def test_linked_random_spn_top_down():
# number small parameters
n_levels = 10
vars = [2, 3, 2, 2, 4]
n_max_children = 2
n_scope_children = 3
max_scope_split = 2
merge_prob = 0.5
# building it
print('creating random spn')
rand_gen = random.Random(789)
#
# doing this for more than once
n_times = 10
for _i in range(n_times):
spn = SpnFactory.linked_random_spn_top_down(vars,
n_levels,
n_max_children,
n_scope_children,
max_scope_split,
merge_prob,
rand_gen=rand_gen)
# printing for comparison
print(spn)
print(spn.stats())
assert spn.is_valid()
# translating to theano representation
theano_spn = SpnFactory.linked_to_theano(spn)
print(theano_spn)
print(theano_spn.stats())
#
# looking for the same computations
# time for some inference comparison
for instance in II:
print('linked')
res_l = spn.eval(instance)
print(res_l)
print('theano')
res_t = theano_spn.eval(instance)
print(res_t)
assert_array_almost_equal(res_l, res_t)
def test_linked_random_spn_top_down():
# number small parameters
n_levels = 10
vars = [2, 3, 2, 2, 4]
n_max_children = 2
n_scope_children = 3
max_scope_split = 2
merge_prob = 0.5
# building it
print('creating random spn')
rand_gen = random.Random(789)
#
# doing this for more than once
n_times = 10
for _i in range(n_times):
spn = SpnFactory.linked_random_spn_top_down(vars,
n_levels,
n_max_children,
n_scope_children,
max_scope_split,
merge_prob,
rand_gen=rand_gen)
# printing for comparison
print(spn)
print(spn.stats())
assert spn.is_valid()
# translating to theano representation
theano_spn = SpnFactory.linked_to_theano(spn)
print(theano_spn)
print(theano_spn.stats())
#
# looking for the same computations
# time for some inference comparison
for instance in II:
print('linked')
res_l = spn.eval(instance)
print(res_l)
print('theano')
res_t = theano_spn.eval(instance)
print(res_t)
assert_array_almost_equal(res_l, res_t)
def test_linked_to_theano_indicator():
# creating single nodes
root = SumNode()
prod1 = ProductNode()
prod2 = ProductNode()
prod3 = ProductNode()
sum1 = SumNode()
sum2 = SumNode()
sum3 = SumNode()
sum4 = SumNode()
ind1 = CategoricalIndicatorNode(var=0, var_val=0)
ind2 = CategoricalIndicatorNode(var=0, var_val=1)
ind3 = CategoricalIndicatorNode(var=1, var_val=0)
ind4 = CategoricalIndicatorNode(var=1, var_val=1)
ind5 = CategoricalIndicatorNode(var=2, var_val=0)
ind6 = CategoricalIndicatorNode(var=2, var_val=1)
ind7 = CategoricalIndicatorNode(var=2, var_val=2)
ind8 = CategoricalIndicatorNode(var=3, var_val=0)
ind9 = CategoricalIndicatorNode(var=3, var_val=1)
ind10 = CategoricalIndicatorNode(var=3, var_val=2)
ind11 = CategoricalIndicatorNode(var=3, var_val=3)
prod4 = ProductNode()
prod5 = ProductNode()
prod6 = ProductNode()
prod7 = ProductNode()
# linking nodes
root.add_child(prod1, 0.3)
root. add_child(prod2, 0.3)
root.add_child(prod3, 0.4)
prod1.add_child(sum1)
prod1.add_child(sum2)
prod2.add_child(ind7)
prod2.add_child(ind8)
prod2.add_child(ind11)
prod3.add_child(sum3)
prod3.add_child(sum4)
sum1.add_child(ind1, 0.3)
sum1.add_child(ind2, 0.3)
sum1.add_child(prod4, 0.4)
sum2.add_child(ind2, 0.5)
sum2.add_child(prod4, 0.2)
sum2.add_child(prod5, 0.3)
sum3.add_child(prod6, 0.5)
sum3.add_child(prod7, 0.5)
sum4.add_child(prod6, 0.5)
sum4.add_child(prod7, 0.5)
prod4.add_child(ind3)
prod4.add_child(ind4)
prod5.add_child(ind5)
prod5.add_child(ind6)
prod6.add_child(ind9)
prod6.add_child(ind10)
prod7.add_child(ind9)
