def get_estimated_mean_and_std(gp: GaussianProcess, array_samples_parameters,
X):
functions_samples = []
X = X.reshape((-1, gp.array_dataset.shape[1]))
for num_samples, sample_gp_parameter in enumerate(
array_samples_parameters):
gp.set_kernel_parameters(*sample_gp_parameter.flatten())
functions_samples.append(gp.get_sample(X))
yield gp.get_sample(X)
if num_samples % 50 == 0:
print(f'num samples: {num_samples} \r')
def plot_vi_gp(obj, mu, Sigma, X, y):
gp = GaussianProcess(GaussianLinearKernel(0., 0., 0., 0., 0., 0.), X, y)
xlim, = obj.boundaries
x_gt = np.linspace(xlim[0], xlim[1], 100)
xx = np.linspace(xlim[0] - 2, xlim[1] + 2, 200)
plt.plot(x_gt, obj.evaluate_without_noise(x_gt), c='c')
plt.title(f"Gaussian Process Regression")
mu = mu.flatten()
for _ in range(500):
sample_gp_parameter = onp.random.multivariate_normal(mu, Sigma)
gp.set_kernel_parameters(*sample_gp_parameter)
function_sample = gp.get_sample(xx.reshape((-1, 1)))
plt.plot(xx, function_sample, alpha=0.3, c='C0')
plt.scatter(gp.array_dataset,
gp.array_objective_function_values,
c='m',
marker="+",
zorder=1000,
s=(30, ))
plt.pause(0.01)
plt.show()
