Pushing the docs to dev/ for branch: master, commit e443c05ea3a4c2634611253759ddbeb4367fe70c

Author: sklearn-ci

dev/_downloads/auto_examples_jupyter.zip

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "print(__doc__)\n\n# Authors: Clay Woolam <[email protected]>\n#          Andreas Mueller <[email protected]>\n# License: BSD\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.semi_supervised import label_propagation\nfrom sklearn.datasets import make_circles\n\n# generate ring with inner box\nn_samples = 200\nX, y = make_circles(n_samples=n_samples, shuffle=False)\nouter, inner = 0, 1\nlabels = -np.ones(n_samples)\nlabels[0] = outer\nlabels[-1] = inner\n\n# #############################################################################\n# Learn with LabelSpreading\nlabel_spread = label_propagation.LabelSpreading(kernel='knn', alpha=0.2)\nlabel_spread.fit(X, labels)\n\n# #############################################################################\n# Plot output labels\noutput_labels = label_spread.transduction_\nplt.figure(figsize=(8.5, 4))\nplt.subplot(1, 2, 1)\nplt.scatter(X[labels == outer, 0], X[labels == outer, 1], color='navy',\n            marker='s', lw=0, label=\"outer labeled\", s=10)\nplt.scatter(X[labels == inner, 0], X[labels == inner, 1], color='c',\n            marker='s', lw=0, label='inner labeled', s=10)\nplt.scatter(X[labels == -1, 0], X[labels == -1, 1], color='darkorange',\n            marker='.', label='unlabeled')\nplt.legend(scatterpoints=1, shadow=False, loc='upper right')\nplt.title(\"Raw data (2 classes=outer and inner)\")\n\nplt.subplot(1, 2, 2)\noutput_label_array = np.asarray(output_labels)\nouter_numbers = np.where(output_label_array == outer)[0]\ninner_numbers = np.where(output_label_array == inner)[0]\nplt.scatter(X[outer_numbers, 0], X[outer_numbers, 1], color='navy',\n            marker='s', lw=0, s=10, label=\"outer learned\")\nplt.scatter(X[inner_numbers, 0], X[inner_numbers, 1], color='c',\n            marker='s', lw=0, s=10, label=\"inner learned\")\nplt.legend(scatterpoints=1, shadow=False, loc='upper right')\nplt.title(\"Labels learned with Label Spreading (KNN)\")\n\nplt.subplots_adjust(left=0.07, bottom=0.07, right=0.93, top=0.92)\nplt.show()"
+        "print(__doc__)\n\n# Authors: Clay Woolam <[email protected]>\n#          Andreas Mueller <[email protected]>\n# License: BSD\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.semi_supervised import label_propagation\nfrom sklearn.datasets import make_circles\n\n# generate ring with inner box\nn_samples = 200\nX, y = make_circles(n_samples=n_samples, shuffle=False)\nouter, inner = 0, 1\nlabels = -np.ones(n_samples)\nlabels[0] = outer\nlabels[-1] = inner\n\n# #############################################################################\n# Learn with LabelSpreading\nlabel_spread = label_propagation.LabelSpreading(kernel='knn', alpha=0.8)\nlabel_spread.fit(X, labels)\n\n# #############################################################################\n# Plot output labels\noutput_labels = label_spread.transduction_\nplt.figure(figsize=(8.5, 4))\nplt.subplot(1, 2, 1)\nplt.scatter(X[labels == outer, 0], X[labels == outer, 1], color='navy',\n            marker='s', lw=0, label=\"outer labeled\", s=10)\nplt.scatter(X[labels == inner, 0], X[labels == inner, 1], color='c',\n            marker='s', lw=0, label='inner labeled', s=10)\nplt.scatter(X[labels == -1, 0], X[labels == -1, 1], color='darkorange',\n            marker='.', label='unlabeled')\nplt.legend(scatterpoints=1, shadow=False, loc='upper right')\nplt.title(\"Raw data (2 classes=outer and inner)\")\n\nplt.subplot(1, 2, 2)\noutput_label_array = np.asarray(output_labels)\nouter_numbers = np.where(output_label_array == outer)[0]\ninner_numbers = np.where(output_label_array == inner)[0]\nplt.scatter(X[outer_numbers, 0], X[outer_numbers, 1], color='navy',\n            marker='s', lw=0, s=10, label=\"outer learned\")\nplt.scatter(X[inner_numbers, 0], X[inner_numbers, 1], color='c',\n            marker='s', lw=0, s=10, label=\"inner learned\")\nplt.legend(scatterpoints=1, shadow=False, loc='upper right')\nplt.title(\"Labels learned with Label Spreading (KNN)\")\n\nplt.subplots_adjust(left=0.07, bottom=0.07, right=0.93, top=0.92)\nplt.show()"
       ]
     }
   ],

dev/_downloads/auto_examples_python.zip

@@ -30,7 +30,7 @@
 
 # #############################################################################
 # Learn with LabelSpreading
-label_spread = label_propagation.LabelSpreading(kernel='knn', alpha=0.2)
+label_spread = label_propagation.LabelSpreading(kernel='knn', alpha=0.8)
 label_spread.fit(X, labels)
 
 # #############################################################################