F-G-Acc Comparison with width.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "code_folding": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The tikzmagic extension is already loaded. To reload it, use:\n",
      "  %reload_ext tikzmagic\n"
     ]
    }
   ],
   "source": [
    "# Import needed files and basic setup\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "import data_gen2\n",
    "import tropical\n",
    "\n",
    "from ipywidgets import Output\n",
    "from IPython.display import display, Markdown, Latex, Math, clear_output\n",
    "\n",
    "%matplotlib notebook\n",
    "#plt.ion()\n",
    "\n",
    "%load_ext tikzmagic\n",
    "\n",
    "device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') # I don't have a GPU to test this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "code_folding": [
     0
    ],
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Set up the ReLULayer Pytorch module\n",
    "# Apply affine transformation then rectified linear unit\n",
    "class ReLULayer(nn.Module):\n",
    "    # Set up a ReLU layer for ease of use\n",
    "    def __init__(self, input_dim, output_dim):\n",
    "        super(ReLULayer, self).__init__()\n",
    "        self.linear = nn.Linear(input_dim, output_dim) # Just one affine transformation\n",
    "\n",
    "    def forward(self, x):\n",
    "        return F.relu(self.linear(x)) # Apply the affine transformation and then the ReLU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "code_folding": [],
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Set up the NeuralNetwork class\n",
    "class NeuralNetwork(nn.Module):\n",
    "    def __init__(self, input_dim, output_dim, hidden_structure, learning_rate=0.001):\n",
    "        super(NeuralNetwork, self).__init__()\n",
    "        self.hidden_structure = hidden_structure\n",
    "\n",
    "        layers = []\n",
    "        layers.append(ReLULayer(input_dim, hidden_structure[0])) # Setup initial hidden layer\n",
    "\n",
    "        for i in range(1, len(hidden_structure)):\n",
    "            layers.append(ReLULayer(hidden_structure[i-1], hidden_structure[i])) # Setup hidden layers\n",
    "\n",
    "        layers.append(nn.Linear(hidden_structure[-1], output_dim)) # Setup output layer (linear)\n",
    "\n",
    "        self.layers = nn.ModuleList(layers)\n",
    "\n",
    "        self.optimizer = torch.optim.Adam(self.parameters(), lr=learning_rate)\n",
    "\n",
    "        model_parameters = filter(lambda p: p.requires_grad, self.parameters())\n",
    "        self.num_params = sum([np.prod(p.size()) for p in model_parameters])\n",
    "\n",
    "    def forward(self, x):\n",
    "        out = x\n",
    "\n",
    "        for layer in self.layers:\n",
    "            out = layer(out) # Apply hidden layers\n",
    "\n",
    "        return out\n",
    "\n",
    "    def train(self, X_train, y_train, loss_function):\n",
    "        pred = self(X_train) # Predict from our inputs\n",
    "        loss = loss_function(pred, y_train) # Calculate the loss\n",
    "        # Backpropgate\n",
    "        self.optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        self.optimizer.step()\n",
    "\n",
    "        return loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "code_folding": [
     0
    ],
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Set up the data generators\n",
    "def circles(n, r1=0.5):\n",
    "    x = np.zeros((n, 2))\n",
    "    y = np.zeros((n, 2))\n",
    "    i = 0\n",
    "    \n",
    "    r1sqr = r1**2\n",
    "    r2sqr = 2*r1sqr\n",
    "    r3sqr = 3*r1sqr\n",
    "\n",
    "    while i < n//2:\n",
    "        sample = 2*np.random.uniform(size=(1,2)) - 1\n",
    "        if np.sum(np.square(sample)) < r1sqr:\n",
    "            x[i, :] = sample\n",
    "            y[i, :] = np.array([0, 1])\n",
    "            i += 1\n",
    "\n",
    "    while i < n:\n",
    "        sample = 2*np.random.uniform(size=(1,2)) - 1\n",
    "        if r2sqr < np.sum(np.square(sample)) < r3sqr:\n",
    "            x[i, :] = sample\n",
