VpRO5Twi6b7Ql0o+78uxi9aRPqG0oB7NxhQOmJtA9AzKbHK9IDpYIZ0k6n2SlfPfQWvvH4IwnV5ypeIpe0pKg01eRSZFyVarZSqfasJTHt48+5uUG7NwuukobSnhkS8iTVrTYH63Wz1D+fj9YnTCNxHyxTRyjk8aR2JckWiULJNasScW0LCangnkAOa7vcn3aOi43QObhCwkLO21l/HbO33RGlXUoPs+aJpu3Sc86/pNoy2BrDodnmNBvfq4feUh0qBPnllstxa94yblRb9vMcf61Ai7yPEMLj++X9y+W8S0ETtPuka/DXNVSlclhq2rUvJGKAw1op8HPlxQsH3oyKwhT1fc9Mrt2/+tlfSvIK7XUze2ECrdt3FlbhRrsWL1kmy5eFxrv57iHLPmu/9oa63mjtR4heV61UG4DY68GXmZZzRnNC+BSQhFWmTxIO36eF+PDzkFNIwlpK8hAlbZ4+04Ac50rl1eyMPmivv7VE167t+rGQ55dw2WX5fpfN2mXysO6J/wDJKIzFhLTvcodmXuQiV2ufVotRIoTgfGP4UlS27eWEwsfxbQS+nB2cnIttp3kQacniva5zcdB5te1ybnz+fIlN21zqUQuL8RGFyxFDx7sC230xlD7ikCX6tGNom/xBsJan2rUswslCEFYigU+Ny32aM8w6v8tsYN2vvGaII4y2AflVooyKcBG9NR+Xvdx1Xd9+6zxEskA+U0Fu5RqqPK3itwQXGWuExs8p90uSdG0DECvQS9vkWD6eVyyyxlsk6/piyPPI6/HH8tdeU4PWdVzhMi7PtQyjkXGT2j26nDDWstaypWrLXZei1uyDFqxzW/fG7bkAYq+RC9aPlG9fKEIiF/h2jlClHQrL/sznGnKcFVVAc3aZXOQ9yqSDM8OjsYph/BhOskB6oi1UuUoC9REqgZwbBEkwFnHJx/RcXosTnEVechwf7yJS7UulETOAWJgKEKZAfPbJUDXgg09JaeNDvcva+emx67y+eVhhUDQXQCcMn4PFmk/IGOverB8Cyz4qYX2mXD/iIeYcOUd+vOt+Qo93jZFz9R2rgb/P15o7IoLk+/m9UxysRr50Dk6+gx+uxcx//XkkwNIQrItcUxdukUtaekx/2lgCfXisDxGwQH6uvjrW9axz0WONxPk4bTyBPsRULo6P4WMtApa/rtqHi95c15eX/qjkHN+nwVo28S+WPHbzloWeSvyagL0U27wlWWDaAv+iDU7ORfcyNb7Qhtq6NzkXmg/98Xum/7yQijbv0Dnzx9oxtE2SXBoimZkec4oVOpdvdRQC7b0vEoOTc9FfEdfZvKU1KqlISlSDJF/tHBK77tyN+/d+IfccOZzKdc89X/qltiQnWPt8JBa6nJe2Pk3t0geRq2IL9KXmKlZel5SnpmZ9is/1AZJLSzk+ZJkt98klsW95zo/R5q2dF4jbN31RAxKWbdOyYUovPD+vz8mkEWjI62MhVGmFEodrdaJtc9m6syhxa4UU+nm25uyah4VQmzKHVJy+SAwaY/2w8mQEvm3mv/48+r6Sn0dD3S3N2Xtodf/qZ39pOXdcdktAtyW6IAlPcwpxQvU5sFzL/7SQx2lfdknCEmmcID5VQvtlaqXrmmn20f4Qe5nlqAmxB7rmTK+nFutpHQNkux/fOQlpba8arON8805zDD/OhSwmGdc2H0LnBLjvLSRu2WWvJZKWtlkC3041C6xw0sw21z33fCnaaXnbJXzq1Ed6rnNbDjFLPaf5AmhKle/j5w09F8FSaSEk61KMHCG2UDkfl/lBG0/7tONdtlF57jRL2lBlZNnUsxBqVuKU13URYpb75UhjJ9WO810zLZkvFvI46HyOLwu06tEqcBVCroAdkiTBidLn6U/juecefzkvgox91bz//NwSeYmUEKomKaWQrmfNj8K0rI6jrqW+LC8X8sW3HDD0nmpmmNAvXxYlG+IQ4nPUHJS+Hzy5PeRaEnltoXnMD0WZLlzjXfuzEn0W0k7zGbKOB8IJlsaS44tINrNZQJKrhObcIvgULo2JJi6IOEThushdlh+jebrIPiSywIWQZZKLuAiyqIc1Truuda608+fPCdIcoI3R4Psi+Gy3WWyaBOtH2/faWqsNbbzrfS+XAgbcPxgSaVYUeeacxkQV4iNwwfe50iILXMeHxvhSXO/M9Fj5yBXQw6JCoC3BtVhUIL3d1gfNZsvnxJ+7ruGzK4fal9JCm0PWpbaPOOQxVoqidT4LLluidf0syi2tYuPHhNrAreunvcdQpHUs8bm49vvOnee1LGKOeeAjWmsM/7xzB9jgh2tx+uRxvPwf/qQ4h5YGuQxPGykQTZ55/zWC1Y6R0MwNliJ12YBd1/GRvc+5RQhVo1kUhfySuH4ALBLQrptWmYXYm12fCdc9hV7XhzQk6BuXluDTXFc7l+uzZtnHs6Ic5o0szrqlgFZDFgC+84/+SbHkGhLU70MIiQJ6ckDoMjf0S6sdpznEfCTHTRH8vtIoWJ9TyKUw0v4oWPdZbqSxn5bjOlmUe57rl+se05oFrHllcfpZKGK1VmnEyiFNB2UhV4kQEia44lq1sQTLKeVSkpotNy3p0ngt318bZ503jVfeNy5EbZWbxKSdVItwAMLeJ5fq4tu0a1vzkvev2bLleeVcilCz1jGuOWRBnvc7bZQCHeNb9qeNwKhkYiXwOPBFIVcgvc0VCLeThhKgSwVb+0PPneb4UK9qqO3OOm/aL3YWNaWRYlpy4EHZBFddhrzOIR/5SvAfhnITn4Ws1ylyvkWQ21JeX/tRLSdhf+/p75nk+mnXgb4UVBktkCZ6QPvCuojRR4R0fpc6yhsNoM1LIwefKYETo0YC1geidO3k2DTwqWVNmbgI0EJy/vNqFnEHom9+vuukhfVFC1V9eUwoaZxjWc5bRGSCPG/aY/j2LPNJ66jzzadcxBpyb6mjBXwhVi6CdcG1/LfGcViE6Up15efMkkkWgpAvbRFfjDz2VJ9t0rLNuZbaFtIuwbO+NlkVvTWPrNdxqWTXtbJmE6aFz7ST5jyEckZJ+BBiR86qZrX7+dFPfly+UKyi4IoMCDUdyPPIc/H9VhJD3g+0z8NetApbLEdUiIMtFIvx2oRgMQjMR67avqVCqENsKVGE2SD0XCH37CLXoKpYvqLY5QC/ZpovQAgRu4jUOj6NMtOW+WmQRQWm+QFywTqP677yLN2W8ku7GMowxObO/xYDZD6TyPu5JZC5qxwo2vThOr/rPkLusazKVQvKD13qhzqogCT5+wq4WChKvZb7nIBftRZVpCbrfLJ+uazIiCywji0iRMplMgk1wxRpfghBmnofWVAORyCQP2PSh1BTgnbuzA6tvAhRU75lfMibb8XcZlW8i2Xv0uD64i3GvLKeX9oRpd3N8sSHnNeCj3B8qjEPGYQ6IUPnxaG9z0UQVdakmDQoyt7Kv4/0ne6dLj23/DnWaxb6PrveQ3lPIe+3U7nWUEMNNdSQDak7EdRQQw011OBHjVxrqKGGGsqAGrnWUEMNNZQBNXKtoYYaaigDauRaQw011FAG1Mi1hhpqqKEM+P8Ba5ZUs1HrD1MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.close()\n",
    "plt.axis('off')\n",
    "plt.imshow(compressed_image_reshaped)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### With scikit-image\n",
