Pushing the docs to dev/ for branch: master, commit f4b230cc65f2a9091545779cb8595848765904fc

Author: sklearn-ci

dev/_downloads/auto_examples_jupyter.zip

@@ -37,7 +37,7 @@
       },
       "outputs": [],
       "source": [
-        "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))"
+        "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/auto_examples_python.zip

@@ -53,7 +53,7 @@
 
 # Convert from uint8 representation with values between 0 and 255 to
 # a floating point representation with values between 0 and 1.
-face = face / 255
+face = face / 255.
 
 # downsample for higher speed
 face = face[::2, ::2] + face[1::2, ::2] + face[::2, 1::2] + face[1::2, 1::2]