From 6d564cdb6b12d5f545b504ce1947de01b3524497 Mon Sep 17 00:00:00 2001
From: Geoff <3062872548@qq.com>
Date: Thu, 6 Aug 2020 18:12:18 +0800
Subject: [PATCH] add english version to logestic regression

---
 3_kmeans/1-k-means.ipynb                           |    2 +-
 4_logistic_regression/1-Least_squares_EN.ipynb     | 1024 +++-----------------
 4_logistic_regression/2-Logistic_regression.ipynb  |    9 +-
 .../2-Logistic_regression_EN.ipynb                 |  135 +--
 4 files changed, 215 insertions(+), 955 deletions(-)

diff --git a/3_kmeans/1-k-means.ipynb b/3_kmeans/1-k-means.ipynb
index 6363188..6de3fda 100644
--- a/3_kmeans/1-k-means.ipynb
+++ b/3_kmeans/1-k-means.ipynb
@@ -955,7 +955,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.6.8"
   }
  },
  "nbformat": 4,
diff --git a/4_logistic_regression/1-Least_squares_EN.ipynb b/4_logistic_regression/1-Least_squares_EN.ipynb
index 02b8bd3..09ca9a7 100644
--- a/4_logistic_regression/1-Least_squares_EN.ipynb
+++ b/4_logistic_regression/1-Least_squares_EN.ipynb
@@ -27,7 +27,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
@@ -115,7 +115,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -127,7 +127,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -220,12 +220,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 2.2 示例代码"
+    "### 2.2 The sample code"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -454,7 +454,13 @@
       "epoch  219: loss = 2093216.552860, a = 338.396285, b = 152.909413\n",
       "epoch  220: loss = 2091726.761728, a = 339.616130, b = 152.908634\n",
       "epoch  221: loss = 2090242.917340, a = 340.833535, b = 152.907857\n",
-      "epoch  222: loss = 2088764.995948, a = 342.048506, b = 152.907081\n",
+      "epoch  222: loss = 2088764.995948, a = 342.048506, b = 152.907081\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "epoch  223: loss = 2087292.973899, a = 343.261047, b = 152.906306\n",
       "epoch  224: loss = 2085826.827634, a = 344.471164, b = 152.905533\n",
       "epoch  225: loss = 2084366.533687, a = 345.678860, b = 152.904762\n",
@@ -650,7 +656,13 @@
       "epoch  415: loss = 1890400.033577, a = 536.359274, b = 152.782975\n",
       "epoch  416: loss = 1889719.674478, a = 537.183258, b = 152.782449\n",
       "epoch  417: loss = 1889042.029945, a = 538.005595, b = 152.781924\n",
-      "epoch  418: loss = 1888367.089140, a = 538.826287, b = 152.781399\n",
+      "epoch  418: loss = 1888367.089140, a = 538.826287, b = 152.781399\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "epoch  419: loss = 1887694.841269, a = 539.645337, b = 152.780876\n",
       "epoch  420: loss = 1887025.275578, a = 540.462750, b = 152.780354\n",
       "epoch  421: loss = 1886358.381359, a = 541.278529, b = 152.779833\n",
@@ -832,7 +844,13 @@
       "epoch  597: loss = 1802246.577240, a = 662.169538, b = 152.702620\n",
       "epoch  598: loss = 1801917.856269, a = 662.741943, b = 152.702255\n",
       "epoch  599: loss = 1801590.446081, a = 663.313203, b = 152.701890\n",
-      "epoch  600: loss = 1801264.341442, a = 663.883321, b = 152.701526\n",
+      "epoch  600: loss = 1801264.341442, a = 663.883321, b = 152.701526\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "epoch  601: loss = 1800939.537143, a = 664.452299, b = 152.701163\n",
       "epoch  602: loss = 1800616.027992, a = 665.020140, b = 152.700800\n",
       "epoch  603: loss = 1800293.808821, a = 665.586845, b = 152.700438\n",
@@ -957,7 +975,13 @@
       "epoch  722: loss = 1769836.957698, a = 725.534359, b = 152.662150\n",
       "epoch  723: loss = 1769637.439670, a = 725.980056, b = 152.661865\n",
       "epoch  724: loss = 1769438.716783, a = 726.424861, b = 152.661581\n",
-      "epoch  725: loss = 1769240.785864, a = 726.868776, b = 152.661297\n",
+      "epoch  725: loss = 1769240.785864, a = 726.868776, b = 152.661297\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "epoch  726: loss = 1769043.643754, a = 727.311805, b = 152.661014\n",
       "epoch  727: loss = 1768847.287304, a = 727.753947, b = 152.660732\n",
       "epoch  728: loss = 1768651.713380, a = 728.195205, b = 152.660450\n",
@@ -1161,13 +1185,7 @@
       "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"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "epoch  929: loss = 1741674.355969, a = 801.143152, b = 152.613859\n",
       "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",
@@ -1187,7 +1205,13 @@
       "epoch  946: loss = 1740235.951555, a = 806.070421, b = 152.610712\n",
       "epoch  947: loss = 1740154.338013, a = 806.355072, b = 152.610530\n",
       "epoch  948: loss = 1740073.049267, a = 806.639154, b = 152.610348\n",
-      "epoch  949: loss = 1739992.084021, a = 806.922668, b = 152.610167\n",
+      "epoch  949: loss = 1739992.084021, a = 806.922668, b = 152.610167\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "epoch  950: loss = 1739911.440986, a = 807.205615, b = 152.609986\n",
