def test_return_value_Student_T(self):
N1 = Normal([0.1, 0.01])
N2 = Normal([0.3, N1])
graph = Normal([N1, N2])
Manager = AcceptedParametersManager([graph])
backend = Backend()
kernel = JointPerturbationKernel([MultivariateStudentTKernel([N1, N2], df=2)])
Manager.update_broadcast(backend, [[0.4, 0.09], [0.2, 0.008]], np.array([0.5, 0.2]))
kernel_parameters = []
for krnl in kernel.kernels:
kernel_parameters.append(Manager.get_accepted_parameters_bds_values(krnl.models))
Manager.update_kernel_values(backend, kernel_parameters)
mapping, mapping_index = Manager.get_mapping(Manager.model)
covs = [[[1, 0], [0, 1]], []]
Manager.update_broadcast(backend, accepted_cov_mats=covs)
pdf = kernel.pdf(mapping, Manager, Manager.accepted_parameters_bds.value()[1], [0.3, 0.1])
self.assertTrue(isinstance(pdf, float))
def test_return_value(self):
B1 = Binomial([10, 0.2])
N1 = Normal([0.1, 0.01])
N2 = Normal([0.3, N1])
graph = Normal([B1, N2])
Manager = AcceptedParametersManager([graph])
backend = Backend()
kernel = DefaultKernel([N1, N2, B1])
Manager.update_broadcast(backend, [[2, 0.4, 0.09], [3, 0.2, 0.008]], np.array([0.5, 0.2]))
kernel_parameters = []
for krnl in kernel.kernels:
kernel_parameters.append(Manager.get_accepted_parameters_bds_values(krnl.models))
Manager.update_kernel_values(backend, kernel_parameters)
mapping, mapping_index = Manager.get_mapping(Manager.model)
covs = [[[1,0],[0,1]],[]]
Manager.update_broadcast(backend, accepted_cov_mats=covs)
pdf = kernel.pdf(mapping, Manager, 1, [2,0.3,0.1])
self.assertTrue(isinstance(pdf, float))