{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The scikit-learn interface"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div id='toc-container'><script type='text/javascript'>\n",
       "$(function() {\n",
       "    function regenTOC(){\n",
       "        element = $(\"#toc-container\");\n",
       "\n",
       "\tvar toc = document.createElement(\"div\");\n",
       "\t$(toc).attr(\"class\", \"table-of-contents\");\n",
       "\n",
       "\tvar curLevel = 0;\n",
       "\tvar containerStack = [toc];\n",
       "\tvar levelOfTag = {\"h2\": 1, \"h3\": 2, \"h4\": 3, \"h5\": 4};\n",
       "\n",
       "\tfunction pushLevel() {\n",
       "            var list = document.createElement(\"ul\");\n",
       "            containerStack.push(list);\n",
       "            curLevel++;\n",
       "\t}\n",
       "\t\n",
       "\tfunction popLevel() {\n",
       "            var lastContainer = containerStack.pop();\n",
       "            $(lastContainer).appendTo(containerStack[containerStack.length - 1]);\n",
       "            curLevel--;\n",
       "\t}\n",
       "\t\n",
       "\t$(\".text_cell_render :header\").each(function (i, elem) {\n",
       "            var level = levelOfTag[ elem.tagName.toLowerCase() ];\n",
       "\n",
       "            if (level === undefined)\n",
       "\t\treturn;\n",
       "\n",
       "            while (curLevel < level)\n",
       "\t\tpushLevel();\n",
       "            while (curLevel > level)\n",
       "\t\tpopLevel();\n",
       "            \n",
       "            var listItem = document.createElement(\"li\");\n",
       "            var link = document.createElement(\"a\");\n",
       "            $(link)\n",
       "\t\t.text($(elem).contents().first().text()) // Remove the pilcrow sign\n",
       "\t\t.attr(\"href\", \"#\" + $(elem).attr(\"id\"))\n",
       "\t\t.appendTo(listItem);\n",
       "            $(listItem).appendTo(containerStack[containerStack.length - 1]);\n",
       "\t});\n",
       "\t\n",
       "\twhile (curLevel > 0)\n",
       "            popLevel();\n",
       "\n",
       "        $(\"<a class='btn-update' href='#'>Update</a>\")\n",
       "          .click(regenTOC).prependTo(toc);\n",
       "\n",
       "\t$(toc).prepend(\"<div class='title'>Contents</div>\")\n",
       "          .wrap(\"<div class='toc-headings'/>\");\n",
       "\n",
       "        $(element).empty();\n",
       "        $(element).append(toc);\n",
       "    }\n",
       "\n",
       "    if (typeof(IPython) !== 'undefined')\n",
       "        $([IPython.events]).on('notebook_loaded.Notebook', regenTOC);\n",
       "    regenTOC();\n",
       "});\n",
       "\n",
       "</script></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import addutils.toc ; addutils.toc.js(ipy_notebook=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".text_cell_render @font-face {\n",
       "    font-family: \"Computer Modern\";\n",
       "    src: url('http://mirrors.ctan.org/fonts/cm-unicode/fonts/otf/cmunss.otf');\n",
       "}\n",
       "\n",
       "div.cell {\n",
       "    width: 900px;\n",
       "    margin-left: 0% !important;\n",
       "    margin-right: 0%;\n",
       "}\n",
       "\n",
       "code {\n",
       "    font-size:10pt;\n",
       "}\n",
       "\n",
       ".text_cell_render  h1 {\n",
       "    font-family: Tahoma, sans-serif;\n",
       "    color: rgb( 10, 88, 126 );\n",
       "    font-size:28pt;\n",
       "}\n",
       ".text_cell_render h2 {\n",
       "    font-family: Tahoma, sans-serif;\n",
       "    color: rgb( 10, 88, 126 );\n",
       "    font-size:24pt;\n",
       "}\n",
       ".text_cell_render h3 {\n",
       "    font-family: Tahoma, sans-serif;\n",
       "    color: rgb( 10, 88, 126 );\n",
       "    font-size:20pt;\n",
       "}\n",
       ".text_cell_render h4 {\n",
       "    font-family: Tahoma, sans-serif;\n",
       "    color: rgb( 10, 88, 126 );\n",
       "    font-size:18pt;\n",
       "    margin-top:12px;\n",
       "    margin-bottom: 3px;\n",
       "}\n",
       "\n",
       ".text_cell_render h5 {\n",
       "    font-weight: 300;\n",
       "    font-size: 11pt;\n",
       "    color: rgb( 48, 48, 48 );\n",
       "    font-style: italic;\n",
       "    margin-bottom: .5em;\n",
       "    margin-top: 0.5em;\n",
       "    display: block;\n",
       "}\n",
       "\n",
       ".text_cell_render ul {\n",
       "    font-family: Tahoma, sans-serif;\n",
       "    color: rgb( 90, 90, 90 );\n",
       "    font-size:11pt;\n",
       "    line-height: 185%;\n",
       "}\n",
       "\n",
       ".text_cell_render yp {\n",
       "    font-family: Tahoma, sans-serif;\n",
       "    color: rgb( 90, 90, 90 );\n",
       "    font-size:11pt;\n",
       "}\n",
       "\n",
       ".text_cell_render strong {\n",
       "    font-family: Tahoma, sans-serif;\n",
       "    color: rgb( 30, 30, 30 );\n",
       "    font-size:11pt;\n",
       "}\n",
       "\n",
       ".text_cell_render a:link {\n",
       "    font-family: Tahoma, sans-serif;\n",
       "    color: rgb( 10, 88, 126 );\n",
       "    font-size:11pt;\n",
       "}\n",
       "\n",
       ".text_cell_render a:visited {\n",
       "    color:rgb( 10, 88, 126 );\n",
       "}\n",
       "\n",
       ".text_cell_render {\n",
       "    font-family: Helvetica, Courier, Computer Modern, \"Helvetica Neue\", Arial, Geneva, sans-serif;\n",
       "    color: rgb( 84, 84, 84 );\n",
       "    font-size:11pt;\n",
       "    line-height: 125%;\n",
       "    font-size: 100%;\n",
       "    width:800px;\n",
       "}\n",
       "\n",
       ".CodeMirror {\n",
       "    font-family: Courier, \"Source Code Pro\", source-code-pro,Consolas, monospace;\n",
       "}\n",
       "\n",
       ".warning {\n",
       "    color: rgb( 240, 20, 20 );\n",
       "}\n",
       "\n",
       "/* Pandas tables */\n",
       "/*\n",
       ".rendered_html td {\n",
       "    text-align: right;\n",
       "}\n",
       "*/\n",
       "\n",
       "table.dataframe td {\n",
       "    text-align: right;\n",
       "}\n",
       "\n",
       ".output .table-of-contents {\n",
       "    border: 1px #cecece solid;\n",
       "    background-color: #fafafa;\n",
       "    padding-top: 10px;\n",
       "    padding-bottom: 5px;\n",
       "    padding-right: 15px;\n",
       "    padding-left: 0px;\n",
       "    margin-bottom: 20px;\n",
       "    display: inline-block;\n",
       "    position: relative;\n",
       "}\n",
       "\n",
       ".output .table-of-contents ul {\n",
       "    list-style-type: none;\n",
       "    padding-left: 20px;\n",
       "}\n",
       "\n",
       ".output .table-of-contents .title {\n",
       "    font-weight: bold;\n",
       "    font-height: 11pt;\n",
       "    padding-left: 20px; /* looks better if it's the same to the <ul> */\n",
       "}\n",
       "\n",
       ".output .table-of-contents .btn-update {\n",
       "    position: absolute;\n",
       "    float: right;\n",
       "    right: 11px;\n",
       "    top: 4px;\n",
       "    font-size: 9pt;\n",
       "}\n",
       "\n",
       "</style>\n",
       "<script>\n",
       "    MathJax.Hub.Config({\n",
       "                        TeX: {\n",
       "                           extensions: [\"AMSmath.js\"]\n",
       "                           },\n",
       "                displayAlign: 'center', // Change this to 'center' to center equations.\n",
       "                \"HTML-CSS\": {\n",
       "                    styles: {'.MathJax_Display': {\"margin\": 4}}\n",
       "                }\n",
       "        });\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy.io\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import warnings\n",
    "from addutils import css_notebook\n",
