def test_8():
# "Sample from a VMHMM and then fit to it"
n_states = 2
vm = VonMisesHMM(n_states=n_states)
means = np.array([[0, 0, 0], [np.pi, np.pi, np.pi]])
kappas = np.array([[1, 2, 3], [2, 3, 4]])
transmat = np.array([[0.9, 0.1], [0.1, 0.9]])
vm.means_ = means
vm.kappas_ = kappas
vm.transmat_ = transmat
x, s = vm.sample(1000)
vm = VonMisesHMM(n_states=2)
vm.fit([x])
mappingcost = np.zeros((n_states, n_states))
for i in range(n_states):
for j in range(n_states):
mappingcost[i, j] = np.sum(
circwrap(vm.means_[i, :] - means[j, :]) ** 2)
mapping = Munkres().compute(mappingcost)
means_ = np.array([vm.means_[j, :] for i, j in mapping])
kappas_ = np.array([vm.kappas_[j, :] for i, j in mapping])
transmat_ = vm.transmat_
print('means\n', means, '\nmeans_\n', means_)
print('kappas\n', kappas, '\nkappas_\n', kappas_)
print('transmat\n', transmat, '\ntransmat_\n', transmat_)
#vm.score(x)
assert np.all(np.abs(kappas - kappas_) < 0.5)
assert np.all(circwrap(means - means_) < 0.25)
def test_6():
# Test that _c_fitkappa is consistent with the two-step python implementation
np.random.seed(42)
vm = VonMisesHMM(n_states=13)
kappas = np.random.randn(13, 7)
posteriors = np.random.randn(100, 13)
obs = np.random.randn(100, 7)
means = np.random.randn(13, 7)
vm.kappas_ = kappas
vm._c_fitkappas(posteriors, obs, means)
c_kappas = np.copy(vm._kappas_)
vm._py_fitkappas(posteriors, obs, means)
py_kappas = np.copy(vm._kappas_)
np.testing.assert_array_almost_equal(py_kappas, c_kappas)
