Pushing the docs to dev/ for branch: master, commit bd0fc236e00c57b9a5212e8802c325c99af7b1b4

Author: sklearn-ci

dev/_downloads/auto_examples_jupyter.zip

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "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 numpy as np\nimport scipy as sp\nimport matplotlib.pyplot as plt\n\nfrom sklearn import cluster\nfrom sklearn.utils.testing import SkipTest\nfrom sklearn.utils.fixes import sp_version\n\nif sp_version < (0, 12):\n    raise SkipTest(\"Skipping because SciPy version earlier than 0.12.0 and \"\n                   \"thus does not include the scipy.misc.face() image.\")\n\ntry:\n    face = sp.face(gray=True)\nexcept AttributeError:\n    # Newer versions of scipy have face in misc\n    from scipy import misc\n    face = misc.face(gray=True)\n\nn_clusters = 5\nnp.random.seed(0)\n    \nX = face.reshape((-1, 1))  # We need an (n_sample, n_feature) array\nk_means = cluster.KMeans(n_clusters=n_clusters, n_init=4)\nk_means.fit(X)\nvalues = k_means.cluster_centers_.squeeze()\nlabels = k_means.labels_\n\n# create an array from labels and values\nface_compressed = np.choose(labels, values)\nface_compressed.shape = face.shape\n\nvmin = face.min()\nvmax = face.max()\n\n# original face\nplt.figure(1, figsize=(3, 2.2))\nplt.imshow(face, cmap=plt.cm.gray, vmin=vmin, vmax=256)\n\n# compressed face\nplt.figure(2, figsize=(3, 2.2))\nplt.imshow(face_compressed, cmap=plt.cm.gray, vmin=vmin, vmax=vmax)\n\n# equal bins face\nregular_values = np.linspace(0, 256, n_clusters + 1)\nregular_labels = np.searchsorted(regular_values, face) - 1\nregular_values = .5 * (regular_values[1:] + regular_values[:-1])  # mean\nregular_face = np.choose(regular_labels.ravel(), regular_values, mode=\"clip\")\nregular_face.shape = face.shape\nplt.figure(3, figsize=(3, 2.2))\nplt.imshow(regular_face, cmap=plt.cm.gray, vmin=vmin, vmax=vmax)\n\n# histogram\nplt.figure(4, figsize=(3, 2.2))\nplt.clf()\nplt.axes([.01, .01, .98, .98])\nplt.hist(X, bins=256, color='.5', edgecolor='.5')\nplt.yticks(())\nplt.xticks(regular_values)\nvalues = np.sort(values)\nfor center_1, center_2 in zip(values[:-1], values[1:]):\n    plt.axvline(.5 * (center_1 + center_2), color='b')\n\nfor center_1, center_2 in zip(regular_values[:-1], regular_values[1:]):\n    plt.axvline(.5 * (center_1 + center_2), color='b', linestyle='--')\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 numpy as np\nimport scipy as sp\nimport matplotlib.pyplot as plt\n\nfrom sklearn import cluster\n\n\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\nn_clusters = 5\nnp.random.seed(0)\n\nX = face.reshape((-1, 1))  # We need an (n_sample, n_feature) array\nk_means = cluster.KMeans(n_clusters=n_clusters, n_init=4)\nk_means.fit(X)\nvalues = k_means.cluster_centers_.squeeze()\nlabels = k_means.labels_\n\n# create an array from labels and values\nface_compressed = np.choose(labels, values)\nface_compressed.shape = face.shape\n\nvmin = face.min()\nvmax = face.max()\n\n# original face\nplt.figure(1, figsize=(3, 2.2))\nplt.imshow(face, cmap=plt.cm.gray, vmin=vmin, vmax=256)\n\n# compressed face\nplt.figure(2, figsize=(3, 2.2))\nplt.imshow(face_compressed, cmap=plt.cm.gray, vmin=vmin, vmax=vmax)\n\n# equal bins face\nregular_values = np.linspace(0, 256, n_clusters + 1)\nregular_labels = np.searchsorted(regular_values, face) - 1\nregular_values = .5 * (regular_values[1:] + regular_values[:-1])  # mean\nregular_face = np.choose(regular_labels.ravel(), regular_values, mode=\"clip\")\nregular_face.shape = face.shape\nplt.figure(3, figsize=(3, 2.2))\nplt.imshow(regular_face, cmap=plt.cm.gray, vmin=vmin, vmax=vmax)\n\n# histogram\nplt.figure(4, figsize=(3, 2.2))\nplt.clf()\nplt.axes([.01, .01, .98, .98])\nplt.hist(X, bins=256, color='.5', edgecolor='.5')\nplt.yticks(())\nplt.xticks(regular_values)\nvalues = np.sort(values)\nfor center_1, center_2 in zip(values[:-1], values[1:]):\n    plt.axvline(.5 * (center_1 + center_2), color='b')\n\nfor center_1, center_2 in zip(regular_values[:-1], regular_values[1:]):\n    plt.axvline(.5 * (center_1 + center_2), color='b', linestyle='--')\n\nplt.show()"
       ]
     }
   ],

