one-vs-all.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sigmoid(prediction, rows):\n",
    "    #this is to reverse the sign and convert from 2d array to 1d for np.exp\n",
    "    prediction = list(-np.array(prediction).ravel())\n",
    "    sigmoid = 1 / (1 + np.exp(prediction))\n",
    "    sigmoid = np.reshape(sigmoid, (-1, rows))#converting back to 2d array\n",
    "    return sigmoid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def succes_rate(X, theta, y):\n",
    "    predictions = list(np.dot(X, np.transpose(theta)))\n",
    "    predictions = np.argmax(predictions, axis=1)\n",
    "    succes_rate = (y == predictions).astype(int)\n",
    "    succes_rate = sum(succes_rate)/len(y)\n",
    "    print(\"succes rate\", succes_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#calculating the Cost with delta modifier to protect from overtraining\n",
    "def Cost(X, theta, y, delta, rows):\n",
    "    prediction = sigmoid(np.dot(X, np.transpose(theta)), rows)\n",
    "    cost = sum(sum( -(y * np.log(prediction)) - (1 - y) * np.log(1 - prediction))/len(y))\n",
    "    + sum((sum(np.power(theta, 2))) * delta/(2*len(y)))\n",
    "    return cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#calculating the Gradient with delta modifier to protect from overtraining\n",
    "def Gradient(X, theta, y, alfa, delta, rows):\n",
    "    prediction = sigmoid(np.dot(X, np.transpose(theta)), rows)\n",
    "    #temp_theta is not to regularize the bias unit \n",
    "    temp_theta = theta.copy()\n",
    "    temp_theta[:, 0] = 0\n",
    "    \n",
    "    theta = theta - alfa*(np.dot(np.transpose(prediction-y), X) / len(y) + (delta/len(y) * temp_theta))\n",
    "    return theta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing, changing pandas data to numpy array and shuffling it\n",
    "iris = pd.read_csv(\"Iris.csv\")\n",
    "iris_np = iris.to_numpy()\n",
    "np.random.seed(42)\n",
    "np.random.shuffle(iris_np)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "location_of_name = len(iris_np[0]) - 1 #where the iris names are located"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Changes \"iris-name\" to numbers from 0 to amount of different names\n",
    "iris_names = []\n",
    "\n",
    "for i in range(len(iris_np)):\n",
    "    has_value = False\n",
    "    for j in range(len(iris_names)):\n",
    "        if(iris_names[j] == iris_np[i][location_of_name]):\n",
    "            iris_np[i][location_of_name] = j\n",
    "            has_value = True\n",
    "    if has_value == False:\n",
    "        iris_names.append(iris_np[i][location_of_name])\n",
    "        iris_np[i][location_of_name] = len(iris_names) - 1\n",
    "        has_value = False\n",
    "number_of_different_names = len(iris_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# getting the answers and deleting unnecessary data\n",
    "iris_y = iris_np[:,location_of_name]\n",
    "iris_np = np.delete(iris_np, location_of_name, 1)\n",
    "iris_np = np.delete(iris_np, 0, 1)\n",
    "iris_np = np.append(np.ones((len(iris_np), 1)), iris_np, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# splitting data into training_set 80% and testing_set 20%, setting delta(helps to avoid overfitting)\n",
    "location_of_name = len(iris_np[0]) - 1\n",
    "training_ratio = 0.2\n",
    "delta = 0.1\n",
    "#iris_np = ComplexVariables(iris_np)            \n",
    "training_set = iris_np[:int((1-training_ratio) * len(iris_np)),:].copy()\n",
    "testing_set = iris_np[int((1-training_ratio) * len(iris_np)):,:].copy()\n",
    "y_train_1d = iris_y[:int((1-training_ratio) * len(iris_np))].copy()\n",
    "y_test = iris_y[int((1-training_ratio) * len(iris_np)):].copy()\n",
    "#converting y_train_1d into 2d array with appropriate data representation\n",
    "y_train = np.zeros((len(y_train_1d),3))\n",
    "for i in range(len(y_train_1d)):\n",
    "    y_train[i, y_train_1d[i]] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#generating theta\n",
    "theta = np.random.rand(number_of_different_names, len(training_set[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#trains for 250 iterations\n",
    "train_number = 250\n",
    "cost_count = np.zeros(train_number)\n",
    "for j in range(train_number):\n",
    "    theta = Gradient(training_set, theta, y_train, 0.1, delta, number_of_different_names)#0.1 is how big are the steps in gradient descent\n",
    "    cost_count[j] = Cost(training_set, theta, y_train, delta, number_of_different_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "succes rate 0.9333333333333333\n"
     ]
    }
   ],
   "source": [
    "# succes rate of training set\n",
    "succes_rate(training_set ,theta, y_train_1d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "succes rate 1.0\n"
     ]
    }
   ],
   "source": [
    "# succes rate of testiing set\n",
    "succes_rate(testing_set ,theta, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "        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",
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       "            warnings.style.display = 'block';\n",
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       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
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       "        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",
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       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
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       "            continue;\n",
       "        }\n",
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       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
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       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
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       "    nav_element.append(fmt_picker_span);\n",
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       "\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",
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       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\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",
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       "}\n",
       "\n",
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       "\n",
       "\n",
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       "    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",
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       "    var x0 = msg['x0'] / mpl.ratio;\n",
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       "    y1 = Math.floor(y1) + 0.5;\n",
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       "    var min_y = Math.min(y0, y1);\n",
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       "\n",
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       "        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=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#shows how Cost has changed\n",
    "plt.plot(cost_count)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}