def try_bec():
# get train and test data
in_provider = InputProvider()
harmonic_sims, tr, te, va = in_provider.get_bec_data()
data = bec.get_within_range(tr, g_low=30, g_high=50, n=100)
# data_test = te.sample(100)
# get gp model and fit
gp = GP()
gp.fit(torch.FloatTensor(data[['g', 'x']].to_numpy()),
torch.FloatTensor(data.psi.to_numpy()), True)
# predict around one some fixed Dim
df = bec.get_closest_sim(harmonic_sims, g=30.)
test_gx = np.stack([30. * np.ones(df.x.shape[0]), df.x]).transpose()
y_pred, sigma = gp.predict(torch.FloatTensor(test_gx))
print(y_pred, sigma)
# plot subplots with multiple fixed dimensions
# specify input dimensions,
# the first entry is fixed for each iteration
# the second entry is plotted against the fixed dimension
input_dimensions = ['g', 'x']
out_dimensions = 'psi'
sub_plot_multiple_gp(gp, harmonic_sims, [5, 30, 60, 90], input_dimensions,
out_dimensions)
y = np.cos(x) + 0.5 * x + np.random.normal(loc=0.0, scale=0.2, size=(50, 1))
#test data X points
x_test = np.linspace(-5, 22, 100)
x_test = np.reshape(x_test, (100, 1))
a_gp = GP(x, y, "matern", params)
b = GP(x, y, "rbf", params)
'''
product_of_experts = distributedGP(x,y,8)
general_poe = distributedGP(x,y,8, method='gpoe')
bcm = distributedGP(x,y,8, method='bcm')
rbcm = distributedGP(x,y,8,method='rbcm')
'''
mean, cov = a_gp.predict(x_test)
m2, c2 = b.predict(x_test)
'''
mean2, cov2 = product_of_experts.predict(x_test)
mean3, cov3 = general_poe.predict(x_test)
mean4, cov4 = bcm.predict(x_test)
mean5, cov5 = rbcm.predict(x_test)
'''
plt.scatter(x, y)
plt.plot(x_test, mean, c='g', label='matern mean')
plt.plot(x_test,
mean + np.sqrt(cov) * 2,
linestyle='--',
color='g',
label='matern var')
import matplotlib.pyplot as plt
from torch.optim import Adam
import torch
import numpy as np
from gp import GP
if __name__ == '__main__':
gp = GP()
f = lambda x: ( torch.cos(2 * x[:, 0]) + torch.sin(x[:, 1]) ).view(-1)
n1, n2, ny = 40, 100, 5
domain = (-5, 5)
X_data = torch.distributions.Uniform(
domain[0] + 2, domain[1] - 2).sample((n1, 2))
y_data = f(X_data)
X_test = torch.linspace(domain[0], domain[1], n2)
X_test = torch.stack([X_test, X_test]).view(-1, 2)
y_test = f(X_test)
gp.fit(X_data, y_data, True, epochs=10000)
y_pred, _ = gp.predict(X_test)
print('MSE : ', ((y_pred - y_test)**2).mean().item() )