    "\n",
    "I used scikit-image because openCV was ridiculously increasing the file size, even though I only read the image into matrix and write to a file again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compress_by_k_means(image_name, compressed_image_name, n_clusters = 8):\n",
    "    start = time.time()\n",
    "    \n",
    "    image = io.imread(image_name)\n",
    "    orig_shape = image.shape\n",
    "    image = image.reshape(-1, image.shape[2]) / 255 # Normalization. It improves the performance so much!\n",
    "    img_shape = image.shape\n",
    "    \n",
    "    \n",
    "    \n",
    "    model = KMeans(n_clusters=n_clusters, init_method = 'var_part', tolerance = 0.01)\n",
    "\n",
    "    cluster_means, image_data_with_clusters = model.fit(image)\n",
    "    \n",
    "    compressed_image = np.zeros(img_shape)\n",
    "\n",
    "    for i, cluster in enumerate(image_data_with_clusters[:, -1]):\n",
    "        compressed_image[i, :] = cluster_means[ int(cluster) ]\n",
    "        \n",
    "    compressed_image = compressed_image * 255\n",
    "    compressed_image_reshaped = compressed_image.reshape(orig_shape).astype('uint8') # Can't write float type matrix to an image file\n",
    "    io.imsave(compressed_image_name, compressed_image_reshaped)\n",
    "    \n",
    "    end = time.time()\n",
    "    \n",
    "    print('Compressed in', end-start, 'seconds') # Calculating the time elapsed while compressing the image. Depends on n_clusters and file size and also on the computer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compressed in 0.48164916038513184 seconds\n"
     ]
    }
   ],
   "source": [
    "compress_by_k_means('example_datasets/car.png', 'example_datasets/car_comp.png', n_clusters=8)\n",
    "\n",
    "### Original Image: 221 kb. Compressed Image: 18 kb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compressed in 2.8309500217437744 seconds\n"
     ]
    }
   ],
   "source": [
    "compress_by_k_means('example_datasets/karakoy.jpg', 'example_datasets/karakoy_comp.jpg', n_clusters=8)\n",
    "\n",
    "### Original Image: 167 kb. Compressed Image: 125 kb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compressed in 24.668309688568115 seconds\n"
     ]
    }
   ],
   "source": [
    "compress_by_k_means('example_datasets/dog.jpg', 'example_datasets/dog_comp.jpg', n_clusters=16)\n",
    "\n",
    "## Original Image: 533 kb. Compressed Image: 412 kb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compressed in 9.435445785522461 seconds\n"
     ]
    }
   ],
   "source": [
    "compress_by_k_means('example_datasets/wolf.jpg', 'example_datasets/wolf_comp.jpg', n_clusters=16)\n",
    "\n",
    "## Original Image: 228 kb. Compressed Image: 183 kb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compressed in 13.793962717056274 seconds\n"
     ]
    }
   ],
   "source": [
    "compress_by_k_means('example_datasets/rabbit.jpg', 'example_datasets/rabbit_comp.jpg', n_clusters=8)\n",
    "\n",
    "### Original Image: 884 kb. Compressed Image: 689 kb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EEG Recordings Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
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       "      <td>-15</td>\n",
       "      <td>-31</td>\n",
       "      <td>-77</td>\n",
       "      <td>-103</td>\n",
       "      <td>-127</td>\n",
       "      <td>-116</td>\n",
       "      <td>-83</td>\n",
       "      <td>-51</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>X15.V1.924</td>\n",
       "      <td>386</td>\n",
       "      <td>382</td>\n",
       "      <td>356</td>\n",
       "      <td>331</td>\n",
       "      <td>320</td>\n",
       "      <td>315</td>\n",
       "      <td>307</td>\n",
