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)
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)