machine-learning/17-决策树学习算法/决策树学习算法.ipynb · Saisimon/AIMindMap

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决策树学习算法.ipynb 936.15 KB

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "生成分类样例数据："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_classification\n",
    "\n",
    "X, y = make_classification(n_features = 2, n_informative = 2, n_redundant = 0, n_samples = 100, n_classes = 2, random_state = 0)\n",
    "y[y == 0] = -1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "决策树学习算法实现（信息增益-Information Gain）："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "class GainNode:\n",
    "    \"\"\"\n",
    "    分类决策树中的结点\n",
    "    基于信息增益-Information Gain\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, feature=None, threshold=None, gain=None, left=None, right=None):\n",
    "        # 结点划分的特征下标\n",
    "        self.feature = feature\n",
    "        # 结点划分的临界值，当结点为叶子结点时为分类值\n",
    "        self.threshold = threshold\n",
    "        # 结点的信息增益值\n",
    "        self.gain = gain\n",
    "        # 左结点\n",
    "        self.left = left\n",
    "        # 右结点\n",
    "        self.right = right\n",
    "\n",
    "class GainTree:\n",
    "    \"\"\"\n",
    "    分类决策树\n",
    "    基于信息增益-Information Gain\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, max_depth = None, min_samples_leaf = None):\n",
    "        # 决策树最大深度\n",
    "        self.max_depth = max_depth\n",
    "        # 决策树叶结点最小样本数\n",
    "        self.min_samples_leaf = min_samples_leaf\n",
    "        \n",
    "    def fit(self, X, y):\n",
    "        \"\"\"\n",
    "        分类决策树拟合\n",
    "        基于信息增益-Information Gain\n",
    "        \"\"\"\n",
    "        y = np.array(y)\n",
    "        self.root = self.buildNode(X, y, 0)\n",
    "        return self\n",
    "    \n",
    "    def buildNode(self, X, y, depth):\n",
    "        \"\"\"\n",
    "        构建分类决策树结点\n",
    "        基于信息增益-Information Gain\n",
    "        \"\"\"\n",
    "        node = GainNode()\n",
    "        # 当没有样本时直接返回\n",
    "        if len(y) == 0:\n",
    "            return node\n",
    "        y_classes = np.unique(y)\n",
    "        # 当样本中只存在一种分类时直接返回该分类\n",
    "        if len(y_classes) == 1:\n",
    "            node.threshold = y_classes[0]\n",
    "            return node\n",
    "        # 当决策树深度达到最大深度限制时返回样本中分类占比最大的分类\n",
    "        if self.max_depth is not None and depth >= self.max_depth:\n",
    "            node.threshold = max(y_classes, key=y.tolist().count)\n",
    "            return node\n",
    "        # 当决策树叶结点样本数达到最小样本数限制时返回样本中分类占比最大的分类\n",
    "        if self.min_samples_leaf is not None and len(y) <= self.min_samples_leaf:\n",
    "            node.threshold = max(y_classes, key=y.tolist().count)\n",
    "            return node\n",
    "        max_gain = -np.inf\n",
    "        max_middle = None\n",
    "        max_feature = None\n",
    "        # 遍历所有特征，获取信息增益最大的特征\n",
    "        for i in range(X.shape[1]):\n",
    "            # 计算特征的信息增益\n",
    "            gain, middle = self.calcGain(X[:,i], y, y_classes)\n",
    "            if max_gain < gain:\n",
    "                max_gain = gain\n",
    "                max_middle = middle\n",
    "                max_feature = i\n",
    "        # 信息增益最大的特征\n",
    "        node.feature = max_feature\n",
    "        # 临界值\n",
    "        node.threshold = max_middle\n",
    "        # 信息增益\n",
    "        node.gain = max_gain\n",
    "        X_lt = X[:,max_feature] < max_middle\n",
    "        X_gt = X[:,max_feature] > max_middle\n",
    "        # 递归处理左集合\n",
    "        node.left = self.buildNode(X[X_lt,:], y[X_lt], depth + 1)\n",
    "        # 递归处理右集合\n",
    "        node.right = self.buildNode(X[X_gt,:], y[X_gt], depth + 1)\n",
    "        return node\n",
    "    \n",
    "    def calcMiddle(self, x):\n",
    "        \"\"\"\n",
    "        计算连续型特征的俩俩平均值\n",
    "        \"\"\"\n",
    "        middle = []\n",
    "        if len(x) == 0:\n",
    "            return np.array(middle)\n",
    "        start = x[0]\n",
    "        for i in range(len(x) - 1):\n",
    "            if x[i] == x[i + 1]:\n",
    "                continue\n",
    "            middle.append((start + x[i + 1]) / 2)\n",
    "            start = x[i + 1]\n",
    "        return np.array(middle)\n",
    "\n",
    "    def calcEnt(self, y, y_classes):\n",
    "        \"\"\"\n",
    "        计算信息熵\n",
    "        \"\"\"\n",
    "        ent = 0\n",
    "        for j in range(len(y_classes)):\n",
    "            p = len(y[y == y_classes[j]])/ len(y)\n",
    "            if p != 0:\n",
    "                ent = ent + p * np.log2(p)\n",
    "        return -ent\n",
    "\n",
    "    def calcGain(self, x, y, y_classes):\n",
    "        \"\"\"\n",
    "        计算信息增益\n",
    "        \"\"\"\n",
    "        x_sort = np.sort(x)\n",
    "        middle = self.calcMiddle(x_sort)\n",
    "        max_middle = -np.inf\n",
    "        max_gain = -np.inf\n",
    "        ent = self.calcEnt(y, y_classes)\n",
    "        # 遍历每个平均值\n",
    "        for i in range(len(middle)):\n",
    "            y_gt = y[x > middle[i]]\n",
    "            y_lt = y[x < middle[i]]\n",
    "            ent_gt = self.calcEnt(y_gt, y_classes)\n",
    "            ent_lt = self.calcEnt(y_lt, y_classes)\n",
    "            # 计算信息增益\n",
    "            gain = ent - (ent_gt * len(y_gt) / len(x) + ent_lt * len(y_lt) / len(x))\n",
    "            if max_gain < gain:\n",
    "                max_gain = gain\n",
    "                max_middle = middle[i]\n",
    "        return max_gain, max_middle\n",
    "    \n",
    "    def predict(self, X):\n",
    "        \"\"\"\n",
    "        分类决策树预测\n",
    "        \"\"\"\n",
    "        y = np.zeros(X.shape[0])\n",
    "        self.checkNode(X, y, self.root)\n",
    "        return y\n",
    "    \n",
    "    def checkNode(self, X, y, node, cond = None):\n",
    "        \"\"\"\n",
    "        通过分类决策树结点判断分类\n",
    "        \"\"\"\n",
    "        # 当没有子结点时，直接返回当前临界值\n",
    "        if node.left is None and node.right is None:\n",
    "            return node.threshold\n",
    "        X_lt = X[:,node.feature] < node.threshold\n",
    "        if cond is not None:\n",
    "            X_lt = X_lt & cond\n",
    "        # 递归判断左结点\n",
    "        lt = self.checkNode(X, y, node.left, X_lt)\n",
    "        if lt is not None:\n",
