diff --git a/4_logistic_regression/Least_squares.ipynb b/4_logistic_regression/Least_squares.ipynb
index 42bbd81..e2af4ce 100644
--- a/4_logistic_regression/Least_squares.ipynb
+++ b/4_logistic_regression/Least_squares.ipynb
@@ -26,7 +26,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -83,7 +83,8 @@
     "\n",
     "如何构建函数来评估模型输出与观测数据之间的误差是一个关键问题，这里我们使用观测数据与模型输出的平方和来作为评估函数（也被称为损失函数Loss function):\n",
     "$$\n",
-    "L = \\sum_{i=1}^{N} (y_i - a x_i - b)^2\n",
+    "L = \\sum_{i=1}^{N} (y_i - a x_i - b)^2 \\\\\n",
+    "L = \\sum_{i=1}^{N} \\{y_i - (a x_i + b)\\}^2\n",
     "$$\n",
     "\n",
     "使误差函数最小，那么我们就可以求出模型的参数:\n",
@@ -114,7 +115,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -220,7 +221,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -1156,7 +1157,13 @@
       "epoch  926: loss = 1741938.470661, a = 800.256095, b = 152.614425\n",
       "epoch  927: loss = 1741850.081082, a = 800.552373, b = 152.614236\n",
       "epoch  928: loss = 1741762.043319, a = 800.848058, b = 152.614047\n",
-      "epoch  929: loss = 1741674.355969, a = 801.143152, b = 152.613859\n",
+      "epoch  929: loss = 1741674.355969, a = 801.143152, b = 152.613859\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "epoch  930: loss = 1741587.017634, a = 801.437656, b = 152.613670\n",
       "epoch  931: loss = 1741500.026924, a = 801.731571, b = 152.613483\n",
       "epoch  932: loss = 1741413.382452, a = 802.024898, b = 152.613295\n",
@@ -1186,13 +1193,7 @@
       "epoch  956: loss = 1739434.277612, a = 808.891453, b = 152.608910\n",
       "epoch  957: loss = 1739355.854363, a = 809.170463, b = 152.608732\n",
       "epoch  958: loss = 1739277.743190, a = 809.448915, b = 152.608554\n",
-      "epoch  959: loss = 1739199.942847, a = 809.726810, b = 152.608376\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "epoch  959: loss = 1739199.942847, a = 809.726810, b = 152.608376\n",
       "epoch  960: loss = 1739122.452096, a = 810.004149, b = 152.608199\n",
       "epoch  961: loss = 1739045.269701, a = 810.280934, b = 152.608022\n",
       "epoch  962: loss = 1738968.394433, a = 810.557165, b = 152.607846\n",
@@ -1433,13 +1434,7 @@
       "epoch 1197: loss = 1727262.359044, a = 862.289047, b = 152.574805\n",
       "epoch 1198: loss = 1727232.327005, a = 862.461279, b = 152.574695\n",
       "epoch 1199: loss = 1727202.414181, a = 862.633167, b = 152.574585\n",
-      "epoch 1200: loss = 1727172.620097, a = 862.804711, b = 152.574476\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "epoch 1200: loss = 1727172.620097, a = 862.804711, b = 152.574476\n",
       "epoch 1201: loss = 1727142.944280, a = 862.975912, b = 152.574366\n",
       "epoch 1202: loss = 1727113.386259, a = 863.146771, b = 152.574257\n",
       "epoch 1203: loss = 1727083.945566, a = 863.317288, b = 152.574148\n",
@@ -1697,13 +1692,7 @@
       "epoch 1455: loss = 1722405.759587, a = 897.030088, b = 152.552616\n",
       "epoch 1456: loss = 1722394.975336, a = 897.132850, b = 152.552550\n",
       "epoch 1457: loss = 1722384.233710, a = 897.235407, b = 152.552485\n",
-      "epoch 1458: loss = 1722373.534540, a = 897.337758, b = 152.552419\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "epoch 1458: loss = 1722373.534540, a = 897.337758, b = 152.552419\n",
       "epoch 1459: loss = 1722362.877657, a = 897.439904, b = 152.552354\n",
       "epoch 1460: loss = 1722352.262894, a = 897.541846, b = 152.552289\n",
       "epoch 1461: loss = 1722341.690083, a = 897.643585, b = 152.552224\n",
@@ -1885,7 +1874,13 @@
       "epoch 1637: loss = 1721003.777210, a = 912.720300, b = 152.542595\n",
       "epoch 1638: loss = 1720998.523911, a = 912.791687, b = 152.542549\n",
       "epoch 1639: loss = 1720993.291280, a = 912.862931, b = 152.542504\n",
-      "epoch 1640: loss = 1720988.079235, a = 912.934032, b = 152.542458\n",
+      "epoch 1640: loss = 1720988.079235, a = 912.934032, b = 152.542458\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "epoch 1641: loss = 1720982.887695, a = 913.004991, b = 152.542413\n",
       "epoch 1642: loss = 1720977.716578, a = 913.075808, b = 152.542368\n",
       "epoch 1643: loss = 1720972.565804, a = 913.146484, b = 152.542322\n",
@@ -2173,7 +2168,13 @@
       "epoch 1925: loss = 1720096.340125, a = 928.361108, b = 152.532605\n",
       "epoch 1926: loss = 1720094.642123, a = 928.401218, b = 152.532579\n",
       "epoch 1927: loss = 1720092.950726, a = 928.441248, b = 152.532554\n",
-      "epoch 1928: loss = 1720091.265906, a = 928.481197, b = 152.532528\n",
+      "epoch 1928: loss = 1720091.265906, a = 928.481197, b = 152.532528\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "epoch 1929: loss = 1720089.587638, a = 928.521067, b = 152.532503\n",
       "epoch 1930: loss = 1720087.915897, a = 928.560858, b = 152.532477\n",
       "epoch 1931: loss = 1720086.250656, a = 928.600568, b = 152.532452\n",
@@ -2495,13 +2496,7 @@
       "epoch 2247: loss = 1719783.365139, a = 937.890737, b = 152.526518\n",
       "epoch 2248: loss = 1719782.874794, a = 937.911791, b = 152.526505\n",
       "epoch 2249: loss = 1719782.386313, a = 937.932803, b = 152.526492\n",
-      "epoch 2250: loss = 1719781.899690, a = 937.953773, b = 152.526478\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "epoch 2250: loss = 1719781.899690, a = 937.953773, b = 152.526478\n",