       "epoch  951: loss = 1739831.118877, a = 807.487996, b = 152.609806\n",
       "epoch  952: loss = 1739751.116414, a = 807.769812, b = 152.609626\n",
@@ -1411,7 +1435,13 @@
       "epoch 1170: loss = 1728120.075320, a = 857.506033, b = 152.577860\n",
       "epoch 1171: loss = 1728086.637887, a = 857.687829, b = 152.577744\n",
       "epoch 1172: loss = 1728053.333232, a = 857.869263, b = 152.577628\n",
-      "epoch 1173: loss = 1728020.160826, a = 858.050333, b = 152.577512\n",
+      "epoch 1173: loss = 1728020.160826, a = 858.050333, b = 152.577512\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "epoch 1174: loss = 1727987.120142, a = 858.231041, b = 152.577397\n",
       "epoch 1175: loss = 1727954.210655, a = 858.411388, b = 152.577282\n",
       "epoch 1176: loss = 1727921.431843, a = 858.591375, b = 152.577167\n",
@@ -1577,7 +1607,13 @@
       "epoch 1336: loss = 1724048.770097, a = 883.208323, b = 152.561444\n",
       "epoch 1337: loss = 1724031.483516, a = 883.338724, b = 152.561361\n",
       "epoch 1338: loss = 1724014.265415, a = 883.468864, b = 152.561277\n",
-      "epoch 1339: loss = 1723997.115522, a = 883.598743, b = 152.561194\n",
+      "epoch 1339: loss = 1723997.115522, a = 883.598743, b = 152.561194\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "epoch 1340: loss = 1723980.033566, a = 883.728363, b = 152.561112\n",
       "epoch 1341: loss = 1723963.019276, a = 883.857724, b = 152.561029\n",
       "epoch 1342: loss = 1723946.072383, a = 883.986826, b = 152.560947\n",
@@ -1780,7 +1816,13 @@
       "epoch 1539: loss = 1721633.558576, a = 904.983357, b = 152.547536\n",
       "epoch 1540: loss = 1721625.823823, a = 905.070215, b = 152.547481\n",
       "epoch 1541: loss = 1721618.119582, a = 905.156899, b = 152.547425\n",
-      "epoch 1542: loss = 1721610.445734, a = 905.243410, b = 152.547370\n",
+      "epoch 1542: loss = 1721610.445734, a = 905.243410, b = 152.547370\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "epoch 1543: loss = 1721602.802156, a = 905.329748, b = 152.547315\n",
       "epoch 1544: loss = 1721595.188730, a = 905.415913, b = 152.547260\n",
       "epoch 1545: loss = 1721587.605335, a = 905.501906, b = 152.547205\n",
@@ -1878,13 +1920,7 @@
       "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"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "epoch 1640: loss = 1720988.079235, a = 912.934032, b = 152.542458\n",
       "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",
@@ -1931,7 +1967,13 @@
       "epoch 1684: loss = 1720777.942117, a = 915.925684, b = 152.540547\n",
       "epoch 1685: loss = 1720773.576714, a = 915.990660, b = 152.540506\n",
       "epoch 1686: loss = 1720769.228461, a = 916.055507, b = 152.540464\n",
-      "epoch 1687: loss = 1720764.897290, a = 916.120224, b = 152.540423\n",
+      "epoch 1687: loss = 1720764.897290, a = 916.120224, b = 152.540423\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "epoch 1688: loss = 1720760.583133, a = 916.184812, b = 152.540382\n",
       "epoch 1689: loss = 1720756.285922, a = 916.249271, b = 152.540341\n",
       "epoch 1690: loss = 1720752.005590, a = 916.313601, b = 152.540300\n",
@@ -2142,7 +2184,13 @@
       "epoch 1895: loss = 1720150.484474, a = 927.119700, b = 152.533398\n",
       "epoch 1896: loss = 1720148.575721, a = 927.162292, b = 152.533371\n",
       "epoch 1897: loss = 1720146.674402, a = 927.204800, b = 152.533343\n",
-      "epoch 1898: loss = 1720144.780489, a = 927.247222, b = 152.533316\n",
+      "epoch 1898: loss = 1720144.780489, a = 927.247222, b = 152.533316\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "epoch 1899: loss = 1720142.893953, a = 927.289560, b = 152.533289\n",
       "epoch 1900: loss = 1720141.014763, a = 927.331812, b = 152.533262\n",
       "epoch 1901: loss = 1720139.142892, a = 927.373981, b = 152.533235\n",
@@ -2172,13 +2220,7 @@
       "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"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "epoch 1928: loss = 1720091.265906, a = 928.481197, b = 152.532528\n",
       "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",
@@ -2312,7 +2354,13 @@
       "epoch 2059: loss = 1719918.651824, a = 933.080108, b = 152.529591\n",
       "epoch 2060: loss = 1719917.642561, a = 933.110782, b = 152.529571\n",
       "epoch 2061: loss = 1719916.637192, a = 933.141394, b = 152.529552\n",
-      "epoch 2062: loss = 1719915.635703, a = 933.171945, b = 152.529532\n",
+      "epoch 2062: loss = 1719915.635703, a = 933.171945, b = 152.529532\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "epoch 2063: loss = 1719914.638078, a = 933.202435, b = 152.529513\n",
       "epoch 2064: loss = 1719913.644301, a = 933.232864, b = 152.529493\n",
       "epoch 2065: loss = 1719912.654359, a = 933.263232, b = 152.529474\n",
@@ -2546,7 +2594,13 @@
