Monti_Sebastiano_Lab1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " # Machine Learning LAB 1\n",
    " Academic Year 2021/22, P. Zanuttigh, U. Michieli, F. Barbato, D. Shenaj, G. Rizzoli\n",
    "\n",
    "The notebook contains some simple tasks to be performed about classification and regression. Complete **all** the required code sections and answer to **all** the questions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### IMPORTANT 1: make sure to rerun all the code from the beginning to obtain the results for the final version of your notebook, since this is the way we will do it before evaluating your notebook!\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### IMPORTANT 2: Place your name and ID number. Also recall to save the file as Surname_Name_LAB1.ipynb . Notebooks without name will be discarded.\n",
    "\n",
    "**Student name**: Sebastiano Monti<br>\n",
    "**ID Number**: 2052399"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# 1) Classification of Music genre\n",
    "\n",
    "### Dataset description\n",
    "\n",
    "A music genre is a conventional category that identifies pieces of music as belonging to a shared tradition or set of conventions. It is to be distinguished from musical form and musical style. The features extracted from these songs can help the machine to assing them to the two genres. \n",
    "\n",
    "This dataset is a subset of the dataset provided [here](https://www.kaggle.com/insiyeah/musicfeatures), containing only the data regarding the classical and metal genres.\n",
    "\n",
    "### We consider 3 features for the classification\n",
    "\n",
    "1) **tempo**, the speed at which a passage of music is played, i.e., the beats per minute of the musical piece<br>\n",
    "2) **chroma_stft**, [mean chromagram activation on Short-Time Fourier Transform](https://librosa.org/doc/0.7.0/generated/librosa.feature.chroma_stft.html)<br>\n",
    "3) **spectral_centroid**, Indicates where the \"center of mass\" of the spectrum is located, i.e., it is the weighted average of the frequency transform<br>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first import all the packages that are needed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using matplotlib backend: nbAgg\n",
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "%matplotlib inline\n",
    "%matplotlib notebook\n",
    "%pylab\n",
    "import csv\n",
    "\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "import sklearn as sl\n",
    "from scipy import stats\n",
    "from sklearn import datasets\n",
    "from sklearn import linear_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Perceptron\n",
    "Firstly we will implement the perceptron algorithm and use it to learn a halfspace."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TO DO** Set the random seed, you can use your ID (matricola) or any other number! Try to make various tests changing the seed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "IDnumber = 2052399 #YOUR_ID , try also to change the seed to see the impact of random initialization on the results\n",
    "np.random.seed(IDnumber)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the dataset and then split in training set and test set (the training set is typically larger, you can use a 75% tranining 25% test split) after applying a random permutation to the datset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A) Load dataset and perform permutation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Header: ['tempo', 'chroma_stft', 'spectral_centroid', 'label']\n",
      "\n",
      "Data shape: (200, 4)\n",
      "\n",
      "Dataset Example:\n",
      "[['92.28515625' '0.22373830597598895' '2192.798091164326' '0']\n",
      " ['161.4990234375' '0.2841730455239421' '1534.0649775815205' '0']\n",
      " ['143.5546875' '0.20811288763962318' '1396.8242648287155' '0']\n",
      " ['95.703125' '0.31289954089595506' '1680.0882644413368' '0']\n",
      " ['123.046875' '0.25857228884109024' '1173.6583080518985' '0']\n",
      " ['67.99958881578948' '0.22521197942744986' '715.1893204778178' '0']\n",
      " ['95.703125' '0.2840176228179304' '1184.632142061296' '0']\n",
      " ['89.10290948275862' '0.2563949682674902' '1358.897902739857' '0']\n",
      " ['99.38401442307692' '0.2614892978768399' '1372.8258066922585' '0']\n",
      " ['184.5703125' '0.2899787266101455' '1953.0485341566464' '0']]\n",
      "\n",
      "Number of samples loaded: 200\n",
      "[[9.93840144e+01 5.33625514e-01 2.81548238e+03]\n",
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      " [1.23046875e+02 5.13206393e-01 2.37833575e+03]\n",
      " [8.61328125e+01 4.79849248e-01 2.80798828e+03]\n",
      " [1.12347147e+02 5.27123255e-01 2.25075610e+03]\n",
      " [8.91029095e+01 5.40479025e-01 2.31589771e+03]\n",
      " [1.35999178e+02 3.69871950e-01 2.21842602e+03]\n",
      " [1.23046875e+02 2.74045740e-01 1.36962755e+03]\n",
      " [2.34907670e+02 2.70478239e-01 1.21629376e+03]\n",
      " [1.03359375e+02 4.40987757e-01 2.64694734e+03]\n",
      " [1.29199219e+02 4.84545951e-01 2.87123564e+03]\n",
      " [1.07666016e+02 3.09652754e-01 1.52561929e+03]\n",
      " [1.17453835e+02 4.66587935e-01 2.96460663e+03]\n",
      " [1.43554688e+02 4.37909370e-01 9.72033527e+02]\n",
      " [1.17453835e+02 4.21571092e-01 1.98812037e+03]\n",
      " [9.22851562e+01 5.02055322e-01 2.74339281e+03]\n",
      " [1.07666016e+02 2.86158732e-01 1.17007475e+03]\n",
      " [1.51999081e+02 2.53349534e-01 1.25317412e+03]]\n",
      "[ 1 -1  1 -1 -1  1 -1 -1 -1 -1  1 -1 -1 -1 -1  1  1  1 -1 -1 -1 -1 -1  1\n",
      " -1  1  1 -1  1  1  1 -1 -1  1  1  1 -1 -1  1  1 -1 -1 -1 -1 -1  1 -1  1\n",
      " -1 -1 -1 -1  1 -1 -1  1 -1  1 -1  1 -1  1 -1 -1  1  1 -1  1 -1 -1 -1  1\n",
      "  1 -1 -1  1  1  1  1 -1  1 -1  1 -1  1 -1  1  1  1  1  1 -1  1  1 -1  1\n",
      " -1 -1 -1 -1  1  1  1 -1  1  1  1 -1  1  1  1  1  1 -1 -1 -1 -1 -1 -1 -1\n",
      "  1  1  1 -1 -1  1 -1 -1 -1  1 -1 -1 -1  1  1  1 -1  1  1  1 -1  1 -1 -1\n",
      " -1  1  1  1  1  1  1  1 -1  1  1  1  1 -1  1  1 -1 -1 -1  1 -1  1 -1 -1\n",
      " -1 -1 -1  1  1  1 -1  1 -1  1  1  1 -1 -1 -1 -1  1  1  1  1  1 -1 -1  1\n",
      "  1 -1  1 -1  1  1 -1 -1]\n"
     ]
    }
   ],
   "source": [
    "# Load the dataset\n",
    "filename = 'data/music.csv'\n",
    "music = csv.reader(open(filename, newline='\\n'), delimiter=',')\n",
    "\n",
    "header = next(music) # skip first line\n",
    "print(f\"Header: {header}\\n\")\n",
    "\n",
    "dataset = np.array(list(music))\n",
    "print(f\"Data shape: {dataset.shape}\\n\")\n",
    "print(\"Dataset Example:\")\n",
    "print(dataset[:10,...])\n",
    "\n",
    "X = dataset[:,:-1].astype(float) #columns 0,1,2 contain the features\n",
    "Y = dataset[:,-1].astype(int)    # last column contains the labels\n",
    "#print(X)\n",
    "#print(Y)\n",
    "\n",
    "Y = 2*Y-1                        # for the perceptron classical--> -1, metal-->1\n",
    "m = dataset.shape[0]\n",
    "print(\"\\nNumber of samples loaded:\", m)\n",
    "permutation = np.random.permutation(m) # random permutation\n",
    "#print(permutation)\n",
    "\n",
    "X = X[permutation]\n",
    "Y = Y[permutation]\n",
    "print(X)\n",
    "print(Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are going to classify class \"1\" (metal) vs class \"-1\" (classical)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "B) **TO DO** Divide the data into training set and test set (75% of the data in the first set, 25% in the second one)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1 -1  1 -1 -1  1 -1 -1 -1 -1  1 -1 -1 -1 -1  1  1  1 -1 -1 -1 -1 -1  1\n",
      " -1  1  1 -1  1  1  1 -1 -1  1  1  1 -1 -1  1  1 -1 -1 -1 -1 -1  1 -1  1\n",
      " -1 -1 -1 -1  1 -1 -1  1 -1  1 -1  1 -1  1 -1 -1  1  1 -1  1 -1 -1 -1  1\n",
      "  1 -1 -1  1  1  1  1 -1  1 -1  1 -1  1 -1  1  1  1  1  1 -1  1  1 -1  1\n",
      " -1 -1 -1 -1  1  1  1 -1  1  1  1 -1  1  1  1  1  1 -1 -1 -1 -1 -1 -1 -1\n",
      "  1  1  1 -1 -1  1 -1 -1 -1  1 -1 -1 -1  1  1  1 -1  1  1  1 -1  1 -1 -1\n",
      " -1  1  1  1  1  1]\n",
      "50\n",
      "\n",
      "Number of classical instances in test: 23\n",
      "Number of metal instances in test: 27\n",
      "Shape of training set: (150, 3)\n",
      "Shape of test set: (50, 3)\n"
     ]
    }
   ],
   "source": [
    "# Divide in training and test: make sure that your training set\n",
    "# contains at least 10 elements from class 1 and at least 10 elements\n",
    "# from class -1! If it does not, modify the code so to apply more random\n",
    "# permutations (or the same permutation multiple times) until this happens.\n",
    "# IMPORTANT: do not change the random seed.\n",
    "\n",
    "m_training = int((75*m)/100)   # m_train needs to be the number of samples in the training set\n",
    "m_test = m - m_training   # m_test needs to be the number of samples in the test set\n",
    "\n",
    "X_training = X[:m_training, :]   # X_training = instances for training set\n",
    "Y_training = Y[:m_training]   #Y_training = labels for the training set\n",
    "\n",
    "X_test = X[m_training:, :]   # X_test = instances for test set\n",
    "Y_test = Y[m_training:]   # Y_test = labels for the test set\n",
    "\n",
    "#New permutation if training set does not contain at least 10 elements from class 1 and at least 10 elements from class -1\n",
    "while (np.sum(Y_training==-1)<10 and np.sum(Y_training==1)<10):\n",
    "    X = X[permutation]\n",
    "    Y = Y[permutation]\n",
    "    X_training = X[:m_training, :]\n",
    "    Y_training = Y[:m_training] \n",
    "    X_test = X[m_training:, :]\n",
    "    Y_test = Y[m_training:]\n",
    "\n",
    "print(Y_training) # to make sure that Y_training contains both 1 and -1\n",
    "print(m_test)\n",
    "\n",
    "print(\"\\nNumber of classical instances in test:\", np.sum(Y_test==-1))\n",
    "print(\"Number of metal instances in test:\", np.sum(Y_test==1))\n",
    "\n",
    "print(\"Shape of training set: \" + str(X_training.shape))\n",
    "print(\"Shape of test set: \" + str(X_test.shape))\n",
    "\n",
    "#Normalization\n",
    "mu, sigma = np.mean(X_training, axis=0), np.std(X_training, axis=0)\n",
    "#print(mu, sigma)\n",
    "X_training = (X_training - mu)/sigma\n",
    "X_test = (X_test - mu)/sigma\n",
    "#check if means and variances of the normalized training and test sets are respectively close to zero and one\n",
    "#print(np.mean(X_training, axis=0), np.std(X_training, axis=0), np.mean(X_test, axis=0), np.std(X_test, axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img 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\" width=\"432\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "ax.scatter(X_training[Y_training==1, 0], X_training[Y_training==1, 1], X_training[Y_training==1, 2], label='Class 1')\n",
    "ax.scatter(X_training[Y_training==-1, 0], X_training[Y_training==-1, 1], X_training[Y_training==-1, 2], label='Class -1')\n",
    "ax.set_title('Training set')\n",
    "ax.set_xlabel('Tempo')\n",
    "ax.set_ylabel('Chroma stft')\n",
    "ax.set_zlabel('Spectral centroid')\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"432\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "ax.scatter(X_test[Y_test==1, 0], X_test[Y_test==1, 1], X_test[Y_test==1, 2], label='Class 1')\n",
    "ax.scatter(X_test[Y_test==-1, 0], X_test[Y_test==-1, 1], X_test[Y_test==-1, 2], label='Class -1')\n",
    "ax.set_title('Test set')\n",
    "ax.set_xlabel('Tempo')\n",
    "ax.set_ylabel('Chroma stft')\n",
    "ax.set_zlabel('Spectral centroid')\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We add a 1 in front of each sample so that we can use a vector in homogeneous coordinates to describe all the coefficients of the model. This can be done with the function $hstack$ in $numpy$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set in homogeneous coordinates:\n",
      "[[ 1.         -0.94137367  1.370052    1.13095169]\n",
      " [ 1.         -1.06670536 -0.6989713  -1.0598299 ]\n",
      " [ 1.          0.07381295  0.88216076  0.57062016]\n",
      " [ 1.          0.85013214 -0.71843231 -0.87526308]\n",
      " [ 1.          0.56260651 -0.82291878 -1.01461537]\n",
      " [ 1.          0.07381295  0.77928078 -0.60257813]\n",
      " [ 1.          0.07381295 -1.47036193 -1.22301693]\n",
      " [ 1.         -1.06670536 -1.09982907 -1.06617514]\n",
      " [ 1.          0.07381295 -0.1785828  -0.12753727]\n",
      " [ 1.         -1.88136129 -1.1116155  -0.60038857]]\n"
     ]
    }
   ],
   "source": [
    "# Add a 1 to each sample (homogeneous coordinates)\n",
    "X_training = np.hstack((np.ones((m_training,1)),X_training))\n",
    "X_test = np.hstack((np.ones((m_test,1)),X_test))\n",
    "\n",
    "print(\"Training set in homogeneous coordinates:\")\n",
    "print(X_training[:10])\n",
    "#print(X_test[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TO DO** Now complete the function *perceptron*. Since the perceptron does not terminate if the data is not linearly separable, your implementation should return the desired output (see below) if it reached the termination condition seen in class or if a maximum number of iterations have already been run, where one iteration corresponds to one update of the perceptron weights. In case the termination is reached because the maximum number of iterations have been completed, the implementation should return **the best model** seen up to now.\n",
    "\n",
    "The input parameters to pass are:\n",
    "- $X$: the matrix of input features, one row for each sample\n",
    "- $Y$: the vector of labels for the input features matrix X\n",
    "- $max\\_num\\_iterations$: the maximum number of iterations for running the perceptron\n",
    "\n",
    "The output values are:\n",
    "- $best\\_w$: the vector with the coefficients of the best model\n",
    "- $best\\_error$: the *fraction* of misclassified samples for the best model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def perceptron_update(current_w, x, y):\n",
    "    # Place in this function the update rule of the perceptron algorithm\n",
    "    new_w = current_w + (x*y)\n",
    "    return new_w\n",
    "\n",
    "def perceptron(X, Y, max_num_iterations):\n",
    "    \n",
    "    #init the algorith with w=0, use a best_w variable to keep track of the best solution\n",
    "    curr_w = np.zeros(X.shape[1])\n",
    "    best_w = np.zeros(X.shape[1])\n",
    "    num_samples = X.shape[0]\n",
    "    best_error = 1\n",
    "    \n",
    "    num_misclassified = num_samples\n",
    "    \n",
    "    #main loop continue until all samples correctly classified or max # iterations reached\n",
    "    num_iter = 0\n",
    "    \n",
    "    while ((num_misclassified != 0) and (num_iter < max_num_iterations)):\n",
    "        # avoid working always on the same sample, you can use a random permutation or randomize the choice of misclassified\n",
    "        index_misclassified = np.array([])\n",
    "        \n",
    "        for i in range(num_samples):\n",
    "            if Y[i]*np.dot(curr_w, X[i]) <= 0:\n",
    "                index_misclassified = np.append(index_misclassified, i)\n",
    "        \n",
    "        num_misclassified = index_misclassified.size\n",
    "        if num_misclassified != 0:\n",
    "            pos = int(np.random.choice(index_misclassified))\n",
    "            curr_w = perceptron_update(curr_w, X[pos], Y[pos])\n",
    "            err = 1 - ((num_samples - num_misclassified)/num_samples)\n",
    "        \n",
    "        if err < best_error:\n",
    "            best_w = curr_w\n",
    "            best_error = err        \n",
    "                         \n",
    "        num_iter += 1\n",
    "    \n",
    "    return best_w, best_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A template is provided, but feel free to build a different implementation\n",
    "\n",
    "#def perceptron_update(current_w, x, y):\n",
    "    # Place in this function the update rule of the perceptron algorithm\n",
    "#    if y*np.dot(current_w, x) <= 0:\n",
    "#        new_w = current_w + (x*y)\n",
    "#    else:\n",
    "#        new_w = current_w\n",
    "#    return new_w\n",
    "\n",
    "#def perceptron(X, Y, max_num_iterations):\n",
    "    # Place in this function the main section of the perceptron algorithm\n",
    "    \n",
    "    #init the algorith with w=0, use a best_w variable to keep track of the best solution\n",
    "#    curr_w = np.zeros(X.shape[1])\n",
    "#    best_w = np.zeros(X.shape[1])\n",
    "#    num_samples = X.shape[0]\n",
    "#    best_error = 1\n",
    "    \n",
    "#    index_misclassified = 0 #will be ovewritten\n",
    "#    num_misclassified = 0 #will be ovewritten\n",
    "    \n",
    "    \n",
    "    #main loop continue until all samples correctly classified or max # iterations reached\n",
    "#    num_iter = 1\n",
    "    \n",
    "#    while ((index_misclassified != -1) and (num_iter < max_num_iterations)):\n",
    "        \n",
    "#        index_misclassified = -1\n",
    "#        num_misclassified = 0\n",
    "        \n",
    "        # avoid working always on the same sample, you can use a random permutation or randomize the choice of misclassified\n",
    "        \n",
    "        # PLACE YOUR CODE TO RANDOMIZE\n",
    "        \n",
