def test_log_likelihood_constant():
pi = np.array([1.0, 0.0, 0.0])
a = np.array([
[1.0, 0.0, 0.0],
[0.5, 0.5, 0.0],
[0.0, 0.5, 0.5]
])
chain = mc.MarkovChain(pi, a)
b = np.array([
[1.0, 0.0, 0.0, 0.0, 0.0],
[0.1, 0.1, 0.2, 0.3, 0.3],
[0.0, 0.0, 0.0, 0.5, 0.5]
])
model = dhmm.DiscreteHiddenMM(chain, b)
sequence = np.random.choice(model.num_outputs, size=10)
for i in range(100):
likelihood = model.likelihood(sequence)
log_likelihood = model.log_likelihood(sequence)
assert np.allclose(np.log(likelihood), log_likelihood)
def test_fit_single():
pi = np.array([0.3, 0.4, 0.3])
a = np.array([
[0.1, 0.1, 0.8],
[0.5, 0.5, 0.0],
[0.0, 0.5, 0.5]
])
chain = mc.MarkovChain(pi, a)
b = np.array([
[0.2, 0.2, 0.2, 0.2, 0.2],
[0.1, 0.1, 0.2, 0.3, 0.3],
[0.0, 0.0, 0.0, 0.5, 0.5]
])
model = dhmm.DiscreteHiddenMM(chain, b)
sequence = np.array([0, 1, 2, 3, 4])
model2 = model.fit_single(sequence)
old_likelihood = model.likelihood(sequence)
new_likelihood = model2.likelihood(sequence)
assert new_likelihood >= old_likelihood
def test_fit_single_random_long():
pi = np.array([0.3, 0.4, 0.3])
a = np.array([
[0.1, 0.1, 0.8],
[0.5, 0.5, 0.0],
[0.0, 0.5, 0.5]
])
chain = mc.MarkovChain(pi, a)
b = np.array([
[0.2, 0.2, 0.2, 0.2, 0.2],
[0.1, 0.1, 0.2, 0.3, 0.3],
[0.0, 0.0, 0.0, 0.5, 0.5]
])
model = dhmm.DiscreteHiddenMM(chain, b)
sequence = np.random.choice(model.num_outputs, size=10000)
model2 = model.fit_single(sequence)
old_likelihood = model.likelihood(sequence)
new_likelihood = model2.likelihood(sequence)
assert new_likelihood >= old_likelihood
def test_likelihood_summation():
pi = np.array([0.1, 0.9])
a = np.array([
[0.1, 0.9],
[0.5, 0.5]
])
chain = mc.MarkovChain(pi, a)
b = np.array([
[0.2, 0.2, 0.2, 0.2, 0.2],
[0.1, 0.1, 0.2, 0.3, 0.3]
])
model = dhmm.DiscreteHiddenMM(chain, b)
summation = 0.0
for i in range(5):
for j in range(5):
for k in range(5):
for z in range(5):
observations = [i, j, k, z]
likelihood = model.likelihood(np.array(observations, dtype=int))
assert likelihood >= 0.0
summation += likelihood
assert np.abs(summation - 1.0) < cnst.EPSILON
def test_successful_creation():
pi = np.array([0.1, 0.9])
a = np.array([
[0.1, 0.9],
[0.5, 0.5]
])
chain = mc.MarkovChain(pi, a)
b = np.array([
[0.2, 0.2, 0.2, 0.2, 0.2],
[0.1, 0.1, 0.2, 0.3, 0.3]
])
dhmm.DiscreteHiddenMM(chain, b)
def test_distribution_negative():
pi = np.array([0.1, 0.9])
a = np.array([
[0.1, 0.9],
[0.5, 0.5]
])
chain = mc.MarkovChain(pi, a)
b = np.array([
[0.2, 0.2, 0.2, 0.6, -0.2],
[0.1, 0.1, 0.2, 0.3, 0.3],
])
dhmm.DiscreteHiddenMM(chain, b)
def test_distribution_does_not_sum_up():
pi = np.array([0.1, 0.9])
a = np.array([
[0.1, 0.9],
[0.5, 0.5]
])
chain = mc.MarkovChain(pi, a)
b = np.array([
[0.2, 0.2, 0.2, 0.2, 0.2],
[0.1, 0.1, 0.2, 0.3, 0.5]
])
dhmm.DiscreteHiddenMM(chain, b)
def test_dimension_mismatch():
pi = np.array([0.1, 0.9])
a = np.array([
[0.1, 0.9],
[0.5, 0.5]
])
chain = mc.MarkovChain(pi, a)
b = np.array([
[0.2, 0.2, 0.2, 0.2, 0.2],
[0.1, 0.1, 0.2, 0.3, 0.3],
[0.1, 0.1, 0.2, 0.3, 0.3]
])
dhmm.DiscreteHiddenMM(chain, b)
def test_solve_for_state_constant_chain():
pi = np.array([0.0, 0.0, 1.0])
a = np.array([
[1.0, 0.0, 0.0],
[0.5, 0.5, 0.0],
[0.0, 0.5, 0.5]
])
chain = mc.MarkovChain(pi, a)
b = np.array([
[0.2, 0.2, 0.2, 0.2, 0.2],
[0.1, 0.1, 0.2, 0.3, 0.3],
[0.0, 0.0, 0.0, 0.5, 0.5]
])
model = dhmm.DiscreteHiddenMM(chain, b)
model.solve_for_states(np.array([0, 1, 2, 3, 4]))
def test_solve_for_state_list():
pi = np.array([1.0, 0.0, 0.0])
a = np.array([
[1.0, 0.0, 0.0],
[0.5, 0.5, 0.0],
[0.0, 0.5, 0.5]
])
chain = mc.MarkovChain(pi, a)
b = np.array([
[0.2, 0.2, 0.2, 0.2, 0.2],
[0.1, 0.1, 0.2, 0.3, 0.3],
[0.0, 0.0, 0.0, 0.5, 0.5]
])
model = dhmm.DiscreteHiddenMM(chain, b)
assert np.allclose(model.solve_for_states(np.array([0, 1, 2, 3, 4])), 0)
def test_generation_1():
pi = np.array([0.1, 0.9])
a = np.array([
[0.1, 0.9],
[0.5, 0.5]
])
chain = mc.MarkovChain(pi, a)
b = np.array([
[0.2, 0.2, 0.2, 0.2, 0.2],
[0.1, 0.1, 0.2, 0.3, 0.3]
])
model = dhmm.DiscreteHiddenMM(chain, b)
result = model.generate(100)
assert result.shape[0] == 100
assert result.shape[1] == 2
def test_likelihood_basic():
pi = np.array([0.1, 0.9])
a = np.array([
[0.1, 0.9],
[0.5, 0.5]
])
chain = mc.MarkovChain(pi, a)
b = np.array([
[0.2, 0.2, 0.2, 0.2, 0.2],
[0.1, 0.1, 0.2, 0.3, 0.3]
])
model = dhmm.DiscreteHiddenMM(chain, b)
assert np.abs(model.likelihood(np.array([0], dtype=int)) - 0.11) < cnst.EPSILON
assert np.abs(model.likelihood(np.array([1], dtype=int)) - 0.11) < cnst.EPSILON
assert np.abs(model.likelihood(np.array([2], dtype=int)) - 0.20) < cnst.EPSILON
assert np.abs(model.likelihood(np.array([3], dtype=int)) - 0.29) < cnst.EPSILON
assert np.abs(model.likelihood(np.array([4], dtype=int)) - 0.29) < cnst.EPSILON