       "epoch 2293: loss = 1719762.633396, a = 938.816897, b = 152.525927\n",
       "epoch 2294: loss = 1719762.221723, a = 938.836099, b = 152.525915\n",
       "epoch 2295: loss = 1719761.811609, a = 938.855262, b = 152.525902\n",
-      "epoch 2296: loss = 1719761.403047, a = 938.874387, b = 152.525890\n",
+      "epoch 2296: loss = 1719761.403047, a = 938.874387, b = 152.525890\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "epoch 2297: loss = 1719760.996032, a = 938.893474, b = 152.525878\n",
       "epoch 2298: loss = 1719760.590557, a = 938.912523, b = 152.525866\n",
       "epoch 2299: loss = 1719760.186616, a = 938.931534, b = 152.525854\n",
@@ -2678,13 +2732,7 @@
       "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"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "epoch 2428: loss = 1719719.012273, a = 941.090716, b = 152.524475\n",
       "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",
@@ -2747,7 +2795,13 @@
       "epoch 2488: loss = 1719705.675518, a = 941.920100, b = 152.523945\n",
       "epoch 2489: loss = 1719705.477479, a = 941.933097, b = 152.523937\n",
       "epoch 2490: loss = 1719705.280173, a = 941.946068, b = 152.523928\n",
-      "epoch 2491: loss = 1719705.083597, a = 941.959012, b = 152.523920\n",
+      "epoch 2491: loss = 1719705.083597, a = 941.959012, b = 152.523920\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "epoch 2492: loss = 1719704.887748, a = 941.971931, b = 152.523912\n",
       "epoch 2493: loss = 1719704.692623, a = 941.984824, b = 152.523903\n",
       "epoch 2494: loss = 1719704.498220, a = 941.997691, b = 152.523895\n",
@@ -2979,7 +3033,13 @@
       "epoch 2720: loss = 1719674.684551, a = 944.334488, b = 152.522403\n",
       "epoch 2721: loss = 1719674.599492, a = 944.342656, b = 152.522398\n",
       "epoch 2722: loss = 1719674.514736, a = 944.350809, b = 152.522392\n",
-      "epoch 2723: loss = 1719674.430281, a = 944.358945, b = 152.522387\n",
+      "epoch 2723: loss = 1719674.430281, a = 944.358945, b = 152.522387\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "epoch 2724: loss = 1719674.346128, a = 944.367064, b = 152.522382\n",
       "epoch 2725: loss = 1719674.262274, a = 944.375168, b = 152.522377\n",
       "epoch 2726: loss = 1719674.178719, a = 944.383255, b = 152.522372\n",
@@ -3126,13 +3186,7 @@
       "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"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "epoch 2870: loss = 1719664.766007, a = 945.394000, b = 152.521726\n",
       "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",
@@ -3249,7 +3303,13 @@
       "epoch 2984: loss = 1719660.022627, a = 946.011279, b = 152.521332\n",
       "epoch 2985: loss = 1719659.988619, a = 946.016094, b = 152.521329\n",
       "epoch 2986: loss = 1719659.954725, a = 946.020900, b = 152.521326\n",
-      "epoch 2987: loss = 1719659.920945, a = 946.025697, b = 152.521323\n",
+      "epoch 2987: loss = 1719659.920945, a = 946.025697, b = 152.521323\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "epoch 2988: loss = 1719659.887278, a = 946.030483, b = 152.521320\n",
       "epoch 2989: loss = 1719659.853724, a = 946.035261, b = 152.521316\n",
       "epoch 2990: loss = 1719659.820283, a = 946.040028, b = 152.521313\n",
@@ -3266,7 +3326,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3307,12 +3367,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 3. 如何可视化迭代过程"
+    "## 3. How to achieve visible iteration process?"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -3328,7 +3388,7 @@
        "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
        "        return MozWebSocket;\n",
        "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\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",
@@ -3545,7 +3605,7 @@
        "mpl.figure.prototype._init_toolbar = function() {\n",
        "    var fig = this;\n",
        "\n",
-       "    var nav_element = $('<div/>')\n",
+       "    var nav_element = $('<div/>');\n",
        "    nav_element.attr('style', 'width: 100%');\n",
        "    this.root.append(nav_element);\n",
        "\n",
@@ -3602,7 +3662,7 @@
        "        var fmt = mpl.extensions[ind];\n",
        "        var option = $(\n",
        "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
+       "        fmt_picker.append(option);\n",
        "    }\n",
        "\n",
        "    // Add hover states to the ui-buttons\n",
@@ -3666,7 +3726,7 @@
        "    var height = Math.abs(y1 - y0);\n",
        "\n",
        "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
        "\n",
        "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
        "}\n",
@@ -3971,7 +4031,7 @@
        "mpl.figure.prototype._init_toolbar = function() {\n",
        "    var fig = this;\n",
        "\n",
-       "    var nav_element = $('<div/>')\n",
+       "    var nav_element = $('<div/>');\n",
