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    "info": {
        "author": "Hannu Parviainen",
        "author_email": "hpparvi@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 5 - Production/Stable",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: GNU General Public License v2 (GPLv2)",
            "Topic :: Scientific/Engineering"
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        "description": "PyDE\r\n====\r\n\r\nGlobal optimization using differential evolution in Python [Storn97]_.\r\n\r\n\r\nInstallation\r\n------------\r\n\r\n::\r\n\r\n    git clone https://github.com/hpparvi/PyDE.git\r\n    cd PyDE\r\n    python setup.py install [--user]\r\n  \r\nBasic usage\r\n-----------\r\n\r\nImport the class from the package\r\n\r\n::\r\n\r\n    from pyde.de import DiffEvol\r\n  \r\nCreate a DiffEvol instance\r\n\r\n::\r\n\r\n    de = DiffEvol(minfun, bounds, npop)\r\n\r\nwhere ``minfun`` is the function to be optimized, ``bounds`` is an initialization array, \r\nand ``npop`` is the size of the parameter vector population.\r\n\r\nNow, you can run the optimizer ``ngen`` generations::\r\n\r\n    res = de.optimize(ngen=100)\r\n  \r\nor run the optimizer as a generator::\r\n\r\n    for res in de(ngen=100):\r\n        do something\r\n  \r\nUsage with emcee\r\n----------------\r\n\r\nThe PyDE parameter vector population can be used to initialize the affine-invariant MCMC sampler \r\n`emcee <http://dan.iel.fm/emcee/current/>`_ when a simple point estimate of the function minimum \r\n(or maximum) is not sufficient::\r\n\r\n    de = DiffEvol(lnpost, bounds, npop, maximize=True)\r\n    de.optimize(ngen)\r\n    \r\n    sampler = emcee.EnsembleSampler(npop, ndim, lnpost)\r\n    sampler.run_mcmc(de.population, 1000)\r\n \r\nReferences\r\n----------\r\n.. [Storn97] Storn, R., Price, K., Journal of Global Optimization 11: 341--359, 1997\r\n\r\n  \r\nAPI\r\n---\r\n\r\n*pyde.de.*\\ **DiffEvol** (``minfun``, ``bounds``, ``npop``, ``F=0.5``, ``C=0.5``,\r\n``seed=0``, ``maximize=False``)\r\n\r\n**Parameters**\r\n\r\n:``minfun``:    Function to be minimized.\r\n:``bounds``:    Parameter space bounds as ``[npar,2]`` array.\r\n:``npop``:      Size of the parameter vector population.\r\n:``F``:         Difference amplification factor. Values between 0.5-0.8 are good in most cases.\r\n:``C``:         Cross-over probability. Use 0.9 to test for fast convergence, and smaller values (~0.1) for a more elaborate search.\r\n:``seed``:      Random seed.\r\n:``maximize``:  An *optional* switch telling whether we want maximize or minimize the function. Defaults to minimization.",
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