    "            y[X_lt] = lt\n",
    "        X_gt = X[:,node.feature] > node.threshold\n",
    "        if cond is not None:\n",
    "            X_gt = X_gt & cond\n",
    "        # 递归判断右结点\n",
    "        gt = self.checkNode(X, y, node.right, X_gt)\n",
    "        if gt is not None:\n",
    "            y[X_gt] = gt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "决策树学习算法拟合（信息增益-Information Gain）："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<__main__.GainTree at 0x7f8b1a65e3d0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gainTree = GainTree(max_depth = 3, min_samples_leaf = 5)\n",
    "gainTree.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "决策树分类可视化（信息增益-Information Gain）："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\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",
       "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",
       "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], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.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,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\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",
       "        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.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\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",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\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(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\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(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\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",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\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",
       "        }\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",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\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",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.which === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.which;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which !== 17) {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    if (event.altKey && event.which !== 18) {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    if (event.shiftKey && event.which !== 16) {\n",
       "        value += 'shift+';\n",
       "    }\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, 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",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\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\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"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\";/* global mpl */\n",
       "\n",
       "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 = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\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;\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",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\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.innerHTML =\n",
       "        '<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 / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<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 () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('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",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\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",
       "\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",
       "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(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    },
    {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/saisimon/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/ipykernel_launcher.py:18: MatplotlibDeprecationWarning: shading='flat' when X and Y have the same dimensions as C is deprecated since 3.3.  Either specify the corners of the quadrilaterals with X and Y, or pass shading='auto', 'nearest' or 'gouraud', or set rcParams['pcolor.shading'].  This will become an error two minor releases later.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import LinearSegmentedColormap\n",
    "\n",
    "%matplotlib notebook\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['PingFang HK']  # 选择一个本地的支持中文的字体\n",
    "fig, ax = plt.subplots()\n",
    "ax.set_facecolor('#f8f9fa')\n",
    "\n",
    "x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n",
    "y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, .05), np.arange(y_min, y_max, .05))\n",
    "Z = gainTree.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "Z = Z.reshape(xx.shape)\n",
    "clist=['#ffadad', '#8ecae6']\n",
    "newcmp = LinearSegmentedColormap.from_list('point_color', clist)\n",
    "plt.pcolormesh(xx, yy, Z, cmap = newcmp)\n",
    "plt.xlim(xx.min(), xx.max())\n",
    "plt.ylim(yy.min(), yy.max())\n",
    "\n",
    "x1 = X[y==-1][:, 0]\n",
    "y1 = X[y==-1][:, 1]\n",
    "x2 = X[y==1][:, 0]\n",
    "y2 = X[y==1][:, 1]\n",
    "p1 = plt.scatter(x1, y1, c='#e63946', marker='o', s=20)\n",
    "p2 = plt.scatter(x2, y2, c='#457b9d', marker='x', s=20)\n",
    "\n",
    "ax.set_title('决策树分类（信息增益-Information Gain）：', color='#264653')\n",
    "ax.set_xlabel('X1', color='#264653')\n",
    "ax.set_ylabel('X2', color='#264653')\n",
    "ax.tick_params(labelcolor='#264653')\n",
    "plt.legend([p1, p2], [\"-1\", \"1\"], loc=\"upper left\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "决策树分类（scikit-learn）："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import tree\n",
    "\n",
    "# 决策树分类\n",
    "clf = tree.DecisionTreeClassifier(criterion = \"entropy\", max_depth = 3, min_samples_leaf = 5)\n",
    "# 拟合数据\n",
    "clf = clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "决策树分类可视化（scikit-learn）："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\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(\n",
       "            '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",
       "\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 = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(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 (fig.ratio !== 1) {\n",
