def test_backward_against_ghmm(self):
from kerehmm.test.util import ghmm_from_gaussian_hmm
import ghmm
hmm = self.new_hmm(nDimensions=1,
random_emissions=True,
random_transitions=True,
lower_bounds=[0],
upper_bounds=[10])
hmm_reference = ghmm_from_gaussian_hmm(hmm)
observation_size = 5
observed = [
np.random.randint(0, 10) for _ in range(self.nDimensions)
for i in range(observation_size)
]
seq = ghmm.EmissionSequence(hmm_reference.emissionDomain,
np.array(observed).flatten().tolist())
# remember that we have to convert stuff from ghmm to log scale
_, scale_reference = map(np.array, hmm_reference.forward(seq))
# print "Forward referece", forward
print "Scale reference", scale_reference
# this is the reference backward array, untransformed (scaled)
backward_reference = np.array(
hmm_reference.backward(seq, scalingVector=scale_reference))
print "Backward reference (scaled)", backward_reference
# unscale the reference array
# get the product of scale_t,scale_t+1,...,scale_T for each t.
# coefficients = np.array([np.prod(scale_reference[i:]) for i, _ in enumerate(scale_reference)])
coefficients = np.array([
np.multiply.reduce(scale_reference[t + 1:])
for t, _ in enumerate(scale_reference)
])
print "Reference coefficients:", coefficients
# multiply each backwards_reference[i] by coefficients[i]
backward_reference[:] = (np.expand_dims(coefficients, axis=1) *
backward_reference)
# test shape
print "Backward reference (unscaled)", backward_reference
# this is our backward array, log transformed
backward = hmm.backward(observed)
print "Backward", np.exp(backward)
assert backward.shape == backward_reference.shape
# test values
# print "Diff:", np.exp(backward) - backward_reference
backward_unscaled = np.exp(backward)
assert np.allclose(backward_unscaled, backward_reference)
def test_backward_against_ghmm(self):
from kerehmm.test.util import ghmm_from_gaussian_hmm
import ghmm
hmm = self.new_hmm(
nDimensions=1, random_emissions=True, random_transitions=True, lower_bounds=[0], upper_bounds=[10]
)
hmm_reference = ghmm_from_gaussian_hmm(hmm)
observation_size = 5
observed = [np.random.randint(0, 10) for _ in range(self.nDimensions) for i in range(observation_size)]
seq = ghmm.EmissionSequence(hmm_reference.emissionDomain, np.array(observed).flatten().tolist())
# remember that we have to convert stuff from ghmm to log scale
_, scale_reference = map(np.array, hmm_reference.forward(seq))
# print "Forward referece", forward
print "Scale reference", scale_reference
# this is the reference backward array, untransformed (scaled)
backward_reference = np.array(hmm_reference.backward(seq, scalingVector=scale_reference))
print "Backward reference (scaled)", backward_reference
# unscale the reference array
# get the product of scale_t,scale_t+1,...,scale_T for each t.
# coefficients = np.array([np.prod(scale_reference[i:]) for i, _ in enumerate(scale_reference)])
coefficients = np.array([np.multiply.reduce(scale_reference[t + 1 :]) for t, _ in enumerate(scale_reference)])
print "Reference coefficients:", coefficients
# multiply each backwards_reference[i] by coefficients[i]
backward_reference[:] = np.expand_dims(coefficients, axis=1) * backward_reference
# test shape
print "Backward reference (unscaled)", backward_reference
# this is our backward array, log transformed
backward = hmm.backward(observed)
print "Backward", np.exp(backward)
assert backward.shape == backward_reference.shape
# test values
# print "Diff:", np.exp(backward) - backward_reference
backward_unscaled = np.exp(backward)
assert np.allclose(backward_unscaled, backward_reference)
def test_forward_against_ghmm(self):
import ghmm
hmm = self.new_hmm(
nDimensions=1, upper_bounds=[10], lower_bounds=[0] # random_transitions=True, random_emissions=True,
)
hmm_reference = ghmm_from_gaussian_hmm(hmm)
observed = [0.4, 0.1, 0.2, 0.2]
seq = ghmm.EmissionSequence(hmm_reference.emissionDomain, observed)
forward = hmm.forward(observed)
# remember that we have to convert stuff from ghmm to log scale
forward_reference, scale_reference = map(np.array, hmm_reference.forward(seq))
forward_reference_log = np.log(forward_reference)
print "Forward reference (scaled):\n", forward_reference
print "Scale reference: {}".format(scale_reference)
for i, c in enumerate(scale_reference):
forward_reference_log[i] += sum(np.log(scale_reference[: i + 1]))
print "Forward reference (unscaled):\n", np.exp(forward_reference_log)
print "Forward:\n", np.exp(forward)
assert np.allclose(forward, forward_reference_log)
def test_forward_against_ghmm(self):
import ghmm
hmm = self.new_hmm(
nDimensions=1, # random_transitions=True, random_emissions=True,
upper_bounds=[10],
lower_bounds=[0])
hmm_reference = ghmm_from_gaussian_hmm(hmm)
observed = [.4, .1, .2, .2]
seq = ghmm.EmissionSequence(hmm_reference.emissionDomain, observed)
forward = hmm.forward(observed)
# remember that we have to convert stuff from ghmm to log scale
forward_reference, scale_reference = map(np.array,
hmm_reference.forward(seq))
forward_reference_log = np.log(forward_reference)
print "Forward reference (scaled):\n", forward_reference
print "Scale reference: {}".format(scale_reference)
for i, c in enumerate(scale_reference):
forward_reference_log[i] += sum(np.log(scale_reference[:i + 1]))
print "Forward reference (unscaled):\n", np.exp(forward_reference_log)
print "Forward:\n", np.exp(forward)
assert np.allclose(forward, forward_reference_log)
def to_ghmm(self, hmm):
if self.nDimensions == 1:
return ghmm_from_gaussian_hmm(hmm)
else:
return ghmm_from_multivariate_continuous_hmm(hmm)
def to_ghmm(self, hmm):
if self.nDimensions == 1:
return ghmm_from_gaussian_hmm(hmm)
else:
return ghmm_from_multivariate_continuous_hmm(hmm)