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