       "epoch 2251: loss = 1719781.414916, a = 937.974701, b = 152.526465\n",
       "epoch 2252: loss = 1719780.931986, a = 937.995587, b = 152.526451\n",
       "epoch 2253: loss = 1719780.450892, a = 938.016431, b = 152.526438\n",
@@ -2679,7 +2674,13 @@
       "epoch 2425: loss = 1719719.760650, a = 941.046575, b = 152.524503\n",
       "epoch 2426: loss = 1719719.510258, a = 941.061318, b = 152.524493\n",
       "epoch 2427: loss = 1719719.260800, a = 941.076032, b = 152.524484\n",
-      "epoch 2428: loss = 1719719.012273, a = 941.090716, b = 152.524475\n",
+      "epoch 2428: loss = 1719719.012273, a = 941.090716, b = 152.524475\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "epoch 2429: loss = 1719718.764672, a = 941.105371, b = 152.524465\n",
       "epoch 2430: loss = 1719718.517995, a = 941.119997, b = 152.524456\n",
       "epoch 2431: loss = 1719718.272238, a = 941.134594, b = 152.524447\n",
@@ -2708,13 +2709,7 @@
       "epoch 2454: loss = 1719712.865738, a = 941.462375, b = 152.524237\n",
       "epoch 2455: loss = 1719712.641027, a = 941.476287, b = 152.524228\n",
       "epoch 2456: loss = 1719712.417151, a = 941.490171, b = 152.524219\n",
-      "epoch 2457: loss = 1719712.194107, a = 941.504027, b = 152.524211\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "epoch 2457: loss = 1719712.194107, a = 941.504027, b = 152.524211\n",
       "epoch 2458: loss = 1719711.971892, a = 941.517856, b = 152.524202\n",
       "epoch 2459: loss = 1719711.750502, a = 941.531657, b = 152.524193\n",
       "epoch 2460: loss = 1719711.529935, a = 941.545430, b = 152.524184\n",
@@ -3127,7 +3122,13 @@
       "epoch 2867: loss = 1719664.917619, a = 945.375777, b = 152.521738\n",
       "epoch 2868: loss = 1719664.866907, a = 945.381863, b = 152.521734\n",
       "epoch 2869: loss = 1719664.816370, a = 945.387938, b = 152.521730\n",
-      "epoch 2870: loss = 1719664.766007, a = 945.394000, b = 152.521726\n",
+      "epoch 2870: loss = 1719664.766007, a = 945.394000, b = 152.521726\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "epoch 2871: loss = 1719664.715819, a = 945.400050, b = 152.521722\n",
       "epoch 2872: loss = 1719664.665804, a = 945.406087, b = 152.521718\n",
       "epoch 2873: loss = 1719664.615962, a = 945.412113, b = 152.521714\n",
@@ -3213,13 +3214,7 @@
       "epoch 2953: loss = 1719661.135884, a = 945.857112, b = 152.521430\n",
       "epoch 2954: loss = 1719661.098120, a = 945.862236, b = 152.521427\n",
       "epoch 2955: loss = 1719661.060484, a = 945.867350, b = 152.521424\n",
-      "epoch 2956: loss = 1719661.022975, a = 945.872453, b = 152.521420\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "epoch 2956: loss = 1719661.022975, a = 945.872453, b = 152.521420\n",
       "epoch 2957: loss = 1719660.985594, a = 945.877546, b = 152.521417\n",
       "epoch 2958: loss = 1719660.948338, a = 945.882629, b = 152.521414\n",
       "epoch 2959: loss = 1719660.911209, a = 945.887702, b = 152.521411\n",
@@ -3313,7 +3308,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -4099,7 +4094,7 @@
     {
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\" width=\"432\">"
+       "<img src=\"data:image/png;base64,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\" width=\"432\">"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -4107,228 +4102,902 @@
      },
      "metadata": {},
      "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "%matplotlib nbagg\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "import matplotlib.animation as animation\n",
-    "\n",
-    "n_epoch = 3000          # epoch size\n",
-    "a, b = 1, 1             # initial parameters\n",
-    "epsilon = 0.001         # learning rate\n",
-    "\n",
-    "fig = plt.figure()\n",
-    "imgs = []\n",
-    "\n",
-    "for i in range(n_epoch):\n",
-    "    for j in range(N):\n",
-    "        a = a + epsilon*2*(Y[j] - a*X[j] - b)*X[j]\n",
-    "        b = b + epsilon*2*(Y[j] - a*X[j] - b)\n",
-    "\n",
-    "    L = 0\n",
-    "    for j in range(N):\n",
-    "        L = L + (Y[j]-a*X[j]-b)**2\n",
-    "    #print(\"epoch %4d: loss = %f, a = %f, b = %f\" % (i, L, a, b))\n",
-    "    \n",
-    "    if i % 50 == 0:\n",
-    "        x_min = np.min(X)\n",
-    "        x_max = np.max(X)\n",
-    "        y_min = a * x_min + b\n",
-    "        y_max = a * x_max + b\n",
-    "\n",
-    "        img = plt.scatter(X, Y, label='original data')\n",
-    "        img = plt.plot([x_min, x_max], [y_min, y_max], 'r', label='model')\n",
-    "        imgs.append(img)\n",
-    "        \n",
-    "ani = animation.ArtistAnimation(fig, imgs)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 4. 如何使用批次更新的方法？\n",
-    "\n",
-    "如果有一些数据包含比较大的错误（异常数据），因此每次更新仅仅使用一个数据会导致不精确，同时每次仅仅使用一个数据来计算更新也导致计算效率比较低。\n",
-    "\n",
-    "\n",
-    "* [梯度下降方法的几种形式](https://blog.csdn.net/u010402786/article/details/51188876)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 5. 如何拟合多项式函数？\n",
-    "\n",
-    "需要设计一个弹道导弹防御系统，通过观测导弹的飞行路径，预测未来导弹的飞行轨迹，从而完成摧毁的任务。按照物理学，可以得知模型为:\n",
-    "$$\n",
-    "y = at^2 + bt + c\n",
-    "$$\n",
-    "我们需要求解三个模型参数$a, b, c$。\n",
-    "\n",
-    "损失函数的定义为：\n",
-    "$$\n",
-    "L = \\sum_{i=1}^N (y_i - at^2 - bt - c)^2\n",
