diff --git a/.gitignore b/.gitignore
index 0d20b648..99ec4282 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1 +1,2 @@
 *.pyc
+.idea
diff --git a/Building Machine Learning Systems with Python, 2nd Edition.pdf b/Building Machine Learning Systems with Python, 2nd Edition.pdf
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diff --git a/Machine Learning Projects for .NET Developers [Brandewinder 2015-06-29].pdf b/Machine Learning Projects for .NET Developers [Brandewinder 2015-06-29].pdf
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diff --git a/Mastering Machine Learning with scikit-learn.pdf b/Mastering Machine Learning with scikit-learn.pdf
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diff --git a/ch01/work.py b/ch01/work.py
new file mode 100644
index 00000000..67d3c648
--- /dev/null
+++ b/ch01/work.py
@@ -0,0 +1,76 @@
+import scipy as sp
+import os
+from matplotlib import pyplot as plt
+
+data = sp.genfromtxt(os.path.join(os.getcwd(), 'data\\web_traffic.tsv') , delimiter='\t')
+
+# preprocessing
+x = data[:, 0]
+y = data[:, 1]
+x = x[~sp.isnan(y)]
+y = y[~sp.isnan(y)]
+
+# plot the data
+plt.scatter(x, y, c='r' ,s=10)
+plt.xlabel('Time/Hours')
+plt.ylabel('Requests')
+plt.title('Number of requests per hour')
+plt.xticks([w*7*24 for w in range(10)], ['week %i' % w for w in range(10)])
+plt.grid(True)
+plt.autoscale(tight=True)
+
+# choosing model
+
+# define the error function
+def error(f, x, y):
+    return sp.sum((f(x) - y)**2)
+
+# simple straighe line
+
+def plot_models():
+    plt.scatter(x, y, c='r', s=10)
+    plt.xlabel('Time/Hours')
+    plt.ylabel('Requests')
+    plt.title('Number of requests per hour')
+    plt.xticks([w * 7 * 24 for w in range(10)], ['week %i' % w for w in range(10)])
+    plt.grid(True)
+    plt.autoscale(tight=True)
+    fx = sp.linspace(0, x[-1], 1000)
+    legends = []
+    errors = []
+    for i in [1, 2, 3, 10, 100]:
+        f = sp.poly1d(sp.polyfit(x, y, i))
+        plt.plot(fx, f(fx), linewidth=2)
+        errors.append(error(f, x, y))
+        legends.append(f.order)
+
+    plt.legend(["d=%i" % o for o in legends], loc='upper left')
+    return errors
+
+plt.show()
+
+inflection = 3.5*7*24
+
+xa = x[:inflection]
+ya = y[:inflection]
+
+xb = x[inflection:]
+yb = y[inflection:]
+
+f1 = sp.poly1d(sp.polyfit(xa, ya, 1))
+f2 = sp.poly1d(sp.polyfit(xb, yb, 1))
+
+plt.scatter(x, y, c='r', s=10)
+plt.xlabel('Time/Hours')
+plt.ylabel('Requests')
+plt.title('Number of requests per hour')
+plt.xticks([w * 7 * 24 for w in range(10)], ['week %i' % w for w in range(10)])
+plt.grid(True)
+plt.autoscale(tight=True)
+
+fx = sp.linspace(0, x[-1], 1000)
+plt.plot(fx, f1(fx), linewidth=2)
+plt.plot(fx, f2(fx), linewidth=2)
+
+
+
diff --git a/ch07/work.ipynb b/ch07/work.ipynb
new file mode 100644
index 00000000..6c90b986
--- /dev/null
+++ b/ch07/work.ipynb
@@ -0,0 +1,415 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import load_boston\n",
+    "boston = load_boston()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'DESCR': \"Boston House Prices dataset\\n===========================\\n\\nNotes\\n------\\nData Set Characteristics:  \\n\\n    :Number of Instances: 506 \\n\\n    :Number of Attributes: 13 numeric/categorical predictive\\n    \\n    :Median Value (attribute 14) is usually the target\\n\\n    :Attribute Information (in order):\\n        - CRIM     per capita crime rate by town\\n        - ZN       proportion of residential land zoned for lots over 25,000 sq.ft.