From e395b2ea485195b1deec8e2dd05b33fd05514ef6 Mon Sep 17 00:00:00 2001 From: Somasree Majumder <56045049+soma2000-lang@users.noreply.github.com> Date: Sat, 4 Feb 2023 01:56:42 +0530 Subject: [PATCH 1/2] Update continuous.py --- pymc/distributions/continuous.py | 19 ------------------- 1 file changed, 19 deletions(-) diff --git a/pymc/distributions/continuous.py b/pymc/distributions/continuous.py index d587a86153..283ceebb5e 100644 --- a/pymc/distributions/continuous.py +++ b/pymc/distributions/continuous.py @@ -2332,25 +2332,6 @@ def logcdf(value, nu): return logcdf(Gamma.dist(alpha=nu / 2, beta=0.5), value) -# TODO: Remove this once logp for multiplication is working! -class WeibullBetaRV(RandomVariable): - name = "weibull" - ndim_supp = 0 - ndims_params = [0, 0] - dtype = "floatX" - _print_name = ("Weibull", "\\operatorname{Weibull}") - - def __call__(self, alpha, beta, size=None, **kwargs): - return super().__call__(alpha, beta, size=size, **kwargs) - - @classmethod - def rng_fn(cls, rng, alpha, beta, size) -> np.ndarray: - return np.asarray(beta * rng.weibull(alpha, size=size)) - - -weibull_beta = WeibullBetaRV() - - class Weibull(PositiveContinuous): r""" Weibull log-likelihood. From e257de3179560b5b27fbcd4ae7b0beba7f59ecd0 Mon Sep 17 00:00:00 2001 From: Somasree Majumder <56045049+soma2000-lang@users.noreply.github.com> Date: Sat, 4 Feb 2023 02:14:35 +0530 Subject: [PATCH 2/2] Update continuous.py --- pymc/distributions/continuous.py | 41 +------------------------------- 1 file changed, 1 insertion(+), 40 deletions(-) diff --git a/pymc/distributions/continuous.py b/pymc/distributions/continuous.py index 283ceebb5e..9e5cca4832 100644 --- a/pymc/distributions/continuous.py +++ b/pymc/distributions/continuous.py @@ -835,22 +835,7 @@ def logcdf(value, loc, sigma): sigma > 0, msg="sigma > 0", ) - - -class WaldRV(RandomVariable): - name = "wald" - ndim_supp = 0 - ndims_params = [0, 0, 0] - dtype = "floatX" - _print_name = ("Wald", "\\operatorname{Wald}") - - @classmethod - def rng_fn(cls, rng, mu, lam, alpha, size) -> np.ndarray: - return np.asarray(rng.wald(mu, lam, size=size) + alpha) - - -wald = WaldRV() - + class Wald(PositiveContinuous): r""" @@ -1015,15 +1000,6 @@ def logcdf(value, mu, lam, alpha): ) -class BetaClippedRV(BetaRV): - @classmethod - def rng_fn(cls, rng, alpha, beta, size) -> np.ndarray: - return np.asarray(clipped_beta_rvs(alpha, beta, size=size, random_state=rng)) - - -beta = BetaClippedRV() - - class Beta(UnitContinuous): r""" Beta log-likelihood. @@ -1660,21 +1636,6 @@ def logcdf(value, mu, sigma): Lognormal = LogNormal -class StudentTRV(RandomVariable): - name = "studentt" - ndim_supp = 0 - ndims_params = [0, 0, 0] - dtype = "floatX" - _print_name = ("StudentT", "\\operatorname{StudentT}") - - @classmethod - def rng_fn(cls, rng, nu, mu, sigma, size=None) -> np.ndarray: - return np.asarray(stats.t.rvs(nu, mu, sigma, size=size, random_state=rng)) - - -studentt = StudentTRV() - - class StudentT(Continuous): r""" Student's T log-likelihood.