    "css_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 Defining the estimator object"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In scikit-learn, almost all operations are done through an **estimator object**. For example, a linear regression estimator can be instantiated as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=True)\n"
     ]
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "model = linear_model.LinearRegression(fit_intercept=True, normalize=True)\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In brackets are displayed the current values for the “hyperparameters” of the estimator. To learn about the specific “hyperparameters” check the documentation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Try: model?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Meta parameters can be changed after the model has been created:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearRegression(copy_X=True, fit_intercept=False, n_jobs=1, normalize=True)\n"
     ]
    }
   ],
   "source": [
    "model.fit_intercept = False\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given a scikit-learn *estimator* object named `model`, the following methods are available:\n",
    "\n",
    "* *Available in all Estimators*\n",
    "  * `model.fit()` : fit training data.\n",
    "      * For supervised learning, this accepts two arguments: `model.fit(X, y)`.\n",
    "      * For unsupervised learning, this accepts only a single argument `model.fit(X)`.\n",
    "* *Available in supervised estimators*\n",
    "  * `model.predict()` : predict the label of a new set of data. This accepts one argument `model.predict(X_new)`).\n",
    "  * `model.predict_proba()`: Returns the probability of a categorical label. The label itself is returned by `model.predict()`.\n",
    "  * `model.score()`: Scores are between 0 and 1, with a larger score indicating a better fit.\n",
    "* *Available in unsupervised estimators*\n",
    "  * `model.transform()`: Transform new data into the new basis. This accepts one argument `X_new`, and returns the new representation of the data.\n",
    "  * `model.fit_transform()`: some estimators implement this method, which more efficiently performs a fit and a transform on the same input data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 Simple estimator example: fit a linear regression model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"https://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"3ce6c613-cfe5-444f-adcb-415329272159\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(root) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\n",
       "\n",
       "  if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n",
       "    root._bokeh_onload_callbacks = [];\n",
       "    root._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "  var JS_MIME_TYPE = 'application/javascript';\n",
       "  var HTML_MIME_TYPE = 'text/html';\n",
       "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
       "  var CLASS_NAME = 'output_bokeh rendered_html';\n",
       "\n",
       "  /**\n",
       "   * Render data to the DOM node\n",
       "   */\n",
       "  function render(props, node) {\n",
       "    var script = document.createElement(\"script\");\n",
       "    node.appendChild(script);\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when an output is cleared or removed\n",
       "   */\n",
       "  function handleClearOutput(event, handle) {\n",
       "    var cell = handle.cell;\n",
       "\n",
       "    var id = cell.output_area._bokeh_element_id;\n",
       "    var server_id = cell.output_area._bokeh_server_id;\n",
       "    // Clean up Bokeh references\n",
       "    if (id !== undefined) {\n",
       "      Bokeh.index[id].model.document.clear();\n",
       "      delete Bokeh.index[id];\n",
       "    }\n",
       "\n",
       "    if (server_id !== undefined) {\n",
       "      // Clean up Bokeh references\n",
       "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
       "      cell.notebook.kernel.execute(cmd, {\n",
       "        iopub: {\n",
       "          output: function(msg) {\n",
       "            var element_id = msg.content.text.trim();\n",
       "            Bokeh.index[element_id].model.document.clear();\n",
       "            delete Bokeh.index[element_id];\n",
       "          }\n",
       "        }\n",
       "      });\n",
       "      // Destroy server and session\n",
       "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
       "      cell.notebook.kernel.execute(cmd);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when a new output is added\n",
       "   */\n",
       "  function handleAddOutput(event, handle) {\n",
       "    var output_area = handle.output_area;\n",
       "    var output = handle.output;\n",
       "\n",
       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
       "    if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
       "      return\n",
       "    }\n",
       "\n",
       "    var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
       "\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
       "      toinsert[0].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
       "      // store reference to embed id on output_area\n",
       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
       "    }\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
       "      var bk_div = document.createElement(\"div\");\n",
       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "      var script_attrs = bk_div.children[0].attributes;\n",
       "      for (var i = 0; i < script_attrs.length; i++) {\n",
       "        toinsert[0].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
       "      }\n",
       "      // store reference to server id on output_area\n",
       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function register_renderer(events, OutputArea) {\n",
       "\n",
       "    function append_mime(data, metadata, element) {\n",
       "      // create a DOM node to render to\n",
       "      var toinsert = this.create_output_subarea(\n",
       "        metadata,\n",
       "        CLASS_NAME,\n",
       "        EXEC_MIME_TYPE\n",
       "      );\n",
       "      this.keyboard_manager.register_events(toinsert);\n",
       "      // Render to node\n",
       "      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
       "      render(props, toinsert[0]);\n",
       "      element.append(toinsert);\n",
       "      return toinsert\n",
       "    }\n",
       "\n",
       "    /* Handle when an output is cleared or removed */\n",
       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
       "    events.on('delete.Cell', handleClearOutput);\n",
       "\n",
       "    /* Handle when a new output is added */\n",
       "    events.on('output_added.OutputArea', handleAddOutput);\n",
       "\n",
       "    /**\n",
       "     * Register the mime type and append_mime function with output_area\n",
       "     */\n",
       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
       "      /* Is output safe? */\n",
       "      safe: true,\n",
       "      /* Index of renderer in `output_area.display_order` */\n",
       "      index: 0\n",
       "    });\n",
       "  }\n",
       "\n",
       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
       "  if (root.Jupyter !== undefined) {\n",
       "    var events = require('base/js/events');\n",
       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
       "\n",
       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
       "      register_renderer(events, OutputArea);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  \n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    var el = document.getElementById(\"3ce6c613-cfe5-444f-adcb-415329272159\");\n",
       "    if (el != null) {\n",
       "      el.textContent = \"BokehJS is loading...\";\n",
       "    }\n",
       "    if (root.Bokeh !== undefined) {\n",
       "      if (el != null) {\n",
       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