prod7.add_child(ind10)
# building layers from nodes
root_layer = SumLayerLinked([root])
prod_layer = ProductLayerLinked([prod1, prod2, prod3])
sum_layer = SumLayerLinked([sum1, sum2, sum3, sum4])
aprod_layer = ProductLayerLinked([prod4, prod5, prod6, prod7])
ind_layer = CategoricalIndicatorLayer(nodes=[ind1, ind2,
ind3, ind4,
ind5, ind6,
ind7, ind8,
ind9, ind10,
ind11])
# creating the linked spn
spn_linked = SpnLinked(input_layer=ind_layer,
layers=[aprod_layer,
sum_layer,
prod_layer,
root_layer])
print(spn_linked)
# converting to theano repr
spn_theano = SpnFactory.linked_to_theano(spn_linked)
print(spn_theano)
# time for some inference comparison
for instance in I:
print('linked')
res_l = spn_linked.eval(instance)
print(res_l)
print('theano')
res_t = spn_theano.eval(instance)
print(res_t)
assert_array_almost_equal(res_l, res_t)
def test_linked_to_theano_categorical():
vars = [2, 2, 3, 4]
freqs = [{'var': 0, 'freqs': [1, 2]},
{'var': 1, 'freqs': [2, 2]},
{'var': 0, 'freqs': [3, 2]},
{'var': 1, 'freqs': [0, 3]},
{'var': 2, 'freqs': [1, 0, 2]},
{'var': 3, 'freqs': [1, 2, 1, 2]},
{'var': 3, 'freqs': [3, 4, 0, 1]}]
# create input layer first
input_layer = CategoricalSmoothedLayer(vars=vars,
node_dicts=freqs)
# get nodes
ind_nodes = [node for node in input_layer.nodes()]
root_node = ProductNode()
sum1 = SumNode()
sum2 = SumNode()
prod1 = ProductNode()
prod2 = ProductNode()
sum3 = SumNode()
sum4 = SumNode()
# linking
root_node.add_child(sum1)
root_node.add_child(sum2)
root_node.add_child(ind_nodes[0])
root_node.add_child(ind_nodes[1])
sum1.add_child(ind_nodes[2], 0.4)
sum1.add_child(ind_nodes[3], 0.6)
sum2.add_child(ind_nodes[3], 0.2)
sum2.add_child(prod1, 0.5)
sum2.add_child(prod2, 0.3)
prod1.add_child(ind_nodes[4])
prod1.add_child(sum3)
prod1.add_child(sum4)
prod2.add_child(sum3)
prod2.add_child(sum4)
sum3.add_child(ind_nodes[5], 0.5)
sum3.add_child(ind_nodes[6], 0.5)
sum4.add_child(ind_nodes[5], 0.4)
sum4.add_child(ind_nodes[6], 0.6)
# creating layers
root_layer = ProductLayerLinked([root_node])
sum_layer = SumLayerLinked([sum1, sum2])
prod_layer = ProductLayerLinked([prod1, prod2])
sum_layer2 = SumLayerLinked([sum3, sum4])
# create the linked spn
spn_linked = SpnLinked(input_layer=input_layer,
layers=[sum_layer2, prod_layer,
sum_layer, root_layer])
print(spn_linked)
# converting to theano repr
spn_theano = SpnFactory.linked_to_theano(spn_linked)
print(spn_theano)
# time for some inference comparison
for instance in I:
print('linked')
res_l = spn_linked.eval(instance)
print(res_l)
print('theano')
res_t = spn_theano.eval(instance)
print(res_t)
assert_array_almost_equal(res_l, res_t)
def test_linked_to_theano_indicator():
# creating single nodes
root = SumNode()
prod1 = ProductNode()
prod2 = ProductNode()
prod3 = ProductNode()
sum1 = SumNode()
sum2 = SumNode()
sum3 = SumNode()
sum4 = SumNode()
ind1 = CategoricalIndicatorNode(var=0, var_val=0)
ind2 = CategoricalIndicatorNode(var=0, var_val=1)
ind3 = CategoricalIndicatorNode(var=1, var_val=0)
ind4 = CategoricalIndicatorNode(var=1, var_val=1)
ind5 = CategoricalIndicatorNode(var=2, var_val=0)
ind6 = CategoricalIndicatorNode(var=2, var_val=1)
ind7 = CategoricalIndicatorNode(var=2, var_val=2)
ind8 = CategoricalIndicatorNode(var=3, var_val=0)
ind9 = CategoricalIndicatorNode(var=3, var_val=1)
ind10 = CategoricalIndicatorNode(var=3, var_val=2)
ind11 = CategoricalIndicatorNode(var=3, var_val=3)
prod4 = ProductNode()