-    "$$\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Traceback (most recent call last):\n",
-      "  File \"/home/bushuhui/.virtualenv/dl/lib/python3.5/site-packages/matplotlib/cbook/__init__.py\", line 215, in process\n",
-      "    func(*args, **kwargs)\n",
-      "  File \"/home/bushuhui/.virtualenv/dl/lib/python3.5/site-packages/matplotlib/animation.py\", line 1462, in _stop\n",
-      "    self.event_source.remove_callback(self._loop_delay)\n",
-      "AttributeError: 'NoneType' object has no attribute 'remove_callback'\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "%matplotlib inline\n",
-    "\n",
-    "t = np.array([2, 4, 6, 8])\n",
-    "#t = np.linspace(0, 10)\n",
-    "\n",
-    "pa = -20\n",
-    "pb = 90\n",
-    "pc = 800\n",
-    "\n",
-    "y = pa*t**2 + pb*t + pc\n",
-    "\n",
-    "\n",
-    "plt.scatter(t, y)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 5.1 如何得到更新项?\n",
-    "\n",
-    "$$\n",
-    "L = \\sum_{i=1}^N (y_i - at^2 - bt - c)^2\n",
-    "$$\n",
-    "\n",
-    "\\begin{eqnarray}\n",
-    "\\frac{\\partial L}{\\partial a} & = & - 2\\sum_{i=1}^N (y_i - at^2 - bt -c) t^2 \\\\\n",
-    "\\frac{\\partial L}{\\partial b} & = & - 2\\sum_{i=1}^N (y_i - at^2 - bt -c) t \\\\\n",
-    "\\frac{\\partial L}{\\partial c} & = & - 2\\sum_{i=1}^N (y_i - at^2 - bt -c)\n",
-    "\\end{eqnarray}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 6. 如何使用sklearn求解线性问题？\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "a = 949.435260, b = 152.133484\n"
-     ]
     },
     {
      "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from sklearn import linear_model\n",
-    "\n",
-    "# load data\n",
-    "d = datasets.load_diabetes()\n",
-    "\n",
-    "X = d.data[:, np.newaxis, 2]\n",
-    "Y = d.target\n",
-    "\n",
-    "# create regression model\n",
-    "regr = linear_model.LinearRegression()\n",
-    "regr.fit(X, Y)\n",
-    "\n",
-    "a, b = regr.coef_, regr.intercept_\n",
-    "print(\"a = %f, b = %f\" % (a, b))\n",
-    "\n",
-    "x_min = np.min(X)\n",
-    "x_max = np.max(X)\n",
-    "y_min = a * x_min + b\n",
-    "y_max = a * x_max + b\n",
-    "\n",
-    "plt.scatter(X, Y)\n",
-    "plt.plot([x_min, x_max], [y_min, y_max], 'r')\n",
-    "plt.show()"
-   ]
-  },
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('<div/>');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            if (mpl.ratio != 1) {\n",
+       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+       "            }\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        fig.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+       "        'ui-helper-clearfix\"/>');\n",
+       "    var titletext = $(\n",
+       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+       "        'text-align: center; padding: 3px;\"/>');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('<div/>');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('<canvas/>');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var backingStore = this.context.backingStorePixelRatio ||\n",
+       "\tthis.context.webkitBackingStorePixelRatio ||\n",
+       "\tthis.context.mozBackingStorePixelRatio ||\n",
+       "\tthis.context.msBackingStorePixelRatio ||\n",
+       "\tthis.context.oBackingStorePixelRatio ||\n",
+       "\tthis.context.backingStorePixelRatio || 1;\n",
+       "\n",
+       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+       "\n",
+       "    var rubberband = $('<canvas/>');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width * mpl.ratio);\n",
+       "        canvas.attr('height', height * mpl.ratio);\n",
+       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('<button/>');\n",
+       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+       "                        'ui-button-icon-only');\n",
+       "        button.attr('role', 'button');\n",
+       "        button.attr('aria-disabled', 'false');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "\n",
+       "        var icon_img = $('<span/>');\n",
+       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+       "        icon_img.addClass(image);\n",
+       "        icon_img.addClass('ui-corner-all');\n",
+       "\n",
+       "        var tooltip_span = $('<span/>');\n",
+       "        tooltip_span.addClass('ui-button-text');\n",
+       "        tooltip_span.html(tooltip);\n",
+       "\n",
+       "        button.append(icon_img);\n",
+       "        button.append(tooltip_span);\n",
+       "\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    var fmt_picker_span = $('<span/>');\n",
+       "\n",
+       "    var fmt_picker = $('<select/>');\n",
+       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+       "    fmt_picker_span.append(fmt_picker);\n",
+       "    nav_element.append(fmt_picker_span);\n",
+       "    this.format_dropdown = fmt_picker[0];\n",
+       "\n",
+       "    for (var ind in mpl.extensions) {\n",
+       "        var fmt = mpl.extensions[ind];\n",
+       "        var option = $(\n",
+       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+       "        fmt_picker.append(option)\n",
+       "    }\n",
+       "\n",
+       "    // Add hover states to the ui-buttons\n",
+       "    $( \".ui-button\" ).hover(\n",
