Machine Learning ScikitLearn. Week 3 and 4.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Binary Classifiers Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "val=pd.read_csv(\"fruit_data_with_colors.txt\", delimiter='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "val=val[(val[\"fruit_label\"]==1) | (val[\"fruit_label\"]==3)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Saurav\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[5 0]\n",
      " [1 4]]\n"
     ]
    }
   ],
   "source": [
    "X=val[[\"mass\", \"width\", \"height\"]]\n",
    "y=val[\"fruit_label\"]\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "temp=LogisticRegression(C=1)\n",
    "\n",
    "temp.fit(X_train, y_train)\n",
    "pred=temp.predict(X_test)\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "con=confusion_matrix(y_test, pred, labels=[1, 3])\n",
    "print(con)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.9\n",
      "precision: 0.8333333333333334\n",
      "recall: 1.0\n",
      "f1 : 0.9090909090909091\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
    "print(\"accuracy:\", end=\" \")\n",
    "print(accuracy_score(y_test, pred))\n",
    "print(\"precision:\", end=\" \")\n",
    "print(precision_score(y_test, pred, labels=[1, 3]))\n",
    "print(\"recall:\", end=\" \")\n",
    "print(recall_score(y_test, pred, labels=[1, 3]))\n",
    "print(\"f1 :\", end=\" \")\n",
    "print(f1_score(y_test, pred, labels=[1, 3]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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, fig.canvas.height);\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",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\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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npi3mydJ/aYBtMonAbLiYzACCCDgCQGfz6fqA+bq0Mnz+uyJ6qp1a05PzNvNk6R+0wBa5ZMAWfExGAEEEPCMQI/pEZq5br+61L9FrzQr5Zl5u3Wi1G8aQKtsEiArPgYjgAACnhGYuXafenyxXrflz6rvu9bxzLzdOlHqNw2gVTYJkBUfgxFAAAHPCBz6+5yqDZirkBBp7RtNdFPGNJ6ZuxsnSv2mAbTKJQGy4mMwAggg4CmBpsMWatvBU/rokUq6+/a8npq72yZL/aYBtMokAbLiYzACCCDgKYG3vt+sCUt36eFqBTXw/ts9NXe3TZb6TQNolUkCZMXHYAQQQMBTAvO3HFKHSatV4Kb0WvxyA4WY88EsKSJA/aYBtAoeAbLiYzACCCDgKYHT5y86Xwt3IdqnhS/VV+EcGT01fzdNlvpNA2iVRwJkxcdgBBBAwHMCrccs16pdR9WvZTn9u0Zhz83fLROmftMAWmWRAFnxMRgBBBDwnMCIudv13uxtalY2j0a3rey5+btlwtRvGkCrLBIgKz4GI4AAAp4TWLfnmFqNXKYs6VJpXa+mCgvlOsCUCAH1mwbQKncEyIqPwQgggIDnBC5Gx6ji27N18txFffNMbVUomM1zBm6YMPWbBtAqhwTIio/BCCCAgCcFnpryi37edFAvNi2hZxsW96RBSk+a+k0DaJVBAmTFx2AEEEDAkwJTVvyhN7/ZqOpFs2v6UzU9aZDSk6Z+0wBaZZAAWfExGAEEEPCkwO6/Tqv+uwuUOixE63s3VYY0qTzpkJKTpn4HWQM4cuRIDRkyRAcOHFDZsmU1fPhw1a1bN86MXbhwQQMHDtTkyZO1f/9+lSxZUoMGDVKzZs38ziQB8puKFRFAAAEE/ivg8/lUZ9B87T9+VhM7VFWDkrmxSWYB6ncQNYDTp09X27ZtZZrA2rVra8yYMRo/frw2b96sQoUKXRWtV155RVOnTtW4ceNUqlQp/fzzz+rRo4eWLVumihUr+hVFAuQXEyshgAACCFwh8OpXG/T56r3qWKeo3rynDD7JLED9DqIGsHr16qpUqZJGjRp1KUalS5dWy5YtnSN9Vy758uVTz5499cwzz1z6kVk3U6ZMTmPoz0KA/FFiHQQQQACBKwV+2BCpZz9bp5I3Z9bPz9cDKJkFqN9B0gBGRUUpQ4YMmjFjhlq1anUpRt27d1dERIQWLlx4VbRy5MihwYMHq2PHjpd+9vDDD2v58uXavXu3X1EkQH4xsRICCCCAwBUCR09HqXK/2fL5pFU9Gyl35nQYJaMA9TtIGsDIyEjlz59fS5cuVa1atS5FaMCAAc41flu3br0qVo888ojWr1+vb775Rrfccovmzp2rFi1aKDo6WufPn48zhubfL/+ZCVDBggV14sQJZcmSJRmjy1shgAACCAS6wD0jFmvj/r81rE15tapYINCnE1DbTwMYZA2guX6vZs3/3VLfv39/TZkyRVu2bLkqmIcPH1anTp30/fffKyQkxGkCGzdurIkTJ+rMmTNxBrlPnz7q27fvVT+jAQyozz0biwACCLhC4J1ZWzR64e+6v1J+DW1dwRXb5JWNoAEMkgYwIaeAY0N+7tw5HTlyROaawFdffVU//PCDNm3axBFAr/wWYJ4IIIBACgks3fGXHh2/UjdnSasVrzVyDkawJI8ADWCQNIAmLuYmkMqVKzt3AccuZcqUcU7rxnUTyJURM4+FMTeNtG7dWubUsT8LAfJHiXUQQAABBOISOHchWuX7huv8xRjNfr6eit+cGahkEqB+B1EDGPsYmNGjRzungceOHes84sUczStcuLDatWvnXCcY2wyuXLnSef5fhQoVnP9rTu/u2rVLa9euVbZs/n03IwFKpk8qb4MAAggEqUDbj1dq8fa/1OueMnq8TtEgnaX7pkX9DqIG0MTLHP0zd/aaB0GXK1dOw4YNU716/397ff369VWkSBFNmjTJ+d/mzuAuXbpo586dzqNfmjdvrnfeecc5FezvQoD8lWI9BBBAAIG4BMYs/F0DZ21Rw1K5NaF9VZCSSYD6HWQNYDLl5tLbEKDkFuf9EEAAgeAS2Lj/hO4ZsUQZ0oQ5XwuXOiw0uCbo0tlQv2kAraJJgKz4GIwAAgh4XiAmxqeq/efoyOkoffFUTVUrmt3zJskBQP2mAbTKGQGy4mMwAggggICkrtPW6fv1kerW8Fb1aFoSk2QQoH7TAFrFjABZ8TEYAQQQQEDS9NV79MpXv6pSoWya+XRtTJJBgPpNA2gVMwJkxcdgBBBAAAFJ+4+fVe135iksNETrejVRlnSpcUliAeo3DaBVxAiQFR+DEUAAAQT+K9Dw3QXa+ddpjWlbWXeWzYNLEgtQv2kArSJGgKz4GIwAAggg8F+BXt9u1CfL/1C7moX1VotyuCSxAPWbBtAqYgTIio/BCCCAAAL/Ffh50596asoaFcuZUfNerI9LEgtQv2kArSJGgKz4GIwAAggg8F+Bv89dUMW3Zis6xqelrzZU/mzpsUlCAeo3DaBVvAiQFR+DEUAAAQQuE7h/5FKt3XNcgx64TW2qFsImCQWo3zSAVvEiQFZ8DEYAAQQQuExgaPhWfTBvh+4tn08jHq6ITRIKUL9pAK3iRYCs+BiMAAIIIHCZwKpdR9V6zHJlz5hGv/RsrNDQEHySSID6TQNoFS0CZMXHYAQQQACBywQuRMeoQt9wnY6K1g9d66hc/qz4JJEA9ZsG0CpaBMiKj8EIIIAAAlcIdJy0WnO3HNJrd5XSU3fcgk8SCVC/aQCtokWArPgYjAACCCBwhcCEJbv01g+bVbd4Tk3pWB2fJBKgftMAWkWLAFnxMRgBBBBA4AqB7QdPqsmwRUqbKlTrezdVutRhGCWBAPWbBtAqVgTIio/BCCCAAAJXCPh8PtUYOFcH/z6vqR2rq07xnBglgQD1mwbQKlYEyIqPwQgggAACcQj0+