\\n        - INDUS    proportion of non-retail business acres per town\\n        - CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\\n        - NOX      nitric oxides concentration (parts per 10 million)\\n        - RM       average number of rooms per dwelling\\n        - AGE      proportion of owner-occupied units built prior to 1940\\n        - DIS      weighted distances to five Boston employment centres\\n        - RAD      index of accessibility to radial highways\\n        - TAX      full-value property-tax rate per $10,000\\n        - PTRATIO  pupil-teacher ratio by town\\n        - B        1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\\n        - LSTAT    % lower status of the population\\n        - MEDV     Median value of owner-occupied homes in $1000's\\n\\n    :Missing Attribute Values: None\\n\\n    :Creator: Harrison, D. and Rubinfeld, D.L.\\n\\nThis is a copy of UCI ML housing dataset.\\nhttp://archive.ics.uci.edu/ml/datasets/Housing\\n\\n\\nThis dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\\n\\nThe Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\\nprices and the demand for clean air', J. Environ. Economics & Management,\\nvol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics\\n...', Wiley, 1980.   N.B. Various transformations are used in the table on\\npages 244-261 of the latter.\\n\\nThe Boston house-price data has been used in many machine learning papers that address regression\\nproblems.   \\n     \\n**References**\\n\\n   - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\\n   - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.\\n   - many more! (see http://archive.ics.uci.edu/ml/datasets/Housing)\\n\",\n 'data': array([[  6.32000000e-03,   1.80000000e+01,   2.31000000e+00, ...,\n           1.53000000e+01,   3.96900000e+02,   4.98000000e+00],\n        [  2.73100000e-02,   0.00000000e+00,   7.07000000e+00, ...,\n           1.78000000e+01,   3.96900000e+02,   9.14000000e+00],\n        [  2.72900000e-02,   0.00000000e+00,   7.07000000e+00, ...,\n           1.78000000e+01,   3.92830000e+02,   4.03000000e+00],\n        ..., \n        [  6.07600000e-02,   0.00000000e+00,   1.19300000e+01, ...,\n           2.10000000e+01,   3.96900000e+02,   5.64000000e+00],\n        [  1.09590000e-01,   0.00000000e+00,   1.19300000e+01, ...,\n           2.10000000e+01,   3.93450000e+02,   6.48000000e+00],\n        [  4.74100000e-02,   0.00000000e+00,   1.19300000e+01, ...,\n           2.10000000e+01,   3.96900000e+02,   7.88000000e+00]]),\n 'feature_names': array(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD',\n        'TAX', 'PTRATIO', 'B', 'LSTAT'], \n       dtype='|S7'),\n 'target': array([ 24. ,  21.6,  34.7,  33.4,  36.2,  28.7,  22.9,  27.1,  16.5,\n         18.9,  15. ,  18.9,  21.7,  20.4,  18.2,  19.9,  23.1,  17.5,\n         20.2,  18.2,  13.6,  19.6,  15.2,  14.5,  15.6,  13.9,  16.6,\n         14.8,  18.4,  21. ,  12.7,  14.5,  13.2,  13.1,  13.5,  18.9,\n         20. ,  21. ,  24.7,  30.8,  34.9,  26.6,  25.3,  24.7,  21.2,\n         19.3,  20. ,  16.6,  14.4,  19.4,  19.7,  20.5,  25. ,  23.4,\n         18.9,  35.4,  24.7,  31.6,  23.3,  19.6,  18.7,  16. ,  22.2,\n         25. ,  33. ,  23.5,  19.4,  22. ,  17.4,  20.9,  24.2,  21.7,\n         22.8,  23.4,  24.1,  21.4,  20. ,  20.8,  21.2,  20.3,  28. ,\n         23.9,  24.8,  22.9,  23.9,  26.6,  22.5,  22.2,  23.6,  28.7,\n         22.6,  22. ,  22.9,  25. ,  20.6,  28.4,  21.4,  38.7,  43.8,\n         33.2,  27.5,  26.5,  18.6,  19.3,  20.1,  19.5,  19.5,  20.4,\n         19.8,  19.4,  21.7,  22.8,  18.8,  18.7,  18.5,  18.3,  21.2,\n         19.2,  20.4,  19.3,  22. ,  20.3,  20.5,  17.3,  18.8,  21.4,\n         15.7,  16.2,  18. ,  14.3,  19.2,  19.6,  23. ,  18.4,  15.6,\n         18.1,  17.4,  17.1,  13.3,  17.8,  14. ,  14.4,  13.4,  15.6,\n         11.8,  13.8,  15.6,  14.6,  17.8,  15.4,  21.5,  19.6,  15.3,\n         19.4,  17. ,  15.6,  13.1,  41.3,  24.3,  23.3,  27. ,  50. ,\n         50. ,  50. ,  22.7,  25. ,  50. ,  23.8,  23.8,  22.3,  17.4,\n         19.1,  23.1,  23.6,  22.6,  29.4,  23.2,  24.6,  29.9,  37.2,\n         39.8,  36.2,  37.9,  32.5,  