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diff --git a/‎dev/_sources/auto_examples/applications/plot_face_recognition.rst.txt
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diff --git a/‎dev/_sources/auto_examples/applications/plot_model_complexity_influence.rst.txt
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Lines changed: 15 additions & 15 deletions
@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "print(__doc__)\n\n# Code source: Ga\u00ebl Varoquaux\n# Modified for documentation by Jaques Grobler\n# License: BSD 3 clause\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn import datasets\n\n# import some data to play with\niris = datasets.load_iris()\nX = iris.data[:, :2]  # we only take the first two features.\nY = iris.target\n\nlogreg = LogisticRegression(C=1e5, solver='lbfgs', multi_class='multinomial')\n\n# we create an instance of Neighbours Classifier and fit the data.\nlogreg.fit(X, Y)\n\n# Plot the decision boundary. For that, we will assign a color to each\n# point in the mesh [x_min, x_max]x[y_min, y_max].\nx_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\ny_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\nh = .02  # step size in the mesh\nxx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\nZ = logreg.predict(np.c_[xx.ravel(), yy.ravel()])\n\n# Put the result into a color plot\nZ = Z.reshape(xx.shape)\nplt.figure(1, figsize=(4, 3))\nplt.pcolormesh(xx, yy, Z, cmap=plt.cm.Paired)\n\n# Plot also the training points\nplt.scatter(X[:, 0], X[:, 1], c=Y, edgecolors='k', cmap=plt.cm.Paired)\nplt.xlabel('Sepal length')\nplt.ylabel('Sepal width')\n\nplt.xlim(xx.min(), xx.max())\nplt.ylim(yy.min(), yy.max())\nplt.xticks(())\nplt.yticks(())\n\nplt.show()"
+        "print(__doc__)\n\n# Code source: Ga\u00ebl Varoquaux\n# Modified for documentation by Jaques Grobler\n# License: BSD 3 clause\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn import datasets\n\n# import some data to play with\niris = datasets.load_iris()\nX = iris.data[:, :2]  # we only take the first two features.\nY = iris.target\n\nlogreg = LogisticRegression(C=1e5, solver='lbfgs', multi_class='multinomial')\n\n# Create an instance of Logistic Regression Classifier and fit the data.\nlogreg.fit(X, Y)\n\n# Plot the decision boundary. For that, we will assign a color to each\n# point in the mesh [x_min, x_max]x[y_min, y_max].\nx_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\ny_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\nh = .02  # step size in the mesh\nxx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\nZ = logreg.predict(np.c_[xx.ravel(), yy.ravel()])\n\n# Put the result into a color plot\nZ = Z.reshape(xx.shape)\nplt.figure(1, figsize=(4, 3))\nplt.pcolormesh(xx, yy, Z, cmap=plt.cm.Paired)\n\n# Plot also the training points\nplt.scatter(X[:, 0], X[:, 1], c=Y, edgecolors='k', cmap=plt.cm.Paired)\nplt.xlabel('Sepal length')\nplt.ylabel('Sepal width')\n\nplt.xlim(xx.min(), xx.max())\nplt.ylim(yy.min(), yy.max())\nplt.xticks(())\nplt.yticks(())\n\nplt.show()"
       ]
     }
   ],
 
@@ -30,7 +30,7 @@
 
 logreg = LogisticRegression(C=1e5, solver='lbfgs', multi_class='multinomial')
 
-# we create an instance of Neighbours Classifier and fit the data.
+# Create an instance of Logistic Regression Classifier and fit the data.
 logreg.fit(X, Y)
 
 # Plot the decision boundary. For that, we will assign a color to each
Original file line number	Diff line number	Diff line change
`@@ -26,7 +26,7 @@`
`26`	`26`	`},`
`27`	`27`	`"outputs": [],`
`28`	`28`	`"source": [`
`29`		- "print(__doc__)\n\n# Code source: Ga\u00ebl Varoquaux\n# Modified for documentation by Jaques Grobler\n# License: BSD 3 clause\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn import datasets\n\n# import some data to play with\niris = datasets.load_iris()\nX = iris.data[:, :2] # we only take the first two features.\nY = iris.target\n\nlogreg = LogisticRegression(C=1e5, solver='lbfgs', multi_class='multinomial')\n\n# we create an instance of Neighbours Classifier and fit the data.\nlogreg.fit(X, Y)\n\n# Plot the decision boundary. For that, we will assign a color to each\n# point in the mesh [x_min, x_max]x[y_min, y_max].\nx_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\ny_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\nh = .02 # step size in the mesh\nxx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\nZ = logreg.predict(np.c_[xx.ravel(), yy.ravel()])\n\n# Put the result into a color plot\nZ = Z.reshape(xx.shape)\nplt.figure(1, figsize=(4, 3))\nplt.pcolormesh(xx, yy, Z, cmap=plt.cm.Paired)\n\n# Plot also the training points\nplt.scatter(X[:, 0], X[:, 1], c=Y, edgecolors='k', cmap=plt.cm.Paired)\nplt.xlabel('Sepal length')\nplt.ylabel('Sepal width')\n\nplt.xlim(xx.min(), xx.max())\nplt.ylim(yy.min(), yy.max())\nplt.xticks(())\nplt.yticks(())\n\nplt.show()"
	`29`	+ "print(__doc__)\n\n# Code source: Ga\u00ebl Varoquaux\n# Modified for documentation by Jaques Grobler\n# License: BSD 3 clause\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn import datasets\n\n# import some data to play with\niris = datasets.load_iris()\nX = iris.data[:, :2] # we only take the first two features.\nY = iris.target\n\nlogreg = LogisticRegression(C=1e5, solver='lbfgs', multi_class='multinomial')\n\n# Create an instance of Logistic Regression Classifier and fit the data.\nlogreg.fit(X, Y)\n\n# Plot the decision boundary. For that, we will assign a color to each\n# point in the mesh [x_min, x_max]x[y_min, y_max].\nx_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\ny_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\nh = .02 # step size in the mesh\nxx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\nZ = logreg.predict(np.c_[xx.ravel(), yy.ravel()])\n\n# Put the result into a color plot\nZ = Z.reshape(xx.shape)\nplt.figure(1, figsize=(4, 3))\nplt.pcolormesh(xx, yy, Z, cmap=plt.cm.Paired)\n\n# Plot also the training points\nplt.scatter(X[:, 0], X[:, 1], c=Y, edgecolors='k', cmap=plt.cm.Paired)\nplt.xlabel('Sepal length')\nplt.ylabel('Sepal width')\n\nplt.xlim(xx.min(), xx.max())\nplt.ylim(yy.min(), yy.max())\nplt.xticks(())\nplt.yticks(())\n\nplt.show()"
`30`	`30`	`]`
`31`	`31`	`}`
`32`	`32`	`],`