        "    nav_element.attr('style', 'width: 100%');\n",
        "    this.root.append(nav_element);\n",
        "\n",
@@ -4044,12 +4104,9 @@
        "    // 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",
+       "        // 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",
@@ -4098,7 +4155,7 @@
     {
      "data": {
       "text/html": [
-       "<img 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\" width=\"431.43333333333334\">"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -4106,810 +4163,6 @@
      },
      "metadata": {},
      "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('<div/>');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            if (mpl.ratio != 1) {\n",
-       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
-       "            }\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        fig.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
-       "        'ui-helper-clearfix\"/>');\n",
-       "    var titletext = $(\n",
-       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
-       "        'text-align: center; padding: 3px;\"/>');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('<div/>');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('<canvas/>');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var backingStore = this.context.backingStorePixelRatio ||\n",
-       "\tthis.context.webkitBackingStorePixelRatio ||\n",
-       "\tthis.context.mozBackingStorePixelRatio ||\n",
-       "\tthis.context.msBackingStorePixelRatio ||\n",
-       "\tthis.context.oBackingStorePixelRatio ||\n",
-       "\tthis.context.backingStorePixelRatio || 1;\n",
-       "\n",
-       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
-       "\n",
-       "    var rubberband = $('<canvas/>');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width * mpl.ratio);\n",
-       "        canvas.attr('height', height * mpl.ratio);\n",
-       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function(type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function() {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'] / mpl.ratio;\n",
-       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
-       "    var x1 = msg['x1'] / mpl.ratio;\n",
-       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch(cursor)\n",
-       "    {\n",
-       "    case 0:\n",
-       "        cursor = 'pointer';\n",
-       "        break;\n",
-       "    case 1:\n",
-       "        cursor = 'default';\n",
-       "        break;\n",
-       "    case 2:\n",
-       "        cursor = 'crosshair';\n",
-       "        break;\n",
-       "    case 3:\n",
-       "        cursor = 'move';\n",
-       "        break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message(\"ack\", {});\n",
-       "}\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            /* FIXME: We get \"Resource interpreted as Image but\n",
-       "             * transferred with MIME type text/plain:\" errors on\n",
-       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "             * to be part of the websocket stream */\n",
-       "            evt.data.type = \"image/png\";\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src);\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig[\"handle_\" + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function(e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e)\n",
-       "        e = window.event;\n",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\n",
-       "\n",
-       "    return {\"x\": x, \"y\": y};\n",
-       "};\n",
-       "\n",
-       "/*\n",
-       " * return a copy of an object with only non-object keys\n",
-       " * we need this to avoid circular references\n",
-       " * http://stackoverflow.com/a/24161582/3208463\n",
-       " */\n",
-       "function simpleKeys (original) {\n",
-       "  return Object.keys(original).reduce(function (obj, key) {\n",
-       "    if (typeof original[key] !== 'object')\n",
-       "        obj[key] = original[key]\n",
-       "    return obj;\n",
-       "  }, {});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event)\n",
-       "\n",
-       "    if (name === 'button_press')\n",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x * mpl.ratio;\n",
-       "    var y = canvas_pos.y * mpl.ratio;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function(event, name) {\n",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
-       "    if (name == 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message(\"toolbar_button\", {name: name});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.close = function() {\n",
-       "        comm.close()\n",
-       "    };\n",
-       "    ws.send = function(m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function(msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['data'])\n",
