Pushing the docs to dev/ for branch: master, commit 2e78011ab56726f3b1590b4a4d5a53a7a72a08d2

Author: sklearn-ci

dev/_downloads/auto_examples_jupyter.zip

@@ -44,7 +44,7 @@
       },
       "outputs": [],
       "source": [
-        "for assign_labels in ('kmeans', 'discretize'):\n    t0 = time.time()\n    labels = spectral_clustering(graph, n_clusters=N_REGIONS,\n                                 assign_labels=assign_labels, random_state=42)\n    t1 = time.time()\n    labels = labels.reshape(face.shape)\n\n    plt.figure(figsize=(5, 5))\n    plt.imshow(face, cmap=plt.cm.gray)\n    for l in range(N_REGIONS):\n        plt.contour(labels == l, contours=1,\n                    colors=[plt.cm.spectral(l / float(N_REGIONS))])\n    plt.xticks(())\n    plt.yticks(())\n    title = 'Spectral clustering: %s, %.2fs' % (assign_labels, (t1 - t0))\n    print(title)\n    plt.title(title)\nplt.show()"
+        "for assign_labels in ('kmeans', 'discretize'):\n    t0 = time.time()\n    labels = spectral_clustering(graph, n_clusters=N_REGIONS,\n                                 assign_labels=assign_labels, random_state=42)\n    t1 = time.time()\n    labels = labels.reshape(face.shape)\n\n    plt.figure(figsize=(5, 5))\n    plt.imshow(face, cmap=plt.cm.gray)\n    for l in range(N_REGIONS):\n        plt.contour(labels == l,\n                    colors=[plt.cm.spectral(l / float(N_REGIONS))])\n    plt.xticks(())\n    plt.yticks(())\n    title = 'Spectral clustering: %s, %.2fs' % (assign_labels, (t1 - t0))\n    print(title)\n    plt.title(title)\nplt.show()"
       ]
     }
   ],

dev/_downloads/auto_examples_python.zip

@@ -71,7 +71,7 @@
     plt.figure(figsize=(5, 5))
     plt.imshow(face, cmap=plt.cm.gray)
     for l in range(N_REGIONS):
-        plt.contour(labels == l, contours=1,
+        plt.contour(labels == l,
                     colors=[plt.cm.spectral(l / float(N_REGIONS))])
     plt.xticks(())
     plt.yticks(())

dev/_downloads/plot_face_segmentation.ipynb

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "# Author : Vincent Michel, 2010\n#          Alexandre Gramfort, 2011\n# License: BSD 3 clause\n\nprint(__doc__)\n\nimport time as time\n\nimport numpy as np\nimport scipy as sp\n\nimport matplotlib.pyplot as plt\n\nfrom sklearn.feature_extraction.image import grid_to_graph\nfrom sklearn.cluster import AgglomerativeClustering\nfrom sklearn.externals._pilutil import imresize\n\n\n# #############################################################################\n# Generate data\ntry:  # SciPy >= 0.16 have face in misc\n    from scipy.misc import face\n    face = face(gray=True)\nexcept ImportError:\n    face = sp.face(gray=True)\n\n# Resize it to 10% of the original size to speed up the processing\nface = imresize(face, 0.10) / 255.\n\nX = np.reshape(face, (-1, 1))\n\n# #############################################################################\n# Define the structure A of the data. Pixels connected to their neighbors.\nconnectivity = grid_to_graph(*face.shape)\n\n# #############################################################################\n# Compute clustering\nprint(\"Compute structured hierarchical clustering...\")\nst = time.time()\nn_clusters = 15  # number of regions\nward = AgglomerativeClustering(n_clusters=n_clusters, linkage='ward',\n                               connectivity=connectivity)\nward.fit(X)\nlabel = np.reshape(ward.labels_, face.shape)\nprint(\"Elapsed time: \", time.time() - st)\nprint(\"Number of pixels: \", label.size)\nprint(\"Number of clusters: \", np.unique(label).size)\n\n# #############################################################################\n# Plot the results on an image\nplt.figure(figsize=(5, 5))\nplt.imshow(face, cmap=plt.cm.gray)\nfor l in range(n_clusters):\n    plt.contour(label == l, contours=1,\n                colors=[plt.cm.spectral(l / float(n_clusters)), ])\nplt.xticks(())\nplt.yticks(())\nplt.show()"
+        "# Author : Vincent Michel, 2010\n#          Alexandre Gramfort, 2011\n# License: BSD 3 clause\n\nprint(__doc__)\n\nimport time as time\n\nimport numpy as np\nimport scipy as sp\n\nimport matplotlib.pyplot as plt\n\nfrom sklearn.feature_extraction.image import grid_to_graph\nfrom sklearn.cluster import AgglomerativeClustering\nfrom sklearn.externals._pilutil import imresize\n\n\n# #############################################################################\n# Generate data\ntry:  # SciPy >= 0.16 have face in misc\n    from scipy.misc import face\n    face = face(gray=True)\nexcept ImportError:\n    face = sp.face(gray=True)\n\n# Resize it to 10% of the original size to speed up the processing\nface = imresize(face, 0.10) / 255.\n\nX = np.reshape(face, (-1, 1))\n\n# #############################################################################\n# Define the structure A of the data. Pixels connected to their neighbors.\nconnectivity = grid_to_graph(*face.shape)\n\n# #############################################################################\n# Compute clustering\nprint(\"Compute structured hierarchical clustering...\")\nst = time.time()\nn_clusters = 15  # number of regions\nward = AgglomerativeClustering(n_clusters=n_clusters, linkage='ward',\n                               connectivity=connectivity)\nward.fit(X)\nlabel = np.reshape(ward.labels_, face.shape)\nprint(\"Elapsed time: \", time.time() - st)\nprint(\"Number of pixels: \", label.size)\nprint(\"Number of clusters: \", np.unique(label).size)\n\n# #############################################################################\n# Plot the results on an image\nplt.figure(figsize=(5, 5))\nplt.imshow(face, cmap=plt.cm.gray)\nfor l in range(n_clusters):\n    plt.contour(label == l,\n                colors=[plt.cm.spectral(l / float(n_clusters)), ])\nplt.xticks(())\nplt.yticks(())\nplt.show()"
       ]
     }
   ],

dev/_downloads/plot_face_segmentation.py

@@ -61,7 +61,7 @@
 plt.figure(figsize=(5, 5))
 plt.imshow(face, cmap=plt.cm.gray)
 for l in range(n_clusters):
-    plt.contour(label == l, contours=1,
+    plt.contour(label == l,
                 colors=[plt.cm.spectral(l / float(n_clusters)), ])
 plt.xticks(())
 plt.yticks(())