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from functools import singledispatch
from sympy.external import import_module
from sympy.stats.crv_types import BetaDistribution, CauchyDistribution, ChiSquaredDistribution, ExponentialDistribution, \
GammaDistribution, LogNormalDistribution, NormalDistribution, ParetoDistribution, UniformDistribution, \
GaussianInverseDistribution
from sympy.stats.drv_types import PoissonDistribution, GeometricDistribution, NegativeBinomialDistribution
from sympy.stats.frv_types import BinomialDistribution, BernoulliDistribution
try:
import pymc
except ImportError:
pymc = import_module('pymc3')
@singledispatch
def do_sample_pymc(dist):
return None
# CRV:
@do_sample_pymc.register(BetaDistribution)
def _(dist: BetaDistribution):
return pymc.Beta('X', alpha=float(dist.alpha), beta=float(dist.beta))
@do_sample_pymc.register(CauchyDistribution)
def _(dist: CauchyDistribution):
return pymc.Cauchy('X', alpha=float(dist.x0), beta=float(dist.gamma))
@do_sample_pymc.register(ChiSquaredDistribution)
def _(dist: ChiSquaredDistribution):
return pymc.ChiSquared('X', nu=float(dist.k))
@do_sample_pymc.register(ExponentialDistribution)
def _(dist: ExponentialDistribution):
return pymc.Exponential('X', lam=float(dist.rate))
@do_sample_pymc.register(GammaDistribution)
def _(dist: GammaDistribution):
return pymc.Gamma('X', alpha=float(dist.k), beta=1 / float(dist.theta))
@do_sample_pymc.register(LogNormalDistribution)
def _(dist: LogNormalDistribution):
return pymc.Lognormal('X', mu=float(dist.mean), sigma=float(dist.std))
@do_sample_pymc.register(NormalDistribution)
def _(dist: NormalDistribution):
return pymc.Normal('X', float(dist.mean), float(dist.std))
@do_sample_pymc.register(GaussianInverseDistribution)
def _(dist: GaussianInverseDistribution):
return pymc.Wald('X', mu=float(dist.mean), lam=float(dist.shape))
@do_sample_pymc.register(ParetoDistribution)
def _(dist: ParetoDistribution):
return pymc.Pareto('X', alpha=float(dist.alpha), m=float(dist.xm))
@do_sample_pymc.register(UniformDistribution)
def _(dist: UniformDistribution):
return pymc.Uniform('X', lower=float(dist.left), upper=float(dist.right))
# DRV:
@do_sample_pymc.register(GeometricDistribution)
def _(dist: GeometricDistribution):
return pymc.Geometric('X', p=float(dist.p))
@do_sample_pymc.register(NegativeBinomialDistribution)
def _(dist: NegativeBinomialDistribution):
return pymc.NegativeBinomial('X', mu=float((dist.p * dist.r) / (1 - dist.p)),
alpha=float(dist.r))
@do_sample_pymc.register(PoissonDistribution)
def _(dist: PoissonDistribution):
return pymc.Poisson('X', mu=float(dist.lamda))
# FRV:
@do_sample_pymc.register(BernoulliDistribution)
def _(dist: BernoulliDistribution):
return pymc.Bernoulli('X', p=float(dist.p))
@do_sample_pymc.register(BinomialDistribution)
def _(dist: BinomialDistribution):
return pymc.Binomial('X', n=int(dist.n), p=float(dist.p))
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