-       "    });\n",
-       "    return ws;\n",
-       "}\n",
-       "\n",
-       "mpl.mpl_figure_comm = function(comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    var width = fig.canvas.width/mpl.ratio\n",
-       "    fig.root.unbind('remove')\n",
-       "\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable()\n",
-       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
-       "    fig.close_ws(fig, msg);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
-       "    fig.send_message('closing', msg);\n",
-       "    // fig.ws.close()\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var width = this.canvas.width/mpl.ratio\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message(\"ack\", {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items){\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        event.shiftKey = false;\n",
-       "        // Send a \"J\" for go to next cell\n",
-       "        event.which = 74;\n",
-       "        event.keyCode = 74;\n",
-       "        manager.command_mode();\n",
-       "        manager.handle_keydown(event);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i<ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code'){\n",
-       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
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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": [
@@ -4953,27 +4206,30 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 4. 如何使用批次更新的方法？\n",
+    "## 4. How to use batch update?\n",
+    "\n",
+    "If some data have relatively large error(abnormal data), which mean a data for each update will leads to inaccurate. At the same time, using only one data at a time to calculate the update also leads to low computational efficiency.\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)"
+    "* [A few form for Gradient descent method](https://blog.csdn.net/u010402786/article/details/51188876)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 5. 如何拟合多项式函数？\n",
+    "## 5. How to fit multiple polynomial function？\n",
+    "\n",
+    "Assume that we need to design a ballistic missile defense system, by observing the flight path of the missile, the flight path of the missile in the future can be predicted so as to complete the mission of destruction. According to physics, it can be known that the model is:\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",
+    "There are three model parameters $a, b, c$ that we need to solve.\n",
+    "\n",
+    "The definitation of loss function is:\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"
@@ -5018,7 +4274,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 5.1 如何得到更新项?\n",
+    "### 5.1 How to get update term?\n",
     "\n",
     "$$\n",
     "L = \\sum_{i=1}^N (y_i - at^2 - bt - c)^2\n",
@@ -5035,7 +4291,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 6. 如何使用sklearn求解线性问题？\n"
+    "## 6. How to use sklearn to solve linear problem？\n"
    ]
   },
   {
@@ -5093,7 +4349,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 7. 如何使用sklearn拟合多项式函数？"
+    "## 7. How to use sklearn fit  polynomial function？"
    ]
   },
   {
diff --git a/4_logistic_regression/2-Logistic_regression.ipynb b/4_logistic_regression/2-Logistic_regression.ipynb
index c180550..fa86c44 100644
--- a/4_logistic_regression/2-Logistic_regression.ipynb
+++ b/4_logistic_regression/2-Logistic_regression.ipynb
@@ -114,7 +114,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "逻辑回归本质上是线性回归，只是在特征到结果的映射中加入了一层函数映射，即先把特征线性求和，然后使用函数$g(z)$将最为假设函数来预测。$g(z)$可以将连续值映射到0到1之间。线性回归模型的表达式带入$g(z)$，就得到逻辑回归的表达式:\n",
+    "逻辑回归本质上是线性回归，只是在特征到结果的映射中加入了一层函数映射，即先把特征线性求和，然后使用函数$g(z)$将做为假设函数来预测。$g(z)$可以将连续值映射到0到1之间。线性回归模型的表达式带入$g(z)$，就得到逻辑回归的表达式:\n",
     "\n",
     "$$\n",
     "h_\\theta(x) = g(\\theta^T x) = \\frac{1}{1+e^{-\\theta^T x}}\n",
@@ -262,6 +262,7 @@
     "        pred_func (callable): 预测函数\n",
     "        data (numpy.ndarray): 训练数据集合\n",
     "        label (numpy.ndarray): 训练数据标签\n",
+    "        散开数据，但是不在原来的数据上做修改\n",
     "    \"\"\"\n",
     "    x_min, x_max = data[:, 0].min() - .5, data[:, 0].max() + .5\n",
     "    y_min, y_max = data[:, 1].min() - .5, data[:, 1].max() + .5\n",
@@ -272,7 +273,7 @@
     "    Z = predict_func(np.c_[xx.ravel(), yy.ravel()])\n",
     "    Z = Z.reshape(xx.shape)\n",
     "\n",
-    "    plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral)\n",
+    "    plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral)   #画出登高线并填充\n",
     "    plt.scatter(data[:, 0], data[:, 1], c=label, cmap=plt.cm.Spectral)\n",
     "    plt.show()\n",
     "\n"
@@ -350,7 +351,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 2. 如何使用sklearn求解逻辑回归"
+    "## 2.如何用sklearn解决逻辑回归问题?"
    ]
   },
   {
@@ -478,7 +479,7 @@
     "\n",
     "针对机器学习的问题，一个比较好的方法是通过降维的方法将原始的高维特征降到2-3维并可视化处理，通过这样的方法可以对所要处理的数据有一个初步的认识。这里介绍最简单的降维方法主成分分析(Principal Component Analysis, PCA).\n",
