Pushing the docs to dev/ for branch: main, commit 51c8e0e19d042a2dbbc1f9477557c07c8a34ab96

Author: sklearn-ci

dev/_downloads/006fc185672e58b056a5c134db26935c/plot_coin_segmentation.ipynb

@@ -15,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "# Author: Gael Varoquaux <gael.varoquaux@normalesup.org>\n#         Brian Cheung\n#         Andrew Knyazev <Andrew.Knyazev@ucdenver.edu>\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport time\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom scipy.ndimage import gaussian_filter\nfrom skimage.data import coins\nfrom skimage.transform import rescale\n\nfrom sklearn.cluster import spectral_clustering\nfrom sklearn.feature_extraction import image\n\n# load the coins as a numpy array\norig_coins = coins()\n\n# Resize it to 20% of the original size to speed up the processing\n# Applying a Gaussian filter for smoothing prior to down-scaling\n# reduces aliasing artifacts.\nsmoothened_coins = gaussian_filter(orig_coins, sigma=2)\nrescaled_coins = rescale(smoothened_coins, 0.2, mode=\"reflect\", anti_aliasing=False)\n\n# Convert the image into a graph with the value of the gradient on the\n# edges.\ngraph = image.img_to_graph(rescaled_coins)\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 = 10\neps = 1e-6\ngraph.data = np.exp(-beta * graph.data / graph.data.std()) + eps\n\n# The number of segmented regions to display needs to be chosen manually.\n# The current version of 'spectral_clustering' does not support determining\n# the number of good quality clusters automatically.\nn_regions = 26"
+        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport time\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom scipy.ndimage import gaussian_filter\nfrom skimage.data import coins\nfrom skimage.transform import rescale\n\nfrom sklearn.cluster import spectral_clustering\nfrom sklearn.feature_extraction import image\n\n# load the coins as a numpy array\norig_coins = coins()\n\n# Resize it to 20% of the original size to speed up the processing\n# Applying a Gaussian filter for smoothing prior to down-scaling\n# reduces aliasing artifacts.\nsmoothened_coins = gaussian_filter(orig_coins, sigma=2)\nrescaled_coins = rescale(smoothened_coins, 0.2, mode=\"reflect\", anti_aliasing=False)\n\n# Convert the image into a graph with the value of the gradient on the\n# edges.\ngraph = image.img_to_graph(rescaled_coins)\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 = 10\neps = 1e-6\ngraph.data = np.exp(-beta * graph.data / graph.data.std()) + eps\n\n# The number of segmented regions to display needs to be chosen manually.\n# The current version of 'spectral_clustering' does not support determining\n# the number of good quality clusters automatically.\nn_regions = 26"
       ]
     },
     {

dev/_downloads/01fdc7c95204e4a420de7cd297711693/plot_feature_union.py

@@ -16,8 +16,7 @@
 
 """
 
-# Author: Andreas Mueller <amueller@ais.uni-bonn.de>
-#
+# Authors: The scikit-learn developers
 # SPDX-License-Identifier: BSD-3-Clause
 
 from sklearn.datasets import load_iris