       "            fig.send_message('set_dpi_ratio', { dpi_ratio: fig.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 = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\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 toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\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",
       "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",
       "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], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.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,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\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",
       "        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.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\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",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\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(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\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(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\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",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\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",
       "        }\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",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\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",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.which === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.which;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which !== 17) {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    if (event.altKey && event.which !== 18) {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    if (event.shiftKey && event.which !== 16) {\n",
       "        value += 'shift+';\n",
       "    }\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, 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",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\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\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"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\";/* global mpl */\n",
       "\n",
       "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 = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\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;\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",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\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.innerHTML =\n",
       "        '<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 / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<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 () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('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",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\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",
       "\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",
       "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(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\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"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/saisimon/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/ipykernel_launcher.py:18: MatplotlibDeprecationWarning: shading='flat' when X and Y have the same dimensions as C is deprecated since 3.3.  Either specify the corners of the quadrilaterals with X and Y, or pass shading='auto', 'nearest' or 'gouraud', or set rcParams['pcolor.shading'].  This will become an error two minor releases later.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import LinearSegmentedColormap\n",
    "\n",
    "%matplotlib notebook\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['PingFang HK']  # 选择一个本地的支持中文的字体\n",
    "fig, ax = plt.subplots()\n",
    "ax.set_facecolor('#f8f9fa')\n",
    "\n",
    "x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n",
    "y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, .05), np.arange(y_min, y_max, .05))\n",
    "Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "Z = Z.reshape(xx.shape)\n",
    "clist=['#ffadad', '#8ecae6']\n",
    "newcmp = LinearSegmentedColormap.from_list('point_color', clist)\n",
    "plt.pcolormesh(xx, yy, Z, cmap = newcmp)\n",
    "plt.xlim(xx.min(), xx.max())\n",
    "plt.ylim(yy.min(), yy.max())\n",
    "\n",
    "x1 = X[y==-1][:, 0]\n",
    "y1 = X[y==-1][:, 1]\n",
    "x2 = X[y==1][:, 0]\n",
    "y2 = X[y==1][:, 1]\n",
    "p1 = plt.scatter(x1, y1, c='#e63946', marker='o', s=20)\n",
    "p2 = plt.scatter(x2, y2, c='#457b9d', marker='x', s=20)\n",
    "\n",
    "ax.set_title('决策树分类（scikit-learn）', color='#264653')\n",
    "ax.set_xlabel('X1', color='#264653')\n",
    "ax.set_ylabel('X2', color='#264653')\n",
    "ax.tick_params(labelcolor='#264653')\n",
    "plt.legend([p1, p2], [\"-1\", \"1\"], loc=\"upper left\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "决策树分类实现（基尼指数-Gini Index）："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "class GiniNode:\n",
    "    \"\"\"\n",
    "    分类决策树中的结点\n",
    "    基于基尼指数-Gini Index\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, feature=None, threshold=None, gini_index=None, left=None, right=None):\n",
    "        # 结点划分的特征下标\n",
    "        self.feature = feature\n",
    "        # 结点划分的临界值，当结点为叶子结点时为分类值\n",
    "        self.threshold = threshold\n",
    "        # 结点的基尼指数值\n",
    "        self.gini_index = gini_index\n",
    "        # 左结点\n",
    "        self.left = left\n",
    "        # 右结点\n",
    "        self.right = right\n",
    "\n",
    "class GiniTree:\n",
    "    \"\"\"\n",
    "    分类决策树\n",
    "    基于基尼指数-Gini Index\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, max_depth = None, min_samples_leaf = None):\n",
    "        # 决策树最大深度\n",
    "        self.max_depth = max_depth\n",
    "        # 决策树叶结点最小样本数\n",
    "        self.min_samples_leaf = min_samples_leaf\n",
    "        \n",
    "    def fit(self, X, y):\n",
    "        \"\"\"\n",
    "        分类决策树拟合\n",
    "        基于基尼指数-Gini Index\n",
    "        \"\"\"\n",
    "        y = np.array(y)\n",
    "        self.root = self.buildNode(X, y, 0)\n",
    "        return self\n",
    "\n",
    "    def buildNode(self, X, y, depth):\n",
    "        \"\"\"\n",
    "        构建分类决策树结点\n",
    "        基于基尼指数-Gini Index\n",
