{
    "info": {
        "author": "Ezinne Nwankwo and Jennifer Wilson",
        "author_email": "enwankwo17@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3"
        ],
        "description": "## Installation Instructions\n\nRun following code in terminal: \npython3 -m pip install bayesHC1\n\nThen import package and load cython as follows: \nimport bayesHC1\n\n## Documentation\nbayesHC.bayes_hier_clust(data_vec, alpha, alpha0, beta, kappa, mu)\n\nGiven a tuple of datapoints, build a hierarchical cluster tree. Algorithm will continue merging clusters until there is only one overarching cluster. This algorithm is recommended for clustering $<$23 observations.\n\nParameters:\n    alpha : the expected number of clusters\n\n    alpha0 : scale hyperparameter for gamma prior \n\n    beta : location hyperparameter for gamma prior \n\n    kappa : precision hyperparameter for normal prior\n\n    mu : mean hyperparameer for normal prior\n\n\n\nReturns:nested tuple\n\nReturns a tuple with the following structure:\n    cluster = (c_num,left,right,value,n_k,p_k,d_k)\n\n    Where:\n        c_num : the number of the cluster\n        left : the entire cluster tuple for one subcluster\n        right : the entire cluster tuple for the other subcluster\n        value : all data values included in the cluster\n        n_k : number of datapoints in the cluster\n        p_k : the prior on merging\n        d_k : a weight on the volume of data in pairs of clusters on the subtree T_k\n\n\n## Example\n\n<<<import pandas as pd\n<<<df_sim = pd.read_table('simulated_data.csv',delimiter = \",\")\n<<<value = tuple([points] for points in df_sim.loc[:4,'values']) \n<<<mu, kappa = 0, 100 # mean and standard deviation\n<<<alpha, beta = 2,10 # scale and location\n<<<z = bayes_hier_clust(value, alpha, beta, kappa,mu)\n<<<z\n\n[(9,\n  (4, 0, 0, [24.649825190000001], 1, 1, 2.0),\n  (8,\n   (5, 0, 0, [20.543137890000001], 1, 1, 2.0),\n   (7,\n    (1, 0, 0, [19.404575810000001], 1, 1, 2.0),\n    (6,\n     (2, 0, 0, [4.4017292919999997], 1, 1, 2.0),\n     (3, 0, 0, [10.766117830000001], 1, 1, 2.0),\n     [4.4017292919999997, 10.766117830000001],\n     2,\n     0.3333333432674408,\n     6.0),\n    [19.404575810000001, 4.4017292919999997, 10.766117830000001],\n    3,\n    0.25,\n    16.0),\n   [20.543137890000001,\n    19.404575810000001,\n    4.4017292919999997,\n    10.766117830000001],\n   4,\n   0.27272728085517883,\n   44.0),\n  [24.649825190000001,\n   20.543137890000001,\n   19.404575810000001,\n   4.4017292919999997,\n   10.766117830000001],\n  5,\n  0.3529411852359772,\n  136.0)]\n\n>>>z[1]\n(4, 0, 0, [24.649825190000001], 1, 1, 2.0)\n\n\n\nLast updated on April 30, 2019.  \n\n\n",
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        "name": "bayesHC1",
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        "summary": "A package for Bayesian Hierarchical Clustering",
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