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WIP Allow downcast in metropolis and nuts functions. #1265

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WIP Allow downcast in metropolis and nuts functions. #1265

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6 changes: 3 additions & 3 deletions pymc3/step_methods/metropolis.py
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Original file line number Diff line number Diff line change
Expand Up @@ -182,7 +182,7 @@ def tune(scale, acc_rate):

class BinaryMetropolis(ArrayStep):
"""Metropolis-Hastings optimized for binary variables

Parameters
----------
vars : list
Expand All @@ -195,7 +195,7 @@ class BinaryMetropolis(ArrayStep):
The frequency of tuning. Defaults to 100 iterations.
model : PyMC Model
Optional model for sampling step. Defaults to None (taken from context).

"""

def __init__(self, vars, scaling=1., tune=True, tune_interval=100, model=None):
Expand Down Expand Up @@ -294,6 +294,6 @@ def delta_logp(logp, vars, shared):

logp1 = CallableTensor(logp0)(inarray1)

f = theano.function([inarray1, inarray0], logp1 - logp0)
f = theano.function([inarray1, inarray0], logp1 - logp0, allow_input_downcast=True)
f.trust_input = True
return f
2 changes: 1 addition & 1 deletion pymc3/step_methods/nuts.py
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Expand Up @@ -212,6 +212,6 @@ def leapfrog1_dE(logp, vars, shared, pot, profile):
E0 = energy(H, q0, p0)
dE = E - E0

f = theano.function([q, p, e, q0, p0], [q1, p1, dE], profile=profile)
f = theano.function([q, p, e, q0, p0], [q1, p1, dE], profile=profile, allow_input_downcast=True)
f.trust_input = True
return f