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        "author": "Haran Rajkumar, Vaibhav Kulshrestha",
        "author_email": "haranrajkumar97@gmail.com, vaibhav1kulshrestha@gmail.com",
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        "classifiers": [
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3"
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        "description": "# BigNmf\n[![Build Status](https://travis-ci.org/thenmf/bignmf.svg?branch=master)](https://travis-ci.org/thenmf/bignmf)\n[![Read the Docs](https://readthedocs.org/projects/bignmf/badge/?version=latest)](https://bignmf.readthedocs.io/en/latest/?badge=latest)\n[![PyPI version](https://badge.fury.io/py/bignmf.svg)](https://badge.fury.io/py/bignmf)\n[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\n\nBigNmf (Big Data NMF) is a python 3 package for conducting analysis using NMF algorithms.\n\n## NMF Introduction \n[NMF](https://en.wikipedia.org/wiki/Non-negative_matrix_factorization)   (Non-negative matrix factorization) factorizes a non-negative input matrix into non-negative factors. The algorithm has an inherent clustering property and has been gaining attention in various fields especially in biological data analysis. \n\n_Brunet et al_ in their [paper](http://www.pnas.org/content/101/12/4164) demonstrated NMF's superior capability in clustering the [leukemia dataset](https://www.kaggle.com/crawford/gene-expression) compared to standard clustering algorithms like Hierarchial clustering and Self-organizeing maps.\n\n## Available algorithms\nThe following are the algorithms currently available. If you would like to know more about the algorithm, the links below lead to their papers of origin.\n* Single NMF\n    1. [Standard Single NMF](https://www.nature.com/articles/44565)\n    1. [Sparse NMF](https://www.merl.com/publications/docs/TR2015-023.pdf)\n* Joint NMF\n    1. [Standard Joint NMF](https://www.ncbi.nlm.nih.gov/pubmed/25411328)\n    2. [Integrative NMF](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0176278)\n\n## Installation\n\nThis package is available on the PyPi repository. Therefore you can install, by running the following.\n\n```bash\npip3 install bignmf\n```\n\n## Usage\nThe following examples illustrate typical usage of the algorithm.\n\n### 1. Single NMF\n\n```python\nfrom bignmf.datasets.datasets import Datasets\nfrom bignmf.models.snmf.standard import StandardNmf\n\nDatasets.list_all()\ndata=Datasets.read(\"SimulatedX1\")\nk = 3\niter =100\ntrials = 50\n\nmodel = StandardNmf(data,k)\n\n# Runs the model\nmodel.run(trials, iter, verbose=0)\nprint(model.error)\n\n# Clusters the data\nmodel.cluster_data()\nprint(model.h_cluster)\n\n#Calculates the consensus matrices\nmodel.calc_consensus_matrices() \nprint(model.consensus_matrix_w)\n```\n\n### 2. Joint NMF\n\n```python\nfrom bignmf.models.jnmf.integrative import IntegrativeJnmf\nfrom bignmf.datasets.datasets import Datasets\n\nDatasets.list_all()\ndata_dict = {}\ndata_dict[\"sim1\"] = Datasets.read(\"SimulatedX1\")\ndata_dict[\"sim2\"] = Datasets.read(\"SimulatedX2\")\n\nk = 3\niter =100\ntrials = 50\nlamb = 0.1\n\nmodel = IntegrativeJnmf(data_dict, k, lamb)\n# Runs the model\nmodel.run(trials, iter, verbose=0)\nprint(model.error)\n\n# Clusters the data\nmodel.cluster_data()\nprint(model.h_cluster)\n\n#Calculates the consensus matrices\nmodel.calc_consensus_matrices() \nprint(model.consensus_matrix_w)\n```\n\n[Here](https://bignmf.readthedocs.io/en/latest/) is the extensive documentation for more details.\n\n\n",
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