       "      }\n",
       "    } else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(display_loaded, 100)\n",
       "    }\n",
       "  }\n",
       "\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "    }\n",
       "    finally {\n",
       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.info(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(js_urls, callback) {\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = js_urls.length;\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var s = document.createElement('script');\n",
       "      s.src = url;\n",
       "      s.async = false;\n",
       "      s.onreadystatechange = s.onload = function() {\n",
       "        root._bokeh_is_loading--;\n",
       "        if (root._bokeh_is_loading === 0) {\n",
       "          console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "          run_callbacks()\n",
       "        }\n",
       "      };\n",
       "      s.onerror = function() {\n",
       "        console.warn(\"failed to load library \" + url);\n",
       "      };\n",
       "      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "    }\n",
       "  };var element = document.getElementById(\"3ce6c613-cfe5-444f-adcb-415329272159\");\n",
       "  if (element == null) {\n",
       "    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '3ce6c613-cfe5-444f-adcb-415329272159' but no matching script tag was found. \")\n",
       "    return false;\n",
       "  }\n",
       "\n",
       "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.13.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.13.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.13.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.13.min.js\"];\n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      \n",
       "    },\n",
       "    function(Bokeh) {\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.13.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.13.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.13.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.13.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.13.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.13.min.css\");\n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if ((root.Bokeh !== undefined) || (force === true)) {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i].call(root, root.Bokeh);\n",
       "      }if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"3ce6c613-cfe5-444f-adcb-415329272159\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(js_urls, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"3ce6c613-cfe5-444f-adcb-415329272159\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n    }\n    finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.info(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(js_urls, callback) {\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = js_urls.length;\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var s = document.createElement('script');\n      s.src = url;\n      s.async = false;\n      s.onreadystatechange = s.onload = function() {\n        root._bokeh_is_loading--;\n        if (root._bokeh_is_loading === 0) {\n          console.log(\"Bokeh: all BokehJS libraries loaded\");\n          run_callbacks()\n        }\n      };\n      s.onerror = function() {\n        console.warn(\"failed to load library \" + url);\n      };\n      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.getElementsByTagName(\"head\")[0].appendChild(s);\n    }\n  };var element = document.getElementById(\"3ce6c613-cfe5-444f-adcb-415329272159\");\n  if (element == null) {\n    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '3ce6c613-cfe5-444f-adcb-415329272159' but no matching script tag was found. \")\n    return false;\n  }\n\n  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.13.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.13.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.13.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.13.min.js\"];\n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    \n    function(Bokeh) {\n      \n    },\n    function(Bokeh) {\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.13.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.13.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.13.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.13.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.13.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.13.min.css\");\n    }\n  ];\n\n  function run_inline_js() {\n    \n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\n      }if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"3ce6c613-cfe5-444f-adcb-415329272159\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(js_urls, function() {\n      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import bokeh.plotting as bk\n",
    "bk.output_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)\n",
      "Model coefficient: 275.99550, and intercept: -145.41186\n",
      "Mean Squared Error: 23.04\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<div class=\"bk-root\">\n",
       "    <div class=\"bk-plotdiv\" id=\"da5d880b-45f3-4a08-b609-b4e2816984e8\"></div>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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       "  var render_items = [{\"docid\":\"0179b26f-7285-4b81-819d-021c077498bf\",\"elementid\":\"da5d880b-45f3-4a08-b609-b4e2816984e8\",\"modelid\":\"62f266b1-96da-4331-9ccc-4b8e40b5fb75\"}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\")\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "62f266b1-96da-4331-9ccc-4b8e40b5fb75"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import datasets, preprocessing, metrics\n",
    "X, y = datasets.samples_generator.make_regression(n_samples=70,\n",
    "                                                  n_features=1, n_informative=1,\n",
    "                                                  random_state=0, noise=5)\n",
    "scaler = preprocessing.MinMaxScaler()\n",
    "X_sc = scaler.fit_transform(X)\n",
    "\n",
    "lin = linear_model.LinearRegression(fit_intercept=True)\n",
    "lin.fit(X_sc, y)\n",
    "\n",
    "print(lin)\n",
    "print(\"Model coefficient: %.5f, and intercept: %.5f\" % (lin.coef_, lin.intercept_))\n",
    "err = metrics.mean_squared_error(lin.predict(X_sc), y)\n",
    "print(\"Mean Squared Error: %.2f\" % err)\n",
    "\n",
    "# Plot the data and the model prediction\n",
    "X_p = np.linspace(0, 1, 2)[:, np.newaxis]\n",
    "y_p = lin.predict(X_p)\n",
    "\n",
    "fig = bk.figure(title='Simple Regression', \n",
    "                x_axis_label='X scaled',\n",
    "                y_axis_label='y',\n",
    "                plot_width=600, plot_height=300)\n",
    "fig.circle(X_sc.squeeze(), y, line_color='darkgreen', size=10,\n",
    "           fill_color='green', fill_alpha=0.5, legend='Measured Data')\n",
    "fig.line(X_p.ravel(), y_p, line_color='blue', legend='Predicted Values')\n",
    "fig.legend.location = 'bottom_right'\n",
    "bk.show(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 Working with MATLAB files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "v4 (Level 1.0), v6 and v7 to 7.2 matfiles are supported.\n",
    "To read matlab 7.3 format mat files an HDF5 python library is required. Please check the scipy documentation for more information."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data can be generated with the following MATLAB code:\n",
    "\n",
    "    % Generate Regression Test Data\n",
    "    X = [1 2 3\n",
    "         4 5 6\n",
    "         7 8 9\n",
    "         0 1 2] + 0.1;\n",
    "    y = sum(X,2);\n",
    "    feat_names = strvcat('Feature One', 'Feature Two', 'Feature Three');\n",