    "#        for i in range(num_samples):\n",
    "            \n",
    "                      \n",
    "            # PLACE YOUR CODE\n",
    "\n",
    "            #check if the i-th randomly selected sample is misclassified\n",
    "            # store the number of randomly classified samples and the index of at least one of them\n",
    "            \n",
    "            \n",
    "            \n",
    "\n",
    "        #update  error count, keep track of best solution\n",
    "        \n",
    "        # PLACE YOUR CODE\n",
    "    \n",
    "#        num_iter += 1\n",
    "        \n",
    "        #call update function using a misclassifed sample\n",
    "    \n",
    "#    best_error = # PLACE YOUR CODE\n",
    "    \n",
    "#    return best_w, best_error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we use the implementation above of the perceptron to learn a model from the training data using 100 iterations and print the error of the best model we have found."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Error of perpceptron (100 iterations): 0.00666666666666671\n",
      "[ 1.         -1.12883028  2.94282454  0.46323162]\n"
     ]
    }
   ],
   "source": [
    "#now run the perceptron for 100 iterations\n",
    "w_found, error = perceptron(X_training, Y_training, 100)\n",
    "print(\"Training Error of perpceptron (100 iterations): \" + str(error))\n",
    "print(w_found)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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\" width=\"432\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "ax.scatter(X_training[Y_training==1, 1], X_training[Y_training==1, 2], X_training[Y_training==1, 3], label='Class 1')\n",
    "ax.scatter(X_training[Y_training==-1, 1], X_training[Y_training==-1, 2], X_training[Y_training==-1, 3], label='Class -1')\n",
    "ax.plot_trisurf(X_training[:, 1], X_training[:, 2], (-(w_found[1]/w_found[3])*X_training[:, 1])+(-(w_found[2]/w_found[3])*X_training[:, 2])+(-(w_found[0]/w_found[3])), color='r', alpha=0.5)\n",
    "ax.set_title('Training set and perceptron, 100 iterations')\n",
    "ax.set_xlabel('Tempo')\n",
    "ax.set_ylabel('Chroma stft')\n",
    "ax.set_zlabel('Spectral centroid')\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TO DO** use the best model $w\\_found$ to predict the labels for the test dataset and print the fraction of misclassified samples in the test set (the test error that is an estimate of the true loss)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.  1.  1.  1.  1.  1.  1. -1.  1.  1. -1. -1. -1.  1.  1.  1. -1. -1.\n",
      " -1. -1. -1.  1.  1.  1. -1.  1. -1.  1.  1.  1. -1.  1. -1. -1.  1.  1.\n",
      "  1.  1.  1. -1. -1.  1.  1.  1.  1.  1.  1.  1. -1. -1.]\n",
      "5\n",
      "Test Error of perpceptron (100 iterations): 0.1\n"
     ]
    }
   ],
   "source": [
    "#now use the w_found to make predictions on test dataset\n",
    "num_errors = 0\n",
    "\n",
    "# PLACE YOUR CODE to compute the number of errors\n",
    "predictions = np.array([])\n",
    "for i in range(m_test):\n",
    "    predictions = np.append(predictions, np.sign(np.dot(w_found, X_test[i])))\n",
    "    if predictions[i] != Y_test[i]:\n",
    "        num_errors += 1\n",
    "print(predictions)\n",
    "print(num_errors)\n",
    "\n",
    "true_loss_estimate = num_errors/m_test  # error rate on the test set\n",
    "#NOTE: you can avoid using num_errors if you prefer, as long as true_loss_estimate is correct\n",
    "print(\"Test Error of perpceptron (100 iterations): \" + str(true_loss_estimate))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"432\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "ax.scatter(X_test[Y_test==1, 1], X_test[Y_test==1, 2], X_test[Y_test==1, 3], label='Class 1')\n",
    "ax.scatter(X_test[Y_test==-1, 1], X_test[Y_test==-1, 2], X_test[Y_test==-1, 3], label='Class -1')\n",
    "ax.plot_trisurf(X_test[:, 1], X_test[:, 2], (-(w_found[1]/w_found[3])*X_test[:, 1])+(-(w_found[2]/w_found[3])*X_test[:, 2])+(-(w_found[0]/w_found[3])), color='r', alpha=0.5)\n",
    "ax.set_title('Test set and perceptron, 100 iterations')\n",
    "ax.set_xlabel('Tempo')\n",
    "ax.set_ylabel('Chroma stft')\n",
    "ax.set_zlabel('Spectral centroid')\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TO DO** **[Answer the following]** What about the difference betweeen the training error and the test error in terms of fraction of misclassified samples)? Explain what you observe. [Write the answer in this cell]\n",
    "\n",
    "**ANSWER QUESTION 1**\n",
    "What is observed is that the test error, computed through the fraction of misclassified samples, is about two orders of magnitude greater than the training error, but still acceptable. In fact, the perceptron algorithm with 100 iterations, correctly classifies the 90% of the test samples."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TO DO** Copy the code from the last 2 cells above in the cell below and repeat the training with 4000 iterations. Then print the error in the training set and the estimate of the true loss obtained from the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Error of perpceptron (4000 iterations): 0.00666666666666671\n",
      "[ 1.         -1.89223465  3.86902552  1.80469599]\n",
      "[ 1.  1.  1.  1.  1.  1.  1. -1.  1.  1. -1. -1. -1.  1.  1.  1. -1. -1.\n",
      " -1. -1. -1.  1.  1.  1. -1.  1. -1.  1.  1.  1. -1. -1. -1. -1.  1.  1.\n",
      "  1.  1.  1. -1. -1.  1.  1. -1.  1. -1.  1.  1. -1. -1.]\n",
      "2\n",
      "Test Error of perpceptron (4000 iterations): 0.04\n"
     ]
    }
   ],
   "source": [
    "#now run the perceptron for 4000 iterations here!\n",
    "\n",
    "# PLACE YOUR CODE\n",
    "w_found, error = perceptron(X_training, Y_training, 4000)\n",
    "print(\"Training Error of perpceptron (4000 iterations): \" + str(error))\n",
    "print(w_found)\n",
    "\n",
    "# PLACE YOUR CODE\n",
    "num_errors = 0\n",
    "predictions = np.array([])\n",
    "for i in range(m_test):\n",
    "    predictions = np.append(predictions, np.sign(np.dot(w_found, X_test[i])))\n",
    "    if predictions[i] != Y_test[i]:\n",
    "        num_errors += 1\n",
    "print(predictions)\n",
    "print(num_errors)\n",
    "\n",
    "true_loss_estimate = num_errors/m_test  # error rate on the test set\n",
    "#NOTE: you can avoid using num_errors if you prefer, as long as true_loss_estimate is correct\n",
    "print(\"Test Error of perpceptron (4000 iterations): \" + str(true_loss_estimate))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"432\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "ax.scatter(X_training[Y_training==1, 1], X_training[Y_training==1, 2], X_training[Y_training==1, 3], label='Class 1')\n",
    "ax.scatter(X_training[Y_training==-1, 1], X_training[Y_training==-1, 2], X_training[Y_training==-1, 3], label='Class -1')\n",
    "ax.plot_trisurf(X_training[:, 1], X_training[:, 2], (-(w_found[1]/w_found[3])*X_training[:, 1])+(-(w_found[2]/w_found[3])*X_training[:, 2])+(-(w_found[0]/w_found[3])), color='r', alpha=0.5)\n",
    "ax.set_title('Training set and perceptron, 4000 iterations')\n",
    "ax.set_xlabel('Tempo')\n",
    "ax.set_ylabel('Chroma stft')\n",
    "ax.set_zlabel('Spectral centroid')\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"432\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "ax.scatter(X_test[Y_test==1, 1], X_test[Y_test==1, 2], X_test[Y_test==1, 3], label='Class 1')\n",
    "ax.scatter(X_test[Y_test==-1, 1], X_test[Y_test==-1, 2], X_test[Y_test==-1, 3], label='Class -1')\n",
    "ax.plot_trisurf(X_test[:, 1], X_test[:, 2], (-(w_found[1]/w_found[3])*X_test[:, 1])+(-(w_found[2]/w_found[3])*X_test[:, 2])+(-(w_found[0]/w_found[3])), color='r', alpha=0.5)\n",
    "ax.set_title('Test set and perceptron, 4000 iterations')\n",
    "ax.set_xlabel('Tempo')\n",
    "ax.set_ylabel('Chroma stft')\n",
    "ax.set_zlabel('Spectral centroid')\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TO DO** [Answer the following] What about the difference betweeen the training error and the test error  in terms of fraction of misclassified samples) when running for a larger number of iterations ? Explain what you observe and compare with the previous case. [Write the answer in this cell]\n",
    "\n",
    "**ANSWER QUESTION 2**\n",
    "In this case the test error, computed through the fraction of misclassified samples, is about an order of magnitude greater than the training error, so the perceptron algorithm with 4000 iterations, correctly classifies the 96% of the test samples. From a comparison with the previous case it is evident that increasing the number of iterations we obtain a more precise solution. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression\n",
    "Now we use logistic regression, exploiting the implementation in Scikit-learn, to predict labels. We will also plot the decision region of logistic regression.\n",
    "\n",
    "We first load the dataset again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Header: ['tempo', 'chroma_stft', 'spectral_centroid', 'label']\n",
      "\n",
      "Data shape: (200, 4)\n",
      "\n",
      "Dataset Example:\n",
      "[['92.28515625' '0.22373830597598895' '2192.798091164326' '0']\n",
      " ['161.4990234375' '0.2841730455239421' '1534.0649775815205' '0']\n",
      " ['143.5546875' '0.20811288763962318' '1396.8242648287155' '0']\n",
      " ['95.703125' '0.31289954089595506' '1680.0882644413368' '0']\n",
      " ['123.046875' '0.25857228884109024' '1173.6583080518985' '0']\n",
      " ['67.99958881578948' '0.22521197942744986' '715.1893204778178' '0']\n",
      " ['95.703125' '0.2840176228179304' '1184.632142061296' '0']\n",
      " ['89.10290948275862' '0.2563949682674902' '1358.897902739857' '0']\n",
      " ['99.38401442307692' '0.2614892978768399' '1372.8258066922585' '0']\n",
      " ['184.5703125' '0.2899787266101455' '1953.0485341566464' '0']]\n",
      "\n",
      "Number of samples loaded: 200\n"
     ]
    }
   ],
   "source": [
    "# Load the dataset\n",
    "filename = 'data/music.csv'\n",
    "music = csv.reader(open(filename, newline='\\n'), delimiter=',')\n",
    "\n",
    "header = next(music) # skip first line\n",
    "print(f\"Header: {header}\\n\")\n",
    "\n",
    "dataset = np.array(list(music))\n",
    "print(f\"Data shape: {dataset.shape}\\n\")\n",
    "print(\"Dataset Example:\")\n",
    "print(dataset[:10,...])\n",
    "\n",
    "X = dataset[:,:-1].astype(float) # columns 0,1,2 contain the features\n",
    "Y = dataset[:,-1].astype(int)    # last column contains the labels\n",
    "\n",
    "Y = 2*Y-1                        # for the perceprton classical--> -1, metal-->1\n",
    "m = dataset.shape[0]\n",
    "print(\"\\nNumber of samples loaded:\", m)\n",
    "permutation = np.random.permutation(m) # random permutation\n",
    "\n",
    "X = X[permutation]\n",
    "Y = Y[permutation]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TO DO** As for the previous part, divide the data into training and test (75%-25%) and add a 1 as first component to each sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1  1 -1 -1 -1  1 -1 -1 -1  1 -1 -1 -1 -1  1 -1 -1  1 -1 -1  1 -1 -1 -1\n",
      "  1 -1 -1 -1  1  1  1  1  1  1 -1  1  1 -1  1 -1 -1 -1  1  1  1 -1  1 -1\n",
      " -1  1 -1  1 -1 -1  1  1  1 -1  1  1 -1 -1 -1 -1 -1 -1  1 -1  1 -1 -1  1\n",
      " -1  1  1 -1  1  1  1 -1  1  1  1 -1  1  1 -1 -1  1 -1  1  1  1  1 -1  1\n",
      " -1  1 -1 -1 -1  1  1  1  1 -1 -1  1  1  1 -1  1  1 -1  1  1 -1 -1 -1  1\n",
      " -1  1 -1  1 -1 -1  1 -1  1  1  1 -1  1  1  1  1  1  1 -1 -1  1 -1  1 -1\n",
      " -1  1 -1  1  1 -1]\n",
      "Number of samples in the test set: 50\n",
      "\n",
      "Number of classical instances in test: 27\n",
      "Number of metal instances in test: 23\n",
      "Shape of training set: (150, 3)\n",
      "Shape of test set: (50, 3)\n",
      "Training set in homogeneous coordinates:\n",
      "[[ 1.          1.59845012  1.0338905   1.54033565]\n",
      " [ 1.         -2.19521109  0.46461217  0.54732802]\n",
      " [ 1.         -1.25042957 -0.43985059 -0.71548199]\n",
      " [ 1.         -1.024328   -0.44608588 -0.38735755]\n",
      " [ 1.          1.59845012 -0.63498369 -0.65920454]\n",
      " [ 1.         -1.024328    0.94115791  1.76922587]\n",
      " [ 1.         -1.024328   -0.50021839 -0.4320355 ]\n",
      " [ 1.         -1.024328   -1.58589159 -0.25616572]\n",
      " [ 1.          0.90418532 -0.65259191 -0.98577942]\n",
      " [ 1.          0.90418532  1.12464185  0.34287534]]\n"
     ]
    }
   ],
   "source": [
    "# Divide in training and test: make sure that your training set\n",
    "# contains at least 10 elements from class 1 and at least 10 elements\n",
    "# from class -1! If it does not, modify the code so to apply more random\n",
    "# permutations (or the same permutation multiple times) until this happens.\n",
    "# IMPORTANT: do not change the random seed.\n",
    "\n",
    "m_training = int((75*m)/100)\n",
    "m_test = m - m_training\n",
    "\n",
    "X_training = X[:m_training, :] \n",
    "Y_training = Y[:m_training]\n",
    "\n",
    "X_test = X[m_training:, :]\n",
    "Y_test = Y[m_training:]\n",
    "\n",
    "#New permutation if training set does not contain at least 10 elements from class 1 and at least 10 elements from class -1\n",
    "while (np.sum(Y_training==-1)<10 and np.sum(Y_training==1)<10):\n",
    "    X = X[permutation]\n",
    "    Y = Y[permutation]\n",
    "    X_training = X[:m_training, :]\n",
    "    Y_training = Y[:m_training] \n",
    "    X_test = X[m_training:, :]\n",
    "    Y_test = Y[m_training:]\n",
    "\n",
    "print(Y_training) # to make sure that Y_training contains both 1 and -1\n",
    "\n",
    "print(\"Number of samples in the test set:\", m_test)\n",
    "print(\"\\nNumber of classical instances in test:\", np.sum(Y_test==-1))\n",
    "print(\"Number of metal instances in test:\", np.sum(Y_test==1))\n",
    "\n",
    "print(\"Shape of training set: \" + str(X_training.shape))\n",
    "print(\"Shape of test set: \" + str(X_test.shape))\n",
    "\n",
    "#Normalization\n",
    "mu, sigma = np.mean(X_training, axis=0), np.std(X_training, axis=0)\n",
    "#print(mu, sigma)\n",
    "X_training = (X_training - mu)/sigma\n",
    "X_test = (X_test - mu)/sigma\n",
    "#check if means and variances of the normalized training and test sets are respectively close to zero and one\n",
    "#print(np.mean(X_training, axis=0), np.std(X_training, axis=0), np.mean(X_test, axis=0), np.std(X_test, axis=0))\n",
    "\n",
    "# Add a 1 to each sample (homogeneous coordinates)\n",
    "X_training = np.hstack((np.ones((m_training,1)),X_training))\n",
    "X_test = np.hstack((np.ones((m_test,1)),X_test))\n",
    "\n",
    "print(\"Training set in homogeneous coordinates:\")\n",
    "print(X_training[:10])\n",
    "#print(X_test[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To define a logistic regression model in Scikit-learn use the instruction\n",
    "\n",
    "$linear\\_model.LogisticRegression(C=1e5)$\n",
    "\n",
    "($C$ is a parameter related to *regularization*, a technique that\n",
    "we will see later in the course. Setting it to a high value is almost\n",
    "as ignoring regularization, so the instruction above corresponds to the\n",
    "logistic regression you have seen in class.)\n",
    "\n",
    "To learn the model you need to use the $fit(...)$ instruction and to predict you need to use the $predict(...)$ function. See the Scikit-learn documentation for how to use it.\n",
    "\n",
    "**TO DO** Define the logistic regression model, then learn the model using the training set and predict on the test set. Then print the fraction of samples misclassified in the training set and in the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  6.27158443 -47.32267331 107.41944852  48.28962119]]\n",
      "Error rate on training set: 0.0\n",
      "Error rate on test set: 0.020000000000000018\n"
     ]
    }
   ],
   "source": [
    "#part on logistic regression for 2 classes\n",
    "logreg = linear_model.LogisticRegression(C=1e5) #a large C disables regularization\n",
    "\n",
    "#learn from training set\n",
    "logreg.fit(X_training, Y_training)\n",
    "print(logreg.coef_)\n",
    "\n",
    "#predict on training set\n",
    "logreg.predict(X_training)\n",
    "\n",
    "#print the error rate = fraction of misclassified samples\n",
    "error_rate_training = 1 - logreg.score(X_training, Y_training)\n",
    "print(\"Error rate on training set: \"+str(error_rate_training))\n",
    "\n",
    "#predict on test set\n",
    "logreg.predict(X_test)\n",
    "\n",
    "#print the error rate = fraction of misclassified samples\n",
    "error_rate_test = 1 - logreg.score(X_test, Y_test)\n",
    "print(\"Error rate on test set: \" + str(error_rate_test)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
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       "    );\n",
       "\n",
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       "    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>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<img 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\" width=\"432\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "ax.scatter(X_training[Y_training==1, 1], X_training[Y_training==1, 2], X_training[Y_training==1, 3], label='Class 1')\n",
