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Prepare for packaging
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‎MANIFEST.in

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include LICENSE.txt
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include *.md

‎bayesml/__init__.py

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from . import bernoulli
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from . import autoregressive
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from . import exponential
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from . import linearregression
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from . import multivariate_normal
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from . import normal
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from . import poisson
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__all__ = ['bernoulli',
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'autoregressive',
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'exponential',
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'linearregression',
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'multivariate_normal',
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'normal',
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'poisson'
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]

‎bayesml/_check.py

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# Yuta Nakahara <yuta.nakahara@aoni.waseda.jp>
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# Yuji Iikubo <yuji-iikubo.8@fuji.waseda.jp>
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import numpy as np
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from ._exceptions import ParameterFormatError, DataFormatError, CriteriaError, ResultWarning
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FLOATS = list({'float128','float64','float32','float16'} & set(dir(np)) | {float})
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INTS = list({'int64','int32','int16','int8'} & set(dir(np)) | {int})
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return val
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raise(exception_class(val_name + " must be a numpy.ndarray whose ndim >= 1."))
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if __name__ == '__main__':
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a = np.ones([1,1,2])
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print(pos_floats(np.ones([3,4])*(1.0e-8),'tmp',DataFormatError))

‎bayesml/autoregressive/_autoregressive.py

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from scipy.stats import t as ss_t
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import matplotlib.pyplot as plt
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import os
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import sys
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sys.path.insert(0, os.path.abspath('.'))
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from bayesml import base
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from bayesml._exceptions import ParameterFormatError, DataFormatError, CriteriaError, ResultWarning, ParameterFormatWarning
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from bayesml import _check
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# from .. import base
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# from .._exceptions import ParameterFormatError, DataFormatError, CriteriaError, ResultWarning, ParameterFormatWarning
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# from .. import _check
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from .. import base
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from .._exceptions import ParameterFormatError, DataFormatError, CriteriaError, ResultWarning, ParameterFormatWarning
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from .. import _check
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class GenModel(base.Generative):
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"""The stochastic data generative model and the prior distribution.

‎bayesml/bernoulli/_bernoulli.py

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# from scipy.stats import betabino as ss_betabinom
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import matplotlib.pyplot as plt
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import os
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import sys
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sys.path.insert(0, os.path.abspath('.'))
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from bayesml import base
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from bayesml._exceptions import ParameterFormatError, DataFormatError, CriteriaError, ResultWarning
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from bayesml import _check
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# from .. import base
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# from .._exceptions import ParameterFormatError, DataFormatError, CriteriaError, ResultWarning
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# from .. import _check
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from .. import base
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from .._exceptions import ParameterFormatError, DataFormatError, CriteriaError, ResultWarning
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from .. import _check
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class GenModel(base.Generative):
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"""The stochastic data generative model and the prior distribution.

‎bayesml/exponential/_exponential.py

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import warnings
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import numpy as np
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from scipy.stats import expon as ss_expon, gamma as ss_gamma, lomax as ss_lomax
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# from scipy.stats import betabino as ss_betabinom
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import matplotlib.pyplot as plt
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import os
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import sys
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sys.path.insert(0, os.path.abspath('.'))
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from bayesml import base
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from bayesml._exceptions import ParameterFormatError, DataFormatError, CriteriaError, ResultWarning, ParameterFormatWarning
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from bayesml import _check
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# from .. import base
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# from .._exceptions import ParameterFormatError, DataFormatError, CriteriaError, ResultWarning
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# from .. import _check
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from .. import base
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from .._exceptions import ParameterFormatError, DataFormatError, CriteriaError, ResultWarning
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from .. import _check
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class GenModel(base.Generative):
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"""The stochastic data generative model and the prior distribution.

‎bayesml/linearregression/_linearregression.py

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from scipy.stats import t as ss_t
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import matplotlib.pyplot as plt
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import os
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import sys
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sys.path.insert(0, os.path.abspath('.'))
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from bayesml import base
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from bayesml._exceptions import ParameterFormatError, DataFormatError, CriteriaError, ResultWarning, ParameterFormatWarning
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from bayesml import _check
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# from .. import base
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# from .._exceptions import ParameterFormatError, DataFormatError, CriteriaError, ResultWarning, ParameterFormatWarning
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# from .. import _check
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from .. import base
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from .._exceptions import ParameterFormatError, DataFormatError, CriteriaError, ResultWarning, ParameterFormatWarning
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from .. import _check
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class GenModel(base.Generative):
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"""The stochastic data generative model and the prior distribution.

