class statistical_kernel_based_interdependence(interdependence):
def __init__(
self,
b: int, m: int, kernel: Callable,
interdependence_type: str = 'attribute',
name: str = 'statistical_kernel_based_interdependence',
require_data: bool = True,
require_parameters: bool = False,
device: str = 'cpu', *args, **kwargs
):
super().__init__(b=b, m=m, name=name, interdependence_type=interdependence_type, require_parameters=require_parameters, require_data=require_data, device=device, *args, **kwargs)
if kernel is None:
raise ValueError('the kernel is required for the statistical kernel based interdependence function')
self.kernel = kernel
self.kernel = kernel
def calculate_A(self, x: torch.Tensor = None, w: torch.nn.Parameter = None, device: str = 'cpu', *args, **kwargs):
if not self.require_data and not self.require_parameters and self.A is not None:
return self.A
else:
assert x is not None and x.ndim == 2
x = self.pre_process(x=x, device=device)
A = self.kernel(x)
A = self.post_process(x=A, device=device)
if self.interdependence_type in ['column', 'right', 'attribute', 'attribute_interdependence']:
assert A.shape == (self.m, self.calculate_m_prime())
elif self.interdependence_type in ['row', 'left', 'instance', 'instance_interdependence']:
assert A.shape == (self.b, self.calculate_b_prime())
if not self.require_data and not self.require_parameters and self.A is None:
self.A = A
return A