def _calc_elbo(vars, model, n_mcsamples, random_seed):
"""Calculate approximate ELBO.
"""
theano.config.compute_test_value = 'ignore'
shared = pm.make_shared_replacements(vars, model)
factors = [var.logp_elemwiset
for var in model.basic_RVs] + model.potentials
wfactors = tt.stack(factors[:-1])
logpt = tt.concatenate([wfactors, factors[-1].reshape([factors[-1].size])])
#wfactors = tt.stack(factors[:-1])
#logpt = tt.concatenate([wfactors, factors[-1]])
#logpt = tt.add(*map(tt.sum, factors))
[logp], inarray = pm.join_nonshared_inputs([logpt], vars, shared)
uw = tt.dvector('uw')
uw.tag.test_value = np.concatenate(
[inarray.tag.test_value, inarray.tag.test_value])
elbo = _elbo_t(logp, uw, inarray, n_mcsamples, random_seed)
return elbo, shared
def delta_logp(logp, vars, shared):
[logp0], inarray0 = pm.join_nonshared_inputs([logp], vars, shared)
tensor_type = inarray0.type
inarray1 = tensor_type("inarray1")
logp1 = pm.CallableTensor(logp0)(inarray1)
f = aesara.function([inarray1, inarray0], logp1 - logp0)
f.trust_input = True
return f
def delta_logp(logp, vars, shared):
[logp0], inarray0 = pm.join_nonshared_inputs([logp], vars, shared)
tensor_type = inarray0.type
inarray1 = tensor_type('inarray1')
logp1 = pm.CallableTensor(logp0)(inarray1)
f = theano.function([inarray1, inarray0], logp1 - logp0)
f.trust_input = True
return f
def delta_logp(point, logp, vars, shared):
[logp0], inarray0 = pm.join_nonshared_inputs(point, [logp], vars, shared)
tensor_type = inarray0.type
inarray1 = tensor_type("inarray1")
logp1 = pm.CallableTensor(logp0)(inarray1)
f = compile_rv_inplace([inarray1, inarray0], logp1 - logp0)
f.trust_input = True
return f
def _calc_elbo(vars, model, n_mcsamples, random_seed):
"""Calculate approximate ELBO.
"""
theano.config.compute_test_value = 'ignore'
shared = pm.make_shared_replacements(vars, model)
factors = [var.logpt for var in model.basic_RVs] + model.potentials
logpt = tt.add(*map(tt.sum, factors))
[logp], inarray = pm.join_nonshared_inputs([logpt], vars, shared)
uw = tt.vector('uw')
uw.tag.test_value = np.concatenate([inarray.tag.test_value,
inarray.tag.test_value]).astype(theano.config.floatX)
elbo = _elbo_t(logp, uw, inarray, n_mcsamples, random_seed)
return elbo, shared
def _make_vectorized_logp_grad(vars, model, X):
theano.config.compute_test_value = 'ignore'
shared = pm.make_shared_replacements(vars, model)
# For some reason can't use model.basic_RVs here as the ordering
# will not match up with that of vars.
factors = [var.logpt for var in vars + model.observed_RVs] + model.potentials
logpt_grad = pm.theanof.gradient(tt.add(*map(tt.sum, factors)))
[logp_grad], inarray = pm.join_nonshared_inputs([logpt_grad], vars, shared)
# Callable tensor
def logp_grad_(input):
return theano.clone(logp_grad, {inarray: input}, strict=False)
logp_grad_vec = theano.map(logp_grad_, X)[0]
return logp_grad_vec
