def test_DefaultKernel(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, 0.27, 0.097], [3, 0.32, 0.012]],
np.array([1, 1]),
accepted_cov_mats=[[[0.01, 0], [0, 0.01]],
[]])
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=kernel_parameters)
rng = np.random.RandomState(1)
perturbed_values_and_models = kernel.update(Manager, 1, rng)
self.assertEqual(perturbed_values_and_models,
[(N1, [0.17443453636632419]),
(N2, [0.25882435863499248]), (B1, [3])])
def test(self):
B1 = Binomial([10, 0.2])
N1 = Normal([0.1, 0.01])
N2 = Normal([0.3, N1])
graph = Normal([B1, N2])
Manager = AcceptedParametersManager([graph])
mapping, mapping_index = Manager.get_mapping([graph])
self.assertEqual(mapping, [(B1,0),(N2,1),(N1,2)])
def test(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()
Manager.update_broadcast(backend, [[2,3,4],[0.27,0.32,0.28],[0.97,0.12,0.99]])
values = Manager.get_accepted_parameters_bds_values([B1,N2,N1])
values_expected = [np.array(x).reshape(-1,) for x in [[2,3,4],[0.27,0.32,0.28],[0.97,0.12,0.99]]]
self.assertTrue(all([all(a == b) for a, b in zip(values, values_expected)]))
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))
def test(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, 0.27, 0.097], [3, 0.32, 0.012]], np.array([1, 1]))
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)
covs = kernel.calculate_cov(Manager)
self.assertTrue(len(covs)==2)
self.assertTrue(len(covs[0])==2)
self.assertTrue(not(covs[1]))