def loglikelihoods_from_seqs(self,
X,
bPosterior=False,
bIdx=False,
startIdx=1):
'''
X: sample x (length*dim)
return: the likelihoods over time (in single data)
'''
ll_likelihoods = []
ll_posteriors = []
for i in xrange(len(X)):
l_likelihood = []
l_posterior = []
for j in xrange(startIdx, len(X[i]) / self.nEmissionDim):
if isinstance(X[i], np.ndarray) or isinstance(X[i], list):
try:
final_ts_obj = ghmm.EmissionSequence(
self.F, X[i, :j * self.nEmissionDim].tolist())
except:
print "failed to make sequence"
continue
else:
final_ts_obj = ghmm.EmissionSequence(
self.F,
list(X[i])[:j * self.nEmissionDim])
try:
logp = self.ml.loglikelihood(final_ts_obj)
if bPosterior:
post = np.array(self.ml.posterior(final_ts_obj))
except:
print "Unexpected profile!! GHMM cannot handle too low probability. Underflow?"
continue
## return False, False # anomaly
#continue
l_likelihood.append(logp)
if bPosterior: l_posterior.append(post[j - 1])
ll_likelihoods.append(l_likelihood)
if bPosterior: ll_posteriors.append(l_posterior)
if bIdx:
ll_idx = []
for ii in xrange(len(X)):
l_idx = []
for jj in xrange(startIdx, len(X[ii]) / self.nEmissionDim):
l_idx.append(jj)
ll_idx.append(l_idx)
if bPosterior: return ll_likelihoods, ll_posteriors, ll_idx
else: return ll_likelihoods, ll_idx
else:
if bPosterior: return ll_likelihoods, ll_posteriors
else: return ll_likelihoods
def _includeOutliers(models, trainData, outliers):
means = []
stds = []
for i in range(len(models)):
mean = 0
variance = 0
#Calculate model mean
for tmp in trainData[i]:
eSeq = ghmm.EmissionSequence(ghmm.Float(), tmp)
a = abs(models[i].loglikelihood(eSeq))
#print a
mean += a
mean /= (len(trainData[i]) * 1.0)
means.append(mean)
#Calculate the model variance
for tmp in trainData[i]:
eSeq = ghmm.EmissionSequence(ghmm.Float(), tmp)
v = abs(models[i].loglikelihood(eSeq))
variance += (mean - v)**2
variance /= (len(trainData[i]) * 1.0)
std = variance**0.5
stds.append(std)
#For each data element in outliers, check for the model that it most
#fits. If the outlier fits the model within one standard deviation
#include it back into the data.
for tmp in outliers:
eSeq = ghmm.EmissionSequence(ghmm.Float(), tmp)
best = -1
bestModel = -1
for j in range(len(models)):
val = abs(models[j].loglikelihood(eSeq))
if val < best or best == -1:
best = val
bestModel = j
#Determine if the best fit is "good" enough.
#If it is, add the outlier back into the model
if (best - means[bestModel]) < 1 * (stds[bestModel]):
trainData[bestModel].append(tmp)
outliers.remove(tmp)
return trainData, outliers
def hidden_state_change_lik(self, ts):
"""
obs.: compare distribution of different states. If they are too similar
than they are merged during the probability computation
"""
obs_seq = self.get_obs_seqs(ts.y)
emission_seq = ghmm.EmissionSequence(self.emission_domain, obs_seq)
# ALERT: I DON'T KNOW WHAT scale IS
forward, scale = self.model.forward(emission_seq)
backward = self.model.backward(emission_seq, scale)
n = len(self.A)
ts_hidden_state_change_lik = time_series.dist_ts(ts)
for t in xrange(len(ts.y) - 1):
lik = 0.0
for i in xrange(n):
for j in xrange(n):
if self.states_are_diff(i, j):
lik += self.xi(n, forward, backward, ts.y[t + 1], i, j,
t)
ts_hidden_state_change_lik.x.append(ts.x[t])
ts_hidden_state_change_lik.y.append(lik)
return ts_hidden_state_change_lik
def predict2(self, X, x1, x2, x3, x4):
X = np.squeeze(X)
X_test = X.tolist()
n = len(X_test)
mu_l = np.zeros(self.nEmissionDim)
cov_l = np.zeros(self.nEmissionDim**2)
final_ts_obj = ghmm.EmissionSequence(self.F, X_test + [x1, x2, x3, x4])
try:
(alpha, scale) = self.ml.forward(final_ts_obj)
except:
if self.verbose: print "No alpha is available !!"