    "        \"\"\"\n",
    "        node = GiniNode()\n",
    "        # 当没有样本时直接返回\n",
    "        if len(y) == 0:\n",
    "            return node\n",
    "        y_classes = np.unique(y)\n",
    "        # 当样本中只存在一种分类时直接返回该分类\n",
    "        if len(y_classes) == 1:\n",
    "            node.threshold = y_classes[0]\n",
    "            return node\n",
    "        # 当决策树深度达到最大深度限制时返回样本中分类占比最大的分类\n",
    "        if self.max_depth is not None and depth >= self.max_depth:\n",
    "            node.threshold = max(y_classes, key=y.tolist().count)\n",
    "            return node\n",
    "        # 当决策树叶结点样本数达到最小样本数限制时返回样本中分类占比最大的分类\n",
    "        if self.min_samples_leaf is not None and len(y) <= self.min_samples_leaf:\n",
    "            node.threshold = max(y_classes, key=y.tolist().count)\n",
    "            return node\n",
    "        min_gini_index = np.inf\n",
    "        min_middle = None\n",
    "        min_feature = None\n",
    "        # 遍历所有特征，获取基尼指数最小的特征\n",
    "        for i in range(X.shape[1]):\n",
    "            # 计算特征的基尼指数\n",
    "            gini_index, middle = self.calcGiniIndex(X[:,i], y, y_classes)\n",
    "            if min_gini_index > gini_index:\n",
    "                min_gini_index = gini_index\n",
    "                min_middle = middle\n",
    "                min_feature = i\n",
    "        # 基尼指数最小的特征\n",
    "        node.feature = min_feature\n",
    "        # 临界值\n",
    "        node.threshold = min_middle\n",
    "        # 基尼指数\n",
    "        node.gini_index = min_gini_index\n",
    "        X_lt = X[:,min_feature] < min_middle\n",
    "        X_gt = X[:,min_feature] > min_middle\n",
    "        # 递归处理左集合\n",
    "        node.left = self.buildNode(X[X_lt,:], y[X_lt], depth + 1)\n",
    "        # 递归处理右集合\n",
    "        node.right = self.buildNode(X[X_gt,:], y[X_gt], depth + 1)\n",
    "        return node\n",
    "    \n",
    "    def calcMiddle(self, x):\n",
    "        \"\"\"\n",
    "        计算连续型特征的俩俩平均值\n",
    "        \"\"\"\n",
    "        middle = []\n",
    "        if len(x) == 0:\n",
    "            return np.array(middle)\n",
    "        start = x[0]\n",
    "        for i in range(len(x) - 1):\n",
    "            if x[i] == x[i + 1]:\n",
    "                continue\n",
    "            middle.append((start + x[i + 1]) / 2)\n",
    "            start = x[i + 1]\n",
    "        return np.array(middle)\n",
    "\n",
    "    def calcGiniIndex(self, x, y, y_classes):\n",
    "        \"\"\"\n",
    "        计算基尼指数\n",
    "        \"\"\"\n",
    "        x_sort = np.sort(x)\n",
    "        middle = self.calcMiddle(x_sort)\n",
    "        min_middle = np.inf\n",
    "        min_gini_index = np.inf\n",
    "        for i in range(len(middle)):\n",
    "            y_gt = y[x > middle[i]]\n",
    "            y_lt = y[x < middle[i]]\n",
    "            gini_gt = self.calcGini(y_gt, y_classes)\n",
    "            gini_lt = self.calcGini(y_lt, y_classes)\n",
    "            gini_index = gini_gt * len(y_gt) / len(x) + gini_lt * len(y_lt) / len(x)\n",
    "            if min_gini_index > gini_index:\n",
    "                min_gini_index = gini_index\n",
    "                min_middle = middle[i]\n",
    "        return min_gini_index, min_middle\n",
    "\n",
    "    def calcGini(self, y, y_classes):\n",
    "        \"\"\"\n",
    "        计算基尼值\n",
    "        \"\"\"\n",
    "        gini = 1\n",
    "        for j in range(len(y_classes)):\n",
    "            p = len(y[y == y_classes[j]])/ len(y)\n",
    "            gini = gini - p * p\n",
    "        return gini\n",
    "    \n",
    "    def predict(self, X):\n",
    "        \"\"\"\n",
    "        分类决策树预测\n",
    "        \"\"\"\n",
    "        y = np.zeros(X.shape[0])\n",
    "        self.checkNode(X, y, self.root)\n",
    "        return y\n",
    "    \n",
    "    def checkNode(self, X, y, node, cond = None):\n",
    "        \"\"\"\n",
    "        通过分类决策树结点判断分类\n",
    "        \"\"\"\n",
    "        if node.left is None and node.right is None:\n",
    "            return node.threshold\n",
    "        X_lt = X[:,node.feature] < node.threshold\n",
    "        if cond is not None:\n",
    "            X_lt = X_lt & cond\n",
    "        lt = self.checkNode(X, y, node.left, X_lt)\n",
    "        if lt is not None:\n",
    "            y[X_lt] = lt\n",
    "        X_gt = X[:,node.feature] > node.threshold\n",
    "        if cond is not None:\n",
    "            X_gt = X_gt & cond\n",
    "        gt = self.checkNode(X, y, node.right, X_gt)\n",
    "        if gt is not None:\n",
    "            y[X_gt] = gt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "决策树分类拟合（基尼指数-Gini Index）："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<__main__.GiniTree at 0x7f8b1d5675d0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "giniTree = GiniTree(max_depth = 3, min_samples_leaf = 5)\n",
    "giniTree.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "决策树分类可视化（基尼指数-Gini Index）："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\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(\n",
       "            '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",
       "\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 = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(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 (fig.ratio !== 1) {\n",
       "            fig.send_message('set_dpi_ratio', { dpi_ratio: fig.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 = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\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 toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\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",
       "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",
       "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], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.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,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\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",