    "            y[i, :] = np.array([1, 0])\n",
    "            i += 1\n",
    "\n",
    "    return x, y\n",
    "\n",
    "def circlesAlt(n, r1=0.5):\n",
    "    x = np.zeros((n, 2))\n",
    "    y = np.zeros((n, 1))\n",
    "    i = 0\n",
    "    \n",
    "    r1sqr = r1**2\n",
    "    r2sqr = 2*r1sqr\n",
    "    r3sqr = 3*r1sqr\n",
    "\n",
    "    while i < n//2:\n",
    "        sample = 2*np.random.uniform(size=(1,2)) - 1\n",
    "        if np.sum(np.square(sample)) < r1sqr:\n",
    "            x[i, :] = sample\n",
    "            y[i, :] = 1\n",
    "            i += 1\n",
    "\n",
    "    while i < n:\n",
    "        sample = 2*np.random.uniform(size=(1,2)) - 1\n",
    "        if r2sqr < np.sum(np.square(sample)) < r3sqr:\n",
    "            x[i, :] = sample\n",
    "            y[i, :] = -1\n",
    "            i += 1\n",
    "\n",
    "    return x, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$$$$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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       "        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",
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       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
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       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
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       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
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       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
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       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
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       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
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       "    this.header = titletext[0];\n",
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       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
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       "    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": {
      "text/html": [
       "<div id='84301627-045b-49ae-965e-7b6c3e534ce0'></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(128, 2)\n",
      "(128,)\n",
      "(1, 128)\n",
      "(1,)\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'breakHerePlease' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-16-f7d5a53864a7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m    109\u001b[0m                 \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mA2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    110\u001b[0m                 \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 111\u001b[0;31m                 \u001b[0mbreakHerePlease\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    112\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    113\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'breakHerePlease' is not defined"
     ]
    }
   ],
   "source": [
    "# Run the network and display steps\n",
    "\n",
    "# Data info\n",
    "num_inputs = 2\n",
    "num_outputs = 1\n",
    "num_samples = 50\n",
    "hidden_structure = [5]\n",
    "data_func = lambda n: circlesAlt(n)\n",
    "\n",
    "# Hyper-parameters \n",
    "num_epochs = 10000\n",
    "learning_rate = 0.001\n",
    "\n",
    "# Setup for the network\n",
    "model = NeuralNetwork(num_inputs, num_outputs, hidden_structure, learning_rate=learning_rate).to(device)\n",
    "\n",
    "# Count the number of trainable parameters and display it\n",
    "#print('Number of parameters:', model.num_params)\n",
    "\n",
    "# Set up optimization technique\n",
    "#loss_function = nn.BCEWithLogitsLoss() # Binary cross entropy\n",
    "#loss_function = nn.CrossEntropyLoss() # Multi-class\n",
    "#loss_function = nn.L1Loss() # L1 norm\n",
    "loss_function = nn.MSELoss() # L2 norm divided by num samples \n",
    "\n",
    "\n",
    "\n",
    "thing = 101\n",
    "x = np.linspace(-1, 1, thing)\n",
    "y = np.linspace(-1, 1, thing)\n",
    "\n",