    "ax.scatter(X_training[Y_training==-1, 1], X_training[Y_training==-1, 2], X_training[Y_training==-1, 3], label='Class -1')\n",
    "ax.plot_trisurf(X_training[:, 1], X_training[:, 2], (-(logreg.coef_[0, 1]/logreg.coef_[0, 3])*X_training[:, 1])+(-(logreg.coef_[0, 2]/logreg.coef_[0, 3])*X_training[:, 2])+(-(logreg.coef_[0, 0]/logreg.coef_[0, 3])), color='r', alpha=0.5)\n",
    "ax.set_title('Training set logistic regression')\n",
    "ax.set_xlabel('Tempo')\n",
    "ax.set_ylabel('Chroma stft')\n",
    "ax.set_zlabel('Spectral centroid')\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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H+yMARX+HQzHwRUByJDqyC6HXTDhAmL6UT0chFYgURLiIGWWxS1JlgqueuzO7pvnJ5MDIEe4hWazNliwPvvZz+qxxx4zfAR4JaQh8678QIFdO+8AWwIY8EKRHQLJ0u7+2dGzg8dLQNjAerH8WSpkzMB1AFjCreH3ZKnwc6EsFb+Hgz4WG2Nxn9eOK7g6ZKkwVgEAcDUAWIxPxhKAC/sCLuAsAbrg8ECWBnxddtllhtQJEdSCJsIqhOIAZnhCgkJveOeKvcfBBDj4NuC94A3DpmRuMQZ4/gULFpjvuj8BBxwmxjzfKtylfB6sfDOqAxwDt9Zk5s4OcAzcq8gXjrjuuuuM1gXuYRZwyGwADEAILn40OFjwmHTY3UAcJW2URY1GGARiJzF/Jvp8nAYmD9I/cdmzEBBbhaTHAm4b7m52/izCECjxdJCOSh/gjtDY0QJIAEbEw/PpcPC8xPpvvfVWcx5ggcUYImRUwMH5ZDtAUCSUxKKLzehbFA4H10G/AbDk1+Fgcvc2QIHV4WAhhIBItgyLoZV19wIFvCMsgmSK8N7wGLAQwz0B6NHggVgOC0z+QjocvAMWILw68BgAioCsYjocQV6RQmPMPnPU5/XainHAcwIqAWU8Lzbi/eM9AmxANMV2AAs4I9iS94AXg7Ai7xPQUl9fb54RewNgsHE+HY5i73GwAA6eHzDJBgDuCsARPhGAC04QAKS/AQfjj5AtoUG+OQc4iq8drnibz0YOcBQfNO4IZ4EgC7BgAgBYMD8K6ppD4XkL7bKzMsoBE3gA8VSS5h3U/CEVQD/eMzxFcHAg4KLdY8clXkU8R3hE8HZyXTKn2BwQvmVzAqEXHhMhTq6Rr+EJxFvlAEfxEeMAhwMcxUeJO8JZwGcBwh6EAvAmMRlD1sXbgTcIV/NQa0P1edMAHI++tUy/uu9Nvb2kQZtOqtOZn5qhj286IbUhgKeQ0BocIoBHGMDBeCQkRzgTTRG8SHhG8LBxPbxLiOXBqcFTyR+OJURHKjDeLJrNhAoKidp+OMAR/lU7wOEAR/jR4o50FshZgNASLm74K/xMCIDJGpe3P8VvKBhtqD5vUsAB2PjKH/6tbknd3ZB7JRaVa4/fJTXQQbiULBoym/IJkRUjjeLNgEOEt4JUVFLOCaNB5vV644455hijJIzQmhX3KzZ+HeAoZqEPf+8AhwMc4UeLOzJTFsDVTCowRFrcv8ST+RMklZypjrvOZMYCSQHH4Vc8rufmrDRgwzZAxw7rjdXfTtktleeE+4MnIoqHA88EfCa4UAAVqwhsFWBR9CWTDM4Q3B+Iw3g94BYBIAAnfEfo3cDZ8tYs8j+UAxzhX7MDHA5whB8t7shMWACAAbGTPywYTKYWZPC3rYDqBSH87IBIJl5fpjqRFHBs+5N7tLq5fa1nGj28Si+eu28qzwqwJvMLjgUhkaDm93BA+IZUSwYLhGI8HJDCbeaavQZgBECBaCDgxNvI9EKSHh0etEryNQc4wr9mBzgc4Ag/WtyRA2oBgAbgAqDBJAyw4Gf+j5/5P+8fb2fzARHrEXFgZEBf7YDdPCng6A8PB8axWSpk+hC6g+gJFwPeEIDBn6VC9hipxGThkHVG9hehEwAHHhMyhazYGeRfCJ94P/B84PGgVAKZJzbziYKD/sZ3xx/OJ3uIc/meCvE9vNdwabEDNuyzc2OXpZKdd+F6Qly8B0T4gYYFCHbSK+TyDQtErGfEG5pxQGRoj8KkgCMfh+P/TthFH9skPeIobwEdDhZ3q0pL2jtFDUld9+twoBND2jxkX2oikbZN+jmAA08GmSik2TPWt912W6OHw99WFZbsFUoNkBlD2m3Qd4Bng995GyUKKNIXpjnAEcZKQ/wYBziG+AseJI8HSLChE+LKSK2jTOqf+MIAjnyPHAaIBIVn6IMDIoNkIBXpZlLAYYDAW8t02X1v6q1clso395mROtgYGtbu+xQOcAzFtxrxmRzgiGgwd3iqFrBAAyAB4KCha4F4GSJe+dy6hTwcUTtIH2j5wjNeEOInqzogEtXaA3t8GoBjYJ9g8N7dAY7B++5S67kDHKmZ0l0oggWCgIb1JAA4EDBCurw/AEchj4gfiKCWigcGUp8NxVRWVvb+7DJnIgyCATjUAY4BMHrulg5wDJztM3NnBzgy8yo+Eh0BaFjip/Us+EMWxJORcZ49e/aAAo6gF0Lf8L7MmjWroEckKDTjUngHfog7wDFw78ABjoGzfWbu7ABHZl7FkO6IlwhK6MSCjKCQBKJT1AuhcNxAejgKAQ6Ief4WJjTjgMjADnMHOAbO/g5wDJztM3NnBzgy8yqGZEf8GSc8ZDESJgXnYNbb4nBew+AdQfwrTQ5HFMOjyohyYxDgKHSdMIRVQjNejojziER5M+GOdYAjnJ1KcZQDHKWw6iC7pgMcg+yFDZLuesW6WGy9Ql3FHoHqoC+++KLRDfC3wQo48j1zGCACCHGiZsVGTbjfO8ARzk6lOMoBjlJYdZBd0wGOQfbCMtzdfBoa1qsRtusQMyk0tfvuu691SpK02LD3L3QcHg4kp3fcccc0Lpf3GnGAiBM1K/5KHOAobqNSHeEAR6ksO4iu6wDHIHpZGe2qzThhQqFZVdC4KaNNTU2maiWiQv420IADfgkZNKUGHFE9IvQL78/EiRN7vSEuhXdtKzrAMXCTiAMcA2f7zNzZAY7MvIpB1xGvWFdLS4sp2b733nsnFsmi3sO///1vo5boAEe4YUFJ8tbWVlPO3HpH/PVmnKiZTC0eODjYKawkd7g34I4qZgEHOIpZ6CPwewc4PgIvOeVHDNLQYHf94IMPat99kxewArw88cQT2muvvdbq+UBzOAbaw5HvVQI4INNSxMvbwmTOWI7IR8EjMlQAR7Hy9Cl/8qlczgGOVMw4uC/iAMfgfn/92ftCYl2QRO+9917tt99+ibvExGS9Jf6L2YqxLI4D0bIKOFiACDf5AUeh0Ay/K6SuikdkqImaDSbAQS2V888/39RS4T2tv/76Ouqoo0xFWH8tlYH4FhYuXKivfe1reuaZZ8TPFIOj5H2+5gDHQLyljN3TAY6MvZAMdieMWBfH3H333QZwxOVu2Ednp/7www+bUtn+BrBh4hqotFgAB1U7d9ppp0y9qffee89IwxMqSNLCeEQGs5bIYAEctlrseeedpy9/+cuaMGGCEcO74IILTAE1f7XYJO887rmLFy/WTTfdZPhMqAI7wLG2JV15ep9NHOCI+7kN/fOiiHVhjXvuucdwOJJ6H9ipE57ZZ5991jIyYASOx7BhwxIDmzhvEI0Qyn1nEXDwvpBcL0UbKkAkFcDxzoPSQxdIS1+XJm4u7fldaeO1w39x3wO2BjhSufWHP/xh4GX8IRW+ve9///uG0FxbW6tDDz1Ul1xyiSlRT7v00kt12WWXCaVcSt1z3RNPPFEAVSrJQtLmu91iiy2Mp5JrRGlsMhzgcICj6JhxgKOoiT5yB8QR68JI9913n8ksqaqqSmSzoPAMIASeAn+sUimTYl1dnUaMGNH7p9REwKwCDuTWmfQ33HDDRLaPenIxIEKf0FUZN27cWuGZpJ6wqH3l+MSAA7Bx3WHEoghIIWOHkp305b+nBjrwoG222WYm/Tqfx8oPOAhB8i1ss8025hs56KCDjGfkBz/4gfHIEeog1XzzzTcXngn+cOyXvvQljRo1SpdffrkxJ8ADj0XU78gBjuDR6DwczsMRZ576SJzDQm6rtkYV68JADzzwgFEHxfuQpHnDM3g0mEDnzJljUj432GADs3DB5WhsbFzrDxOfF4DYn6NOoPn6n1XAQeYFoY7+BhyF3rPl/BAe+8QnPtHHI8V7KlSFN8n4KXRuYsDxv/tJc/+dAxv2TmXS9F2kE+5OpduPPfaYEb3Dk1dTUxPKw+E/CG/GHXfcYbwVjI2tttpK1113nQ488MBerwfnHHPMMUJb5uKLL9amm24au/8OcDjAEWrwOA9HKDMN2YPSEuvCQIRBdt111z4TWlzD3XXXXQZczJ8/X5MnTzYLKeChEIeDZ2FBCQtE2BFG9cZkGXDgEsdmWWoAw0ceeaRPxtFAipolBhwXrC+1rFrbxDVjpO9+kIrpCdnhiYji4cAz8b3vfc+UBACosHHAS0KYg3bDDTfoiiuu0LPPPmu+0Ysuush4Pah8/OMf/9iAE0DDscceq3POOWctjtSf/vQnQxClQV6l1pG3OcDhAEeowe8ARygzDbmD/ECDByxW46SYEdjJwm2IGv/1XpeUWOLKeDWmT59uOAk2Ds1xcUijfiCCkqkFJSzSQaGZfEAEwAF5b+eddy5mjn79PYsTfWYxyFJj4WPHHiTi5u9nWCBiPSP8HVVdNTHg6AcPh+XiwLEgJBLU/CEVspOOO+44k8ECMMfDcc011+iFF17oczpgBEABoAeceNsrr7xiiNq//e1vdfjhh0caRg5wOMARasA4wBHKTEPmIK9Y16JFi8QfYrlpxNOJI2+//fYmlhy1oS4KD4HY8jrrrGNCKJ/85CdN+MTb4gCOfH3BFohlBXlEACJBoRkA0WuvvZY5wAFZkFDWeuutF9X0JT2eBR4RN0IqcVsYIBJW1Cwx4MjH4Tj6ZmmjtYXq4j6zzVL5+c9/bngWED3hYlx44YUGMPizVCZNmqRzzz1Xp556qhmfhxxyiAHqAA48JnxPhGkILf7sZz/TLbfcYrwfeD7weADu582bZ37+3e9+p4MPPjhU1/keaNyL98xcwj2CsshcWmwokw7tgxzgGNrv1z5dkIbGsmXLzESUlkw3O9mtt97akNDCNrwNAI2lS5eaSY+QABNWPgJqmoAjDhBhIqUPU6dONYAEcIWHJGpoJqx9wh6XVcDBgoROQ1AhvrDPVug98btCWiJ+UTNCPIy3REqj/iyVvb6XKtiwz4sOB+AAHQ4aYPLoo4/WGWecsZYOx80336yzzjrLfEdUMkal99ZbbzWAA08GmSivvvqqAQLbbrutfvOb35i/v/Od74hwCdkrY8eONZkxpN2G3YAEHUdoNUgl2AGOpCN+CJzvAMcQeIkFHsFqaDDRslDSbOhk+fLlJk5MDn0aDXVQ0urGjBlT9HJkLkBmW7FihQkDMJl6F+3777/fLFJ+Amp/AI5CCxw2Y6e57rrryoZm8M7giQkKzfg9NEUNE/MA+sQuE9CWpYYLn530brvt1m/dKpQ5Q4gH3oIfcESpZtxvDzLEbuQAxxB7oXEexwGOOFbL/jlhxLpgp+N+nT17dioPxE5sxowZJgUyX0M4C6DB33gzWCCDFmV2SfTLz9IfSMDBMwGU2Cl6QRq2ZifvD81YIBIUmkkbiOA25z4AoSw1bMIOG1f9QDeb7o0qJtwgAK7NBPEDjnz/HuhnGMz3d4BjML+9lPruAEdKhszIZaKIdUGAZDEglTWN9tRTT5mdI/Fmf8NlC9DAK0DGCQtjIYGwhx56yCzqXsIo1xxowEH/YeiH8QrlAyIswix2XiBiQzNxgQhEVq6RNcARBNDSGGtxr4GHA1lwxinvgP5hNwCG9YwEXbsYIInbn4/SeQ5wfJTedp5ndYBjaAwCFmLCJvyxE2exrBMWT8SAdt9991SMQKye8Ah6GTT6QciEsA0eAHaVEELDyJKT8QK3hEXZ2wYT4MhnVC8Q8WbM4BGBvxLkESmm3grgGDlypLFvllrW0ogLAY5C7yvf7xwQCT/aHOAIb6she6QDHIP71SYR6ypUBj6OVQAvLHgw5iGv4dFgggdoQLIMAzTsffNlvAwFwFFoYeOdBIVmigERQmOjR4/WtGnT4ry6kp1D2I5xAJExC80LOPAmAfgAanGa84hEs5oDHNHsNSSPdoBj8L3WtMS6SAkls4T00zQa5EA4F3g1aLitEe0Ky3j39gGGPix6/2IwlAFHEiCCB4nsIEIqkFeLeUTSeN9hrsFYQDNi1qxZYQ4v+TEWcFgORxLA4Twi0V6XBRxohvizusriTBLRbj8gRztpc5/ZHeAYkHEY66Zpi3UVKpIWpYOAAPQ8IFMykZCpQlglyRwCEEK6rNfzAAAgAElEQVSOmV27t2UBcCCQlAUSJLawHhHE0gAZhNRscTt/aGYggAhZPXPnzi1YtjzKWEt6LN8Q/cHjBpgljBVHNyZOP+J4RLhPku8oTj9LdQ7eLuYJFFD9z+QAR6msnrHrOsCRsRcS0B2rocEOgVg9ssfF+BlhniqoSFqY8+wxnA8BD10D0lcJmeDRSEN8Kl+KLfekvspATcJZyrrwviuIrJB1p0yZYoi1QaGZgQAihNbICkEQKiuNhQ+yKGOVb6u/AEccjwgAktBP0HgfqG8gznsE2AE2GKOW4+W9jgMccaw6CM9xgCO7L80v1sVCgiDWfvvtl8qCy/Xvvvtu7bvvvpH4FUyCqBKyq2YXTeiEVFgWPXaNaQAOUmzZCSFG5PdwOMCx9pjF68JEziKar0UBIjZrJgrvJui+KMcCOvBWZaUx7knLBngwlpIWGyzVc1lJ/kL9C/AUlKo7ia5LPwEbEyZMyAeehmT0YUg+VJKR4ABHEuuV5txCYl0AhH322ScSQCjUS6pJ7rXXXoFaGP7zCMHgjiYmD18AoOEV+YK4SBprGgXEkEkm1utPsXUejuC3SXozZN1CgKMQEGEH6ierwguBkxMUmgkLRPBuwOPYcsstS/OxJLiqJbTCFcpis+J4FFmzrZCoGcf468zwjBY8DtQzAjbyeWlsn5yHY6DeTj/f1wGOfjZ4gduF0dDAw0Eaa1rl1lH0pM5FoeuxC0QCnYJqeDIg3AXJlyM+BYeD3ydtaHpwHXZE3jbQHA4W5Zdeeik1sbSkdrLnAzgAG4COtBq2TgpECLnhTYDXk7WWNUKr3z70j2+O+kTFWj4g8o1vfMPo7Jx88snFLjGgv3eAY0DN3383d4Cj/2yd705hgIY9N58CZ9ynKHQ9OCNMeIAN3PUAgELxbup5sJvBM5G0+TU9vDs8+jVQ8WsW4BdffDFzgAMQBBEyKD6e9F34zy8ERPBw4RGxMu/s0gGs8I6y1qglNH/+fJMNlcWWBv/lqKOOMsXfvvCFL2TxEXv75ABHpl9Pep1zgCM9W0a9UhyxrkceecRoGvgFsaLe2x4fdD3SZQmbwNNg1wzQCFNyHr0F+B3Imydtzz77rJE99+/YbVzbAY6+FgYEoYHi9wglfQ9Rzufd+z0ieDcAHEFiZoCTsKGZKP0Ie2wW+SXevhOOQqF35syZYR9preM+85nPmAJtBxxwQOxr9MeJDnD0h5UzcA8HOPr/JSQR64pTkbXQE3r1LshigAjKRMduGQlyv7R4oWuRrYL3IY3dLCJiiFiRdeFtWQAcVODsz2JkYUZoFgBHUD/xkAFgGU9+jgj/bz0iXkDSX0CEcA8chyQLeph3E/cY+FJ8k0kA/B577KHf/va3mfPI+W3iAEfcUTLIznOAo39eWD6xrqg79XzZG3Gf4vHHHzfkT9y37PjYJUP69BdNC3N9vCJMkGnE61nU8a6wUDnAUdz62AuPUFAdm+Jnl+4IACxjP4jXE+QRQYgL0IpHzRua4WeASNTvpdCT4cEDBJENlcWG7dic8H3Gadgd/sftt9+eyjcZpw9hz3GAI6ylBvlxDnCU9gXa1FZb54S7JdHQePrpp43nIQ3XOZM75EwmNWqg8CcJGRWuBzH7NDIS2LHDR/BLdTsPR/B4ReWV91eoUm9pR3rw1QmzETZhzIZtfCt+bwj/9gIRv0ckDhDB+0K4Jw3OUdhni3IcNYggYfNe4zQL9EiZ9nsK41yvlOc4wFFK62bo2g5wlOZl+DU0kgIN20tbryRO+qO9hk23gwWPOiU7vDQmJHaMxJy33nrrxEaFBMni6a9+il1xxScBbUk6hweHxT1rIRXGBZ6prAGOpIum912lDUQIAdLSyKpKMqbynZu0IB8bCTYmfO9xPJaleKZ813SAoz+tPYD3coAjXeN7gQYseHZ4O++8c2quYFznECnjFOkiXs0kC5GPxQkXPJ6EIHJmHKsQEyc0kwbrn10Zsub0zdush8MCuDj9THJOlgEHXgS/UFqSZ03j3DfffNOEQvzvMY1r22ugD+Mnq+K9y0dWZfEFsKYJhtJ8HnstvgEAQ9zNAEBj0003NYBjIMm5YWzjAEcYKw2BYxzgSOclBol1UZqb+iJp7obzLcSFngJPBsAHtzSLEl4DW9wrH1cijlWQLoZwGkY3oNj186mWZgFw4E1A2yBLjawevxBbFvqHNgvhD7+nqj/6BhAJCs3w/3BE+Ju+Ad752wKR/uhbmHskJQLjcaQwIxuBOCGnMH1M6xgHONKyZMav4wBHshdUSEODXRaLE0JdaTUADJNlMTVP+kXhLIAGipG4jSGE+nc6hC4gGvK7pA3SKZNcGqXI8z2nAxzBbwndEnaz/mJ3Sd9p0vNRn6VPcTxySe+d73wLRNCNAXizGANMLBDxp/AOFBBJCiL5ho455hhTfynrzQGOrL+hlPrnAEc8Q4YR62KhpwgZ0uFpNXaMyATnY67TL8IaAA0mUI7DJZvPpYrHBNXQNOqfcF+IeDvuuGPix2WhYqL3kw0BUYAafocIWSmyFwp1nnfKQpA1D0dWAYe3qFziQZHyBfz1ZwjBeEMzbBgAInBHgnREqHFSSs8BhG4yvqhPFKeRgXbOOeeIMgFZbwGAo1pSraQ2SV2SuiV1SOrM+rN4++dqqfjelgMc0YZvFLEuJrCHHnrI1D5JqxF3BlSwm/U2/g8PA0CDBtCAWFpsQkyz/on1qMBZSdr8MumEhXh2OBTslrEtiwGLgt2ZWgBi/ybjptjzR+1nVgEH2UuQf4Mk56M+Y5rHJ6nxkmY/gq4VNmRhx5o/PFNqIAJgIDwZRQvH+5x33nmnrrrqKt1zzz2lNmXi6wcAjqMk/UzSW5LWSFoiqVlSq6SVkpZJapH0gqTXEnegRBdwgMMBjlhDy4p1McnQ7EJWaEHjHD72tKq7cl9y88nSsOJa3APuBEDDej5IJw270KZZ/4QMFa636667xrKx9yTIhri7IUFaoAGIQpcDgOF9B9ZFbnek9m+OYWdqAYj9GTvFbVkFHEl3w3HtUey8/pRcL9YX/++TZvbkAyJ8k1ZHxHpGGINRATAqwLNnzzapsXHaX//6V1Gc8YYbbohzer+eEwA4kFc9kHp0kthdIZVaIWmOpNGS1pNUI+kHkn6R+13mvB8OcDjAEfpDSkOsC8Cx995795I0Q988z4FW6wJXK3UgyDohvMBiDBcjLNCwlyeOTfN7TOL0k+wXXOhpkGRZqAAwNJ4NrwZhobAcDnuc3ZVaEMK/ARx+EMICYYm0hZ4dwEH44uMf/3gcE5XsHNzmKGbGdb+XqmNZFSTjeUsVhgKI2PFGiMb+zJgMCs0EARGOxTuKUmjcDJMrr7zSbAB+//vfl+r1pnbdIhwOgMdukq6S9H7upvtJ2l/SlZIgqbC2E3bJVHOAwwGOogMyTbGuBx54wMT7ifem0ZA7hsOAl4PJi8U4ifYCgIUJMg21RbJyAApJFmNABh4NwMuYMWM0a9asPhNuWMCRz9acD2jwe0MI1dhS7N7QjF/dErsTvkjyjGmMA/81ABwIrhUqrleK+xa7ZlIvQrHrJ/l9f9sM4bKgrJkgIMK4Q1U4Cf/roosuMmP9l7/8ZRIz9cu5AYCDtZpJk7DJA5Jw0/xPzpPB73Bz/l3SfZJ+l/OEwPXIVHOAwwGOvAOyFGJdDz/8sHbaaadQxc8KfSmEDQAbhE7YiRPbZUFO2tKUI2cRRxSLcvdRmwUaTMiAKCZnntkPhHhHACT+jurNKdQnWwHV6wmxMtt+tzheIQBHmvePai//8SxOCK6lVdQvaX/s+UkzLdLqR9B14Ehst912ib/NpH0MAiKWm0SGDyDShmj4mRBLmLH3wx/+0Kj18nfWWx4PB+EUQMQluRDKeZLeznkyJkm6UdIFOeBhj83UozrA4QDHWgPSamhATOSDtiqWYT7qYqOb4mjbbLNNbDIfiythFLI/8GTAaSAbJI1MEPrOtfFMbLXVVsUepejvcR+z+8cNHLb5gYZN3YWrwu7MX5elVIAjX3+9Wg4sAogo4X0hLOPlh1ivSNx4e1h75TuObCjGWdYAR1bJrNiRb5PNQFrex6Tv0Hs+Yw3CLYDb7xVhXsoXmvFe4/TTTzfzxGmnnZZm10pyrSIhlY0k/UYS5KuXct6NwyQ9KenbOUJpSfqV9KIOcDjA0WsBCzRYVPgZghV8iySkwqCdJ5NGVAVIdj2ADAABuxR0NFjUyAQh5LDLLrsk/RbM+fBAuCaLVdIWJQ0Y1VOeg4kVj4ZfIwTPCwDGX8mzvwGH3yaEVCBoYn+bJeP9G++TP1uGxSEMPySJ/bOyW/c/Q1a5JfQT7yPhzjS/9yTv0HsumwD4FwAib/Nzk7xgxAIRQq6ci7fxoIMO0sknn5xWtwKvQzgS7xrKynzXcVoIHQ6qOH5F0g45r8ddkv6cS5uNc8t+OccBDgc4DLgAZJBxgivdejTgW+AqT3PHww4PkS5AQ5jGgsbuHiBAWitAA3eqbWkSM7kmyqCk0+JaTtoASf/6178MaMvXvECDZ0OBMogUl68Q3EADDp6RRTQobGT5IV4Aws8AJ8aUH4ikWYY9aQpl0nef7/yshnrSIGWWymZcF28rG46wqr1eIIK36/rrrzd6MVyH+QN+z6GHHqpvfOMbqXf7W9/6lvFsQhBOEXCQkZK5rJOoxnOA4yMMOIqJdZGGhkpmmm5pdhmkcharh8AuAaABAOB4BK+C8u9x6fNhx+FJBH0sS5YsMdyQNNRBAXEPPvhgoO5IWKBh+0ifAFf+UE+WAUe+yQhQy/v1E1UBl97YvA3RxBGUeuyxx8w7zFqRrqwCId4JHo4kpMyoi0+U4wmbMhck8Tzuueee+vWvf23mErLH8LIV2gxE6Z89FlLw0UcfrUsvvVRHHnlkmoDjGUl7SmqQ9EdJ7eAwSbhQlud0OOpzpNHMkUWtfRzg+AgCjrBiXUyO7ATSlIfOV/XUvgZ2v2SK4GVgt483pJCHheNx6TOZpNFwgwJ0/K7bONfGY3TfffcZ3RHbABoQXQFKhTwa/vvhFmZ35p9wByPgyGdLW/3UT1Tl//1pu4AR0ifzNfgIxOuzBjiyypOAG4UnIM2yA3G+mXznADbgN/lDimHvwXcCAL3lllvMnFaKxgaD0OIll8DplA455JA0AQfhk2tz/f6DpMmSYMmPyv09QlJdTovDAY5SvOBSXHMoK41GFetiIbd6FmnZmnoGLBbrr79+n0uyyAA02MkgK87vCy0o9mR2xexmKcqURmNRR2QrDbEuJrm7777bAA5i0HA0ogIN+0z5Ks8OJcCR7/15dRy8YITQUxBRlZ1rVhd2QmwsSmHGdhrjOew1sprebPuPhw+v2IwZM8I+Up/j+E422WQTw+NIo05SUCcuvPBCU6fl6quvNpohKQOOWM+dtZOch8P3RoYa4Egi1oV7kI8T7kRazV/7hIUYoMFCD8gAbETJbCgUtojT57Q5IXfddZcpqe0FGnEIk/m4JVkAHPAS+ntnzHOzSPqJqni8WMz5HR4ypM0BJYRq4gpGxRlH+c7JKjETuyFtjpJnFhteRzZM+WomFesz42XSpEm9HI5ix0f9PV5LwlEAGgQHSwA4vGmu6HEcJGmLXD9fzGWoIG+e6eYAxxAFHGmIdaVZOdWamQ8TFzkfPz8DOAibTJ8+PRY73utFSCNtF2DAxJtUyArggkcDjw1ZOQCpOEDD2g25drwcCH/5Gx4AS/YNO9ukpduRNVe85YdA2gNwsCsGlJAxBAfIT1Tt78qncHoAZ0nGQth3HOU4xj11hNKo+xPlvmGP5VtiI+L3jIY9n3HANwiwKgXwvOaaa3TKKaf0hp8hUxM+ZZ677bbbItu1QJYKYONESWfm+BuQSXEXP5rLWlkd1iYDcZwDHEMMcKQp1pUv/JFkoHJNiJk0yFssCkkn3zTl0lmckHiOop3htYcNnQA4eD4mShaYpC70QmTWKIBjzopm/fv9lVq0pk0T6qq0ywZjtdGED7N+or7brAEO239/3Q1ArldW24Zm8JB5hcxsiCbp+wqyI98mgIOdcBrgOOq7KnQ84xUBt7T0bNLsG9ciVIFMfdxwCB5CiOUA91LYHkCDDW2D/3bccceZdFw8HlG8tlwjj9IoUuWkz10n6ZuS7s3db0JO0nyepDOyKmtunivtgTHYrzdYQyppAg37Dm3BsLhuTHsd+mYrpzLRM6mzk0prp3H//febySSNRSKKdkYhoIHHBiDFAoObOil5sVCp+7CAY8HqFl3/zAJ1dHVr9PAq1bd0qFvdOnz7qdpwfDzQkVXAETZ04S045s2asRoOfo9IEo2KLGeCRE077e95/pVXXjGhyWLZbfn6BWA56qijDD+rP1oJQyqHSDpL0u454S/WcDJWKOZ2jiRiYplNoXWAY4h4OIhZM6Hl0HEqKD5pXRGABgsloRN2kha4kIESNp8+zOTAxw0RL27Zau89wmhnhAEa9hj6BrjyaoeEeSb/MWTP8D6CXN4smuzgiwG4O19dopfmrdF64z4EF/NWNWvjCSN02HZT4nTLyKqzm4vrEYp10xAnYXdAaBzvmdVw8Kft4v0C1PqJqmH5IXwDEJyzZivMmUbaaYjXEvsQwpx4NwAdcRo8o+9///tGH2MwtAIeDtjx50v6rqR/5US/yFA5WxIVZY90gGMwvOFcHwerh4OFMmocv9hrySc2Vew8JmxABUCDBtCAeMqusdBOvdh18/0e5j8AJo1CXSwKCJ7tu+++BbtD6ITnI05L6MR6NPwnpaVlwg4Ul3eQompYwHHdU/O0oqlDE+s+TCdd2dSuqooynfQxqltHb1ldRJMAjnxWYFxbTogXjFh+iD804y90l1VvEM9LqAFQm4akf/RRVPyMpDVoyBa74oorhDd0MLQCHI6qHNj4TC6kghYHMsvTJFEk5qGsFm7D7s7D4Rt9gxVwWKXQND8mSIpwB8KqbgJ4iJWyE8f1DNBAUdQbMy20cMbte5p6IcVIqJDr4GUANCC7FiODkp657bbbJi6TjgYB8eCgdN2wgOOht5brsXdWaKMJbIh62vvLm7Td9FE6YCa1n6K3rAIOQllJSplHsYTlh/gzZngvg6HQHc/Ktw4HwV+rJ4odSnksKfr0DR5HnHbjjTfqjjvu0E033RTn9H4/pwDgQNIcdyQVIQEdaHE8J+lySa/0e0cj3tABDgc48g6ZsKqbAA2kxwEacBWsdkcQOQvPAEWYqNmQVsNdSn5+krL03r6wG9pnn336hCgs0GDhx6NRDGjY66UFhgA4EG532223tcwWFnDgzbjx+QVaWt+m2uoKNbd1akxtlY7YfqomjYT8Hr1lFXDgpRpocqYtdGe9IYx9/gDGg+rLRCUWRn9b+c9IqnORZl+CrpVUofWqq64y884f/oBmVvZbAOCwvIxLJeE6/m32n2LtHjrA4QBH3nFbzBvBzg4FTHLkmUBRziy26LMLxD2apm5D1PosxT5UitahXMoC4AcaUdN3UW/cfPPNIxer8/exEFALCzi45urmdv1nYb0WrWnVxLph2nJqncaNyK/YWchWKxrbtLqpTa+/9KwO2Dt8Rdxi9k/6+6xmg5Algww/mSBB/BAvELE8EfghcXgoUW1IcUAAEuJYWWz+rKOofUT9k2/ZqoBGPb+/jw8AHFaH45eUfJIE8Bh0zQGOIQI4WPyZMNJsLHJocfg1KbgPOyImKWTP8WiElT8n3s1uJS1lUJ4XgbJp06bFZrD7bcbumDASHBZAl+VoxMlQoLAZkzipcUlaIX2QKIAjSh+a2jrV0t6pumGVqq5kvutp7Z1dIjzz0vx6tbZ3atmi+Tpi922164ZjVF428FMKHjc4HGmOsSh2y3csIIMaHkE8HFvozg9E4IzgNfQSVa2QWZrpnXgnuR5jPWstjcJy55xzjtkM/ehHP8ra4wX2pwDgoNQtxNDrc2JfzZKoodIiaaUkUmcz2wZ+dsiYaQYrh6MUgMNfp4SFjUWYqo18vHg0UHKM0tJWBuXeMNjhigA6kjYWdrwS7Cpt6CQO0LD9wPvCdeKy6+11WIgAVkGeIWzKn2JZKmFtA6B4eUG93l7SqNaOLo2sqdQ200Zqo4k93I9nPlile15fqmmjazSsokwvv/62Rk2erkO3nazNJlPOYWBbVtNP85VYL2QtnsWrH2J5IpDE8X74QzNxCt1xfwjJnEuoMGuNuQ1ieJJ6SWeccYYhlp9++ulZe7yogIPU1+MksbtckCtHz8+k7/D/8DhY1zMJPBzg8L3uwQo4mJgABGk2JjVcmSxyeDPwarCwAzTiZoQUI2XG6T85+nhYCHfEbSzokEHxaNBH3N5hvTaF7kn4iH6hOJiksegAXoJSKtMGHK8uqtfT76/WpJHVqqks15qWDjW2depTm08wXI9rnpyrxtZOTairNplR2G3EpPW06aQR+szW6cngx7VXGgtU3HsXOi9NcS3LD/ESVfmZ9+H3hvCtFuOHQEjmPIT4stbSqPNy7LHHmtomVHIdDK0AaZTU17G5Im3s9shxr5E0TtLVuaqxmX1EBzgc4Mg7OHHnIqDEDh/BHYBGUj0JbgZHAkJfEs+Bt9NIMpOCSNZI1OYFGjbrhDDI1ltvHdl7E3RvaitMnTo1cbiHd0G/gnZ5aQKOru5u3f7yYnV3y4iD2TZ/dYu2mFKnWdNH66rH56i9o8twPyzgGDl5Pa03brgO2w4S/cA27EF2UJIdcSmeAMIxfKcgefq07scmwZ8tw7/x2AURVS0/BELy2LFjzVjNWqP/ED6TFFQ87LDDdNppp+nggw/O2uMF9icAcPAxsqNEf+OyXAjFe+7mkt7OeT4y+4wOcAwRwGHFitIYaSxuTIykyjF5w+GI69EI6k9a6pv22nEUUQEa6GiggEp9Bv5YAJQW0ZP+QRLEu5E03FNIATVNwNHZ1a1bX1qk6opyw92wbeGaFiMQtvMGY/ToOyv08FvLtcH4WpWrW6+9+bZqJqyrz249WVtNi5e2mMa4tdfIauYMY42QZJqid2HsZvkhfm8IXjPCKHzb/A6eEeMU8J5WeC5M/4odEycU5b/m3nvvrYsvvjhVsnqxfif5fQEPB+qOfJj+EvRwOHBPZbqAmwMcDnD0WoBJB6CBaBeuVXb8EDzTkOb2mjktMSx7TVsQLkzpags0EDni+bxAw14vLaIn14N0C98lqas6nwIq7mYIfyyyLBx2F5vEe/TMnFV6dWGD1h1TY0igbZ1dWrymVXvOGK/pY4cLMumd/1mid5bhwu82KdEH7TpTe282QVUVH5JLk0y4Sc7NqsAWYw5bocuShWYL3fFNMIYIu2A7xhSeTL+QWX8XurM2SkN2nRApWhzbbLNNFkxftA8BgIPslFU5RVHqqCwn6Sz3B8/HA5KIKacbVy/a02gHOMDhAIdJ0WPCQQUU0hiLsK1LAjjABZymhwN5ZxQN0+BI8Prgl+CVKSRaBJgCmPCM+YCGHQoUb8MG8FWSNvglEGuTkvFYCAhvfepTnzJd4t+AQ3g1hLt4Xzyj/WN3rjaez/vzK1/me7bG1g49/i4F3loN/YxJYsakOu2w3mhVlPdMGXhCqM2yprndpMUett+eqcjpJ7U35wPO0GZJM/U6jX6hawOYJ1yXtQYhGXIzYRU4MH5vCHME/++XdeffadQwKmSPpLLreHio2Ix4WBKeV3++Mx/gwKNxbY6v8dmcmuhwSeSz8zsIo7dL+lp/9jHOvRzgGCKAg8dgZxKlWXluYssssCyKfnIZ4QUW8jFjELRLp+FB2HTTTYtqdoS9G25qniVIljkK0LD3SzPNNq2Kuza7B+4LWUKALOTiSUnmnXmzVLw7VxYK+8dWRvV6QvIRCslUWVLf1pOlMqzCEESD0jCzmBEC4GCMUUslSw35cMIqW265ZZa6ZfoCIZlFuVDWGSDXpu16AQnhl6D6Mkm8bF4DoV7MHDVzJnzJ6A3AASgH7MVVKo1+12RnBHg4ABUQQ/9L0k8lkQ4G4ECxD2/He8nu2D9nO8AxhAAHEy0fV7GGaiW7fRZpdvuFxKzSSuv09gkPAuAmaeaGvSZcE3ZBXle1F2hYjkYxpr69Xlq8C65HlUq8DUn1DZjsqQPBbpJYO9oeTPI0dp78vljc3VZG9YIQ7GQFp7ykQm9BMsDHu8uatLyxXSOGVWiTCbUakeN3ZBFwpJHVUOwb4vd8a/NWtWhFU7vG1VaZEFQhbQzGKd9e3IUzTJ/iHgNAAwhF9WRiA+ztJ6rCD2Gs+omqYQvdeZ8jqQoq/CcKv+EF7Q8RtbjvwHteAQ4Hh7Fukw5GOmxbjs/Bz/A4Mt0c4PgIAQ5ioQANJgcyTvgIi32AaWVZeM3Mgs7uPC1GPDtHdkGQ8ZIADdtHGPG4lpPyLrheHEKr11Ys6Ki58t6Y2JGE9+/SwgKO5vZOzVnRrK5uaZ0xNRpVU2kWTSZkPwjh/1gchg0foaeWSB/Ud6mqskrlFRWaPHKYqS5r02KzJrJF30lHTlM+3z+L4/m58bkFembOapPRA9eFkNORO0zNy2OBv4HOC8qzWWtJpcP9z2O9bP7QjLfQnReMFOKHEDrkerbadFTb4dkgw4VNSZpiaVH7EeX4PIADjwb1U47CoZ3T2kD4i/aapIuzrMFhkVIUOwz5YwerDgcvhh2sLVFvXxQLCm5cu2ABNGCiF9sNl2Lx9V6TEE1a8VQmEp6PHT9x8qgeDf+gRg2SyZDrJG0IKtEIIUVpvDd2xEbjYsQIcz7hrf3222+tSTNMlsrclc36x8tLhBw5DU