def eval1():
n_observations = 2000 # number of data points
n_features = 1 # number of features
X_train, X_test, y_train, y_test = build_econ1_dataset(n_observations)
print("Size of features in training data: {}".format(X_train.shape))
print("Size of output in training data: {}".format(y_train.shape))
print("Size of features in test data: {}".format(X_test.shape))
print("Size of output in test data: {}".format(y_test.shape))
fig, ax = plt.subplots()
fig.set_size_inches(10, 8)
sns.regplot(X_train, y_train, fit_reg=False)
# plt.savefig('toydata.png')
# plt.show()
# plot.figure.size = 100
# plt.show()
kmn = KernelMixtureNetwork(train_scales=True, n_centers=20)
kmn.fit(X_train, y_train, n_epoch=300, eval_set=(X_test, y_test))
kmn.plot_loss()
# plt.savefig('trainplot.png')
samples = kmn.sample(X_test)
print(X_test.shape, samples.shape)
jp = sns.jointplot(X_test.ravel(), samples, kind="hex", stat_func=None, size=10)
jp.ax_joint.add_line(Line2D([X_test[0][0], X_test[0][0]], [-40, 40], linewidth=3))
jp.ax_joint.add_line(Line2D([X_test[1][0], X_test[1][0]], [-40, 40], color='g', linewidth=3))
jp.ax_joint.add_line(Line2D([X_test[2][0], X_test[2][0]], [-40, 40], color='r', linewidth=3))
plt.savefig('hexplot.png')
plt.show()
d = kmn.predict_density(X_test[0:3, :].reshape(-1, 1), resolution=1000)
df = pd.DataFrame(d).transpose()
df.index = np.linspace(kmn.y_min, kmn.y_max, num=1000)
df.plot(legend=False, linewidth=3, figsize=(12.2, 8))
plt.savefig('conditional_density.png')
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)