{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n# Demo of affinity propagation clustering algorithm\n\nReference:\nBrendan J. Frey and Delbert Dueck, \"Clustering by Passing Messages\nBetween Data Points\", Science Feb. 2007\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport numpy as np\n\nfrom sklearn import metrics\nfrom sklearn.cluster import AffinityPropagation\nfrom sklearn.datasets import make_blobs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Generate sample data\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "centers = [[1, 1], [-1, -1], [1, -1]]\nX, labels_true = make_blobs(\n    n_samples=300, centers=centers, cluster_std=0.5, random_state=0\n)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Compute Affinity Propagation\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "af = AffinityPropagation(preference=-50, random_state=0).fit(X)\ncluster_centers_indices = af.cluster_centers_indices_\nlabels = af.labels_\n\nn_clusters_ = len(cluster_centers_indices)\n\nprint(\"Estimated number of clusters: %d\" % n_clusters_)\nprint(\"Homogeneity: %0.3f\" % metrics.homogeneity_score(labels_true, labels))\nprint(\"Completeness: %0.3f\" % metrics.completeness_score(labels_true, labels))\nprint(\"V-measure: %0.3f\" % metrics.v_measure_score(labels_true, labels))\nprint(\"Adjusted Rand Index: %0.3f\" % metrics.adjusted_rand_score(labels_true, labels))\nprint(\n    \"Adjusted Mutual Information: %0.3f\"\n    % metrics.adjusted_mutual_info_score(labels_true, labels)\n)\nprint(\n    \"Silhouette Coefficient: %0.3f\"\n    % metrics.silhouette_score(X, labels, metric=\"sqeuclidean\")\n)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Plot result\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "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()"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
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    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
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      "file_extension": ".py",
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      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.21"
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  "nbformat": 4,
  "nbformat_minor": 0
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