CJCM9fuV+c7btGrd5XCKAkEqN80gFaxIkBWfAxGAAEEEIhD4Ot1+/T89PUqlz+LfuhaF6MkEKB+0wBaxYoAWfExGAEEEEAgDoFDJ8+pWv+5CgmR1rzRxHkuIEviClC/aQCtEkWArPgYjAACCCBwDYFmwxdpy58n9eEjFXXP7flwSmQB6jcNoFWkCJAVH4MRQAABBK4h8PYPm/Xxkl16qGpBvfPA7TglsgD1mwbQKlIEyIqPwQgggAAC1xCYv/WQOkxcrfzZ0mvJKw0UYs4HsySaAPWbBtAqTATIio/BCCCAAALXEDgTdVHl+4brQrRP81+sr6I5M2KViALUbxpAqzgRICs+BiOAAAIIXEegzZjlWrnrqN5uWU5taxTGKhEFqN80gFZxIkBWfAxGAAEEELiOwIfztuvd8G26s+zNGtO2ClaJKED9pgG0ihMBsuJjMAIIIIDAdQQi9h5Xy4+WKnO6VFr3ZhOlCgvFK5EEqN80gFZRIkBWfAxGAAEEELiOQHSMTxXfCtff5y7q66drqWKhm/BKJAHqNw2gVZQIkBUfgxFAAAEEbiDQecoa/WfTn3qhSQl1bVQcr0QSoH7TAFpFiQBZ8TEYAQQQQOAGAlNX/KE3vtmoakWz64unauKVSALUbxpAqygRICs+BiOAAAII3EDgjyOndceQBUodFqKIXk2VMW0qzBJBgPpNA2gVIwJkxcdgBBBAAIEbCPh8PtUdPF/7jp3VxPZV1aBUbswSQYD6TQNoFSMCZMXHYAQQQAABPwRem7lB01bt1eO1i6rXvWX8GMEqNxKgftMA3igj1/05AbLiYzACCCCAgB8CP244oGc+W6sSN2dS+PN3+DGCVW4kQP2mAbxRRmgArYQYjAACCCBgK3DsdJQq9Zstn09a9Xoj5c6SzvYlPT+eBpAG0OpDQICs+BiMAAIIIOCnwL0jlujX/Sc0tHV53V+pgJ+jWO1aAtRvGkCrTwcBsuJjMAIIIICAnwKD/rNFoxb8rvsr5tfQNhX8HMVqNIDXzkCIz9xixJIgARrABLExCAEEEEAgngLLdvylR8avVO7MabXy9UYKCQmJ5yuw+uUC1G+OAFp9IgiQFR+DEUAAAQT8FDh3IVrl+4br/MUYhT9fTyVuzuznSFaLS4D6TQNo9ckgQFZ8DEYAAQQQiIdA249XavH2v/TmPWXUsU7ReIxk1SsFqN80gFafCgJkxcdgBBBAAIF4CIxd9LsG/LRFDUrm0sQO1eIxklVpAK/OANcAWnwuaAAt8BiKAAIIIBAvgc2Rf6v5B4uVIU2Y87VwaVKFxms8K/9PgPrNEUCrzwMBsuJjMAIIIIBAPARiYnyq2n+OjpyO0vQna6h6sRzxGM2qlwtQv2kArT4RBMiKj8EIIIAAAvEU6DZtnb5bH6muDW/VC01LxnM0q8cKUL9pAK0+DQTIio/BCCCAAALxFPjil716+csNqlgom75+unY8R7M6DeD/MsA1gBafBxpACzyGIoAAAgjEWyDy+FnVemeeQkOkdb2aKmv61PF+DQZI1G+OAFp9DgiQFR+DEUAAAQQSINDwvQXaefi0Rv+7spqVy5OAV2AI9ZsG0OpTQICs+BiMAAIIIJAAgd7fbtTk5X/o3zUKqV/L2xLwCgyhftMAWn0KCJAVH4MRQAABBBIgEL7pTz05ZY2K5syo+S/WT8ArMIT6TQNo9SkgQFZ8DEYAAQQQSIDA3+cuqOJbsxUd49OSVxqowE0ZEvAq3h5C/aYBtPoEECArPgYjgAACCCRQ4IFRy7Tmj2N65/7b9FC1Qgl8Fe8Oo37TAFqlnwBZ8TEYAQQQQCCBAsNmb9P7c7frntvz6sNHKiXwVbw7jPpNA2iVfgJkxcdgBBBAAIEECqzefVT/Gr1c2TOm0S89GyvUPBeGxW8B6jcNoN9hiWtFAmTFx2AEEEAAgQQKXIiOca4DPHX+on7oWkfl8mdN4Ct5cxj1mwbQKvkEyIqPwQgggAACFgJPTF6tOb8d0ivNSqlL/VssXsl7Q6nfNIBWqSdAVnwMRgABBBCwEJi4dJf6fr9ZdW7NqalPVLd4Je8NpX7TAFqlngBZ8TEYAQQQQMBCYMehk2o8dJHSpArVht5NlS51mMWreWso9TvIGsCRI0dqyJAhOnDggMqWLavhw4erbt2610y1+fmoUaO0Z88e5cyZUw8++KAGDhyodOnS+fVJIEB+MbESAggggEASCPh8PtUcOE9//n1OUzpWU93iuZLgXYLzJanfQdQATp8+XW3btpVpAmvXrq0xY8Zo/Pjx2rx5swoVuvoZSZ9++qk6duyoCRMmqFatWtq2bZvat2+vNm3aaNiwYX4lngD5xcRKCCCAAAJJJPDijPX6cs0+PXVHMb12V+kkepfge1nqdxA1gNWrV1elSpWcI3qxS+nSpdWyZUvnqN6Vy7PPPqvffvtNc+fOvfSjF154QatWrdLixYv9SjsB8ouJlRBAAAEEkkjgm3X79dz0CJXNl0U/drv2Ga8kevuAfVnqd5A0gFFRUcqQIYNmzJihVq1aXQpk9+7dFRERoYULF14V0s8//1ydO3dWeHi4qlWrpp07d+ruu+/WY489pldffdWvUBMgv5hYCQEEEEAgiQQOnzyvqv3nOK++5o3GypEpbRK9U3C9LPU7SBrAyMhI5c+fX0uXLnVO58YuAwYM0OTJk7V169Y4kztixAiZo37mOoqLFy+qS5cuzinkay3nz5+X+S92MQEqWLCgTpw4oSxZsgTXp4PZIIAAAggEhECz4Yu05c+T+uDhirqvfL6A2OaU3kgawCBrAJctW6aaNWteylX//v01ZcoUbdmy5aqsLViwQA899JD69esnc/p4x44dMkcMO3XqpDfffDPObPbp00d9+/a96mc0gCn9Ueb9EUAAAe8K9Pths8Yv2aU2VQpq0IO3exciHjOnAQySBjAhp4DN3cE1atRw7hqOXaZOnaonn3xSp06dUmho6FVR4ghgPD5drIoAAgggkCwCC7YeUvuJq5U/W3oteaWBQkL4WrgbwdMABkkDaHa0OYpXuXLlf5zCLVOmjN7cEQEAACAASURBVFq0aBHnTSBm3caNG2vQoEGXcjJt2jQ9/vjjTgMYFnbj5ykRoBt9xPg5AggggEBSC5yNilb5vuGKio7RvBfuULFcmZL6LQP+9anfQdQAxj4GZvTo0c5p4LFjx2rcuHHatGmTChcurHbt2jnXCcbeEWxO5w4dOtRZL/YUsLkG0DSG5rX8WQiQP0qsgwACCCCQ1AIPj12h5TuP6O0WZdW2ZpGkfruAf33qdxA1gCaN5gaOwYMHOw+CLleunPM8v3r16jlBrV+/vooUKaJJkyY5/9vc9BF7jeD+/fuVK1cu3Xvvvc6/ZcuWza9wEyC/mFgJAQQQQCCJBT6av0NDft6qpmVu1th2VZL43QL/5anfQdYAJnckCVByi/N+CCCAAAJxCazfe1wtPlqqzGlTaV2vJkoVdvV17Mj9T4D67YIGMDo62jkqZx7IfOjQIcXExPwjo/PmzXNtZgmQa3cNG4YAAgh4SiA6xqdKb8/WibMX9FWXWqpc+CZPzT++k6V+u6ABNN/IYRpA8xDmvHnzXnX3kr9fyxbfnZ8Y6xOgxFDkNRBAAAEEEkOgy9Q1mrXxT/VoUkLdGhVPjJcM2tegfrugAcyZM6c++eQTNW/ePOCCRoACbpexwQgggEDQCny68g/1/HqjqhXJri86/++ZuEE7YYuJUb9d0ADmy5dP5qHMJUqUsNiVKTOUAKWMO++KAAIIIHC1wJ4jZ1RvyHylCg1RRO+mypQ2FUzXEKB+u6ABfO+995zv4f3www8D7uGVBIjfLQgggAACbhKoN3i+9hw9owntq6hhqZvdtGmu2hbqtwsawFatWmn+/PnKnj27ypYtq9SpU/8jJDNnznRVaC7fGALk2l3DhiGAAAKeFHht5q+atmqPOtQuot73lvWkgT+Tpn67oAHs0KHDdffVxIkT/dmXKbIOAUoRdt4UAQQQQOAaAj/9ekBPf7pWxXNn0uwed+DEKeBrZiDE5/P5SEjCBGgAE+bGKAQQQACBpBE4fiZKFd+eLVPZV7zWSHmypkuaNwrwV6V+u+AIYGyGDh8+rK1btzrXAZobQsw3c7h9IUBu30NsHwIIIOA9gfs+XKIN+07ovX+V1wOVC3gPwI8ZU79d0ACePn1aXbt2dR4FE/sQ6LCwMOe7e0eMGKEMGTL4sStTZhUClDLuvCsCCCCAwLUFBv9ni0Yu+F2tKubXsDYVoIpDgPrtggbwqaee0pw5c5y7gGvXru3spiVLlqhbt25q0qSJRo0a5drwEiDX7ho2DAEEEPCswLLf/9Ij41YqV+a0WvV6o4B7wkZy7DjqtwsaQPMg6C+//FL169f/xz43dwa3bt1a5tSwWxcC5NY9w3YhgAAC3hU4fzFa5fuG69yFGP38XD2VzJPZuxjXmDn12wUNoDnFu2bNGpUuXfofu2nTpk2qVq2azClity4EyK17hu1CAAEEvC3QbsIqLdp2WG/cXVpP1C3mbQxOAce5/1P8LuBGjRopR44czjWA6dL9/91KZ8+e1WOPPaajR486p4fdutAAunXPsF0IIICAtwXGLdqp/j/9pvolc2lSh2rexqABdGcDuHHjRjVr1kznzp1T+fLlnWsVIiIinGbw559/dh4O7daFBtCte4btQgABBLwt8NuBv3XX+4uVPnWY1vduqjSpQr0NcsXsqd8uOAUce8Rv6tSp2rJli8