def test_4variables(self):
l1 = Variable('l1', list(range(10)))
l2 = Variable('l2', list(range(10)))
l3 = Variable('l3', list(range(10)))
y1 = Variable('y1', list(range(10)))
@AsNAryFunctionRelation(l1, l2, l3, y1)
def scene_rel(l1_, l2_, l3_, y1_):
if y1_ == round((l1_ + l2_ + l3_) / 3):
return 0
return 10000
@AsNAryFunctionRelation(l3)
def cost_l3(l3_):
return l3_
self.assertEqual(scene_rel(9, 6, 0, 5), 0)
self.assertEqual(scene_rel(3, 6, 0, 5), 10000)
joined = dpop.join_utils(scene_rel, cost_l3)
self.assertEqual(joined(9, 6, 0, 5), 0)
self.assertEqual(joined(3, 6, 0, 5), 10000)
util = dpop.projection(joined, l3, 'min')
def test_projection_min_oneVarRel(self):
# u1 is a relation with a single variable :
x1 = Variable('x1', ['a', 'b', 'c'])
u1 = NAryMatrixRelation([x1], np.array([2, 4, 8], np.int8))
# take the projection of u1 along x1
p = dpop.projection(u1, x1, mode='min')
# the dimension must be one less than the dimension of u1
self.assertEqual(p.arity, 0)
# this means that p is actually a signle value, corresponding to the
# max of u1
self.assertEqual(p.get_value_for_assignment(), 2)
def test_projection_min_twoVarsRel(self):
x1 = Variable('x1', ['a', 'b', 'c'])
x2 = Variable('x2', ['1', '2'])
u1 = NAryMatrixRelation([x1, x2],
np.array([[2, 16], [4, 32], [8, 64]], np.int8))
# take the projection of u1 along x1
p = dpop.projection(u1, x1, mode='min')
# the dimension must be one less than the dimension of u1, it should
# contain only x2
self.assertEqual(p.arity, 1)
self.assertListEqual(p.dimensions, [x2])
# the min of u1 when setting x2<-1 is 2
self.assertEqual(p.get_value_for_assignment(['1']), 2)
# the min of u1 when setting x2<-2 is 16
self.assertEqual(p.get_value_for_assignment(['2']), 16)