+       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
+       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
+       "    );\n",
+       "\n",
+       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+       "    // which will in turn request a refresh of the image.\n",
+       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_message = function(type, properties) {\n",
+       "    properties['type'] = type;\n",
+       "    properties['figure_id'] = this.id;\n",
+       "    this.ws.send(JSON.stringify(properties));\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_draw_message = function() {\n",
+       "    if (!this.waiting) {\n",
+       "        this.waiting = true;\n",
+       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    var format_dropdown = fig.format_dropdown;\n",
+       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+       "    fig.ondownload(fig, format);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+       "    var size = msg['size'];\n",
+       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+       "        fig._resize_canvas(size[0], size[1]);\n",
+       "        fig.send_message(\"refresh\", {});\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+       "    var x0 = msg['x0'] / mpl.ratio;\n",
+       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+       "    var x1 = msg['x1'] / mpl.ratio;\n",
+       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+       "    x0 = Math.floor(x0) + 0.5;\n",
+       "    y0 = Math.floor(y0) + 0.5;\n",
+       "    x1 = Math.floor(x1) + 0.5;\n",
+       "    y1 = Math.floor(y1) + 0.5;\n",
+       "    var min_x = Math.min(x0, x1);\n",
+       "    var min_y = Math.min(y0, y1);\n",
+       "    var width = Math.abs(x1 - x0);\n",
+       "    var height = Math.abs(y1 - y0);\n",
+       "\n",
+       "    fig.rubberband_context.clearRect(\n",
+       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "\n",
+       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+       "    // Updates the figure title.\n",
+       "    fig.header.textContent = msg['label'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+       "    var cursor = msg['cursor'];\n",
+       "    switch(cursor)\n",
+       "    {\n",
+       "    case 0:\n",
+       "        cursor = 'pointer';\n",
+       "        break;\n",
+       "    case 1:\n",
+       "        cursor = 'default';\n",
+       "        break;\n",
+       "    case 2:\n",
+       "        cursor = 'crosshair';\n",
+       "        break;\n",
+       "    case 3:\n",
+       "        cursor = 'move';\n",
+       "        break;\n",
+       "    }\n",
+       "    fig.rubberband_canvas.style.cursor = cursor;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+       "    fig.message.textContent = msg['message'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+       "    // Request the server to send over a new figure.\n",
+       "    fig.send_draw_message();\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+       "    fig.image_mode = msg['mode'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Called whenever the canvas gets updated.\n",
+       "    this.send_message(\"ack\", {});\n",
+       "}\n",
+       "\n",
+       "// A function to construct a web socket function for onmessage handling.\n",
+       "// Called in the figure constructor.\n",
+       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+       "    return function socket_on_message(evt) {\n",
+       "        if (evt.data instanceof Blob) {\n",
+       "            /* FIXME: We get \"Resource interpreted as Image but\n",
+       "             * transferred with MIME type text/plain:\" errors on\n",
+       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
+       "             * to be part of the websocket stream */\n",
+       "            evt.data.type = \"image/png\";\n",
+       "\n",
+       "            /* Free the memory for the previous frames */\n",
+       "            if (fig.imageObj.src) {\n",
+       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
+       "                    fig.imageObj.src);\n",
+       "            }\n",
+       "\n",
+       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+       "                evt.data);\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+       "            fig.imageObj.src = evt.data;\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        var msg = JSON.parse(evt.data);\n",
+       "        var msg_type = msg['type'];\n",
+       "\n",
+       "        // Call the  \"handle_{type}\" callback, which takes\n",
+       "        // the figure and JSON message as its only arguments.\n",
+       "        try {\n",
+       "            var callback = fig[\"handle_\" + msg_type];\n",
+       "        } catch (e) {\n",
+       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        if (callback) {\n",
+       "            try {\n",
+       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+       "                callback(fig, msg);\n",
+       "            } catch (e) {\n",
+       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+       "            }\n",
+       "        }\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+       "mpl.findpos = function(e) {\n",