26.4,  29.6,  50. ,  32. ,  29.8,\n         34.9,  37. ,  30.5,  36.4,  31.1,  29.1,  50. ,  33.3,  30.3,\n         34.6,  34.9,  32.9,  24.1,  42.3,  48.5,  50. ,  22.6,  24.4,\n         22.5,  24.4,  20. ,  21.7,  19.3,  22.4,  28.1,  23.7,  25. ,\n         23.3,  28.7,  21.5,  23. ,  26.7,  21.7,  27.5,  30.1,  44.8,\n         50. ,  37.6,  31.6,  46.7,  31.5,  24.3,  31.7,  41.7,  48.3,\n         29. ,  24. ,  25.1,  31.5,  23.7,  23.3,  22. ,  20.1,  22.2,\n         23.7,  17.6,  18.5,  24.3,  20.5,  24.5,  26.2,  24.4,  24.8,\n         29.6,  42.8,  21.9,  20.9,  44. ,  50. ,  36. ,  30.1,  33.8,\n         43.1,  48.8,  31. ,  36.5,  22.8,  30.7,  50. ,  43.5,  20.7,\n         21.1,  25.2,  24.4,  35.2,  32.4,  32. ,  33.2,  33.1,  29.1,\n         35.1,  45.4,  35.4,  46. ,  50. ,  32.2,  22. ,  20.1,  23.2,\n         22.3,  24.8,  28.5,  37.3,  27.9,  23.9,  21.7,  28.6,  27.1,\n         20.3,  22.5,  29. ,  24.8,  22. ,  26.4,  33.1,  36.1,  28.4,\n         33.4,  28.2,  22.8,  20.3,  16.1,  22.1,  19.4,  21.6,  23.8,\n         16.2,  17.8,  19.8,  23.1,  21. ,  23.8,  23.1,  20.4,  18.5,\n         25. ,  24.6,  23. ,  22.2,  19.3,  22.6,  19.8,  17.1,  19.4,\n         22.2,  20.7,  21.1,  19.5,  18.5,  20.6,  19. ,  18.7,  32.7,\n         16.5,  23.9,  31.2,  17.5,  17.2,  23.1,  24.5,  26.6,  22.9,\n         24.1,  18.6,  30.1,  18.2,  20.6,  17.8,  21.7,  22.7,  22.6,\n         25. ,  19.9,  20.8,  16.8,  21.9,  27.5,  21.9,  23.1,  50. ,\n         50. ,  50. ,  50. ,  50. ,  13.8,  13.8,  15. ,  13.9,  13.3,\n         13.1,  10.2,  10.4,  10.9,  11.3,  12.3,   8.8,   7.2,  10.5,\n          7.4,  10.2,  11.5,  15.1,  23.2,   9.7,  13.8,  12.7,  13.1,\n         12.5,   8.5,   5. ,   6.3,   5.6,   7.2,  12.1,   8.3,   8.5,\n          5. ,  11.9,  27.9,  17.2,  27.5,  15. ,  17.2,  17.9,  16.3,\n          7. ,   7.2,   7.5,  10.4,   8.8,   8.4,  16.7,  14.2,  20.8,\n         13.4,  11.7,   8.3,  10.2,  10.9,  11. ,   9.5,  14.5,  14.1,\n         16.1,  14.3,  11.7,  13.4,   9.6,   8.7,   8.4,  12.8,  10.5,\n         17.1,  18.4,  15.4,  10.8,  11.8,  14.9,  12.6,  14.1,  13. ,\n         13.4,  15.2,  16.1,  17.8,  14.9,  14.1,  12.7,  13.5,  14.9,\n         20. ,  16.4,  17.7,  19.5,  20.2,  21.4,  19.9,  19. ,  19.1,\n         19.1,  20.1,  19.9,  19.6,  23.2,  29.8,  13.8,  13.3,  16.7,\n         12. ,  14.6,  21.4,  23. ,  23.7,  25. ,  21.8,  20.6,  21.2,\n         19.1,  20.6,  15.2,   7. ,   8.1,  13.6,  20.1,  21.8,  24.5,\n         23.1,  19.7,  18.3,  21.2,  17.5,  16.8,  22.4,  20.6,  23.9,\n         22. ,  11.9])}"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "boston"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(506L, 13L)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "boston.data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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qx9KOY3IuF5q4/mORdc6StCpInJWW/CtrvVySpWW5B6ur7se6tP/AgeYlcyPN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\no26CqucAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x9083dd8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "from matplotlib import pyplot as plt\n",
+    "plt.scatter(boston.data[:, 5], boston.target, color='r')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "lr = LinearRegression()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = boston.data[:, 5]\n",
+    "y = boston.target\n",
+    "import numpy as np\n",
+    "x = np.transpose(np.atleast_2d(x))\n",
+    "\n",
+    "lr.fit(x, y)\n",
+    "y_predicted = lr.predict(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "43.600551771169563"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import mean_squared_error\n",
+    "\n",
+    "mse = mean_squared_error(y, y_predicted)\n",