       "      <td>272</td>\n",
       "      <td>244</td>\n",
       "      <td>...</td>\n",
       "      <td>164</td>\n",
       "      <td>150</td>\n",
       "      <td>146</td>\n",
       "      <td>152</td>\n",
       "      <td>157</td>\n",
       "      <td>156</td>\n",
       "      <td>154</td>\n",
       "      <td>143</td>\n",
       "      <td>129</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>X8.V1.1</td>\n",
       "      <td>-32</td>\n",
       "      <td>-39</td>\n",
       "      <td>-47</td>\n",
       "      <td>-37</td>\n",
       "      <td>-32</td>\n",
       "      <td>-36</td>\n",
       "      <td>-57</td>\n",
       "      <td>-73</td>\n",
       "      <td>-85</td>\n",
       "      <td>...</td>\n",
       "      <td>57</td>\n",
       "      <td>64</td>\n",
       "      <td>48</td>\n",
       "      <td>19</td>\n",
       "      <td>-12</td>\n",
       "      <td>-30</td>\n",
       "      <td>-35</td>\n",
       "      <td>-35</td>\n",
       "      <td>-36</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>X16.V1.60</td>\n",
       "      <td>-105</td>\n",
       "      <td>-101</td>\n",
       "      <td>-96</td>\n",
       "      <td>-92</td>\n",
       "      <td>-89</td>\n",
       "      <td>-95</td>\n",
       "      <td>-102</td>\n",
       "      <td>-100</td>\n",
       "      <td>-87</td>\n",
       "      <td>...</td>\n",
       "      <td>-82</td>\n",
       "      <td>-81</td>\n",
       "      <td>-80</td>\n",
       "      <td>-77</td>\n",
       "      <td>-85</td>\n",
       "      <td>-77</td>\n",
       "      <td>-72</td>\n",
       "      <td>-69</td>\n",
       "      <td>-65</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>X20.V1.54</td>\n",
       "      <td>-9</td>\n",
       "      <td>-65</td>\n",
       "      <td>-98</td>\n",
       "      <td>-102</td>\n",
       "      <td>-78</td>\n",
       "      <td>-48</td>\n",
       "      <td>-16</td>\n",
       "      <td>0</td>\n",
       "      <td>-21</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>-12</td>\n",
       "      <td>-32</td>\n",
       "      <td>-41</td>\n",
       "      <td>-65</td>\n",
       "      <td>-83</td>\n",
       "      <td>-89</td>\n",
       "      <td>-73</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11495</th>\n",
       "      <td>X22.V1.114</td>\n",
       "      <td>-22</td>\n",
       "      <td>-22</td>\n",
       "      <td>-23</td>\n",
       "      <td>-26</td>\n",
       "      <td>-36</td>\n",
       "      <td>-42</td>\n",
       "      <td>-45</td>\n",
       "      <td>-42</td>\n",
       "      <td>-45</td>\n",
       "      <td>...</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>-1</td>\n",
       "      <td>-18</td>\n",
       "      <td>-37</td>\n",
       "      <td>-47</td>\n",
       "      <td>-48</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11496</th>\n",
       "      <td>X19.V1.354</td>\n",
       "      <td>-47</td>\n",
       "      <td>-11</td>\n",
       "      <td>28</td>\n",
       "      <td>77</td>\n",
       "      <td>141</td>\n",
       "      <td>211</td>\n",
       "      <td>246</td>\n",
       "      <td>240</td>\n",
       "      <td>193</td>\n",
       "      <td>...</td>\n",
       "      <td>-65</td>\n",
       "      <td>-33</td>\n",
       "      <td>-7</td>\n",
       "      <td>14</td>\n",
       "      <td>27</td>\n",
       "      <td>48</td>\n",
       "      <td>77</td>\n",
       "      <td>117</td>\n",
       "      <td>170</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11497</th>\n",
       "      <td>X8.V1.28</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>-13</td>\n",
       "      <td>-16</td>\n",