+       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+       "    var targ;\n",
+       "    if (!e)\n",
+       "        e = window.event;\n",
+       "    if (e.target)\n",
+       "        targ = e.target;\n",
+       "    else if (e.srcElement)\n",
+       "        targ = e.srcElement;\n",
+       "    if (targ.nodeType == 3) // defeat Safari bug\n",
+       "        targ = targ.parentNode;\n",
+       "\n",
+       "    // jQuery normalizes the pageX and pageY\n",
+       "    // pageX,Y are the mouse positions relative to the document\n",
+       "    // offset() returns the position of the element relative to the document\n",
+       "    var x = e.pageX - $(targ).offset().left;\n",
+       "    var y = e.pageY - $(targ).offset().top;\n",
+       "\n",
+       "    return {\"x\": x, \"y\": y};\n",
+       "};\n",
+       "\n",
+       "/*\n",
+       " * return a copy of an object with only non-object keys\n",
+       " * we need this to avoid circular references\n",
+       " * http://stackoverflow.com/a/24161582/3208463\n",
+       " */\n",
+       "function simpleKeys (original) {\n",
+       "  return Object.keys(original).reduce(function (obj, key) {\n",
+       "    if (typeof original[key] !== 'object')\n",
+       "        obj[key] = original[key]\n",
+       "    return obj;\n",
+       "  }, {});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+       "    var canvas_pos = mpl.findpos(event)\n",
+       "\n",
+       "    if (name === 'button_press')\n",
+       "    {\n",
+       "        this.canvas.focus();\n",
+       "        this.canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    var x = canvas_pos.x * mpl.ratio;\n",
+       "    var y = canvas_pos.y * mpl.ratio;\n",
+       "\n",
+       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
+       "                             step: event.step,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "\n",
+       "    /* This prevents the web browser from automatically changing to\n",
+       "     * the text insertion cursor when the button is pressed.  We want\n",
+       "     * to control all of the cursor setting manually through the\n",
+       "     * 'cursor' event from matplotlib */\n",
+       "    event.preventDefault();\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    // Handle any extra behaviour associated with a key event\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.key_event = function(event, name) {\n",
+       "\n",
+       "    // Prevent repeat events\n",
+       "    if (name == 'key_press')\n",
+       "    {\n",
+       "        if (event.which === this._key)\n",
+       "            return;\n",
+       "        else\n",
+       "            this._key = event.which;\n",
+       "    }\n",
+       "    if (name == 'key_release')\n",
+       "        this._key = null;\n",
+       "\n",
+       "    var value = '';\n",
+       "    if (event.ctrlKey && event.which != 17)\n",
+       "        value += \"ctrl+\";\n",
+       "    if (event.altKey && event.which != 18)\n",
+       "        value += \"alt+\";\n",
+       "    if (event.shiftKey && event.which != 16)\n",
+       "        value += \"shift+\";\n",
+       "\n",
+       "    value += 'k';\n",
+       "    value += event.which.toString();\n",
+       "\n",
+       "    this._key_event_extra(event, name);\n",
+       "\n",
+       "    this.send_message(name, {key: value,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+       "    if (name == 'download') {\n",
+       "        this.handle_save(this, null);\n",
+       "    } else {\n",
+       "        this.send_message(\"toolbar_button\", {name: name});\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+       "    this.message.textContent = tooltip;\n",
+       "};\n",
+       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+       "\n",
+       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+       "\n",
+       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
+       "    // object with the appropriate methods. Currently this is a non binary\n",
+       "    // socket, so there is still some room for performance tuning.\n",
+       "    var ws = {};\n",
+       "\n",
+       "    ws.close = function() {\n",
+       "        comm.close()\n",
+       "    };\n",
+       "    ws.send = function(m) {\n",
+       "        //console.log('sending', m);\n",
+       "        comm.send(m);\n",
+       "    };\n",
+       "    // Register the callback with on_msg.\n",
+       "    comm.on_msg(function(msg) {\n",
+       "        //console.log('receiving', msg['content']['data'], msg);\n",
+       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+       "        ws.onmessage(msg['content']['data'])\n",
+       "    });\n",
+       "    return ws;\n",
+       "}\n",
+       "\n",
+       "mpl.mpl_figure_comm = function(comm, msg) {\n",
+       "    // This is the function which gets called when the mpl process\n",
+       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+       "\n",
+       "    var id = msg.content.data.id;\n",
+       "    // Get hold of the div created by the display call when the Comm\n",
+       "    // socket was opened in Python.\n",
+       "    var element = $(\"#\" + id);\n",