    "X, Y = np.meshgrid(x, y)\n",
    "\n",
    "x = np.reshape(X, thing**2)\n",
    "y = np.reshape(Y, thing**2)\n",
    "\n",
    "X_train2 = np.vstack((x, y)).T\n",
    "\n",
    "indices = X_train2[:, 0]**2 + X_train2[:, 1]**2 < r1sqr\n",
    "X_trainCirc = X_train2[indices, :]\n",
    "y_trainCirc = np.ones((X_trainCirc.shape[0], 1))\n",
    "\n",
    "indices = np.logical_and(r2sqr < X_train2[:, 0]**2 + X_train2[:, 1]**2, X_train2[:, 0]**2 + X_train2[:, 1]**2 < r3sqr)\n",
    "X_trainCirc2 = X_train2[indices, :]\n",
    "y_trainCirc2 = -np.ones((X_trainCirc2.shape[0], 1))\n",
    "\n",
    "X_trainTrueCirc = np.vstack((X_trainCirc, X_trainCirc2))\n",
    "y_trainTrueCirc = np.vstack((y_trainCirc, y_trainCirc2))\n",
    "\n",
    "X_test = torch.from_numpy(np.vstack((x, y)).T).float()\n",
    "\n",
    "out = Output(layout={'border': '1px solid black'})\n",
    "\n",
    "tmp = display(Math(''))\n",
    "\n",
    "\n",
    "\n",
    "colors = ['#ff0000', '#00ff00', '#0000ff', '#ffff00',\\\n",
    "          '#ff00ff', '#00ffff', '#000000']\n",
    "\n",
    "colors2 = ['#770000', '#007700', '#000077', '#777700',\\\n",
    "           '#770077', '#007777', '#000000']\n",
    "\n",
    "big_num_fterms = []\n",
    "big_num_fterms_used = []\n",
    "big_num_gterms = []\n",
    "big_num_gterms_used = []\n",
    "big_accuracy = []\n",
    "big_epochs = []\n",
    "\n",
    "for hidden_structure in [[i] for i in range(1, 11)]:\n",
    "\n",
    "    num_fterms = [[] for i in range(7)]\n",
    "    num_fterms_used = [[] for i in range(7)]\n",
    "    num_gterms = [[] for i in range(7)]\n",
    "    num_gterms_used = [[] for i in range(7)]\n",
    "    accuracy = [[] for i in range(7)]\n",
    "    epochs = [[] for i in range(7)]\n",
    "    \n",
    "    # Setup for plotting progress\n",
    "    fig = plt.figure(figsize=(10, 20))\n",
    "    axTrain = fig.add_subplot(411)\n",
    "    axF = fig.add_subplot(412)\n",
    "    axG = fig.add_subplot(413)\n",
    "    axPlane = fig.add_subplot(427)\n",
    "\n",
    "    for i in range(7):\n",
    "        model = NeuralNetwork(num_inputs, num_outputs, hidden_structure, learning_rate=learning_rate).to(device)\n",
    "        for epoch in range(num_epochs):\n",
    "            # Get the training data in a useful data structure\n",
    "            X_train, y_train = data_func(num_samples)\n",
    "            X_train = torch.from_numpy(X_train).float().to(device)\n",
    "            y_train = torch.from_numpy(y_train).float().to(device)\n",
    "\n",
    "            loss = model.train(X_train, y_train, loss_function)\n",
    "\n",
    "            if (epoch+1) %100 == 0  or epoch == 0:\n",
    "                # Display the running accuracy\n",
    "                #print ('Epoch [{}/{}], Loss: {:.4f}'.format(epoch, num_epochs, loss.item()))\n",
    "\n",
    "\n",
    "\n",
    "                # Plot the classification map\n",
    "                axTrain.clear()\n",
    "                axF.clear()\n",
    "                axG.clear()\n",
    "                axPlane.clear()\n",
    "\n",
    "                pred2 = model(X_test).detach().numpy()\n",
    "\n",
    "                data_gen2.plotClassifierDataAlt(X_test, pred2, plot=(fig, axPlane), alpha=0.5)\n",
    "                X_train2, y_train2 = data_func(1000)\n",
    "                data_gen2.plotClassifierDataAlt(X_train2, y_train2, plot=(fig, axPlane))\n",
    "\n",
    "                pred = model(torch.from_numpy(X_trainTrueCirc).float()).detach().numpy()\n",
    "                epochs[i].append(epoch)\n",
    "                accuracy[i].append(np.sum(np.equal(pred > 0, y_trainTrueCirc > 0))/(len(y_trainTrueCirc)))\n",
    "\n",
    "                for j in range(7):\n",
    "                    axTrain.plot(epochs[j], accuracy[j], c=colors[j])\n",
    "                    \n",
    "                axTrain.set_title('Accuracy for ' + str(hidden_structure[0]) + ' hidden units')\n",
    "                axTrain.set_ylim([0.4, 1.1])\n",
    "                \n",
    "\n",
    "                params = []\n",
