def __init__(self, var_set, pt_tensor=None, discrete_pgm=None, name=None):
if (discrete_pgm is None):
discrete_pgm = DiscretePGM.get_context()
if (discrete_pgm is None):
raise Exception("No DiscretePGM specified, neither explicit nor as current context")
self.discrete_pgm = discrete_pgm
self.var_set = frozenset(var_set)
self.scope = discrete_pgm.map_var_set(self.var_set)
var_indices = sorted([ discrete_pgm.var_index(v) for v in var_set])
var_idx_map = {}
for i, sorted_idx in enumerate(var_indices):
var_idx_map[sorted_idx] = i
self.var_indices = var_indices
self.var_idx_map = var_idx_map
shp = [self.discrete_pgm.cardinalities[vidx] for vidx in var_indices]
self.shape = shp
self.is_shared = False
if (pt_tensor=="ones"):
pt_tensor = T.ones(shp, dtype=theano.config.floatX)
if (pt_tensor=='zeros'):
pt_tensor = T.zeros(shp, dtype=theano.config.floatX)
if (pt_tensor=="shared"):
pt_tensor = theano.shared(np.zeros(shp, dtype=theano.config.floatX))
self.is_shared = True
if (pt_tensor is None):
bcast = [False,]*len(self.var_set)
tensor_type = T.TensorType(dtype=theano.config.floatX, broadcastable=bcast)
self.pt_tensor = T.TensorVariable(type=tensor_type, name=name)
else:
self.pt_tensor = T.as_tensor_variable(pt_tensor)
def compact_shape(ndarr, var_set, discrete_pgm=None):
''' Helper function: Create ndarray in compact format from ndarray in expanded format'''
if (discrete_pgm is None):
discrete_pgm = DiscretePGM.get_context()
if (discrete_pgm is None):
raise Exception("No DiscretePGM specified, neither explicit nor as current context")
scope = discrete_pgm.map_var_set(var_set)
return np.reshape(ndarr, [discrete_pgm.cardinalities[i] for i in sorted(scope)])
