def test_MDN_with_2d_gaussian2(self):
mu = -5
std = 2.5
X, Y = self.get_samples(mu=mu, std=std)
model = MixtureDensityNetwork("mdn2",
1,
1,
n_centers=5,
weight_normalization=True)
model.fit(X, Y)
y = np.arange(mu - 3 * std, mu + 3 * std, 6 * std / 20)
x = np.asarray([mu for i in range(y.shape[0])])
p_est = model.pdf(x, y)
p_true = norm.pdf(y, loc=mu, scale=std)
self.assertLessEqual(np.mean(np.abs(p_true - p_est)), 0.1)
p_est = model.cdf(x, y)
p_true = norm.cdf(y, loc=mu, scale=std)
self.assertLessEqual(np.mean(np.abs(p_true - p_est)), 0.1)
def test_MDN_dropout(self):
mu = -8
std = 2.5
X, Y = self.get_samples(mu=mu, std=std)
dropout_model = MixtureDensityNetwork("mdn_dropout_reasonable",
1,
1,
n_centers=5,
weight_normalization=True,
dropout=0.5,
n_training_epochs=400)
dropout_model.fit(X, Y)
y = np.arange(mu - 3 * std, mu + 3 * std, 6 * std / 20)
x = np.asarray([mu for i in range(y.shape[0])])
p_est = dropout_model.pdf(x, y)
p_true = norm.pdf(y, loc=mu, scale=std)
self.assertLessEqual(np.mean(np.abs(p_true - p_est)), 0.1)
p_est = dropout_model.cdf(x, y)
p_true = norm.cdf(y, loc=mu, scale=std)
self.assertLessEqual(np.mean(np.abs(p_true - p_est)), 0.1)