/F/jMnmVTXoGbj+M+8t0x3vLpc46s61NHebtSEVnZUacsptfr0VpNM3/BYZUnVk50sYbFSAo6H3lymG59fZLwaw6rKjSLrglWt+vysqdp902BV2aQ79SjjJ+qxCGuhgFpqPoYtdOdXVLXhPn9oBq8k3zZ/x/0e+QY/97nPmW9pkAIOK+S1s6RrJD0r6ftuwS4AACAASURBVOVcWAUdDshmr0v6RZYrxTrAEfBVDhXAwYJlF2I+ZnYHxDHDAg1rmiQaF/kmvTSviesWngSgCjBFiChs6CRf/9IKg3B9JkvsT3w8TOO94bFhcmTy93JdgorKcc1igIOQyDVPzhPF26aNHmYm3eWNbYb4efzs6X30Nvx9vO2lRXp9caPxiLCV7+js1Io1TapvbtWhm1SqKUdUtaqXXn7IQGU1ADjwzAUVugvzDsIcc8n972pJQ6vx9thGJs/UUcN05ic3CrwEXCO8VFHBZ5j+JD0GQjIALS3eRdT+2HCfPzTDWOUP4wuvaJwsLMLCZ599tgGhg6X5PBy2jspJklAu232wPIe/ny6k4rPIYAcc7CBYsMg6oQE0+FDjInt2B5ybZlGnpGEGngugwWKOR4PnQ8UxrR1tGv2zwwp3MG7kYmW/LUC09mZRQg7d+97yCaYVAxzzVjXrj0/OM8Jc3lonlKY/dNspmjk1v3bGfa8v01Pvr9L64yHF9zTASk1lhU76WI+yK7oqO++8c5+wjM1qsAAkrZTdMBMtYwO5e9K5S9XiAA6IvnyfcUMDpXqWrBa7s9pCZHpZ8GqzsADj/rRd/h20obrvvvt0+eWXixpJg6X5AIf1cDCgPy3pZ5JsKGWwPJLppwMcQwhwECPGe8AuhUmNtM64QMO7YLIrSzPuDBhiRxN21+99RSwmnE9c1qbwQpZlR5tWVgLeBUJTYXQ9in3tZJQwSRYiCuKdAWgAHAAakGmD3hsTJ9Lmfg9OMQ4H6at4OHD/27RW+v3e8iYdsd1UbZ4nrMIx81e16M9Pz1dFRZkhRqLBAXn0gJkTteP6YwwHBMARVAreq3ppOSLYwpIJvWAkbMpuMXvz+zSUKYvdB+GzG55bqHXGDFNNVcWHIZUdpmr3TYJDKlktkJZVKXj7DgCP8M1sPSLGHF4svzcEYA8wsV4QAAheXjyg//jHP3TzzTcXe62Z+X0eDwf8jV9J+o+k/8sVbUPoq1HSHPZhmXmAPB1xgGMIAQ6IkyyUZDEkBRrWLMSd8R4EpZzGHdxhFmH/tYOAho03F5L9jtNHvBJpgaxC9iNjAW8KEyUeJEi0hd4bWSqAKn+cnQWDxT0fAZjQyXVPzRfy5D2ZFDJCXsMqK3TCbtNVW11R0ExvLG7QI2+vMPyP4dUVRt589oZje8ELO8cgwBF0URYL3qVdLCwQof9ezRC7aMThFPQH4Gjr6DKA49k5q01oCiC34/qjDYcjn/gZQJbNQBwJ/jjjOOw5WRVKs/2HAMwGqlhqPnOf9YAwrpgPjzvuODN/ke570EEHmXkMHZR99tknlTmSeQLJdDYDCLsBjL797W/r+OOPD2v+wOPyeDi+KOnruXRY0mRp6HDgajwrJ3nOx9yZ6OYlPNkBjiEEOEpRMZaPFm8C5drTamRdkDETRvXPCzQgmTJZ+xehfCqccfsbBxDluxdeJzwY3mclHRmPBhkLTKRMUmG4Nfkk4ZlosUGhayytb9UdrywxoAPH5vgRVdp/5kQjTx6mdXR1q76lQ8Orys2O3tuCAIfV8aDSLNkwxZotRubNluFnWwPE6w0pllpZqAx8sX5E+T3gCXuuaGzX+BHVvfyYfNfIakXW/sjqiWJX/7EIdhGSjKuhccEFF5jvDdE8wjN837fccksqgAOAc+GFF+qYY44xHDLSiw844ABdf/312nfffWM/dp4sFQhDrNk2/RVXGrFOPuKlufL0se/ZHyc6wOEAR8FxhkuSDxRp4LSaN4013zXxWsDRAOzkAxr2XIAWu/8kH7i3H3gl8D6koQhJpgk8EwCbLRSHiBGTE88VBmjYvpF+SiYB4QdvCwM4OJ6dOOqhFGiD7FhdGU6GnIX1ifdW6b7Xl5rFdZt1RurArSZryqgewqQfcMD5uOG5BVrW0K5hleX6+CZjdeSsaWvdj/DMO0sbjUdgk0kjVJlTMbXP5q8BYsGITa3080MAoniIAHKEFuGVZKlltSJrf3iEkryHpCm7P/nJT0yY5cc//nGSboQ+l0JxeFJ++lP0ueK1AMBheRxkpMDlYBL4Zy6MQrrsoOB0OMAxhABHKUrUsziStZEmAQ8QA4Fup512WutrBGgQ6waUFAMa3oWJDI6glNE4n3uQkFic63AOz4FHh8XQ1m+Be5IvG4BFlkX49cUNpoDa9tNHa2IuE4JMAmzmr9gbFnDEfYZ7X1uqq5+cZ8IGlKtf3dKhdUYP07kHztCY2ioDOKjM2tLRrZcX1OvKRz8w4IFqsqSLLm1o0yHbTNbnZk3r7QL1Wq5+oidzBpwBofXru6+vdcf0BVNBfbYpu/6wDLZjYcHWuNEBjIRpimnNxLVL1PMAQbj28WhlqWUVoFkbUV6B+Sdu9tlZZ51lRM3OPPPMkpvdhkcvu+wyHXHEEbHvl8fDsYWkH0jCs7GfpL1zFWIJs1xJlYfYN+ynEx3gcICj4FBjMoK0RcXYtBoght2et9y0F2gguIUyZ5T4fZol78N4YMLYgskHF27YlF0WzBufna+bX1xovBEIShGOOH2vjbXd9NHKV9cGwEEsuRQLK6GRs2561ZBFCRvQ6OeclS06dtd1dcCWk3TfAw9q2agZuvf1ZUZ5s6G1UzOn1vWmjK5qaje8kUsPn2m8Gcsa2vS9W19XZ3e3JtZVGyEyyKkbT6zVjw+c0YfYGsbOtk+ElfCCYG/eIQRCQnL87eeHDETKblZLwFvl4R122CGsufvtOMYanj3I0lG8gd4OnnDCCSbMAZ+jlI2+Hn300aZAHx7XuP2lj3lIozfkwiZnS3pc0omS/i2JfN8TJD3vhL9K+YZLcO3BnBZbCg8HQIBCWJAC02peEJMUaNg+8YEDipART9qShpFY+PDSMPFQKZZ/Ewop1vBs/Oi21zR6eKUpDW80Oda0moX+l4dvpaeefNzIt9tYNmDgxXmrtWh1iyaNqNDW63xYXK3YvcL+nhTYM//2qslQITxiG0JiVIc98WPr6aLrH9LT9SNNv6m/Al8EYLHNOqOMBwSw0tDaocuO2NIQVB94Y5muenyupo/9UAq8o7PLPOtPP72ZNpoQjleS7xnwbhCzJwzINwHo8ApN8bO/rowNz5RShwLwSeYYadxZagA0NEJQ6s1aSyODBk/DySefrEMPPbRkj8e3esoppxiFWwikSQtT5gEccyURJ1woiZ8PkPSKpHk5b8cbJXvAlC7sPBw+Qw5mwGHz1lMaG+YyMNjZYcDqTquxAEAEI/0TUmocj4a/L/SRmL0/3BCnz1aZNSoHAFsRKmLyJuMEngbPyuIXBnDc8fIiXfvkXBNesI3y73gNzj94Cy1+60UTJsAtv7q5XRfe/aZeW9RgWGRd3V2ate5ofWOvDYtmnUSxCaDmv/7+mhpaOoyUOY1xNndVi46bPV37bj5BX7riYQ2rG6PxddXGe0HdFbwWiGBtNnmEACeAj//aeyPDsUDb4w9P9H1OU312VYt+8ukZ2mRiT42YuI0dOxkhhXhH3kJk3pRd3PZ+bwicGXarcF+oogtHBUC156bjtPU6o0J386WXXjLie2nVEAp94yIHJq3GmlY/gq6TRl0c5i6Io4T9StH4Hk499VSzMWPjQ1mEpC1PSAWvBvLlf5e0QNIGOWVRVEfhdZAim+nmAIcDHAUHKB9TmvwIwgwswOz+CZuQdZKGVwJpZnZoUYtPBT18VN4Ku2VbwZXFhMwTC3xY/Ijdh+HA3Pf6El35yPtaf9zwXgY96ZdLG1p14WFbae5rzxs9D9IDr3niA/39+YWaPna4Ksql1vYOLVzTpmN2WVef2TrdHfSDb/Z4JGhwOMhWARSdc+AMjaiu0BG/fVgTxo3VqOFV6uqS3lnWqMX1raqtqtC6Y4cboHLmXhua56IRPvnhP94wHhM8IEbGfXWrIaH+/LOb5U0rDTuT8v7wMEUNEVh9By8A4WcWPd7nze9Jj8/vkXcHgMBTgXdy2HZTQ3XNrycR6qR+OCjLVWzTILSycfjLX/6Saqad97UANh599FHDZUKSII2WB3Dgovl2Tt78d5JIg/mSpNWSvpXldFhrEwc4hhDg4FGilqgP83HAj2B3EJe0xT0AGiwCeDSo5YIXAJJnWg0mO8SwpK5M+kPaKgXciimX4u4lowXdDnY1aGn4AQ/ufWqzhJHZRufiOzf/R42tHZoyqkakoi5c3aLt1h2t7x8ww6TcIZbGvb72p+fV1NqpsYZX0W1CB4QzSHP92WfCyaiHtT0LMUXK7n9jmSF5bj1tpKnDYj0eZ1z9kBZ0juwNkXR0duvtpY3aeYMx+vjG47TD9NEGWHjb/a8v1Z+eWaDW9i6zgJOme9oeG2izycG1XcL2leMAHLyTWbNmRTkt77EAymffW6pv/+Nd1VRIlWXd6uzqUkuHVFlRpov2maSp40f1Ck7li90jTkc9EMJsWWqAf8Z8MTXcgegz/YLvFUQwD9Mfxi6ihY899ljiis1B92OjYTdN3nDcl7/8Zf3P//xPmC4GHpMHcLBeU57+YE95+hclIfqR+UqxPKgDHEMMcIQtUR/lS0D/AYKnPx0zzDW8QAN2viWD4jUhjTUJscp7fxZjb92RMH3Ldwy72kLKpSzuTNKk7RLe4L75NAKikm5fnr9GVz/+gfEC4LnYatooffUTG2hC3TADOOwznn79S1re0Gr+3wKORWvaTDG2Hx0QrVBcEltx7o13PqQHV4832Sh2UkG99Dv7bNxbxj7oHkvqW/X6ogZVVZYbEANvJY2GxguhrbQAB30izffKR+do7PAPgROgY0VTm87ebbw2qus0PBE4SXwn/rAMXjxqeRBmS8Plnoad7DWyXFSOd8miHpdfAuBg3uEZiwmHpWnTpNcqUp6emCNiX3A58G4MmuYAhwMcRQcr7kIvWbHoCTmPBrtMUkwt0PCGTvLJdIe5dtAxVCxl9wgpL2mzHBN/zJfJCw8N/AAWFRb/YpMYLmGIZLvvHr7eEgRKeA/oZEwb/SGxEt4LCxYSz7e8sEBXPz7HpMwOrypTfUu7VjV16NQ9NtAeeaqVJrVLvvPhz2y5w656es4aLW9oM6ETvBt+gbBS3d9/3VKQIO96dakuuvcdjR1e2Rvu4j2tae3UFV/YqtczY9UuvQJmjAH+n/GDd4M/cYqQlcp+Wa3xwvMm5ZfA1SHMWaosrlK9kwDAAVnoC5LuysmY21uTa46TEPCR+eYAhwMcRQcpZdFxS4bZmeHRsECD0AkLZBBHI59qZtHO5DkAjwRETUh5SRuTEy5YW3KdhQIBMitNDdAIG6tNU3bdC6rgdlz12Pt6+M1lau3o0rCKMu2/5SR9YYdpsdJKg2z23rImPfruCk0YUaVPbjaxT5aK93gAB5LrpUjLjfMuARzsaNNUx4W3cvx1Lxoxs5HDKtQlmcwbPDO/+dyWRVUr8Tzy/gDE3sqofBsWfNhMmTTryoSxH546PI14H7PWAPiEyArVIirUZ7Rv4PIwJtIq99AfNvIADiv4Rc7y9ZKQfG7wpL8emwMi+2e9ND12c4BjiAEOJjN2U2k2yjsTpyzkPWCRhqOBRwOgweSF1kG+libJk3uQAQAISENUCRsisrX33nsbsS5IrrSgCq7F7OwHL8WOL/R7PCWIoXmzHEiJXbymRRNqyzVxZH57R7kvAOu8O9/SbS8vVmdXzyQxbkSVWViDKssCOEhJLmVKaZT+884IeeGVS7PBS/nVA+/pjUX1qupo046Tq3XSjpM0qqxDHU1N6mxq6vm7tVUTd95ZwydN6nN7shhQoLQ8H75TAKmfpMr481ZCtUAkCYeqkB2yKrlOn5OGewBTpMPawnlpjodSXisAcJACe66kXeFu5wii7ZLQK7hUEjnNma6j4gBHwIgZzGmxPE4pAMcLL7xgtAPwIPhbVKBhz0+T5Mk1IWbCo0DFM2mDDApRlnAJ9gRo8PxxdkjsbBHsoo5D0gYHAEDl13FIW2n0n/9Zoh/e9oZRAK2uKDO7ebwoqIDeevKOfUrc80yAMwi2WQEcUd3w3V1dBih0NDero7FRnS0t6uRnCyByv2vPgYpVq+pV1tERmH5cjrx6ZaVmnHCCqkeO7PPK8RQCgoqlblsPiD8sg30t+LAckWJ1ZcKMOZSE+XbSAOth7hflGLyljG8yv+I0vpkzzjhDzGGDqQUADnQJ0GZHvtQbPqFoG/m+n3WAYzC94VxfBzvggFHPgplmQ7CI7A9217YBNJgM2ElazYlCHg1/f+AjMImEDU0Uex5ST7l/Urcwaazs+HDBonmBtyYO0LD95X0QPkpDx4SwESEjP/BLG3B8/a8v64n3VvZZUMk8IWvm+hNmraWTkQRwIH1+0/OLTAZMe1e3PrbRWB25wzSN9WW1FHv/3t8vXrhQC95/X5ttuKHxOrQDIlpbe4CEBRbNzQZU2H93dXTI5PQmaIANvEPDRo/WjJNOWmvcEKbDvR/lO7HdsXVl/CCEECagww9EbF2ZMI8DWIdXErShCHN+KY8hjIlnB35WnEaq6iWXXGJA/2BqARyOMZJ+IYkUtN/nirUhAkZq7BW5cAvKfMkGcYmN5EIqPgM7wLH2iGMHRKyZxTwp0LBXDwoPJBnrlHknDk1qapxGNglAgzQ8gBAy1HglkvISAAN4S9JIAUbHgbAWIMjb0gYcJ/3pRT0zZ41qqz9UFgVwAAj+ctz2fVJXCTPccP8z+uwnttVW64zRgtUtevDN5XpzcYNRSP3EJuO03bqj8oK2X977jh5+a7nJZuE5IGFuOaVOFx66hdHjYKHtam83ngc8EAYgtLSopb5Rj722UG/OXaqR6tAOU2o0aXiFOtvatGbFCq1ZuVJTJk1SeVWVObeitlYdDQ2olsUZHkXPqaipUVdnp7rb2zVqxgxtGFBHgzAiAnBRJPuL3djWlfFX2QUk+0FIvroypIDjNcuaIBnPntT7QlXYG2+8UbfddlsxU2bq93myVGbkaqlsKclWjr1I0rWZ6nyBzjjAMcQARylK1LPLwM3LJBbXo+Efg7g4meD8i2fcD4dYLd4EdCqiNDIIABqk39kKroAMFAMhQiZdHNIUTsvHU+G5AUppuNex3Z+fnq+L73tXKJmz6KOwSWG2SSOrdeepO5uQSkdXl77655f13JzV5veMDTQ0Zkyq1ermDuOhaGrvVHtHt47aaZp23qBHfRF7GODQ3Kw5C1fqvJtf0ojuNq1Z06S2hkZVdrSosr1NB20ySttPHmYABiCiu6PDUPEr8CJUVunGFxYZsTB1dqqqq0PlnR3aZ+ZEbTS+Vo3NzaZ05sS6OnV7vH2VtbXGy0EIJc1WMXy4uuhjZ6fKyss1drvtNH1/OHx9W9IiZGH7bBWH/SAEvgjeFW+VXUAIizqhyLS8jWH7GeY4vKtkZcUlg19zzTWCg3bttYNmTTZmKVAtll+vK4n87PdyNrTE0jAmHdBjHOBwgKPgAISDADgg1IAEOYtyHJew/yZBYZokXwKpfUyoYcWLOBYgRel4CLG4bL0chDSzaO655x5DQE3qLWEnCq/EhrZMrZVFi4R3x9bRse51YvJxyYZkwJxx4yv69werex0CKIpefNgW2nXDHuDw03++qZteWCQYHlUtzarobFdlW4smVndpj+m1Km9vUVlLi1avqlddd4cO2GSkuuBFtLSYxZkLL1zTqgffWGZCNXBEqEbLhNTaJdWNHKEjd57eE9bp7lYnZOjWVvMzlXQffmtFLhuwZwrDFjXDqnTCpzbX6kWL1NTQELhjN+Cgvd0AmDQaIIZnsuGYqpEjNWn2bE3Ycce1Lj/Q2TzeujJeMMI3DvBgbHm9Ilng5CRVZ/31r39tMswuv/zyNF53v12jgPCX10U3aICGNZwDHA5wBH5ETEJwNCitzuJFHDWu+E7QDdLiXNhrw2ZH1ZMsgELNK0TGrg6wEcT+z1cKPs6Mk5bmiJcYSyYGQIMGqRUBMqsBQXjIu6BYsqEXhORLveyEA9TYqNbGJj35+kK99M5ijSrr1J7Th2t4d3svkfKi2/9jvA/VXe0q6+wwO3vqoVAVdltPfZH2zm4BYPaYMV5VsFBzjVmzubtcN72yXKtbu4w3pby7W+Ud7epub1PtsEoDbjYOKOQG3+OtJY29YZru8nK1VtWoqrVZX5w1RVVdrWpqbtakPJos5RT4I1QD8EnQKkeMMPbwhmkqR47U+p/9rOoCOAfwCdB2SUvsLkHX+5yKoByEUUCbN2MG757fGwKgTcJpitpnQq+EOIvp3eS77k9/+lOzQTrvvPOi3npAjy8i/DWgfUtycwc4hhjgSFox1gs0cGPi0WAhJx8+TcBBGIOJKy7nwj/oScclOyFfKqT3uZhcea5C4ZI4Ymf5PsS0KtnCK2GxAlDgoZkxY4aJvVsXetBC1tHerjWLFhleQ8OyZapfsUJNK1eKjAtE0YeVlamqu1tVXV2q6Ow0u/8wkwK1VRC+Ki8vM/fnXVJoDiABZ8M2OBkjR9Zq980n9xRAQwALUIO3oqtLj727Uq8sqDcZMVyDaw2vqlBVZZkBLpsHSJ1TQI1zugzQGK5hbSz6hEnK9JVd1lFna7NampsLpnHD7SirqDAhljgNT0Z7fX3fU8vKDPCaefrpwvPhbTwXXjOr7RLnnqU6h3RdCNJ4OXoBYXd3n5RdC0SsN8QPRJKGHvM9G+RyvJb5lHyL2eTss882YdazziKZY/C0HODgUwz6HK2XozSEpBKaKczcUsLbZ+/Sg500GhdwMJEQlsBTYIGGlTJnN423I249g6C3zPUgoCIolkbDbQq/xC9nDb+Be1G7heditxQmJJRm2m4a4RmrWMp7AmgQ3rIAA45Dw5Ilalu1Sq0rV6ptxQrzt0nrzJEtK4cPNwssgMJka3R1qb2jw3BzuGZbR4f5u7KqSsNqagwYgyjMz/yfmShwX+TaDc8t1AcrmgxQMCijrMxodtRWlWnKuBEmnbazs1sV3V3aecOxpjCbaVzDQ9zEA/Knp+ebarTlFeWmGFxNZZmaO7p0wBYTNdlzHos55y+tb9E1j81Rl8pU1dVmOB/wSlA43W/mRAPI8GQVU53FHhXDhvV4KSI0wEZbff1aKwEeD9qWZ5yx1tXSKLMeoYuRDiV7hu8mTOkCvifGop8fQrjQC0L4OQ1OEd8hG50wfQt66JNOOsmQv0888cRINhnog52HY6DfQD/df7ADjqgl6r1Ag90yC7L/445S8TTsawIAsDBQcC2N5gdFTIzcAxAF6QxPSjH9A28/2PXZQmlJ+5ckPMPCCddk0bx5Gt7drbryck2uqzOAojUHLNrWrDGp0BYOoAPBDrt9zZrgrlPpFABSWdkDQJqbDQhg7PQCkLY2tQJEcnyL6mHDDAiprqpSDfevqNF1zyw0VWrLO9rUWVGl2rIufWbzsWpo69TKxnYNr67QumNqiqa4zlvVoqfeW6nW8kp1lVcYYLTZlJHaZb1RhohJ5gccDq8r/73lTXrk7RVqbutUV0WF1p86TvvPnKiqznatXr5cLYhvTZhQ/NWVlRlbAczCtKpRo9S2enVgWIHfDRs7VhsfddRal8Ku6HBEkbgP0580jkmaPWNTdv1AhLHLXOIHIlFSdpMSbY888kgdf/zxOiIgaygN25XqGjnA8V1JuMogLKEuyh/cat4/uOjm52B/qbqT2nWdh8Nnyo8K4PADDUIM+RZkdjMI6KQ5WeKNACSkpQaJ/DFVJSlFjZcGZcF8FVzDfD0w20kDBqwkbWFVVQkzWBDRuGSJ5r7xhha/955GlpdrVHW1Ie6yyx83toe4aRsOBgs4WPRYPL3ZGUX7bwEIHhDCHTkAovJymXTPXBXilqYmtdbXq7WlxWQEqaJS7zYP09LmTk0dXq5Z641RXQ2BmjytrMx4FMorK6nv3nNQV5cBEytXN2rBqhZDIJ1YV208G8Ump65uaXVLu5Fcr61CZLGHPNrY2iokGMmCCmsLuBcd/hCJz8bVo0cbL1IQh8Fk0FRVady222qdfZFG6Nv43uBKkPmUtVYqMitjkrpEfm8I78gPQoJSdjmOvu2xxx6xeS/777+/4HFA2h5MLQc4/ptkq5xkOR8W1Q35w8fD58GniVsNFdI8u4tsPXWxbzpbve2H3gx1wMHER/VFdv94NAoBDWtudirszvbaCxXddFq+EEjcq8MzARSxGBDvtUTKuNdLUycElzUxcoidZDQYUGH/eDwVLI6kmPIsq1auVO2IEUaQqYoFWtLyFWx0ZP6vvKLCeChMWKW83KSpsuDZlM8+9PVC2hN4NnpW6t5QBxM9oQvDubAAxGdIk1haXa3GFSu0fNky4/1gbNGHYSNGaFhtbU9IprpalRUVvbyN/iAckiLc1t6uCePHGwCCN8eIcnV2mpTcIIEvjgNQBHmFsE/1qFH5PUasAnhJmpq07gEHaPz2qEz3bXxDjCkUWbPULLeEb7s/3g3PzjjxgxCACaFOb5VdvCMA/yTzzuzZs/XHP/7RCK4NppYDHJNzMuZImQ/PgQtik/zh3/ypywmBpZvrXSJjOcDhM+xgBxw8DtwIf8OlS3ghCtCw10hTLdNeE+8GXgg8EkmareBKxgYTGTyTMEXmit0zbjE4+oOHwHoq+PulJ5/UpLo6VeAZ8PEFLHmRRa2+sVHLli9XzfDhJsOCMIYJdXR1Gc8GgAOODr8zfAy8A/AxWlvVDeGynX392s2QOsvLzTn2bwCFWWByf9ufDdiwAIRr5jQrjFdC6kkBragwgIe+cd6777yj6YiRdXaqPReGaWtt7eGGtLUZvgihGBOS4Q/hmaqq2LvWYu9u9Zo1JjQE4FirEUIZMeJDPovnfRiV0DFj1ErIJHeiARtBBFHfhfEsAVY2OfpojfAo8trDWFBJL99tt92Kdb9ff58Vbok3ZdeGZgi5Mn4A6n6Sapi6MrxPQrZ4SeJKo/fry/DczHE4Bsry/XzfoQA4+EjtwuEFGriY+fCicBl61p5uoSWBPHdaICGoHAAAIABJREFUKX1JeSH0CQ0Nsl0grJHeioBRkt2Qd6gVKgZnxKsIL+Q4FPxNXB8uBYsOvAhDjmQxlwzQg8A4vKamh5OQy9QwctplZYaEh5R6ZU2NJk2Zotq6up7zOzt79Cc6OlRVW2vEydpbWjS2rq7PbpRwQMvKlb33S+2TIQRSU2PUOs04yHkIzP9VVvaAj44OfTBnjtadPr2HXEq/c+ROSzTFa2P5IIBhxif/xnMD+MALgjcEMEIKb5n1uuRKupuxnPvZ+3e+5wRwkJ0TSsiqoqKHz4I3p63NPJPXmxGYjRJwY1Jt0QnZ6qyzjM38jR092jO77krtrey0LId6+C74DiGWe/kh/OytK+Otsuudnxg3kKv5/vAKDqYWADiI7Z6QE/1amuNsLJa0UtKNg+XZnIfD96aGCuCwHA08GnGBhtc0aEnA4Ugr/Y3dC6I+VBmN0phEWJwBGuyKCJ3Ykt9wJdKK1VphsmmTJqlh7lzVz5tnwASpkIANvAlm94+eA2qWud2+9Uh4wxRz58wxvAtDxmURp8BXRYXRili6eLFR0hw3apQBgiYPDtJn7lgj693UZBbylatWGe6ElwxZWVdn+oQXgRb7g6ZfhB5yngxSV034ISAcAw/DeFWQOxwxQu+9/76mjhtnQidhmyGokhnj8YRYoNzrCfF4Q/zufpuKC7gxXhv+5MDOqtWr1dHZ2ZOlkgN9FgDmlTbPgRyuw6KFtofxHoUgkwI2OJaQzMzTTgs0AeMd7ZmkHr2w9g17HOnVhCKzFuqh/4TG4GX5s+N49/TbT1IFzNoqu8wtfG+nnnqqAeqlEDFjM/fNb35Tf/7zn425jzrqKP3qV79K5V4+wEHYhDoqFGkDZHxS0iuSEB16XdLMwVCaPtH8FHZAD7bjhgLg4CNF6jsNoGHfH25JJsuo3pF8758JAxIdQkhhG8RQGzoh64Q0V7sQ4RoGFCWpWWIyfFavVuO8eXrtscfUvWqVxgwfbhZWyIIsWux+WWyN6qWvGU4FO3QW3pwmA39DYh03ZoxGILXd1WVSUNEMaWls1Li6Oo0aM6YHYNhddg5g+K+PV4hJzqZ7cj+a4VnkJLxDAQ4PSdScbzNVQryI+atb9NaaLpW3tmijCbVaZ0yNAFTrrL++akaONAtvEkEtnsN6Q2xIBm8F3hOTqmvDMtXVeZVbARxcZ3ycXS26JBBIm5rMO1lLayPARjacMnKjjbTRF74QaEV4OYDkHQMUSEOYvWSHsGijYJs1zwsPDFCAbxZW/8ebsnvllVcKoTVAHt6NbbbZxvz5+te/bnhrabRzzz1Xt956q+68805zuQMOOECHHXaYzjnnnMSX9+hwMP1sI+kOSRBRcDfeJWlrST+S9Iykng4MghZqfhoEz5FaF4cC4MCrgZvRK+ST1EAIYfHBEk9No7GYhPVI2MmayZGQEPVX/KEdwMLdd9+tfffdN3TYh8W/adEiAzAa585VQ86LwUK+orHRlCCfkMt08D8zi0zYxRXRtJGkTFZXm0mUeP7YceM0YZ11esIvZINUV5uwAV6FfLLb2IHFGA6HITnCG6ivN4AoH+AwWhM1NT0gCHCRCxvE+fCf/mCVnpmzWm3VwzWsvdk4C7Zfd5SmVdRr2jrr9JBDEREj5IMoV32DVjS2GqXRUTWVqhvWA5CiNkIyJl3X5w0BbPZyQnJAhNj+6lWrjPcrihvdEkO975SCb6ipFmv2uIk776xpn/pU4OGAZThLWSMv5vMiFHvm/vg9oJxvh3knTkN/56CDDhJaHoAqwjOkySatKG37QokBPBo25ZYicQiNAZKSNh/gIO3pAkmzJH1a0s9zIITd2mVo7Q2G0vTYJM68k9SWmT5/KAAOJmcm3DQbuhQITkWZxAvdP0wVVdzQaFCwswdoeMWugq5NVVY4HPncp8Tnm+bPNyESAEbjggV9duNkMsCjaGto0EpSJLu6NHbkyPyPkdsNQxI1wCFPY9I0WgWtrRo7caImTJmiLsIyAe/Im1XB5Yxbv63NfKhwE0hJxXNld9UcYwEHRE4TFmHhh1AKYbOlJZXsg9UtHaaoG61l2AjVtH6oW7HHtDLN2KAHcNjW0t6lZ+fXa2lnpZErr+xoMx4RCrylMenwzOxorRekl6DagYBYhSrKy022kg3PFOIeVSDYhb0IIXmaeReADt//e4+B32LeT1mZph90kEmLDWpRd+tpfruFrpVVIESf+W7o38yZRAyiN0i6hFQAGmk3+sVciNfKqiXzM3Mk89Xo0aMT3dIHOA6SdK6kXXJhlfMlHS/pcEkgXNIHQfPpFAhK1PPCJ6fx7Zewe/1/6aEAOJiI7Y43LQummSZq+4RHIoiIiicDoAFXg90INU/CFD7DhUosGgJib3gk57kAYDQvWRLMSYAEWV3d4z5nhz5qlBZ/8IEBbYGZDn6j5kIt7ehfeIqCcT6TD9yLEePGafKkSaQQRQYAZVVVBggBOBpWrTKAgwWUxc6krpIdQgZMa6tR3CxFe21RgxHaMpVjPYCjoqxMW4yVZm82rQ/Qe3H+Gs1Z0azRw6tUrm61VAxTQ2eZZk+p1uS6HhJqKRo2J5uHmjAATwtEGD+94mU5b8iwujoTAuO950sJLabP4QV+mx57rGrJ1gloZGWhPZOW7kxatuMbI+S33XZskrPV6JeV8I/TMwT3LrzwQuNJTbvRN+YlvDBWq4ef+Tb5HZujJM3H4YDxCth4OSf69RNJ+0laJukaSf/rOBxJrD2A5zrAEWz8tMvJcxd/CXgmF7gnaHRQvZXMk7BkLxbdB2+9VZuOH6/OZcuMF6NY/N14A6qqjHfCci9YsAipsIMhxXPKpEm9GT8mwySXpmp+zv3pJSLmPB4Aj1UrVmh1Y6NqCb00Nam2psaEo9iZGw0Nm6Ka09Ewst2e1qujkdPHwGNBSKWhvl6TSZkltEUWC+ROSrhH4XDE+L7eXdake15bavrfMqxWNa09oQYW6u3GSztu+iHgQLyLAmuV5WVGlMu2Vc0dWm98rWZtNs2QbgkflQIe8e5oNj2aPnsVVNvh0ZC+3NjYhxOCN6Squnot0Abgy5dybAq45YilW519dk9oLKCRUUVl37jhgRivLNQp9IvvDZ2YrDVCInyPcVNab7vtNv3lL3/R7bffnvqjWQ8HGyPbP36GxJ6ih8P2GzSIBwO+Bg19joMl/UfSY6k/XAkvWIrvvYTdLf2lhwLgYPExSpApNm+l0rQuCxF1l112Mbt04tsUYCMuCtAolg3TGx6ZM6eHgzF/vt5/5x1NnjLFLCK9DQErPAG5Yl2GzFlZ2ZNlQoYJoZCurp6/PRkZDagkrliRt+KoAQ5esJALJ6ByyU4Hl/7UadOMpsaiBQtUhSAZBFQATUwD1jc0qLGhwZBlLefAiIVxTTJfIJEWELeKeVtzGiDiuqfmq6W9Uy3VtaZgGs9RXVmuvaZ1a/o64QAH9U62ntYTpjLptdXVodVAw/bfAI6yMo0dM6bPKYA8AIL1ZPGdeEMy8GO8BFUbjqmbOFFdDahK921czyq6kqGyxamn5u0iizrjolg147DPmNZxhC0I96RVYiCtfnEdFnA4OWw+4rRrr73WCBZed911cU4veg5z1WWXXabDDyeyIf3tb38zReLg0CVtOQ8HMcpOSZfnlER/kBP78sb++mj8Jb1vqc+PO/eVul8Ddn0HOIJNT+YLH39aDG/ugquTstPssiCCcm3CIf5mwiOrVhlgYfgX8+b1hEdI5QRI5DJDIGvh0qwhpIL3gdASNTjKy036Jq2dhcMCC/6/rk4dDQ1r8SmaurtVv2SJUWMt1ExV0MpKNaxZo6Xz55tro/9QS9ZJri1dtsxoTkAULWeRzWWxmF/npL1NZkchRVAKKTQ0CNAxdcqU3muz+2ZX3ZKrm2I/aOu94ZqFiKhRPrTlje3Gy7G4o9KQRkfXVGqfLSaqeeViTZ06tVcRlWu+NH+N3iekUlOpivIytXZ0CV7HTuuP0aSRPi9AzjNEto1JAY7SqYBjV6xcabwUvSXNbeZJCMl3M9ZsQTuEyyCqUqcGkq5XvKy6WsPHjVNnDoiM2mQTbfj5z+fteVI+QkKT5D2dMA9cqbSKKKbZT3R14OFQ3TlOu/zyyzVv3jz97ne/i3N60XPIRsF78s9//tMce+CBB+qQQw5JM0sF9yBkvK9KQocDsuigbkm/7UH98EGdHwqAI27F2EIvk1AHO0JIUUkb3hfEeLgmKZ6Un/YWjCM80rx4sfFaNC9cqKYlS3rd1kY0i1AC4mYdHX1i70yeLOpmsWeRAWSUl/eAjAIk2t5iZzkOB8/XjMT44sXGm+BtLOSAHKPy2dSk5qYmwzXhmQAapL76PyrUQ/F4FFJANbU4cOdbAJUTXOMZTdpqe7uaGhsNj2Pa1Kl9+sQiWZEHOHGg+T1AJ6dcSkqvJaJGfZdGEVXV6mxq1Jjaniqyc+bOXQtwtHR06YV5a7Ssoc3coqJc2mTCCG0yaURBQEEfbQXXfNk6xfrMjh3vE4CDFFeTxgygi9mMN6u2Vo3LlvVJ2e2CW9PZabxxE3fdVevts4/JDgtSwWRskhHCWM9SYzeOfgWhgKw1tHDgR/i/wbD9PP/88804+PnPS7NOE6Y788wz++hw4PEIGwYu9Bw+Dwflh/lzr6QHct4O4oZ4Ot7M/R3WLAN6nAMcPvM7wBE8HvEeQOaMyxjnqgAWJjhis0wkpIfC8OZnQiRryEh5802tee+9Hs8FPAcgfq4Sqp8/4Q+DLFi4UOOmTNFIamIQV8/xGsJ+Ybj3WZgR+GpublDL/P9oEpgFFcmR66ijamJP3ZFcPQjIidTIgK0+auTIvMRDjuM5kmT44KVpbm/Xqvp6TV9vvZ5O5EJBkEWNjSorTf/x2BRrPCvPBQejN5ul2Em53wO6vJkbQYDDAB1Jq5rajXeDtNja6vDCYABGRM14Rt5llIkKeyNHPianCxLysQoeZkAQhF97VFmZIdDaVN0Rs2era9o0840AOKz6JTt0foY0yninAnGWWlKeRCmfBfEuvBtxCyh++9vfNqRz/h5szZelAtiAs0EskhCLwcA51dEjJD2Ryzi101NmHzfKd5zZh0izYw5wBFuTHRo7+TikN7wBuDbxaLDrBGQwET/92GMa1dysyuXLTYgE0a3AsIJVwURiG89CbofOsYbsmas+unDZMo0eOVKjRo/uXfwNSMnJidt6IeYJPdkcfYKgKIZWlKv1zQfVsWqhRoybgqa3ytCdGLux2sdtKkIkkDfHjR9vFESNMmUuHGLukfvZfv14OPDM4M0J+r3f4t5Frfe6hFSamrRi+XLDc7HPwPUMFyV3EXO8JZp2dJhnJ1RRzFtg9DqsxDceFWTL84R4ACtejwGs/Cm+kEqa36QFgiY0VCD9mHvyDMvwcLS1JU5N9D+DlyAKGPICu02PP161cGtyKpgm/FVf31ukjN0wO1/GAACE8R9UITVNu4W5Ft+kLQ0Q5vj+PIbMOAiZvaGxiDf/2te+ZoQFv/pVIhKDq/myVDbJpbzymVOuHu184pJkrzw+WCrFmml3cL2G0vd2KAAOE4dO4EIOsnKc6q4ADYigTGpMrrhtawhVvPGGVr3+ut55/nmNGj9ewz2LdNg3TOjBCFrlKpqyGC1GYGviRNX6Mj7s4mx29bkMEVPvwwKDHN8DLoYBNMvnqmv+c2osq9Go2h6CY3dHi1oaVmvRsA1VN2aCISSGSdXlXEiMSI/bFFt/pgp9sQRU+1H2blUATLlaIixiy5cu1bpTp/YQRS3IyHmBOMf7f/BLTNpnjqNiyLO5KrNWCtwAslzmDR4hvEbGW4IaKfaFYOsjonrJknSh1IDDjgkjpZ6rM+MvJW89NninjJR1VZVGpyRSZ+/vBRxVeNEAyOaFlWnrs8/urTkTNIb5BsjCIlPJVkq1FVL93hBCNP1VuRXlXsKZvSA27AfYD8ehRIxHFXAWp33xi1/UMccco8997nNxTh/Qc3KAA1BBLPBqSd9HmsTXqcMk3ecAx4C+qmQ3d4Aj2H54N5g0w9SCYAEjBRCWOZPn+lTtXLTIgIymhQt7aoWMGKEF77xjiHiFRHLMImoLoeWAhP/f1luxZMUKkxUCV4JF0uz+DVroKbdu6pzYQmA5LgieBzwALNTE+/EElDctUse857SquVzjxo9T/ao1hqtRqxZVbvRxVY2a0JvW2qeWh8d09r7cb+Xy5QYATkBqm0U917eoI7W5pcWUgvfn+HMvrukFHN5r2+wMb12Wgve2mT0AMABKzo6W7GpCMO3tQoODRrjNkEZz2UHeXYz10PSgtogeX8/x3jO5vlVQJRQHkPICEFMIrwSAw4qyGTE1gFguE6x67