       "      <td>10</td>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
       "      <td>-9</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>-65</td>\n",
       "      <td>-48</td>\n",
       "      <td>-61</td>\n",
       "      <td>-62</td>\n",
       "      <td>-67</td>\n",
       "      <td>-30</td>\n",
       "      <td>-2</td>\n",
       "      <td>-1</td>\n",
       "      <td>-8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11498</th>\n",
       "      <td>X10.V1.932</td>\n",
       "      <td>-40</td>\n",
       "      <td>-25</td>\n",
       "      <td>-9</td>\n",
       "      <td>-12</td>\n",
       "      <td>-2</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>19</td>\n",
       "      <td>22</td>\n",
       "      <td>...</td>\n",
       "      <td>121</td>\n",
       "      <td>135</td>\n",
       "      <td>148</td>\n",
       "      <td>143</td>\n",
       "      <td>116</td>\n",
       "      <td>86</td>\n",
       "      <td>68</td>\n",
       "      <td>59</td>\n",
       "      <td>55</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11499</th>\n",
       "      <td>X16.V1.210</td>\n",
       "      <td>29</td>\n",
       "      <td>41</td>\n",
       "      <td>57</td>\n",
       "      <td>72</td>\n",
       "      <td>74</td>\n",
       "      <td>62</td>\n",
       "      <td>54</td>\n",
       "      <td>43</td>\n",
       "      <td>31</td>\n",
       "      <td>...</td>\n",
       "      <td>-59</td>\n",
       "      <td>-25</td>\n",
       "      <td>-4</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>-2</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11500 rows × 180 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0   X1   X2   X3   X4   X5   X6   X7   X8   X9  ...  X170  \\\n",
       "0      X21.V1.791  135  190  229  223  192  125   55   -9  -33  ...   -17   \n",
       "1      X15.V1.924  386  382  356  331  320  315  307  272  244  ...   164   \n",
       "2         X8.V1.1  -32  -39  -47  -37  -32  -36  -57  -73  -85  ...    57   \n",
       "3       X16.V1.60 -105 -101  -96  -92  -89  -95 -102 -100  -87  ...   -82   \n",
       "4       X20.V1.54   -9  -65  -98 -102  -78  -48  -16    0  -21  ...     4   \n",
       "...           ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   ...   \n",
       "11495  X22.V1.114  -22  -22  -23  -26  -36  -42  -45  -42  -45  ...    15   \n",
       "11496  X19.V1.354  -47  -11   28   77  141  211  246  240  193  ...   -65   \n",
       "11497    X8.V1.28   14    6  -13  -16   10   26   27   -9    4  ...   -65   \n",
       "11498  X10.V1.932  -40  -25   -9  -12   -2   12    7   19   22  ...   121   \n",
       "11499  X16.V1.210   29   41   57   72   74   62   54   43   31  ...   -59   \n",
       "\n",
       "       X171  X172  X173  X174  X175  X176  X177  X178  y  \n",
       "0       -15   -31   -77  -103  -127  -116   -83   -51  4  \n",
       "1       150   146   152   157   156   154   143   129  1  \n",
       "2        64    48    19   -12   -30   -35   -35   -36  5  \n",
       "3       -81   -80   -77   -85   -77   -72   -69   -65  5  \n",
       "4         2   -12   -32   -41   -65   -83   -89   -73  5  \n",
       "...     ...   ...   ...   ...   ...   ...   ...   ... ..  \n",
       "11495    16    12     5    -1   -18   -37   -47   -48  2  \n",
       "11496   -33    -7    14    27    48    77   117   170  1  \n",
       "11497   -48   -61   -62   -67   -30    -2    -1    -8  5  \n",
       "11498   135   148   143   116    86    68    59    55  3  \n",
       "11499   -25    -4     2     5     4    -2     2    20  4  \n",
       "\n",
       "[11500 rows x 180 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('example_datasets/epileptic_seizure.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>29</td>\n",
