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zdR`_5RWJXJXM5A-$4GR&yozMR*2~pC9eI5mQ94(%-P{0-)XUXmiVnhTii#s;K6Q$x z`NH+`p(r~KgzVJy^5+UypN`ahQ;U>@15pwt!__G6??T)Pr7D`uejOFJ_`*fA$s#~#68T6dYvwtV;8Erd0?zKuA6BU*Ug>$D)e!n`YvoB2P*m;s&4)u zyeE0jsQPb!V%c*CEY42(O#|&UBk`gmiJQba7MTxt&*$mj5io-dGcm7YebiMhR_X9iQ;yAN&~2U3dPEyYVD- z%MEVl8^7>_o7l!eesDk6_<All modules for which code is available
    -
  • update_vector() (EDApy.optimization.univariate.continuous.UMDAc method) +
  • update_vector() (EDApy.optimization.multivariate.EDA_multivariate_gaussian.EDA_multivariate_gaussian method)
    • diff --git a/docs/build/html/index.html b/docs/build/html/index.html index 495180f..d83066d 100644 --- a/docs/build/html/index.html +++ b/docs/build/html/index.html @@ -63,8 +63,8 @@

    Requirements

    Documentation for the code

    -
    -

    EDA multivariate

    +
    +

    EDA multivariate with evidences

    EDApy.optimization.multivariate.EDA_multivariate
    @@ -237,6 +237,99 @@

    EDA multivariate +

    EDA multivariate with no evidences

    +
    +
    +class EDApy.optimization.multivariate.EDA_multivariate_gaussian(SIZE_GEN, MAX_ITER, DEAD_ITER, ALPHA, aim, cost_function, mus, sigma)[source]
    +

    Multivariate Estimation of Distribution algorithm continuous. +New individuals are sampled from a multivariate normal distribution. Evidences are not allowed

    +
    +
    Parameters
    +
      +

    • SIZE_GEN (int) – total size of the generations in the execution of the algorithm

      • +

    • MAX_ITER (int) – total number of iterations in case that optimum is not yet found. If reached, the optimum found is returned

      • +

    • DEAD_ITER (int) – total number of iteration with no better solution found. If reached, the optimum found is returned

      • +

    • ALPHA (float [0-1]) – percentage of the generation tu take, in order to sample from them. The best individuals selection

      • +

    • aim ('minimize' or 'maximize'.) – Represents the optimization aim.

      • +

    • cost_function (callable function which receives a dictionary as input and returns a numeric) – a callable function implemented by the user, to optimize.

      • +

    • mus (pandas dataframe (one row)) – pandas dataframe with initial mus of the multivariate gaussian

      • +

    • sigma (pandas dataframe (one row)) – pandas dataframe with the sigmas of the variable (diagonal of covariance matrix)

      • +
    +
    +
    Raises
    +

    Exception – cost function is not callable

    +
    +
    +
    + +
    +
    +class EDApy.optimization.multivariate.EDA_multivariate_gaussian.EDA_multivariate_gaussian(SIZE_GEN, MAX_ITER, DEAD_ITER, ALPHA, aim, cost_function, mus, sigma)[source]
    +

    Multivariate Estimation of Distribution algorithm continuous. +New individuals are sampled from a multivariate normal distribution. Evidences are not allowed

    +
    +
    Parameters
    +
      +

    • SIZE_GEN (int) – total size of the generations in the execution of the algorithm

      • +

    • MAX_ITER (int) – total number of iterations in case that optimum is not yet found. If reached, the optimum found is returned

      • +

    • DEAD_ITER (int) – total number of iteration with no better solution found. If reached, the optimum found is returned

      • +

    • ALPHA (float [0-1]) – percentage of the generation tu take, in order to sample from them. The best individuals selection

      • +

    • aim ('minimize' or 'maximize'.) – Represents the optimization aim.

      • +

    • cost_function (callable function which receives a dictionary as input and returns a numeric) – a callable function implemented by the user, to optimize.

      • +

    • mus (pandas dataframe (one row)) – pandas dataframe with initial mus of the multivariate gaussian

      • +

    • sigma (pandas dataframe (one row)) – pandas dataframe with the sigmas of the variable (diagonal of covariance matrix)

      • +
    +
    +
    Raises
    +

    Exception – cost function is not callable

    +
    +
    +
    +
    +check_generation()[source]
    +

    Check the cost of each individual in the cost function implemented by the user

    +
    + +
    +
    +new_generation()[source]
    +

    Build a new generation sampled from the vector of probabilities. Updates the generation pandas dataframe

    +
    + +
    +
    +run(output=True)[source]
    +

    Run method to execute the EDA algorithm

    +
    +
    Parameters
    +

    output (bool) – True if wanted to print each iteration

    +
    +
    Returns
    +

    best cost, best individual, history of costs along execution

    +
    +
    Return type
    +

    float, pandas dataframe, list

    +
    +
    +
    + +
    +
    +truncation()[source]
    +

    Selection of the best individuals of the actual generation. Updates the generation by selecting the best individuals

    +
    + +
    +
    +update_vector()[source]
    +

    From the best individuals update the vector of normal distributions in order to the next +generation can sample from it. Update the vector of normal distributions

    +
    + +
    +

    EDA discrete

    diff --git a/docs/build/html/objects.inv b/docs/build/html/objects.inv index 852a8aa..a74dfbd 100644 --- a/docs/build/html/objects.inv +++ b/docs/build/html/objects.inv @@ -2,6 +2,9 @@ # Project: EDApy # Version: # The remainder of this file is compressed using zlib. -xڵn0 /QKh5k9ꞛkɪX[ ŭHS% VY\~׏Pji.I ]D[c +}]=EڰSPO^ Date: Tue, 9 Jun 2020 17:51:20 +0200 Subject: [PATCH 2/2] setup.py --- setup.py | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/setup.py b/setup.py index 6b408e1..17368dd 100644 --- a/setup.py +++ b/setup.py @@ -4,19 +4,23 @@ long_description = fh.read() setuptools.setup( - name="EDApy", # Replace with your own username - version="0.0.1", + name="EDApy", + version="0.1.0", author="Vicente P. Soloviev", author_email="vicente.perez.soloviev@gmail.com", description="This is a package where some estimation of distribution algorithms are implemented.", long_description=long_description, long_description_content_type="text/markdown", + download_url="https://github.com/VicentePerezSoloviev/EDApy/archive/0.1.0.tar.gz", url="https://github.com/VicentePerezSoloviev/EDApy", packages=setuptools.find_packages(), classifiers=[ + 'Development Status :: 5 - Production/Stable', "Programming Language :: Python :: 3", - "License :: OSI Approved :: MIT License", + "License :: OSI Approved :: LGPL-2.1", "Operating System :: OS Independent", ], + keywords=['EDA', 'estimation', 'bayesian', 'evolutionary', 'algorithm', 'optimization'], python_requires='>=3.6', + license="LGPL-2.1", )