       "        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.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\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",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\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(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\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(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\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",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\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",
       "        }\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",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\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",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.which === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.which;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which !== 17) {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    if (event.altKey && event.which !== 18) {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    if (event.shiftKey && event.which !== 16) {\n",
       "        value += 'shift+';\n",
       "    }\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, 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",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\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\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"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\";/* global mpl */\n",
       "\n",
       "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 = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\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;\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",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\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.innerHTML =\n",
       "        '<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 / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<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 () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('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",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\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",
       "\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",
       "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(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\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"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/saisimon/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/ipykernel_launcher.py:18: MatplotlibDeprecationWarning: shading='flat' when X and Y have the same dimensions as C is deprecated since 3.3.  Either specify the corners of the quadrilaterals with X and Y, or pass shading='auto', 'nearest' or 'gouraud', or set rcParams['pcolor.shading'].  This will become an error two minor releases later.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import LinearSegmentedColormap\n",
    "\n",
    "%matplotlib notebook\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['PingFang HK']  # 选择一个本地的支持中文的字体\n",
    "fig, ax = plt.subplots()\n",
    "ax.set_facecolor('#f8f9fa')\n",
    "\n",
    "x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n",
    "y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, .05), np.arange(y_min, y_max, .05))\n",
    "Z = giniTree.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "Z = Z.reshape(xx.shape)\n",
    "clist=['#ffadad', '#8ecae6']\n",
    "newcmp = LinearSegmentedColormap.from_list('point_color', clist)\n",
    "plt.pcolormesh(xx, yy, Z, cmap = newcmp)\n",
    "plt.xlim(xx.min(), xx.max())\n",
    "plt.ylim(yy.min(), yy.max())\n",
    "\n",
    "x1 = X[y==-1][:, 0]\n",
    "y1 = X[y==-1][:, 1]\n",
    "x2 = X[y==1][:, 0]\n",
    "y2 = X[y==1][:, 1]\n",
    "p1 = plt.scatter(x1, y1, c='#e63946', marker='o', s=20)\n",
    "p2 = plt.scatter(x2, y2, c='#457b9d', marker='x', s=20)\n",
    "\n",
    "ax.set_title('决策树分类（基尼指数-Gini Index）', color='#264653')\n",
    "ax.set_xlabel('X1', color='#264653')\n",
    "ax.set_ylabel('X2', color='#264653')\n",
    "ax.tick_params(labelcolor='#264653')\n",
    "plt.legend([p1, p2], [\"-1\", \"1\"], loc=\"upper left\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "决策树分类（scikit-learn）："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import tree\n",
    "\n",
    "clf = tree.DecisionTreeClassifier(criterion = \"gini\", max_depth = 3, min_samples_leaf = 5)\n",
    "clf = clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "决策树分类可视化（scikit-learn）："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\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(\n",
       "            '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",
       "\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 = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(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 (fig.ratio !== 1) {\n",
       "            fig.send_message('set_dpi_ratio', { dpi_ratio: fig.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 = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\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 toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\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",
       "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",
       "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], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.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,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\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",
       "        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.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\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",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\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(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\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(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\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",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\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",
       "        }\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",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\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",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.which === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.which;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which !== 17) {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    if (event.altKey && event.which !== 18) {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    if (event.shiftKey && event.which !== 16) {\n",