    "    save ('matlab_test_data_01', 'X','y', 'feat_names')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mat_data = scipy.io.loadmat('example_data/matlab_test_data_01.mat')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Variables names included in the `.mat` file are keys of the `mat_data` dictionary. Moreover the key `'__header__'` contains the mat-file information.\n",
    "Here we load the two variables in Pandas varialbles:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['__header__', '__version__', '__globals__', 'X', 'y', 'feat_names'])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat_data.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the following code the `.strip()` method is used to remove the trailing white spaces used by MATLAB to make all the variable names of the same lenght:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Feature One  Feature Two  Feature Three\n",
      "0          1.1          2.1            3.1\n",
      "1          4.1          5.1            6.1\n",
      "2          7.1          8.1            9.1\n",
      "3          0.1          1.1            2.1 \n",
      "\n",
      "    measured\n",
      "0       6.3\n",
      "1      15.3\n",
      "2      24.3\n",
      "3       3.3\n"
     ]
    }
   ],
   "source": [
    "X = pd.DataFrame(mat_data['X'], columns=[s.strip() for s in list(mat_data['feat_names'])])\n",
    "y = pd.DataFrame(mat_data['y'], columns=['measured'])\n",
    "print(X, '\\n\\n', y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4 Datasets available in scikit-learn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Typical *scikit-learn* dataset are dictionary-like object that holds all the data and metadata.\n",
    "\n",
    "* **Features** are usually stored in the `.data` field in the form of a 2D array `[n_samples, n_features]`.\n",
    "* **Explanatory variables (targets)** are usually stored in the `.target` field in the form of a 1D array.\n",
    "\n",
    "Scikit-learn makes available a host of datasets for testing learning algorithms:\n",
    "\n",
    "- **Packaged Data:** these small datasets can be downloaded with ``sklearn.datasets.load_*``\n",
    "- **Downloadable Data:** larger datasets that can be fetched from the web with ``sklearn.datasets.fetch_*``\n",
    "- **Generated Data:** can be created with `sklearn.datasets.make_*`\n",
    "\n",
    "Try by yourself:\n",
    "\n",
    "* `datasets.load_<TAB>`\n",
    "* `datasets.fetch_<TAB>`\n",
    "* `datasets.make_<TAB>`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#datasets.make_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 Example: the \"Iris\" Packaged Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Features in the Iris dataset:\n",
    "\n",
    "  1. sepal length in cm\n",
    "  2. sepal width in cm\n",
    "  3. petal length in cm\n",
    "  4. petal width in cm\n",
    "\n",
    "- Target classes to predict:\n",
    "\n",
    "  1. Iris Setosa\n",
    "  2. Iris Versicolour\n",
    "  3. Iris Virginica"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "d = datasets.load_iris()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Try by yourself one of the following commands where *'d'* is the variable containing the dataset:\n",
    "\n",
    "    print d.keys()           # Structure of the contained data\n",
    "    print d.DESCR            # A complete description of the dataset\n",
    "    print d.data.shape       # [n_samples, n_features]\n",
    "    print d.target.shape     # [n_samples,]\n",
    "    print d.feature_names\n",
    "    datasets.get_data_home() # This is where the datasets are stored"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names'])\n",
      "['setosa' 'versicolor' 'virginica']\n",
      "['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n"
     ]
    }
   ],
   "source": [
    "print(d.keys())\n",
    "print(d.target_names)\n",
    "print(d.feature_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 Example: the \"Digits\" Packaged Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The *Digits* contains 1797 samples made of 64 features: each feature represents the grey-scale value of a 8x8 digit image:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"https://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"3548d94f-2f10-48c6-9f14-2c29cbd564f3\">Loading BokehJS ...</span>\n",
       "    </div>"
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     "metadata": {},
     "output_type": "display_data"
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    {
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       "\n",
       "(function(root) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\n",
       "\n",
       "  if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n",
       "    root._bokeh_onload_callbacks = [];\n",
       "    root._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "  var JS_MIME_TYPE = 'application/javascript';\n",
       "  var HTML_MIME_TYPE = 'text/html';\n",
       "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
       "  var CLASS_NAME = 'output_bokeh rendered_html';\n",
       "\n",
       "  /**\n",
       "   * Render data to the DOM node\n",
       "   */\n",
       "  function render(props, node) {\n",
       "    var script = document.createElement(\"script\");\n",
       "    node.appendChild(script);\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when an output is cleared or removed\n",
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       "  function handleClearOutput(event, handle) {\n",
       "    var cell = handle.cell;\n",
       "\n",
       "    var id = cell.output_area._bokeh_element_id;\n",
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       "    // Clean up Bokeh references\n",
       "    if (id !== undefined) {\n",
       "      Bokeh.index[id].model.document.clear();\n",
       "      delete Bokeh.index[id];\n",
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       "\n",
       "    if (server_id !== undefined) {\n",
       "      // Clean up Bokeh references\n",
       "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
       "      cell.notebook.kernel.execute(cmd, {\n",
       "        iopub: {\n",
       "          output: function(msg) {\n",
       "            var element_id = msg.content.text.trim();\n",
       "            Bokeh.index[element_id].model.document.clear();\n",
       "            delete Bokeh.index[element_id];\n",
       "          }\n",
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       "      });\n",
       "      // Destroy server and session\n",
       "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
       "      cell.notebook.kernel.execute(cmd);\n",
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       "\n",
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       "\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
       "      toinsert[0].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
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       "  }\n",
       "\n",
       "  function register_renderer(events, OutputArea) {\n",
       "\n",
       "    function append_mime(data, metadata, element) {\n",
       "      // create a DOM node to render to\n",
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       "        metadata,\n",
       "        CLASS_NAME,\n",
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       "      );\n",
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       "      render(props, toinsert[0]);\n",
       "      element.append(toinsert);\n",
       "      return toinsert\n",
       "    }\n",
       "\n",
       "    /* Handle when an output is cleared or removed */\n",
       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
       "    events.on('delete.Cell', handleClearOutput);\n",
       "\n",
       "    /* Handle when a new output is added */\n",
       "    events.on('output_added.OutputArea', handleAddOutput);\n",
       "\n",
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       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