     "\n",
-    "PCA寻求具有最大方差的特征的正交线性组合，因此可以更好地了解数据的结构。在这里，我们将使用Randomized PCA，因为当数据个数$N$比较大是，计算的效率更好。\n"
+    "PCA寻求具有最大方差的特征的正交线性组合，因此可以更好地了解数据的结构。在这里，我们将使用Randomized PCA，因为当数据个数$N$比较大时，计算的效率更好。\n"
    ]
   },
   {
diff --git a/4_logistic_regression/2-Logistic_regression_EN.ipynb b/4_logistic_regression/2-Logistic_regression_EN.ipynb
index c180550..042df24 100644
--- a/4_logistic_regression/2-Logistic_regression_EN.ipynb
+++ b/4_logistic_regression/2-Logistic_regression_EN.ipynb
@@ -4,32 +4,32 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# 逻辑回归 Logistic Regression\n",
+    "# Logistic Regression\n",
     "\n",
-    "逻辑回归(Logistic Regression, LR)模型其实仅在线性回归的基础上，套用了一个逻辑函数，但也就由于这个逻辑函数，使得逻辑回归模型成为了机器学习领域一颗耀眼的明星，更是计算广告学的核心。本节主要详述逻辑回归模型的基础。\n",
+    "Logistic Regression model is actually apply a logical function at the basis of linear regression. Beacause of this logical function, Logistic regression has become a shining star at the field of machine learning and the core of computational advertising. This chapter is mainly introducing the basis of logistic regression. \n",
     "\n",
     "\n",
-    "## 1. 逻辑回归模型\n",
-    "回归是一种比较容易理解的模型，就相当于$y=f(x)$，表明自变量$x$与因变量$y$的关系。最常见问题有如医生治病时的望、闻、问、切，之后判定病人是否生病或生了什么病，其中的望闻问切就是获取自变量$x$，即特征数据，判断是否生病就相当于获取因变量$y$，即预测分类。\n",
+    "## 1. Logestic regression model\n",
+    "logestic is a model which can be understood easily, which is equal to $y=f(x)$, shows the relationship between independent variable $x$ and dependent variable $y$. The most common questions are like the doctor's observation, hearing, asking and cutting during treatment, and then determining whether the patient is ill or what kind of disease he has. The observation, hearing and cutting is to obtain the independent variable $x$, that is, the characteristic data, while the determination of whether the patient is ill is equivalent to the acquisition of the dependent variable $y$, that is, the prediction classification.\n",
     "\n",
-    "最简单的回归是线性回归，在此借用Andrew NG的讲义，有如图所示，$X$为数据点——肿瘤的大小，$Y$为观测值——是否是恶性肿瘤。通过构建线性回归模型，如$h_\\theta(x)$所示，构建线性回归模型后，即可以根据肿瘤大小，预测是否为恶性肿瘤$h_\\theta(x)) \\ge 0.5$为恶性，$h_\\theta(x) \\lt 0.5$为良性。\n",
+    "The most simple regression is linear regression. Using Andrew NG's handout, as shown in the figure, $X$ is number point -- tumor size, $Y$ is the observed value -- whether a malignant tumor or not. By building a linear regression model, as shown in $h_\\theta(x)$, we can predict whether $h_\\theta(x)) \\ GE 0.5$ is malignant and $h_\\theta(x) \\lt 0.5$ is benign according to the size of the tumor.\n",
     "\n",
     "![LinearRegression](images/fig1.gif)\n",
     "\n",
-    "然而线性回归的鲁棒性很差，例如在上图的数据集上建立回归，因最右边噪点的存在，使回归模型在训练集上表现都很差。这主要是由于线性回归在整个实数域内敏感度一致，而分类范围，需要在$[0,1]$。\n",
+    "However the linear regression have poor robust, for example if we want to built regression in upper graph, the regression model perform badly in the training set because of the noise point on the right. This is mainly because that the linear regression have a same sensitivity in the whole real number field, however for the range for sort is in $[0,1]$.\n",
     "\n",
-    "逻辑回归就是一种减小预测范围，将预测值限定为$[0,1]$间的一种回归模型，其回归方程与回归曲线如图2所示。逻辑曲线在$z=0$时，十分敏感，在$z>>0$或$z<<0$处，都不敏感，将预测值限定为$(0,1)$。\n",
+    "Logestic regression is a kind of regression model which decrease the predict range and limit the prediction value to $[0,1]$, the regression function and curve is shown in graph 2. When the logestic curve is in $z=0$, it is very sensitive, while at $z>>0$ or $z<<0$, the result is not sensitive, we set the predict value to $(0,1)$.\n",
     "\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -59,15 +59,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 1.1 逻辑回归表达式\n",
+    "### 1.1 Logestic regression expression\n",
     "\n",
-    "这个函数称为Logistic函数(logistic function)，也称为Sigmoid函数(sigmoid function)。函数公式如下：\n",
+    "This function is called logestic function, which is also called sigmoid function. The formula of function is as following:\n",
     "\n",
     "$$\n",
     "g(z) = \\frac{1}{1+e^{-z}}\n",
     "$$\n",
     "\n",
-    "Logistic函数当z趋近于无穷大时，g(z)趋近于1；当z趋近于无穷小时，g(z)趋近于0。Logistic函数的图形如上图所示。Logistic函数求导时有一个特性，这个特性将在下面的推导中用到，这个特性为：\n",
+    "For logistic function, when z is approach infinity, g(z) is approach to 1. While z is approach minus infinity, g(z) is approach to 0. The graph for logestic function is shown as upper figure. Logistic funciton has an attribute when doing derivative which will be used in the following derivation, the characteristic is:\n",
+    "\n",
     "$$\n",
     "g'(z) =  \\frac{d}{dz} \\frac{1}{1+e^{-z}} \\\\\n",
     "      =  \\frac{1}{(1+e^{-z})^2}(e^{-z}) \\\\\n",
@@ -79,12 +80,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -114,7 +115,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "逻辑回归本质上是线性回归，只是在特征到结果的映射中加入了一层函数映射，即先把特征线性求和，然后使用函数$g(z)$将最为假设函数来预测。$g(z)$可以将连续值映射到0到1之间。线性回归模型的表达式带入$g(z)$，就得到逻辑回归的表达式:\n",