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       "      <td>48</td>\n",
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       "      <td>-12</td>\n",
       "      <td>-30</td>\n",
       "      <td>-35</td>\n",
       "      <td>-35</td>\n",
       "      <td>-36</td>\n",
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       "      <th>3</th>\n",
       "      <td>-105</td>\n",
       "      <td>-101</td>\n",
       "      <td>-96</td>\n",
       "      <td>-92</td>\n",
       "      <td>-89</td>\n",
       "      <td>-95</td>\n",
       "      <td>-102</td>\n",
       "      <td>-100</td>\n",
       "      <td>-87</td>\n",
       "      <td>-79</td>\n",
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       "      <td>-80</td>\n",
       "      <td>-82</td>\n",
       "      <td>-81</td>\n",
       "      <td>-80</td>\n",
       "      <td>-77</td>\n",
       "      <td>-85</td>\n",
       "      <td>-77</td>\n",
       "      <td>-72</td>\n",
       "      <td>-69</td>\n",
       "      <td>-65</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-9</td>\n",
       "      <td>-65</td>\n",
       "      <td>-98</td>\n",
       "      <td>-102</td>\n",
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       "      <td>-16</td>\n",
       "      <td>0</td>\n",
       "      <td>-21</td>\n",
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       "      <td>-12</td>\n",
       "      <td>-32</td>\n",
       "      <td>-41</td>\n",
       "      <td>-65</td>\n",
       "      <td>-83</td>\n",
       "      <td>-89</td>\n",
       "      <td>-73</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>11495</th>\n",
       "      <td>-22</td>\n",
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       "      <td>-42</td>\n",
       "      <td>-45</td>\n",
       "      <td>-42</td>\n",
       "      <td>-45</td>\n",
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       "      <td>5</td>\n",
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       "      <td>-37</td>\n",
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       "      <th>11496</th>\n",
       "      <td>-47</td>\n",
       "      <td>-11</td>\n",
       "      <td>28</td>\n",
       "      <td>77</td>\n",
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       "      <td>211</td>\n",
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       "      <td>240</td>\n",
       "      <td>193</td>\n",
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       "      <td>-94</td>\n",
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       "      <td>-7</td>\n",
       "      <td>14</td>\n",
       "      <td>27</td>\n",
       "      <td>48</td>\n",
       "      <td>77</td>\n",
       "      <td>117</td>\n",
       "      <td>170</td>\n",
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       "      <th>11497</th>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
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       "      <td>-16</td>\n",
       "      <td>10</td>\n",
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       "      <td>-30</td>\n",
       "      <td>-2</td>\n",
       "      <td>-1</td>\n",
       "      <td>-8</td>\n",
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       "    <tr>\n",
       "      <th>11498</th>\n",
       "      <td>-40</td>\n",
       "      <td>-25</td>\n",
       "      <td>-9</td>\n",
       "      <td>-12</td>\n",
       "      <td>-2</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>19</td>\n",
       "      <td>22</td>\n",
       "      <td>29</td>\n",
       "      <td>...</td>\n",
       "      <td>114</td>\n",
       "      <td>121</td>\n",
       "      <td>135</td>\n",
       "      <td>148</td>\n",
