def test1_KMN_with_2d_gaussian_noise_y(self):
X, Y = self.get_samples(std=0.5)
with tf.Session():
model_no_noise = KernelMixtureNetwork("kmn_no_noise_y",
1,
1,
n_centers=5,
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 = KernelMixtureNetwork("kmn_noise_y",
1,
1,
n_centers=5,
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 test9_data_normalization(self):
np.random.seed(24)
mean = -80
std = 7
data = np.random.normal([mean, mean, mean, mean], std, size=(4000, 4))
X = data[:, 0:2]
Y = data[:, 2:4]
with tf.Session():
model = KernelMixtureNetwork("kmn_data_normalization_2",
2,
2,
n_centers=5,
x_noise_std=None,
y_noise_std=None,
data_normalization=True,
n_training_epochs=2000,
random_seed=22,
keep_edges=False,
train_scales=True,
weight_normalization=True,
init_scales=np.array([1.0]))
model.fit(X, Y)
cond_mean = model.mean_(Y)
print(np.mean(cond_mean))
mean_diff = np.abs(mean - np.mean(cond_mean))
self.assertLessEqual(mean_diff, np.abs(mean) * 0.1)
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_KMN_with_2d_gaussian_sampling(self):
np.random.seed(22)
X, Y = self.get_samples(mu=5)
import time
t = time.time()
model = KernelMixtureNetwork("kmn_sampling",
1,
1,
center_sampling_method='k_means',
n_centers=5,
n_training_epochs=1000,
data_normalization=True)
print("time to build model:", time.time() - t)
t = time.time()
model.fit(X, Y)
print("time to fit model:", time.time() - t)
x_cond = 5 * np.ones(shape=(2000000, 1))
_, y_sample = model.sample(x_cond)
print(np.mean(y_sample), np.std(y_sample))
self.assertAlmostEqual(np.mean(y_sample),
float(model.mean_(x_cond[1])),
places=1)
self.assertAlmostEqual(np.std(y_sample),
float(model.covariance(x_cond[1])),
places=1)
x_cond = np.ones(shape=(400000, 1))
x_cond[0, 0] = 5.0
_, y_sample = model.sample(x_cond)
self.assertAlmostEqual(np.mean(y_sample),
float(model.mean_(x_cond[1])),
places=1)
self.assertAlmostEqual(np.std(y_sample),
float(np.sqrt(model.covariance(x_cond[1]))),
places=1)