+    "mse"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6.6030713892225608"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rmse = np.sqrt(mse)\n",
+    "rmse"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.48352545599133423"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import r2_score\n",
+    "r2 = r2_score(y, y_predicted)\n",
+    "r2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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YyGeLx1RyJE7ijuM2ncqbzvlbHlnnGd45c/IxNIwZPqDJfTn5B8xRbDh4KgJV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\nKhxTBIZhGBXO/wdY1VmAsFJq8AAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa8f8400>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = boston.data\n",
+    "y = boston.target\n",
+    "lr.fit(x, y)\n",
+    "p = lr.predict(x)\n",
+    "plt.scatter(p, y)\n",
+    "plt.xlabel('Predicted price')\n",
+    "plt.ylabel('Actual price')\n",
+    "plt.plot([y.min(), y.max()], [[y.min()], [y.max()]])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6.097044039669913"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.cross_validation import KFold\n",
+    "kf = KFold(len(x), n_folds=5)\n",
+    "p = np.zeros_like(y)\n",
+    "\n",
+    "for train, test in kf:\n",
+    "    lr.fit(x[train], y[train])\n",
+    "    p[test] = lr.predict(x[test])\n",
+    "    \n",
+    "rmse_cv = np.sqrt(mean_squared_error(y, p))\n",
+    "rmse_cv"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# P greater than N example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import load_svmlight_file\n",
+    "import os\n",
+    "data, target = load_svmlight_file(os.path.join(os.getcwd(), 'ch07\\\\data\\\\E2006.train'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## OLS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.00238297744011\n0.999985795589\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.metrics import mean_squared_error, r2_score\n",
+    "import numpy as np\n",
+    "\n",
+    "lr = LinearRegression()\n",
+    "\n",
+    "lr.fit(data, target)\n",
+    "pred = lr.predict(data)\n",
+    "\n",
+    "rmse_train = np.sqrt(mean_squared_error(target, pred))\n",
+    "r2 = r2_score(target, pred)\n",
+    "print(rmse_train)\n",
+    "print(r2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "rmse:0.396742077005\nr2:0.606268739456\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import ElasticNet\n",
+    "from sklearn.cross_validation import KFold\n",
+    "met = ElasticNet(alpha=0.1)\n",
+    "\n",
+    "kf = KFold(len(target), n_folds=5)\n",
+    "p = np.zeros_like(target)\n",
+    "\n",
+    "for train, test in kf:\n",
+    "    met.fit(data[train], target[train])\n",
+    "    p[test] = met.predict(data[test])\n",
+    "\n",
+    "print('rmse:' + str(np.sqrt(mean_squared_error(target, p))))\n",
+    "print('r2:' + str(r2_score(target, p)))"
+   ]
+  },
+  {
+   "cell_type": "heading",
+   "metadata": {},
+   "level": 3,
+   "source": [
+    ""
+   ]
+  },
+  {
+   "cell_type": "heading",
+   "metadata": {},
+   "level": 3,
+   "source": [
+    "Hyperparameter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "...\n0.653689239305\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import ElasticNetCV\n",
+    "from sklearn.metrics import mean_squared_error, r2_score\n",
+    "import numpy as np\n",
+    "\n",
+    "met = ElasticNetCV(n_jobs=-1)\n",
+    "\n",
+    "kf = KFold(len(target), n_folds=5)\n",
+    "p = np.zeros_like(target)\n",
+    "for train, test in kf:\n",
+    "    met.fit(data[train], target[train])\n",
+    "    p[test] = met.predict(data[test])\n",
+    "    print('...')\n",
+    "    \n",
+    "r2_cv = r2_score(target, p)\n",
+    "print(r2_cv)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print('dd')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2.0
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file