def _sample_numpy(cls, dist, size, seed):
"""Sample from NumPy."""
import numpy
if seed is None or isinstance(seed, int):
rand_state = numpy.random.default_rng(seed=seed)
else:
rand_state = seed
numpy_rv_map = {
'MultivariateNormalDistribution': lambda dist, size: rand_state.multivariate_normal(
mean=matrix2numpy(dist.mu, float).flatten(),
cov=matrix2numpy(dist.sigma, float), size=size),
'MultivariateBetaDistribution': lambda dist, size: rand_state.dirichlet(
alpha=list2numpy(dist.alpha, float).flatten(), size=size),
'MultinomialDistribution': lambda dist, size: rand_state.multinomial(
n=int(dist.n), pvals=list2numpy(dist.p, float).flatten(), size=size)
}
dist_list = numpy_rv_map.keys()
if dist.__class__.__name__ not in dist_list:
return None
samples = numpy_rv_map[dist.__class__.__name__](dist, size)
if samples.shape[0:len(size)] != size:
samples = samples.reshape((size[0],) + samples.shape)
return samples
def _sample_pymc3(cls, dist, size, seed):
"""Sample from PyMC3."""
import pymc3
pymc3_rv_map = {
'MultivariateNormalDistribution': lambda dist:
pymc3.MvNormal('X', mu=matrix2numpy(dist.mu, float).flatten(),
cov=matrix2numpy(dist.sigma, float), shape=(1, dist.mu.shape[0])),
'MultivariateBetaDistribution': lambda dist:
pymc3.Dirichlet('X', a=list2numpy(dist.alpha, float).flatten()),
'MultinomialDistribution': lambda dist:
pymc3.Multinomial('X', n=int(dist.n),
p=list2numpy(dist.p, float).flatten(), shape=(1, len(dist.p)))
}
dist_list = pymc3_rv_map.keys()
if dist.__class__.__name__ not in dist_list:
return None
with pymc3.Model():
pymc3_rv_map[dist.__class__.__name__](dist)
samples = pymc3.sample(size, chains=1, progressbar=False, random_seed=seed)[:]['X']
if samples.shape[0:len(size)] != size:
samples = samples.reshape((size[0],) + samples.shape)
return samples
class SampleJointPymc:
"""Returns the sample from pymc3 of the given distribution"""
def __new__(cls, dist, size):
return cls._sample_pymc3(dist, size)
pymc3_rv_map = {
'MultivariateNormalDistribution': lambda dist:
pymc3.MvNormal('X', mu=matrix2numpy(dist.mu, float).flatten(),
cov=matrix2numpy(dist.sigma, float), shape=(1, dist.mu.shape[0])),
'MultivariateBetaDistribution': lambda dist:
pymc3.Dirichlet('X', a=list2numpy(dist.alpha, float).flatten()),
'MultinomialDistribution': lambda dist:
pymc3.Multinomial('X', n=int(dist.n),
p=list2numpy(dist.p, float).flatten(), shape=(1, len(dist.p)))
}
@classmethod
def _sample_pymc3(cls, dist, size):
"""Sample from PyMC3."""
dist_list = cls.pymc3_rv_map.keys()
if dist.__class__.__name__ not in dist_list:
return None
with pymc3.Model():
cls.pymc3_rv_map[dist.__class__.__name__](dist)
return pymc3.sample(size, chains=1, progressbar=False)[:]['X']
class SampleJointNumpy:
"""Returns the sample from numpy of the given distribution"""
def __new__(cls, dist, size):
return cls._sample_numpy(dist, size)
numpy_rv_map = {
'MultivariateNormalDistribution': lambda dist, size: numpy.random.multivariate_normal(
mean=matrix2numpy(dist.mu, float).flatten(),
cov=matrix2numpy(dist.sigma, float), size=size),
'MultivariateBetaDistribution': lambda dist, size: numpy.random.dirichlet(
alpha=list2numpy(dist.alpha, float).flatten(), size=size),
'MultinomialDistribution': lambda dist, size: numpy.random.multinomial(
n=int(dist.n), pvals=list2numpy(dist.p, float).flatten(), size=size)
}
@classmethod
def _sample_numpy(cls, dist, size):
"""Sample from NumPy."""
dist_list = cls.numpy_rv_map.keys()
if dist.__class__.__name__ not in dist_list:
return None
return cls.numpy_rv_map[dist.__class__.__name__](dist, size)
class SampleJointScipy:
"""Returns the sample from scipy of the given distribution"""
def __new__(cls, dist, size):
return cls._sample_scipy(dist, size)
scipy_rv_map = {
'MultivariateNormalDistribution': lambda dist, size: scipy.stats.multivariate_normal.rvs(
mean=matrix2numpy(dist.mu).flatten(),
cov=matrix2numpy(dist.sigma), size=size),
'MultivariateBetaDistribution': lambda dist, size: scipy.stats.dirichlet.rvs(
alpha=list2numpy(dist.alpha, float).flatten(), size=size),
'MultinomialDistribution': lambda dist, size: scipy.stats.multinomial.rvs(
n=int(dist.n), p=list2numpy(dist.p, float).flatten(), size=size)
}
@classmethod
def _sample_scipy(cls, dist, size):
"""Sample from SciPy."""