FhtccopBU1K6ILzvaRrWyHV6wkBjNCsF8SCEUoKpFU40dtRpL8BQXGJmVHHcJTjUQhF1tzL74pyPiROiJ1o/Qy25vNwADpgoRNO8TY4HJDq/j975wFlV1W98f3ezCSTnpBCQm/SBKL0FnoVpCmoCEhRVJooiIi0P6L0LiWAiCggIEgPXap0AoReQoCQkF4myfT3/ut33tuTk5tXbns156w1a1JuOWefe+/5zt7f/vaXtTI+5+HwzFQ9AA6GlKtEfZSHEg0IUmMLldfmg0rqH4p7fBz5uDbNmiULJk40Jb/ZfXYsXNgjWjVt5kzjJRg6ZEhPGKAn1GEtNrlCDD1jsY6bPm2auR5hjsz6lgEY5kOd1b3QYl49gABSZtbDAWmTBawxtUhk8ksyq5XCbCLNfXrLgD6N0tiYlK6Vt5VEQzbLQsM12bCFck28drYlyaPMAXwRwjkre0SFigEO+55aoK0Tye6AHBdj02y5dqNu2t5uvEyff/aZLD9smDQQjooyQL/nKr9Ds47w9jQ09ITX4Ev0am5ewhXfAzSZM+acMFOIFGUDSvH4WJLnA9deW1b/PqH0/M1v6EK9k14QwtwDOrxAxE+Z9kL94p2G7ByWmOl3ysIc9+yzz8pWW22Vk4Tr53qIAN54442+Nkl+rlfOY7KA4zERQfAGJdG/Zz0ckEUR/OIVvFREYCEX198vZ+cL3Kss34cqGauvbtQL4LBL1PsaeJGDIMMRU6VibK7GrhL3LMRQQiekp+aq8Il7v23WLOmcO1c+nTBBuubPl+UokjV/fiY1FZ2MrKaFLgwsbH4e1LDkTD7y9JUAKWGE1vZ2aZv0sjQtmCp9+w2URCqzv04PW1tk8CrmGEIVnGdqb1i8gp4sk2wFU8YD4MB9jhfAeFmyix2LXj6QksvGSKOTQryKcjiyBwUBHD3XzaYKc3+0PPy48FX7g7miqcqmCamgf9KvX2giqp9nFK+Y0e1YtChn8Ty9hgEcvXsbIm/Bhicn68Ux3hvmjBMsfo/yf0x2FPOVSmVq2Gg4RURGbLONjNp++4K3AoST8o06ZZjGe6WhGPs3vBAlptpl2v3MJ/1A9pvnUgsChulbKc7h3UKnZ/vttw/l2eF8+GaPPfZYLJl1pRhjoWtmAccfRQQxGQRe7hcRCllROZYcf35fm62xUu7uhb6fn+946IvX4okOcOSeNTwmL7zwguy0E5WRFzf4CXxM2YFBBuXj5fdjh5uZ80gVNFVU586VNlIPKVI2d64hkfJvhpxnkzxx8We9CoARddV7uRJBnz9i7IbgmU7L4IEDZOG0ibLKyAGSaOolMmAFSfUdahYko+/R1mb6rOmsZDFotVdv7Q12qwsXLZKReTQ9VJTM8Cq00qwdDsqGf+jf1K++kpVXXHEpAEa4BU+OqWfSmdUFalqsBVLIFmiDGO9OEZ0KW8ZbrwcI+OzDD83YvLvtIETUfP3Dg4JXBmCE5kWu+jPec/ECsbgXBRzZE1XHxWRFWYC357qqLkv4rbvbgKpuUqizbZX99pMhRep9lIIrYRNUFYTwrJGireDD/p2Lc4SCMPwNvBzV1ABY6PQEqSZt9x/bIBiG3asNTPmxsxVSgSD6g6yHA5BBqhcZKhk53RprDnB4JswBjtxPsLf8O5oCAA0+cACNXBVci70L7IzZ/RdTX4Qz0D57tvnBO9Ixf77ZYfLbeD+y3A4yOPDsINZlSraTaeGjiB1gCg8Ngk+UNO8/YIBZWD7/4gtZY/XVe4ZhuCdZjQ8jyw3ZE4If988WSzNl66n+ykLZ2Gh2xHNnzJCWefOWEOwqZptc/8/Ypn79tay6yiqGQ8DunFL3gCS8H8nuVhmwaLL0kUWSTDSI9B0qstxakuzVL8NpKXZTDVVQm8Xj9QBsAAC9YJKxfzl1qoxYbjnDxynYChBR7fNM7RjsjJJne7sQ/inWd0PEJWMpmZQZs2ZJn379Mtwg21vh5e9kQatfgLz4Qch4RtSztfZPfyp9qJNToEUVsSo2dfb/kxFje0F4R70EVQ3NUEWV9zdsJkiQfgU5lnfy1VdflW233TbIaT3H8r3S1PtiqsWhblDikyzAwUuFW/kDEclUT8w0hMB4LWaUuCuxXr7YexzrzWrhYvUCOOKuGMuOAfckMVXi0dQLgKPB7igsmY0MFkIEYQtH0Sfc6+0suLNny+QPPpC5U6bIqOWWy5RtZ5G1SI+AAiWJAmI6WlsFVUoIoQiGkdWgiw9A4bNJk2TNNdbI+diywBFawTOgImAaEjIiW+iIEKbp6pJ5M2dKy8KFsjLghawaSJcFKpDme08ARNhstVVXla7ubpkze7ZRHiVjZkD/ftIw7W1Jt82Tzoa+GWn7tvnSlmiWuX1WMyGG3n36SHO/foZYC8dhiXnLptIyFiOoBicCMiZpy4S8FiwwYzX21KqwpON2dxtJ+RVXW02a+/bNeH24txJ9dTBWVpD+k+FQYCf4ICjCZs/h/9NkWam3gd92UTrNrNHwlAKH7IXhETHGAWS0lKjB5TBF35JJ2eC3v82oxxZo7733nlnUAeaVaEpQtT0h/JnvBCEZ+qbeEDLKwr7TcY2NEC7hHr43YRrfJ+Ta2dAEBpRhbhjzOYlEAi8GnxlK5cLfoFjb7OxteGVIvdlZRH5eKxoc9N0BDs+D4gBH7jeHHRIuTmLGiOngrvSbFprvXQRs4OXYZJNNYnldkY8mfRcAY0I0lHOfNavHM0K4hkWivaVFZs2ZI/NZrIcONe5kpLANtyK7eEEepb7Lmmut5fslaejbN+PVyEp066AWLFwoLYsWySqrr24WKKObwaKeDaGYUvKEaIrUv+ns6jJ6Juzc+aCyUECQZR5SC2dJEsDRTMg3+1rDTWmfJ53DN5T2RG/j/aFeCGJVABJCIAaIZKunshNk524AE9yGbK0ZwE2vvn0zfYdoqlk/2QGaLJUVVsh4OJSom/V2mMwYyKVZvRTjDQJAKKk3Gy4ymiq9eplwWqMCG61V462Aa2cbWcRdrWsyfcYMk9lQSsBhMlb69jVqtuv40HmoRnImY3jppZcMr8T2itgEVTtlNypBNchLjgeVcgqbbbZZkNN6juWZ3GOPPcz3pcYBB+XnIYduJCLUfSBThUJZkIaoscK/a82VULYq50kOcNQp4IirRD0fHzwapLnygeIDUCiNNcjDSxiD1FkKuMXRigEYlVSfNHGiDO/bV0YNHCgpAAjhmqyXBCBgvBPJpHz48ceyzrrrZhbHbPwe4Sk/JFbOB4DgDVgwZ47MmDJl6ZLVCJplvSE948+GZ4ycueUFMaqdc+eatFjk0wFJdggjtWCGJKZNEOmDFtDilmibI93LbyjSnMnc0caOl69WVzot7Z2dhkvTvmiRNKRSiwFI797GU2DXsTULLX1ubu4RYft84kQTxrL7Y3Q8+vc3wIrwTFO2Mi/390qN96S4Zquuhn0WDPm3sdFk8mAjBOAMETTrIfEm5S5BCPVW/vXRCfgr/Sg2eAByCIUboQtsBJm6mhq8LAB/M/OZbYQj8DB4xcvYbHg1QwB2pVjQ0VIBNJAWG6bhHUHwC6BXi83ycOwpIqeLyDaecRwhIodlM1gc4KjFSabP9eLhiAo4iKFqBVd0H/BqEFOFbxEX4PCTahvkOcqnFcLiyk4H4MRCTRYNKYa5GiEEBR//e+wxWY/dHyTWWbPM4gk5VMMNRjyMltXyMKJZOYqwQeicPXeurEYdmiLF1Fg0TU2V7H2418KWFpk2ZQo11qW9rS1nmKe7o1WSU16XRLJB0o0ZsmiiM5Mt173CJtLQt7/xIJhnHH5Le3tPpVO1A/fWMu7GE9LVJR2plAEckDCNNyT7w+KjC80XX30lK6y0kgEc3V1dxkNharV4PCGZTmXCI2lCNlnyL3Y03gmPAJx3N+TV4rDnz9b7ABz379fPcHHUk9JzrH0Pr+Cc/t3KMOoJ89jqsVleEPYcuummMtzHohhVpjvIexDkWL+pp9gXUJpLQZUQjA1E+HvUeiJsHpjHMMrGjJ9xUUsFLY9abBaHg+qRl2UVRq8QEdTmKFdPidpHsl4OLfJW9UN1Hg7PFNUL4Ahbop4Fh+wRFmgyTuBp6O4H5T8IZnEx2vl4jR8/XsaMGRPLi0KWCuQ8jfvykSTMArmVjyCF5xA58tuefPJJQ1pjkTWLMd4QPCFWmAbvCJk0ho8AvyFbvbWHO5JOy6KWFvn6q69kJQrBEQLI8j9YsHo0HXKIYrHok+JJyfShw4ZJv8GDZdKXX8o6FAADuCDxDnkVUMEi2DJVGud+Jg2NCGMlRBJN0j10benutVxPRd0l5N2zYQ2z0GcXUcNDyWZlaPgD8NqJJ6S9XVoXLJB2Qk9UT21uNpV5W+bPl+EjRphFx3gTCMXgvYGvApDIAjKt4Mu5KIYaLgvE3pjbtOnTDeBgzgM3UmWpqdOnj+mj+RkwIPMzaJCp0QKB1qToFuFt2PfmOSd0Ede7E3hceU4g9ZT3L2x41EtQ5Z3GO8I74/WG8G9+vSFRq+tSxZXy9OPGjYvLVGW9jkf4a2sROTULOjqzNVQeFJHfl7VTMdzMAQ4HOIwFWFRwYVLFFdcvNQy8Cn/ocODtKFay3e8fZ9mdAAAgAElEQVRzyY7pxRdflB13RNcmeiPui+sacTIWalLiIL9RMCtM4TQ+xoR7iikdsqh2zJljiKs2Z4Q/Q7Zkof562jRZdfXVMx4SMkyy3hGzIPOT3VXjJWlraTHy3FSHRTUVj5IhoGaJrIA+463Iil1hObwRhhPSsUCSrXMyYma9B4vg7dC0TrvOCtyJEEJdKukNoIUTws+cuXMNuIDr0bt/f+ndt68hkDa0tRmlWR2rER1raipavTfqkwCPh8VuCcCRVU/V7B7Dv+jf34R9eoAFQGLAAOk9aJAJdcXZ3njjDeMlLFQxOM77+bkW8//f//7XvH9+gYCf6/KcksLtFS/j373CZfkIqmx4uEbY6tS33nqrPPPMM3L77bf76XLVHWMBDuOky3aQ+NKKIvKmiEyuuk776JADHMs44GDh4OUmfMLui8Us386QuCjHxCWDzO6Ij8Iuu8CLit7YWb3yyismXMJCyMcqlwBZ3julUyKpLpFkk3Hv45bdeOONzUcybCN0MfuLL+StF1+Ub6666hKgxGiIZBuVR6nlMnf+fJm3YIEMHjJEhgwaJE0AFKI2WXXMj95/X9ZcbbXFxemUX4IXAX5FdlePpgbZJSb8o5k6Vqn7sC++t2gZ3Z88daqssNpqhrkGWOpqbJRWwFdHhwnR4B3oRUoxgKS52XBEwu6ol5oHS4ZdNUymTp9uqgYPGTlyMaigDwMHSq9BgwyoCOKdCDv39nmvvfaaCeXFFY6Mo09sMuBwIK5VjsY76QUhgAoAvReIUCwSgMLGJ0y75pprzObp2mvRxqq95qkWC8ggI4UPEQiKsApkIBRHoWLVTAv73amZAQbtaL2EVHhZ+djna1rBFaBBmIGPYbEiSaT2AUbIUImj0QdSbXfffffIOyxcuYRT8GyQDgcoCpTaN3uiJKa9LYm2+ZLuO1TSo74lz7/9mYkhBwnD5LILGT7wX+wPu0npXbjQhGdaZ8yQrz76SCZ/+KE0d3fLoF69pNEWOmtoyICHpiYzxnXWXjuT6UIGCEqnEF0toTAlT+JRMKXrrbCFCXmgH9LcbOqNGB5KlhCLhkgx5VM8NIb/QViI+h4NDUb4a8SwYdJMSCWZNGESQ4bt1ct4Mzra2hZnyGSzZOgHnBA8IJolQ3/sD5Ih71JwLputopoa9IF7m59+/QyI6DV4sAl1ACre+egj44kLW9Y8jmfbew3mH09b1Gcpzr4BAAiTxhXSDNM3m6Bqa4fwDBOC0eq6ABI2En49Meedd54BLOeff36YblX8HE9aLIXbIGbtLyI/FZE7ROTsLPh4o+KdDdABBzg8xqp3wMGLjJYD2SG8wAANv6I/hCjYmYbddRhTd7WLLJol0qufSPMgAzh23nnn0DtewjKMBTc6QAj+SeBiTXMmSfKTx0VQ5uzVX6RtrkktfaVlhKw1eivf9sn33uULHTEXaEaQ/sfHlQWJHXCP6qqVPdM+c6YBJ++PHy9rrLaKIZUmmzMfYAAHTYWvjBYGypxwPPAwoI/R0GAUWw2PgoNtzkiWyMnHwGSMcG3SYgExWnukoyOjspkV5TKZJlkPzeSvvpJRq6xiQBKZOQAYo1HhTWfN+oYBKd2JhJDmS4YMISd+GAcgpO/gwdJ30CDpP2KEDBwxQnpbYAJQQR8KtWpUz2RhBwhH8ZYF+K77OhTvAtySQvWRfF0o5oN45sgugXjKe6FEVQASGx6vlHsuguqpp55qNIL4XYstkUg0ZlNgb85WjEXenNoq14jIXSLyjIhcLCIPOB2OWpzhbJ/rBXDw0tol6vk7izIESnaVAI2gvAa8IXhNWBjDtMTXb0vyo0dEWmeJNPaW9AqbyJOTe8vW2+1oPixBGmMDXKBLwW4Wcitg6PHHHzcekyAt8dEjklgwTWTAyMWnzftS3p2ZkBGb7ReZ6Edf0TABWGkjQwdvhdqT3Xih3ZtZ+BfOlE/vOFOGzvtUutvT0tpvbekcvoV0LOqU9jlzDEAwNV4AG62tPWXmDTjRrBIW+6wsu4pzGWChBNJsBw344M9ZQin3Nwt9VkzN3CPrXZk6b54sP2KENGW9J0qYVY+EAUB4UTo7TT/wQhgCpv0bMm+fPtKWThvSIa53flQTQhcZ/V0oC6IaCZpwlUaPHp03OyrI8xrXsSzkLOxxpaXH1S+u88477xgPlV1UjhCQV0GVZ4XvmT4Xmml12WWXGcL3L4tU8Y3a54ceekguuOACwx1Dp4RaU5dffvnSKfABb2QBjgkicqSIvCoi5PieLCIwYfn3X4nIUw5wBDRuNR1eb4CDF5AwA0CDBtAotrjlm48vvvjCLALs1AK3eV9Kw6tjRRqaRfoNz9T7mD9Z3m4bJavv/kvfH2JcsJBbARuQV+Gc2BoCjz76qPFwBAmnJCfcmRlO86DFw1owXT6aMlcGbf7DyLUY+FBCzqNfhFeYC0ih9N1P6CcjFNYhjU+eIekpb0lb42Dp6uqU5KIZMrvP6vLFukdL/wEDpT8CXh0d0tjebjJnSOfFq6ES8AZoZD0bCFbhckaQi9YJQOnoyHg0+AfrN39VaXNjVyTQCac0NRkQ8eXnn8vwwYMNP6MnmwMwkQ1xGHABH2XgQJPyG6TZRcvs+D8A1QYh/FklrAEceLuCAuog/Qp6LFwJ+EDFCMhBrxvl+KjiWlHuXexcv2nEPK+8UwpEIHr/8Y9/NECVrKDddtvNAD0qx4bdKBXq62233Wa8koRL2TAcf/zxZiMRNR03kaA2gaknSdE2vBmXZOXNKWY1hbp72Ror7xezZTX9vwupeGajngCHpoSy4LG4sUD7jYHmekgJxRAC4AUO2hKfPC7JT54QGbrW4lNb58iXX0yS/nv9UQYOKVw8isURIhnhE0JAAKdc7uknnnjCvPxBVBETn78gienvigzOclMgj86ZJO+1DpUh620fOStH69CwCDIGfq+22mpFtQr4mHKu8S5MmyC9//t/kh60MrGTjA27O0Xmfymztvi9zGkaacAgiwgfYMJlLMLmp39/aYYzki2GR2E8Te01vA3CJWS5EBLJEk0NP2TRInNvQxRtazN8Ca3YajI7siDiI1J1R4+W5VZYoQfABH0+ghxvy3QrCOE3YIjxsviQ0s3uuFTCVEH6y7HPP/+8Ec0L6skLep8gx5NGDnAHCFVbIyOO0K3fcK+3/3vvvbdRGgWEAl423HBD+e1vf1vyYUKsR6yMEGEULRIrS2VzEblSRJ7MpsYeLyJbish0ETlLRBaWfFAx3sABjjoFHCxUfORA+UEquBZ6toopeRY6N/nB/ZL8/EVJL2fVJmlfIFMmfSC9dj9bhgwflfN0OxTEx5rMk0IfITwJ6HDYXo+i78u8ydIw/u8isz4xPI50/xGSHvlteat1eRk2apVI9S9YHPHIwNMg9OP1yOTqm/FoEILIpq6ykDZ88YI0/e9SSQ9eXEyOcxNzJkrnmN9JamW+QVkcklWKBHxoaILfpoLqwIE9QIQ/q1fAeDaQdQd8ZAXMjM5HZ6chrMKjMCm92fRc3VkipsZ9kJO3QR59VoXPIN6monOV5wBbmAquEfPPLlcrp9rekErUCvErsBV2/GHOw/MJAA6zgQhzvyDnwHlZf/31ixLZ812T0AYZKltuufi9CHL/sMdeddVVMnbsWBMSitI8OhxUi+UHFyziMh9mS9YviHKPSpzrAEedAg6GBXdAXehxPFyEAQgHhIn5Jqa9I8nxt4gMWsXwNwQvwuxPZWLbYOm7zc9yhi1QDmXxYOEFaPgJBfFhR6rZt+hTV7sk3/23JKZTjJHYwgKRRFJS6x8gb80bYNzyAIWgDbujlEj/8cTgGcK9W2jxNYt+FmyYTBOraFli3pfS65GTJI1EOcRWWvt8kY4W6dzzcknb/JMcnVWvgHpBFIjAe2ExtoEIi3UhT5hKxMOfgZiHt8aoh1p917HYzx/HKBChi6UCIvbuGI6M7QXB+wFZUj1AdjpmlB1pseeD9G/c+qW8R7E+eP+fDQS8Lnb/1dYISeApCBOC4pkDAD/88MOy7rrrhh4azw4bt3zNK2RGKA9Nk7vuuis4cd1zEw/g0P9dTUQmhR5QFZzoAIcDHL4fQxXW4sMZuKW6Jfn+fZKY/HJPhkR64ArydnIjGbba+kuQw7gPCzWEMEInQTw0Lz39qIwekZZ+7dNEeg+S9MiNlvSqeF/sGR9I8p1/Z44xBRpFpHWOJNIpmdB3a+k/eKjxEgVpADM8Gnz4iBujXQKZlY9RrgXHCzS4lw029N4Nb/5DGt//TybXI42SaEK6NjhQujf8YZDu9RzLfVWgyQYi/HtPOCYLRlQKnjAdYS0ErJibQp4k9dDo73wgRMeqgCTUYKyTimle+OWFAEbiCoHgeSPUVyqQFcZmUdU8w9zT7zlRPEI8Z4iskcYfRaSQsAyk0HyNcBRgmwZpFD2hSy65RA455BC/w8x7nKc8PeRQirQh3POpiDwhIq9FvkkFLuAARx0DjrhL1OfSkyj6zOLJMCtoMuPVmDNJEgunizT1lfTQteTt9z/p8SLYhEqyTtg9+/5At7cYHsaUZ26RYUMHS++hq4p0tUqiu0tS6+4t6eG5dzqJya+YlNj0cpbAUKpbEvO/lPcHbi9Ng0aaj5efxu4ZoARgwiNjAyVk0tE70BCGXs8On+QDGosRQsrUS0l+BWE9KamVNpPUqI0zmSQxNuLP3nAM/2Y4Hg0NJsQEPyJMaALwQVNuSi4Qwv9HCcmE0bwoxguxPSFBeSGMsRSKnlGnHA8VoL4UZMoofcNekD/DAjTmEm8o72NcgLHQeAifkIGG5scRR1BTLXrLAg60N9Db2E1E3oK7LSJ8yMiDR4+DTJWaavF+qWpq6Lk7Wy+kUUYXN+DIld6Zd8o7Fkriy5eEUIpJuxw5WtIrbZ7RurAauxA+Cixo7Lj8Eirta1AlNfn2vyQxZ5K0fv2xNAxZSRrX2lFkwCiRBdNFeveX1LcOWezBsE5OzPxIku/cJenBq4kkIYZLRickkZAPBoyRht59i+qO0He4DPQfcMIYvEqaTz31lNE7UI+ATQgtCjQq+F7hASGMBsEQoAFPQ8Mx/J/qItghmaAqogpCbN6KAhF76H5BSBjAkcvENi/EDssE5YUArtixxyXhH9fjQNYZzy6eqmpq2Is08h122CFUt5grxqRE4lAX8XkSacU77bSTyYyhOm1cLQs41st6M5BitrNRKFe/QRaI1EzhNvOdi8tA9XKdegIcUSvGeueUBYHQAFyEgtku6ZQkJ9wliRnvSZoUWElLYuFMAzpS6+/fsyOnf7i/2U3jzYCV7vUAFH2u2hdIw7Pni6S7Tapty+T3TEGxXn0HSmqdvUTwVlCifdOfGq/KUq27Q5Lv/kcSMz+UNGmxAKW5kyTdPFhmdveX1iHry4pb7CvSsLTYFB9G5JP5YTEu1H+tywLg8BJCo2QOFbVPyANy8TS8QILx26RU5pEdMx4ADckoEAmSNaRd9oZkFJjYvJBcIAR5+/XWWy8c4bB1jjRMeUMS7XMlPXAl6R45WqRxyboqQXkh5ZYQ9zvlhASwJd7EamqAIEAjOhphGp4bPA4QYkv9buHR+Pvf/75UWj8bqaChWB2ryUpLJlmbMQCVYjfz2GFTEblFRNY3rs5M+mxNNAc4PNPkAEfh5xZlUBB9QfIbmhtv3CxpCKKULqV1tUuiZYp0b/JTSfUbIeyuEBLDu0HWSShtDxDz1xOMvkd62NoiLV/Lws9ek4b+w6VPar6k1to18z72GSSp0T/O6eEwfetcZDwxiRkfSmLKGyLd7SbEMmf2LEm0zpXB395HUmvv0WMYPgiaoksOPuGTYiRVdrhKgmPh1FBRqT+IQb9CjC0ITyMXKAV0eEMyAA5vhkwYd3cxXgj9p1AagENlxP2G5Qwx95WrJbFwGpKrJgSYWn5D6dj0F0t55rzjLsQL4dmA1wM5M05eSNC59R4PF4f3WHkIUa8X1/k8P6SXatXnoNfF6wAQeP/9mpKoWGKYWQ8HBBFSX9HcQCyoVURI5/t+tq4Keb7shPLXsAhqvBIf7wCHAxyBHjFvaCDXyQlDxLxD0stZmhuAg1mfyNRRu8r70zvNh5eFmkwUXPMsEGEaQKHhlbGGDyKpTpn/8f+kOdUqvZNdklpxc0n0HSqp9b4r6aHF3caJWZ9KcvzNkh6yugmv0LeuRfNk5MBe0r3FsSJ9BhsRNQih7PaJfRer/qmhAVj3eDcAVyyE/ITZ9Yexkd9z5s6da8YGCCqWfuz3mhynKbTqDVEwwn28GTJB+RFcX0EI1yf8gweCGji2R8YmpuYjpza9ep00THlNUkMgECeMdyw5+1Pp/PYR0r3adkGGbI5VXgjhKEJugFNbLyQKLyRwZ3KcAN8Ie+NdrKYWVZAMkbWzzz5bXnrppWoalu++GEG+hgbWZnZMCH/xZ8S/KNqGCBKpsdeLyJysd2O8iPzP9w0qeKADHHUMONh14c6Ns/mqoLpwujS8dqOk+w7L1ExJp2XB7K9lztRJMmXlfWXNDTbpUYHE/ckHOXRqXmerNDx3kUj7PJFBK8uMqV9K/5aJ0rdfX+ne4CBThK1H0KuIIRJTxkvyvf9IemiGQDpn9hwDhlZsbpX56/1I3p+60IhqsRj7EVGzCaFchwUdIpsuuCrJrDt/Wxcjzjkrdi3ladA/Yt8QQkvteVF+hBeE8MzmypAp5KUAYOAtwzNDeMBOY/ZNTk11SZ8nTpE0YbfeA3tMhtcjNXK0dG7682JmzPv/ds0Sxk3IQMetdUKC8kJCd8Y6EQ8AIIhwYDU1vEFo1+ARDNNIh73pppsE1eFabVkPB7EuSvmyTsNcR4cDj8bgrKcD2V7+/QYRObMWxuoAhwMcgZ5TduoI8hRTAEx8+qQkP39B2jq7ZPasWdLd1Sl9N/yuDPrW3kssZiwSaAGQNx+2Gc/EhH9JYsHXMm/efOnuP0oGbv9L30Cj575zP5eG128ysXtCQS1fT5JFc6dJsrFZ3hmyq6z6jW+auGwxF70fQqju+u3QA39mV24DEBZfb75/WDt5z/PD04jrXn6voyXMbbsg4GWDkEEN7dI/3SKy3OoyZdYCk6ZLVVFE1QpxgOz0Y2+qLuGTvs/8nyQ6FxnhtwTf+IRIw5zPpGvNXaRrA3SXwjVCBKRNFhKhCsoLCdeTJc/KVa8kjutGvQb6IGjY4KUK026//XYhK+yOOyiqWpstjw5HbQ7G6rUDHHUMOIqVqA/z9ELIYxdZrPT3gpb58uX4JyU94yMZOWoFGfKNLSQx7BtLpXAiiMVuZtNN4UFlW6ork13S2EsEL4mfBkdk3pfyOcz7vqPkG+uECNFAdn3vPklMfMqk2HbOn2YKoHUOXVca97pAGkcULlpnezSyxK9AngJbF0MXXH57Qw8AkmLiXIVMFpWn4Wc64jwGYGQ8AnNny+BXL5EhXz0liVSndCeb5bORe0n7t4+UEcsvb0BJGGGtHkXXTx6Xpgm3Sap5OUk39ZNk60zjnVu05Ykiy625hDZKMdBpj5++QyIMKphXar0QeBKkbgPWqqlF1Qe57rrrTHr6DTew8a+dxnvJuw4HaZNNNjkIRXwRmZr1auwjIlSl/EpE/pnV44Awauou1sooHeBwgCPQs4qantapyHUi7mN2nAAJUkTxCBRKk/Sql5ItYlJc5002XobUqG9LCmGr5sVu7kIdhnmPyzqswmCqdb5033WEyKxPpbN5OelqGiBD+zZIesCK0rXnRUtlLNAX7845ECF04QxJTn9HpLGPpAj/eDIi1AVvAxAWMBZJm/8ACPHDf1CeBv2Gg1LMUxXo4SjxwY0vXSmNL18jnekG6ZYGaZIuSUq3fDb6t/Jl3w1NuAog5s2Q8Z35lOqWhk8ekeSnT5psJcjNnevsK13Lb9Qzx3aGjFe6PR8IicpJsM3KvNtibRqS4d7KCVEZ92LPQzUWuWOsX375pRkjocsw7cILLzSy9hdddFGY0yt2jpLJf/Ob38hll112rohcKCItWZLoH0VkroisKCLEiqgaC6ejppoDHA5wBHpg2RXlkvvGFU4cnewNSGiADT+kSMq049o16qWLZkvj0+earBEWeEl1GK9FapWtJLWZvxg63hJc2IR9gjQWEkDSlNceknU+/6f0HrWupNIi8+e3yIjhQ41gWfeOZ0h6hcWFrrxAwyYm+rl3wwf3S+OrYyXR1ZrZpgxcSTp3PGtJEbI8F1IegA1E8AR4F1syJOhXLDwNSJTTJoi0zZH00LUlPZBvX3kaH+OmsVuKtM4zvKAmqtwi/d42T7pX2Vo69r+ph6TpzZAJLN/e1WYAhzQPXqzNYpFTgyqnAvIgjSK5X4oWlhdCSjphqGoDnWwasDFp5mHaH/7wB8Ox4nctNQUc1IF57rnnDs16MhgChNA3ROQ4EcHli/zpadmCbqzhzsNRSxNt97We0mL5EAEE4mwQzdg5aSqdrUUB2ZCPRJDCaYADal/wkiUmPSsNr/8tk1Gi6pmkrC6aKV07n5Mpa1+kQULFaxIk/gvooaQ0cfQNBy2UoW9fJzJkdWnv6JS58+bK8sOHmyJpXdudKulskTQ7fEKXckmRF+pqYvan0uuBYySNR4OFDa2S+ZMlPWxd6dj76lDqoV7+Awsv/0aYgbEhsc684Q0JEhIw41gwXXo9dYYkZ5BqmDAVa7s2+pF0bXxUqL4Wm0f7/41U/AcfyLav/EwaG5OSsPVU2lskPXx9af/Rv3Ne0g5T5ZJvt1N1kW8PSpb1I1oGKZrnEp5SXPLtfuxXjBdCFhbh0REjRoQKRfnpQ5hj8JCyWUFAL0w77rjjZPPNN5djjz02zOkVO0cBB9+uCRMmQBZ9VkRGisgLIoKEKX+nAT5OyYqCOcBRsRmL4cYOcBQ2ImmHfDT5UPERZeemdTWKaVHkujKuT4ioaHsgMZ5867aMpoa2VJck5n4uXTudLTKoeBG1ICRUW0qdnd6KK64oyfb50vjQCQYAdDQNltlz5sjI/siyp6Vrrysk3WdIjyx3GKChw2qYcIeQhpkabH1UuzuNBkTHvjdkUnMjNOVpMF+42slGUFc8v9X9rgsufy8U+mp68kxpmPikUP8GsCFIybe3SMduFyxRqTZCl5c6lWeDWDyAEPf6Sq+cI40Tn5B070EZkJNOmT50bnGcdG3J5s9f83oEFIiwQHszZMLIt9MLFg/AOORHPH9kgniLAPpVTvU3Kn9H2bwQFnY2BzwPEJO9IZkwOin+elH4KMA/88D7GKYdeuihctBBB8nBBx8c5vSKnaOAY//995d77733DBEhrMLH6EARoUDL59nOfSIicDwAHg5wVGzGYrhxPQEOzIHrPc7Gx5MdG54JPlbwAFjMwjbCANSZ2HXXXY1OR8NzF0i634ieiqiJuV+YrAHCGWahK9JguBMDLuS+ZmEBKBH+ySWlbjwtr1wn3W0thhcwaOhI6d7s59K16hizkEQBGksCjrGSGmwVhuvuMHVmOva9UdJDMkWhwrRiPA1sbqfn4gkBfOWVKW9vkebbD5A0svSkOWcbPJuudfeRrm3RH4qvsSgSGkMcTqvRAoYSsydK73t+YjxeAEBAR2rYutJ+wM0iqMRGbOoRsEMyKt9ue0JYDIvJt2NfvIGMBY0Z3hE/yqnlqqiLqZ5//nlD1objEhcvJOIUmNOjZs/ss88+cvLJJ8tee+0VR3fKfo0333yTlOD3KPgtInwg4GtAFEXgC/XR6ywCadn7F+WGjsPhsV69AY44S9TjguVjwII1evRo46IP6oL2PqzsNsmX33333Q1UT753jyQ/fkSETBVa82Dp3uxoSQ/3l3VCHwETuFS9jQ++KpyOXG6ArDVEpHeffpkQjle6vGWqdH7+qnz88Uey7o4/lFT/kWbB8EUI7WozUumJ9gWS7r/8kpVodbGePVF6PfBLSTf2FqHkPLv1+V+ZInNhQypK2AUQBtXT0J2v7vZtmfLBfRrkm6+eIsne/aWheYAkjOqyGGJv1zrfla4xeHejN54FFVZjtw2YXao8OdLjHz4oyZYpBmx0r7VbURXQKD2z5dsViBSSb+d45TIRXsSrUegd8QtC7FBM4HBYHgMUqsgalhcSxdZ67ltvvWW8G8Uy4fLdixosV155ZYYXVqMtkUhsJSIMYJqIkN+raqJHZjU4YMTGGy8vg60c4HCAo+hjpix7FjQ+AnhNwory5LrZEqXbSQ2b/anZzbIYp5ffQKTv0KJ91APY3bOztGWR+Xji2sY9z0K2/qBWGfDBnSKts81pSLB3b/7zJXQ7VJiKXSAEWN/qoItmSuMbN0ty7mdZb2dCulfZyoiQeT00DR88KI2vXieJrkUZ0uigVaRzhzMzACVAY5GDaEeIy/YIBLhEzkNZDFWmfOALf5IB016WBY3LSbKhUXpJh/TuXiTzxpwpvdfZxRdBuFB/uA8qpzxjAI2wi03UMfs537aLAjQ8Gng9AONwQeDKQK4OE5YoJt9uwB6EWesnDAjBsxi0ImshXog3JBMmRZmxwekCrIUhs/Le4t289957Q5dL8PMMlPoYp8NRagtXyfXrzcMRpWIsbnYWaXbMulvDg7CUbkbEufMjl+73FiwA7JC08BN9JybMh4iFbGgfkcYnTpd0qjtTTRbPAsXahqwh3TueabISlBCqMXjbxU4YScGHpqXaH9aGt26Thi+elxRS64mkqSGTnPeFIVemrAyXnvEsnCnJGe9m0mJNobDefodq+mnXPYGHspRHwPfVCh+YaJkqTU+eLlTXNcJm6YTMWHFX+XT4HtKyYIFxy3vTdP2ko7JAA5YIb7FI+xFWi2lIsV2G94RnjN8QpwEMqiQal4aKH3IqA/LLC+F6FBSkgm1ULyXvCYDRVk/V0vBheCEvv/yyyTLjeQraeDZ5DwhLVJuCapCxOMARxFo1fKwDHBneh5ZbZxGA56CLKgs4O9FCqolBpx/XLruSMKRT77346FNpktg0/ak5j/QAACAASURBVAQsQDhEO8TsCCc9Jw2vXpep86KZMEpM3fFs6Rq0qllQ7V2k3iPXzpaPrIKQAf36yCrvXiONfQdIsu+Qnq4l5kyS1IqbSTcF5GJqxXgaMd1myct0d0py6nhJtM2V1LB1JJ0lvKpqqp0FUkw1Vb1OkFrxBLBIBMluKsn4Al6U54FKwYTpeEf4sT0N6iWz7cKfbfl25YZo+nLALuTkhdhaIflASKkr2PrRC1Ew4tULgUSuhQ7D2IOsG75TeJpqtTnAUaszF7DfyzLg0N0mH9B85da9HoSA5s15OMWWNthgg0jkU70w/XvxxReNe5tQCIvAEkW8DCGU6rJWMbfubpG5n0n7dqcbDYxEd7skOlWHoTBRVUGIWVTmzZHh46+Qzo5OSTUPkV69e0lz72bp1zZVEmvuKDL6h5HNReYGi7RvnsaimdLw5UuS7tVfUqtsszRXJUePAEiJ2Z9Iut9wSS+/Yf4quwVGo4utrROiqqksBIROtEgcC0TUXXZkwwa8AJ4+vBoAhZxckwLXg1fllbQH5HszZFiQw4RK/IRk8HyiaKk8hzD3CWgyA+RVP0YFy3hvvHVkeL7Z0IQJR7HhYJOEx6UcYwpqA7/HO8Dh11I1fly9AQ5eZnZUhZpNpiR2DuEwn2uehQKXJ8SsuBrX457sdMM2Ww+EMeMqzunSXzhdGh8/XdINjSJ9hksa9sScSZIeMEo6djhTGic+KQ2fPmbSLcmW6V5/f0mttIXvbjV89LAkP7hf2vuMlPbupHS3TJfuRXPkw2F7SMegxVwQ3dn6EUfj5jo+MnD88jQa37xFmv57NjmaJs033W95af/eLYaYmrOlU9L4wqXS+P49plIqDfXTjl3PF+mz2GPj2xieA1loCdEhsEbWBiBDd/x2mIqFN4wmRth+BTnPTtVFzTYuWXAAgJ05hF1YPLGDN0MmDDfCBiEsxpre/s1vfrNn+H6VU4PYy8+xXl4ItZUAC4w9KC9kypQphpdCqLHWQKxtKwc4/Dw5dXDMsgQ42HHwgvLx4SNP6KFY3JSPwzPPPCO77LJLbLON4iGeiDAfb3sMLGIAFzwmu+22W/4Pzuf/k4bxt4h0tGQkq/uPlK7Njpbk/K+k8bUbjNaG9BogCUilqU7p3PYU4/lIfvWqwGXgz6mRG+Xe+Xe1ScP792aO7WqTNFk23/iOUcNUAqa9uwUUeTkhNggJy9NITH9Pmv+xZ2aOEg2Z3+luSQ9eTdqOfDqnWFfDx49I01NnSbr3ABHEtdAFWTRDutf/nnRu9/vQ880Y4GgQpsObAR/IBoP5VFNZbGzbhA07hO64dSILNqRcMlDIPMF7Viw1Nup9vZlDgJAo8u2MAf4VP/Tf6M4kkz26Mqqc6yckE3Vs+c5nzM8995yMGTOmKC9EwYjtCYEwfsghhxhgW8vNAY5anr0Afa83wJGrRD0fFHaZvJTslnAJI97lpy2RxqocCD8nFjgGghdSxPAsgjRNoeTDb4+BNFt0PXK5VHsUQhfOkuTsTyTR0EvSw9cxC2zTE6dLYuEM4+3QlpjzmaRGbSzJaW9lVDaN4FRaulcdI52on3pqn/ScuGhmJi3WaIrkjiXTFwUhdpxfQQjAA5VNxsduOghrv+m5C6Xx1WuXBEXplPFctB02TtIjFu9stc9Nj/5WGiY9a1J5exoCX4mEtP3kscI6KO0LpOHTxw0gg9+RWm07E76Ba0LoQecIAOGnqWqqnabLvykIUYJqWGEuP33QYzTziXlhHuLgGgW5v30soAHPhx2qAoTwHntDMnaVYcZAATk8l4whnwczbnJq0HECPuFgKenbO3bVC7FDMlpH5oEHHjDPGVlvFJksZ0hl7Nix8otf/IIaKHLiiScGHfZSxzvAEdmEtXGBegccxP4BGngq8GjgVQjqelwijTWGaaV0N4DHq8SY79