+       "    var ws_proxy = comm_websocket_adapter(comm)\n",
+       "\n",
+       "    function ondownload(figure, format) {\n",
+       "        window.open(figure.imageObj.src);\n",
+       "    }\n",
+       "\n",
+       "    var fig = new mpl.figure(id, ws_proxy,\n",
+       "                           ondownload,\n",
+       "                           element.get(0));\n",
+       "\n",
+       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+       "    // web socket which is closed, not our websocket->open comm proxy.\n",
+       "    ws_proxy.onopen();\n",
+       "\n",
+       "    fig.parent_element = element.get(0);\n",
+       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+       "    if (!fig.cell_info) {\n",
+       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    var output_index = fig.cell_info[2]\n",
+       "    var cell = fig.cell_info[0];\n",
+       "\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+       "    var width = fig.canvas.width/mpl.ratio\n",
+       "    fig.root.unbind('remove')\n",
+       "\n",
+       "    // Update the output cell to use the data from the current canvas.\n",
+       "    fig.push_to_output();\n",
+       "    var dataURL = fig.canvas.toDataURL();\n",
+       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+       "    // the notebook keyboard shortcuts fail.\n",
+       "    IPython.keyboard_manager.enable()\n",
+       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+       "    fig.close_ws(fig, msg);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+       "    fig.send_message('closing', msg);\n",
+       "    // fig.ws.close()\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+       "    // Turn the data on the canvas into data in the output cell.\n",
+       "    var width = this.canvas.width/mpl.ratio\n",
+       "    var dataURL = this.canvas.toDataURL();\n",
+       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Tell IPython that the notebook contents must change.\n",
+       "    IPython.notebook.set_dirty(true);\n",
+       "    this.send_message(\"ack\", {});\n",
+       "    var fig = this;\n",
+       "    // Wait a second, then push the new image to the DOM so\n",
+       "    // that it is saved nicely (might be nice to debounce this).\n",
+       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items){\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) { continue; };\n",
+       "\n",
+       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        event.shiftKey = false;\n",
+       "        // Send a \"J\" for go to next cell\n",
+       "        event.which = 74;\n",
+       "        event.keyCode = 74;\n",
+       "        manager.command_mode();\n",
+       "        manager.handle_keydown(event);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i<ncells; i++) {\n",
+       "        var cell = cells[i];\n",
+       "        if (cell.cell_type === 'code'){\n",
+       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+       "                var data = cell.output_area.outputs[j];\n",
+       "                if (data.data) {\n",
+       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<img 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\" width=\"432\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Traceback (most recent call last):\n",
+      "  File \"/home/bushuhui/.virtualenv/dl/lib/python3.5/site-packages/matplotlib/cbook/__init__.py\", line 215, in process\n",
+      "    func(*args, **kwargs)\n",
+      "  File \"/home/bushuhui/.virtualenv/dl/lib/python3.5/site-packages/matplotlib/animation.py\", line 1462, in _stop\n",
+      "    self.event_source.remove_callback(self._loop_delay)\n",
+      "AttributeError: 'NoneType' object has no attribute 'remove_callback'\n"
+     ]
+    }
+   ],
+   "source": [
+    "%matplotlib nbagg\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.animation as animation\n",
+    "\n",
+    "n_epoch = 3000          # epoch size\n",
+    "a, b = 1, 1             # initial parameters\n",
+    "epsilon = 0.001         # learning rate\n",
+    "\n",
+    "fig = plt.figure()\n",
+    "imgs = []\n",
+    "\n",
+    "for i in range(n_epoch):\n",
+    "    for j in range(N):\n",
+    "        a = a + epsilon*2*(Y[j] - a*X[j] - b)*X[j]\n",
+    "        b = b + epsilon*2*(Y[j] - a*X[j] - b)\n",
+    "\n",
+    "    L = 0\n",
+    "    for j in range(N):\n",
+    "        L = L + (Y[j]-a*X[j]-b)**2\n",
+    "    #print(\"epoch %4d: loss = %f, a = %f, b = %f\" % (i, L, a, b))\n",
+    "    \n",
+    "    if i % 50 == 0:\n",
+    "        x_min = np.min(X)\n",
+    "        x_max = np.max(X)\n",
+    "        y_min = a * x_min + b\n",
+    "        y_max = a * x_max + b\n",
+    "\n",
+    "        img = plt.scatter(X, Y, label='original data')\n",
+    "        img = plt.plot([x_min, x_max], [y_min, y_max], 'r', label='model')\n",
+    "        imgs.append(img)\n",
+    "        \n",
+    "ani = animation.ArtistAnimation(fig, imgs)\n",
+    "plt.show()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 7. 如何使用sklearn拟合多项式函数？"
+    "## 4. 如何使用批次更新的方法？\n",
+    "\n",
+    "如果有一些数据包含比较大的错误（异常数据），因此每次更新仅仅使用一个数据会导致不精确，同时每次仅仅使用一个数据来计算更新也导致计算效率比较低。\n",
+    "\n",
+    "\n",
+    "* [梯度下降方法的几种形式](https://blog.csdn.net/u010402786/article/details/51188876)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. 如何拟合多项式函数？\n",
+    "\n",
+    "需要设计一个弹道导弹防御系统，通过观测导弹的飞行路径，预测未来导弹的飞行轨迹，从而完成摧毁的任务。按照物理学，可以得知模型为:\n",
+    "$$\n",
+    "y = at^2 + bt + c\n",
+    "$$\n",
+    "我们需要求解三个模型参数$a, b, c$。\n",
+    "\n",
+    "损失函数的定义为：\n",
+    "$$\n",
+    "L = \\sum_{i=1}^N (y_i - at^2 - bt - c)^2\n",
+    "$$\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "image/png": 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\n",
       "text/plain": [
-       "array([800.,  90., -20.])"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "# Fitting polynomial functions\n",
-    "\n",
-    "from sklearn.preprocessing import PolynomialFeatures\n",
-    "from sklearn.linear_model import LinearRegression\n",
-    "from sklearn.pipeline import Pipeline\n",
+    "%matplotlib inline\n",
     "\n",
     "t = np.array([2, 4, 6, 8])\n",
+    "#t = np.linspace(0, 10)\n",
     "\n",
     "pa = -20\n",
     "pb = 90\n",
@@ -4336,50 +5005,50 @@
     "\n",
     "y = pa*t**2 + pb*t + pc\n",
     "\n",
-    "model = Pipeline([('poly', PolynomialFeatures(degree=2)),\n",
-    "                  ('linear', LinearRegression(fit_intercept=False))])\n",
-    "model = model.fit(t[:, np.newaxis], y)\n",
-    "model.named_steps['linear'].coef_\n"
+    "\n",
+    "plt.scatter(t, y)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5.1 如何得到更新项?\n",
+    "\n",
+    "$$\n",
+    "L = \\sum_{i=1}^N (y_i - at^2 - bt - c)^2\n",
+    "$$\n",
+    "\n",
+    "\\begin{eqnarray}\n",
+    "\\frac{\\partial L}{\\partial a} & = & - 2\\sum_{i=1}^N (y_i - at^2 - bt -c) t^2 \\\\\n",
+    "\\frac{\\partial L}{\\partial b} & = & - 2\\sum_{i=1}^N (y_i - at^2 - bt -c) t \\\\\n",
+    "\\frac{\\partial L}{\\partial c} & = & - 2\\sum_{i=1}^N (y_i - at^2 - bt -c)\n",
+    "\\end{eqnarray}"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 8. 如何通过模型来估计缺失的值？\n"
+    "## 6. 如何使用sklearn求解线性问题？\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "lines_to_next_cell": 0
-   },
+   "execution_count": 12,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Y_est  =  [148.6844971  167.17376752 174.36403934 195.93485483 109.65159289\n",
-      " 120.95059148 245.23957594 204.15230835 269.89193649  75.75459712\n",
-      " 241.13084918 104.51568444 141.49422527 126.08649992 208.26103511\n",
-      " 233.94057735 151.76604217 158.956314   161.01067738 228.8046689\n",
-      " 220.58721538 129.16804499 100.40695768 117.86904641 168.2009492\n",
-      " 226.75030552 114.78750134 163.06504076 113.76031965 119.92340979\n",
-      " 157.92913231 237.02212242 120.95059148  98.3525943  123.00495485\n",
-      " 205.17949004  95.27104923 153.82040555 130.19522668  81.91768726\n",
-      " 171.28249427 137.38549851 137.38549851 189.77176469  82.94486895]\n",
-      "Y_test =  [198. 242. 232. 175.  93. 168. 275. 293. 281.  72. 140. 189. 181. 209.\n",
-      " 136. 261. 113. 131. 174. 257.  55.  84.  42. 146. 212. 233.  91. 111.\n",
-      " 152. 120.  67. 310.  94. 183.  66. 173.  72.  49.  64.  48. 178. 104.\n",
-      " 132. 220.  57.]\n",
-      "err = 8.437628 (56.601327), score = 0.422889\n",
-      "a = 953.024850, b = 152.544562\n"
+      "a = 949.435260, b = 152.133484\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -4391,33 +5060,18 @@
     }
    ],
    "source": [
+    "from sklearn import linear_model\n",
+    "\n",
     "# load data\n",
     "d = datasets.load_diabetes()\n",
     "\n",
-    "N = d.target.shape[0]\n",
-    "N_train = int(N*0.9)\n",
-    "N_test = N - N_train\n",
-    "\n",
-    "X = d.data[:N_train, np.newaxis, 2]\n",
-    "Y = d.target[:N_train]\n",
-    "\n",
-    "X_test = d.data[N_train:, np.newaxis, 2]\n",
-    "Y_test = d.target[N_train:]\n",
+    "X = d.data[:, np.newaxis, 2]\n",
+    "Y = d.target\n",
     "\n",
     "# create regression model\n",
     "regr = linear_model.LinearRegression()\n",
     "regr.fit(X, Y)\n",
     "\n",
-    "Y_est = regr.predict(X_test)\n",
-    "print(\"Y_est  = \", Y_est)\n",
-    "print(\"Y_test = \", Y_test)\n",
-    "err = (Y_est - Y_test)**2\n",
-    "err2 = sklearn.metrics.mean_squared_error(Y_test, Y_est)\n",
-    "score = regr.score(X_test, Y_test)\n",
-    "print(\"err = %f (%f), score = %f\" % (np.sqrt(np.sum(err))/N_test, np.sqrt(err2), score))\n",
-    "\n",
-    "\n",
-    "# plot data\n",
     "a, b = regr.coef_, regr.intercept_\n",
     "print(\"a = %f, b = %f\" % (a, b))\n",
     "\n",
@@ -4426,13 +5080,54 @@
     "y_min = a * x_min + b\n",
     "y_max = a * x_max + b\n",
     "\n",
-    "\n",
-    "plt.scatter(X, Y, label='train data')\n",
-    "plt.scatter(X_test, Y_test, label='test data')\n",
-    "plt.plot([x_min, x_max], [y_min, y_max], 'r', label='model')\n",
-    "plt.legend()\n",
+    "plt.scatter(X, Y)\n",
+    "plt.plot([x_min, x_max], [y_min, y_max], 'r')\n",
     "plt.show()"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 7. 如何使用sklearn拟合多项式函数？"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([800.,  90., -20.])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Fitting polynomial functions\n",
+    "\n",
+    "from sklearn.preprocessing import PolynomialFeatures\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "\n",
+    "t = np.array([2, 4, 6, 8])\n",
+    "\n",