xjCcuUKaNHH31U6dOnd3VgCZCrdw8bhwACCHhWICbGp2oD5uivU1H6/MkaqlEsh2ct4po49dslDWCgppIABeqeY7sRQACB4Bfo/vk6fRsRqWcb3KoX7ywZ/BOOxwyp3ynUAH733Xe66667nO/9Nf//9Zb77rsvHrs0eVclQMnrzbshgAACCPgvMOOXvXrpyw0qXzCbvn3m/x+zxvL/AtTvFGoAQ0ND9eeffyp37twy//+1FnM9YHR0tGvzSoBcu2vYMAQQQMDzAgdOnFXNgfMUGiKte7OpsmZI7XmTWADqdwo1gMGSQAIULHuSeSCAAALBKdDovQX6/fBpjf53JTUrlzc4J5mAWVG/XdoAHj9+XNmyZUvALk3eIQQoeb15NwQQQACB+An0+W6TJi3brUerF1L/VrfFb3AQr039dkEDOGjQIBUpUkRt2rRxovavf/1LX331lfLmzauffvrJeTSMWxcC5NY9w3YhgAACCBiBOZsP6olPflGRHBm04KUGoPxXgPrtggawWLFiMo+AqVWrlmbPnu18/dv06dP1xRdfaM+ePQoPD3dtYAmQa3cNG4YAAgggIOnkuQuq8NZsRcf4tPjlBiqYPQMu3ATiZCDFvwnEPOtv27ZtKliwoLp37+48EHrMmDHOv1WvXl3Hjh1zbVhpAF27a9gwBBBAAIH/Cjw4apl++eOYBt5/mx6uVggXGkB3NID58uXTl19+6RwBLFmypPr16+ecBt66dauqVq3q3Krt1oUG0K17hu1CAAEEEIgVGD5nm4bP2a67b8urjx6tBAwNoDsawGeffVY//PCDihcvrnXr1mn37t3KlCmTcxrYXB+4du1a14aVBtC1u4YNQwABBBD4r8Avu4/qwdHLlS1Daq19o4lCzXNhPL5Qv11wCvjChQt6//33tXfvXrVv314VK1Z0Yjl8+HCnEXziiSdcG1MC5Npdw4YhgAACCPxX4EJ0jCq+NVunzl/U98/W0W0FsnrehvrtggYwkFNIgAJ577HtCCCAgHcEnpj8i+b8dlAvNyupp+vf6p2JX2Om1O8UagD5KjjPf/YAQAABBBBIRoHJy3ar93ebVPvWHPr0iRrJ+M7ufCsawBRqAPkqOHd+INgqBBBAAIHgFNhx6JQaD12oNKlCtaF3U6VLHRacE/VzVjSAKdQA+rl/XL8aAXL9LmIDEUAAAQQk+Xw+1Xpnng6cOKdPHq+meiVyedqF+k0DaPUBIEBWfAxGAAEEEEhGgZdmrNeMNfv0ZL1ier156WR8Z/e9FfXbBQ1gt27ddOutt8r838uXDz/8UDt27HDuBnbrQoDcumfYLgQQQACBKwW+jdiv7p9HqEzeLPqpe11PA1G/XdAA5s+fX+amkMqVK/8jjOb5f/fdd5/27dvn2pASINfuGjYMAQQQQOAKgb9OnVeVfnOcf/3ljcbKmSmtZ42o3y5oANOlS6eNGzc6RwEvX8zRv3LlyjlfDefWhQC5dc+wXQgggAACcQnc9f5i/Xbgb73/UAW1qJDfs0jUbxc0gKbJ69y5s8w3gly+jBgxQqNGjdLmzZtdG1AC5Npdw4YhgAACCMQhMOCn3zR20U61rlJAgx8s71kj6rcLGsAJEyY4zd9LL72khg0bOmGcO3eu3nvvPef6v06dOrk2oATItbuGDUMAAQQQiENg4bbDemzCKuXLmk5LX22okBBvfi0c9dsFDaDJpznS179/f0VGRjpxLVKkiPr06aN27dq5+gNMgFy9e9g4BBBAAIErBM5GRat833BFRcdo7gt36JZcmTxpRP12SQMYm77Dhw8rffr0zncAB8JCgAJhL7GNCCCAAAKXCzwyboWW/X5Efe8rq8dqFfEkDvXbJQ3gxYsXtWDBAv3+++965JFHlDlzZudoYJYsWVzdDBIgT/7eYNIIIIBAQAt8NH+Hhvy8VU3K3Kxx7aoE9FwSuvHUbxc0gH/88YeaNWumPXv26Pz589q2bZuKFSum5557zrkDePTo0Qndv0k+jgAlOTFvgAACCCCQyAIb9h3XfR8uVea0qbSuVxOlCgtN5Hdw/8tRv13QALZs2dI54vfxxx8rR44cWr9+vdMALly4UE888YS2b9/u2iQRINfuGjYMAQQQQOAaAtExPlXuN1vHz1zQV11qqnLh7J6zon67oAHMmTOnli5dqpIlSzqNYGwDuHv3bpUpU0ZnzpxxbTAJkGt3DRuGAAIIIHAdgWc+Xasffz2g5xuXUPfGxT1nRf12QQOYPXt2LVmyxGn2Lm8Azb898MADOnjwoGuDSYBcu2vYMAQQQACB6wh8tnKPXv/6V1UtcpNmdK7lOSvqtwsawDZt2ihr1qwaO3as0wBu2LBBuXLlUosWLVSoUCFNnDjRtcEkQK7dNWwYAggggMB1BPYePaO6g+crVWiIIno3Vaa0qTzlRf12QQNo7vZt0KCBwsLCnOv9qlSp4vxfc2p40aJFyp07t2tDSYBcu2vYMAQQQACBGwjcMWS+/jhyRuPbVVHjMjd7yov67YIG0CTu7Nmz+vzzz7VmzRrFxMSoUqVKevTRR51nArp5IUBu3jtsGwIIIIDA9QTMKWBzKrh9rSLqc19ZT2FRv1O4Abxw4YKefPJJvfnmm86dv4G2EKBA22NsLwIIIIBArMCsXw+oy6drdWvuTJrT4w5PwVC/U7gBNGnLli2b1q5dSwPoqY8ek0UAAQQQSGmBE2cuqOLb4YrxSctfa6i8Wd191i0xvWgAXdAAdujQQbfddpt69OiRmPs2WV6LACULM2+CAAIIIJBEAi0+Wqr1e4/r3X+V14OVCyTRu7jvZanfLmgA+/fvr3fffVeNGjVS5cqVlTFjxn8kpVu3bu5Lzn+3iAC5dtewYQgggAACfggM+XmLPpr/u1pWyKfhD1X0Y0RwrEL9dkEDWLRo0WumKSQkRDt37nRt2giQa3cNG4YAAggg4IfA8t+P6OFxK5QzU1qt7tlIpu56YaF+u6ABvDxoPp/P+Z+BEkAC5IVfE8wRAQQQCF6B8xejVaHvbJ29EK3/PFdXpfJkCd7JXjYz6rdLGkDzPcDDhg279L2/xYsX13PPPed8F7CbFwLk5r3DtiGAAAII+CPw2IRVWrjtsN64u7SeqBt4T+TwZ45XrkP9dkEDaB4BY5q/rl27qmbNms4+Wr58uT788EN1795d/fr1S8i+TZYxBChZmHkTBBBAAIEkFBi/eKf6/fib7iiRS5Mfr5aE7+Sel6Z+u6ABNN/4MWLECD388MP/SMa0adOcpvCvv/5yT2Ku2BIC5Npdw4YhgAACCPgpsOXPv9Vs+GKlSx2q9b2bKm2qMD9HBu5q1G8XNIA33XSTVq1aJXPa9/Jl27Ztqlatmo4fP+53wkaOHKkhQ4bowIEDKlu2rIYPH666devGOb5+/fpauHDhVT9r3ry5fvzxR7/ekwD5xcRKCCCAAAIuFjDX31ftP1d/nTqvaZ1qqOYtOVy8tYmzadRvFzSA5ihf6tSpNXTo0H/s1RdffNH5iriPPvrIr709ffp0tW3bVqYJrF27tsaMGaPx48dr8+bNKlSo0FWvcfToUUVFRV369yNHjqh8+fLOmPbt2/v1ngTILyZWQgABBBBwucBzn6/TNxGReqbBLXrpzlIu31r7zaN+u6QB/OSTT1SwYEHVqFHD2asrVqzQ3r171a5dO6c5jF2ubBIvj0D16tWd7xAeNWrUpX8uXbq0WrZsqYEDB94wLeZoYa9evZyjh1c+i/BagwnQDVlZAQEEEEAgAAS+XLNPL85Yr/IFsurbZ+sEwBbbbSL12wUNYIMGDfzai+bRMPPmzYtzXXMkL0OGDJoxY4ZatWp1aR1zE0lEREScp3qvfCHzbSTmJpSxY8f6tT1mJQLkNxUrIoAAAgi4WODPE+dUY+BcmccARrzZVFkz/O/gi4s3O8GbRv12QQOY4L132cDIyEjlz59fS5cuVa1atS79ZMCAAZo8ebK2bt163bcx1yCaI4grV650rju81nL+/HmZ/2IXEyBz5PLEiRPKksUbz05KjP3FayCAAAIIuE+g8dCF2nHolEY9Wkl33ZbXfRuYiFtEAxhkDeCyZcsuPUrG5MR8zdyUKVO0ZcuW68bmqaeekhn766+/Xne9Pn36qG/fvletQwOYiJ9KXgoBBBBAIEUE+ny3SZOW7dYj1QtpQKvbUmQbkutNaQCDpAG0OQV85swZ5c2bV2+99Zbz3MHrLRwBTK6PJu+DAAIIIJDcAnN/O6iOk39RoewZtOhl/y7PSu5tTKz3owEMkgbQBMKcwq1cubJzF3DsUqZMGbVo0eK6N4FMmjRJnTt31v79+5UjR/xufSdAifVR5HUQQAABBFJa4NT5i6rQN1wXY3xa9FIDFcqRIaU3Kcnen/odRA1g7GNgRo8efelmjnHjxmnTpk0qXLiwc0exuU7wyjuCzXMCzb9//vnn8Q4aAYo3GQMQQAABBFws8K/Ry7R69zHnFLA5FRysC/U7iBpAE1Jz9G/w4MHOo1zKlSvnfMVcvXr1nPyaBz8XKVJE5ohf7GIeNl2yZEmFh4erSZMm8c45AYo3GQMQQAABBFws8P6c7Ro2Z5ua35ZHIx+t7OIttds06neQNYB2cYj/aAIUfzNGIIAAAgi4V2DNH8f0wKhlypYhtda80URhoSHu3ViLLaN+0wBaxIfnAFrhMRgBBBBAwHUCF6NjVPGt2Tp5/qK+e7a2bi+QzXXbmBgbRANIA2iVIwJkxcdgBBBAAAEXCnT65BfN3nxQL91