       "      <td>143</td>\n",
       "      <td>116</td>\n",
       "      <td>86</td>\n",
       "      <td>68</td>\n",
       "      <td>59</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11499</th>\n",
       "      <td>29</td>\n",
       "      <td>41</td>\n",
       "      <td>57</td>\n",
       "      <td>72</td>\n",
       "      <td>74</td>\n",
       "      <td>62</td>\n",
       "      <td>54</td>\n",
       "      <td>43</td>\n",
       "      <td>31</td>\n",
       "      <td>23</td>\n",
       "      <td>...</td>\n",
       "      <td>-94</td>\n",
       "      <td>-59</td>\n",
       "      <td>-25</td>\n",
       "      <td>-4</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>-2</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11500 rows × 178 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        X1   X2   X3   X4   X5   X6   X7   X8   X9  X10  ...  X169  X170  \\\n",
       "0      135  190  229  223  192  125   55   -9  -33  -38  ...     8   -17   \n",
       "1      386  382  356  331  320  315  307  272  244  232  ...   168   164   \n",
       "2      -32  -39  -47  -37  -32  -36  -57  -73  -85  -94  ...    29    57   \n",
       "3     -105 -101  -96  -92  -89  -95 -102 -100  -87  -79  ...   -80   -82   \n",
       "4       -9  -65  -98 -102  -78  -48  -16    0  -21  -59  ...    10     4   \n",
       "...    ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   ...   ...   \n",
       "11495  -22  -22  -23  -26  -36  -42  -45  -42  -45  -49  ...    20    15   \n",
       "11496  -47  -11   28   77  141  211  246  240  193  136  ...   -94   -65   \n",
       "11497   14    6  -13  -16   10   26   27   -9    4   14  ...   -42   -65   \n",
       "11498  -40  -25   -9  -12   -2   12    7   19   22   29  ...   114   121   \n",
       "11499   29   41   57   72   74   62   54   43   31   23  ...   -94   -59   \n",
       "\n",
       "       X171  X172  X173  X174  X175  X176  X177  X178  \n",
       "0       -15   -31   -77  -103  -127  -116   -83   -51  \n",
       "1       150   146   152   157   156   154   143   129  \n",
       "2        64    48    19   -12   -30   -35   -35   -36  \n",
       "3       -81   -80   -77   -85   -77   -72   -69   -65  \n",
       "4         2   -12   -32   -41   -65   -83   -89   -73  \n",
       "...     ...   ...   ...   ...   ...   ...   ...   ...  \n",
       "11495    16    12     5    -1   -18   -37   -47   -48  \n",
       "11496   -33    -7    14    27    48    77   117   170  \n",
       "11497   -48   -61   -62   -67   -30    -2    -1    -8  \n",
       "11498   135   148   143   116    86    68    59    55  \n",
       "11499   -25    -4     2     5     4    -2     2    20  \n",
       "\n",
       "[11500 rows x 178 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df.iloc[:, 1:-1]\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_clusters = df['y'].unique().size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = KMeans(n_clusters=n_clusters, init_method='var_part')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.3206572532653809"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = time.time()\n",
    "clusters, labeled_X = model.fit(X)\n",
    "b = time.time()\n",
    "\n",
    "b-a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15794238.290983565"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.cost_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.798281192779541"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = time.time()\n",
    "model = KMeans(n_clusters=16, init_method='forgy')\n",
    "model.fit(X)\n",
    "\n",
    "b = time.time()\n",
    "\n",
