def test_join_bothsamevar(self):
x1 = Variable('x1', ['a', 'b', 'c'])
u1 = NAryMatrixRelation([x1], np.array([1, 2, 3], np.int8))
u2 = NAryMatrixRelation([x1], np.array([1, 2, 3], np.int8))
# x1 = Variable('x1', ['a', 'b', 'c'])
# u1 = dpop.NAryRelation([x1], np.array([1, 2, 3], np.int8))
self.assertEqual(u1.get_value_for_assignment(['b']), 2)
u_j = dpop.join_utils(u1, u2)
self.assertEqual(u_j.arity, 1)
self.assertEqual(u_j.get_value_for_assignment(['a']), 2)
self.assertEqual(u_j.get_value_for_assignment(['b']), 4)
self.assertEqual(u_j.get_value_for_assignment(['c']), 6)
def _eff_cost(self, rel: NAryMatrixRelation, val) -> float:
"""
Compute the effective cost of a constraint with combining its base
cost with the associated modifier value (i.e. the weight of the
constraint at the current step)
:param rel: a constraint given as NAryMatrixRelation.
:param val: the value of the agent's variablefor which to compute the
_eff_cost
:return: the effective cost of the constraint for the current
assignment.
"""
# Keep only the variables present in the relation rel
global_asgt = self._neighbors_values.copy()
global_asgt[self.name] = val
asgt = filter_assignment_dict(global_asgt, rel.dimensions)
c = rel.get_value_for_assignment(asgt)
modifier = self._get_modifier_for_assignment(rel, asgt)
if self._modifier_mode == 'A':
c += modifier
else: # modifier_mode == 'M'
c *= modifier
return c