From e2307d105aaca55f158f5996489455d7528e9599 Mon Sep 17 00:00:00 2001 From: Jessica Moore Date: Sat, 15 Apr 2023 08:03:40 -0400 Subject: [PATCH] More fixes --- pymc/distributions/discrete.py | 34 +++++++++++++++++----------------- 1 file changed, 17 insertions(+), 17 deletions(-) diff --git a/pymc/distributions/discrete.py b/pymc/distributions/discrete.py index 2ed25e10aa6..3414c173c68 100644 --- a/pymc/distributions/discrete.py +++ b/pymc/distributions/discrete.py @@ -256,9 +256,9 @@ def dist( *args, **kwargs, ): - alpha = at.as_tensor_variable(floatX(alpha)) - beta = at.as_tensor_variable(floatX(beta)) - n = at.as_tensor_variable(intX(n)) + alpha = pt.as_tensor_variable(floatX(alpha)) + beta = pt.as_tensor_variable(floatX(beta)) + n = pt.as_tensor_variable(intX(n)) return super().dist([n, alpha, beta], **kwargs) def moment(rv, size, n, alpha, beta): @@ -484,8 +484,8 @@ def DiscreteWeibull(q, b, x): @classmethod def dist(cls, q: DIST_PARAMETER_TYPES, beta: DIST_PARAMETER_TYPES, *args, **kwargs): - q = at.as_tensor_variable(floatX(q)) - beta = at.as_tensor_variable(floatX(beta)) + q = pt.as_tensor_variable(floatX(q)) + beta = pt.as_tensor_variable(floatX(beta)) return super().dist([q, beta], **kwargs) def moment(rv, size, q, beta): @@ -573,7 +573,7 @@ class Poisson(Discrete): @classmethod def dist(cls, mu: DIST_PARAMETER_TYPES, *args, **kwargs): - mu = at.as_tensor_variable(floatX(mu)) + mu = pt.as_tensor_variable(floatX(mu)) return super().dist([mu], *args, **kwargs) def moment(rv, size, mu): @@ -816,7 +816,7 @@ class Geometric(Discrete): @classmethod def dist(cls, p: DIST_PARAMETER_TYPES, *args, **kwargs): - p = at.as_tensor_variable(floatX(p)) + p = pt.as_tensor_variable(floatX(p)) return super().dist([p], *args, **kwargs) def moment(rv, size, p): @@ -920,9 +920,9 @@ def dist( *args, **kwargs, ): - good = at.as_tensor_variable(intX(k)) - bad = at.as_tensor_variable(intX(N - k)) - n = at.as_tensor_variable(intX(n)) + good = pt.as_tensor_variable(intX(k)) + bad = pt.as_tensor_variable(intX(N - k)) + n = pt.as_tensor_variable(intX(n)) return super().dist([good, bad, n], *args, **kwargs) def moment(rv, size, good, bad, n): @@ -1057,8 +1057,8 @@ class DiscreteUniform(Discrete): @classmethod def dist(cls, lower: DIST_PARAMETER_TYPES, upper: DIST_PARAMETER_TYPES, *args, **kwargs): - lower = intX(at.floor(lower)) - upper = intX(at.floor(upper)) + lower = intX(pt.floor(lower)) + upper = intX(pt.floor(upper)) return super().dist([lower, upper], **kwargs) def moment(rv, size, lower, upper): @@ -1254,7 +1254,7 @@ class DiracDelta(Discrete): @classmethod def dist(cls, c: DIST_PARAMETER_TYPES, *args, **kwargs): - c = at.as_tensor_variable(c) + c = pt.as_tensor_variable(c) if c.dtype in continuous_types: c = floatX(c) return super().dist([c], **kwargs) @@ -1561,8 +1561,8 @@ class _OrderedLogistic(Categorical): @classmethod def dist(cls, eta: DIST_PARAMETER_TYPES, cutpoints: DIST_PARAMETER_TYPES, *args, **kwargs): - eta = at.as_tensor_variable(floatX(eta)) - cutpoints = at.as_tensor_variable(cutpoints) + eta = pt.as_tensor_variable(floatX(eta)) + cutpoints = pt.as_tensor_variable(cutpoints) pa = sigmoid(cutpoints - pt.shape_padright(eta)) p_cum = pt.concatenate( @@ -1674,8 +1674,8 @@ def dist( *args, **kwargs, ): - eta = at.as_tensor_variable(floatX(eta)) - cutpoints = at.as_tensor_variable(cutpoints) + eta = pt.as_tensor_variable(floatX(eta)) + cutpoints = pt.as_tensor_variable(cutpoints) probits = pt.shape_padright(eta) - cutpoints _log_p = pt.concatenate(