def test_compute_graph_up_noconst(self):
"""Computing value assuming no constant functions"""
# Number of times val_fun was called
# Use list to avoid creating local fun variable during assignment
counter = [0]
def val_fun(node, *inputs):
counter[0] += 1
if isinstance(node, spn.graph.node.VarNode):
return 1
elif isinstance(node, spn.graph.node.ParamNode):
return 0.1
else:
weight_val, iv_val, *values = inputs
return weight_val + sum(values) + 1
# Generate graph
v1 = spn.RawLeaf(num_vars=1)
v2 = spn.RawLeaf(num_vars=1)
v3 = spn.RawLeaf(num_vars=1)
s1 = spn.Sum(v1, v1, v2) # v1 included twice
s2 = spn.Sum(v1, v3)
s3 = spn.Sum(v2, v3, v3) # v3 included twice
s4 = spn.Sum(s1, v1)
s5 = spn.Sum(s2, v3, s3)
s6 = spn.Sum(s4, s2, s5, s4, s5) # s4 and s5 included twice
spn.generate_weights(s6)
# Calculate value
val = spn.compute_graph_up(s6, val_fun)
# Test
self.assertAlmostEqual(val, 35.2)
self.assertEqual(counter[0], 15)
def test_traversing_on_dense(self):
"""Compare traversal algs on dense SPN"""
def fun1(node, *args):
counter[0] += 1
def fun2(node, *args):
counter[0] += 1
if node.is_op:
return [None] * len(node.inputs)
# Generate dense graph
v1 = spn.IndicatorLeaf(num_vars=3, num_vals=2, name="IndicatorLeaf1")
v2 = spn.IndicatorLeaf(num_vars=3, num_vals=2, name="IndicatorLeaf2")
gen = spn.DenseSPNGenerator(num_decomps=2,
num_subsets=3,
num_mixtures=2,
input_dist=spn.DenseSPNGenerator.InputDist.MIXTURE,
num_input_mixtures=None)
root = gen.generate(v1, v2)
spn.generate_weights(root)
# Run traversal algs and count nodes
counter = [0]
spn.compute_graph_up_down(root, down_fun=fun2, graph_input=1)
c1 = counter[0]
counter = [0]
spn.compute_graph_up(root, val_fun=fun1)
c2 = counter[0]
counter = [0]
spn.traverse_graph(root, fun=fun1, skip_params=False)
c3 = counter[0]
# Compare
self.assertEqual(c1, c3)
self.assertEqual(c2, c3)
def test_compute_graph_up_const(self):
"""Computing value with constant function detection"""
# Number of times val_fun was called
# Use list to avoid creating local fun variable during assignment
counter = [0]
# Generate graph
v1 = spn.RawLeaf(num_vars=1)
v2 = spn.RawLeaf(num_vars=1)
v3 = spn.RawLeaf(num_vars=1)
s1 = spn.Sum(v1, v1, v2) # v1 included twice
s2 = spn.Sum(v1, v3)
s3 = spn.Sum(v2, v3, v3) # v3 included twice
s4 = spn.Sum(s1, v1)
s5 = spn.Sum(s2, v3, s3)
s6 = spn.Sum(s4, s2, s5, s4, s5) # s4 and s5 included twice
def val_fun(node, *inputs):
counter[0] += 1
# s3 is not needed for calculations since only parent is s5
self.assertIsNot(node, s3)
# Fixed value or compute using children
if node == s5:
return 16
else:
if isinstance(node, spn.graph.node.VarNode):
return 1
else:
weight_val, iv_val, *values = inputs
return sum(values) + 1
def const_fun(node):
if node == s5:
return True
else:
return False
# Calculate value
val = spn.compute_graph_up(s6, val_fun, const_fun)
# Test
self.assertEqual(val, 48)
self.assertEqual(counter[0], 8)