       "        value += 'shift+';\n",
       "    }\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, 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",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\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\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"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\";/* global mpl */\n",
       "\n",
       "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 = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\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;\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",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\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.innerHTML =\n",
       "        '<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 / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<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 () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('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",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\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",
       "\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",
       "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(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\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"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/saisimon/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/ipykernel_launcher.py:18: MatplotlibDeprecationWarning: shading='flat' when X and Y have the same dimensions as C is deprecated since 3.3.  Either specify the corners of the quadrilaterals with X and Y, or pass shading='auto', 'nearest' or 'gouraud', or set rcParams['pcolor.shading'].  This will become an error two minor releases later.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import LinearSegmentedColormap\n",
    "\n",
    "%matplotlib notebook\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['PingFang HK']  # 选择一个本地的支持中文的字体\n",
    "fig, ax = plt.subplots()\n",
    "ax.set_facecolor('#f8f9fa')\n",
    "\n",
    "x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n",
    "y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, .05), np.arange(y_min, y_max, .05))\n",
    "Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "Z = Z.reshape(xx.shape)\n",
    "clist=['#ffadad', '#8ecae6']\n",
    "newcmp = LinearSegmentedColormap.from_list('point_color', clist)\n",
    "plt.pcolormesh(xx, yy, Z, cmap = newcmp)\n",
    "plt.xlim(xx.min(), xx.max())\n",
    "plt.ylim(yy.min(), yy.max())\n",
    "\n",
    "x1 = X[y==-1][:, 0]\n",
    "y1 = X[y==-1][:, 1]\n",
    "x2 = X[y==1][:, 0]\n",
    "y2 = X[y==1][:, 1]\n",
    "p1 = plt.scatter(x1, y1, c='#e63946', marker='o', s=20)\n",
    "p2 = plt.scatter(x2, y2, c='#457b9d', marker='x', s=20)\n",
    "\n",
    "ax.set_title('决策树分类（scikit-learn）', color='#264653')\n",
    "ax.set_xlabel('X1', color='#264653')\n",
    "ax.set_ylabel('X2', color='#264653')\n",
    "ax.tick_params(labelcolor='#264653')\n",
    "plt.legend([p1, p2], [\"-1\", \"1\"], loc=\"upper left\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "生成回归样例数据："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_regression\n",
    "\n",
    "X, y = make_regression(n_features = 1, n_targets = 1, n_samples = 100, noise=5, random_state = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "决策树回归实现（均方差-MSE）："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "class RegressorNode:\n",
    "    \"\"\"\n",
    "    回归决策树中的结点\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, feature=None, threshold=None, mse=None, left=None, right=None):\n",
    "        # 结点划分的特征下标\n",
    "        self.feature = feature\n",
    "        # 结点划分的临界值，当结点为叶子结点时为分类值\n",
    "        self.threshold = threshold\n",
    "        # 结点的均方差值\n",
    "        self.mse = mse\n",
    "        # 左结点\n",
    "        self.left = left\n",
    "        # 右结点\n",
    "        self.right = right\n",
    "\n",
    "class RegressorTree:\n",
    "    \"\"\"\n",
    "    回归决策树\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, max_depth = None, min_samples_leaf = None):\n",
    "        # 决策树最大深度\n",
    "        self.max_depth = max_depth\n",
    "        # 决策树叶结点最小样本数\n",
    "        self.min_samples_leaf = min_samples_leaf\n",
    "        \n",
    "    def fit(self, X, y):\n",
    "        \"\"\"\n",
    "        回归决策树拟合\n",
    "        \"\"\"\n",
    "        self.root = self.buildNode(X, y, 0)\n",
    "        return self\n",
    "\n",
    "    def buildNode(self, X, y, depth):\n",
    "        \"\"\"\n",
    "        构建回归决策树结点\n",
    "        \"\"\"\n",
    "        node = RegressorNode()\n",
    "        # 当没有样本时直接返回\n",
    "        if len(y) == 0:\n",
    "            return node\n",
    "        y_classes = np.unique(y)\n",
    "        # 当样本中只存在一种分类时直接返回该分类\n",
    "        if len(y_classes) == 1:\n",
    "            node.threshold = y_classes[0]\n",
    "            return node\n",
    "        # 当决策树深度达到最大深度限制时返回样本中分类占比最大的分类\n",
    "        if self.max_depth is not None and depth >= self.max_depth:\n",
    "            node.threshold = np.average(y)\n",
    "            return node\n",
    "        # 当决策树叶结点样本数达到最小样本数限制时返回样本中分类占比最大的分类\n",
    "        if self.min_samples_leaf is not None and len(y) <= self.min_samples_leaf:\n",
    "            node.threshold = np.average(y)\n",
    "            return node\n",
    "        min_mse = np.inf\n",
    "        min_middle = None\n",
    "        min_feature = None\n",
    "        # 遍历所有特征，获取均方差最小的特征\n",
    "        for i in range(X.shape[1]):\n",
    "            # 计算特征的均方差\n",
    "            mse, middle = self.calcMse(X[:,i], y)\n",
    "            if min_mse > mse:\n",
    "                min_mse = mse\n",
    "                min_middle = middle\n",
    "                min_feature = i\n",
    "        # 均方差最小的特征\n",
    "        node.feature = min_feature\n",
    "        # 临界值\n",
    "        node.threshold = min_middle\n",
    "        # 均方差\n",
