Pushing the docs to dev/ for branch: master, commit 6f7394953b0dbcd6400b9f39f1dbd2e430a4a9e5

Author: sklearn-ci

dev/_downloads/auto_examples_jupyter.zip

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "# Authors: Gael Varoquaux, Nelle Varoquaux\n# License: BSD 3 clause\n\nimport time\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.cluster import AgglomerativeClustering\nfrom sklearn.neighbors import kneighbors_graph\n\n# Generate sample data\nn_samples = 1500\nnp.random.seed(0)\nt = 1.5 * np.pi * (1 + 3 * np.random.rand(1, n_samples))\nx = t * np.cos(t)\ny = t * np.sin(t)\n\n\nX = np.concatenate((x, y))\nX += .7 * np.random.randn(2, n_samples)\nX = X.T\n\n# Create a graph capturing local connectivity. Larger number of neighbors\n# will give more homogeneous clusters to the cost of computation\n# time. A very large number of neighbors gives more evenly distributed\n# cluster sizes, but may not impose the local manifold structure of\n# the data\nknn_graph = kneighbors_graph(X, 30, include_self=False)\n\nfor connectivity in (None, knn_graph):\n    for n_clusters in (30, 3):\n        plt.figure(figsize=(10, 4))\n        for index, linkage in enumerate(('average',\n                                         'complete',\n                                         'ward',\n                                         'single')):\n            plt.subplot(1, 4, index + 1)\n            model = AgglomerativeClustering(linkage=linkage,\n                                            connectivity=connectivity,\n                                            n_clusters=n_clusters)\n            t0 = time.time()\n            model.fit(X)\n            elapsed_time = time.time() - t0\n            plt.scatter(X[:, 0], X[:, 1], c=model.labels_,\n                        cmap=plt.cm.spectral)\n            plt.title('linkage=%s\\n(time %.2fs)' % (linkage, elapsed_time),\n                      fontdict=dict(verticalalignment='top'))\n            plt.axis('equal')\n            plt.axis('off')\n\n            plt.subplots_adjust(bottom=0, top=.89, wspace=0,\n                                left=0, right=1)\n            plt.suptitle('n_cluster=%i, connectivity=%r' %\n                         (n_clusters, connectivity is not None), size=17)\n\n\nplt.show()"
+        "# Authors: Gael Varoquaux, Nelle Varoquaux\n# License: BSD 3 clause\n\nimport time\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.cluster import AgglomerativeClustering\nfrom sklearn.neighbors import kneighbors_graph\n\n# Generate sample data\nn_samples = 1500\nnp.random.seed(0)\nt = 1.5 * np.pi * (1 + 3 * np.random.rand(1, n_samples))\nx = t * np.cos(t)\ny = t * np.sin(t)\n\n\nX = np.concatenate((x, y))\nX += .7 * np.random.randn(2, n_samples)\nX = X.T\n\n# Create a graph capturing local connectivity. Larger number of neighbors\n# will give more homogeneous clusters to the cost of computation\n# time. A very large number of neighbors gives more evenly distributed\n# cluster sizes, but may not impose the local manifold structure of\n# the data\nknn_graph = kneighbors_graph(X, 30, include_self=False)\n\nfor connectivity in (None, knn_graph):\n    for n_clusters in (30, 3):\n        plt.figure(figsize=(10, 4))\n        for index, linkage in enumerate(('average',\n                                         'complete',\n                                         'ward',\n                                         'single')):\n            plt.subplot(1, 4, index + 1)\n            model = AgglomerativeClustering(linkage=linkage,\n                                            connectivity=connectivity,\n                                            n_clusters=n_clusters)\n            t0 = time.time()\n            model.fit(X)\n            elapsed_time = time.time() - t0\n            plt.scatter(X[:, 0], X[:, 1], c=model.labels_,\n                        cmap=plt.cm.nipy_spectral)\n            plt.title('linkage=%s\\n(time %.2fs)' % (linkage, elapsed_time),\n                      fontdict=dict(verticalalignment='top'))\n            plt.axis('equal')\n            plt.axis('off')\n\n            plt.subplots_adjust(bottom=0, top=.89, wspace=0,\n                                left=0, right=1)\n            plt.suptitle('n_cluster=%i, connectivity=%r' %\n                         (n_clusters, connectivity is not None), size=17)\n\n\nplt.show()"
       ]
     }
   ],