    "b-a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15017239.790582392"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.cost_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Letter Recognition Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>19999</th>\n",
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       "<p>20000 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      0   1   2   3   4   5   6   7   8   9   10  11  12  13  14  15  16\n",
       "0      T   2   8   3   5   1   8  13   0   6   6  10   8   0   8   0   8\n",
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       "4      G   2   1   3   1   1   8   6   6   6   6   5   9   1   7   5  10\n",
       "...   ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..\n",
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       "19996  C   7  10   8   8   4   4   8   6   9  12   9  13   2   9   3   7\n",
       "19997  T   6   9   6   7   5   6  11   3   7  11   9   5   2  12   2   4\n",
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       "19999  A   4   9   6   6   2   9   5   3   1   8   1   8   2   7   2   8\n",
       "\n",
       "[20000 rows x 17 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('example_datasets/letter-recognition.data', header=None)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19995</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19996</th>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19997</th>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19998</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19999</th>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20000 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       1   2   3   4   5   6   7   8   9   10  11  12  13  14  15  16\n",
       "0       2   8   3   5   1   8  13   0   6   6  10   8   0   8   0   8\n",
       "1       5  12   3   7   2  10   5   5   4  13   3   9   2   8   4  10\n",
       "2       4  11   6   8   6  10   6   2   6  10   3   7   3   7   3   9\n",
       "3       7  11   6   6   3   5   9   4   6   4   4  10   6  10   2   8\n",
       "4       2   1   3   1   1   8   6   6   6   6   5   9   1   7   5  10\n",
       "...    ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..\n",
       "19995   2   2   3   3   2   7   7   7   6   6   6   4   2   8   3   7\n",
       "19996   7  10   8   8   4   4   8   6   9  12   9  13   2   9   3   7\n",
       "19997   6   9   6   7   5   6  11   3   7  11   9   5   2  12   2   4\n",
       "19998   2   3   4   2   1   8   7   2   6  10   6   8   1   9   5   8\n",
       "19999   4   9   6   6   2   9   5   3   1   8   1   8   2   7   2   8\n",
       "\n",
       "[20000 rows x 16 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df.iloc[:, 1:]\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_clusters = df.iloc[:, 0].unique().size\n",
    "n_clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.8468894958496094"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = KMeans(n_clusters=n_clusters, init_method='var_part', tolerance = 0.001)\n",
    "a = time.time()\n",
    "clusters, labeled_X = model.fit(X)\n",
    "b = time.time()\n",
    "\n",
    "b-a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "107698.44917338352"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.cost_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.6438746452331543"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = time.time()\n",
    "model = KMeans(n_clusters=n_clusters, init_method='forgy', tolerance = 0.001)\n",
    "model.fit(X)\n",
    "\n",
    "b = time.time()\n",
    "\n",
    "b-a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "107502.15354435568"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.cost_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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