‎bayesml/multivariate_normal/_multivariatenormal.py

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# Keito Tajima <wool812@akane.waseda.jp>
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# Naoki Ichijo <1jonao@fuji.waseda.jp>
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# Yuta Nakahara <yuta.nakahara@aoni.waseda.jp>
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r"""
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The multivariate normal distribution with normal-wishart prior distribution.
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The stochastic data generative model is as follows:
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* :math:`x \in \mathbb{R}^degree`: a data point
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* :math:`\bm{\mu} \in \mathbb{R}^degree`: a parameter
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* :math:`\Sigma \in \mathbb{R}^{degree\times degree}` : a parameter
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.. math:: \mathcal{N}(\bm{x}|\bm{\mu},\Sigma) = \frac{1}{(2\pi)^{\frac{degree}{2}}|\Sigma|^{\frac{1}{2}}}e^{-\frac{1}{2}(\bm{x}-\bm{\mu})\Sigma^{-1}(\bm{x}-\bm{\mu})^\top}
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The prior distribution is as follows:
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* :math:`\bm{\mu}_0 \in \mathbb{R}^{degree}`: a hyperparameter
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* :math:`\kappa_0 \in \mathbb{R}_{>0}`: a hyperparameter
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* :math:`\nu_0 \in \mathbb{R}_{>degree-1}`: a hyperparameter
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* :math:`V_0 \in \mathbb{R}^{degree\times degree}`: a hyperparameter
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.. math:: f(\bm{\mu},\Lambda|\bm{\mu}_0,\kappa_0,\V_0,\nu_0) = mathcal{N}(\bm{\mu}|\bm{\mu}_0,(\lambda_0\Lambda)^{-1})\mathcal{W}(\Lambda|V_0,\nu_0)
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The posterior distribution is as follows:
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* :math:`\bm{x}^n = (\bm{x}_1, \bm{x}_2, \dots , \bm{x}_n) \in \mathbb{R}^{degree\times n}`: given data
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* :math:`\bm{\mu}_n \in \mathbb{R}^{degree}`: a hyperparameter
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* :math:`\kappa_n \in \mathbb{R}_{>0}`: a hyperparameter
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* :math:`\nu_n \in \mathbb{R}_{>degree-1}`: a hyperparameter
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* :math:`V_n \in \mathbb{R}^{degree\times degree}`: a hyperparameter
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.. math:: f(\bm{\mu},\Lambda|\bm{\mu}_n,\kappa_n,V_n,\nu_n) = \mathcal{N}(\bm{\mu}|\bm{\mu}_n,(\lambda_n\Lambda)^{-1})\mathcal{W}(\Lambda|V_n,\nu_n)
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where the updating rule of the hyperparameters is
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.. math::
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\bm{\mu}_n=\frac{\kappa_0\bm{\mu}+n\bar{\bm{x}}}{\kappa_0+n}\\
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\kappa_n=\kappa_0+n\\
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V_n=\left(V_0^{-1}+C+\frac{\kappa_0 n}{\kappa_0+n}(\bar{\bm{x}}-\bm{\mu}_0)(\bar{\bm{x}}-\bm{\mu}_0)^\top\right)^{-1}\\
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\nu_n=\nu_0+n\\
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C=\sum_{i=1}^{n}(\bm{x}_i-\bar{\bm{x}})(\bm{x}_i-\bar{\bm{x}})^\top
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The predictive distribution is as follows:
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* :math:`x \in \mathbb{R}`: a new data point
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* :math:`\bm{\mu}_p`: the hyperparameter of the posterior
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* :math:`\kappa_p`: the hyperparameter of the posterior
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* :math:`V_p`: the hyperparameter of the posterior
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* :math:`\nu_p`: the hyperparameter of the posterior
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.. math::
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p(x|\bm{\mu}_p,\kappa_p,V_p,\nu_p) = t_{\nu_p-degree+1}\left(\bm{\mu}_p,\frac{\kappa_p+1}{\kappa_p(\nu_p-degree+1)}V_p^{-1}\right)
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"""
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import warnings
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import numpy as np
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from scipy.stats import multivariate_normal as ss_multivariate_normal
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from scipy.stats import wishart as ss_wishart
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from scipy.stats import multivariate_t as ss_multivariate_t
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import matplotlib.pyplot as plt
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# from .. import base
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# from .._exceptions import ParameterFormatError, DataFormatError, CriteriaError, ResultWarning, ParameterFormatWarning
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# from .. import _check
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import matplotlib.pyplot as plt
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import os
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import sys
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sys.path.insert(0, os.path.abspath('.'))
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from bayesml import base
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from bayesml._exceptions import ParameterFormatError, DataFormatError, CriteriaError, ResultWarning, ParameterFormatWarning
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from bayesml import _check
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from .. import base
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from .._exceptions import ParameterFormatError, DataFormatError, CriteriaError, ResultWarning, ParameterFormatWarning
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from .. import _check
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class GenModel(base.Generative):
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"""The stochastic data generative model and the prior distribution

‎bayesml/normal/_normal.py

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from scipy.stats import norm as ss_norm
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from scipy.stats import gamma as ss_gamma
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from scipy.stats import t as ss_t
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# from scipy.stats import betabino as ss_betabinom
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import matplotlib.pyplot as plt
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import os
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import sys
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sys.path.insert(0, os.path.abspath('.'))
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from bayesml import base
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from bayesml._exceptions import ParameterFormatError, DataFormatError, CriteriaError, ResultWarning
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from bayesml import _check
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from .. import base
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from .._exceptions import ParameterFormatError, DataFormatError, CriteriaError, ResultWarning, ParameterFormatWarning
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from .. import _check
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class GenModel(base.Generative):
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"""The stochastic data generative model and the prior distribution

‎bayesml/poisson/_poisson.py

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from scipy.stats import nbinom as ss_nbinom
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import matplotlib.pyplot as plt
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import matplotlib.pyplot as plt
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import os
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import sys
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sys.path.insert(0, os.path.abspath('.'))
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from bayesml import base
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from bayesml._exceptions import ParameterFormatError, DataFormatError, CriteriaError, ResultWarning
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from bayesml import _check
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# from .. import base
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# from .._exceptions import ParameterFormatError, DataFormatError, CriteriaError, ResultWarning
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# from .. import _check
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from .. import base
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from .._exceptions import ParameterFormatError, DataFormatError, CriteriaError, ResultWarning
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from .. import _check
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class GenModel(base.Generative):
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"""The stochastic data generative model and the prior distribution.

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