def sums(inputs,
indices,
latent_indicators,
num_sums,
inf_type=None,
log=True,
output=None):
if indices is None:
inputs = [inputs for _ in range(num_sums)]
else:
inputs = [(inputs, indices) for _ in range(num_sums)]
# Generate a single Sums node, modeling 'num_sums' sum nodes within,
# connecting it to inputs and latent_indicators
s = spn.Sums(*inputs,
num_sums=num_sums,
latent_indicators=latent_indicators[-1])
# Generate weights of the Sums node
weights = s.generate_weights()
# Connect the Sums nodes to a single root Sum node and generate its weights
root = spn.Sum(s)
root.generate_weights()
if log:
mpe_path_gen = spn.MPEPath(value_inference_type=inf_type, log=True)
else:
mpe_path_gen = spn.MPEPath(value_inference_type=inf_type,
log=False)
mpe_path_gen.get_mpe_path(root)
path_op = [mpe_path_gen.counts[weights]]
return spn.initialize_weights(root), path_op
def sums(inputs,
indices,
latent_indicators,
num_sums,
inf_type,
log=True,
output=None):
if indices is None:
inputs = [inputs for _ in range(num_sums)]
else:
inputs = [(inputs, ind) for ind in indices]
# Generate a single Sums node, modeling 'num_sums' sum nodes within,
# connecting it to inputs and latent_indicators
s = spn.Sums(*inputs,
num_sums=num_sums,
latent_indicators=latent_indicators[0])
# Generate weights of the Sums node
s.generate_weights()
# Connect the Sums nodes to a single root Sum node and generate its weights
root = spn.Sum(s)
root.generate_weights()
if log:
value_op = root.get_log_value(inference_type=inf_type)
else:
value_op = root.get_value(inference_type=inf_type)
return spn.initialize_weights(root), value_op