dist_list = cls.scipy_rv_map.keys()
if dist.__class__.__name__ not in dist_list:
return None
return cls.scipy_rv_map[dist.__class__.__name__](dist, size)
def _sample_scipy(cls, dist, size, seed):
"""Sample from SciPy."""
from scipy import stats as scipy_stats
scipy_rv_map = {
'MultivariateNormalDistribution':
lambda dist, size: scipy_stats.multivariate_normal.rvs(
mean=matrix2numpy(dist.mu).flatten(),
cov=matrix2numpy(dist.sigma),
size=size,
random_state=seed),
'MultivariateBetaDistribution':
lambda dist, size: scipy_stats.dirichlet.rvs(alpha=list2numpy(
dist.alpha, float).flatten(),
size=size,
random_state=seed),
'MultinomialDistribution':
lambda dist, size: scipy_stats.multinomial.rvs(
n=int(dist.n),
p=list2numpy(dist.p, float).flatten(),
size=size,
random_state=seed)
}
dist_list = scipy_rv_map.keys()
if dist.__class__.__name__ not in dist_list:
return None
samples = scipy_rv_map[dist.__class__.__name__](dist, size)
if samples.shape[0:len(size)] != size:
samples = samples.reshape((size[0], ) + samples.shape)
return samples
def _sample_pymc3(cls, dist, size, seed):
"""Sample from PyMC3."""
import pymc3
pymc3_rv_map = {
'MultivariateNormalDistribution':
lambda dist: pymc3.MvNormal('X',
mu=matrix2numpy(dist.mu, float).
flatten(),
cov=matrix2numpy(dist.sigma, float),
shape=(1, dist.mu.shape[0])),
'MultivariateBetaDistribution':
lambda dist: pymc3.Dirichlet(
'X', a=list2numpy(dist.alpha, float).flatten()),
'MultinomialDistribution':
lambda dist: pymc3.Multinomial('X',
n=int(dist.n),
p=list2numpy(dist.p, float).flatten(
),
shape=(1, len(dist.p)))
}
sample_shape = {
'MultivariateNormalDistribution':
lambda dist: matrix2numpy(dist.mu).flatten().shape,
'MultivariateBetaDistribution':
lambda dist: list2numpy(dist.alpha).flatten().shape,
'MultinomialDistribution':
lambda dist: list2numpy(dist.p).flatten().shape
}
dist_list = pymc3_rv_map.keys()
if dist.__class__.__name__ not in dist_list:
return None
import logging
logging.getLogger("pymc3").setLevel(logging.ERROR)
with pymc3.Model():
pymc3_rv_map[dist.__class__.__name__](dist)
samples = pymc3.sample(draws=prod(size),
chains=1,
progressbar=False,
random_seed=seed,
return_inferencedata=False,
compute_convergence_checks=False)[:]['X']
return samples.reshape(size +
sample_shape[dist.__class__.__name__](dist))
def _sample_scipy(cls, dist, size, seed):
"""Sample from SciPy."""
import numpy
if seed is None or isinstance(seed, int):
rand_state = numpy.random.default_rng(seed=seed)
else:
rand_state = seed
from scipy import stats as scipy_stats
scipy_rv_map = {
'MultivariateNormalDistribution':
lambda dist, size: scipy_stats.multivariate_normal.rvs(
mean=matrix2numpy(dist.mu).flatten(),
cov=matrix2numpy(dist.sigma),
size=size,
random_state=rand_state),
'MultivariateBetaDistribution':
lambda dist, size: scipy_stats.dirichlet.rvs(
alpha=list2numpy(dist.alpha, float).flatten(),
size=size,
random_state=rand_state),
'MultinomialDistribution':
lambda dist, size: scipy_stats.multinomial.rvs(
n=int(dist.n),
p=list2numpy(dist.p, float).flatten(),
size=size,
random_state=rand_state)
}
sample_shape = {
'MultivariateNormalDistribution':
lambda dist: matrix2numpy(dist.mu).flatten().shape,
'MultivariateBetaDistribution':
lambda dist: list2numpy(dist.alpha).flatten().shape,
'MultinomialDistribution':
lambda dist: list2numpy(dist.p).flatten().shape
}
dist_list = scipy_rv_map.keys()
if dist.__class__.__name__ not in dist_list:
return None
samples = scipy_rv_map[dist.__class__.__name__](dist, size)
return samples.reshape(size +
sample_shape[dist.__class__.__name__](dist))
def _sample_numpy(cls, dist, size):
"""Sample from NumPy."""
import numpy
numpy_rv_map = {
'MultivariateNormalDistribution': lambda dist, size: numpy.random.multivariate_normal(
mean=matrix2numpy(dist.mu, float).flatten(),
cov=matrix2numpy(dist.sigma, float), size=size),
'MultivariateBetaDistribution': lambda dist, size: numpy.random.dirichlet(
alpha=list2numpy(dist.alpha, float).flatten(), size=size),
'MultinomialDistribution': lambda dist, size: numpy.random.multinomial(
n=int(dist.n), pvals=list2numpy(dist.p, float).flatten(), size=size)
}
dist_list = numpy_rv_map.keys()
if dist.__class__.__name__ not in dist_list:
return None
return numpy_rv_map[dist.__class__.__name__](dist, size)