+    "Logistic regression is essentially linear regression, only add a layer of function map in the mapping from characteristic and result, which is add linear characteristic first and then use function $g(z)$ as the hypothesis function to predict. $g(z)$ can map continuous value between 0 and 1. We put the expression of logestic model into $g(z)$ to get the expression of logestic regression\n",
     "\n",
     "$$\n",
     "h_\\theta(x) = g(\\theta^T x) = \\frac{1}{1+e^{-\\theta^T x}}\n",
@@ -125,16 +126,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 1.2 逻辑回归的软分类\n",
+    "### 1.2 Soft classification of logestic regression\n",
     "\n",
-    "现在我们将y的取值$h_\\theta(x)$通过Logistic函数归一化到(0,1)间，$y$的取值有特殊的含义，它表示结果取1的概率，因此对于输入$x$分类结果为类别1和类别0的概率分别为：\n",
+    "Now we normalize the value of y $h_\\theta(x)$ to between (0,1) by Logistic function. The value of $y$ have special meaning, which represent the probability that the result getting 1. Therefore the probabilities of input $x$ classification result as category 1 and category 0 are respectively:\n",
     "\n",
     "$$\n",
     "P(y=1|x,\\theta) = h_\\theta(x) \\\\\n",
     "P(y=0|x,\\theta) = 1 - h_\\theta(x)\n",
     "$$\n",
     "\n",
-    "对上面的表达式合并一下就是：\n",
+    "We merge the upper function can get:\n",
     "\n",
     "$$\n",
     "p(y|x,\\theta) = (h_\\theta(x))^y (1 - h_\\theta(x))^{1-y}\n",
@@ -146,25 +147,28 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 1.3 梯度上升\n",
+    "### 1.3 Gradient rise\n",
     "\n",
-    "得到了逻辑回归的表达式，下一步跟线性回归类似，构建似然函数，然后最大似然估计，最终推导出$\\theta$的迭代更新表达式。只不过这里用的不是梯度下降，而是梯度上升，因为这里是最大化似然函数。\n",
+    "When we get the expression for logestic regression, the next step is similar to linear regerssion. We bulid likelihood function then do the maximum likelihood estimation to derive the iteration udate expression for $\\theta$. However we use gradient rise rather than gradient descent beacuse this is maximum likelihood function.\n",
     "\n",
-    "我们假设训练样本相互独立，那么似然函数表达式为：\n",
+    "We assume that the training sample are independent with each other, so that the linkelihood function expression is:\n",
     "![Loss](images/eq_loss.png)\n",
     "\n",
-    "同样对似然函数取log，转换为：\n",
+    "Similarly, take the log of likelihood function, we can get:\n",
+    "\n",
     "![LogLoss](images/eq_logloss.png)\n",
     "\n",
-    "转换后的似然函数对$\\theta$求偏导，在这里我们以只有一个训练样本的情况为例：\n",
+    "Takes the partial derivative of $\\theta$ to converted likelihood function, we take a case where there is only one training sample:\n",
+    "\n",
     "![LogLossDiff](images/eq_logloss_diff.png)\n",
     "\n",
-    "这个求偏导过程中：\n",
-    "* 第一步是对$\\theta$偏导的转化，依据偏导公式：$y=lnx$, $y'=1/x$。\n",
-    "* 第二步是根据g(z)求导的特性g'(z) = g(z)(1 - g(z)) 。\n",
-    "* 第三步就是普通的变换。\n",
+    "In the process of getting patial derivative:\n",
+    "* The first step is to convert the partial derivative of $\\theta$, according to the partial formula: $y= LNX $, $y'=1/x$.\n",
+    "* The second step is to use the characteristic of $g(z)$ when doing derivative: $g'(z) = g(z)(1 - g(z))$ \n",
+    "* The third step was normal translation.\n",
+    "\n",
+    "In this way, we get the update direction of graient rise in every iteration, so that the iteration expression of $\\theta$ is: \n",
     "\n",
-    "这样我们就得到了梯度上升每次迭代的更新方向，那么$\\theta$的迭代表达式为：\n",
     "$$\n",
     "\\theta_j := \\theta_j + \\alpha(y^i - h_\\theta(x^i)) x_j^i\n",
     "$$\n",
@@ -175,17 +179,15 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 1.4 示例程序"
+    "### 1.4 The sample program"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
-    "%matplotlib inline\n",
-    "\n",
     "from __future__ import division\n",
     "import numpy as np\n",
     "import sklearn.datasets\n",
@@ -196,7 +198,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -222,13 +224,13 @@
        "Text(0.5, 1.0, 'Original Data')"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -252,16 +254,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
     "def plot_decision_boundary(predict_func, data, label):\n",
-    "    \"\"\"画出结果图\n",
+    "    \"\"\"Draw result\n",
     "    Args:\n",
-    "        pred_func (callable): 预测函数\n",
-    "        data (numpy.ndarray): 训练数据集合\n",
-    "        label (numpy.ndarray): 训练数据标签\n",
+    "        pred_func (callable): Predict function\n",
+    "        data (numpy.ndarray): Training data set\n",
+    "        label (numpy.ndarray): Training data label\n",
+    "        ravel() split the data, without changing the original data\n",
     "    \"\"\"\n",
     "    x_min, x_max = data[:, 0].min() - .5, data[:, 0].max() + .5\n",
     "    y_min, y_max = data[:, 1].min() - .5, data[:, 1].max() + .5\n",
@@ -280,7 +283,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -288,7 +291,7 @@
     "    return 1.0 / (1 + np.exp(-x))\n",
