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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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@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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@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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@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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