+    "pa = -20\n",
+    "pb = 90\n",
+    "pc = 800\n",
+    "\n",
+    "y = pa*t**2 + pb*t + pc\n",
+    "\n",
+    "model = Pipeline([('poly', PolynomialFeatures(degree=2)),\n",
+    "                  ('linear', LinearRegression(fit_intercept=False))])\n",
+    "model = model.fit(t[:, np.newaxis], y)\n",
+    "model.named_steps['linear'].coef_\n"
+   ]
   }
  ],
  "metadata": {
diff --git a/4_logistic_regression/Logistic_regression.ipynb b/4_logistic_regression/Logistic_regression.ipynb
index 80b841d..954707d 100644
--- a/4_logistic_regression/Logistic_regression.ipynb
+++ b/4_logistic_regression/Logistic_regression.ipynb
@@ -180,7 +180,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -196,7 +196,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -222,7 +222,7 @@
        "Text(0.5, 1.0, 'Original Data')"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -252,7 +252,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -436,7 +436,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -483,16 +483,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.colorbar.Colorbar at 0x7f89bae4c550>"
+       "<matplotlib.colorbar.Colorbar at 0x7f9a151d86d8>"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -529,16 +529,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.colorbar.Colorbar at 0x7f89b82a8f98>"
+       "<matplotlib.colorbar.Colorbar at 0x7f9a140d50b8>"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -573,7 +573,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -608,6 +608,8 @@
     "x_test  = digits[N_train:, :]\n",
     "y_test  = dig_label[N_train:]\n",
     "\n",
+    "# FIXME: need to use Isomap to transform data\n",
+    "\n",
     "# do logistic regression\n",
     "lr=LogisticRegression()\n",
     "lr.fit(x_train,y_train)\n",
@@ -628,12 +630,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 288x288 with 2 Axes>"
       ]
diff --git a/References.md b/References.md
index ae2551d..fa58e5a 100644
--- a/References.md
+++ b/References.md
@@ -3,6 +3,16 @@
 
 
 
+## Notebook, Book, Tutorial
+* [Deep Learning with PyTorch](https://pytorch.org/deep-learning-with-pytorch-thank-you)
+* [Machine Learning Yearning 中文版 - 《机器学习训练秘籍》](https://github.com/deeplearning-ai/machine-learning-yearning-cn) ([在线阅读](https://deeplearning-ai.github.io/machine-learning-yearning-cn/))
+* [ipython-notebooks: A collection of IPython notebooks covering various topics](https://github.com/jdwittenauer/ipython-notebooks)
+* [Learn Data Science](http://learnds.com/)
+* [AM207 2016](https://github.com/AM207/2016/tree/master)
+* [Python机器学习](https://ljalphabeta.gitbooks.io/python-/content/)
+* [scientific-python-lectures](http://nbviewer.jupyter.org/github/jrjohansson/scientific-python-lectures/tree/master/)
+
+
 ## Python & IPython
 
 * [Python Numpy Tutorial - 简明Python, Numpy, Matplotlib教程](http://cs231n.github.io/python-numpy-tutorial/)
@@ -29,16 +39,6 @@
 
 
 
-## Notebook, Book, Tutorial
-
-* [Machine Learning Yearning 中文版 - 《机器学习训练秘籍》](https://github.com/deeplearning-ai/machine-learning-yearning-cn) ([在线阅读](https://deeplearning-ai.github.io/machine-learning-yearning-cn/))
-* [ipython-notebooks: A collection of IPython notebooks covering various topics](https://github.com/jdwittenauer/ipython-notebooks)
-* [Learn Data Science](http://learnds.com/)
-* [AM207 2016](https://github.com/AM207/2016/tree/master)
-* [Python机器学习](https://ljalphabeta.gitbooks.io/python-/content/)
-* [scientific-python-lectures](http://nbviewer.jupyter.org/github/jrjohansson/scientific-python-lectures/tree/master/)
-
-
 
 ## Awesome series & Collections
 
@@ -48,7 +48,6 @@
 * [Awesome Deep Vision](https://github.com/kjw0612/awesome-deep-vision)
 * [Awesome 3D Reconstruction](https://github.com/openMVG/awesome_3DReconstruction_list)
 * [awesome-algorithm](https://github.com/apachecn/awesome-algorithm)
-
 * [Papers with code. Sorted by stars. Updated weekly.](https://github.com/zziz/pwc)
 
 
diff --git a/requirements.txt b/requirements.txt
index 236d726..aa4300a 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -8,11 +8,23 @@
 # pip
 #   sudo apt-get install python-pip python3-pip
 #   pip install pip -U
+#   pip config set global.index-url 'https://mirrors.aliyun.com/pypi/simple/'
 #   pip config set global.index-url 'https://mirrors.ustc.edu.cn/pypi/web/simple'
 #
 
 #
-# Install virtualenv
+# Install virtualenv by apt-get
+#   sudo apt-get install virtualenvwrapper
+#
+# Add following lines to `~/.bashrc`
+#   # virtualenv
+#   export VIRTUALENVWRAPPER_PYTHON=/usr/bin/python
+#   export WORKON_HOME=/home/bushuhui/virtualenv
+#   source /usr/share/virtualenvwrapper/virtualenvwrapper.sh
+#
+
+
+# Install virtualenv by pip
 #   pip install setuptools
 #   pip install virtualenv
 #   pip install virtualenvwrapper
@@ -24,6 +36,8 @@
 #   export WORKON_HOME=/home/bushuhui/virtualenv
 #   source /usr/local/bin/virtualenvwrapper.sh　
 #
+
+#
 # Usage:
 #   # create virtual env
 #   mkvirtualenv --python=/usr/local/python3.5.3/bin/python venv
@@ -35,7 +49,8 @@
 #
 # Install this list packages:
 #   pip install -r requirements.txt
-#
+# Save package list
+#   pip freeze >> requirements.txt
 
 
 attrs==19.1.0
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index 0000000..c7db959
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