Pushing the docs to dev/ for branch: master, commit 11f97429929568e3061997e9cd878086edccb27f

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -0,0 +1,54 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# An example of K-Means++ initialization\n\nAn example to show the output of the :func:`sklearn.cluster.kmeans_plusplus`\nfunction for generating initial seeds for clustering.\n\nK-Means++ is used as the default initialization for `k_means`.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "print(__doc__)\n\nfrom sklearn.cluster import kmeans_plusplus\nfrom sklearn.datasets import make_blobs\nimport matplotlib.pyplot as plt\n\n# Generate sample data\nn_samples = 4000\nn_components = 4\n\nX, y_true = make_blobs(n_samples=n_samples,\n                       centers=n_components,\n                       cluster_std=0.60,\n                       random_state=0)\nX = X[:, ::-1]\n\n# Calculate seeds from kmeans++\ncenters_init, indices = kmeans_plusplus(X, n_clusters=4,\n                                        random_state=0)\n\n# Plot init seeds along side sample data\nplt.figure(1)\ncolors = ['#4EACC5', '#FF9C34', '#4E9A06', 'm']\n\nfor k, col in enumerate(colors):\n    cluster_data = y_true == k\n    plt.scatter(X[cluster_data, 0], X[cluster_data, 1],\n                c=col, marker='.', s=10)\n\nplt.scatter(centers_init[:, 0], centers_init[:, 1], c='b', s=50)\nplt.title(\"K-Means++ Initialization\")\nplt.xticks([])\nplt.yticks([])\nplt.show()"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.8.5"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip

@@ -0,0 +1,45 @@
+"""
+===========================================================
+An example of K-Means++ initialization
+===========================================================
+
+An example to show the output of the :func:`sklearn.cluster.kmeans_plusplus`
+function for generating initial seeds for clustering.
+
+K-Means++ is used as the default initialization for :ref:`k_means`.
+
+"""
+print(__doc__)
+
+from sklearn.cluster import kmeans_plusplus
+from sklearn.datasets import make_blobs
+import matplotlib.pyplot as plt
+
+# Generate sample data
+n_samples = 4000
+n_components = 4
+
+X, y_true = make_blobs(n_samples=n_samples,
+                       centers=n_components,
+                       cluster_std=0.60,
+                       random_state=0)
+X = X[:, ::-1]
+
+# Calculate seeds from kmeans++
+centers_init, indices = kmeans_plusplus(X, n_clusters=4,
+                                        random_state=0)
+
+# Plot init seeds along side sample data
+plt.figure(1)
+colors = ['#4EACC5', '#FF9C34', '#4E9A06', 'm']
+
+for k, col in enumerate(colors):
+    cluster_data = y_true == k
+    plt.scatter(X[cluster_data, 0], X[cluster_data, 1],
+                c=col, marker='.', s=10)
+
+plt.scatter(centers_init[:, 0], centers_init[:, 1], c='b', s=50)
+plt.title("K-Means++ Initialization")
+plt.xticks([])
+plt.yticks([])
+plt.show()