def test_kernels(self):
from GPy.kern import RBF,Linear,MLP,Bias,White
Q = self.Z.shape[1]
kernels = [RBF(Q,ARD=True), Linear(Q,ARD=True),MLP(Q,ARD=True), RBF(Q,ARD=True)+Linear(Q,ARD=True)+Bias(Q)+White(Q)
,RBF(Q,ARD=True)+Bias(Q)+White(Q), Linear(Q,ARD=True)+Bias(Q)+White(Q)]
for k in kernels:
k.randomize()
self._test_kernel_param(k)
self._test_Z(k)
self._test_qX(k)
self._test_kernel_param(k, psi2n=True)
self._test_Z(k, psi2n=True)
self._test_qX(k, psi2n=True)
def get_data(kernel_name, variance_value=1.0, n_traces=3, lengthscale=1.0):
n_dims = 100
n_frames = 20
#n_traces = 3
x = np.linspace(0, 10, n_dims)[:, np.newaxis]
if kernel_name == "RBF":
kernel = RBF(input_dim=1,
variance=variance_value,
lengthscale=lengthscale)
elif kernel_name == "Brownian":
kernel = Brownian(input_dim=1, variance=variance_value)
elif kernel_name == "Matern32":
kernel = Matern32(input_dim=1, variance=variance_value)
elif kernel_name == "Cosine":
kernel = Cosine(input_dim=1, variance=variance_value)
elif kernel_name == "Exponential":
kernel = Exponential(input_dim=1, variance=variance_value)
elif kernel_name == "Linear":
kernel = Linear(input_dim=1)
elif kernel_name == "GridRBF":
kernel = GridRBF(input_dim=1, variance=variance_value)
elif kernel_name == "MLP":
kernel = MLP(input_dim=1, variance=variance_value)
elif kernel_name == "PeriodicMatern32":
kernel = PeriodicMatern32(input_dim=1, variance=variance_value)
elif kernel_name == "Spline":
kernel = Spline(input_dim=1, variance=variance_value)
elif kernel_name == "White":
kernel = White(input_dim=1, variance=variance_value)
elif kernel_name == "StdPeriodic":
kernel = StdPeriodic(input_dim=1, variance=variance_value)
else:
raise ValueError("Unknown Kernel name")
kernel_matrix = kernel.K(x, x)
gaussian_process_animation = GaussianProcessAnimation(kernel_matrix,
n_dims=n_dims,
n_frames=n_frames)
frames = gaussian_process_animation.get_traces(n_traces)
data = np.stack(frames).transpose((2, 0, 1))
return data