def test_eval_parametric(self):
data = np.array([1, 1, 1, 1, 1, 1, 1], dtype=np.float32).reshape(
(1, 7))
spn = (Gaussian(mean=1.0, stdev=1.0, scope=[0]) *
Exponential(l=1.0, scope=[1]) *
Gamma(alpha=1.0, beta=1.0, scope=[2]) *
LogNormal(mean=1.0, stdev=1.0, scope=[3]) *
Poisson(mean=1.0, scope=[4]) * Bernoulli(p=0.6, scope=[5]) *
Categorical(p=[0.1, 0.2, 0.7], scope=[6]))
ll = log_likelihood(spn, data)
tf_ll = eval_tf(spn, data)
self.assertTrue(np.all(np.isclose(ll, tf_ll)))
spn_copy = Copy(spn)
tf_graph, data_placeholder, variable_dict = spn_to_tf_graph(
spn_copy, data, 1)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
tf_graph_to_spn(variable_dict)
str_val = spn_to_str_equation(spn)
str_val2 = spn_to_str_equation(spn_copy)
self.assertEqual(str_val, str_val2)
raise Exception("Unknown parametric " + str(type(node)))
if __name__ == '__main__':
node = Gaussian(np.inf, np.inf)
data = np.array([1, 2, 3, 4, 5]).reshape(-1, 1)
update_parametric_parameters_mle(node, data)
assert np.isclose(node.mean, np.mean(data))
assert np.isclose(node.stdev, np.std(data))
node = Gamma(np.inf, np.inf)
data = np.array([1, 2, 3, 4, 5]).reshape(-1, 1)
update_parametric_parameters_mle(node, data)
assert np.isclose(node.alpha / node.beta, np.mean(data)), node.alpha
node = LogNormal(np.inf, np.inf)
data = np.array([1, 2, 3, 4, 5]).reshape(-1, 1)
update_parametric_parameters_mle(node, data)
assert np.isclose(node.mean, np.log(data).mean(), atol=0.00001)
assert np.isclose(node.stdev, np.log(data).std(), atol=0.00001)
node = Poisson(np.inf)
data = np.array([1, 2, 3, 4, 5]).reshape(-1, 1)
update_parametric_parameters_mle(node, data)
assert np.isclose(node.mean, np.mean(data))
node = Categorical(np.array([1, 1, 1, 1, 1, 1]) / 6)
data = np.array([0, 0, 1, 3, 5]).reshape(-1, 1)
update_parametric_parameters_mle(node, data)
assert np.isclose(node.p[0], 2 / 5)
assert np.isclose(node.p[1], 1 / 5)
#
# gamma, alpha=20, beta=1
gamma = Gamma(alpha=20, beta=1.1, scope=[0])
pdf_x, pdf_y = approximate_density(gamma, x_range)
fig, ax = plt.subplots(1, 1)
ax.plot(pdf_x, pdf_y, label="gamma")
print('Gamma Mode:', gamma.mode)
plt.axvline(x=gamma.mode, color='r')
if show_plots:
plt.show()
#
# lognormal
lognormal = LogNormal(mean=-5, stdev=1.1, scope=[0])
pdf_x, pdf_y = approximate_density(lognormal, x_range)
fig, ax = plt.subplots(1, 1)
ax.plot(pdf_x, pdf_y, label="lognormal")
print('LogNormal Mode:', lognormal.mode)
plt.axvline(x=lognormal.mode, color='r')
if show_plots:
plt.show()
#
# poisson
poisson = Poisson(mean=5, scope=[0])
pdf_x, pdf_y = approximate_density(poisson, x_range)
fig, ax = plt.subplots(1, 1)