IsQhBC+fAyBsCKPYYnnnjCuFW9ngLAVz5CKCCi6aHjM94AK3yANoYsminJlq8k3X9UpraGEeiaKZ3b/CaT7hpjo3/MESqQyvTnI2xngfhJ0W165k/S+PqNHsCRNh6b9h8/kCkU52lN434jDV+8sDTgEJG2wx/PCziod9Prvp8ZD1E6C8i6lv+WvP2NE2Tm/NbAmiD5zKmud82GYNENo5rqd7oAOFqIkOeMDJSg74rfe0U5jmdGF2IbiHBNABohRt4VPEuE48IsxH70QvxmyBQaKwCcek12Wnuh421eyBVXXCEvvfSS0QoCeKAVhJw8ICBMGMrvnBC+ASARAjrqqKMc4ChgOKfD4TFOvQIOPtKETvggqYx32I8n+ft8EOLKKvDWZ8n3vCphEu8M0ur5Uijt/vV4NHwohDa+er0kJz2zOIMl1S2JOZ9Kcs5nkibEYKlZogiaGrGBdOyD6J/PZjRGPs4IfPUbYWqA9GTKFOBpaBYIc6c/zCeASsGHxvk1VJH86jXpffsBGcABSCLzJtVlwETb0S/mBA8IajU9/UdJM048N2TvLJgu3evuI52kDOdpTeNOkoZPHjVALZ1ISmd7m8lkmbH+kTJwl5NK+rFXYS57obW5Dzb/wW8IRMN0gA3ALAu1X66Nzyeh5IcBEDRcijcDbwcA1pZvtzNk4gAhuUIyQZVTo1bWvfPOO2XcuHFy/fXXm9RYNia//OUvS2rvAw44QFA3vfnmm2W//fZzgMMBDv/PW70BDj6+uFL9lov3YylirOwcivE9/FyLY/C4sBjkqw5pZ8/gBQFsFFoASLPdeOONzY6DDy8/fiq5AgQaX7xMEvOnZDwd6ZSkhq8nDV/+T6SzdUnPx4JpJtTSsfdV/obZ3SGNL14hDROf6inuSKps55hTDZhBmAxAiKfHj56GF4Torp+dnIKQlT66WQZO+FsmM4VwSmNvaT/g7/lJsKkuaXr2PGn46KFs/cm0pIavLx27XyTSjyKVOVo6Jc1jM6Ta7mRvae9ol0QiKc2pVkmPGi0d3/+HP/vEeFQ+7gMLry6y+tu788WOAGAaoQe/IaAYux/5UgBz1QVBVt0Ol9qhKn1mvPLtjLlYbZ18nfSTIVNItIwQIhyTsMKCN9xwg7z77rvy17/+NbId/Vzg7rvvlr/85S+mPANEegc4ClvNeTg89qk3wMGuBmElv+Xi/bxkpJ3yMfbL+yh2TciEgAp4GHZToh7/j4x6oewZPY+FmDx+XOCaCRGokmvrHElOeT3D5Ri0shHrahx/izSO/5uk+w7LCHN1LJRE+zzp3P506V5n72LDM/+P9wDAYfgheA8Iy8yfLC1rHyhvNn67R5gsCE/De2MbhCj3IT39fRmx4D1p6r+cdKyxm/QftqJZRPOKcuGFmfWRJBH46jdcUituWpi7kU5LrxvHSPeiedKZbDapwI1kALXONmm4gTxAviwZ7qB86qDs/AHO8DJ4V1jweM4gVIb1AIbrYfSzmH+ymCC2EjrhnffjufDW1uHZUfl2b4ZMGE+P8kI0pFmInAovi3pIQao925a7+OKLTd/5HaXhCSqU3cdzQ1FBgNEjjzxivl0OcBS3uAMcdQ44eLnjLlEPqYtc9zBZJbkeSXY0fCRQF6QpqRV3KC+23wJxGj7hPNzJeE3skEPBhbbQu9KxSJqePkcaPn8u4ymgNPv6+0vXlr/KhCt8tKZHT5bkjA97CKl8zNpmTZY26SUte43tESbzcamlDwEkTH9XkrM/zYKEzXr0NhSE2ORL/qwkQ9s+fpRB7Ztrqm6vl66QNWY+LsnmQYaEK52LJJHqkI7dLpTub+wRakjlOElBCNknxOF5XhiThqqUmBr6uSnHILL3YE7xZAIweI+iEluxjU3MJNTB372KstgojF5GLnKqpk1zD7x8tKC8kDPOOMNsTvgdpe29997y0EMP5b0Em7g///nPBpieddZZ5jgHOIpb3AEOBziKPyWeI8aPH28IdEGzSvLdiA8+O0t2NewacAcHIbWyqHoJodwL17LNe+DPdsjBy3soaAit8bLga1PJNT1wpUB2IwMkOeMDUwQOcNXaukgGJNul96DlpfN7Nwe61hIHd3cKBFEyRLRkO0XNWOzzhUGUZGjzQWxlUDvskGsxsQEhx669xioy8MXzpeHjRyWR6jQhoq6Nj5SuzX6RM/02/GDjPVPrt0DKVY+dF6ApUAOM2ABEF9pKe0HsYnF4ZhDsK1WfNKtKQzFqG0CB7Qnhz/C7/PaD6+LV4L2HM0PIlHN1A6GbEJ39QiDkhBNOMOHU448/Pt6HJcfVVGBMuUF8wxj3HnvsIXBJojSXpRLFejV0br2FVErh4YAFTrgCt20cDf4CoEPTQJXUWswdrB8kddEWC514F1olGnJfezHhgxnGdVzIFskPHhR57hKZl+ojjX36S/8+vaRx4VTp+tbh0v2tQ0ObseGDB6TpufMk3Scb7oHoumCKdK+zj3Ruf5rv63ozHRSMeBdaABsuexZpU5tm6OLCeoSIEi1fS4ric33Ci7j57nSxA9tbpGHSM+aobtJ0e2dScm0RNT/1W7CNglfbUxRXnZRiw8j3/4QfWKR5FwkhhvE0hL23nqe2ySffbgORXDoqPEeMQT2cuUKKfnkhfC+OOOII2X///eWwww6LOrSi58OLs8MukEf33HNPOe644yKHmx3gKGr++jig3gAHs8JLHWeDVAeSJ0YcteHJAMCww2FnwzWLpbApwNAPEX0oBjby9TNfBogtyKVhh2L9yncPNBA+/uA9Wfnzf8tKi96RhmRSJCGSWnlL6dzmlLw6HX5s2/TISdIw+RUjXtbT2ucbYmr7oeN8h3xy3cteaBkDc8TCCwiBv2OLcsWVseRnzH6OSU58Sno/cIxIV2umam9DL+nY+y8ybfDGZoEDYAKYwvZb0zHt7JiOWZ9L88Ipkhj+DWketmrPrj9u1VRCD4QNmRNIodVYWVfF3GwgojoqqhfCMZRRUL6J3ywi5j8XCEFAELBx2mmnmZ9yNxdSKW5xF1Lx2KgeAQcvOh/IuBrZFDRcuGGbLafODo2Pz9Zbb13wcjoGdhV+M0/C9E9dx3Y4RjUxvGmohUCIXfcEr82okSMlOXeSJND0IC126NqRQw5Nj/0uq51hiaa1zTNAo/3QhyNfH1vAb2DOWdjIJOLfvOEYAJ9XMdUWngozD6HPaZ8vfa7bzHBJbJXVVKJRnt34Gllzw83iXaTJ7nn0FGmc8C9JGLH8hMxeY1/5aK2fyfyWhT21QuxQVRjVVOwON4l0XcKZzEWQRTq0PWM6UXVUCD2gPAtZlTFhC9s2ZMgEAfdk2Zx77rly6623Gl4FSqNhgWRMQ418GefhiGzC2riAAxzF54kKmbzk7K6CNj4whFBIheXDgiuYf0PsJ5e6oF5fwydKNgvr0QjaXz3eznKwpck11dJebDkHG5ExQBovbvsgH9AgfUx+8rj0+u/ZkiZc0Kt/JvtlwTTp2uhH0rXVr4JcaqljGSfeAGyPNyBfBk2hkIO9kPDncoCQhvfvk14PZ8ee5QKks6nR7btdIOmNohfRs43V+NKV0vT8RZJIdfX8czrZKJ3b/8HwWFhobU8Iu34AdhDVVEIOeBZZpCGF1mK6Ls8JoVOyztDQ4b3g37w1ZGz5djsk4yU1cy51mNDZ4Ft07bXXxsYri/TixHCyAxwxGLEWLlGPgIMPni7UccwBHw3UMDfccMNAl+McewHT+L+WlEcd1NsqDTQKDVBBCERXG4RwDgsrBD68AmE1DXwZl4JrL18tje/ebVREIY52r7SFdO54tgg1UUI0W2GTXXSYFFFvbF89IjbvQUFa3CCk4b17pNe43xitM1OcTxKSxJebSEjHrudJ94bxAo7mazc1SrTelhq8urQd/b+cM+BXNZVwDMCV0AMhR8IPfsmYIaa+ZKeoHhDvDICJdyJfsysw2yEZeFWcd8cdd5hrvP7663LffffJ5ZdfLj/84Q99pQCXbIAxX9gBjpgNWq2Xc4Cj+MzgocDN7lecR7MBWHRyMenZ7T3//POy8847L94hptM9cVoWr3J7NIpbYckjiKfjteFjyaJghx0YP25jb5ZDMVJskD4gWpaYMzFSqIa+a5E1spDiVti0eQ92SIZ/94aqgmQ5eO3UOW+a9L9xS5MxI4nGjJhrutukM7f+/BWRfsODmLbosX2uXFcShLE8Dd2W1uMmFD1fD/CqpgLQ8SQSNgGcK2+G56hWQinMLWnvePzgZ+HZCAOYlNQMUZM01LfeestcFwCyySabmGJvp59+um9bV/uBDnBU+wzF1L96BBx+StQHMR/sePLQN9tss4Kn6U4ZcMLHhoqwuT6UfGiffPJJU+E1TkJokDGFPXYpnsaoUUt9UFX50uaEsOPT2LUutnw84wQhQcak3id2kYRP4lKRLdYHLwjpESyzQIgCtWIgxHbZr5/+QFYdf8ESt0cxtfub3y/WpcD/3+vB46Thg/s8IZUm6drgIOncI7gAFd4PODO8Zyp2Z4cd+LOtmqokzLgzqwIbwnMC/bS1QfDWRGkA93POOUfuuusu49U46KCDzMbnjTfeMErKRx55ZJTLV9W5DnBU1XSUrjMOcBS3rVbPzFdgSdMO2dVAboMwWUhUioVCK7wq4KAX1ezVsFMrw/A07N2sAhEtRGbv9ktdDdUGTLkK4RV/GuI/IlcGCDZST4jtKVIQwv/DcQCwoalhANPCGdLwyWMmY6d7zV1F7Cq4MXY70TJFev9jL0ksmtUjW29q1hzyUKB7Mj4ygchAQVSP9yYXiCimmlqI9xDjsPNeiv7x7hMGilq3iZtglxdeeEGOOeYYo7Fx1VVXGb2Oem4OcNTz7Fpjc4Cj+ESzc0H8Czem3ZRFz+5MtQH8KB5yHh4OvCCcxwezWl3GSnpljJAo/citF7do5gg8UV5FUACBkgsViITJcPD2gUUBlzQ/ACZsX602175jC9s+gAzGQb/xCsCZwYvG7j+M297vPOU8rn2BNL53tynOlxq6jnSvf4BIr36+LwnYhN+kZOygZQN4LvEAeMmpXtXUsMqgfgfC/allAi8HnkXUbBHGdPbZZ8s999wjVIM98MADyz+3fgcf43EOcMRozGq+VD0CDnbTLGZxNT781FPZcccdey6J+5edGR9/XPJ+Ppg2IRReCG59PljecAMgpNQ7fT+2UZ5GsawNP9fyewzz5q0SC+fFK1QWROtB54pzmKuorm6/Y4nzOAV+PHNqC5XiBoR4+TIVASE+Bsw48AQQotTMjbjCalybd8kmXqqirNpHf0cl7qriKWm7gHA8m1FAH32nSOSxxx5rQrd4NeIqpeBjWip+iAMcFZ+C8nTAAY7idmYRpDrirrvuaj5mfPT5sPl1ySvQ0PCJN3TiDTeQBQLI0Y+jekGCLLLFR5X/CD88jSjXD3quZjjYQAS+jDcF1Wsf5oi5YtcI0KjVDzjAAm8AzyHhE2+6LoDMqxPCM+XVCak0CNEwECCdtE4/3sCgz4r3eM0esj0h/DmKaipAHK4G/Wc+oiqeMr9nnnmm3H///QZooOAZBbxEtVklzneAoxJWr8A96xFwsONjkYqrKeeCdEnIWqTrsTsrtjOLQgj17vQBIfybF4QUIxYGsUFUnkaQe0U9VsuO2yAE+7DIEpLBVY8HiZAD+gfF5ipqf0pxvl03hGeOUJDfcSgIsUMyCkJsb0g5QAj3RYsCwiMch1LWP/EzD8qZ8YbzvPbBTjaI5f8RIcM7CdCIyqugH88++6zhasAPu/LKK+MVaPNjjCo5xgGOKpmIUnfDAY7CFmYRw/3LB5PFKx+xzb4KHxIawCdO4S4WWXuBBYR4UyzxhgTdcZWSp1Hq59e+PossGg78sItWz5K3gm6cIK1U41MyJd6MuOqGeJ8fFlwbxKrHKE5PGuEsyK2MAy9T0Aq9pbJvrut65cl517APAJZ+A2B5v+BqBH3HvPfD9lR4ffDBB+Xqq6+W/fbbb5nzatg2cYCjnE96Be9Vj4CDhSZqiXqAgqoEogkwa9Ys2WKLLYrG/22eBtNa6syTXBVitZqln1LsgBbCDuXkaZTicdewA54tFrbllssUU7PDDQrWvCCtXIqgfsatZErCQeyi7WJxfs4PeowNQnTHb4MQ9YYEBSHYnecKmzOOaqx/4sdWzAPhLN4TgAf2Cqqa6t2MPP3006bgGUrDpLuWeo79jLPSxzjAUekZKNP9HeBY0tAsRoRN+Fiyi2HxYleDUNfo0aPz6jXYQIM/a0npMk1jz224N4uW7Qnhz97ibPydVD7qPJi6JxFJb+Uep94PgEE1V9z1fsMOuUCaXRvFVgQt17jsLBo8afz4DZ/E3cd84SrVv1D75AIhduYWIUjmpNqzgfLZj+8A3hnAEl4mleu3s6sUpPHO2Toz2MqruMt7iFjXuHHj5JprrpF99tlnmfZqOA9H3G9uDVzPAY7Fk8Quht0MixgfGEiGSt566aWXcmajFCOEVsMjYKcQMkY+pOzSSCFkd6Wk1FpTdNTCXqrhENZdrzF9L0hjwfdmx0R1ped6HvCe8dyxgOMNgFdRbc0LQlho+TfbPizIhB+xJ2GHcompxW0rrU7Lu8I41FtW6D5K/LZ5IXhHAMOPP/644a1ACt1pp53kL3/5i69rxj2uar6e83BU8+zE2Ld6BBzqSvdrJj4M6Ezobp+dmXd3SSloiKIjRowwl41CCPXbrziPU54GpDcABjLe7Kq1LgqLrUqS26GYSqqB5hu/AkPAoHqg4rSVzm8uTwiLqheEhAU6gD7k4cl6YBz6bMU9llJdT4u0MR8QKXl+mBMbwPIsxaGjUqox2NflHYE7A/hD6h7PXxTvDCDkzTfflIsuushck4aUPt6r448/3hRhcy1jAQc4lpEnoV4Bh58S9XwwIYPyEQBMIAaVr8opHw4WBEIPtLgJoaV83JSnQZ8LaYaoJLkNQnAVexfYSi0gzKktgV3uMFCucBU7Wp4ZLzG1kOy28oPY/dZ62AEiJWEH9c6wQHtTUNUTYj9HlXqG8r1ngD9AAaAJr0a+SsF+31OelSeeeMIAi1122UUuueQSo9XDN4eUWuy0wQYb+L1c3R/nAEfdT3FmgMsi4OCDj/gQH3xABDuZYgqB77zzjllUWCDizDwp5WPGDh2PRhSehjc9l8VENTDsRbaU6ZX2Ag3IgBdQLXU0VGzKK1bm5cyw2NJnwBwLNCCF8EmhKqKlfDaiXtuuf6LemXzaEfl0VLyKsoCWcvNWmD/4P3ia4lKgxWN12mmnyVNPPWVKyH/nO99xXI0iD5wDHFHfyBo5v14BR64S9RpW4OPCxw6ehp84M+cpUx1XK7sfzq9WcZ5S62nYrnRdaAEF3l1+VDVHXiHAEgReFnAWtlpYoG3OjC3IxWKKnQC5AFfslc+jVq2fD8Y2bdo0MycK1sOAPy8IwVNkZ39oim4pFXfx3gH+ANB4NbhnlIZtHnvsMTnhhBNk9913l4svvjiyp8Rvf+CF3HzzzTJhwgTZc8895d5778176uGHHy633XbbEinK8Ezy1Yry24coxznAEcV6NXTusgI4tDqopn/6SUWzCaF8nCBb6gKictsar+Z3pfUdvDyNOOueFHukNf3UDsdo5ofaiA+6X74D3hmAIdcDGLK4VSvAK2QbewcN+RCwCmdIww5aBdVWBY3CGyg2T1H+365/EkfYwdsXG4Qo+TJKCmq+sTInmvIel7w635ff//738swzz8j1119vKkGX83ml9gqAljAOYysGOHgOScmtluYAR7XMRIn7Ue+Ag5isLlwswH5UDv0QQr27fFuEywYhfhfYqNPsl6cR9T5+z8eGNumS/tl8B5tYaO+Q8c5QYI2QV1wubr99jvs4xotnDK8GUt7eHbRdBVWBLHoihBa8tVEqCULoJ2JqZKCUO2VXQ3p29ofK/ns5IX7CMYA9OBSMCdAU1WPGc/7II4/Ir371KxM6gSDKs12pRuE3+GYOcFRqBpa8b6I6ulE9vahXwKFAg/gsZFA+lMU+2nw8aGEJobrA2rt8XOb2Dj9uN3ocPI1yPY1evgN2YoEl9KIVc/Ei8edaLbKGLVkk4QeRtks2EMDJ726XZw+b2JwQnmVAiB2yYrH1s8BGnVv6wQINKIRzUo76J8X6nKvKMN6XXFWG1UY8ewBZtGf4HuDZ8Dsn+fpDuO93v/udKSV/ww03GHJo1GsWG3ux//cLOEjRpcGJOvLII+XXv/51WZ6nfP13Ho5iM1sn/1+vgIPYLIsxhFA/XgZbuIupjUMh1F5gFYSwU8ONboOQMItHqXka5Xq8WWCRv4bcqtog/LYXWGxVjem5XhtpWiX8BsInhIL8PHvFbK3ZQzYI8VYY1hoycYEQFnUyuEh3jaMaarExRv3/fGJcPDe8b9gO8E9mSBxejYceesgs0t/97nflwgsvjMz/iDp+Pd8P4HjjjTdk5ZVXNs/oq6++KgcddJAZCz+Vag5wVMryZb5vvQIOvyXqbaDBn0utEKo7WNsLwuLBR9AOxeSTkq4kTyPuR1Pr1JCWTIE1LYiHjWwXOouF2oiFVe1UTamVKoENyCV8QgpkKZu3wjD2sm2k3pAwpEu8TISCGENcoKmUtsh3bU2jBjQB6nne1BNih2OCgFlE2k455RRBCPDGG280Ql6V9mrY4/cDOLz2QvX0lltuMWOqVHOAo1KWL/N9l1XAYRNC+XMcHo2wU6e7MxuEwBGxXegsskqkLKanEbYf5TpPQROaGuyy/KQle93ogBAl7tp2ClrzI+qYbU+TDZqiXjfM+V4Qgo1y6ajkSz+1658AmvwQq8P0sxznMPZ3333XhOvgamjaez4b2bLkubxFPLMUWsMLQKG1888/v2q8GlEBx3XXXWcyXBzgiP/JdBwOj02XNcDhhxAa/2MX/IpaVAsQQl4/PwANdmZIecMy58MYJiUxeG/iO0OJlHz44QREEVhS4m6uEvUKQrR6bil2oYSC8AQwJ3BOimm5xGdF/1cqBNTsXT5ZFoRQal2IjOdK+TN+suKiMwAAIABJREFUQ0E2CFHPGt4iwMrYsWPN3BKGgHyOV2PHHXesKq8GTwPzzM+5554rb7/9ttx5553GW5srpMf/7bHHHua5ff311+X73/++HHvssfLb3/7W/4MV85HOwxGzQav1cvUKOFicWZC0RSWEVmL+7N0zCwEELwiEtieED4odiqnWeiiq6oqGgxZZKwUIyFUdlrnzeouicCvwNMHTYHFiMQIA1lKz008hPgI2FMwSRilUnK2axwkoh+CKtwIwG6XuDe8eMud//vOf5ZVXXumRbme+t956a1NSvpoaoZT/+7//W6JL22+/vVCZ9he/+IX5dzwZtO22286AEgAK35WjjjpKTj75ZEcaLcGEOg/HMuLhsAEHYIMPiIKOSoZP/DzTfnkaKjClAITfmtFgg5AwcXw//fRzDH2Eo8HuGS0NsjaiLPZ+7mkfw/0BITZIUwJhEDlyrmmnh0K6I4RSLPMpaH/LdbwNZpmT5ZdffinejF2mXm1VSkXZsGNnLJCO4WoANBhL1AaPhUWYFFMyUFi8AZos1GS6/OAHP4h6C3e+ZQHn4VhGHod69XDoQqMpruUghMbxyETV08hVD0V1C+wwQzkWDnachBxYlNkZRlVyjMO+XMObnqsiXJqeawMRBRSMhcwnwFK1pIeGtQdeDcYCWbJQKEjDerZaKiDEC9QqKXgHiZOxEJqLg+DKs/Gf//zHgA1AxZ/+9KfIWS1h52lZOs8BjmVktusVcNx3333y1ltvySabbGJ+CDWUwoUf12NSSj0NdaHbnhCvFLlyHeIYDx4Frb5LHB05+Gq2vYIQb7gKPQyAmXrLVPSqVr0aPAfwEFikAU1hqtPaISvlO5RK1r7Qs6i1XHQscYS1CKGcdNJJRh4crsaYMWPK9twGkSZn7JBXkSen/fjHP5bLLrus5mTy7fl1gCOOL28NXKNeAcfzzz8v//rXv0yeOYXXcH8DPDbddFPz881vfrMqCJeV0tPwhhkAIyykXj5IEFKqFsXD5YyiK1yNWqsVoq+syl/jqgessovX1FNCVLaOSiVDVn4+MRqiA2wQbiArKM55ASx705jpl7fKcFykWsIdeDWGDRtmvBpRx4J97r77bkOaPPjgg41Xg0yecrYg0uRnnXWWsKEaN26c6SK1Uw444AA588wzy9nlWO/lAEes5qzei9Ur4LAXDhZXYrGQvwAg/IZXsNFGGy0BQtjBxiWcVGzG/fI0il0nrv+nP6RQ2lwHFhEWCTsUk0+kjJ0mREqOx01fDYqUYW1DCIFQEI30ULvAn53RoLbSeh82CCl3em6+sWqBMvrIWKJkBfm1p4Yz7ewh/qy1deyQTBBiJyEenjHsTqorKdVRGyRmvAUAmL/+9a+yzTbblM2rkavvfnQ04A/h0SC7hHbXXXeZEBBKqrXaHOCo1ZkL2O96Bxy5zMEHkZg8wOPll182IIQfdugagtlss83Mn2Htxx0OiMrTCDjFoQ/XWh92KMYrwAWngWJRdsZG3PYKPYCAJ9qZNHgB/NTd4RZ21ofaSgmXNggpJ9fB9jaVu/5JvnfOrq2jYASvmpcT4iUVq4IrIJDwHHMTNayFff79738bafJDDjnEpJMSPqt0KwY4yCgCaBGyxA40/oynh29aJeu4RLGdAxxRrFdD5y6LgCPX9OgHGvEbgAg/cED4wNkgBK9I2LLrpeRplOuR0x0+HzdqhcB7AGDwodMfFpByLq5Rx24LkeGm9yuHX+i+NuFSQQj3sb1F/DnIDt/vOLkfO3bCYXg1yh0e8NtP9arZpFRvBhEggHpIeGriqlBLNgvF1lio8WqQ5lotILkY4KCIHoq8hJV4Vmn8GT4O/0fdnlpsDnDU4qyF6LMDHPmNxs4VtUK8IApCSO/kw2fzQSBGFgrFeCugwieJGncOMdWxnMIiwQcO1zYLpsbQWShsTwjH2XyQIKXpY+moz4sAmNg5AxBKGXLIFWZQ3owt1x5FzM2uf8K81AJZ1ztNdgYRQIOMGhrAzFt/KAi/iGuwqbjjjjtMGfnDDz/c6FZUg1fDtkExwKEeDrhFpDPT+DPfIOfh8PnSl/Ewp8PhMbYDHP6fPj6GZC6gzocnREMxhBM23nhjA0IIxUBKZcfB8Y8//rjZYfKx5KNQbR84/6MX481gcYYPAE8jn/R1Pu0LFggvH6RSwAsQSLl1wkFUDyUuXi7+jtrcu8PXnT4hBW+YoZid6qX+CbZR3gkgEHAPh4ZQns0J4Z0DhHiJqfnsBHjBq4EKKV6NLbfcsmq8GkEAB8fyrF5++eXyve99z5xKaOg3v/mNfPHFF0Fe56o61nk4qmo6StcZBzii2ZZFg9CCekEAIQASQgr8EGuG4LXVVlsZ4FEtrtsgo7bLrYddnHXn6iWlVqIqrC7OgEA8AXFlTwSxab5jVczNu7gCVG2whoYGzxbgDxDIAlzr9U80MwgvImEDPIH5QGA+OynJGdBGttTmm28uDz/8sPzhD38wZdjxIFTTfOtzEESanGwU6rowLtp3vvMdU9/FZanE8QbGew3n4XAejnifKM/VcHmyk3rkkUdk7733NqET6jAguYyHw+aDoIVQbOda0s4WuTgfdXaGxLqV2xAn58BbORcwYhcbUxd6XECNaxMKwlOD7WulOJnayQYhjAGPEWE/iM2kIANIyu2liev5xIPBO8JY8WqEKSGvJGfsxLUghAI6aDvvvLOpH7LFFlsYEFJtLYg0OXN+4oknLqHDgcejmr8lxeztPBzFLFQn/+88HPFOJMTQSy+9VI4//viedEp1nY8fP36JrBiEhr71rW8tAUKobVANi4amhtJ3wiflSKdkJtjp2QsrIMRWt1QQEmSXykJEyiA/xXbO8T4NpbkaYT24RYSFELxSMa5clWFJT65mrxrPF/MCMMB7xvxE7S/zfeutt8rpp59uvBpoVCBJjueR95PKqK5VlwUc4Kiu+ShZbxzgKJlpC16YDy3aFZBRbT4Iu1Y4IEpK5Tc716gfYb+jJG4OCY2wA6Q0AFC57p2vj/TJJqQCSABlfsiWkA4JORCSADhVa8aGn/mxeSfMDRkJ9tx4wRp2wnZePkg5ZO39jAfghCeCucSrEcfcoK8D2CfMCVcDTpVr1W8BBziqf45i6aEDHLGYMZaLsDOD1AYfhJ/XXnvNpOZCElMQwgd0gw02MO70OIEA99Yia2Q3sKAFzQKIxQg+LgJYY6dqgxAlESoIYVEl/RHmvlZ0jdNeProZ6yF+6594b+qthwIIySVFHsRjFHVg3B+PBiRHUpDjALVc8x//+IfhMVAdFe9GnOG/qGMu9/m8I/bzThimWt9nbOMAR7mfkArdzwGOChne521xl+MO1rRcSKm4oAEdNgjBHR02FAPvBC8AMWC4Dbayps9uVvwwJRECMOCd8JsPrsqQayim2mXIcwEGrUsDcApT/8R7zVwCXKqlYntDSlHVF8BDOAgwgFcjDqCD/sRxxx1nysn/7W9/Mxlj5W5B6puQkksdFNu+ZLNBLI+r8czAGbvlllvksMMOM5cFoCvZOK77xHUdBzjismSVX8cBjiqfIE/3WFj5cOD90MwY/swiYmuDAEYgRRba1XMOHyYAB9ka1NmoZS+ACl6RvUHGBl4Ou8YH/w+A01RKBSHVEmKwp7rU9U+891IQol4j7AYAtbUvACNhiYmEg/DeEepgIRw1alTkZw2vxt///nehtsixxx5rMlFKAZL8fCGC1DcBcMCJgugZdwNob7fddgZoc31ADHwWUsAfeOABU4MlDtAad78d4IjbolV6PQc4qnRiAnSLxYldni1QBkGVzBKbDwJBlR0liy7F7Vg8cGfjHQm7kAToZskOZXcJ74QdbrHFjGO9pFQ7xKALbCXd8WRsqN4JXoBKyFXbAlw2CLHTcwEggLdiMuMsgnA1WATxoMVhW8IxgAxCTTfddJN8+9vfLtnz5efCQeqblBJwXHPNNSZD7v777zfdpiAdlWjxjvFvkHKrsTnAUY2zUoI+OcBRAqNWwSUhEPKRt+vFUC2U+iCQ9aiWS1VMfhdbMKpgODm7YKftsmuDDxAmTm2HGHRxBYB5SamlBmV2/RM0KFgcwobJSjFn3to6ADfSc1XYTsMxuO3pN14NgCBcGoAGHrSojT4QNkEl9IQTTpBTTz21Yl4NHUvQ+iYADgUEeHrIpKGAXBxzDeA455xzjM2vu+46Oe+888z7TZgJgTDmhPtUmyfTAY6ob0aNnO8AR41MVMRuwvs45phjDAmVctbsOgnF8LFkd2h7QuJwd0fsbtHTAU3UC+EDSvgkTi+AvbtXyXZCDLq7Vy9Ivsq5RTuf4wCADgCR3T+LcxwZG2H6EfQc7M9cqNdItVTUkwb4wOtECCHqIgfRFK4G98CrMXr06KDdLcnxQeuboMuDR4QibHCyDjroIAM4+AnSbGKoTQrl+Zk5c6Z5pwEdHEdoBfBxxBFHmPBrHNyZIH0tdqwDHMUsVCf/7wBHnUxkkWGw06SMNRLI6tJWnoASUvmNVgGub5sPAiBh4Yi6YMRhaVv1NFdqaBz3yHWNQpVzbZ5DUN0LxsPcUCa9Vuuf2PZi4UNcjZRvQnoAEk3PDcudwfY33nij/PGPfzSLMoJeYTxZpXo2otY3wSsBuZP0+KANQjkeC0jktOeee06233574/XheoRRAcvc44orrjCEXYTiaITuqgXYOsARdOZr9HgHOGp04krUbT7uhF5sqXY+UvA8bBACt6CcH33AERwN+sYHk11zHFyAKGbUxdQuXMeCy8Lqpyw942FxZqfLeCpFeIxiA/tctFvwOgE0tKif/n8u7gz2y1U91wa2EE3xahC6waux4YYbxtXdWK8Tpb4JXgjEyIICDrwYKI4CVimfsO+++8ppp51mVFUBaPfcc4/xmtEg65Ktgm1POeUU+eUvf2lCU2PGjInVDmEv5gBHWMvV2HkOcNTYhJW5u1qIDRIqHhBcwPzmA4ZLm9iwFqwrFeeAnRgLM0qauItZoKu15SpLT19tPghAiYWUUAThoGoejx87M2bmh1AHQNTveFQh1Q7HsCBSJ4Tnjh+qu5588smG/FhOgOtn3PYxQeqb3HnnnUZmHWCKR/H73/++IcAyRj/NDqVA/qaIG2ES5NE1LEP2Gf8O+ACMcA4hqZ///OcmjHr00UfLz372Mz+3K8sxDnCUxcyVv4kDHJWfg1rrAR8vPlo2IRUgQrNrxfDnKLF7WyBq1VVXFX7iINaV09525VwWZLwAACgIqCzM2Ed3+bVG3lWvExk1iMVB2o0yBhV0Q8CLRRlQhq3waAFq2b1XQmPDz/NSqL4JQmQ0PBk00lYJhRBOI0vsqKOOMqDKz7ONV0htzPsB+ZTr41EjnAJ5mutyzL333muuTZE3quPSsCfArdrAmwMcfp6yOjjGAY46mMQqGILWK8EtrJ4QCKoQUG0QgkucHX4xPggcABYyOBGk9EHYrOWmMt6MGy8Nv+2dPf+vlXM1HKPZHtU4bq1SS6gDr0YctXZYTMeOHWvIjbj9TzrpJLMIw3GB4AzxkdDestpsz8af//xnAxrIeKGdccYZJjX+oYce6jEPc4PXBO4WQLeamwMc1Tw7MfbNAY4YjekutYQF2PW98847S+iDIEDEAmWDEHbGurtDJZSPI94AFmaKk9Vys+ufFJLxBrCpSJmm5uImB3TYfJC4KueGtamdiqwaLlG8GtoPQAVZVNiLGig8I64tbQE4MhSj4zlA2GujjTYyz8ejjz5qBNAOPPBAA9RoPD9aU4Y0YuapGNCvlM0d4KiU5ct8Xwc4ymzwZfh2LFYsqsSt7YJ17O7JhMGLgVuYBWf33XevaTEyphlRKkh7hEzw0gQludrF2BSEaOVcBSH8DnrdsI8gHBoWPDgbAALGFbUBMMiguPDCC42mBhyEUuudePscRJY8yLFRbeM9H7tTmA6Nlt///vfmv/GSMQ88Fwh8AUJQX73qqqtMuIbMlFpoDnDUwizF0EcHOGIwortEaAsAQpBbhm3P7gsvAHwQ4vbE69ES4If0vkrv7v0OkoWBbBp4LnF7aWyipYIQvENeCfI4Y/TM0eTJk+XTTz81YmQseH74BsXshY3wajDvgExsVYkWRJY8yLGlGAtcDMAeHgsEvlBcJZxFuvsWW2xhgAjcDQT94MLUSijSAY5SPC1VeE0HOMJPCu5/WN/El/kzmRwsjK4Fs8Ahhxwie++9t/zgBz8wiw+7XvgbtheEnTWpo3bBOrwG5d4NFxqZHW7Qirul7h/3xPNgp+Zq5VwbhPiRIM81NkiGeGkI+bDQEeKJ2tiNX3311XLJJZcYIuivfvWrSGTTqP0JIkse5Nio/cp1PuCcmjEQqAmRkGmCZ4OUamql0AijoChcS80BjlqarQh9dYAjvPHIf7/77rvNIsjuwgGO8LYstpCzqKLQiD4IHhBAHjoWhGJsPkgcpc7DjIKFGVCEyz1u5dOg/VGRMhuEQCDUyrmaoltIpAwggzotqZQQNfFsxBH/B0ji1cADg1YEoLGSLYgseZBjw4zJzkDhfG+Jeb0mhGo8XQoqINiS8URIqlabAxy1OnMB++0AR0CD5Tmcj7EDHPHY0s9V+BgjfERGjO0Jgc9gy7QTlmGBjWOxzNUvzc5hca7G+ifaZxYzLylVK+fafBAkrwEneDUIm+DViEONUjkGeDXIqICLEAfZ1M+zUuiYILLkQY4N0i/VHNEwFSmzADG8Fjy3PGPeEBbghzAX6quAcNKI2fTUanOAo1ZnLmC/HeAIaDAHOOIxWAmuwocZngEfYH7wgvDxZndugxBkoONQ9aQeDV4NQA5ejVqJl6vpbfVP5YPwbyyAQ4cOlZVWWslwQ6LaChuhbIl98GoQGquWFkSWPMixYcb34osvGkEuQAZqrWScYDdvgyMEyMazscYaaxiSaJycnTB9j3qOAxxRLVgj5zvAkXui+PCyK8zXvFoSzsNRnQ88u3j0QFSkDBAC0Q49EJsPEoQIadc/YSdKFdRSeVDKZVXCL0jYwzlBO4WUSrtyrpeU6oebgp2uvPJKkzmBEidqmtXg1fDaNIgseZBjg8wdHgqI05A+Dz74YAMibr31VkFvg4wtb7iFECMhXZ7bemgOcNTDLPoYgwMcuY0EidEW0fEehZ6E/bI7wOHjYauCQ9i9a6Vcu14MANOuFQMYIS7uBRLwRuAh4AGgXkit7yxZyFD0hGiI58FbKdiunKsARCvnekmpttufkAwKmJBM8WqQfVStLYgseZBj843XCx549sgsgdiL5Dnthz/8oZF4BxiPGzfOZKLkCq1Uq02D9ssBjqAWq9HjHeCIZ+Ic4IjHjpW4CosqXg8lpPL7zTffNKJjCkLY2SJNTSnx/fbbr6fiZiX6G9c9AV4AA8ijpKT61fNg4dOS9ApCIM2yS8fdD/B45JFHTOomhdfiSKGNa8y5rhNElrzQsX76aBNBv/76awNqCVnx/EECxQN36KGHmuqvhFPI4Nlnn33kggsu8HP5mj3GAY6anbpgHXeAI5i9vEfjeqYRn2ahQvmPD0i1f2Sjjbr+zyYcQIiBWDnubRYCFmU8GpoVgxcEL0c1hgkKzRA7bJQ9WfAYEyGhqA17UWiNqqd4S7SSLrbCK0DJ9GW52d4Jnik8GHBk8JSR6qoNrxBAFzIo3rTddtvNeOTwgNRzyr0DHMvI2+EAR7SJzhW7/+9//ys77LBDtAsHOLuS6ocBulmTh0LiI4SCdwPyKcADPgg//JnFgEwY9YRQZKyaOR2kVELixEUPWIpKCGVSef4uvfRSo63BQok2DYAbiXpsBM8FcuOy2LwZKACzI444QrbZZhvzzCBTDkeDsBNzQ70Y0obRpKGgG2Fb7MmzV8/NAY56nl1rbA5w1P5EV1r9sPYtmH8ELJrsQnN5rFhM8BIoFwQQgmw7sXgFIPwGkBTSvCiH/QAFlCxnUYtT/XTChAnG9U9o4IYbblimi6t559H2ajzzzDMGVGy99dbmGeHPNEJahE8IR1GmHmn3Z5991migwOf45z//WY7Ho+L3cICj4lNQng44wFEeO5fyLpVWPyzl2Grt2iwyH374YQ8IQaSM0Myaa665BCmVNNpyEU4BTXg1SLXEq+Enw6SY3eFqXHzxxcbz86c//ckoXlZDGPGFF14wHgLAFWO99tprjdcgV1NRM8Cgth133LFHsbOYDfz+/0033SRnn322KUvP80AYFkChDbVVgAU6PoBbUrvJrlqWCtg5wOH3aarx4xzgqO0JLLX6YW1bp/K9xwvCAsNiggeEBYffeEZGjx5tvB+EYeCDABzjXLQBBYAfiJ0sXngh4mikGePVGDFihFx//fVVk5pJsTyAHYXgDjvsMLnllluM+iYLOCEkb1PAwTuU6/+j2gouC/VOSF8l1IQng3ArnJYxY8aYlFdtZAfxb3g6lsXmAMcyMusOcNT2RJdK/bC2rVLdvQeEsDiqNggghB/Ahi3TDhhhIQyq8cH1NX2Xmi6kpMZBbGXXfdFFF5nQyXnnnSdHHnlkrAAp6qxRAO6yyy6Td955p+dSFDGDCwFvopSAA88W82TPFcRZMkyogHzXXXcZrgbZPLfddpup4kqVXEAGDWCIJ2rbbbeNaoaaPN8BjpqctuCddoAjuM2q6YxSqx9W01jruS8sWOy4yWBQTwieBGrD2CAEXQaVvM5lD0ABJFfkyfFqxLVzJ02YDAr6M3bs2KokMZJCCkfF5j38+Mc/Np4YgEg+wMGYIHNuvvnmxjsStGqtzdXAk0VDfwRdE5Ruf/rTnxoSKGEnZPZJgSUcRfgH/k8cIa5afzcc4Kj1GfTZfwc4fBqqig8rlfphFQ95megaRE9ImbZAGYJz7NptEEIYgfbvf//bLK6kW1JwLS6vBhoQeA/4ffjhh1fEq+FH+ZeFnfR0qqdqQ90UEKYkTfvBQUsE0iYFAAFohD0IacC5ARgEbUcddZQ8/PDDJn0VmXL4LaS/4s0g9ZUS8ggK0p588kmTQgz/pd4zUPzY0QEOP1aqg2Mc4Kj9SYxD/bD2rVD/IyBUggQ5WQ52wToWziFDhpgaKIhtoXmBlkPQUIzXglTnxavBgghXAyBTqeZH+RcvBqGqf/zjHz3dPOSQQ4wtcnk4vGPBfnApAAKq+JlvvF61UPgieFaoHk2WEkAC8bOTTjpJ9t13XyMYxxydcMIJhssBgCoXabhScxbkvg5wBLFWDR/rAEcNT16261HVD0tlAXaafLzZpe+5555GvMi1+CzAAkkWBtVX4QegVAkYAShAELW9IOy62f37ASGQXM8//3wzd0rAjJPMGp8FlrwSXhjqtvC8aWNxx7MA38RPA3D87W9/yws4vCXjf/e73xlQ8Yc//MGorCISpw3iKnbDjqS64gE5/fTTjZJoLdjTj73iOsYBjrgsWeXXcYCjyieohrt3zz33mA/rE088YUppO8AR/2RC3mQHbadQsvsmDdYOxcDrQIDLBiH83Rt2QaiLDBSEuggJwG+olaZZKvAjWNTxdFBRFVVVvAvehn0InZA+SzE0Qiqcg+2oE1OoTZ061WSZcG1IoIBrCNwANOXNUAuFfsDZwOvx9NNPl1UQsFbmjX46wFFLsxWhrw5wRDCeO9WXBdAggHToAIcvc8V+kBZgw/Oh9WIAFmRFwF8gJRdJfngHgMRLLrlEIFvW4i78+eefX0qHA7EtGgs/wAzeBmGi22+/3XgcSFHu27evbLHFFsazg1fEbl6vBmETvBXwMwBlNLwiAA88KT/60Y/Mv/3nP/8x2Sn1UD4+9ofSc0EHOEpt4Sq5vgMcVTIRddwNBziqb3JZRAEcZMTAB3nggQdMyiauf8IKy3ojrNTc3GzCJF75dwAL3BCK3QHStJElQ5YK3iFCWOeee64Jd/HvrhW2gAMcy8gT4gDHMjLRFRymAxwVNL7PWwNAaH44Hj4vWbOHASZQAkU/g0baK6nAAAm8JYRH8FwcfPDBBnDgIaIhsMbfyUjhzxBuURd1rbgFHOAobqO6OMIBjrqYxqoehAMcVT09y3znvCETSKdbbrmlIXtSZA3tE7Q50Emh2BokUYqqkYaLhwitDQUmtRiGqoYHwAGOapiFMvTBAY4yGHkZv4UDHMv4A1Bk+BAwqYgKr4Q/E5YoVoo9SM2UQre3RbsIlSDYBb8D8iekUNJb+bef/OQnhpMBTwPlVsi6eEH22msvk4GFAqt9LTfjwSzgAEcwe9Xs0Q5w1OzULdFxiG98tKk4ucMOO5i6HJVuqDfyQyybXSCiSuwA4yiJXumxufvHZwFqjUDEJDQBcbMY4AhaM6VYT2fOnGlIoC0tLSa1GDCB5shuu+1map+QXaKS46TdPvroo+Z43jUIobx3EydONOe6Fs4CDnCEs1vNneUAR81NWc4OUzuDMvUUqqIUNnFm3MAUBmMHBggp90KPZwMhKrshSsUH3DVngVwWgENSDHAErZlSyNL333+/AQ8HHnigyc4BOKDkSiM9ljRiMlCQJqcB7PF6fPXVV0Z5Fflysl+cWmi059kBjmj2q5mzHeComany3VHEiAAeO+20k0lHxVWMlwHQAesehcNlnRyI3PVxxx1nNELY4aI3gWaDX4Eo35PhDgxkAT+AI2jNlHwd4BmgqBvvxdFHH73EYcrrwNtBuAT+hsqdoyCK7savf/1r2XnnnQONzx2c2wIOcCwjT4YDHPUx0cSP+aEQFHFl2PSoH2rzEuPsUeeqdFkfVsk/CmpnsEMlNk/2AfoUeILuuOMO40p3LR4L+KmBYoNfP4ADj0SQmin5RgIP5Dvf+Y4hfiKCZsuV6/vCbwik1Kb517/+1XMpQOqwYcPiMZK7ihP+WlaeAQc46mum0VbYb7/9jJuY3ToLK9wJaj1AyGM3hzaAHwXJQiClvqyWGQ0ZCIg+nXPOOfU4vIqMyU8NFDI+tPkBHHg4otRM0XshREd45PHHHy9YVZdy9wijIRSmoZWKGLOOb+o8HHXG0hYTAAAIsElEQVQ8ufbQHOCoj4lWhjwkNwAFDHs0AChadfXVV/eU7EYZEdlmKlgibkTqH3U3kHceOXKk+VkWG7Yg+wBSIGRA1ypjAT+AI46aKYwOjgYAE08HXgz1cOhvvF2IewHguSdew/XWW68yhqnzuzrAUecTrMNzgKM+Jlo/kgAMSl8DLGD/n3zyyaYQFbwOGtkilOzmY0odDWo9QJQjFg34IKwA8x5vCD9bbbWVUVz0NrwfGoqpde0BxoIdIAJiu1ofTy0+0QA+GqESwlt4FCA555qLoDVTCtkDcEk9FLhO3nb88ccbKXRqy7hWWgs4wFFa+1bN1R3gqJqpiNQR9XAgRgSQuPLKK41uAFkixJtpq666qpGtppAaHo7ll1/eqCWSyQJAofgUFS6JTROzRu6a61KanOwSCKeUR6+n2DVggwWFKqsQSIsV7Yo0Se7kvBbIRWLGW0cI0FsDhYsUqpkSxMyADZ5t0l6POeYYAzDIROGZAOygu0GKrGultYADHKW1b9Vc3QGOqpmKWDpCGIW4OZ4N0gtPPPFEs2uHvPfYY48ZghwABPb9//73P5MKeNVVV5kdJYsvWgjoCdxwww0mvEIlUoAJoIPMF0p9k+VCOuBnn31mPtJjxozJmfXC9fipVo8BfcPbQy0RbJSromgsk+IuUtUWwLuBB/DVV1+VzTffXD788EOTJov4l2vlsYADHOWxc8Xv4gBHxacgtg5AGCW973vf+54pLjV37lyTsQIooPqnt/FBRbODXRw7e0p0Ay4eeughAyJohBrgeBCqocQ5AIS/kxKI6xvPAN4UVYYk1ZAQDaRUTSOMbYAxXwiwwU75qaeekqFDh8Z8dXe5WrIAzy2eFDx6hHWcrkZ5Z88BjvLau2J3c4CjYqYv2Y1tiWV2bz/72c9MGAVSJK5jmPakAZ566qkmi+XGG280fXn33Xdl9OjRBqgg50zbfffdjcv5tNNOk3HjxpkQDBVFqS9BIxwDCNEy32R4cE/ACffg2iqkVLIBh7gwstRkR0AKJJVYG0BNS46HuKw7xVnAWSCEBRzgCGG0WjzFAY5anLXcfS6Uxkr4g9ABYGSfffaRWbNmmWqWAAz0KGgw8Qm1ENemwek46KCD5IQTTpAf/ehHJoMDQimhGr0XHJGPP/7YZMFwHF4PUnABG4RfpkyZYq7br1+/+jF0mUYCaZHUTSqPohyrbv5yK8aWabjuNsuwBRzgWEYm3wGO+p7oQiCEXT4NMimN3T2ZLegS0B588EG5+OKL5fzzzzeeDHQ9iG8DXLguBFJScCGTosbIMZdeeqkRU6IBciDh4aoePnx4fRu6BKMjbRPXPmCNcBngj2wivEmuOQvUkwUc4Kin2XRjcRbIWCApIqkCxiD/dTAlI7LHnEhURUSOEZFWEfmniKDStIeI4AY5VER+KiKXishkEblWRI4UkXey539LRO4Skf2tf3NzEc4CIDakLrHzT8Jdwp3lLOAsUE4LJMp5M3cvZ4E6sMBAEZkvIluKCLT9N0QEIEEbICIPiciZIrIRURkR+ZOI3GsBlu+JCIzVL+rAFpUYwqkigkY9pJpZWbD3WiU64u7pLOAsEMwCDnAEs5c72llALYD8JsBiWxFht72riHwkIpR+Va/JWBFpEJGzRGRjEblGRC4XkUucGSNbAIlLgNt1WS9H5Au6CzgLOAuU1gIOcJTWvu7q9WmB3iJyRhZkbFFgiGuJyNkislN2UWRxvE1EMjKSrkW1wIEi8nMR2SXqhdz5zgLOAqW3gAMcpbexu0P9WQDAcTD80iygaBKR7iJ8ENJSFtafKSo6IubgvOw8VLQj7ubOAs4CxS3gAEdxG7kjnAXCWgBSKu8YIZZ02Iu484wF4Gzg0fiPiMwTkQ1E5A5UvUXkaGcjZwFngeq3gAMc1T9HrofOAs4CIniIIN/ChcHDNF1E7s7yYxY5AzkLOAtUvwUc4Kj+OXI9dBZwFnAWcBZwFqh5CzjAUfNT6AbgLOAs4CzgLOAsUP0WcICj+ufI9dBZwFnAWcBZwFmg5i3gAEfNT6EbgLOAs4CzgLOAs0D1W8ABjuqfI9dDZwFnAWcBZwFngZq3gAMcNT+FbgDOAs4CzgLOAs4C1W8BBziqf45cD50FnAWcBZwFnAVq3gIOcNT8FLoBOAs4CzgLOAs4C1S/BRzgqP45cj10FnAWcBZwFnAWqHkLOMBR81PoBuAs4CzgLOAs4CxQ/RZwgKP658j10FnAWcBZwFnAWaDmLeAAR81PoRuAs4CzgLOAs4CzQPVbwAGO6p8j10NnAWcBZwFnAWeBmreAAxw1P4VuAM4CzgLOAs4CzgLVbwEHOKp/jlwPnQWcBZwFnAWcBWreAg5w1PwUugE4CzgLOAs4CzgLVL8FHOCo/jlyPXQWcBZwFnAWcBaoeQs4wFHzU+gG4CzgLOAs4CzgLFD9FnCAo/rnyPXQWcBZwFnAWcBZoOYt4ABHzU+hG4CzgLOAs4CzgLNA9VvAAY7qnyPXQ2cBZwFnAWcBZ4Gat4ADHDU/hW4AzgLOAs4CzgLOAtVvAQc4qn+OXA+dBZwFnAWcBZwFat4CDnDU/BS6ATgLOAs4CzgLOAtUvwUc4Kj+OXI9dBZwFnAWcBZwFqh5CzjAUfNT6AbgLOAs4CzgLOAsUP0WcICj+ufI9dBZwFnAWcBZwFmg5i3gAEfNT6EbgLOAs4CzgLOAs0D1W8ABjuqfI9dDZwFnAWcBZwFngZq3gAMcNT+FbgDOAs4CzgLOAs4C1W8BBziqf45cD50FnAWcBZwFnAVq3gIOcNT8FLoBOAs4CzgLOAs4C1S/BRzgqP45cj10FnAWcBZwFnAWqHkLOMBR81PoBuAs4CzgLOAs4CxQ/RZwgKP658j10FnAWcBZwFnAWeD/261jGgAAAIRh/l1jgyV1AOUhL+Bw5CdUgAABAgQI/As4HP8bSUiAAAECBPICDkd+QgUIECBAgMC/wAALYz0k8pSHEwAAAABJRU5ErkJggg==\" width=\"432\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "ax.scatter(X_test[Y_test==1, 1], X_test[Y_test==1, 2], X_test[Y_test==1, 3], label='Class 1')\n",
    "ax.scatter(X_test[Y_test==-1, 1], X_test[Y_test==-1, 2], X_test[Y_test==-1, 3], label='Class -1')\n",
    "ax.plot_trisurf(X_test[:, 1], X_test[:, 2], (-(logreg.coef_[0, 1]/logreg.coef_[0, 3])*X_test[:, 1])+(-(logreg.coef_[0, 2]/logreg.coef_[0, 3])*X_test[:, 2])+(-(logreg.coef_[0, 0]/logreg.coef_[0, 3])), color='r', alpha=0.5)\n",
    "ax.set_title('Test set and logistic regression')\n",
    "ax.set_xlabel('Tempo')\n",
    "ax.set_ylabel('Chroma stft')\n",
    "ax.set_zlabel('Spectral centroid')\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TO DO** Now pick two features and restrict the dataset to include only two features, whose indices are specified in the $feature$ vector below. Then split into training and test. Which features are you going to select ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to make the plot we need to reduce the data to 2D, so we choose two features\n",
    "features_list = ['tempo', 'chroma_stft', 'spectral_centroid']\n",
    "index_feature1 = 1\n",
    "index_feature2 = 2\n",
    "features = [index_feature1, index_feature2]\n",
    "\n",
    "feature_name0 = features_list[features[0]]\n",
    "feature_name1 = features_list[features[1]]\n",
    "\n",
    "X_reduced = X[:,features]\n",
    "\n",
    "X_training = X_reduced[:m_training, :] \n",
    "Y_training = Y[:m_training]\n",
    "\n",
    "X_test = X_reduced[m_training:, :]\n",
    "Y_test = Y[m_training:]\n",
    "\n",
    "#New permutation if training set does not contain at least 10 elements from class 1 and at least 10 elements from class -1\n",
    "while (np.sum(Y_training==-1)<10 and np.sum(Y_training==1)<10):\n",
    "    X = X[permutation]\n",
    "    Y = Y[permutation]\n",
    "    X_training = X[:m_training, :]\n",
    "    Y_training = Y[:m_training] \n",
    "    X_test = X[m_training:, :]\n",
    "    Y_test = Y[m_training:]\n",
    "\n",
    "#Normalization\n",
    "mu, sigma = np.mean(X_training, axis=0), np.std(X_training, axis=0)\n",
    "#print(mu, sigma)\n",
    "X_training = (X_training - mu)/sigma\n",
    "X_test = (X_test - mu)/sigma\n",
    "#check if means and variances of the normalized training and test sets are respectively close to zero and one\n",
    "#print(np.mean(X_training, axis=0), np.std(X_training, axis=0), np.mean(X_test, axis=0), np.std(X_test, axis=0))\n",
    "\n",
    "# Add a 1 to each sample (homogeneous coordinates)\n",
    "#X_training = np.hstack((np.ones((m_training,1)),X_training))\n",
    "#X_test = np.hstack((np.ones((m_test,1)),X_test))\n",
    "\n",
    "#print(\"Training set in homogeneous coordinates:\")\n",
    "#print(X_training[:10])\n",
    "#print(X_test[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now learn a model using the training data and measure the performances."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[5.46593799 3.54348224]]\n",
      "Error rate on training set: 0.013333333333333308\n",
      "Error rate on test set: 0.0\n"
     ]
    }
   ],
   "source": [
    "# learning from training data\n",
    "\n",
    "#logreg = linear_model.LogisticRegression(C=1e5) #a large C disables regularization\n",
    "\n",
    "#learn from training set\n",
    "logreg.fit(X_training, Y_training)\n",
    "print(logreg.coef_)\n",
    "\n",
    "#predict on training set\n",
    "logreg.predict(X_training)\n",
    "\n",
    "#print the error rate = fraction of misclassified samples\n",
    "error_rate_training = 1 - logreg.score(X_training, Y_training)\n",
    "print(\"Error rate on training set: \"+str(error_rate_training))\n",
    "\n",
    "#predict on test set\n",
    "logreg.predict(X_test)\n",
    "\n",
    "#print the error rate = fraction of misclassified samples\n",
    "error_rate_test = 1 - logreg.score(X_test, Y_test)\n",
    "print(\"Error rate on test set: \" + str(error_rate_test)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TO DO** [Answer the following] Which features did you select and why ? Compare the perfromances with the ones of the case with all the 3 features and comment about the results. [Write the answer in this cell]\n",
    "\n",
    "**ANSWER QUESTION 3**\n",
    "I selected the features chroma_stft and spectral_centroid for their lower error rates on training and test sets. Comparing these results with those of the case with all the 3 features we can observe that in this case a small error rate on training set is present, but the error rate on test set is reduced to zero. So we have better performances on the prediction of the test set, but slightly worse performances on the prediction of the training set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If everything is ok, the code below uses the model in $logreg$ to plot the decision region for the two features chosen above, with colors denoting the predicted value. It also plots the points (with correct labels) in the training set. It makes a similar plot for the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "loop of ufunc does not support argument 0 of type NoneType which has no callable sqrt method",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'sqrt'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-23-2ef61150b86e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     19\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     20\u001b[0m \u001b[1;31m# Plot also the training points\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 21\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_training\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX_training\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mc\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mY_training\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0medgecolors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'k'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPaired\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     22\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfeature_name0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     23\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfeature_name1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\pyplot.py\u001b[0m in \u001b[0;36mscatter\u001b[1;34m(x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, edgecolors, plotnonfinite, data, **kwargs)\u001b[0m\n\u001b[0;32m   2809\u001b[0m         \u001b[0mverts\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcbook\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdeprecation\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_deprecated_parameter\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2810\u001b[0m         edgecolors=None, *, plotnonfinite=False, data=None, **kwargs):\n\u001b[1;32m-> 2811\u001b[1;33m     __ret = gca().scatter(\n\u001b[0m\u001b[0;32m   2812\u001b[0m         \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mc\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmarker\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmarker\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcmap\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnorm\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnorm\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2813\u001b[0m         \u001b[0mvmin\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvmin\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvmax\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0malpha\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlinewidths\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlinewidths\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\mpl_toolkits\\mplot3d\\axes3d.py\u001b[0m in \u001b[0;36mscatter\u001b[1;34m(self, xs, ys, zs, zdir, s, c, depthshade, *args, **kwargs)\u001b[0m\n\u001b[0;32m   2237\u001b[0m         \u001b[0mxs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mys\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mzs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcbook\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdelete_masked_points\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mys\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mzs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2238\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2239\u001b[1;33m         \u001b[0mpatches\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mys\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mc\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2240\u001b[0m         art3d.patch_collection_2d_to_3d(patches, zs=zs, zdir=zdir,\n\u001b[0;32m   2241\u001b[0m                                         depthshade=depthshade)\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\__init__.py\u001b[0m in \u001b[0;36minner\u001b[1;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1563\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0minner\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1564\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1565\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msanitize_sequence\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1566\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1567\u001b[0m         \u001b[0mbound\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnew_sig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\cbook\\deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    356\u001b[0m                 \u001b[1;34mf\"%(removal)s.  If any parameter follows {name!r}, they \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    357\u001b[0m                 f\"should be pass as keyword, not positionally.\")\n\u001b[1;32m--> 358\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    359\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    360\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py\u001b[0m in \u001b[0;36mscatter\u001b[1;34m(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, edgecolors, plotnonfinite, **kwargs)\u001b[0m\n\u001b[0;32m   4431\u001b[0m         \u001b[0moffsets\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumn_stack\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4432\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4433\u001b[1;33m         collection = mcoll.PathCollection(\n\u001b[0m\u001b[0;32m   4434\u001b[0m                 \u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscales\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4435\u001b[0m                 \u001b[0mfacecolors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcolors\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\collections.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, paths, sizes, **kwargs)\u001b[0m\n\u001b[0;32m    911\u001b[0m         \u001b[0mCollection\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    912\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_paths\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpaths\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 913\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_sizes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msizes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    914\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstale\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    915\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\collections.py\u001b[0m in \u001b[0;36mset_sizes\u001b[1;34m(self, sizes, dpi)\u001b[0m\n\u001b[0;32m    883\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_sizes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msizes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    884\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_transforms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_sizes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 885\u001b[1;33m             \u001b[0mscale\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_sizes\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mdpi\u001b[0m \u001b[1;33m/\u001b[0m \u001b[1;36m72.0\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_factor\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    886\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_transforms\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mscale\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    887\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_transforms\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mscale\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: loop of ufunc does not support argument 0 of type NoneType which has no callable sqrt method"
