def test_gp_callable_white_noise(N=50, seed=1234):
np.random.seed(seed)
x = np.random.uniform(0, 5)
y = 5 + np.sin(x)
gp = GP(10. * kernels.ExpSquaredKernel(1.3), mean=5.0,
white_noise=LinearWhiteNoise(-6, 0.01),
fit_white_noise=True)
gp.compute(x)
check_gradient(gp, y)
gp.freeze_parameter("white_noise:m")
check_gradient(gp, y)
def test_gp_callable_white_noise(N=50, seed=1234):
np.random.seed(seed)
x = np.random.uniform(0, 5)
y = 5 + np.sin(x)
gp = GP(10. * kernels.ExpSquaredKernel(1.3),
mean=5.0,
white_noise=LinearWhiteNoise(-6, 0.01),
fit_white_noise=True)
gp.compute(x)
check_gradient(gp, y)
gp.freeze_parameter("white_noise:m")
check_gradient(gp, y)
def test_parameters():
kernel = 10 * kernels.ExpSquaredKernel(1.0)
kernel += 0.5 * kernels.RationalQuadraticKernel(log_alpha=0.1, metric=5.0)
gp = GP(kernel, white_noise=LinearWhiteNoise(1.0, 0.1))
n = len(gp.get_parameter_vector())
assert n == len(gp.get_parameter_names())
assert n - 2 == len(kernel.get_parameter_names())
gp.freeze_parameter(gp.get_parameter_names()[0])
assert n - 1 == len(gp.get_parameter_names())
assert n - 1 == len(gp.get_parameter_vector())
gp.freeze_all_parameters()
assert len(gp.get_parameter_names()) == 0
assert len(gp.get_parameter_vector()) == 0
gp.kernel.thaw_all_parameters()
gp.white_noise.thaw_all_parameters()
assert n == len(gp.get_parameter_vector())
assert n == len(gp.get_parameter_names())
assert np.allclose(kernel[0], np.log(10.))
def test_parameters():
kernel = 10 * kernels.ExpSquaredKernel(1.0)
kernel += 0.5 * kernels.RationalQuadraticKernel(log_alpha=0.1, metric=5.0)
gp = GP(kernel, white_noise=LinearWhiteNoise(1.0, 0.1))
n = len(gp.get_parameter_vector())
assert n == len(gp.get_parameter_names())
assert n - 2 == len(kernel.get_parameter_names())
gp.freeze_parameter(gp.get_parameter_names()[0])
assert n - 1 == len(gp.get_parameter_names())
assert n - 1 == len(gp.get_parameter_vector())
gp.freeze_all_parameters()
assert len(gp.get_parameter_names()) == 0
assert len(gp.get_parameter_vector()) == 0
gp.kernel.thaw_all_parameters()
gp.white_noise.thaw_all_parameters()
assert n == len(gp.get_parameter_vector())
assert n == len(gp.get_parameter_names())
assert np.allclose(kernel[0], np.log(10.))
