Pushing the docs to dev/ for branch: main, commit 7ec1bfc2ab7425bcd984d631b5bfeb9082ce11bf

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib.pyplot as plt\n\nfrom sklearn.datasets import make_blobs\nfrom sklearn.cluster import BisectingKMeans, KMeans\n\n\nprint(__doc__)\n\n\n# Generate sample data\nn_samples = 1000\nrandom_state = 0\n\nX, _ = make_blobs(n_samples=n_samples, centers=2, random_state=random_state)\n\n# Number of cluster centers for KMeans and BisectingKMeans\nn_clusters_list = [2, 3, 4, 5]\n\n# Algorithms to compare\nclustering_algorithms = {\n    \"Bisecting K-Means\": BisectingKMeans,\n    \"K-Means\": KMeans,\n}\n\n# Make subplots for each variant\nfig, axs = plt.subplots(\n    len(clustering_algorithms), len(n_clusters_list), figsize=(15, 5)\n)\n\naxs = axs.T\n\nfor i, (algorithm_name, Algorithm) in enumerate(clustering_algorithms.items()):\n    for j, n_clusters in enumerate(n_clusters_list):\n        algo = Algorithm(n_clusters=n_clusters, random_state=random_state)\n        algo.fit(X)\n        centers = algo.cluster_centers_\n\n        axs[j, i].scatter(X[:, 0], X[:, 1], s=10, c=algo.labels_)\n        axs[j, i].scatter(centers[:, 0], centers[:, 1], c=\"r\", s=20)\n\n        axs[j, i].set_title(f\"{algorithm_name} : {n_clusters} clusters\")\n\n\n# Hide x labels and tick labels for top plots and y ticks for right plots.\nfor ax in axs.flat:\n    ax.label_outer()\n    ax.set_xticks([])\n    ax.set_yticks([])\n\nplt.show()"
+        "import matplotlib.pyplot as plt\n\nfrom sklearn.datasets import make_blobs\nfrom sklearn.cluster import BisectingKMeans, KMeans\n\n\nprint(__doc__)\n\n\n# Generate sample data\nn_samples = 1000\nrandom_state = 0\n\nX, _ = make_blobs(n_samples=n_samples, centers=2, random_state=random_state)\n\n# Number of cluster centers for KMeans and BisectingKMeans\nn_clusters_list = [2, 3, 4, 5]\n\n# Algorithms to compare\nclustering_algorithms = {\n    \"Bisecting K-Means\": BisectingKMeans,\n    \"K-Means\": KMeans,\n}\n\n# Make subplots for each variant\nfig, axs = plt.subplots(\n    len(clustering_algorithms), len(n_clusters_list), figsize=(15, 5)\n)\n\naxs = axs.T\n\nfor i, (algorithm_name, Algorithm) in enumerate(clustering_algorithms.items()):\n    for j, n_clusters in enumerate(n_clusters_list):\n        algo = Algorithm(n_clusters=n_clusters, random_state=random_state, n_init=3)\n        algo.fit(X)\n        centers = algo.cluster_centers_\n\n        axs[j, i].scatter(X[:, 0], X[:, 1], s=10, c=algo.labels_)\n        axs[j, i].scatter(centers[:, 0], centers[:, 1], c=\"r\", s=20)\n\n        axs[j, i].set_title(f\"{algorithm_name} : {n_clusters} clusters\")\n\n\n# Hide x labels and tick labels for top plots and y ticks for right plots.\nfor ax in axs.flat:\n    ax.label_outer()\n    ax.set_xticks([])\n    ax.set_yticks([])\n\nplt.show()"
       ]
     }
   ],

dev/_downloads/13db5212719118ea59532c291af3a8f9/plot_bisect_kmeans.ipynb

@@ -44,7 +44,7 @@
 
 for i, (algorithm_name, Algorithm) in enumerate(clustering_algorithms.items()):
     for j, n_clusters in enumerate(n_clusters_list):
-        algo = Algorithm(n_clusters=n_clusters, random_state=random_state)
+        algo = Algorithm(n_clusters=n_clusters, random_state=random_state, n_init=3)
         algo.fit(X)
         centers = algo.cluster_centers_