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)
X, Y = density_simulator.simulate(n_samples=3000)
""" fit density model """
model = KernelMixtureNetwork("KDE_demo",
ndim_x=1,
ndim_y=1,
n_centers=50,
x_noise_std=0.2,
y_noise_std=0.1,
random_seed=22)
model.fit(X, Y)
""" query the conditional pdf and cdf"""
x_cond = np.zeros((1, 1))
y_query = np.ones((1, 1)) * 0.1
prob = model.pdf(x_cond, y_query)
cum_prob = model.cdf(x_cond, y_query)
""" compute conditional moments & VaR """
x_cond = np.zeros((1, 1))
mean = model.mean_(x_cond)[0][0]
std = model.std_(x_cond)[0][0]
skewness = model.skewness(x_cond)[0]
VaR = model.value_at_risk(x_cond, alpha=0.01)[0]
print("Mean:", mean)
print("Std:", std)
print("Skewness:", skewness)
print("Value-at-Risk", VaR)
""" plot the fitted distribution """
x_cond_plot = np.array([-0.5, 0, 0.5])
model.plot2d(x_cond_plot, ylim=(-0.25, 0.15), show=True)