ZUs80uNWFW2i/SdRvGkCrFBEgKz4GI4AAAgi4UOCT5bvV69tNqlksh6Y9+f9f0RpsC/WbBtAq0wTIio/BCCCAAAIuFPj98Ck1em+h0oSFan3vpkqfJsyFW2m3SdRvGkCrBBEgKz4GI4AAAgi4UMDn86n2O/MUeeKcJj9eTXeUyOXCrbTbJOo3DaBVggiQFR+DEUAAAQRcKvDyl+v1xS/71KluUfW8u4xLtzLhm0X9pgFMeHrEXcBWeAxGAAEEEHCtwHfrI9Vt2jqVzptFs7rXde12JnTDaABpABOaHWccAbLiYzACCCCAgEsF/jp1XlX6zXG2bnXPxsqVOa1LtzRhm0X9pgFMWHL+O4oAWfExGAEEEEDAxQLN31+szQf+1vsPVVCLCvldvKXx3zTqNw1g/FNz2QgCZMXHYAQQQAABFwsM/Ok3jVm0Uw9WLqB3/1XexVsa/02jftMAxj81NIBWZgxGAAEEEAgMgUXbDqvdhFXKmzWdlr3aUCEhwfO1cDSANIBWn0ICZMXHYAQQQAABFwucuxCt2/uGK+pijOb0uEO35s7k4q2N36ZRv2kA45eYK9YmQFZ8DEYAAQQQcLnAo+NXaOmOI+pzbxm1r13U5Vvr/+ZRv2kA/U9LHGsSICs+BiOAAAIIuFxg1ILfNeg/W9S49M0a/1gVl2+t/5tH/aYB9D8tNIBWVgxGAAEEEAg8gV/3ndC9Hy5RprSptK5XE6UOCw28SVC/49xnIT7znS8sCRLgL4gEsTEIAQQQQCBABGJifKrcb7aOnbmgLzvXVJUi2QNky6+/mdRvjgBaBZkAWfExGAEEEEAgAASe+WytftxwQN0bFdfzTUoEwBbfeBOp3zSAN07JddYgQFZ8DEYAAQQQCACBaav26LWZv6pK4Zv0ZZdaAbDFN95E6jcN4I1TQgNoZcRgBBBAAIHAFth79IzqDp6vsNAQRfRqoszpUgf2hPgqV2f/cQ2gRYz5C8ICj6EIIIAAAgEjUH/IfO0+ckbj2lVRkzI3B8x2X2tDqd80gFYhJkBWfAxGAAEEEAgQgTe++VVTV+xR+1pF1Oe+sgGy1dfeTOo3DaBViAmQFR+DEUAAAQQCROA/Gw+o89S1uiVXRs19oX6AbDUN4PV2FKeALWJMA2iBx1AEEEAAgYAROHH2giq+Fa4Yn7T8tYbKmzV9wGx7XBtK/eYIoFWACZAVH4MRQAABBAJIoOVHSxWx97iGPHi7/lWlYABt+dWbSv2mAbQKMAGy4mMwAggggEAACbz781Z9OH+HWlTIp/cfqhhAW04DGNfO4hSwRYRpAC3wGIoAAgggEFACK3Ye0UNjVyhnpjRa9XpjhYaGBNT2X76x1G+OAFqFlwBZ8TEYAQQQQCCABKIuxqjCW+E6ExWtn7rVVZl8WQJo6/+5qdRvGkCr8BIgKz4GI4AAAggEmECHias0f+th9WxeWp3qFQuwrf/f5lK/aQCtwkuArPgYjAACCCAQYALjF+9Uvx9/U70SufTJ49UCbOtpAC/fYVwDaBFfGkALPIYigAACCAScwNY/T+rO4YuULnWoIno1VbrUYQE3B7PB1G+OAFoFlwBZ8TEYAQQQQCDABHw+n6oNmKvDJ8/rs07VVeuWnAE2g//fXOo3DaBVcAmQFR+DEUAAAQQCUOD56RH6et1+PV3/Fr3crFQAzoAG0Ow0TgFbRJcG0AKPoQgggAACASnw1Zp9emHGet1eIKu+e7ZOQM6B+k0DaBVcAmTFx2AEEEAAgQAUOPj3OVUfMFchIdLaN5ropoxpAm4W1G8aQKvQEiArPgYjgAACCASoQNNhC7Xt4CmNfLSSmt+WN+BmQf2mAbQKLQGy4mMwAggggECACvT9fpMmLt2th6sV0sD7bwu4WVC/aQCtQkuArPgYjAACCCAQoALzthzU45N+UcHs6bX45YYBNwvqNw2gVWgJkBUfgxFAAAEEAlTg9PmLKt83XBdjfFr0UgMVypEhoGZC/aYBtAosAbLiYzACCCCAQAALtB69XKt2H1X/VuX0aPXCATUT6jcNoFVgCZAVH4MRQAABBAJY4IO52zV09jbdVS6PRv27ckDNhPpNA2gVWAJkxcdgBBBAAIEAFli755juH7lMWdOn1to3mygsNCRgZkP9pgG0CisBsuJjMAIIIIBAAAtcjI5Rxbdn6+S5i/r2mdoqXzBbwMyG+k0DaBVWAmTFx2AEEEAAgQAXePKTXxS++aBeurOknmlwa8DMhvpNA2gVVgJkxcdgBBBAAIEAF5iyfLfe/HaTahTLrs+frBkws6F+0wBahZUAWfExGAEEEEAgwAV2/XVaDd5doDRhoYro3UQZ0qQKiBlRv2kArYJKgKz4GIwAAgggEOACPp9PdQbN1/7jZzWpQ1XVL5k7IGZE/aYBtAoqAbLiYzACCCCAQBAIvPLlBk3/Za+eqFNUb9xTJiBmRP2mAbQKKgGy4mMwAggggEAQCHy/PlJdp61TqTyZ9Z/n6gXEjKjfNIBWQSVAVnwMRgABBBAIAoEjp86rcr85zkxW9Wyk3JnTuX5W1G8aQKuQEiArPgYjgAACCASJwN0fLNamyL81vE0FtayY3/Wzon7TAFqFlABZ8TEYAQQQQCBIBAbO+k1jFu7UA5UK6L3W5V0/K+o3DaBVSAmQFR+DEUAAAQSCRGDJ9r/0749XKk+WdFr+WkOFhLj7a+Go3zSAVh89AmTFx2AEEEAAgSAROHchWrf3DVfUxRjN6VFPt+bO7OqZUb9pAK0CSoCs+BiMAAIIIBBEAv8ev1JLdvyl3veWUYfaRV09M+o3DaBVQAmQFR+DEUAAAQSCSGD0wt/1zqwtalQqtz5uX9XVM6N+0wBaBZQAWfExGAEEEEAgiAQ27j+he0YsUcY0YYro3VSpw0JdOzvqNw2gVTgJkBUfgxFAAAEEgkggJsanKv3n6OjpKM3oXFNVi2R37eyo30HWAI4cOVJDhgzRgQMHVLZsWQ0fPlx169a9ZgCPHz+unj17aubMmTp27JiKFi2q9957T82bN/crtATILyZWQgABBBDwiMCzn63VDxsOqFuj4urRpIRrZ039DqIGcPr06Wrbtq1ME1i7dm2NGTNG48eP1+bNm1WoUKGrQhgVFeWslzt3br3++usqUKCA9u7dq8yZM6t8ef+eYUSAXPvZZsMQQAABBFJAYPrqPXrlq19VufBN+qpLrRTYAv/ekvodRA1g9erVValSJY0aNerS3i9durRatmypgQMHXpWI0aNHO0cLt2zZotSpU/uXmCvWIkAJYmMQAggggECQCuw7dkZ1Bs1XWGiI1vVqoizpElZfk5qH+h0kDaA5mpchQwbNmDFDrVq1upSb7t27KyIiQgsXLrwqS+Y0b/bs2Z1x3377rXLlyqVHHnlEr7zyisLCwuLM3vnz52X+i11MgAoWLKgTJ04oS5YsSZ1XXh8BBBBAAAHXCzR4d4F2/XVaY9tWVtOyeVy5vTSAQdIARkZGKn/+/Fq6dKlq1frfIecBAwZo8uTJ2rp161UBLFWqlHbv3q1HH31UTz/9tLZv365nnnlGpmns1atXnIHt06eP+vbte9XPaABd+flmoxBAAAEEUkDgzW82asqKP/RYzcLq26JcCmzBjd+SBjDIGsBly5apZs2al/Z8//79NWXKFOc075VLiRIldO7cOe3atevSEb+hQ4deuokkrvhwBPDGHyrWQAABBBDwtsB/Nv6pzlPXqFiujJr3Qn1XYtAABkkDmJBTwHfccYdz7d+cOXMuhXPWrFnOHcCm0UuTJs0NQ0uAbkjECggggAACHhM4cfaCKr4VrhiftOzVhsqXLb3rBKjfQdIAmmSZm0AqV67s3AUcu5QpU0YtWrSI8yYQc+fvZ599pp07dyo09P8fVvn+++9r0KBBMqeU/VkIkD9KrIMAAggg4DWBViOXat2e4xr8wO1qXbWg66ZP/Q6iBjD2MTDm7l5zGnjs2LEaN26cNm3apMKFC6tdu3bOdYKxdwSbR76YBrF9+/bq2rWrcw3g448/rm7dujnPBvRnIUD+KLEOAggggIDXBIaGb9UH83bovvL59MHDFV03fep3EDWAJl3m6N/gwYOdB0GXK1dOw4YNU7169Zzg1a9fX0WKFNGkSZMuBXH58uV6/vnnnTuFTXPYsWPH694FfGWCCZDrPtNsEAIIIICACwRW7jyiNmNXKEfGNFrds7FCQ0NcsFX/2wTqd5A1gMmdLgKU3OK8HwIIIIBAIAhEXYxRhbfCdSYqWj92q6Oy+bK6arOp3zSAVoEkQFZ8DEYAAQQQCGKBxyet1rwth/R681J6st4trpop9ZsG0CqQBMiKj8EIIIAAAkEs8PGSXXr7h82qWzynpnSs7qqZUr9pAK0CSYCs+BiMAAIIIBDEAtsOnlTTYYuUNlWo1vduqnSp4/6WrZQgoH7TAFrljgBZ8TEYAQQQQCCIBXw+n2oMnKuDf5/Xp09UV+1bc7pmttRvGkCrMBIgKz4GI4AAuhHTpQAAGUBJREFUAggEuUCPLyI0c+1+dal/i15pVso1s6V+0wBahZEAWfExGAEEEEAgyAVmrt2nHl+s1235s+r7rnVcM1vqNw2gVRgJkBUfgxFAAAEEglzg0N/nVG3AXIWESGvfaKKbMt74a1aTg4T6TQNolTMCZMXHYAQQQAABDwjcOWyRth48qY8eqaS7b8/rihlTv2kArYJIgKz4GIwAAggg4AGBt77frAlLd+nhagU18P7bXTFj6jcNoFUQCZAVH4MRQAABBDwgMH/LIXWYtFoFbkqvxS83UIg5H5zCC/WbBtAqggTIio/BCCCAAAIeEDgTdVHl+4brQrRPC16sryI5M6b4rKnfNIBWISRAVnwMRgABBBDwiECbMcu1ctdR9WtZTv+uUTjFZ039pgG0CiEBsuJjMAIIIICARwRGzN2u92ZvU7OyeTS6beUUnzX1mwbQKoQEyIqPwQgggAACHhFYt+eYWo1cpizpUmldr6YKC03Z6wCp3zSAVh89AmTFx2AEEEAAAY8IRMf4VOGtcJ08d1HfPFNbFQpmS9GZU79pAK0CSICs+BiMAAIIIOAhgaem/KKfNx3Ui01L6NmGxVN05tRvGkCrABIgKz4GI4AAAgh4SGDKij/05jcbVb1odk1/qmaKzpz6TQNoFUACZMXHYAQQQAABDwns/uu06r+7QKnDQhTRq6kypk2VYrOnftMAWoWPAFnxMRgBBBBAwEMCPp9PdQbN1/7jZzWxQ1U1KJk7xWZP/aYBtAofAbLiYzACCCCAgMcEXv1qgz5fvVcd6xTVm/eUSbHZU79pAK3CR4Cs+BiMAAIIIOAxgR82ROrZz9ap5M2Z9fPz9VJs9tRvGkCr8BEgKz4GI4AAAgh4TODo6ShV7jdbPp+0qmcj5c6cLkUEqN80gFbBI0BWfAxGAAEEEPCgwD0jFmvj/r81rE15tapYIEUEqN80gFbBI0BWfAxGAAEEEPCgwDuztmj0wt91f6X8Gtq6QooIUL9pAK2CR4Cs+BiMAAIIIOBBgaU7/tKj41cqd+a0Wvl6I4WEJP/XwlG/aQCtPnoEyIqPwQgggAACHhQ4dyFa5fuG6/zFGM1+vp6K35w52RWo3zSAVqEjQFZ8DEYAAQQQ8KhA249XavH2v9TrnjJ6vE7RZFegftMAWoWOAFnxMRgBBBBAwKMCYxb+roGztqhhqdya0L5qsitQv2kArUJHgKz4GIwAAggg4FGBTZEndPcHS5QhTZjW926q1GGhySpB/aYBtAocAbLiYzACCCCAgEcFYmJ8qtp/jo6cjtIXT9VUtaLZk1WC+k0DaBU4AmTFx2AEEEAAAQ8LdJ22Tt+vj1S3hreqR9OSySpB/aYBtAocAbLiYzACCCCAgIcFvli9Vy9/tUGVCmXTzKdrJ6sE9ZsG0CpwBMiKj8EIIIAAAh4W2H/8rGq/M09hoSFa16uJsqRLnWwa1G8aQKuwESArPgYjgAACCHhcoOG7C7Tzr9Ma07ay7iybJ9k0qN80gFZhI0BWfAxGAAEEEPC4QK9vN+qT5X+obY3CertluWTToH7TAFqFjQBZ8TEYAQQQQMDjAuGb/tSTU9aoWM6Mmvdi/WTToH7TAFqFjQBZ8TEYAQQQQMDjAn+fu6CKb81WdIxPS19tqPzZ0ieLCPWbBtAqaATIio/BCCCAAAII6P6RS7V2z3ENeuA2talaKFlEqN80gFZBI0BWfAxGAAEEEEBAQ2dv0wdzt+ve8vk04uGKySJC/aYBtAoaAbLiYzACCCCAAAJateuoWo9ZruwZ0+iXno0VGhqS5CrUbxpAq5ARICs+BiOAAAIIIKAL0TGq0Ddcp6Oi9UPXOiqXP2uSq1C/aQCtQkaArPgYjAACCCCAgCPQcdJqzd1ySK/eVUqd77glyVWo3zSAViEjQFZ8DEYAAQQQQMARmLh0l/p+v1l1i+fUlI7Vk1yF+k0DaBUyAmTFx2AEEEAAAQQcge0HT6rJsEVKmypU63s3VbrUYUkqQ/2mAbQKGAGy4mMwAggggAACjoDP51ONgXN18O/zmtqxuuoUz5mkMtRvGkCrgBEgKz4GI4AAAgggcEnghS/W66u1+5xrAM21gEm5UL9pAK3yRYCs+BiMAAIIIIDAJYGv1+3T89PXq1z+LPqha90klaF+0wBaBYwAWfExGAEEEEAAgUsCh06eU7X+cxUSIq15o4nzXMCkWqjfNIBW2SJAVnwMRgABBBBA4B8CzYYv0pY/TzrfCGK+GSSpFuo3DaBVtgiQFR+DEUAAAQQQ+IdAvx82a/ySXXqoakG988DtSaZD/aYBtAoXAbLiYzACCCCAAAL/EJi/9ZA6TFyt/NnSa8krDRRizgcnwUL9pgG0ihUBsuJjMAIIIIAAAv8QOBN1URX6zlZUdIzmv1hfRXNmTBIh6jcNoFWwCJAVH4MRQAABBBC4SuChscu1YudRvd2ynNrWKJwkQtRvGkCrYBEgKz4GI4AAAgggcJXAh/O2693wbbqz7M0a07ZKkghRv2kArYJFgKz4GIwAAggggMBVAhF7j6vlR0uVOV0qrXuziVKFhSa6EvWbBtAqVATIio/BCCCAAAIIXCUQHeNTpbdn68TZC5r5dC1VKnRToitRv4OsARw5cqSGDBmiAwcOqGzZsho+fLjq1o37aeKTJk1Shw4drgrV2bNnlS5dOr/CRoD8YmIlBBBAAAEE4iXQZeoazdr4p15oUkJdGxWP11h/VqZ+B1EDOH36dLVt21amCaxdu7bGjBmj8ePHa/PmzSpUqNBVeTANYPfu3bV169Z//CxPnjz+ZMdZhwD5TcWKCCCAAAII+C0wdcUfeuObjapWNLu+eKqm3+P8XZH6HUQNYPXq1VWpUiWNGjXq0v4vXbq0WrZsqYEDB8bZAD733HM6fvy4v3m5aj0ClGA6BiKAAAIIIHBNgT+OnNYdQxYodViIIno1Vca0qRJVi/odJA1gVFSUMmTIoBkzZqhVq1aXQmKO8EVERGjhwoVxNoBPPPGE8ufPr+joaFWoUEFvv/22KlaseM2QnT9/Xua/2MUEqGDBgjpx4oSyZMmSqOHkxRBAAAEEEPCyQN3B87T36FlNbF9VDUrlTlQKGsAgaQAjIyOdRm7p0qWqVavWpZAMGDBAkydPvuo0r1lhxYoV2rFjh2677TbnVO7777+vn376SevXr1fx4nFfb9CnTx/17dv3qhDSACbq55IXQwABBBBAQK/N3KBpq/bq8dpF1eveMokqQgMYZA3gsmXLVLPm/64V6N+/v6ZMmaItW7bcMDgxMTHOKeR69erpgw8+iHN9jgDekJEVEEAAAQQQSBSBlTuPaO2e42pYKrdK5smcKK95+Rm8rFmzevoMXojP5/MlqmoKvFhCTgHHtZmdOnXSvn37NGvWLL9mwV8QfjGxEgIIIIAAAq4SoH4HyRFAkypzE0jlypWdu4BjlzJlyqhFixZx3gRyZRJNH1ytWjXnlPCECRP8CioB8ouJlRBAAAEEEHCVAPU7iBrA2MfAjB492jkNPHbsWI0bN06bNm1S4cKF1a5dO+c6wdg7gs21fDVq1HCu9zNBMKd9zelicx2haQT9WQiQP0qsgwACCCCAgLsEqN9B1ACaaJmjf4MHD3YeBF2uXDkNGzbMuabPLPXr11eRIkVknv9nlueff14zZ87Un3/+KXMdgLn719zkcfk1hDeKKwG6kRA/RwABBBBAwH0C1O8gawCTO2IEKLnFeT8EEEAAAQTsBajfNIBWKSJAVnwMRgABBBBAIEUEqN80gFbBI0BWfAxGAAEEEEAgRQSo3zSAVsEjQFZ8DEYAAQQQQCBFBKjfNIBWwSNAVnwMRgABBBBAIEUEqN80gFbBI0BWfAxGAAEEEEAgRQSo3zSAVsEjQFZ8DEYAAQQQQCBFBKjfNIBWwSNAVnwMRgABBBBAIEUEqN80gFbBI0BWfAxGAAEEEEAgRQSo3zSAVsEjQFZ8DEYAAQQQQCBFBKjfNIBWwTtx4oSyZcumvXv3KkuWLFavxWAEEEAAAQQQSB4B0wAWLFhQx48fd74O1otLiM/n83lx4okx53379jkBYkEAAQQQQACBwBMwB3AKFCgQeBueCFtMA2iBGBMTo8jISGXOnFkhISEWr3T10Ni/Tji6mKisV70YzknrG/vqOOOcPALJ8y7kOfCdzbGvkydPKl++fAoNDU2eCbnsXWgAXbZDLi+Y5rC0Oc3M6eWk20lcB5J0tpe/Ms44J49A8rwLecY5eQSS9l1oAJPWN8Gvzi+YBNPFayDO8eJK8Mo4J5guXgNxjhdXglfGOcF08RqIc7y44r0yDWC8yZJnAMHHOXkEkuddyDPOySOQPO9CnnFOHoGkfRcawKT1TfCrnz9/XgMHDtRrr72mtGnTJvh1GHh9AZyTJyE445w8AsnzLuQZ5+QRSNp3oQFMWl9eHQEEEEAAAQQQcJ0ADaDrdgkbhAACCCCAAAIIJK0ADWDS+vLqCCCAAAIIIICA6wRoAF23S9ggBBBAAAEEEEAgaQVoAJPWl1dHAAEEEEAAAQRcJ0ADmEK7ZOTIkRoyZIgOHDigsmXLavjw4apbt+41t+arr77Sm2++qd9//1233HKL+vfvr1atWqXQ1gfW28bHety4cfrkk0+0ceNGZ5KVK1fWgAEDVK1atcCadApsbXycL9+8zz//XA8//LBatGihb775JgW2PLDeMr7O5rtOe/bsqZkzZ+rYsWMqWrSo3nvvPTVv3jywJp7MWxtfZ/M7fNSoUdqzZ49y5sypBx980HmSQ7p06ZJ5ywPn7RYtWuTUwTVr1ji18Ouvv1bLli2vO4GFCxeqR48e2rRpk/MtHi+//LI6d+4cOJN20ZbSAKbAzpg+fbratm0r8wumdu3aGjNmjMaPH6/NmzerUKFCV23R8uXLnebw7bffdpo+8yHp1auXlixZourVq6fADALnLeNr/eijjzr7pFatWs4v7sGDBzuF0/yyyZ8/f+BMPJm3NL7OsZv3xx9/ON7FihVT9uzZaQBvsN/