def test_by_batch_restore_gaussian(self):
"""
Test Gaussian HMM with spherical covariance by restoring an existing HMM via batch training.
"""
config = {
'num_states': 3,
'output_dim': 2,
'output_covariance': 'spherical'
}
hmm_reference = HMM(**config)
hmm_reference.markov.initial_probs.set_(torch.as_tensor(
[0.75, 0.25, 0], dtype=torch.float
))
hmm_reference.markov.transition_probs.set_(torch.as_tensor([
[0, 1, 0],
[0.5, 0, 0.5],
[1, 0, 0]
], dtype=torch.float))
hmm_reference.emission.means.set_(torch.as_tensor([
[-1, -1], [0, 1], [1, -1]
], dtype=torch.float))
hmm_reference.emission.covars.set_(torch.as_tensor(
[0.1, 0.25, 0.2]
))
torch.manual_seed(42)
sequences = hmm_reference.sample(8192, 8)
hmm = HMM(**config)
hmm.fit(sequences.chunk(32))
order = hmm.markov.initial_probs.argsort(descending=True)
self.assertTrue(torch.allclose(
hmm.markov.initial_probs[order], hmm_reference.markov.initial_probs,
atol=0.01, rtol=0
))
self.assertTrue(torch.allclose(
hmm.markov.transition_probs[order][:, order], hmm_reference.markov.transition_probs,
atol=0.01, rtol=0
))
self.assertTrue(torch.allclose(
hmm.emission.means[order], hmm_reference.emission.means,
atol=0.05, rtol=0
))
self.assertTrue(torch.allclose(
hmm.emission.covars[order], hmm_reference.emission.covars,
atol=0.02, rtol=0
))
def test_by_restore_discrete(self):
"""
Test Discrete HMM with by restoring an existing HMM.
"""
config = {
'num_states': 3,
'output': 'discrete',
'output_num_states': 4
}
hmm_reference = HMM(**config)
hmm_reference.markov.initial_probs.set_(torch.as_tensor(
[0.75, 0.25, 0], dtype=torch.float
))
hmm_reference.markov.transition_probs.set_(torch.as_tensor([
[0.9, 0.1, 0],
[0.5, 0, 0.5],
[0, 1, 0]
], dtype=torch.float))
hmm_reference.emission.probabilities.set_(torch.as_tensor([
[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 0.5, 0.5]
]))
torch.manual_seed(21)
sequences = hmm_reference.sample(16536, 16)
hmm = HMM(**config)
hmm.fit(sequences, epochs=100, eps=1e-7, patience=3)
order = hmm.markov.initial_probs.argsort(descending=True)
self.assertTrue(torch.allclose(
hmm.markov.initial_probs[order], hmm_reference.markov.initial_probs,
atol=0.01, rtol=0
))
self.assertTrue(torch.allclose(
hmm.markov.transition_probs[order][:, order], hmm_reference.markov.transition_probs,
atol=0.01, rtol=0
))
self.assertTrue(torch.allclose(
hmm.emission.probabilities[order], hmm_reference.emission.probabilities,
atol=0.05, rtol=0
))