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       "      index: 0\n",
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       "  }\n",
       "\n",
       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
       "  if (root.Jupyter !== undefined) {\n",
       "    var events = require('base/js/events');\n",
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       "\n",
       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
       "      register_renderer(events, OutputArea);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  \n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    var el = document.getElementById(\"3548d94f-2f10-48c6-9f14-2c29cbd564f3\");\n",
       "    if (el != null) {\n",
       "      el.textContent = \"BokehJS is loading...\";\n",
       "    }\n",
       "    if (root.Bokeh !== undefined) {\n",
       "      if (el != null) {\n",
       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
       "      }\n",
       "    } else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(display_loaded, 100)\n",
       "    }\n",
       "  }\n",
       "\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "    }\n",
       "    finally {\n",
       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.info(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(js_urls, callback) {\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = js_urls.length;\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var s = document.createElement('script');\n",
       "      s.src = url;\n",
       "      s.async = false;\n",
       "      s.onreadystatechange = s.onload = function() {\n",
       "        root._bokeh_is_loading--;\n",
       "        if (root._bokeh_is_loading === 0) {\n",
       "          console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "          run_callbacks()\n",
       "        }\n",
       "      };\n",
       "      s.onerror = function() {\n",
       "        console.warn(\"failed to load library \" + url);\n",
       "      };\n",
       "      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "    }\n",
       "  };var element = document.getElementById(\"3548d94f-2f10-48c6-9f14-2c29cbd564f3\");\n",
       "  if (element == null) {\n",
       "    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '3548d94f-2f10-48c6-9f14-2c29cbd564f3' but no matching script tag was found. \")\n",
       "    return false;\n",
       "  }\n",
       "\n",
       "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.13.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.13.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.13.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.13.min.js\"];\n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      \n",
       "    },\n",
       "    function(Bokeh) {\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.13.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.13.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.13.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.13.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.13.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.13.min.css\");\n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if ((root.Bokeh !== undefined) || (force === true)) {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i].call(root, root.Bokeh);\n",
       "      }if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"3548d94f-2f10-48c6-9f14-2c29cbd564f3\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(js_urls, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"3548d94f-2f10-48c6-9f14-2c29cbd564f3\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n    }\n    finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.info(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(js_urls, callback) {\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = js_urls.length;\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var s = document.createElement('script');\n      s.src = url;\n      s.async = false;\n      s.onreadystatechange = s.onload = function() {\n        root._bokeh_is_loading--;\n        if (root._bokeh_is_loading === 0) {\n          console.log(\"Bokeh: all BokehJS libraries loaded\");\n          run_callbacks()\n        }\n      };\n      s.onerror = function() {\n        console.warn(\"failed to load library \" + url);\n      };\n      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.getElementsByTagName(\"head\")[0].appendChild(s);\n    }\n  };var element = document.getElementById(\"3548d94f-2f10-48c6-9f14-2c29cbd564f3\");\n  if (element == null) {\n    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '3548d94f-2f10-48c6-9f14-2c29cbd564f3' but no matching script tag was found. \")\n    return false;\n  }\n\n  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.13.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.13.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.13.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.13.min.js\"];\n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    \n    function(Bokeh) {\n      \n    },\n    function(Bokeh) {\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.13.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.13.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.13.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.13.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.13.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.13.min.css\");\n    }\n  ];\n\n  function run_inline_js() {\n    \n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\n      }if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"3548d94f-2f10-48c6-9f14-2c29cbd564f3\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(js_urls, function() {\n      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import bokeh.plotting as bk\n",
    "bk.output_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div class=\"bk-root\">\n",
       "    <div class=\"bk-plotdiv\" id=\"9daf7d1f-bbea-4866-a42e-a9910a81c913\"></div>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = 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       "  var render_items = [{\"docid\":\"0760670d-c2c1-444f-8dce-0cb8d2923f22\",\"elementid\":\"9daf7d1f-bbea-4866-a42e-a9910a81c913\",\"modelid\":\"1187a336-4cf6-4a55-a884-71c04390e69b\"}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\")\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "1187a336-4cf6-4a55-a884-71c04390e69b"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from bokeh.palettes import Greys9\n",
    "from bokeh.models.ranges import Range1d\n",
    "import addutils.palette as pal\n",
    "import addutils.imagegrid as ig\n",
    "\n",
    "digits = datasets.load_digits()\n",
    "\n",
    "# plot the digits: each image is 8x8 pixels\n",
    "images = [ digits.images[i][::-1, :] for i in range(40) ]\n",
    "txt =    [ str(i) for i in range(10) ] * 4\n",
    "\n",
    "fig = ig.imagegrid_figure(figure_plot_width=760, figure_plot_height=100,\n",
    "                          figure_title=None,\n",
    "                          images=images, grid_size=(20, 2), \n",
    "                          text=txt, text_font_size='9pt', text_color='red',\n",
    "                          palette=Greys9[::-1], padding=0.2)\n",
    "bk.show(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3 Example: the \"Blob\" Generated Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "cat_colors = list(map(pal.to_hex, sns.color_palette('Paired', 7)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div class=\"bk-root\">\n",
       "    <div class=\"bk-plotdiv\" id=\"542f30bc-56c7-4841-bd06-1faf248d7109\"></div>\n",
