def test_categorical_indicator_node_create_and_eval_keras():
alpha = 0.0
data = numpy.array([[1, 1, 1, 0],
[0, 0, 1, 0],
[0, 0, 1, 0],
[1, 0, 0, 0],
[1, 0, 1, 0],
[0, 1, 1, 0],
[MARG_IND, 0, 0, 1],
[MARG_IND, MARG_IND, MARG_IND, MARG_IND]]).astype(numpy.int32)
input = K.placeholder(ndim=2, dtype='int32')
for i, var in enumerate(vars):
for j in [0, 1]:
log_vals = []
print('var {} val {}'.format(i, j))
smi = CategoricalIndicatorNode(i, j)
print(smi)
smi.build_k(input)
for d in data:
# print(d)
smi.eval(d)
log_vals.append(smi.log_val)
print('smi values')
print(smi.log_val)
eval_input_node_f = K.function([input], [smi.log_vals])
keras_log_vals = eval_input_node_f([data])[0]
print('keras vals')
print(keras_log_vals)
assert_array_almost_equal(numpy.array(log_vals)[:, numpy.newaxis],
keras_log_vals,
decimal=4)
def test_categorical_indicator_node_create_and_eval():
# created a node on the first var and its first value
ind = CategoricalIndicatorNode(0, 0)
# seen x0 = 0 -> 1.
ind.eval(0)
assert ind.log_val == 0.
# this indicator is not fired
ind.eval(1)
assert ind.log_val == LOG_ZERO
# all indicators for that var are fired
ind.eval(MARG_IND)
assert ind.log_val == 0.
# the var has only 2 values, but the node does not know!
ind.eval(2)
assert ind.log_val == LOG_ZERO