     "\n",
     "class Logistic(object):\n",
-    "    \"\"\"logistic回归模型\"\"\"\n",
+    "    \"\"\"logestic regression model\"\"\"\n",
     "    def __init__(self, data, label):\n",
     "        self.data = data\n",
     "        self.label = label\n",
@@ -298,25 +301,25 @@
     "        self.b = 1\n",
     "\n",
     "    def train(self, num_iteration=150):\n",
-    "        \"\"\"随机梯度上升算法\n",
+    "        \"\"\"Random gradient rise algorithm\n",
     "        Args:\n",
-    "            data (numpy.ndarray): 训练数据集\n",
-    "            labels (numpy.ndarray): 训练标签\n",
-    "            num_iteration (int): 迭代次数\n",
+    "            data (numpy.ndarray): Training data set\n",
+    "            labels (numpy.ndarray): Training label\n",
+    "            num_iteration (int): Iteration times\n",
     "        \"\"\"\n",
     "        for j in range(num_iteration):\n",
     "            data_index = list(range(self.data_num))\n",
     "            for i in range(self.data_num):\n",
-    "                # 学习速率\n",
+    "                # learning rate\n",
     "                alpha = 0.01\n",
-    "                rand_index = int(np.random.uniform(0, len(data_index)))\n",
+    "                rand_index = int(np.random.uniform(0, len(data_index))) #Choose trianing radomly\n",
     "                error = self.label[rand_index] - sigmoid(sum(self.data[rand_index] * self.weights + self.b))\n",
     "                self.weights += alpha * error * self.data[rand_index]\n",
     "                self.b += alpha * error\n",
-    "                del(data_index[rand_index])\n",
+    "                del(data_index[rand_index]) \n",
     "\n",
     "    def predict(self, predict_data):\n",
-    "        \"\"\"预测函数\"\"\"\n",
+    "        \"\"\"Predict function\"\"\"\n",
     "        result = list(map(lambda x: 1 if sum(self.weights * x + self.b) > 0 else 0,\n",
     "                     predict_data))\n",
     "        return np.array(result)\n"
@@ -324,12 +327,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -343,14 +346,14 @@
    "source": [
     "logistic = Logistic(data, label)\n",
     "logistic.train(200)\n",
-    "plot_decision_boundary(lambda x: logistic.predict(x), data, label)"
+    "plot_decision_boundary(lambda x:logistic.predict(x) , data, label)　#lambda generate callable function"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 2. 如何使用sklearn求解逻辑回归"
+    "## 2. How to use sklearn solve logestic regression?"
    ]
   },
   {
@@ -424,14 +427,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 3. 多类识别问题"
+    "## 3. Multiple recognition problem"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 3.1 加载显示数据"
+    "### 3.1 Load display data"
    ]
   },
   {
@@ -474,11 +477,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 3.2 可视化特征\n",
+    "### 3.2 Visual characteristics\n",
     "\n",
-    "针对机器学习的问题，一个比较好的方法是通过降维的方法将原始的高维特征降到2-3维并可视化处理，通过这样的方法可以对所要处理的数据有一个初步的认识。这里介绍最简单的降维方法主成分分析(Principal Component Analysis, PCA).\n",
+    "For the problem in machine learing, one good way is to reduce original high dimension to 2-3 dimension by dimension reduction and visual processing. In this way, we can have a  preliminary understanding of the data to be processed. The simplist dimension reduction method Principal Component Analysisi(PCA) is introduced.\n",
     "\n",
-    "PCA寻求具有最大方差的特征的正交线性组合，因此可以更好地了解数据的结构。在这里，我们将使用Randomized PCA，因为当数据个数$N$比较大是，计算的效率更好。\n"
+    "PCA seeks orthogonal linear combinations of features with maximum variance so that we can better understand the structure of the data. At here, we will use Randomized PCA, because it will ahcieve a better computational effenciency when the data number $N$ is relatively large.\n",
+    "\n"
    ]
   },
   {
@@ -522,9 +526,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "PCA的一个缺点是它可能会丢失数据中一些有趣的相互关系。如果想看到非线性的降维与映射\n",
-    "我们可以使用几种流形模块中的方法。在这里，我们将使用[Isomap](https://blog.csdn.net/VictoriaW/article/details/78497316)（串联\n",
-    "等距映射）是一种基于图论的流形降维方法。"
+    "One disadvantage of PCA is that it may loss some interesting interconnections. We can use some manifold module method to see the nolinear dimension reduction and mapping.\n",
+    "Here we will use[Isomap](https://blog.csdn.net/VictoriaW/article/details/78497316)(Series isometric mapping) which is a manifold dimension redution method based on graph theory."
    ]
   },
   {
@@ -568,7 +571,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 3.3 示例程序"
+    "### 3.3 The sample program"
    ]
   },
   {
@@ -664,10 +667,10 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 4. 练习 - 如何画出错误分类的数据？\n",
+    "## 4. Pratice-How to draw misclassified data?\n",
     "\n",
-    "1. 如何得到错误分类数据的下标？\n",
-    "2. 如何根据下标，将这些错误的数据可视化出来？"
+    "1. How to get the index of misclassified data？\n",
+    "2. How to do visiualizaion to these misclassified data according to their index？"
    ]
   },
   {