i6xwVFeX45s6dW6+//rrznafm6yXN11eWL18+mVMSOG8XX+dPP/1UHTt21IQJE5zfHdu2bVP79u3Vpk0bDRs2LHAmnsxbOmvWLC1dulSVKlXSAw88cMMGcNeuXSpXrpw6deqkp556yhn79NNPa9q0ac54lvgJ0ADGzytR1jZNmwm8+WsxdildurTzl4/5i/HKxfwSMQ8eNR+W2KVZs2a66aabnOCzXFsgvtZXvlJ0dLTj/OGHH6pdu3ZQX0MgIc7G9o477lCHDh20ePFimSNVHAG8fsTi6zx69GjnCMuWLVuUOnVq8uunQHydn332Wf3222+aO3fupXd44YUXtGrVKifbLDcWCAkJuWED+Morr+i7775zrGMXc/Rv/fr1MgdKWOInQAMYPy/rtc1f5BkyZNCMGTP+cQq3e/fuioiIkDm8feVijgo+//zzzn+xi/mr0pxyMEdQWOIWSIj1la9kvizcHD0x++uee+6BOg6BhDr37t1bGzZscH7pm6MlNIDXj1dCnM1pXnNk1fzO+fbbb5UrVy498sgjMoU0LCyMPCdSns1lDKYRCQ8Pdy4X2blzp+6++2499thjevXVV3H2Q8CfBrBevXqqWLGi3n///UuvaH5/tG7dWmfOnOGPHD+cL1+FBjCeYLarR0ZGOqcSzaFrc6ogdjHXmU2ePFlbt2696i3SpEmjSZMmOb+4Y5fPPvvMOXJinkjPErdAQqyvfKVnnnlGP//8s3NNINfyJJ6zyb85sm3+6DHXS9EA3vhTnJA8lypVSrt375a5tMGcKtu+fbtMps0fnOYyEparBRLibF5lxIgRMkf9fD6fLl68qC5dujiX+bD4J+BPA1iiRAnnd4W5nCF2WbZsmXOZg9lvefPm9e/NWMsRoAFM5iDE/nIxoa1Zs+aldzc3dUyZMsU5VXPlYhpA0xyaC+Vjl9hrTs6dO5fMMwict0uI9eWzM9f/vfPOO1qwYIFuv/32wJl4Mm9pfJ3NUVXjaYrjXXfd5WwtDeCNd1p8nc0rmoJpfkeYa6dij/gNHTr00g1oN35X762REGfzO+Khhx5Sv379nOuyd+zY4TTZ5lo1c/Mey40F/G0AzYEP8xWpsYv5Y7JOnTrOTSR58uS58RuxxiUBGsBkDkNCTuNwCjhhOykh1rHv9O677zq/zOfMmaMqVaokbAM8Miq+zuaonzmNc/kpyJiYGEcrNDTUOQpu7nRn+adAfJ3NaHONpbn2z+Q4djHXEptTw+bsgfnjksXe2dykV6NGDaexjl2mTp2qJ598UqdOnXJyzXJ9AX8aQE4BJ26KaAAT19OvVzN/IZrHi1x+eqBMmTLOYzCudROIOWry008/XXp9c+QkW7Zs3ARyA/H4WpuXM7/ETfNnTv2aX+osNxaIj7M5ImWOkFy+vPHGGzIZN9f2mKNWNCZxm8fH2byCOVVmLhcx16TFNiHGeNCgQc4pM5bEcTa/zxs3buy4xi7mBr3HH3/caQC53vLGSfOnATTXrn7//ffOEzNiF3Oq3fxRyU0gNza+cg0awPibWY+IfcSAuUPPnAYeO3aszPPnzKNGChcu7Nxtaq4TjG0Gzeli85ePOU1smkRzMbcpmDwG5sa7Ir7W5rSvOWVjiqa5riR2yZQpk8x/LHELxNf5ylfhFLB/yYqvs3nki/nj0vh27drVuQbQNCXdunVzng3Ikjh57tOnj8ypdfO7PPYUsGlMTGNo9hlL3AKmOY79Y9CcFTCGDRo0cG5cMme+zKne/fv3O89mNUvsY2DMI2DM6XXT9Jmbb3gMTMISRgOYMDfrUebon2k2zHUL5rlG5q5e0+SZpX79+ipSpIhz40fs8uWXXzpNn/lLPvZB0Pfff7/1dnjhBeJjbdzjurPa3LFqfsmzXFsgPs40gAlPUnydTZE0TxAwR0nMH5bmeXXcBXxj//g4m5s+Yq/jNg2Ludv63nvvdf7NnKlhiVvAXDtpGr4rF3P3tKl/5g8XcxOTWS92MU/KMHmOfRC0yTIPgk5YwmgAE+bGKAQQQAABBBBAIGAFaAADdtex4QgggAACCCCAQMIEaAAT5sYoBBBAAAEEEEAgYAVoAAN217HhCCCAAAIIIIBAwgRoABPmxigEEEAAAQQQQCBgBWgAA3bXseEIIIAAAggggEDCBGgAE+bGKAQQQAABBBBAIGAFaAADdtex4QgggAACCCCAQMIEaAAT5sYoBBBA4B8CsQ+1PXbsmPPwX/Mg2+eee07Hjx9HCgEEEHCdAA2g63YJG4QAAoEoQAMYiHuNbUbAuwI0gN7d98wcAc8IREVFKU2aNEk6XxrAJOXlxRFAIJEFaAATGZSXQwCBlBcw36dtvmPbNH3mi+TLli2r7777Ti+99JK++eYbnTt3TlWqVHG+g7t8+fKXNtis89Zbb2njxo3KlCmT8/3cM2fOdH4+depUDR8+XFu3blXGjBnVsGFD53/nzp3b+TkNYMrvd7YAAQT8F6AB9N+KNRFAIEAETAO4Zs0adenSRR07dlRMTIw6deqk7Nmzq1evXsqaNavGjBnjXKe3bds2599//PFHtWjRQj179tRDDz0kc9TQ/Nvrr7/uzHrChAnKmzevSpYsqUOHDjlfSH/TTTfpp59+ogEMkFywmQgg8D8BGkDSgAACQSdgGsATJ05o3bp1ztzmzZunVq1aOY1b2rRpL8331ltv1csvv6wnn3xStWrVUrFixZwjff4sq1evVrVq1XTy5EnnaCFHAP1RYx0EEHCLAA2gW/YE24EAAokmYBrA4sWLa9y4cc5rDhkyRK+++qrSp0//j/c4e/asXnzxRQ0aNEgZMmTQRx99pA4dOsS5HaaZ7NOnjyIiInT06FHnqOKZM2e0adMmlSlThgYw0fYeL4QAAskhQAOYHMq8BwIIJKuAaQArVKjgXKNnFtPgjRgxwmnSrlzMI1ty5sypHDly6N13342zATx9+rSKFCmipk2bqnPnzsqVK5f27NmjO++80znKaN6LI4DJuot5MwQQsBSgAbQEZDgCCLhP4MoGcPbs2brrrru0Y8cOp5GLa2nQoIHy588f5ylgcz2huWnENH0FCxZ0hptTxW3btqUBdN/uZ4sQQMAPARpAP5BYBQEEAkvgygbQ5/M5d/Sa6/XM0UBzI0dkZKRzA0fLli2d5s4cwWvUqJHeeOMN5yaQixcvatasWc41gocPH1aBAgXUvXt35wiguUvY3FFsbiDhCGBgZYOtRQCB/xegASQJCCAQdAJXNoBmgqb5M3f4fvXVV05DlydPHqcpHDhw4KWjeuaRL2+//bY2b96sLFmyOD8365tl2rRpzh3BBw4cUKVKlfTaa6/pvvvuowEMuvQwIQS8IUAD6I39zCwRQAABBBBAAIFLAjSAhAEBBBBAAAEEEPCYAA2gx3Y400UAAQQQQAABBGgAyQACCCCAAAIIIOAxARpAj+1wposAAggggAACCNAAkgEEEEAAAQQQQMBjAjSAHtvhTBcBBBBAAAEEEKABJAMIIIAAAggggIDHBGgAPbbDmS4CCCCAAAIIIEADSAYQQAABBBBAAAGPCdAAemyHM10EEEAAAQQQQIAGkAwggAACCCCAAAIeE6AB9NgOZ7oIIIAAAggggMD/AdxpIyKwdCzZAAAAAElFTkSuQmCC\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'precision')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import precision_recall_curve\n",
    "precision, recall, thresholds = precision_recall_curve(y_test, pred, pos_label=3)\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(recall, precision)\n",
    "plt.xlabel(\"recall\")\n",
    "plt.ylabel(\"precision\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(3, 1.5257138786785736), (3, -0.4327128672904404), (1, -0.3257943650139726), (1, -0.30398221641660733), (1, -0.462642771639517), (3, 1.789731954746577), (3, 0.4185243885017837), (1, -0.8586263669850333), (3, 0.08837611689578698), (1, -0.20217199749602485)]\n"
     ]
    }
   ],
   "source": [
    "a=temp.decision_function(X_test)\n",
    "l=list(zip(y_test, a))\n",
    "print(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(3, array([0.17862166, 0.82137834])), (3, array([0.60652129, 0.39347871])), (1, array([0.58073573, 0.41926427])), (1, array([0.57541571, 0.42458429])), (1, array([0.61364093, 0.38635907])), (3, array([0.14310559, 0.85689441])), (3, array([0.3968699, 0.6031301])), (1, array([0.70237358, 0.29762642])), (3, array([0.47792034, 0.52207966])), (1, array([0.55037154, 0.44962846]))]\n"
