You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
97 lines
2.9 KiB
97 lines
2.9 KiB
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
|
|
|
|
|
|
pymc3 = import_module('pymc3')
|
|
|
|
|
|
@singledispatch
|
|
def do_sample_pymc3(dist):
|
|
return None
|
|
|
|
|
|
# CRV:
|
|
|
|
@do_sample_pymc3.register(BetaDistribution)
|
|
def _(dist: BetaDistribution):
|
|
return pymc3.Beta('X', alpha=float(dist.alpha), beta=float(dist.beta))
|
|
|
|
|
|
@do_sample_pymc3.register(CauchyDistribution)
|
|
def _(dist: CauchyDistribution):
|
|
return pymc3.Cauchy('X', alpha=float(dist.x0), beta=float(dist.gamma))
|
|
|
|
|
|
@do_sample_pymc3.register(ChiSquaredDistribution)
|
|
def _(dist: ChiSquaredDistribution):
|
|
return pymc3.ChiSquared('X', nu=float(dist.k))
|
|
|
|
|
|
@do_sample_pymc3.register(ExponentialDistribution)
|
|
def _(dist: ExponentialDistribution):
|
|
return pymc3.Exponential('X', lam=float(dist.rate))
|
|
|
|
|
|
@do_sample_pymc3.register(GammaDistribution)
|
|
def _(dist: GammaDistribution):
|
|
return pymc3.Gamma('X', alpha=float(dist.k), beta=1 / float(dist.theta))
|
|
|
|
|
|
@do_sample_pymc3.register(LogNormalDistribution)
|
|
def _(dist: LogNormalDistribution):
|
|
return pymc3.Lognormal('X', mu=float(dist.mean), sigma=float(dist.std))
|
|
|
|
|
|
@do_sample_pymc3.register(NormalDistribution)
|
|
def _(dist: NormalDistribution):
|
|
return pymc3.Normal('X', float(dist.mean), float(dist.std))
|
|
|
|
|
|
@do_sample_pymc3.register(GaussianInverseDistribution)
|
|
def _(dist: GaussianInverseDistribution):
|
|
return pymc3.Wald('X', mu=float(dist.mean), lam=float(dist.shape))
|
|
|
|
|
|
@do_sample_pymc3.register(ParetoDistribution)
|
|
def _(dist: ParetoDistribution):
|
|
return pymc3.Pareto('X', alpha=float(dist.alpha), m=float(dist.xm))
|
|
|
|
|
|
@do_sample_pymc3.register(UniformDistribution)
|
|
def _(dist: UniformDistribution):
|
|
return pymc3.Uniform('X', lower=float(dist.left), upper=float(dist.right))
|
|
|
|
|
|
# DRV:
|
|
|
|
@do_sample_pymc3.register(GeometricDistribution)
|
|
def _(dist: GeometricDistribution):
|
|
return pymc3.Geometric('X', p=float(dist.p))
|
|
|
|
|
|
@do_sample_pymc3.register(NegativeBinomialDistribution)
|
|
def _(dist: NegativeBinomialDistribution):
|
|
return pymc3.NegativeBinomial('X', mu=float((dist.p * dist.r) / (1 - dist.p)),
|
|
alpha=float(dist.r))
|
|
|
|
|
|
@do_sample_pymc3.register(PoissonDistribution)
|
|
def _(dist: PoissonDistribution):
|
|
return pymc3.Poisson('X', mu=float(dist.lamda))
|
|
|
|
|
|
# FRV:
|
|
|
|
@do_sample_pymc3.register(BernoulliDistribution)
|
|
def _(dist: BernoulliDistribution):
|
|
return pymc3.Bernoulli('X', p=float(dist.p))
|
|
|
|
|
|
@do_sample_pymc3.register(BinomialDistribution)
|
|
def _(dist: BinomialDistribution):
|
|
return pymc3.Binomial('X', n=int(dist.n), p=float(dist.p))
|