def test3_MDN_with_2d_gaussian_noise_y(self):
X, Y = self.get_samples(std=0.5)
with tf.Session():
model_no_noise = MixtureDensityNetwork("mdn_no_noise_y",
1,
1,
n_centers=1,
x_noise_std=None,
y_noise_std=None)
model_no_noise.fit(X, Y)
var_no_noise = model_no_noise.covariance(
x_cond=np.array([[2]]))[0][0][0]
model_noise = MixtureDensityNetwork("mdn_noise_y",
1,
1,
n_centers=1,
x_noise_std=None,
y_noise_std=1)
model_noise.fit(X, Y)
var_noise = model_noise.covariance(x_cond=np.array([[2]]))[0][0][0]
print("Training w/o noise:", var_no_noise)
print("Training w/ noise:", var_noise)
self.assertGreaterEqual(var_noise - var_no_noise, 0.1)
def test8_data_normalization(self):
np.random.seed(24)
mean = 10
std = 2
data = np.random.normal([mean, mean, mean, mean], std, size=(2000, 4))
X = data[:, 0:2]
Y = data[:, 2:4]
with tf.Session() as sess:
model = MixtureDensityNetwork("mdn_data_normalization",
2,
2,
n_centers=2,
x_noise_std=None,
y_noise_std=None,
data_normalization=True,
n_training_epochs=2000,
random_seed=22)
model.fit(X, Y)
y_mean, y_std = sess.run([model.mean_y_sym, model.std_y_sym])
print(y_mean, y_std)
cond_mean = model.mean_(Y)
mean_diff = np.abs(mean - np.mean(cond_mean))
self.assertLessEqual(mean_diff, 0.5)
cond_cov = np.mean(model.covariance(Y), axis=0)
print(cond_cov)
self.assertGreaterEqual(cond_cov[0][0], std**2 * 0.7)
self.assertLessEqual(cond_cov[0][0], std**2 * 1.3)
self.assertGreaterEqual(cond_cov[1][1], std**2 * 0.7)
self.assertLessEqual(cond_cov[1][1], std**2 * 1.3)
def test_MDN_with_2d_gaussian_sampling(self):
X, Y = self.get_samples()
model = MixtureDensityNetwork("mdn_gaussian_sampling",
1,
1,
n_centers=5,
n_training_epochs=200)
model.fit(X, Y)
x_cond = np.ones(shape=(10**6, 1))
_, y_sample = model.sample(x_cond)
self.assertAlmostEqual(np.mean(y_sample),
float(model.mean_(y_sample[1])),
places=0)
self.assertAlmostEqual(np.std(y_sample),
float(model.covariance(y_sample[1])),
places=0)
def test_covariance_mixture(self):
np.random.seed(24)
from tensorflow import set_random_seed
set_random_seed(24)
scale = 2.0
data = np.random.normal(loc=[2, 2, 7, -2],
scale=scale,
size=(10000, 4))
X = data[:, 0:2]
Y = data[:, 2:4]
model = MixtureDensityNetwork("mdn_cov",
2,
2,
n_centers=5,
x_noise_std=0.1,
y_noise_std=0.1)
model.fit(X, Y)
cov_est = model.covariance(x_cond=np.array([[0, 1]]))
print(cov_est)
self.assertLessEqual(np.abs(cov_est[0][1][0] - 0.0), 0.2)
self.assertLessEqual(np.abs(np.sqrt(cov_est[0][0][0]) - scale), 1.0)