def test_define_default_kernel_2(self):
kernel = ScaledKernel.define_default_kernel(1, None, None, None,
*([MATERN52_NAME], ))
assert kernel.name == MATERN52_NAME
assert kernel.dimension == 1
assert kernel.dimension_parameters == 2
assert kernel.sigma2.value == [1.0]
assert kernel.kernel.length_scale.value == [1.0]
def get_kernel_default(kernel_name,
dimension,
bounds=None,
default_values=None,
parameters_priors=None,
**kernel_parameters):
"""
Returns a default kernel object associated to the kernel_name
:param kernel_name: [str]
:param dimension: [int]. It's the number of tasks for the task kernel.
:param bounds: [[float, float]], lower bound and upper bound for each entry. This parameter
is to compute priors in a smart way.
:param default_values: np.array(k), default values for the parameters of the kernel
:param parameters_priors: {
SIGMA2_NAME: float,
LENGTH_SCALE_NAME: [float],
LOWER_TRIANG_NAME: [float],
}
:param kernel_parameters: additional kernel parameters,
- SAME_CORRELATION: (boolean) True or False. Parameter used only for task kernel.
:return: kernel object
"""
if kernel_name[0] == SCALED_KERNEL:
if kernel_name[1] == MATERN52_NAME:
return ScaledKernel.define_default_kernel(dimension[0], bounds,
default_values,
parameters_priors,
*([MATERN52_NAME], ))
if kernel_name[0] == MATERN52_NAME:
return Matern52.define_default_kernel(dimension[0], bounds,
default_values,
parameters_priors)
if kernel_name[0] == TASKS_KERNEL_NAME:
return TasksKernel.define_default_kernel(dimension[0], bounds,
default_values,
parameters_priors,
**kernel_parameters)
if kernel_name[0] == PRODUCT_KERNELS_SEPARABLE:
values = []
cont = 0
bounds_ = []
cont_b = 0
for name, dim in zip(kernel_name[1:], dimension[1:]):
n_params = get_number_parameters_kernel([name], [dim],
**kernel_parameters)
if default_values is not None:
value_kernel = default_values[cont:cont + n_params]
else:
value_kernel = None
if bounds is not None:
if name == MATERN52_NAME:
bounds_.append(bounds[cont_b:cont_b + dim])
cont_b += dim
if name == TASKS_KERNEL_NAME:
bounds_.append(bounds[cont_b:cont_b + 1])
cont_b += 1
cont += n_params
values.append(value_kernel)
if len(bounds_) > 0:
bounds = bounds_
return ProductKernels.define_default_kernel(dimension[1:], bounds,
values, parameters_priors,
kernel_name[1:],
**kernel_parameters)