def test_spn_construction_by_init_and_evaluation():
# building the same levels
input_layer = build_spn_indicator_layer(vars)
sum_layer, prod_layer = build_spn_layers(input_layer)
spn = Spn(input_layer=input_layer, layers=[sum_layer, prod_layer])
res = spn.eval(I)
print('First evaluation')
print(res)
assert_log_array_almost_equal(root_vals, res)
def test_spn_construction_by_add_and_evaluation():
spn = Spn()
# building the same levels
input_layer = build_spn_indicator_layer(vars)
sum_layer, prod_layer = build_spn_layers(input_layer)
# adding all layers to the spn
spn.set_input_layer(input_layer)
spn.add_layer(sum_layer)
spn.add_layer(prod_layer)
res = spn.eval(I)
print('First evaluation')
print(res)
assert_log_array_almost_equal(root_vals, res)
def test_spn_construction_by_add_and_evaluation_II():
spn = Spn()
# print('empty spn')
# print(spn)
input_layer = build_spn_smoothed_layer(vars, dicts, alpha)
prod_layer = build_spn_layers_II(input_layer)
# adding all layers to the spn
spn.set_input_layer(input_layer)
spn.add_layer(prod_layer)
# print('created spn')
# print(spn)
res = spn.eval(I)
print('First smoothed evaluation')
print(res)
assert_log_array_almost_equal(root_vals, res)
def test_categorical_smoothed_node_data_smooth():
data_1 = numpy.array([[1],
[0],
[1],
[0],
[1]])
data_2 = numpy.array([[1, 0],
[0, 1],
[1, 1],
[0, 1],
[1, 0]])
alpha = 0
freqs = CategoricalSmoothedNode.smooth_freq_from_data(data_1, alpha)
print('freqs', freqs)
exp_freqs = CategoricalSmoothedNode.smooth_ll([2 / 5, 3 / 5], alpha)
print('exp freqs', exp_freqs)
assert_array_almost_equal(exp_freqs, freqs)
# now create a node
input_node = CategoricalSmoothedNode(var=0,
var_values=2,
instances={0, 2, 4})
input_node.smooth_probs(alpha, data=data_1)
exp_probs = CategoricalSmoothedNode.smooth_ll([0, 1], alpha)
print('exp probs', exp_probs)
print('probs', input_node._var_probs)
assert_log_array_almost_equal(exp_probs,
input_node._var_probs)
input_node.smooth_probs(alpha, data=data_2)
assert_log_array_almost_equal(exp_probs,
input_node._var_probs)
