def construct_var(self, var_num):
rv = str(var_num)
values = self.domains[var_num]
self.pgm.add_var(
Variable(rv,
values,
detect_boolean=self.detect_bool,
force_boolean=self.force_bool))
def parse_node(self, s, l, t):
# print(t)
rv = t[0]
for key, val in t[1]:
if key == 'states':
self.domains[rv] = val
self.pgm.add_var(
Variable(rv,
val,
detect_boolean=self.detect_bool,
force_boolean=self.force_bool))
def parse_domains(self, root):
for cpt in root.find("nodes").findall('cpt'):
rv = cpt.get('id')
states = cpt.findall('state')
values = [state.get('id') for state in states]
self.domains[rv] = values
self.pgm.add_var(
Variable(rv,
values,
detect_boolean=self.detect_bool,
force_boolean=self.force_bool))
def parseNode(s, l, t):
global domains
# print(t)
rv = t[0]
for key, val in t[1]:
if key == 'states':
domains[rv] = val
pgm.add_var(
Variable(rv,
val,
detect_boolean=detect_bool,
force_boolean=force_bool))
def parse_domains(self, root):
for cpt in root.find("nodes").findall("cpt"):
rv = cpt.get("id")
states = cpt.findall("state")
values = [state.get("id") for state in states]
self.domains[rv] = values
self.pgm.add_var(
Variable(
rv,
values,
detect_boolean=self.detect_bool,
force_boolean=self.force_bool,
))
def parse_domains(self, root):
for cpt in root.findall('VARIABLE'):
rv = cpt.find('NAME').text
node_type = cpt.get("TYPE")
if node_type != "nature":
logger.error(
"Only probabilistic variables are supported. Found type {} for variable {}"
.format(node_type, name),
halt=True)
states = cpt.findall('OUTCOME')
values = [state.text for state in states]
self.domains[rv] = values
self.pgm.add_var(
Variable(rv,
values,
detect_boolean=self.detect_bool,
force_boolean=self.force_bool))
def clause_to_cpt(clause, number, pgm):
logger = logging.getLogger('problog')
# print('Clause: {} -- {}'.format(clause, type(clause)))
if isinstance(clause, Clause):
if isinstance(clause.head, Or):
heads = clause.head.to_list()
elif isinstance(clause.head, Term):
heads = [clause.head]
else:
heads = []
logger.error('Unknown head type: {} ({})'.format(
clause.head, type(clause.head)))
# CPT for the choice node
rv_cn = 'c{}'.format(number)
parents = term_to_atoms(clause.body)
parents_str = [str(t) for t in parents]
probs_heads = []
for head in heads:
if head.probability is not None:
probs_heads.append(head.probability.compute_value())
else:
probs_heads.append(1.0)
probs = [1.0 - sum(probs_heads)] + probs_heads
table_cn = dict()
for keys in itertools.product([False, True], repeat=len(parents)):
truth_values = dict(zip(parents, keys))
truth_value = term_to_bool(clause.body, truth_values)
if truth_value is None:
logger.error('Expected a truth value. Instead god:\n'
' {} -> {}'.format(truth_values, truth_value))
table_cn[keys] = [1.0] + [0.0] * len(heads)
elif truth_value:
table_cn[keys] = probs
else:
table_cn[keys] = [1.0] + [0.0] * len(heads)
pgm.add_var(Variable(rv_cn, list(range(len(heads) + 1))))
cpd_cn = Factor(pgm, rv_cn, parents_str, table_cn)
cpd_cn.latent = True
pgm.add_factor(cpd_cn)
# CPT for the head random variable
for idx, head in enumerate(heads):
rv = str(head.with_probability())
pgm.add_var(Variable(rv, [0, 1]))
pgm.add_factor(OrCPT(pgm, rv, [(rv_cn, idx + 1)]))
return
elif isinstance(clause, Or):
heads = clause.to_list()
# CPT for the choice node
rv_cn = 'c{}'.format(number)
parents = []
probs_heads = [head.probability.compute_value() for head in heads]
table_cn = [1.0 - sum(probs_heads)] + probs_heads
pgm.add_var(Variable(rv_cn, list(range(len(heads) + 1))))
cpd_cn = Factor(pgm, rv_cn, parents, table_cn)
cpd_cn.latent = True
pgm.add_factor(cpd_cn)
# CPT for the head random variable
for idx, head in enumerate(heads):
rv = str(head.with_probability())
pgm.add_var(Variable(rv, [0, 1]))
pgm.add_factor(OrCPT(pgm, rv, [(rv_cn, idx + 1)]))
return
elif isinstance(clause, Term):
# CPT for the choice node
rv_cn = 'c{}'.format(number)
parents = []
prob = clause.probability.compute_value()
table_cn = [1.0 - prob, prob]
pgm.add_var(Variable(rv_cn, [0, 1]))
cpd_cn = Factor(pgm, rv_cn, parents, table_cn)
cpd_cn.latent = True
pgm.add_factor(cpd_cn)
# CPT for the head random variable
rv = str(clause.with_probability())
pgm.add_var(Variable(rv, [0, 1]))
pgm.add_factor(OrCPT(pgm, rv, [(rv_cn, 1)]))
return
return