dev/_downloads/auto_examples_python.zip

@@ -65,7 +65,7 @@
             model.fit(X)
             elapsed_time = time.time() - t0
             plt.scatter(X[:, 0], X[:, 1], c=model.labels_,
-                        cmap=plt.cm.spectral)
+                        cmap=plt.cm.nipy_spectral)
             plt.title('linkage=%s\n(time %.2fs)' % (linkage, elapsed_time),
                       fontdict=dict(verticalalignment='top'))
             plt.axis('equal')

dev/_downloads/plot_agglomerative_clustering.ipynb

@@ -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(rescaled_coins.shape)\n\n    plt.figure(figsize=(5, 5))\n    plt.imshow(rescaled_coins, 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()"
+        "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(rescaled_coins.shape)\n\n    plt.figure(figsize=(5, 5))\n    plt.imshow(rescaled_coins, cmap=plt.cm.gray)\n    for l in range(N_REGIONS):\n        plt.contour(labels == l,\n                    colors=[plt.cm.nipy_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/plot_agglomerative_clustering.py

@@ -72,7 +72,7 @@
     plt.imshow(rescaled_coins, cmap=plt.cm.gray)
     for l in range(N_REGIONS):
         plt.contour(labels == l,
-                    colors=[plt.cm.spectral(l / float(N_REGIONS))])
+                    colors=[plt.cm.nipy_spectral(l / float(N_REGIONS))])
     plt.xticks(())
     plt.yticks(())
     title = 'Spectral clustering: %s, %.2fs' % (assign_labels, (t1 - t0))

dev/_downloads/plot_coin_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\nfrom scipy.ndimage.filters import gaussian_filter\n\nimport matplotlib.pyplot as plt\n\nfrom skimage.data import coins\nfrom skimage.transform import rescale\n\nfrom sklearn.feature_extraction.image import grid_to_graph\nfrom sklearn.cluster import AgglomerativeClustering\n\n\n# #############################################################################\n# Generate data\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\")\n\nX = np.reshape(rescaled_coins, (-1, 1))\n\n# #############################################################################\n# Define the structure A of the data. Pixels connected to their neighbors.\nconnectivity = grid_to_graph(*rescaled_coins.shape)\n\n# #############################################################################\n# Compute clustering\nprint(\"Compute structured hierarchical clustering...\")\nst = time.time()\nn_clusters = 27  # number of regions\nward = AgglomerativeClustering(n_clusters=n_clusters, linkage='ward',\n                               connectivity=connectivity)\nward.fit(X)\nlabel = np.reshape(ward.labels_, rescaled_coins.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(rescaled_coins, 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()"
+        "# 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\nfrom scipy.ndimage.filters import gaussian_filter\n\nimport matplotlib.pyplot as plt\n\nfrom skimage.data import coins\nfrom skimage.transform import rescale\n\nfrom sklearn.feature_extraction.image import grid_to_graph\nfrom sklearn.cluster import AgglomerativeClustering\n\n\n# #############################################################################\n# Generate data\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\")\n\nX = np.reshape(rescaled_coins, (-1, 1))\n\n# #############################################################################\n# Define the structure A of the data. Pixels connected to their neighbors.\nconnectivity = grid_to_graph(*rescaled_coins.shape)\n\n# #############################################################################\n# Compute clustering\nprint(\"Compute structured hierarchical clustering...\")\nst = time.time()\nn_clusters = 27  # number of regions\nward = AgglomerativeClustering(n_clusters=n_clusters, linkage='ward',\n                               connectivity=connectivity)\nward.fit(X)\nlabel = np.reshape(ward.labels_, rescaled_coins.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(rescaled_coins, cmap=plt.cm.gray)\nfor l in range(n_clusters):\n    plt.contour(label == l,\n                colors=[plt.cm.nipy_spectral(l / float(n_clusters)), ])\nplt.xticks(())\nplt.yticks(())\nplt.show()"
       ]
     }
   ],

