Pushing the docs to dev/ for branch: master, commit 96dd337ae073652ed4d77d331b285e67629c519d

Author: sklearn-ci

dev/_downloads/auto_examples_jupyter.zip

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "print(__doc__)\n\n# Author: Gael Varoquaux <[email protected]>, Brian Cheung\n# License: BSD 3 clause\n\nimport time\n\nimport numpy as np\nimport scipy as sp\nimport matplotlib.pyplot as plt\n\nfrom sklearn.feature_extraction import image\nfrom sklearn.cluster import spectral_clustering\n\n\n# load the raccoon face as a numpy array\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 = sp.misc.imresize(face, 0.10) / 255.\n\n# Convert the image into a graph with the value of the gradient on the\n# edges.\ngraph = image.img_to_graph(face)\n\n# Take a decreasing function of the gradient: an exponential\n# The smaller beta is, the more independent the segmentation is of the\n# actual image. For beta=1, the segmentation is close to a voronoi\nbeta = 5\neps = 1e-6\ngraph.data = np.exp(-beta * graph.data / graph.data.std()) + eps\n\n# Apply spectral clustering (this step goes much faster if you have pyamg\n# installed)\nN_REGIONS = 25"
+        "print(__doc__)\n\n# Author: Gael Varoquaux <[email protected]>, Brian Cheung\n# License: BSD 3 clause\n\nimport time\n\nimport numpy as np\nimport scipy as sp\nimport matplotlib.pyplot as plt\n\nfrom sklearn.feature_extraction import image\nfrom sklearn.cluster import spectral_clustering\nfrom sklearn.externals._pilutil import imresize\n\n\n# load the raccoon face as a numpy array\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\n# Convert the image into a graph with the value of the gradient on the\n# edges.\ngraph = image.img_to_graph(face)\n\n# Take a decreasing function of the gradient: an exponential\n# The smaller beta is, the more independent the segmentation is of the\n# actual image. For beta=1, the segmentation is close to a voronoi\nbeta = 5\neps = 1e-6\ngraph.data = np.exp(-beta * graph.data / graph.data.std()) + eps\n\n# Apply spectral clustering (this step goes much faster if you have pyamg\n# installed)\nN_REGIONS = 25"
       ]
     },
     {

dev/_downloads/auto_examples_python.zip

@@ -30,6 +30,7 @@
 
 from sklearn.feature_extraction import image
 from sklearn.cluster import spectral_clustering
+from sklearn.externals._pilutil import imresize
 
 
 # load the raccoon face as a numpy array
@@ -40,7 +41,7 @@
     face = sp.face(gray=True)
 
 # Resize it to 10% of the original size to speed up the processing
-face = sp.misc.imresize(face, 0.10) / 255.
+face = imresize(face, 0.10) / 255.
 
 # Convert the image into a graph with the value of the gradient on the
 # edges.

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\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 = sp.misc.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, contours=1,\n                colors=[plt.cm.spectral(l / float(n_clusters)), ])\nplt.xticks(())\nplt.yticks(())\nplt.show()"
       ]
     }
   ],

dev/_downloads/plot_face_segmentation.py

@@ -23,6 +23,7 @@
 
 from sklearn.feature_extraction.image import grid_to_graph
 from sklearn.cluster import AgglomerativeClustering
+from sklearn.externals._pilutil import imresize
 
 
 # #############################################################################
@@ -34,7 +35,7 @@
     face = sp.face(gray=True)
 
 # Resize it to 10% of the original size to speed up the processing
-face = sp.misc.imresize(face, 0.10) / 255.
+face = imresize(face, 0.10) / 255.
 
 X = np.reshape(face, (-1, 1))