    "        node.mse = min_mse\n",
    "        X_lt = X[:,min_feature] < min_middle\n",
    "        X_gt = X[:,min_feature] > min_middle\n",
    "        # 递归处理左集合\n",
    "        node.left = self.buildNode(X[X_lt,:], y[X_lt], depth + 1)\n",
    "        # 递归处理右集合\n",
    "        node.right = self.buildNode(X[X_gt,:], y[X_gt], depth + 1)\n",
    "        return node\n",
    "    \n",
    "    def calcMiddle(self, x):\n",
    "        \"\"\"\n",
    "        计算连续型特征的俩俩平均值\n",
    "        \"\"\"\n",
    "        middle = []\n",
    "        if len(x) == 0:\n",
    "            return np.array(middle)\n",
    "        start = x[0]\n",
    "        for i in range(len(x) - 1):\n",
    "            if x[i] == x[i + 1]:\n",
    "                continue\n",
    "            middle.append((start + x[i + 1]) / 2)\n",
    "            start = x[i + 1]\n",
    "        return np.array(middle)\n",
    "\n",
    "    def calcMse(self, x, y):\n",
    "        \"\"\"\n",
    "        计算均方差\n",
    "        \"\"\"\n",
    "        x_sort = np.sort(x)\n",
    "        middle = self.calcMiddle(x_sort)\n",
    "        min_middle = np.inf\n",
    "        min_mse = np.inf\n",
    "        for i in range(len(middle)):\n",
    "            y_gt = y[x > middle[i]]\n",
    "            y_lt = y[x < middle[i]]\n",
    "            avg_gt = np.average(y_gt)\n",
    "            avg_lt = np.average(y_lt)\n",
    "            mse = np.sum((y_lt - avg_lt) ** 2) + np.sum((y_gt - avg_gt) ** 2)\n",
    "            if min_mse > mse:\n",
    "                min_mse = mse\n",
    "                min_middle = middle[i]\n",
    "        return min_mse, min_middle\n",
    "    \n",
    "    def predict(self, X):\n",
    "        \"\"\"\n",
    "        回归决策树预测\n",
    "        \"\"\"\n",
    "        y = np.zeros(X.shape[0])\n",
    "        self.checkNode(X, y, self.root)\n",
    "        return y\n",
    "    \n",
    "    def checkNode(self, X, y, node, cond = None):\n",
    "        \"\"\"\n",
    "        通过回归决策树结点判断分类\n",
    "        \"\"\"\n",
    "        if node.left is None and node.right is None:\n",
    "            return node.threshold\n",
    "        X_lt = X[:,node.feature] < node.threshold\n",
    "        if cond is not None:\n",
    "            X_lt = X_lt & cond\n",
    "        lt = self.checkNode(X, y, node.left, X_lt)\n",
    "        if lt is not None:\n",
    "            y[X_lt] = lt\n",
    "        X_gt = X[:,node.feature] > node.threshold\n",
    "        if cond is not None:\n",
    "            X_gt = X_gt & cond\n",
    "        gt = self.checkNode(X, y, node.right, X_gt)\n",
    "        if gt is not None:\n",
    "            y[X_gt] = gt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "决策树回归拟合（均方差-MSE）："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<__main__.RegressorTree at 0x7f8b1f2fcc10>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressorTree = RegressorTree(max_depth = 3, min_samples_leaf = 5)\n",
    "regressorTree.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "决策树回归可视化（均方差-MSE）："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\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(\n",
       "            '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",
       "\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 = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(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 (fig.ratio !== 1) {\n",
       "            fig.send_message('set_dpi_ratio', { dpi_ratio: fig.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 = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\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 toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\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",
       "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",
       "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], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.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,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\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",
       "        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.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\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",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\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(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\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(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\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",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\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",
       "        }\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",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\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",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.which === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.which;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which !== 17) {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    if (event.altKey && event.which !== 18) {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    if (event.shiftKey && event.which !== 16) {\n",
       "        value += 'shift+';\n",
       "    }\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, 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",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\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\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"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\";/* global mpl */\n",
       "\n",
       "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 = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\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;\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",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\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.innerHTML =\n",
       "        '<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 / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<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 () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('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",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\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",