     ]
    }
   ],
   "source": [
    "b=temp.predict_proba(X_test)\n",
    "m=list(zip(y_test, b))\n",
    "print(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.83      1.00      0.91         5\n",
      "           3       1.00      0.80      0.89         5\n",
      "\n",
      "   micro avg       0.90      0.90      0.90        10\n",
      "   macro avg       0.92      0.90      0.90        10\n",
      "weighted avg       0.92      0.90      0.90        10\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test, pred,labels=[1,3] ,target_names=['1', '3']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dummy Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[5 0]\n",
      " [5 0]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.dummy import DummyClassifier\n",
    "dum=DummyClassifier(strategy=\"constant\", constant=1)\n",
    "dum.fit(X_train, y_train)\n",
    "p=dum.predict(X_test)\n",
    "c=confusion_matrix(y_test, p, labels=[1, 3])\n",
    "print(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# MultiClass Classifiers Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2 0 2 0]\n",
      " [0 1 0 0]\n",
      " [5 0 2 1]\n",
      " [0 0 0 2]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Saurav\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\Saurav\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "val=pd.read_csv(\"fruit_data_with_colors.txt\", delimiter='\\t')\n",
    "X=val[[\"mass\", \"width\", \"height\"]]\n",
    "y=val[\"fruit_label\"]\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "temp=LogisticRegression(C=1)\n",
    "\n",
    "temp.fit(X_train, y_train)\n",
    "pred=temp.predict(X_test)\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "con=confusion_matrix(y_test, pred, labels=[1, 2, 3, 4])\n",
    "print(con)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.4666666666666667\n",
      "precision: 0.4904761904761904\n",
      "recall: 0.6875\n",
      "f1 : 0.6242424242424243\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Saurav\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
    "print(\"accuracy:\", end=\" \")\n",
    "print(accuracy_score(y_test, pred))\n",
    "print(\"precision:\", end=\" \")\n",
    "print(precision_score(y_test, pred, labels=[1, 2, 3, 4, 5], average=\"macro\"))\n",
    "print(\"recall:\", end=\" \")\n",
    "print(recall_score(y_test, pred, labels=[1, 2, 3, 4], average=\"macro\"))\n",
    "print(\"f1 :\", end=\" \")\n",
    "print(f1_score(y_test, pred, labels=[1, 2, 3, 4], average=\"macro\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.29      0.50      0.36         4\n",
      "           2       1.00      1.00      1.00         1\n",
      "           3       0.50      0.25      0.33         8\n",
      "           4       0.67      1.00      0.80         2\n",
      "\n",
      "   micro avg       0.47      0.47      0.47        15\n",
      "   macro avg       0.61      0.69      0.62        15\n",
      "weighted avg       0.50      0.47      0.45        15\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test, pred,labels=[1, 2, 3, 4] ,target_names=['1', '2', '3', '4']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Regression Evaluation (We use R^2 score )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fruit_label</th>\n",
       "      <th>fruit_name</th>\n",
       "      <th>fruit_subtype</th>\n",
       "      <th>mass</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>color_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>granny_smith</td>\n",
       "      <td>192</td>\n",
       "      <td>8.4</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>granny_smith</td>\n",
       "      <td>180</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>granny_smith</td>\n",
       "      <td>176</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.2</td>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>mandarin</td>\n",
       "      <td>mandarin</td>\n",
       "      <td>86</td>\n",
       "      <td>6.2</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>mandarin</td>\n",
       "      <td>mandarin</td>\n",
       "      <td>84</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fruit_label fruit_name fruit_subtype  mass  width  height  color_score\n",
       "0            1      apple  granny_smith   192    8.4     7.3         0.55\n",
       "1            1      apple  granny_smith   180    8.0     6.8         0.59\n",
       "2            1      apple  granny_smith   176    7.4     7.2         0.60\n",
       "3            2   mandarin      mandarin    86    6.2     4.7         0.80\n",
