Fixed import error.

03-Gaussians.ipynb

@@ -16,19 +16,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The autoreload extension is already loaded. To reload it, use:\n",
-      "  %reload_ext autoreload\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
@@ -264,7 +256,7 @@
        "<IPython.core.display.HTML object>"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -276,6 +268,8 @@
     "%autoreload 2  \n",
     "from __future__ import division, print_function\n",
     "import sys\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
     "sys.path.insert(0,'./code')\n",
     "from book_format import load_style\n",
     "load_style()"
@@ -386,7 +380,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 2,
    "metadata": {
     "collapsed": false
    },
@@ -395,8 +389,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1.81\n",
-      "1.85\n"
+      "1.81\n"
      ]
     }
    ],
@@ -419,7 +412,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 3,
    "metadata": {
     "collapsed": false
    },
@@ -478,7 +471,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 4,
    "metadata": {
     "collapsed": true
    },
@@ -498,7 +491,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 5,
    "metadata": {
     "collapsed": false
    },
@@ -556,7 +549,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 6,
    "metadata": {
     "collapsed": false
    },
@@ -597,7 +590,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 7,
    "metadata": {
     "collapsed": false
    },
@@ -634,16 +627,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 8,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x71b3cc0>"
+       "<matplotlib.figure.Figure at 0x4940160>"
       ]
      },
      "metadata": {},
@@ -670,16 +663,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 9,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvQAAADxCAYAAACkjiOMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8050320>"
+       "<matplotlib.figure.Figure at 0x7cc9828>"
       ]
      },
      "metadata": {},
@@ -689,8 +682,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "mean = 1.808\n",
-      "std  = 0.121\n"
+      "mean = 1.788\n",
+      "std  = 0.139\n"
      ]
     }
    ],
@@ -733,7 +726,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 10,
    "metadata": {
     "collapsed": false
    },
@@ -768,7 +761,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 11,
    "metadata": {
     "collapsed": false
    },
@@ -796,16 +789,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 12,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
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+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtUAAACxCAYAAAALSUEOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\npysLuOkAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7847e80>"
+       "<matplotlib.figure.Figure at 0x7afb710>"
       ]
      },
      "metadata": {},
@@ -840,7 +833,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 13,
    "metadata": {
     "collapsed": false
    },
@@ -890,16 +883,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 14,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7e472e8>"
+       "<matplotlib.figure.Figure at 0x7b18da0>"
       ]
      },
      "metadata": {},
@@ -928,16 +921,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 15,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7d86710>"
+       "<matplotlib.figure.Figure at 0x887c940>"
       ]
      },
      "metadata": {},
@@ -967,16 +960,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 16,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7da3048>"
+       "<matplotlib.figure.Figure at 0x88a95f8>"
       ]
      },
      "metadata": {},
@@ -1049,7 +1042,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 17,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -1057,9 +1050,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x981b550>"
+       "<matplotlib.figure.Figure at 0x89a5828>"
       ]
      },
      "metadata": {},
@@ -1097,7 +1090,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 18,
    "metadata": {
     "collapsed": false
    },
@@ -1147,7 +1140,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 19,
    "metadata": {
     "collapsed": false
    },
@@ -1175,7 +1168,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 20,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -1183,9 +1176,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9820f28>"
+       "<matplotlib.figure.Figure at 0x9ab1b70>"
       ]
      },
      "metadata": {},
@@ -1231,16 +1224,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 21,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x90ab2e8>"
+       "<matplotlib.figure.Figure at 0x9b77be0>"
       ]
      },
      "metadata": {},
@@ -1269,16 +1262,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 22,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x99fdf98>"
+       "<matplotlib.figure.Figure at 0x9ac8710>"
       ]
      },
      "metadata": {},
@@ -1288,7 +1281,6 @@
    "source": [
     "import math\n",
     "from IPython.html.widgets import interact, interactive, fixed\n",
-    "from IPython.html.widgets import FloatSliderWidget\n",
     "\n",
     "set_figsize(y=3)\n",
     "def plt_g(mu,variance):\n",
@@ -1345,7 +1337,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 23,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -1376,7 +1368,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 24,
    "metadata": {
     "collapsed": false
    },
@@ -1409,7 +1401,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 25,
    "metadata": {
     "collapsed": false
    },
@@ -1418,9 +1410,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ -1.67    1.966   2.794   2.159   2.462  -0.012\n",
-      "  12.025   6.336   3.566  -1.321  -1.545   2.25\n",
-      "   4.888   2.674   1.885]\n"
+      "[ 2.447  4.083 -0.846  0.879  3.904  0.432  5.224\n",
+      "  2.652  2.469  5.71  -1.672 -2.814 -3.919 -2.951\n",
+      " -1.152]\n"
      ]
     }
    ],
@@ -1438,7 +1430,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 26,
    "metadata": {
     "collapsed": false
    },
@@ -1465,7 +1457,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 27,
    "metadata": {
     "collapsed": false
    },
@@ -1515,16 +1507,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 28,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuwAAADaCAYAAAD9nBdlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x803e5c0>"
+       "<matplotlib.figure.Figure at 0x89fc828>"
       ]
      },
      "metadata": {},
@@ -1552,7 +1544,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 29,
    "metadata": {
     "collapsed": false
    },
@@ -1572,16 +1564,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 30,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f4d908>"
+       "<matplotlib.figure.Figure at 0x9a17860>"
       ]
      },
      "metadata": {},
@@ -1604,7 +1596,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 31,
    "metadata": {
     "collapsed": false
    },
@@ -1625,16 +1617,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 32,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7efcb38>"
+       "<matplotlib.figure.Figure at 0x88432b0>"
       ]
      },
      "metadata": {},
@@ -1732,7 +1724,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.4.3"
+   "version": "3.5.0"
   }
  },
  "nbformat": 4,