def test_categorical_smoothed_node_create_and_eval():
for alpha in alphas:
for i, var in enumerate(vars):
var_freq = freqs[i]
smo = CategoricalSmoothedNode(i, var, alpha, var_freq)
smo.eval(obs)
print('smo values')
print(smo.log_val)
ll = compute_smoothed_ll(obs[i], var_freq, var, alpha)
print('log values')
print(ll)
assert_almost_equal(ll, smo.log_val, 15)
def test_categorical_smoothed_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):
log_vals = []
var_freq = freqs[i]
smo = CategoricalSmoothedNode(i, var, alpha, var_freq)
smo.build_k(input)
for d in data:
smo.eval(d)
log_vals.append(smo.log_val)
print('smo values')
print(smo.log_val)
eval_input_node_f = K.function([input], [smo.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_smoothed_node_resmooth():
for i, var in enumerate(vars):
alpha = alphas[0]
var_freq = freqs[i]
smo = CategoricalSmoothedNode(i, var, alpha, var_freq)
smo.eval(obs[i])
print('smo values')
print(smo.log_val)
# checking the right value
ll = compute_smoothed_ll(obs[i], var_freq, var, alpha)
print('log values')
print(ll)
assert_almost_equal(ll, smo.log_val, 15)
# now setting another alpha
print('Changing smooth level')
for alpha_new in alphas:
smo.smooth_probs(alpha_new)
smo.eval(obs[i])
print('smo values')
print(smo.log_val)
ll = compute_smoothed_ll(obs[i], var_freq, var, alpha_new)
print('log values')
print(ll)
assert_almost_equal(ll, smo.log_val, 15)