{
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    {
      "cell_type": "code",
      "execution_count": null,
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      "source": [
        "%matplotlib inline"
      ]
    },
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      "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": [
        "from sklearn.cluster import AffinityPropagation\nfrom sklearn import metrics\nfrom sklearn.datasets import make_blobs\n\n# #############################################################################\n# Generate sample data\ncenters = [[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)\n\n# #############################################################################\n# Compute Affinity Propagation\naf = 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)\n\n# #############################################################################\n# Plot result\nimport 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()"
      ]
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