     ]
    }
   ],
   "source": [
    "# Plot the decision boundary. For that, we will assign a color to each\n",
    "# point in the mesh [x_min, x_max]x[y_min, y_max].\n",
    "\n",
    "# NOTICE: This visualization code has been developed for a \"standard\" solution of the notebook, \n",
    "# it could be necessary to make some fixes to adapt to your implementation\n",
    "\n",
    "h = .02  # step size in the mesh\n",
    "x_min, x_max = X_reduced[:, 0].min() - .5, X_reduced[:, 0].max() + .5\n",
    "y_min, y_max = X_reduced[:, 1].min() - .5, X_reduced[:, 1].max() + .5\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
    "\n",
    "Z = logreg.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "\n",
    "# Put the result into a color plot\n",
    "Z = Z.reshape(xx.shape)\n",
    "\n",
    "plt.figure(1, figsize=(4, 3))\n",
    "plt.pcolormesh(xx, yy, Z, cmap=plt.cm.Paired)\n",
    "\n",
    "# Plot also the training points\n",
    "plt.scatter(X_training[:, 0], X_training[:, 1], c=Y_training, edgecolors='k', cmap=plt.cm.Paired)\n",
    "plt.xlabel(feature_name0)\n",
    "plt.ylabel(feature_name1)\n",
    "\n",
    "plt.xlim(xx.min(), xx.max())\n",
    "plt.ylim(yy.min(), yy.max())\n",
    "plt.xticks(())\n",
    "plt.yticks(())\n",
    "plt.title('Training set')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "# Put the result into a color plot\n",
    "Z = Z.reshape(xx.shape)\n",
    "plt.figure(1, figsize=(4, 3))\n",
    "plt.pcolormesh(xx, yy, Z, cmap=plt.cm.Paired)\n",
    "\n",
    "# Plot also the test points \n",
    "plt.scatter(X_test[:, 0], X_test[:, 1], c=Y_test, edgecolors='k', cmap=plt.cm.Paired, marker='s')\n",
    "plt.xlabel(feature_name0)\n",
    "plt.ylabel(feature_name1)\n",
    "\n",
    "plt.xlim(xx.min(), xx.max())\n",
    "plt.ylim(yy.min(), yy.max())\n",
    "plt.xticks(())\n",
    "plt.yticks(())\n",
    "plt.title('Test set')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2) Linear Regression on the Boston House Price dataset\n",
    "\n",
    "### Dataset description: \n",
    "\n",
    "The Boston House Price Dataset involves the prediction of a house price in thousands of dollars given details about the house and its neighborhood.\n",
    "\n",
    "The dataset contains a total of 500 observations, which relate 13 input features to an output variable (house price).\n",
    "\n",
    "The variable names are as follows:\n",
    "\n",
    "CRIM: per capita crime rate by town.\n",
    "\n",
    "ZN: proportion of residential land zoned for lots over 25,000 sq.ft.\n",
    "\n",
    "INDUS: proportion of nonretail business acres per town.\n",
    "\n",
    "CHAS: Charles River dummy variable (= 1 if tract bounds river; 0 otherwise).\n",
    "\n",
    "NOX: nitric oxides concentration (parts per 10 million).\n",
    "\n",
    "RM: average number of rooms per dwelling.\n",
    "\n",
    "AGE: proportion of owner-occupied units built prior to 1940.\n",
    "\n",
    "DIS: weighted distances to five Boston employment centers.\n",
    "\n",
    "RAD: index of accessibility to radial highways.\n",
    "\n",
    "TAX: full-value property-tax rate per $10,000.\n",
    "\n",
    "PTRATIO: pupil-teacher ratio by town.\n",
    "\n",
    "B: 1000*(Bk – 0.63)2 where Bk is the proportion of blacks by town.\n",
    "\n",
    "LSTAT: % lower status of the population.\n",
    "\n",
    "MEDV: Median value of owner-occupied homes in $1000s.\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#needed if you get the IPython/javascript error on the in-line plots\n",
    "#%matplotlib nbagg  \n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DescribeResult(nobs=500, minmax=(array([6.3200e-03, 0.0000e+00, 4.6000e-01, 0.0000e+00, 3.8500e-01,\n",
      "       3.5610e+00, 2.9000e+00, 1.1296e+00, 1.0000e+00, 1.8700e+02,\n",
      "       1.2600e+01, 3.2000e-01, 1.7300e+00, 5.0000e+01]), array([ 88.9762, 100.    ,  27.74  ,   1.    ,   0.871 ,   8.78  ,\n",
      "       100.    ,  12.1265,  24.    , 711.    ,  22.    , 396.9   ,\n",
      "        37.97  , 500.    ])), mean=array([3.65578576e+00, 1.15000000e+01, 1.11317400e+01, 7.00000000e-02,\n",
      "       5.54451400e-01, 6.28297000e+00, 6.84246000e+01, 3.81193180e+00,\n",
      "       9.64200000e+00, 4.09624000e+02, 1.84286000e+01, 3.56208040e+02,\n",
      "       1.26987400e+01, 2.25680000e+02]), variance=array([7.47252195e+01, 5.48905812e+02, 4.76198228e+01, 6.52304609e-02,\n",
      "       1.35838329e-02, 4.97618330e-01, 7.99314163e+02, 4.46304043e+00,\n",
      "       7.59617595e+01, 2.85605197e+04, 4.67663531e+00, 8.41657137e+03,\n",
      "       5.13361942e+01, 8.52982926e+03]), skewness=array([ 5.17851447,  2.19924065,  0.29469001,  3.37060575,  0.72944382,\n",
      "        0.40691344, -0.58266003,  0.99061328,  0.9886802 ,  0.65098333,\n",
      "       -0.79127942, -2.85972639,  0.89051925,  1.09625625]), kurtosis=array([36.3384518 ,  3.88460431, -1.25254394,  9.3609831 , -0.10223624,\n",
      "        1.84311394, -0.99523636,  0.43299159, -0.90519243, -1.16696682,\n",
      "       -0.30903922,  7.00864873,  0.44527324,  1.4266838 ]))\n",
      "Shape of data array: (500, 14)\n"
     ]
    }
   ],
   "source": [
    "#Import Data: Load the data from a .csv file\n",
    "\n",
    "filename = \"data/house.csv\"\n",
    "Data = np.genfromtxt(filename, delimiter=';',skip_header=1)\n",
    "\n",
    "#A quick overview of data, to inspect the data you can use the method describe()\n",
    "\n",
    "dataDescription = stats.describe(Data)\n",
    "print(dataDescription)\n",
    "print (\"Shape of data array: \" + str(Data.shape))\n",
    "\n",
    "\n",
    "#for more interesting visualization: use Panda!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Split data in training and test sets\n",
    "\n",
    "\n",
    "\n",
    "Given $m$ total data, denote with $m_{t}$ the part used for training. Keep $m_t$ data as training data, and $m_{test}:= m-m_{t}$. For instance one can take $m_t=0.7m$ of the data as training and $m_{test}=0.3m$ as testing. Let us define as define\n",
    "\n",
    "$\\bullet$ $S_{t}$ the training data set\n",
    "\n",
    "$\\bullet$ $S_{test}$ the testing data set\n",
    "\n",
    "\n",
    "The reason for this splitting is as follows:\n",
    "\n",
    "TRAINING DATA: The training data are used to compute the empirical loss\n",
    "$$\n",
    "L_S(h) = \\frac{1}{m_t} \\sum_{z_i \\in S_{t}} \\ell(h,z_i)\n",
    "$$\n",
    "which is used to estimate $h$ in a given model class ${\\cal H}$.\n",
    "i.e. \n",
    "$$\n",
    "\\hat{h} = {\\rm arg\\; min}_{h \\in {\\cal H}} \\, L_S(h)\n",
    "$$\n",
    "\n",
    "TESTING DATA: The test data set can be used to estimate the performance of the final estimated model\n",
    "$\\hat h_{\\hat d_j}$ using:\n",
    "$$\n",
    "L_{{\\cal D}}(\\hat h_{\\hat d_j}) \\simeq \\frac{1}{m_{test}} \\sum_{ z_i \\in S_{test}} \\ell(\\hat h_{\\hat d_j},z_i)\n",
    "$$\n",
    "\n",
    "\n",
    "**TO DO**: split the data in training and test sets (70%-30%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of samples:  500\n",
      "Cardinality of Training Set:  350\n",
      "Training input data size:  (350, 13)\n",
      "Training output data size:  (350,)\n",
      "Test input data size:  (150, 13)\n",
      "Test output data size:  (150,)\n"
     ]
    }
   ],
   "source": [
    "#get number of total samples\n",
    "num_total_samples = Data.shape[0]\n",
    "\n",
    "print (\"Total number of samples: \", num_total_samples)\n",
    "\n",
    "m_t = int(num_total_samples*.7)\n",
    "\n",
    "print (\"Cardinality of Training Set: \", m_t)\n",
    "\n",
    "#shuffle the data\n",
    "np.random.shuffle(Data)\n",
    "\n",
    "#training data \n",
    "\n",
    "X_training = Data[:m_t, :-1]\n",
    "Y_training = Data[:m_t, -1]\n",
    "print (\"Training input data size: \", X_training.shape)\n",
    "print (\"Training output data size: \", Y_training.shape)\n",
    "\n",
    "#test data, to be used to estimate the true loss of the final model(s)\n",
    "X_test = Data[m_t:, :-1]\n",
    "Y_test = Data[m_t:, -1]\n",
    "print (\"Test input data size: \", X_test.shape)\n",
    "print (\"Test output data size: \", Y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Normalization\n",
    "It is common practice in Statistics and Machine Learning to scale the data (= each variable) so that it is centered (zero mean) and has standard deviation equal to 1. This helps in terms of numerical conditioning of the (inverse) problems of estimating the model (the coefficients of the linear regression in this case), as well as to give the same scale to all the coefficients."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean of the training input data: [-0. -0.  0. -0. -0.  0.  0. -0. -0.  0.  0.  0.  0.]\n",
      "Std of the training input data: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n",
      "Mean of the test input data: [ 0.05526017  0.15011602  0.04246145  0.18463724  0.08007631  0.16598841\n",
      "  0.09365783  0.01509741  0.06570654  0.0368837  -0.11012705 -0.08002211\n",
      "  0.02459851]\n",
      "Std of the test input data: [1.32353141 1.22303047 0.98597963 1.29246066 1.09533369 1.01243277\n",
      " 0.9612403  1.06360171 1.01905412 1.01196613 1.10143589 1.10720514\n",
      " 0.94338814]\n"
     ]
    }
   ],
   "source": [
    "# scale the data\n",
    "\n",
    "# standardize the input matrix\n",
    "from sklearn import preprocessing\n",
    "# the transformation is computed on training data and then used on all the 3 sets\n",
    "scaler = preprocessing.StandardScaler().fit(X_training) \n",
    "\n",
    "np.set_printoptions(suppress=True) # sets to zero floating point numbers < min_float_eps\n",
    "X_training = scaler.transform(X_training)\n",
    "print (\"Mean of the training input data:\", X_training.mean(axis=0))\n",
    "print (\"Std of the training input data:\",X_training.std(axis=0))\n",
    "\n",
    "X_test = scaler.transform(X_test) # use the same transformation on test data\n",
    "print (\"Mean of the test input data:\", X_test.mean(axis=0))\n",
    "print (\"Std of the test input data:\", X_test.std(axis=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Training \n",
    "\n",
    "The model is trained (= estimated) minimizing the empirical error\n",
    "$$\n",
    "L_S(h) := \\frac{1}{m_t} \\sum_{z_i \\in S_{t}} \\ell(h,z_i)\n",
    "$$\n",
    "When the loss function is the quadratic loss\n",
    "$$\n",
    "\\ell(h,z) := (y - h(x))^2\n",
    "$$\n",
    "we define  the Residual Sum of Squares (RSS) as\n",
    "$$\n",
    "RSS(h):= \\sum_{z_i \\in S_{t}} \\ell(h,z_i) = \\sum_{z_i \\in S_{t}} (y_i - h(x_i))^2\n",
    "$$ so that the training error becomes\n",
    "$$\n",
    "L_S(h) = \\frac{RSS(h)}{m_t}\n",
    "$$\n",
    "\n",
    "We recal that, for linear models we have $h(x) = <w,x>$ and the Empirical error $L_S(h)$ can be written\n",
    "in terms of the vector of parameters $w$ in the form\n",
    "$$\n",
    "L_S(w) = \\frac{1}{m_t} \\|Y - X w\\|^2\n",
    "$$\n",
    "where $Y$ and $X$ are the matrices whose $i-$th row are, respectively, the output data $y_i$ and the input vectors $x_i^\\top$.\n",
    "\n",
    "\n",
    " **TO DO:** compute the linear regression coefficients using np.linalg.lstsq from scikitlear \n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LS coefficients with numpy lstsq: [224.47142857  -9.55964617   9.88643694  -0.07050866   6.31037553\n",
      " -16.62566997  26.58797041  -4.77975636 -32.32814954  28.80456815\n",
      " -20.94109628 -17.66184865   6.99677852 -37.55801084]\n",
      "RSS calculated by hand:  786213.7708515079\n",
      "RSS with numpy lstsq:  [786213.77085151]\n",
      "Empirical risk with numpy lstsq: [2246.32505958]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-28-6e74725273f2>:12: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  w_np, RSStr_np, rank_Xtr, sv_Xtr = np.linalg.lstsq(X_trainingH, Y_training)\n"
     ]
    }
   ],
   "source": [
    "#compute linear regression coefficients for training data\n",
    "\n",
    "#add a 1 at the beginning of each sample for training, and testing (use homogeneous coordinates)\n",
    "m_training = X_training.shape[0]\n",
    "X_trainingH = np.hstack((np.ones((m_training,1)),X_training)) # H: in homogeneous coordinates\n",
    "\n",
    "m_test = X_test.shape[0]\n",
    "X_testH = np.hstack((np.ones((m_test,1)),X_test))  # H: in homogeneous coordinates\n",
    "\n",
    "# Compute the least-squares coefficients using linalg.lstsq\n",
    "#w_np, RSStr_np, rank_Xtr, sv_Xtr =  # PLACE YOUR CODE\n",
    "w_np, RSStr_np, rank_Xtr, sv_Xtr = np.linalg.lstsq(X_trainingH, Y_training)\n",
    "print(\"LS coefficients with numpy lstsq:\", w_np)\n",
    "\n",
    "# compute Residual sums of squares \n",
    "RSStr_hand = 0\n",
    "for i in range(m_training):\n",
    "    RSStr_hand += (Y_training[i] - np.dot(w_np, X_trainingH[i]))**2\n",
    "\n",
    "print(\"RSS calculated by hand: \", RSStr_hand)\n",
    "print(\"RSS with numpy lstsq: \", RSStr_np)\n",
    "print(\"Empirical risk with numpy lstsq:\", RSStr_np/m_training)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data prediction \n",
    "\n",
    "Compute the output predictions on both training and test set and compute the Residual Sum of Squares (RSS). \n",
    "\n",
    "**TO DO**: Compute these quantities on  training and test sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RSS on test data: 318914.8871942978\n",
      "Loss estimated from test data: 2126.0992479619854\n"
     ]
    }
   ],
   "source": [
    "#compute predictions on training and test\n",
    "\n",
    "prediction_training = np.array([])\n",
    "for i in range(m_training): prediction_training = np.append(prediction_training, np.dot(w_np, X_trainingH[i]))\n",
    "prediction_test = np.array([])\n",
    "for i in range(num_total_samples - m_training): prediction_test = np.append(prediction_test, np.dot(w_np, X_testH[i]))\n",
    "#print(prediction_training)\n",
    "#print(prediction_test)\n",
    "    \n",
    "#what about the loss for points in the test data?\n",
    "RSS_test = 0\n",
    "for i in range(m_test): RSS_test += (Y_test[i] - np.dot(w_np, X_testH[i]))**2\n",
    "\n",
    "print(\"RSS on test data:\",  RSS_test)\n",
    "print(\"Loss estimated from test data:\", RSS_test/m_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### QUESTION 4: Comment on the results you get and on the difference between the train and test errors.\n",
    "\n",
    "The obtained results about the empirical risk and the loss from test data are relatively high. Moreover, the estimated loss from test data is higher than the empirical risk. I would have expected a lower value of the empirical risk, considering the fact that the algorithm learns from the training data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ordinary Least-Squares using scikit-learn\n",
    "Another fast way to compute the LS estimate is through sklearn.linear_model (for this function homogeneous coordinates are not needed)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept: 224.47142857142873\n",
      "Least-Squares Coefficients: [ -9.55964617   9.88643694  -0.07050866   6.31037553 -16.62566997\n",
      "  26.58797041  -4.77975636 -32.32814954  28.80456815 -20.94109628\n",
      " -17.66184865   6.99677852 -37.55801084]\n",
      "Measure on training data: 0.2804861832986715\n"
     ]
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "\n",
    "# build the LinearRegression() model and train it\n",
    "LinReg = linear_model.LinearRegression()\n",
    "\n",
    "LinReg.fit(X_training, Y_training)\n",
    "\n",
    "print(\"Intercept:\", LinReg.intercept_)\n",
    "print(\"Least-Squares Coefficients:\", LinReg.coef_)\n",
    "\n",
    "# predict output values on training and test sets\n",
    "\n",
    "prediction_training_sk = np.array([])\n",
    "for i in range(m_training): prediction_training_sk = np.append(prediction_training_sk, np.dot(LinReg.coef_, X_training[i]) + LinReg.intercept_)\n",
    "prediction_test_sk = np.array([])\n",
    "for i in range(num_total_samples - m_training): prediction_test_sk = np.append(prediction_test_sk, np.dot(LinReg.coef_, X_test[i]) + LinReg.intercept_)\n",
    "#print(prediction_training_sk)\n",
    "#print(prediction_test_sk)\n",
    "\n",
    "# return a prediction score based on the coefficient of determination\n",
    "print(\"Measure on training data:\", 1-LinReg.score(X_training, Y_training))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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