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\",\"dtype\":\"float64\",\"shape\":[2000]},\"y\":{\"__ndarray__\":\"U0MKFls5CMCq8yIad1PGP3VZX3wZ3ApAxHqAIKOBC8BxOD7PJn3vv2oy0guj4t+/5FZATdh2C8Dae8La8gvYPwIQXcsgpwNA8Ey1O8gilT9FsbJwUcYJQBxpOrmCt78/BnjuCc7CBUBgxC39++gAQNaZ2LRS/QzA6x0Y1Uhq7j+W5Ob2J3DPP2Pn34bfrQzAtf41++9eAUDLAjpE1X8IwEV4AETb7Q3AZd6zXstwDUDq2pSolt0MQDPjFhiXysk/QATaONzQC8DyyXbU59bpP1eVWNhQeAZAlaJBqhHGB0BQAcRVd6YGQBCrUzA2xAJABo3NIlfiDsA4aY35/bjrP5vU1ENHcwlAoSU5OZkBCUBcW3d0fNIJQOuYEg5Czw7Ae7pYRSL2EEAelwBhid4GQNQ+dJ2tH9U/jMzm30jv6z8oK80ae0GvP94g/gFWHxLADC38uUhQCUCySjRKD3wNQPXFz+/QExLA+S8j0k0EC0Bfjqz0WaMLwDuoWE8A6g7AJLPX26BvBEAl2oB6TyoMwJ6eXohrhwvAA7wUxoYx/z+N/kG3iDYOwHAZ56ThsPo/5QPq5CnIDMA8ebNWyHILQCSHQkwv2rU//K/zONu82T/AME+u2Ia+v3XqW3uniA/AL0uXZ5v0DEDg742ML7nuPxgvcfI0FAdA5cGlX08tBUCx5lKgz64PQHh4QE9ehPM/AlUHzi2GA0DlVsDZ1icQwOZEgNCRWc0/BZHcj+cADsDSjNi1B93av5rn0jR5cMU/F2JCk/KMDEAFe4ZMk7wQQE6Tn3VdqAZAMHPW+e8QBUBGo7zy5S7TP4KW3VuxgBFAbksFNVgtB0Ay3V+uvSgQwPrcAes9IQ7ACS2JkxbmA0C6dPkPDfj/P9ywx9s8WArA9R9frPQsCUB0VxPzDdsRwJ7P7IflIwRAJ+UuQtN3EMB+gEuUcjoSwP4wH3P8hQxAHighMw1ADEBAnfsRYveWP7feA7bx6QZA2tspUzaADsAJeETbKJcKQILu1bZ6i+q/Rb7DKXyG4D9WarSah0QQQJ6mJt1E3g9ArrKdy4+k0D+VJ1TwTPgNQBsGRLBwlgJA5i3MeQFQ5T8gbg2g4y8RQPsGE2W4OBHALdtl1T4WDsAGVUwM3x0HwLi3rgRNG88/OANQ3dFbDUA4I9Amog4OQAvWhs00WA5AMsyRbBrGC8B/NVuzy78JQGCmUAElWghAO+yOpIB03j9JqHMl3nkHQKsDImGiBuW/fvFN5kHLBECrUgbNH/oFQFzvjGfYxL8/HiILO5T9BcBGdOeHvhsNQCNpy8S7TOG/b6F6voPfCEBgdNSxeDwPQAjPGPAXQMC/SpNw+QQzCECXzT6UDxHiv7GL9yQ0zOQ/QQ0gR6zcC8AywALVPwgTwGVNvIFKqtm/5CQyzMgADcCC/GRqdn8PwFf9mavs/O0/FK1ImkZbAkBAoQ8/rY11v9ZRnpnrcAvA3y3DxEbz17/aQWeirWXkPwwbNHnLsfw/kcvyYooFEcDUuCPSvmvlv1gPFtK7F+E/E14aNfh0CUBIQk5MxKn1v3TmWsklrr8/J2kYyKrI1j98NhutRQ0LQBgMDq/xFO8/rLN52rgn3b/Eb5JutvzKv38zz377XQBAFdAROrRbB0BiDGFt8M4QwEo6ndzgkBFA5Ts0jeD1DMABGRs5eBXXP7wKluRzwANAknqTMyD5B8DhTPdG513rv3dPyJBERBDAGy5Y+MxUyz9+VYbIxYoQwL2TpPoS6RDA3BGhy3oxCEBA+U83E/Obv/IiKOhfpgRAr2KX6R/zCsC8sjT6K2/Hv+PLssyFtghAzyZQpOp8C0BwsyH8ZAMLQM7AwY/LnAdAZw8628oMEUBoQmqDf7ECQO607bpBnfA/kMaqWeOn8z+q+Lh/2ckMwEVBzYr8JwxAqOqeiL1XDcB60a8bZDPoPy7dVPfcW9m/6Dzrz9S1CkBBlIv4HJINwARgG22/c+s/xISk98REzb/2T0iPpsYOwG5iXnDsRt8/YGQnY/ATDMDnvND01csIQFpBOLrLhRDAwpEEZdwxDUDDu48n+AIKQPlicsQrYP4/VdzPrTRQ4D9KYYM2oS/RP8QE+NConwNAdjk4QWbPAkCImgJ8OiYJwCOb+Iy6dNE/7jcEeDtLEsASrBc2nUTCv+BiujmP/LS/zJgb67kcC0DzGkCDHn4NQBvNBXLfXQdAi9rDXoniDMAjWbDZD3kPwGBLjcIixBDASIC1QIPhD0D1FGXZ4KUCQJQNIHvBJvE/MPrYJVYh2j9FYYjezw4PwAXw003kJeg/1v5WbgBeBEAaWyJev7AKwBqoxC34wAlAXt7hZ/07C0BoNsJ6N6UPQEfFYPXgmRLA6pbEQZOuEMBE8Zf/hSUMQAjQXf4idwJAO/fH+GFGEsDd7BEBxDYBQAxmML8zFhHANNP4B6mn5T9YwGTkXvesvyQMHMAU4fY/8ylnzPi8B8DsnNdD6a0DQCW4sJyzuhHAPkXARMMeEMAyfZp1bvMIQMyqT27FrARAsN17Kd555T8iMpHvj6sMwHoJvzD0pPU/j3GjB2yfDMBdsXSn+eT/P0wGHT/48N4/1ptjSGyk1z9bKq1JhAsQwLb+EpnDzgnAQNt3WNBpEMBo6LAigokRwDT5A76z4g3AWoJThDzEEcD425rq1kQRwGeiS8pMKghACVgVxbLGBkClt6CVZtEHQBg3jPX7YgdAV73HUzxvE8BfNTxvJFIOwK5ykjklQdO/RMZvQnNxz7/XZkTzDUsIQOI5iWd5TdS/bsNB9J5zBkC3oHOdy8ENQLtrBOieTxLApBb3Fnzo6j82LAM4vwUPwMJfnDt9HxDAsyU+ugqEC8BatTRO9SgPQMIWGJ2M0fI/EFnRyMS1AEDQn/PjRQ0LwCL+uGHBHdY/HHwGYZe+8b+beK1e97gEQKlKNEx4mgZAuMyuUV+kB0C1OPS1Wk0FQBur7CevehHAY3BCjiqzAkA2+z95rGgMQK1xBUuoaQVABnAPcHgmC8C4nV+9DW8QwPzvhLGWhwtAkmB4FvDF3T8ApwPR+U0KQJ2Q/c8QTgdAfPqeMNrL0D8FOyP/j8sKQHaMeqYPEQtAowb7xKoXBkAFsX/wSn8LwDDEHU66rw1AMEiJXEWGDsCgTezwxOIMQMJWBZLMUA5AIACC1NpBEcCRKZlsJgzsP9ZXr/fmHg/AkAeRygVG1z+cSvd9XDXpP4rsKCt7n8+/YgncYXZ3EMBtfj7H35MPQFz2lDu2hw/Asns3XRyDu7/0CQ3WLckBQDLXPAKqIwRAQZZQ/3BzDsDalidWDtwIQMdpwgBBsQ3A4Z+lcXyZD8BfoCIL8RgKQPRV1qTTcuG/OmD6fupZzT+LOpgqq3kFQL8kMAXjsg/AWzqJ4iRSEcCtoFo4xycMQGOQykzNkARAg9FYbaeQB0DICFgBQwIKQFgw8hOMFA5A22JfDHwaAEBIKBumq/IQQE9TncaUUg3AJWys/xo67D93+EuKJw0JQNwylbGEAgvAKLznlACI6T+2qW351mgPwIUA5siXUAJA9GoNREmVyj/HdF3rLhMSwBk0U/TW8gzAdyxhueKtD8BM+2z3XOsKQBmPQJC4z8s/tiViFZE8/D9mFXmONV3+P4kk/M7+owVADb6mPYnDC0AuP5GvxvLuv7phtZSH6xDADc/YqPjfC0CcLSB6L6kFQHUVqHW6qw7A9L/8sv+LEcBCAKdqZW8NwJS0U/f8Euq/Xzyqw7gP1T+DvH0ZM0QRQJJVynjMygrA2Xg40FhcCkDj0JfTWTYRwD+EcSsmTQ/AGOKdO+gJB0AY8kNHcS0FQLRACmnHxfC/KOpRhRNvCEBquJpChb8LwHeJpE9HbwZAcJEKGZK1AEDgDdMwlPrxPyYg7v281QhArRVB25ywC8BlisW1c1YQwGKAlZV4xgdAuGcRMJQvCkA3LWabscMCQBGhUt5WogtAOBlhP7bC/j/wQwW1sqL+PyGpcz6LuwRA/Nbr/lisCMBYwsSQCd0TwHDuxUuKsghAxMP2W5RAsL+ITQzZ1wzjv2ju5RFg5dC/58v1QXHVDMCWAGZNkTQIQKvdB3cbwQVABArc8khM3j9Kj6HoNarkv+C8x+uZwYa//IUHBSohEcAcWZ9yGXABQPsPltpg8wtANlPBj3tb2T9CIoBcnzDpP4ol0WkesdC/0cQVXu1cEMBbhVGlk+wKwKiZ6OAMgrg/DA+oeuyFAkCjokaioDMEQN2gtT2B7g9Aow6d4mACEMAK9EJFrXALQBYK/Tf6xwnA5Knqi5duB0D65p8lMu8MwGCqLB3TvfA/xthc0grzDsDgLeIHARAFQGvwXocC4RDAgVFwEQ7cCsBef7xhNjgGQCewS5dWehPAbjjFSirvCsA+hOltRGgHwDisQ/18bwJADW+M8WdUEMDxUWV8ql8HQAFllSEsFw7AzGyqg9ELBkDDrIF2pbIQwO0ArRyliQVA42DL9EGvEECL44Sh9C3hv75xBHAvzOY/vvTvwq8rEMCyiVAJ+b0OQClTPTEnzsu//ODHryABAkAF4hRjMfsSwGGFu96dQxLAqLrJP0sQBkAjRnWkoWUOwL2UD0wFyAZAYZWe9SfNEMAuudZCCYzQv0IgX+26++A/p/2lsjmEDMBInHBSoifEP2IvlEqMBgVAaTeQQPLUEsBqxBiktS32PzEEt8qa2AVAMgw9RITfBUBhDxb9E6EHQCbu5orCsxDARRaRS6FPDMC5E2SR3OrDP5snp1JoJhBA2JPUU7IsEsBCagXUODbWP2xQEg8D/AZABul63SpVEcBCZD9YPm8JQAuuE/pefxBAD9dKJzWgCkAK2XiZHX0OwJlZ4xgXJsM//gq3qO5O8L9i5ukyd/wIwCODF+O9zgZAu38iWgffEcD+yXkAl60AQJ36ODZORgvAQekXVXRVB0DoYJMMoOINQLrIN/Hiyg3AKLHmem39B0C+XuLgvwkFQCCvlo/tfdC/fymvyg1UEcAhhO7DjrQRwIOJk4OOoArAfCtHStHYEMD3lHifxjTxv7EeboDg6xDAthNHt4syBsBVxfju2gYRwL/zWIdHGQRAu4xPLjDb4b8SwDgsIdzVP0IpsvfL+AtACK8pqvSjBEBY17gaPn0RwH4jHD74tARAaVALbdI2CUAJ6XuadPDEP2vacpU0nQVA+A+eKV/MBcDIbh6UCNnGv3kamCFqcg9AhHUZ0EbCAUAQDMl/rXjcP+dOHLPRKBFABIhcGkxhEEAKCqfCfBL8P4NXcQaGBwBAU7PcAi3c47/oP1LfLFWmv1d+OyikX9i/QIAuRJCcBkA65upn1ooOwACrJY3dcnI/XUyrQpENBUAi8Kr45MkFQExy4RfbceC/EmdSvVFaEsBJxqAVg0YQwGr3Tg0bkwnAoD28p2lhyz+DW/IulvEOwAJVComMM7w/8WKgwuAQCUB8DisTBDIMwKNuhEM0JhDAbT5UtNhPCEBbU0konCUQQOofHQa/wAVAaNesJtFhDcCA91PMNgLoP7ID6AaNe+Q/Xn6LiZChE8AGPLqcdd8MwBclry7IStY/fxSTGLG2BcDwlvlvZePLv/yHyrcyowRA2GScbS2aB0Anvs/Ax6TYv8IvAOiOd9Y/E27wWVLND0BBfGkSqUMGQIQZoptPrew/Zz4bra2IEcD2pFN5tE69v9HUhWVaKQhAMj9hn/ZtEsBf7eIovJTWv6ql/+ZVRwtAgHE6lq8w/j8QFR8akKnFvwahEGFVnw/Ax+cIRFckBECGse7ZjlYEQGiZYuFDHAhA10k8DUR6D8CQ2uNEyqrtP/A63xR6Dea/AGy+uX9jDMC37Dp6LbwKwOLiLkQFGvC/CJcttEjADUDHPj1oiwQHQBI2v7pEPxDAQGYV1vWGyr9+sJ1eooXJv+ARijQ2oA/A3IYVPxS3EMC3u0FprkzvP9UZJqyuKQRAqu8hH529EsBcg7nBASECQCRgCSEn/QvA9sdefqO90r+eVyGJI4kJQDeWMFj6/gVANh4YglzRvb9G4yEg3n4OwGp+W9+rvg5Ae5hS6Q4qB0DhuxEgplQKwFdnHO/WDg3AGGlIBGkGEsCHKN3wXZEQQMie9LMEuRDAnt7I9K3TDsCji34jx+fhv85N9F7JdeE/5LEBmuetBUDYwxBgh1bRv0ij3EHSCxLAgJjQT/jADEAXw+Tq0vHQP7SH62J2LRFAcy+tBLFuB0BAwcPxmaeKP3DJApXmxqc/GDBiC+XhpD9WZqPRCLEJwBiHwHkoVQtA+JgsBw8zA0DYhWs7L0L+P4f1KR4vJgZABHyH3K/ZBUAfs5f1Pb0QwF2EQOUbx9Y/R4YDJ/AmDcCYhpmSbIYEQKOATVnMeQ/A9uDb15pMDECEhgbdOjvRv+Jwcvf5dw3AelpYICAPAUDu3teRMCDuv62TpKg/HuA/Vbdj4+2hEsDTJUCMvDoTwPL9LUteaeW/+M12vQbP4D9SdIdiv1UEQMxGScUVVwlA+bwapFcBDEBqUUzsZrUNQC3lF//R8QZAUYMx8rFpEMDwKyCyKxukv1ZAJ8IEfQ3AUxkwGOlJEMCLjYuJE3AMQCjArGYoosQ/XBCAZZ7zDMBPOAyBbSMSwOd9WwbmLQ3Ar26sfCXpDEBwSbauRdcFQOqyw3xGsg/A/oc+qBq+AUCc7rjVCce6v6E3z1emEg5Akpnq5xdGCkCbOJP/zAsQwC7czVs6D9m/cIJ2Drju8L90Y3ktZ+gRwAAl53EWQ5C/mNzZVU3isj/zsq8vQYoNwIh+Vj7xdP8/ea6FAhX/DkDfZdFnHGbQP8p9P+NIHgxAVZNzV9em9L+2FsyrZ6gEQJS2ipNOdQJAKiqgdS4iCUAipM3JTEfaP+zxWjk+f9g/kDZZ0nr/BkAUxvOyYXwNQEcSwBPSlts/rQD1/RTuDsDfNPOc/8vaP0yUlDxO+MW/i7Ld4I+WCkBqbafn/qTzv1r/AY4gNgRAGMTvVYmb2b+Z+Psc/KgPwF2mcIzZw+0/RdkTC31EB0CmA6LkmogDQJTc5q3PiARA/yg2Go3/AkCiF/YALkYAQNZO2e6L8AbAWPybb4euDsCJlucc8ogNwIRI2ciHlAVAEEUBh1T72j8Q3jxNzREQwOkc8297nAdAH+9zONGmDsAGk0mb0jkRwN+Zp/jNRhBAmPaznGYqpL+MwrgVlCHcP7hNcrCJMglAKntQYy78A0BI3f0Qu8AJQNKpOSRFqQ7AiHd+H9u9C8ArV3h1fzQLwKcd9GqfqRLAFP8dQiUPBUAhYSSITGoGQOc7M7YrMgtAZXYQAjypAkD8H7x88CjNvzelAljw0BLAqqOdjK1P0z9fxtFUgg0FQLZ7VXTFDwBAUv+vazk4BEB70cdwJJ8QwIRXJlmJEdY/dAkn7r5LDECG1w/zeNfWPyTBTfCGGRTAFbdQmtOSCcDQGm3SEPqdP4uE13naGfI/n4CGEassyD9I7GgcoVQSwGdN82DlqOu/WGHbOHK9w79QWczj6UPov9AtDck1tRDAR3XTqcBtCEBIObY/Zx+0v6UYqAuf9tS/WaMP3mQJEcCZSmknxkDgP9iGjPGh7+2/mL7aH0uwxr8Pwtf7dhkSQN0nIeCabhBAMYmGkXw2AEDKksGglNADQDQeVGQyhtS/XJ0c6mluCEC1Piwrk2MQwLCTPQ9IlAJAAESUFzPIB0BppDaQhYgBQJzsTMc8SBDA0ebCDZsu6T/b0TurL0gSwPU/Yen9ZwtAy/mdfOkRAUBvH7vjbjgGQEpHqMIua+S/eMQGbJ/NAUALrNW2QDAGQAtrNcDFkQNAIDVUZ9IBFMBuJrbU2KUGQOrGvsHyatC/9LLo9TyrCUCnOUt4R0wIQNKqkbFMNg1AwoICS2ocDsAYGuR/IM4EQJFzJd8s1/E/lr/nFwYBBkCYdLQQTNznvx3QxHEPIAlAklcUsbt6/j82S+Qgnlfwv0W0kyy2MhDAcee0pbvrEMCAyvd8XOwLQMhjR/piGQpAu3pYx2FfAkBBJF05JO8KwK7Bi1U9pgJABOqrHeWxBECtPdc09g/Mv+rz4sigthLAugpN2UWmEUBusuPcLhEHQDf5jIwv0+q/lsqFs0UdCUBPf/twEqkMwJIvNvHbbghA0poreE7vDMAY5uFeiiwAQHUlS1vbFQdAoBomqM5Ivj87EHzI7BEJQKwYZZZ43g1ABsJsEwP1AED4pFlykw/kP+B0++mO9wtAN7GdZtoF+L+46e3rf8e8vwp76/yUNgVAyglJTs+JA0BCuyhg5AYIQJRUBf2cPgdAorOUTfZdCUDFfr80GoYHQOiGCSolBe8/dEhMhDfYCkCYdE5YzlTBv7j9Yvz+3ApAuB4PNK/TBECngV49nykNwPzJZqmAYwJAMFPd1OBeB0CeA+Tfr5sPQJOhhU0rIgzAkio2HA590b9Pj6csjwX0vyKis4vZiQfAomnDUEzBDsDq9H9CPiHgP3BOTEhG5AZA9hSzRhJNy7/51Q6TKcTIvx3CsH7LhgVA3urPjpy65j+K7Djw/urnPzAREKpywwFAkPh0WfuSBUA26zTiwkYMQIJCjotMbgzA6lg5046F2z9c2QSt2nAMwGPNxrieaxHA67zIkKrsBkA0QFnNigEKQD1Sq44MCfU/2GBvoEbTEcCbas1RAzkHQBujV8q4sgNA/N9yDuSB/D/0WiPTOiQLQMMJjf7DfPG/lnI1upNVCkAQ7pnQuDzdv6JT12h4dgVAl+AK5OMCDcDmQAP9n6MMwIKSlSY1uPO/6ixthN7o9r89T1FW8/cBQGA9ERcSeQlAt+ZDRZ6BEsD4pPTT1NLTP0htJSVwubG/2cc/fE3O9D/UDPu+VDcQwFbjDHqLKRNAoLcaFeViDUDLzSDJmBUJQNj4XNoqvwnAyIsf5aU66z9MTXQviVoGQFMTMdViiBBAYO3gv/pGrj8PhCyLfJMOwBxMR4rbogpABkQMp8O9DEA4iPbhxXoTwOjPGR2j9da/5XDxzn3m/D9utEwnmKLgPy+ZSyZ8j+2/dTtSvkZRDcCc8yQFBtgFQMuJyoJe7AVAgU5ikuwF+j+OSkdsEz0KwA7ucqzzrAnABmIxvx2/AkB1Cwo79doIQJA+j/uzuAVAPecbmUMuDUB4VD9MwToNwNjJUmBZ6BBA86X/fi6r/j8rG5ih52IGQLbCR6JdSghA3BxlUHLIBUBl7rQdFjYGQDLEIVP9IwnAMV4TdhYEBEBLuTs+6EHgP+KGl0KpiPw/ELoS7TlDAEAH70IqNZbYP6Gf8gVsYw7AFIbhWA8dw78ol7mjCekGQCiULk0Gdg1AqIYKNMdx779Fq2ABHrAAQIfQT11ECQVAwNxHaFkWBEBj7uNNCcQBQCQ2BFVSIhNA87BBqUlRDMBqN/DOHYnxvyKkIzdKcwfA2iXZgfCoEEBVTK7KGZANwJyuCu+Na9e/k5lMhb+DCsByVyCIGMMNQJZ55yWEOBLAgKCiKT7qiD/DlUXHlbkQwKQAAnPbAdu/U9xQ/0whDcCzobcEngALQP4wwkgpwtu/cdxTZ2Bt5j/Kwg5/6xIDQPDt4ExaMs4/42JvHB1XDUBozRJp8fMDQJckmJEKpBDAleCN2MRMC0AGnnVdE/AQwCQXbGdnuwPAUri+dsIECsDjPQYn2iL0v1j7igpungZALrWE+r8O5L/+n7XO0mDVv9I4b+u5kO0/jq7UME+oEcAN04MBMU0OwEJzIz075xBA+vZjzELmBECBkRjb+y4BQGXUwNRwSA5AL8+Al0mP5D9NGgGCB2kNQDaqenfbd+Y/qSG6ceBq4b9GwXkh5uTTP3dssZPISNs/fEhZuj84DUDGjZ8o6ecHQJkzyh6d+w1A3V04Yum3BUCSdqimRZsEQItsRRq4tuI/WGVLVz/Q/z/Y9HTYiP8KwO2oZIYhgxHAnS4KSskLEsB6dfJSCv3SvxoS7DldR+m/zuJl8Tk1DMAur+sxOokNwK1cuxcTAQlAQTKwl/4dDEBoD8zYVwTWv+e952ALdxHA2g+WGms7EcB0u7nME2TFP/GsYqDZLRBAKamEguwKAkANhH8vJCcLQB7Gv+8ureM/UACYAhR1BkBgEGjY9msQwCExu5b3ZxHAZjzsVrDf/D9FEwD04KUGQJCQ64MCqwZAKMZtMGbt279wyY0ma+4PQGTguZBibw/Af/ic33JO6D/8bkpQ06gQwJrDmak+ndS/AC26Qv1h1b9OIafx3Knhv2jm3yKU8aE/aQdpjzJaB0DWgRfo0L0RQBcHjug2Sg/ANLZfpp5nC0BUjQaaC0LIP6b9pjKssdU/wBZaziJ9B0AcIry79SIAQPhn+/rWjwJAdB8O6wQBEcA2NeVMcQ0FQOywqy/9p9m/9Znl4JTaCEDe0AHvBAkQQL3u+8HrjuW/agXljihi2b+3eP2HFRsMQAzPbTEk6gVA+OCsuJaUD0CsveKtvzjKP6yi3H17uOW/rGHX2ra0BUD6Tf+8LxMQwASC8gQ0CQVAohu+nxU4CkAK2uqtxWT5vxpk3pc0mvo/mm1UH8jM7L+i6aBGLiXxP1BbVr29vuO/XEMWilqVDcCRRdLfaQbtP2SNtwU2aA5Aopf5MsycEUCXioLgZhPAP3V0Kp0PkRDAkfFV1aSG8j96oHGsNDkJQJRyI+fWrwhAGLTUrfnoxL/0DRMNc16xP7luzxE+QARAYTfzQlweDMB68y9razDzv68f4st3ohDAKK6CBJYD4b9SkfPMJGDYP0i5Ip6M1xPATFVgRMmixb+gLlOHXDQFQCbWIsOfngVAMW9EXjJhEcB7Z2sgAZ0GQFCwewT42bI/exv3jQDCCUAAMQW4odcLwDe9FZUlCtI/zTeiPIm+E8As5ivWxNYOwFNe1eScesg/vRGov+JnAEA7IiOvgK0TwMrMVXWW7AVA4P1GiOjID8AQSFto34z+P5a8qkYR6v4/RBlXIvqpBkDzVdSdWH4QQMbEG1tYrew/3Hb0WXslCUCAGYWsULz7Pzq8av/NeA7Aq4tXOpoJCUCLqhmPsYAKQPIj2WTXBeC/oNJwnX3CAkAzK52zNEIFQId51dYPiO8/C5bSNkp1EMAZtFC4uogKQMlAkNCf2g5AXh+EKAIv3L+3GhLHdcIFQFe7J//4XgpAJHOB4YZKEcDlwhRn1+0HQFm0h+pP1gdAvl8G2MadDEAtzHyoZlETwF0V26H78cU/DgkUSAK0D8DbZCKYfy4GQEts/hvoa+C/Gz8ToyJzyT+P+nreROcFQJoVeTchmfo/y0J6WQHfB8CeoT7y3+sNwNFDXQwtNw3AEKKG9uyPoz90sItnb6cLQNPvWfYYX9S/IK2BPX2LB0D92JV58c4LQBPApyNBZhBAb1UxhFJLEcAlgh/X70MGQL9pYq+tahDAPZ8dzsJIFMA+5m9fzOkPwGK1g3aL/dY/HXR2Lh0yCkBLK5ojbeThP9EOKAstLfA/NIb2l/FZBsDdoyWOTLUSwKLak+Rz2v8/eeMfKcMT3T/QQ8qV5+rjP2+e8XRiDw1AxrRQn8/c4j+2vJ/eFJIGwFUkNhasDw1AQ2OQ7cqx6b8MR5WJBUSyP6ikR1+oOxJAEWq0cnlNEcDFtyYhRCoCQB22cbhC9g/AMtpX06JX5T+iUQutlYgJQHo8jE7wCwtAavEmYb8BDcAsQ2mBpr0KQNlMr473KAZAOZ0vPsK1EMC4mmoWomYLQAGgmBr+FfW/GY+eTXg98T8GaiFgkuLJP1y+VR3taL4/HzScVywbAEDPJ70DqFcHQNMaGu4Y1vo/EDbomGLnCUBuYP7Yl4bTv3H9/1BLWda/5HK8ZKXF2D8ZswfcjEIQwIdbber3hgVA/BJJj7Z/BEDpRgm4dZMEQL7Tx5Bj9t8/Umtuf4j527/5TcaOSd8DQJxb+zxMuBPABE6aa/fbCEAUOdOiJxXgvx1TT50lQAvABPHikBm6DcAN88tkRiAQwBPifBSNEBDAw3FbD25ED0CBf9iLhUkNwKS6eV4TzgZA2YRaRz25BkALJoXLNkITwPpCu2/ZRBJAY2rtjb1EA0DM0K1o5UzaP6JeDaMROAdAgJs2FGzUB0ClwEgHdBbxvwK6XXcenfg/ULU8Jqlq1z/tjjyWbOALQFC7fiTT2wZAWkBunq+B0r/T9IRHbQIQwIbYsemBnuI/sSx5pPpqCkB0auArIcDOPwIynlVULBDA89bAFXzWEMBUhsav5tcKwD0NwCaYdQfAgsGFkQKuC8Apaf4HJLMQwOyW6RKqqgVA9gT7EtK1/T+AsRIIjBqkv9IaFYM5MRFAsJP6898+A0CdXVERiHPWP5jqFgudJQpATx2izrPL2T8umfHmEdrdv+bcS5z2kBHAKXIhgZ44B0Cso3/ZHlEPwK/SOlWn4+6/TSY4HIlV/j8tFqPw3VoOwDwnTufPMdk/sI0XWuX5EMA6EH7QO3MOQOqLOit/+Q7ARHevAMJL4r995XUgkBLyP5vFyZYmXRHA5nykgm6hDcDxxElcc6IQwFwN7NrC1Q3A6NtgRNHVC0CbWgQCdHkMQOQCHeuOgw/A7v6T2gjiDEDEo0LWmUULwC6JsjsMRxDARRhrfJGZCUD+7nGtzncEQNLe3Svujg3AoKuKOy1j/D8cWZtjYPICQM0Q14k/wxPAbktPzTO3AkCic0kx8EkGQPgKQ2kkewZAAYa596owxb95MyNjMBHyv3ZsgjHYSQjAc2E20vO7EEDSMTDqnnERwCtD8SloCAdADEZjQIREEcD2+NEgJ80LwKQOX0iwSf8/FRvT1vnoDcDSnIabPsn7Pz/XxWWdDwVAgWY+ehyQDcDufUlNmygQwCKkw+LJWg/AWlWDQDtPB0Bn6DPBWIj8P7Kws6WfOBLA/XQnuazbC0ByZbsPq3QFQF5yUnm0Pb+/5vC8cPRk2T/JnUufbpASwPrKdzOyQwRASqYimkDaCEDZI66fofwHwEC4JiJ8G6A/IKpfFhlBD8DmqqTw7vkKQBzGAtRBZtM/6DVP08WyEsBxmkc0xiXEPyGHt3F6VRLA8teFdymq7L9RqcE2I6Xzv3xA0T0sVgVA5HuIsaJjxL9q6JbUazcMQDSQpha/kM0/Nw7SYkITB0AmQsZ6gM4IQKVXkexpQwhA9t7R2rVrCkDczMoN++0DQHCEg3mgkQ3ATq9Pc6ibCUCU+aE3Jo0NwBpccjcopwZAtyAwGuzf4r85IN1IIzAGQJyKxGqYytq/68veQcgQD8DBLSC9L0oRwPtQ/o69DAZAeGqpifXM57/80xadmeQGQGb4DvFjxBLA0tBfs8SdCEBCiP5VtLkQwBZl1BakXgBAPKK69s3VE8Az6CNBfekCQHwOixqqaRDAOIqdQztXvr970L6lprQJQDa5zR1nCf8/2nBsTkyR9L9w8GPGO+oOQP76QTzQ1gBAT87F5v4YA0C8MUkSCVPOv8APpjGHy6S/0M3wVQKhEcDvWqiSOuMJQPjft6R/194/z+0nV1mBD8CojdrN3asIQEaY23hEow3AHpCEjwo/+D8PBCcXfsMOwPJPuzkz+wrAxHus+ecc6D/QDW91NHgQQDbcelFhrsY/5CI4uGBSD8CaXfC1sdEHQBGNzP4Ivg/AUre/d2OUEsCgLUEqDKL5P6MhRXLYyRLAzEpwEEIWDUBnSBUAz7sCQD6JqnpkyNg/kE+HeUN7EEBCwxzgW1X/P+1GZlg01OI/kDjTsaCFrD8BokpwRmoQwAZKmMQRqxHA3N+Ps624v7+Gr9FHTT4OQLSqqUDzT7U/rFC7p6tcCsCCoiB/0j8FQMyaThXDFLq/+sKuA9edEEDKL3Mk0e4FQAkpbI7iA+E/SIGj5PHNB0AOKr6WN8QNQAjS2aM1LOC/ql/m7pjtDcB2pM+GBN/mvzLsfKjMZxDA7onGiWDz578gmFfulLKuP7fzdJU5GQxAdm5oKTgMCkBXSrlkFf4BQFGHvo85FhDAMCYinSoHD8DRgRpsxtzDP9W/lJBJbQ/AiIIyO6I2BUAmAScWZMwIQAh4oUhKx6K/Yqij9Cq14T/MYfUXaFINwHVaWntfPwlAGYSlg44K4b9LRUpbp70RwI8dKkrDXv8/qHHmNsKnD8A0rJiIiEK8v37uVOn59ABAcYPa/v423L9HeA9rRdgNwMTrPto7Fg1AgWFQBQXH8T+FIoPUqTQFQAkxwvoBJQRAEBrvtqZd/j8MirZGV43/PyjF7Ct5YhDA4TqwnN1UBkDll+pZNK8QQF1Dnkma9vQ/hY2PX+51DsA9jsI/Ys8KQKbuPYtFXwdAYHYYD8Ul5D+Sem7Z2OcPwIfCccYPCwNAznc8HKXeBkBdEpmOKWz8PzInAaKjFglADmYakSnZD0CYHIiynePmP0haHUOIEdU/yxkBIlLODUCRqmEg1JXCv4R7nen1dsS/lLCV48b4CkAM59tmI94KQLwKPD1gK9a/1q9n357aD8B0dA0qvM4GQBtM5rRvt+2/HMp4LUlHBECijQDXigoJQDNhlDnjdP0/H3LYVkrBE8AQjK2bSqkRQEg7T3yozw7AqaSPuPFpEcBklq6Fo4kKQPWxHMeGAQvA8ifUG/8U6T+7KJz+Pkzcv/TO6wRA4w3Awk/mt/BI1T/DU7PYIbkDQEqc5c8lsgLAXf2HoaR2EMDdHh+NMq8NwKdQ1fxwrtk/u/ZZcb+PA0D+ot2HVSUKQG/aQAEXMQJAbKspSGmNBUBohB82rvj+P05+efT4ZQVABl8DobyZ9z/v7i4wYnbov+JUDQOdNsk/TavP4gQID8DQ4zybaLQOwFuCt2qLvwJASe7PZziLEsD1tjH+5r3kPwiJmhVjOcI/HGsWByFj5r/KTF7VQRHVvzh4VAMbGMA/2VGtyNgUDcAmGMN6tsrKP2sbzUEelw7AVb/MVpb2+T/MpA3jG2kQwBYv5LFa+uY/tF0pXCWA+j8OvRhRwqTnv5yibLwuowpASblKOTvGDkCEyCzcqpzav84i27UEVQdALfBe5HQ0DsD6VesIM+0BQDhrq5UHAw1AKBFf9AU6DcAIpaKWjFsKwJjl8fFh7M2/VHXxZufdtT/ePMVnGZQRwADAGpoIAyM/nBqPsTQA5L8GnHoPe28RwAwUJwAwDOO/qc1woE5EDMBWslZRwqcCQC1I/5DQIhHAcC/Zcl/lCUBmYqctfjzXv3DpfQa47uk/GJHzzLUD4b9hTeFXFyXIP9ePJ/+UwgnAUOgVgyIoo78DtG0ZhCsEwIn1IBxHhgFA8KMwNM7RAUCssYoCnlkIQIv9QVCPEgtAOvEQ8o/KB0Bmwg6GgI0RwHedQx4WbQlAch1pI0OFA0AwC6wyFe3KP9RsULp6mw3AbZQq29rCDMBPvgAvHf4DQDSjBuYNlRDASuscEJSbCECHIdkQHkoQQB5mz7R70A7AA0y9ZvlCEUAaTVf7APUOwH5X006tqQxAw+CFAvyaDcCK/OYgtuIGQDvgZGig/xLAzbXfksXzDUC49IhM/ToOQKMTIhDbTxDA/1Lskjem3r/8hFg2P80NwHwlokfR9ghARzmM77ML7D9wM6L5pksKQFTg4dnZKdm/nkzfi40dAEB1gXmaEi0RQM7JN8U3twJAIFS5XBkoz7/2xYA/VhYRwA3+yLXArAhAyETdRDmCy7/8WdYfyKUIQARlnAF/fuI/3HSwXA0Wtj/QKF0oCmkRQJcPRcUmFwRAzy2VUWyfD8AannsuYPAMwK/i+7yy/gFAx78n0ps7CcDrMc4tLqESwFVVyoTTRNu/aB4hoPs6AUBc3TIwrZsIQIM82x0z/gdAdtqfd6GVBUDDYsokDEcKQK8iwaT3BO4/tuCdodQSvT+xymMfXY4DQOS8JxT1ihDAvyf65GqSDMAJ40SOPJTFP8129VPTLQhAGLZnRv3pE8DWE4CoOJnQvwVfuMWQkgzANXIbwYod4r9NDGYMndwNwLTrT1VUrRLAbzdPm307/j/+8b94FTgQQF6wt/u6ZBHAxzNljj0U1r9ye81ioVECQLdan7EW4AdAlhlpZh15BkBxB72qixUIQMwYMgpN9QJAevi/d1M+D0CXOEFPcAkOwNnvKAlu/BDA1mmH1bfRCUCa3qRLHxkOwLmn7WD+MgBA7kjduIMMC8DaSUdbrtkLwDJAoBlX6BDAlJRNed1Q4j8SBu72gB4DQNJVxVUmyw1AGPRelwgbpz+WXQww5JEQwAYc+Z3Ncua/Cudk4yUb2j/Qs630hzADQIddswBM1AfAkPBAbccEC0BhmKa5yfsCQEZ4Y3+DRhLAXSOjssxAB0AYv19coXAJwDzCuIRbKQpAGcikZHoB0b8dx99c96QPQM1smy3wkdm/qblWHmnv/j8U3QKYV3qwv6EmmzXcBQvA59jllCQaD0D9nUptVqcLQAF8z6zwQ+k/0yO/eQomC8Am7hhzZtELQKJuAwyp+BBA4N8iA0PBsD8J4IaZcKnQP4l8d7DXZBHAzZ2JuwPRE8DNfVCELtEFQDLOI7GI/whAkUej1IGfCUBwQRkWQVqXv97mFFk849o/wAFZkcLDD0B8nccKAmnXP8DNDEMaDAJAEIcqWvMQDMBC7hBbjpQBQOC+NMoLOwZAfG1EZ3YrC8AEdj72nDXhv5NNhYmJahHAdE6S1PuA1j8AeSGMKB2rv3i3JFe1ZA1AqKLhD2rErb+4fMz0SrGuv+mmagogqA1AOQMu9ycyCEA35L2M6h8JQA44s6FRzQvAsrAB7yoSD0DQeNMtyGWgP5KY21IyMwVAyfUTrPD10j/v8Yixc4IKQIb3WkjQlRHA/+RW4/0pEMDtaQ5FJloOQHo1vXXFMek/si6xUG2/BUBaRO/s8iIPwGVqJdodeQhAQmqlVz6e2r8iAik7elgSwDIAHUWu1A9AhOeIOrjJAEAXiM5dcIzhvyH1ZX109MG/iKXrmA6jtD/DHizQJgPJv0FtchRpHA1AeB0CbXDGE8Dx5z4wtJcDQJMkqDI65A3ATDaGi8geD8Bpp78MWRkJQOhlYC3Yoqi/OIXqF5+p2r/e5CPlEUcKQKFTGxMhIg/A4EDI4e/Jh78vKQbsYAUFQEbQhEYsX/o/pvTsQ49wE0A7yr+AqQQPwEhOryKMQAzAkJpWD4ROvD9rM6tRVw8OwOR7lPn1hglAJK5XQbGuBECU6yQ7eMMBQNrPEf8cFPU/QkOeEvPW8L9ifcXKlybrv7uMxzoMlRDAT1xPHQopAkBAKGNhfSSzv+TtnPAtOQVAhghzpq6KA0AkGLJfyWwKQEo0R/o4eeK/Lx6bmlW3yj+onTP1vavev2qFyzv/pwBAp31znAWMEcCeQv3rsmUJQE55axSkBw/Al3ugjFQeEEBolsLbyfYIQH/+RA2xWxbAYl84qfAB7r/HEy7sJWUGQHAxf309brc/jTdrWyogC8CDBQdN8LfOPxQMhawiwAzASP0A8ChJCkCYfLYnk2UHQBidXj73TKi/Hg8eB8qqCUDKel5ybjcOwNwliTgKh7g/F8bKidPRD8DIQS1oGujiv9b7H87WtcK/3LISlZHKCUBBnnJxXcMLQDT20IaT/tC/dLSaecOPCMC8/xfzSEQBQOmRRKZC7AzAsiEViilcxD820hzOb9LMv1/d5vCj5wdAMTcuvd454j8ANE2bKq2nP+rxJxnlrQnAprN/tlci4D9H8drBtMQCQFDhkjivtwrANfhS1cveB0AeqnUdb94RwHhfJd7L3QRAAQxifjExAUAA8NnCra0ev3lDFB8208i/ZldQCr/BCsCHecthTGkOwMddc4C+6wBAJ7CYBIiSDsDy10XjkjLAP8Kn+lZncuq/yBln/OWfzL+uT+q9s8sMwIIFBPMZece/yi6VZNnHBsCJ1Pla0HADQMTrJlYcQuo/HC+nzYdp+j/uns9UC6MHQCvvnLghiQRAbIrwPals77/q3qwMzRsGQNbDE8iejRHACAKv1AwJsr/OiVPvdqUNwFJ2R5rgwAzAWSaRNeunD8B6DaJuQzoNwORxkOXSUAfATgDhm6dEEMCB5AxiS6EJwIl68Bs/+gdAmjN7+o4cDUBIgCFwiaIDQCmQ2cehTA5AXZCo5++FCsAlmQlpCPjFvxaZRp9s7hHAMGmArD+vCsAH+Ri5zCMGQOM16ZH2ZghAjJj8BraQ+j+95XAdKT4CQHS84TA99gbA99z+g5/5B0CsyyxIEh/wv7OWyyO4XQ/AmkSf+5561D9v2R62Jh/oP3fc8WgX+eM/EGXTn50sEcBSbcBUGCUOwBBGJ/ovLre/0yi5iNL6AEDAsO+0IB0NQFrXqi4tE8M/JEblthQBEcDO3yQBItLpvxpDiYCeHwBA5yn+5ru7DEDZSxoPcSQRwIJbQC2zCRDACE2BlVjoCsAEUV/oMFUFQBZCPCGcv/A/qjLgUNhw1L/e/qvoNe//P9mzVfQyUQzAVeH6XhgPE8Cv3StbDVPIPygSOC735OC/2q3sALHrCEBFeQiWFMcNQP0XYiSPOhDAxO4Lo8eLvj+UCYpM2BgNQLrrBepKrQlAu3kj6K1uBUDsD4Py3BHGP//irhCwdA9ALxJoU9gQCUDElMiLISoRwI0heg6GHwpAgrgsK/OzCkCKVXvlaM8CQA12Ry6i3QFAWCl30EZE1b8UVCDrJCgGQIG03TxxDe4/2OBML45rDkBDqu6A5+/fP0SeT4kvA9S/eny1+XJvDcAgp5DRcqfov01yNfV+nAtA2upLu2qUyD9yGSMrCd7ivzIMnOYF4Q/ABAHe+8fIEsBo/kZRFO4MwLbK7kpWahHA9D3QfoB+tD8YjUS2+ObnPxUY3xnv/RFA+qo92Nhv9L/COMEPF4EQQOtkZVi1UBHA/IgQze+0DsAxnKTqzMYSwIY9pIWyW+K/JiCSSEJpDcA8zC62+IrHv7X6OGL0Dw1A8jbowO1lB0A7sjeLvJgHQMC7z6oVzvk/9SPEcHFWEMAUIvWSieAHQOwa20wlcRHA2mD3mOfc379IvsCg6DwQwKpHCFAyFQ/AAJCnonBEh7+xEBN62p0SwI2VrffuvxHA7SsHTsknEUBUsJqK6JIEQMUhiZZIZQtAGIpGLHAA3b+p4Qd7/b4PwPAtLktUuLC/D2jgOrLUEcDcBdZQvCwOwKFDZYxhwwtAboufji0N97/M8OveevsBQC1LMebnNQnAMa9/uYMYEMDcqHlMFzQRwOjcTHKX7PI/PhHKw3Pj3T/sVCtPm/IPwPfGQazLlwlABK2/OQ/E/j+YBb+K0Q/dv0yyHm3gowxA2zI0qncjEMBLQaZ737EIQOhrHCPq6AzAgQPLMrdVEEC0YyRAEMAIQM07DW8EYwzAd/L6lbSQDUAWr3nHUioQQNEG0MZY/gNAxB62CWqQDsBp+tIU5iUKQGf5R8K8dwpAm8Txr6QGEEA3Z7uDTKAMQKRpiZNIrg/AaFwXgV8wDkCOT+Jp//LwP0n0AZ0jsxHApTThz0HyEMCD5JcP5TjtP2K7tQBYXARApj4Tc+AjD8BrmWqsaOgCQKL+iktyoQrA4pm3Jn3zBEBRojbLshcOQL26t0wopAzASu6gzt5jCkDwRMpPC08IQJfri1cdTwZAS7N8+08nEcBch/WGBJAHQBF1gN0lDgtAZZ8fx8vh0D/88AdnUBYOQFK7AiOTwg1ACFX/SMtYyL8c3djNb5foPy99BbnKNw7A9ct4fICgDEAP4Rravo0GQO8x5PSFfwNAOqU84E9tDsC9r13T+tILQFVipP4cygRAYOOMdGdE5T+8sppTT/q1P73j6M/bpwNAhOv6JCv9AEC2sAjNNf8AQKXGAxZldgZA9AY5kPq6DkAq4ke1WD7wv+hcmDkRJtC/BZ75W+b2DsCEYkJeIPUHQA6gbP2VOgpAhMmz05yMCsA3gWsow8fcP4DJEOlJRQxASJHHoZ78sL9Ey8bT/z/+v7xWUg2m2bk/5bs6Xsm0DsBlamDdXTcJQLSjDB+enwpAUML1xNWXvD/WE0PnU8ANQL5/ApySTeq/rgIZU+me47+co/CiGhb+P1bu1TWkiQFA56UWvkT7AkCMuY1iiMgCQKHHqWye4A7AqfABEHlw4D9AJjQ6F62/P8xp0OYp5wlARrJtZrCeEMCZm5OX7x8OwLWSYoy0ZgrAJ9ksDGO6DcCCNoYVAHMAQDTX1mfZ3gdAlKa/Mb61DsAUqxUgqLgFQJw2JW9eg7U/wAGDONL9pD/n9/N0uKz/P9bhLsvNHQBASoIct1UeAEBgfxuDZnoEQIK5lC0pjwVA7Glz94nFtz9h7MU4FMMRwMIeLkZpU9M/CGLcsLdiDcAl6Y9lRB3tP9jTPGM/igpAokKhHnoyDUB4ZJ6aCBKlP2O4IFY3XgvAaEcCW0tbBUCKXBZkuTMLQFwCppAj682/zTNTafi7AkCmm34aj7oPQLBxGoEe8uK/ViPrEsoECkBP2q+rnVwQQKa5tPwJphBAUvW4tqpm3L/oHKBI2brgv1gkXEaduay/f4iVrrF2EcA1c/xjjPILQEY4W2etTwvA8K+UaeI85r+3uRhIRC3KP/OxZTN1kg9AQmKu8uTqCEC7lOwIfYYQQNzsOqHtQtM/lslkwpu0B8DqqGQFmwoIQDYiDTsT8ei/39n79763BUCZTccQiGEGQIy/Qqef7rU/bcCOVeM6EsAwkJ7iCbq1Pxz+sgQVVgdA3RIpbZXoDsDmtuT8ucAPQB0MxmT0gxDAUTKyvAYMB0BGEgRYdpzbPzlM+vsXIwNAfiamVOsBAECpGW4BoTTgP8qE3eoyC+W/nsWfJVBt8r/9BaGobHAMQPDv1F2pdps/hKXaKWT6A0Axbn+g3twKwC046/Mehg3A82GkFXBcBUCHHPiFQgPsv1Ijx1gbcgxAE6MjJZi8EEDAUu6LbywQwAHYxI9Xeg9AXOz/zji3s7/5Ye/LkUASwLB+2pkY+Oa/OuXab5MWEMDmfpSPx60HQFPVxvqBdAZAip954j+ZC0BITiInAFLYP29ef03qDA9AfNMi5lFD6r9jXoB80SIKwAApGslHlw3AL4NFkwYW478gkR8UdZERQHf4mlHh7gdASYj4jztmEMDWizD0JH8EQPBqTZf3f9K/aUH7ivINDkBRvY0Dhprqvz6Jrw+6YPO/8tSlo2pdzb+dW+jS9tcQwLPi/dqmOxDAJeF0m3DVDUCvyU/384QHQMuYHaBhRw7AZi68KKJqCkCMqAtwdqa5v9Z7pBNfVgNAfxDO8YTxCMDxpnsiXGwRwKy9L6EPleU/E3JFwAjMDUCkGg815ETMP0tmug1bS9E/XB4fba15DsC4UXJKftkLwBbn9UlJ8ghAummy8Tb+EMCMHCaDeY/mP34qAp592L2/O6ZDO7soEsBukVFp4IsOQA==\",\"dtype\":\"float64\",\"shape\":[2000]}}},\"id\":\"0a301be0-f3eb-4d49-b570-b5496f18f27a\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"bottom_units\":\"screen\",\"fill_alpha\":{\"value\":0.5},\"fill_color\":{\"value\":\"lightgrey\"},\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":{\"value\":1.0},\"line_color\":{\"value\":\"black\"},\"line_dash\":[4,4],\"line_width\":{\"value\":2},\"plot\":null,\"render_mode\":\"css\",\"right_units\":\"screen\",\"top_units\":\"screen\"},\"id\":\"77b36ab4-aef5-4aee-b51a-8d90ca4eae19\",\"type\":\"BoxAnnotation\"},{\"attributes\":{},\"id\":\"79311563-b6e2-4383-8120-718763072eaf\",\"type\":\"PanTool\"},{\"attributes\":{\"callback\":null},\"id\":\"ff94a545-49cd-4862-a5a4-7e41b0a67aa2\",\"type\":\"DataRange1d\"},{\"attributes\":{},\"id\":\"2bde389d-87cd-4f33-969b-a1624c3d432a\",\"type\":\"ResetTool\"},{\"attributes\":{\"fill_color\":{\"field\":\"fill_color\"},\"line_alpha\":{\"value\":0.5},\"size\":{\"units\":\"screen\",\"value\":8},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1a495936-1030-49ab-a5f3-aa36e7182987\",\"type\":\"Circle\"},{\"attributes\":{},\"id\":\"fda0a28f-a79c-4eb5-b71d-c9f3a739b4af\",\"type\":\"WheelZoomTool\"}],\"root_ids\":[\"54818e84-5795-4175-a3e3-9439de75335e\"]},\"title\":\"Bokeh Application\",\"version\":\"0.12.13\"}};\n",
       "  var render_items = [{\"docid\":\"513be5cb-5511-4d5e-aeaf-f329915e3dc8\",\"elementid\":\"542f30bc-56c7-4841-bd06-1faf248d7109\",\"modelid\":\"54818e84-5795-4175-a3e3-9439de75335e\"}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\")\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "54818e84-5795-4175-a3e3-9439de75335e"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data, color_indices = datasets.make_blobs(n_samples=2000, n_features=2, centers=7,\n",
    "                                   center_box=(-4.0, 6.0), cluster_std=0.5)\n",
    "\n",
    "fig = bk.figure(title=None)\n",
    "fig.circle(data[:,0], data[:,1],\n",
    "            line_color='black', line_alpha=0.5, size=8,\n",
    "            fill_color=pal.linear_map(color_indices, cat_colors,\n",
    "                                      low=0, high=6))\n",
    "bk.show(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "Visit [www.add-for.com](<http://www.add-for.com/IT>) for more tutorials and updates.\n",
    "\n",
    "This work is licensed under a <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-sa/4.0/\">Creative Commons Attribution-ShareAlike 4.0 International License</a>."
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda env:addfor_tutorials]",
   "language": "python",
   "name": "conda-env-addfor_tutorials-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}