scikit-learn
diff --git a/‎dev/_downloads/auto_examples_jupyter.zip
23 Bytes b/‎dev/_downloads/auto_examples_jupyter.zip
23 Bytes
diff --git a/‎dev/_downloads/auto_examples_python.zip
23 Bytes b/‎dev/_downloads/auto_examples_python.zip
23 Bytes
diff --git a/‎dev/_downloads/plot_dbscan.ipynb
Lines changed: 1 addition & 1 deletion b/‎dev/_downloads/plot_dbscan.ipynb
Lines changed: 1 addition & 1 deletion
diff --git a/‎dev/_downloads/plot_dbscan.py
Lines changed: 3 additions & 3 deletions b/‎dev/_downloads/plot_dbscan.py
Lines changed: 3 additions & 3 deletions
diff --git a/‎dev/_downloads/scikit-learn-docs.pdf
2.54 KB b/‎dev/_downloads/scikit-learn-docs.pdf
2.54 KB
diff --git a/‎dev/_images/sphx_glr_plot_agglomerative_clustering_001.png
133 Bytes b/‎dev/_images/sphx_glr_plot_agglomerative_clustering_001.png
133 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_agglomerative_clustering_0011.png
133 Bytes b/‎dev/_images/sphx_glr_plot_agglomerative_clustering_0011.png
133 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_agglomerative_clustering_002.png
116 Bytes b/‎dev/_images/sphx_glr_plot_agglomerative_clustering_002.png
116 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_agglomerative_clustering_0021.png
116 Bytes b/‎dev/_images/sphx_glr_plot_agglomerative_clustering_0021.png
116 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_agglomerative_clustering_003.png
2 Bytes b/‎dev/_images/sphx_glr_plot_agglomerative_clustering_003.png
2 Bytes
@@ -80,7 +80,7 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib.pyplot as plt\n\n# Black removed and is used for noise instead.\nunique_labels = set(labels)\ncolors = [plt.cm.Spectral(each)\n          for each in np.linspace(0, 1, len(unique_labels))]\nfor k, col in zip(unique_labels, colors):\n    if k == -1:\n        # Black used for noise.\n        col = 'k'\n\n    class_member_mask = (labels == k)\n\n    xy = X[class_member_mask & core_samples_mask]\n    plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=col,\n             markeredgecolor='k', markersize=14)\n\n    xy = X[class_member_mask & ~core_samples_mask]\n    plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=col,\n             markeredgecolor='k', markersize=6)\n\nplt.title('Estimated number of clusters: %d' % n_clusters_)\nplt.show()"
+        "import matplotlib.pyplot as plt\n\n# Black removed and is used for noise instead.\nunique_labels = set(labels)\ncolors = [plt.cm.Spectral(each)\n          for each in np.linspace(0, 1, len(unique_labels))]\nfor k, col in zip(unique_labels, colors):\n    if k == -1:\n        # Black used for noise.\n        col = [0, 0, 0, 1]\n\n    class_member_mask = (labels == k)\n\n    xy = X[class_member_mask & core_samples_mask]\n    plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col),\n             markeredgecolor='k', markersize=14)\n\n    xy = X[class_member_mask & ~core_samples_mask]\n    plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col),\n             markeredgecolor='k', markersize=6)\n\nplt.title('Estimated number of clusters: %d' % n_clusters_)\nplt.show()"
       ]
     }
   ],
 
@@ -57,16 +57,16 @@
 for k, col in zip(unique_labels, colors):
     if k == -1:
         # Black used for noise.
-        col = 'k'
+        col = [0, 0, 0, 1]
 
     class_member_mask = (labels == k)
 
     xy = X[class_member_mask & core_samples_mask]
-    plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=col,
+    plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col),
              markeredgecolor='k', markersize=14)
 
     xy = X[class_member_mask & ~core_samples_mask]
-    plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=col,
+    plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col),
              markeredgecolor='k', markersize=6)
 
 plt.title('Estimated number of clusters: %d' % n_clusters_)
Original file line number	Diff line number	Diff line change
`@@ -80,7 +80,7 @@`
`80`	`80`	`},`
`81`	`81`	`"outputs": [],`
`82`	`82`	`"source": [`
`83`		- "import matplotlib.pyplot as plt\n\n# Black removed and is used for noise instead.\nunique_labels = set(labels)\ncolors = [plt.cm.Spectral(each)\n for each in np.linspace(0, 1, len(unique_labels))]\nfor k, col in zip(unique_labels, colors):\n if k == -1:\n # Black used for noise.\n col = 'k'\n\n class_member_mask = (labels == k)\n\n xy = X[class_member_mask & core_samples_mask]\n plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=col,\n markeredgecolor='k', markersize=14)\n\n xy = X[class_member_mask & ~core_samples_mask]\n plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=col,\n markeredgecolor='k', markersize=6)\n\nplt.title('Estimated number of clusters: %d' % n_clusters_)\nplt.show()"
	`83`	+ "import matplotlib.pyplot as plt\n\n# Black removed and is used for noise instead.\nunique_labels = set(labels)\ncolors = [plt.cm.Spectral(each)\n for each in np.linspace(0, 1, len(unique_labels))]\nfor k, col in zip(unique_labels, colors):\n if k == -1:\n # Black used for noise.\n col = [0, 0, 0, 1]\n\n class_member_mask = (labels == k)\n\n xy = X[class_member_mask & core_samples_mask]\n plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col),\n markeredgecolor='k', markersize=14)\n\n xy = X[class_member_mask & ~core_samples_mask]\n plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col),\n markeredgecolor='k', markersize=6)\n\nplt.title('Estimated number of clusters: %d' % n_clusters_)\nplt.show()"
`84`	`84`	`]`
`85`	`85`	`}`
`86`	`86`	`],`