prod5 = ProductNode()
prod6 = ProductNode()
prod7 = ProductNode()
# linking nodes
root.add_child(prod1, 0.3)
root.add_child(prod2, 0.3)
root.add_child(prod3, 0.4)
prod1.add_child(sum1)
prod1.add_child(sum2)
prod2.add_child(ind7)
prod2.add_child(ind8)
prod2.add_child(ind11)
prod3.add_child(sum3)
prod3.add_child(sum4)
sum1.add_child(ind1, 0.3)
sum1.add_child(ind2, 0.3)
sum1.add_child(prod4, 0.4)
sum2.add_child(ind2, 0.5)
sum2.add_child(prod4, 0.2)
sum2.add_child(prod5, 0.3)
sum3.add_child(prod6, 0.5)
sum3.add_child(prod7, 0.5)
sum4.add_child(prod6, 0.5)
sum4.add_child(prod7, 0.5)
prod4.add_child(ind3)
prod4.add_child(ind4)
prod5.add_child(ind5)
prod5.add_child(ind6)
prod6.add_child(ind9)
prod6.add_child(ind10)
prod7.add_child(ind9)
prod7.add_child(ind10)
# building layers from nodes
root_layer = SumLayerLinked([root])
prod_layer = ProductLayerLinked([prod1, prod2, prod3])
sum_layer = SumLayerLinked([sum1, sum2, sum3, sum4])
aprod_layer = ProductLayerLinked([prod4, prod5, prod6, prod7])
ind_layer = CategoricalIndicatorLayer(nodes=[
ind1, ind2, ind3, ind4, ind5, ind6, ind7, ind8, ind9, ind10, ind11
])
# creating the linked spn
spn_linked = SpnLinked(
input_layer=ind_layer,
layers=[aprod_layer, sum_layer, prod_layer, root_layer])
print(spn_linked)
# converting to theano repr
spn_theano = SpnFactory.linked_to_theano(spn_linked)
print(spn_theano)
# time for some inference comparison
for instance in I:
print('linked')
res_l = spn_linked.eval(instance)
print(res_l)
print('theano')
res_t = spn_theano.eval(instance)
print(res_t)
assert_array_almost_equal(res_l, res_t)
def test_linked_to_theano_categorical():
vars = [2, 2, 3, 4]
freqs = [{
'var': 0,
'freqs': [1, 2]
}, {
'var': 1,
'freqs': [2, 2]
}, {
'var': 0,
'freqs': [3, 2]
}, {
'var': 1,
'freqs': [0, 3]
}, {
'var': 2,
'freqs': [1, 0, 2]
}, {
'var': 3,
'freqs': [1, 2, 1, 2]
}, {
'var': 3,
'freqs': [3, 4, 0, 1]
}]
# create input layer first
input_layer = CategoricalSmoothedLayer(vars=vars, node_dicts=freqs)
# get nodes
ind_nodes = [node for node in input_layer.nodes()]
root_node = ProductNode()
sum1 = SumNode()
sum2 = SumNode()
prod1 = ProductNode()
prod2 = ProductNode()
sum3 = SumNode()
sum4 = SumNode()
# linking
root_node.add_child(sum1)
root_node.add_child(sum2)
root_node.add_child(ind_nodes[0])
root_node.add_child(ind_nodes[1])
sum1.add_child(ind_nodes[2], 0.4)
sum1.add_child(ind_nodes[3], 0.6)
sum2.add_child(ind_nodes[3], 0.2)
sum2.add_child(prod1, 0.5)
sum2.add_child(prod2, 0.3)
prod1.add_child(ind_nodes[4])
prod1.add_child(sum3)
prod1.add_child(sum4)
prod2.add_child(sum3)
prod2.add_child(sum4)
sum3.add_child(ind_nodes[5], 0.5)
sum3.add_child(ind_nodes[6], 0.5)
sum4.add_child(ind_nodes[5], 0.4)
sum4.add_child(ind_nodes[6], 0.6)
# creating layers
root_layer = ProductLayerLinked([root_node])
sum_layer = SumLayerLinked([sum1, sum2])
prod_layer = ProductLayerLinked([prod1, prod2])
sum_layer2 = SumLayerLinked([sum3, sum4])
# create the linked spn
spn_linked = SpnLinked(
input_layer=input_layer,
layers=[sum_layer2, prod_layer, sum_layer, root_layer])
print(spn_linked)
# converting to theano repr
spn_theano = SpnFactory.linked_to_theano(spn_linked)
print(spn_theano)
# time for some inference comparison
for instance in I:
print('linked')
res_l = spn_linked.eval(instance)
print(res_l)
print('theano')
res_t = spn_theano.eval(instance)
print(res_t)
assert_array_almost_equal(res_l, res_t)