       "4            2   mandarin      mandarin    84    6.0     4.6         0.79"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val=pd.read_csv(\"fruit_data_with_colors.txt\", delimiter='\\t')\n",
    "val.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9772727272727273\n",
      "0.5333333333333333\n"
     ]
    }
   ],
   "source": [
    "X=val[[\"mass\", \"width\", \"height\"]]\n",
    "y=val[\"fruit_label\"]\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "temp=RandomForestClassifier(n_estimators=10, max_depth=5)\n",
    "temp.fit(X_train, y_train)\n",
    "print(temp.score(X_train, y_train))\n",
    "print(temp.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Gradient Boosting Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n",
      "0.6\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "temp=GradientBoostingClassifier(n_estimators=10, max_depth=5, learning_rate=0.1)\n",
    "temp.fit(X_train, y_train)\n",
    "print(temp.score(X_train, y_train))\n",
    "print(temp.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Neural Networks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8181818181818182\n",
      "0.5333333333333333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Saurav\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "temp=MLPClassifier(hidden_layer_sizes=[120, 120], activation=\"tanh\", alpha=5)\n",
    "temp.fit(X_train, y_train)\n",
    "print(temp.score(X_train, y_train))\n",
    "print(temp.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# KMeans Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fruit_label</th>\n",
       "      <th>fruit_name</th>\n",
       "      <th>fruit_subtype</th>\n",
       "      <th>mass</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>color_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>granny_smith</td>\n",
       "      <td>192</td>\n",
       "      <td>8.4</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>granny_smith</td>\n",
       "      <td>180</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>apple</td>\n",
       "      <td>granny_smith</td>\n",
       "      <td>176</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.2</td>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>mandarin</td>\n",
       "      <td>mandarin</td>\n",
       "      <td>86</td>\n",
       "      <td>6.2</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>mandarin</td>\n",
       "      <td>mandarin</td>\n",
       "      <td>84</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fruit_label fruit_name fruit_subtype  mass  width  height  color_score\n",
       "0            1      apple  granny_smith   192    8.4     7.3         0.55\n",
       "1            1      apple  granny_smith   180    8.0     6.8         0.59\n",
       "2            1      apple  granny_smith   176    7.4     7.2         0.60\n",
       "3            2   mandarin      mandarin    86    6.2     4.7         0.80\n",
       "4            2   mandarin      mandarin    84    6.0     4.6         0.79"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val=pd.read_csv(\"fruit_data_with_colors.txt\", delimiter='\\t')\n",
    "val.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 1 1 3 3 3 3 3 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 2 2 2 1 1 0 0 0 0 1 1 1 1\n",
      " 0 1 1 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]\n",
      "[[0.30953257 0.39196676 0.52712551 0.77839335]\n",
      " [0.28409091 0.30921053 0.64951923 0.42105263]\n",
      " [0.96969697 0.9122807  0.81025641 0.51754386]\n",
      " [0.01818182 0.03684211 0.05846154 0.64736842]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Saurav\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\data.py:334: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by MinMaxScaler.\n",
      "  return self.partial_fit(X, y)\n"
     ]
    }
   ],
   "source": [
    "X=val[[\"mass\", \"width\", \"height\", \"color_score\"]]\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler=MinMaxScaler()\n",
    "X_scaled=scaler.fit_transform(X)\n",
    "\n",
    "from sklearn.cluster import KMeans\n",
    "temp=KMeans(n_clusters=4)\n",
    "\n",
    "temp.fit(X_scaled)\n",
    "print(temp.labels_)\n",
    "print(temp.cluster_centers_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# DBSCAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-1 -1 -1  0  0  0  0  0  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]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cluster import DBSCAN\n",
    "temp=DBSCAN(eps=0.2, min_samples=5)\n",
    "temp.fit(X_scaled)\n",
    "print(temp.labels_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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