dev/_downloads/plot_coin_segmentation.py

@@ -63,7 +63,7 @@
 plt.imshow(rescaled_coins, cmap=plt.cm.gray)
 for l in range(n_clusters):
     plt.contour(label == l,
-                colors=[plt.cm.spectral(l / float(n_clusters)), ])
+                colors=[plt.cm.nipy_spectral(l / float(n_clusters)), ])
 plt.xticks(())
 plt.yticks(())
 plt.show()

dev/_downloads/plot_coin_ward_segmentation.ipynb

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "print(__doc__)\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 matplotlib.pyplot as plt\n\nfrom sklearn import datasets, cluster\nfrom sklearn.feature_extraction.image import grid_to_graph\n\ndigits = datasets.load_digits()\nimages = digits.images\nX = np.reshape(images, (len(images), -1))\nconnectivity = grid_to_graph(*images[0].shape)\n\nagglo = cluster.FeatureAgglomeration(connectivity=connectivity,\n                                     n_clusters=32)\n\nagglo.fit(X)\nX_reduced = agglo.transform(X)\n\nX_restored = agglo.inverse_transform(X_reduced)\nimages_restored = np.reshape(X_restored, images.shape)\nplt.figure(1, figsize=(4, 3.5))\nplt.clf()\nplt.subplots_adjust(left=.01, right=.99, bottom=.01, top=.91)\nfor i in range(4):\n    plt.subplot(3, 4, i + 1)\n    plt.imshow(images[i], cmap=plt.cm.gray, vmax=16, interpolation='nearest')\n    plt.xticks(())\n    plt.yticks(())\n    if i == 1:\n        plt.title('Original data')\n    plt.subplot(3, 4, 4 + i + 1)\n    plt.imshow(images_restored[i], cmap=plt.cm.gray, vmax=16,\n               interpolation='nearest')\n    if i == 1:\n        plt.title('Agglomerated data')\n    plt.xticks(())\n    plt.yticks(())\n\nplt.subplot(3, 4, 10)\nplt.imshow(np.reshape(agglo.labels_, images[0].shape),\n           interpolation='nearest', cmap=plt.cm.spectral)\nplt.xticks(())\nplt.yticks(())\nplt.title('Labels')\nplt.show()"
+        "print(__doc__)\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 matplotlib.pyplot as plt\n\nfrom sklearn import datasets, cluster\nfrom sklearn.feature_extraction.image import grid_to_graph\n\ndigits = datasets.load_digits()\nimages = digits.images\nX = np.reshape(images, (len(images), -1))\nconnectivity = grid_to_graph(*images[0].shape)\n\nagglo = cluster.FeatureAgglomeration(connectivity=connectivity,\n                                     n_clusters=32)\n\nagglo.fit(X)\nX_reduced = agglo.transform(X)\n\nX_restored = agglo.inverse_transform(X_reduced)\nimages_restored = np.reshape(X_restored, images.shape)\nplt.figure(1, figsize=(4, 3.5))\nplt.clf()\nplt.subplots_adjust(left=.01, right=.99, bottom=.01, top=.91)\nfor i in range(4):\n    plt.subplot(3, 4, i + 1)\n    plt.imshow(images[i], cmap=plt.cm.gray, vmax=16, interpolation='nearest')\n    plt.xticks(())\n    plt.yticks(())\n    if i == 1:\n        plt.title('Original data')\n    plt.subplot(3, 4, 4 + i + 1)\n    plt.imshow(images_restored[i], cmap=plt.cm.gray, vmax=16,\n               interpolation='nearest')\n    if i == 1:\n        plt.title('Agglomerated data')\n    plt.xticks(())\n    plt.yticks(())\n\nplt.subplot(3, 4, 10)\nplt.imshow(np.reshape(agglo.labels_, images[0].shape),\n           interpolation='nearest', cmap=plt.cm.nipy_spectral)\nplt.xticks(())\nplt.yticks(())\nplt.title('Labels')\nplt.show()"
       ]
     }
   ],

dev/_downloads/plot_coin_ward_segmentation.py

@@ -54,7 +54,7 @@
 
 plt.subplot(3, 4, 10)
 plt.imshow(np.reshape(agglo.labels_, images[0].shape),
-           interpolation='nearest', cmap=plt.cm.spectral)
+           interpolation='nearest', cmap=plt.cm.nipy_spectral)
 plt.xticks(())
 plt.yticks(())
 plt.title('Labels')