       "\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",
       "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(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\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": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import LinearSegmentedColormap\n",
    "\n",
    "%matplotlib notebook\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['PingFang HK']  # 选择一个本地的支持中文的字体\n",
    "fig, ax = plt.subplots()\n",
    "ax.set_facecolor('#f8f9fa')\n",
    "\n",
    "x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n",
    "xx = np.arange(x_min, x_max, .05)\n",
    "yy = regressorTree.predict(np.c_[xx.ravel()])\n",
    "\n",
    "plt.scatter(X, y, c='#e63946', marker='o', s=20)\n",
    "plt.plot(xx, yy)\n",
    "\n",
    "ax.set_title('决策树回归（均方差-MSE）', color='#264653')\n",
    "ax.set_xlabel('X', color='#264653')\n",
    "ax.set_ylabel('Y', color='#264653')\n",
    "ax.tick_params(labelcolor='#264653')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "决策树回归（scikit-learn）："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import tree\n",
    "\n",
    "# 决策树回归\n",
    "clf = tree.DecisionTreeRegressor(max_depth = 3, min_samples_leaf = 5)\n",
    "# 拟合数据\n",
    "clf = clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "决策树回归可视化（scikit-learn）："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\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(\n",
       "            '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",
       "\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 = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(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 (fig.ratio !== 1) {\n",
       "            fig.send_message('set_dpi_ratio', { dpi_ratio: fig.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 = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\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 toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\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",
       "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",
       "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], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.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,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\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",
       "        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.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\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",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\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(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\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(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\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",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\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",
       "        }\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",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\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",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.which === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.which;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which !== 17) {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    if (event.altKey && event.which !== 18) {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    if (event.shiftKey && event.which !== 16) {\n",
       "        value += 'shift+';\n",
       "    }\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, 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",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\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\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"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\";/* global mpl */\n",
       "\n",
       "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 = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\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;\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",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\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.innerHTML =\n",
       "        '<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 / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<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 () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('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",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\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",
       "\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",
       "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(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\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": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import LinearSegmentedColormap\n",
    "\n",
    "%matplotlib notebook\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['PingFang HK']  # 选择一个本地的支持中文的字体\n",
    "fig, ax = plt.subplots()\n",
    "ax.set_facecolor('#f8f9fa')\n",
    "\n",
    "x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n",
    "xx = np.arange(x_min, x_max, .05)\n",
    "yy = clf.predict(np.c_[xx.ravel()])\n",
    "\n",
    "plt.scatter(X, y, c='#e63946', marker='o', s=20)\n",
    "plt.plot(xx, yy)\n",
    "\n",
    "ax.set_title('决策树回归（scikit-learn）', color='#264653')\n",
    "ax.set_xlabel('X', color='#264653')\n",
    "ax.set_ylabel('Y', color='#264653')\n",
    "ax.tick_params(labelcolor='#264653')\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": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}

一键复制编辑原始数据按行查看历史

提交于 2021-12-26 21:19 . 17-决策树学习算法

Python

https://gitee.com/saisimon/AIMindMap.git

git@gitee.com:saisimon/AIMindMap.git

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AIMindMap

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Saisimon / AIMindMap

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Saisimon / AIMindMap .gitee-modal { width: 500px !important; }

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