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    "info": {
        "author": "Li Yao",
        "author_email": "yaol17@mails.tsinghua.edu.cn",
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            "Programming Language :: Python",
            "Topic :: Scientific/Engineering"
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        "description": "URF: Unsupervised Random Forest\n==============================================\n\nURF (Unsupervised Random Forest, or Random Forest Clustering) is a python implementation of the paper: Shi, T., & Horvath, S. (2006). Unsupervised learning with random forest predictors. *Journal of Computational and Graphical Statistics*, 15(1), 118-138.\n\nPrerequisite\n------------\n::\n\n    conda install -c bioconda pycluster\n\nor::\n\n  wget http://bonsai.hgc.jp/~mdehoon/software/cluster/Pycluster-1.54.tar.gz\n  tar -zxvf Pycluster-1.54.tar.gz\n  cd Pycluster-1.54\n  python setup.py install\n\nInstallation\n------------\n::\n\n  pip install URF\n\nUsage\n-----\n::\n\n  from sklearn.datasets import load_iris\n  from URF.main import random_forest_cluster, plot_cluster_result\n  iris = load_iris()\n  X = iris.data\n  y = iris.target\n  print(len(list(set(y))))\n\n  clf, prox_mat, cluster_ids = random_forest_cluster(X, k=3, max_depth=20, random_state=0)\n  plot_cluster_result(prox_mat, cluster_ids, marker=y)\n\nIf you encountered an error like\n\n  > QXcbConnection: Could not connect to display\n\nthen you need to add these codes to the very beginning of your file::\n\n  import matplotlib as mpl\n  mpl.use(\"Agg\")\n\nand you must assign the output file when you call `plot_cluster_result`, like this::\n\n  plot_cluster_result(prox_mat, cluster_ids, marker=y, output=\"test_123.png\")",
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        "license": "Apache 2.0",
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        "name": "URF",
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