dev/_downloads/auto_examples_python.zip

@@ -23,23 +23,17 @@
 import matplotlib.pyplot as plt
 
 from sklearn import cluster
-from sklearn.utils.testing import SkipTest
-from sklearn.utils.fixes import sp_version
 
-if sp_version < (0, 12):
-    raise SkipTest("Skipping because SciPy version earlier than 0.12.0 and "
-                   "thus does not include the scipy.misc.face() image.")
 
-try:
+try:  # SciPy >= 0.16 have face in misc
+    from scipy.misc import face
+    face = face(gray=True)
+except ImportError:
     face = sp.face(gray=True)
-except AttributeError:
-    # Newer versions of scipy have face in misc
-    from scipy import misc
-    face = misc.face(gray=True)
 
 n_clusters = 5
 np.random.seed(0)
-    
+
 X = face.reshape((-1, 1))  # We need an (n_sample, n_feature) array
 k_means = cluster.KMeans(n_clusters=n_clusters, n_init=4)
 k_means.fit(X)

dev/_downloads/plot_face_compress.ipynb

@@ -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\nfrom sklearn.utils.testing import SkipTest\nfrom sklearn.utils.fixes import sp_version\n\nif sp_version < (0, 12):\n    raise SkipTest(\"Skipping because SciPy version earlier than 0.12.0 and \"\n                   \"thus does not include the scipy.misc.face() image.\")\n\n\n# load the raccoon face as a numpy array\ntry:\n    face = sp.face(gray=True)\nexcept AttributeError:\n    # Newer versions of scipy have face in misc\n    from scipy import misc\n    face = misc.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\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"
       ]
     },
     {

dev/_downloads/plot_face_compress.py

@@ -30,21 +30,14 @@
 
 from sklearn.feature_extraction import image
 from sklearn.cluster import spectral_clustering
-from sklearn.utils.testing import SkipTest
-from sklearn.utils.fixes import sp_version
-
-if sp_version < (0, 12):
-    raise SkipTest("Skipping because SciPy version earlier than 0.12.0 and "
-                   "thus does not include the scipy.misc.face() image.")
 
 
 # load the raccoon face as a numpy array
-try:
+try:  # SciPy >= 0.16 have face in misc
+    from scipy.misc import face
+    face = face(gray=True)
+except ImportError:
     face = sp.face(gray=True)
-except AttributeError:
-    # Newer versions of scipy have face in misc
-    from scipy import misc
-    face = misc.face(gray=True)
 
 # Resize it to 10% of the original size to speed up the processing
 face = sp.misc.imresize(face, 0.10) / 255.

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.utils.testing import SkipTest\nfrom sklearn.utils.fixes import sp_version\n\nif sp_version < (0, 12):\n    raise SkipTest(\"Skipping because SciPy version earlier than 0.12.0 and \"\n                   \"thus does not include the scipy.misc.face() image.\")"
+        "# 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"
       ]
     },
     {
@@ -44,7 +44,7 @@
       },
       "outputs": [],
       "source": [
-        "try:\n    face = sp.face(gray=True)\nexcept AttributeError:\n    # Newer versions of scipy have face in misc\n    from scipy import misc\n    face = misc.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))"
+        "try:  # 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))"
       ]
     },
     {

dev/_downloads/plot_face_segmentation.py

@@ -23,22 +23,15 @@
 
 from sklearn.feature_extraction.image import grid_to_graph
 from sklearn.cluster import AgglomerativeClustering
-from sklearn.utils.testing import SkipTest
-from sklearn.utils.fixes import sp_version
-
-if sp_version < (0, 12):
-    raise SkipTest("Skipping because SciPy version earlier than 0.12.0 and "
-                   "thus does not include the scipy.misc.face() image.")
 
