def test_KMN_adaptive_noise(self):
adaptive_noise_fn = lambda n, d: 0.0 if n < 1000 else 5.0
X, Y = self.get_samples(mu=0, std=1, n_samples=999)
est = KernelMixtureNetwork("kmn_999",
1,
1,
n_centers=5,
y_noise_std=0.0,
x_noise_std=0.0,
adaptive_noise_fn=adaptive_noise_fn)
est.fit(X, Y)
std_999 = est.std_(x_cond=np.array([[0.0]]))[0]
X, Y = self.get_samples(mu=0, std=1, n_samples=1002)
est = KernelMixtureNetwork("kmn_1002",
1,
1,
n_centers=5,
y_noise_std=0.0,
x_noise_std=0.0,
adaptive_noise_fn=adaptive_noise_fn)
est.fit(X, Y)
std_1002 = est.std_(x_cond=np.array([[0.0]]))[0]
self.assertLess(std_999, std_1002)
self.assertGreater(std_1002, 2)
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)