Pushing the docs to dev/ for branch: master, commit cc810151906b3a1b1a48f9e589d3cbda451a8d48

Author: sklearn-ci

dev/_downloads/auto_examples_jupyter.zip

@@ -24,7 +24,7 @@
       "execution_count": null, 
       "cell_type": "code", 
       "source": [
-        "print(__doc__)\n\n\n# Code source: Ga\u00ebl Varoquaux\n# Modified for documentation by Jaques Grobler\n# License: BSD 3 clause\n\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\nfrom sklearn import datasets\nfrom sklearn.decomposition import PCA\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\nx_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\ny_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n\nplt.figure(2, figsize=(8, 6))\nplt.clf()\n\n# Plot the training points\nplt.scatter(X[:, 0], X[:, 1], c=Y, cmap=plt.cm.Paired)\nplt.xlabel('Sepal length')\nplt.ylabel('Sepal width')\n\nplt.xlim(x_min, x_max)\nplt.ylim(y_min, y_max)\nplt.xticks(())\nplt.yticks(())\n\n# To getter a better understanding of interaction of the dimensions\n# plot the first three PCA dimensions\nfig = plt.figure(1, figsize=(8, 6))\nax = Axes3D(fig, elev=-150, azim=110)\nX_reduced = PCA(n_components=3).fit_transform(iris.data)\nax.scatter(X_reduced[:, 0], X_reduced[:, 1], X_reduced[:, 2], c=Y,\n           cmap=plt.cm.Paired)\nax.set_title(\"First three PCA directions\")\nax.set_xlabel(\"1st eigenvector\")\nax.w_xaxis.set_ticklabels([])\nax.set_ylabel(\"2nd eigenvector\")\nax.w_yaxis.set_ticklabels([])\nax.set_zlabel(\"3rd eigenvector\")\nax.w_zaxis.set_ticklabels([])\n\nplt.show()"
+        "print(__doc__)\n\n\n# Code source: Ga\u00ebl Varoquaux\n# Modified for documentation by Jaques Grobler\n# License: BSD 3 clause\n\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\nfrom sklearn import datasets\nfrom sklearn.decomposition import PCA\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\nx_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\ny_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n\nplt.figure(2, figsize=(8, 6))\nplt.clf()\n\n# Plot the training points\nplt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Paired)\nplt.xlabel('Sepal length')\nplt.ylabel('Sepal width')\n\nplt.xlim(x_min, x_max)\nplt.ylim(y_min, y_max)\nplt.xticks(())\nplt.yticks(())\n\n# To getter a better understanding of interaction of the dimensions\n# plot the first three PCA dimensions\nfig = plt.figure(1, figsize=(8, 6))\nax = Axes3D(fig, elev=-150, azim=110)\nX_reduced = PCA(n_components=3).fit_transform(iris.data)\nax.scatter(X_reduced[:, 0], X_reduced[:, 1], X_reduced[:, 2], c=y,\n           cmap=plt.cm.Paired)\nax.set_title(\"First three PCA directions\")\nax.set_xlabel(\"1st eigenvector\")\nax.w_xaxis.set_ticklabels([])\nax.set_ylabel(\"2nd eigenvector\")\nax.w_yaxis.set_ticklabels([])\nax.set_zlabel(\"3rd eigenvector\")\nax.w_zaxis.set_ticklabels([])\n\nplt.show()"
       ], 
       "outputs": [], 
       "metadata": {

dev/_downloads/auto_examples_python.zip

@@ -31,7 +31,7 @@
 # import some data to play with
 iris = datasets.load_iris()
 X = iris.data[:, :2]  # we only take the first two features.
-Y = iris.target
+y = iris.target
 
 x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
 y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
@@ -40,7 +40,7 @@
 plt.clf()
 
 # Plot the training points
-plt.scatter(X[:, 0], X[:, 1], c=Y, cmap=plt.cm.Paired)
+plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Paired)
 plt.xlabel('Sepal length')
 plt.ylabel('Sepal width')
 
@@ -54,7 +54,7 @@
 fig = plt.figure(1, figsize=(8, 6))
 ax = Axes3D(fig, elev=-150, azim=110)
 X_reduced = PCA(n_components=3).fit_transform(iris.data)
-ax.scatter(X_reduced[:, 0], X_reduced[:, 1], X_reduced[:, 2], c=Y,
+ax.scatter(X_reduced[:, 0], X_reduced[:, 1], X_reduced[:, 2], c=y,
            cmap=plt.cm.Paired)
 ax.set_title("First three PCA directions")
 ax.set_xlabel("1st eigenvector")