 
 ###############################################################################
 # Generate data
-try:
+try:  # SciPy >= 0.16 have face in misc
+    from scipy.misc import face
+    face = face(gray=True)
+except ImportError:
     face = sp.face(gray=True)
-except AttributeError:
-    # Newer versions of scipy have face in misc
-    from scipy import misc
-    face = misc.face(gray=True)
 
 # Resize it to 10% of the original size to speed up the processing
 face = sp.misc.imresize(face, 0.10) / 255.

dev/_downloads/plot_face_ward_segmentation.ipynb

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "print(__doc__)\n\nfrom time import time\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport scipy as sp\n\nfrom sklearn.decomposition import MiniBatchDictionaryLearning\nfrom sklearn.feature_extraction.image import extract_patches_2d\nfrom sklearn.feature_extraction.image import reconstruct_from_patches_2d\nfrom sklearn.utils.testing import SkipTest\nfrom sklearn.utils.fixes import sp_version\n\nif sp_version < (0, 12):\n    raise SkipTest(\"Skipping because SciPy version earlier than 0.12.0 and \"\n                   \"thus does not include the scipy.misc.face() image.\")"
+        "print(__doc__)\n\nfrom time import time\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport scipy as sp\n\nfrom sklearn.decomposition import MiniBatchDictionaryLearning\nfrom sklearn.feature_extraction.image import extract_patches_2d\nfrom sklearn.feature_extraction.image import reconstruct_from_patches_2d"
       ]
     },
     {
@@ -37,7 +37,7 @@
       },
       "outputs": [],
       "source": [
-        "try:\n    from scipy import misc\n    face = misc.face(gray=True)\nexcept AttributeError:\n    # Old versions of scipy have face in the top level package\n    face = sp.face(gray=True)\n\n# Convert from uint8 representation with values between 0 and 255 to\n# a floating point representation with values between 0 and 1.\nface = face / 255\n\n# downsample for higher speed\nface = face[::2, ::2] + face[1::2, ::2] + face[::2, 1::2] + face[1::2, 1::2]\nface /= 4.0\nheight, width = face.shape\n\n# Distort the right half of the image\nprint('Distorting image...')\ndistorted = face.copy()\ndistorted[:, width // 2:] += 0.075 * np.random.randn(height, width // 2)\n\n# Extract all reference patches from the left half of the image\nprint('Extracting reference patches...')\nt0 = time()\npatch_size = (7, 7)\ndata = extract_patches_2d(distorted[:, :width // 2], patch_size)\ndata = data.reshape(data.shape[0], -1)\ndata -= np.mean(data, axis=0)\ndata /= np.std(data, axis=0)\nprint('done in %.2fs.' % (time() - t0))"
+        "try:  # 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# Convert from uint8 representation with values between 0 and 255 to\n# a floating point representation with values between 0 and 1.\nface = face / 255\n\n# downsample for higher speed\nface = face[::2, ::2] + face[1::2, ::2] + face[::2, 1::2] + face[1::2, 1::2]\nface /= 4.0\nheight, width = face.shape\n\n# Distort the right half of the image\nprint('Distorting image...')\ndistorted = face.copy()\ndistorted[:, width // 2:] += 0.075 * np.random.randn(height, width // 2)\n\n# Extract all reference patches from the left half of the image\nprint('Extracting reference patches...')\nt0 = time()\npatch_size = (7, 7)\ndata = extract_patches_2d(distorted[:, :width // 2], patch_size)\ndata = data.reshape(data.shape[0], -1)\ndata -= np.mean(data, axis=0)\ndata /= np.std(data, axis=0)\nprint('done in %.2fs.' % (time() - t0))"
       ]
     },
     {

dev/_downloads/plot_face_ward_segmentation.py

@@ -42,19 +42,13 @@
 from sklearn.decomposition import MiniBatchDictionaryLearning
 from sklearn.feature_extraction.image import extract_patches_2d
 from sklearn.feature_extraction.image import reconstruct_from_patches_2d
-from sklearn.utils.testing import SkipTest
-from sklearn.utils.fixes import sp_version
 
-if sp_version < (0, 12):
-    raise SkipTest("Skipping because SciPy version earlier than 0.12.0 and "
-                   "thus does not include the scipy.misc.face() image.")
 
 ###############################################################################
-try:
-    from scipy import misc
-    face = misc.face(gray=True)
-except AttributeError:
-    # Old versions of scipy have face in the top level package
+try:  # SciPy >= 0.16 have face in misc
+    from scipy.misc import face
+    face = face(gray=True)
+except ImportError:
     face = sp.face(gray=True)
 
 # Convert from uint8 representation with values between 0 and 255 to