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    "info": {
        "author": "Makoto Okada, Kenji Yamanishi and Naoki Masuda",
        "author_email": "naoki.masuda@bristol.ac.uk",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Software Development"
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        "description": "# exp_mixture_model\nMaximum likelihood estimation and model selection for the exponential mixture model (i.e., mixture of exponential distributions)\n\nWhen you use this code, please cite the following paper:\n\nMakoto Okada, Kenji Yamanishi, Naoki Masuda.\nLong-tailed distributions of inter-event times as mixtures of exponential distributions.\narXiv:19xx.xxxxx\n\n\n\n\n## Installation\n``exp_mixture_model`` is hosted on PyPI. So, one can install it by running\n```python\npip install exp_mixture_model\n```\nIf you want to install from source, clone the exp_mixture_model git repository by running\n```python\ngit clone https://github.com/naokimas/exp_mixture_model.git\n```\n\nThen, navigate to the top-level of the cloned directory and run\n```python\npython setup.py install\n```\n\nYou can test our code by running\n```python\npython setup.py test\n```\n\nIf you use Anaconda, you may install required packages by running\n```python\nconda install --file requirements.txt\n```\n\n## Quick Use\nFit an EMM to data stored in a file (e.g. sample.dat) by running\n```python\npython emmfit.py -f sample.dat -k 10\n```\n- '10' is the initial number of components. \n- 'sample.dat' is provided as part of this package. It is synthetic data that we generated by running\n```python\nfrom emm import generate_emm\nx = generate_emm(1000, 10)\n```\n\nTo select the best model among EMMs with different numbers of components, don't specify 'k' and instead specify the model selection criterion using '-c' as follows.\n```python\npython emmfit.py -f sample.dat -c DNML\n```\n- One can specify either 'marginal_log_likelihood', 'joint_log_likelihood', 'AIC', 'BIC', 'AIC_LVC', 'BIC_LVC', 'NML_LVC', or 'DNML' as the argument of '-c'.\n- Default is 'DNML'. \n\nCheck details of the usage by running\n```python\npython emmfit.py --help\n```\n\n## Usage\nFit an EMM to data by running\n```python\nfrom exp_mixture_model import EMM\nx = [1.5, 2.3, ...]  # data can be either a list or numpy array\n#\n# Alternatively, one can load data from a file using numpy as follows.\n#\n# import numpy as np\n# x = np.loadtxt(\"sample.dat\")\n#\nmodel = EMM()\npi, mu = model.fit(x)  # estimate the parameters\nmodel.print_result()  # print 'k_final' (i.e., the estimated effective number of components) and the estimated parameters\nmodel.plot_survival_probability()  # plot the survival probability (= cumulative complementary distribution function) for the estimated EMM and the given data 'x'.\n```\n\nSelect the number of components based on a model selection criterion by running\n```python\nfrom exp_mixture_model import EMMs\nx = [1.5, 2.3, ...]\nemms = EMMs()\nemms.fit(x)  # fit EMMs with different values of 'k', i.e., the number of components. The default uses 13 values of 'k'. This process is computationally heavy.\nbest_model = emms.select('DNML')  # select the best number of components under the 'DNML' criterion. One can specify either 'marginal_log_likelihood', 'joint_log_likelihood', 'AIC', 'BIC', 'AIC_LVC', 'BIC_LVC', 'NML_LVC', or 'DNML' as the argument of 'emms.select'.\nemms.print_result_table()  # print the values of 'k_final', likelihoods, and 'DNML' for each 'k' value\nbest_model.print_result() # print 'k_final' and the estimated parameter values of the selected EMM\n```\n\n\n",
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        "keywords": "exponential mixture model,model selection,normalized maximum likelihood",
        "license": "MIT License",
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        "name": "exp-mixture-model",
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        "summary": "Maximum likelihood estimation and model selection of EMMs",
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