Pushing the docs to dev/ for branch: main, commit 53acd0fe52cb5d8c6f5a86a1fc1352809240b68d

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -8,6 +8,7 @@
 Between Data Points", Science Feb. 2007
 
 """
+import numpy as np
 
 from sklearn.cluster import AffinityPropagation
 from sklearn import metrics
@@ -48,27 +49,26 @@
 # Plot result
 # -----------
 import matplotlib.pyplot as plt
-from itertools import cycle
 
 plt.close("all")
 plt.figure(1)
 plt.clf()
 
-colors = cycle("bgrcmykbgrcmykbgrcmykbgrcmyk")
+colors = plt.cycler("color", plt.cm.viridis(np.linspace(0, 1, 4)))
+
 for k, col in zip(range(n_clusters_), colors):
     class_members = labels == k
     cluster_center = X[cluster_centers_indices[k]]
-    plt.plot(X[class_members, 0], X[class_members, 1], col + ".")
-    plt.plot(
-        cluster_center[0],
-        cluster_center[1],
-        "o",
-        markerfacecolor=col,
-        markeredgecolor="k",
-        markersize=14,
+    plt.scatter(
+        X[class_members, 0], X[class_members, 1], color=col["color"], marker="."
+    )
+    plt.scatter(
+        cluster_center[0], cluster_center[1], s=14, color=col["color"], marker="o"
     )
     for x in X[class_members]:
-        plt.plot([cluster_center[0], x[0]], [cluster_center[1], x[1]], col)
+        plt.plot(
+            [cluster_center[0], x[0]], [cluster_center[1], x[1]], color=col["color"]
+        )
 
 plt.title("Estimated number of clusters: %d" % n_clusters_)
 plt.show()

dev/_downloads/2f930acda654766f8ba0fee08887bb41/plot_affinity_propagation.py

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.cluster import AffinityPropagation\nfrom sklearn import metrics\nfrom sklearn.datasets import make_blobs"
+        "import numpy as np\n\nfrom sklearn.cluster import AffinityPropagation\nfrom sklearn import metrics\nfrom sklearn.datasets import make_blobs"
       ]
     },
     {
@@ -80,7 +80,7 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib.pyplot as plt\nfrom itertools import cycle\n\nplt.close(\"all\")\nplt.figure(1)\nplt.clf()\n\ncolors = cycle(\"bgrcmykbgrcmykbgrcmykbgrcmyk\")\nfor k, col in zip(range(n_clusters_), colors):\n    class_members = labels == k\n    cluster_center = X[cluster_centers_indices[k]]\n    plt.plot(X[class_members, 0], X[class_members, 1], col + \".\")\n    plt.plot(\n        cluster_center[0],\n        cluster_center[1],\n        \"o\",\n        markerfacecolor=col,\n        markeredgecolor=\"k\",\n        markersize=14,\n    )\n    for x in X[class_members]:\n        plt.plot([cluster_center[0], x[0]], [cluster_center[1], x[1]], col)\n\nplt.title(\"Estimated number of clusters: %d\" % n_clusters_)\nplt.show()"
+        "import matplotlib.pyplot as plt\n\nplt.close(\"all\")\nplt.figure(1)\nplt.clf()\n\ncolors = plt.cycler(\"color\", plt.cm.viridis(np.linspace(0, 1, 4)))\n\nfor k, col in zip(range(n_clusters_), colors):\n    class_members = labels == k\n    cluster_center = X[cluster_centers_indices[k]]\n    plt.scatter(\n        X[class_members, 0], X[class_members, 1], color=col[\"color\"], marker=\".\"\n    )\n    plt.scatter(\n        cluster_center[0], cluster_center[1], s=14, color=col[\"color\"], marker=\"o\"\n    )\n    for x in X[class_members]:\n        plt.plot(\n            [cluster_center[0], x[0]], [cluster_center[1], x[1]], color=col[\"color\"]\n        )\n\nplt.title(\"Estimated number of clusters: %d\" % n_clusters_)\nplt.show()"
       ]
     }
   ],