    "                for param in model.parameters():\n",
    "                    params.append(param.detach().numpy())\n",
    "\n",
    "zz                A1 = params[0]\n",
    "                b1 = params[1]\n",
    "                A2 = params[2]\n",
    "                b2 = params[3]\n",
    "\n",
    "                Fterms, Gterms = tropical.getTropCoeffs(A1, b1, A2, b2)\n",
    "\n",
    "                num_fterms[i].append(len(Fterms))\n",
    "                for j in range(7):\n",
    "                    axF.plot(epochs[j], num_fterms[j], '-^', c=colors[j], markeredgecolor=colors[j], markerfacecolor='none')\n",
    "\n",
    "                x_stuff = X_train.detach().numpy().T\n",
    "                temp = np.ones((1, x_stuff.shape[1]))\n",
    "                x_temp = np.concatenate((x_stuff, temp), axis=0)\n",
    "                x_results = np.matmul(np.array(list(Fterms)), x_temp)\n",
    "                Fmax = set(np.argmax(x_results, axis=0))\n",
    "\n",
    "                num_fterms_used[i].append(len(Fmax))\n",
    "\n",
    "                for j in range(7):\n",
    "                    axF.plot(epochs[j], num_fterms_used[j], '--o', c=colors[j], markeredgecolor=colors[j], markerfacecolor='none')\n",
    "\n",
    "                axF.set_title('Number of F terms')\n",
    "\n",
    "                num_gterms[i].append(len(Gterms))\n",
    "                for j in range(7):\n",
    "                    axG.plot(epochs[j], num_gterms[j], '-^', c=colors[j], markeredgecolor=colors[j], markerfacecolor='none')\n",
    "\n",
    "                x_results = np.matmul(np.array(list(Gterms)), x_temp)\n",
    "                Gmax = set(np.argmax(x_results, axis=0))\n",
    "\n",
    "                num_gterms_used[i].append(len(Gmax))\n",
    "\n",
    "                for j in range(7):\n",
    "                    axG.plot(epochs[j], num_gterms_used[j], '--o', color=colors[j], markeredgecolor=colors[j], markerfacecolor='none')\n",
    "\n",
    "                axG.set_title('Number of G terms')\n",
    "\n",
    "\n",
    "                fig.canvas.draw()\n",
    "                fig.canvas.flush_events()\n",
    "                \n",
    "    big_num_fterms.append(num_fterms)\n",
    "    big_num_fterms_used.append(num_fterms_used)\n",
    "    big_num_gterms.append(num_gterms)\n",
    "    big_num_gterms_used.append(num_gterms_used)\n",
    "    big_accuracy.append(accuracy)\n",
    "    big_epochs.append(epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "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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width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(211)\n",
    "ax2 = fig.add_subplot(212)\n",
    "\n",
    "for j in range(7):\n",
    "    ax1.plot(epochs[j], num_fterms_used[j], c=colors[j], markeredgecolor=colors[j], markerfacecolor='none')\n",
    "    ax2.plot(epochs[j], num_gterms_used[j], c=colors[j], markeredgecolor=colors[j], markerfacecolor='none')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10, 7, 101)\n"
     ]
    }
   ],
   "source": [
    "print(np.array(big_epochs).shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.savez('widthVsTropicalTerms', epochs=np.array(big_epochs), \\\n",
    "         gterms=big_num_gterms, fterms=big_num_fterms, \\\n",
    "         gterms_used=big_num_gterms_used, fterms_used=big_num_fterms_used, \\\n",
    "         accuracies=big_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['epochs', 'gterms', 'fterms', 'gterms_used', 'fterms_used', 'accuracies']\n",
      "(10, 7, 101)\n"
     ]
    }
   ],
   "source": [
    "npzfile = np.load('widthVsTropicalTerms.npz')\n",
    "print(npzfile.files)\n",
    "print(npzfile['epochs'].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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    "version": 3
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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  "latex_envs": {
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   "hotkeys": {
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