sys.exit()
alpha = np.array(alpha)
for j in xrange(self.nState):
[[mu1, mu2, mu3, mu4], [cov11, cov12, cov13, cov14, cov21, cov22, cov23, cov24,
cov31, cov32, cov33, cov34, cov41, cov42, cov43, cov44]] = self.B[j]
mu_l[0] = x1
mu_l[1] += alpha[n/self.nEmissionDim, j] * (mu2 + cov21/cov11*(x1 - mu1) )
mu_l[2] += alpha[n/self.nEmissionDim, j] * (mu3 + cov31/cov21*(x2 - mu2)) # TODO Where does this come from?
mu_l[3] += alpha[n/self.nEmissionDim, j] * (mu4 + cov41/cov31*(x3 - mu3)) # TODO Where does this come from?
## cov_l[0] += (cov11)*(total**2)
## cov_l[1] += (cov12)*(total**2)
## cov_l[2] += (cov21)*(total**2)
## cov_l[3] += (cov22)*(total**2)
return mu_l, cov_l
def allLikelihoods(self, X1, X2, X3, X4):
# n, m = np.shape(X1)
X_test = self.convert_sequence(X1, X2, X3, X4, emission=False)
# i = m - 1
m = len(np.squeeze(X1))
ll_likelihood = np.zeros(m)
ll_state_idx = np.zeros(m)
ll_likelihood_mu = np.zeros(m)
ll_likelihood_std = np.zeros(m)
for i in xrange(1, m):
final_ts_obj = ghmm.EmissionSequence(self.F, X_test[0,:i*self.nEmissionDim].tolist())
logp = self.ml.loglikelihood(final_ts_obj)
post = np.array(self.ml.posterior(final_ts_obj))
# Find the best posterior distribution
min_index, min_dist = self.findBestPosteriorDistribution(post[i-1])
ll_likelihood[i] = logp
ll_state_idx[i] = min_index
ll_likelihood_mu[i] = self.ll_mu[min_index]
ll_likelihood_std[i] = self.ll_std[min_index] #self.ll_mu[min_index] + ths_mult*self.ll_std[min_index]
return ll_likelihood, ll_state_idx, ll_likelihood_mu, ll_likelihood_std
def predict2(self, X, x1):
X = np.squeeze(X)
X_test = X.tolist()
n = len(X_test)
mu_l = np.zeros(self.nEmissionDim)
cov_l = np.zeros(self.nEmissionDim**2)
final_ts_obj = ghmm.EmissionSequence(self.F, X_test + [x1])
try:
(alpha, scale) = self.ml.forward(final_ts_obj)
except:
print "No alpha is available !!"
sys.exit()
alpha = np.array(alpha)
for j in xrange(self.nState):
[[mu1], [cov11]] = self.B[j]
mu_l[0] = x1
return mu_l, cov_l
def test_backward_against_ghmm(self):
from kerehmm.test.util import ghmm_from_discrete_hmm
import ghmm
hmm = self.new_hmm(random_emissions=True, random_transitions=True)
hmm_reference = ghmm_from_discrete_hmm(hmm)
observation_size = 10
observed = np.random.choice(range(self.nSymbols),
size=observation_size).tolist()
seq = ghmm.EmissionSequence(hmm_reference.emissionDomain, observed)
_, scale = hmm.forward(observations=observed)
# 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
assert np.allclose(scale, 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
backward = hmm.backward(observed, scale_coefficients=scale)
print "Backward", 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, backward_reference)
def forecast(self, data, future = 1):
"""Forecast for a model the probability of each observation.
equation is:
p(o_t+1) = sum_j(p(o_t+1|s_t+1^j)p(s_t+1^j)
where
p(s_t+1^j) is found through forward algorithm
"""
state = self.model.asMatrices()[0]
observe = self.model.asMatrices()[1]
ps1 = [0.0] * len(state[0])
po1 = [0.0] * len(observe[0])
tmp = ghmm.EmissionSequence(ghmm.Float(), data)
ps = self.model.forward(tmp)[0][-1]
for j in range(len(ps1)):
for i in range(len(ps)):
ps1[j] += state[i][j] * ps[i]
for k in range(len(po1)):
for j in range(len(ps1)):
po1[k] += observe[j][k]*ps1[j]
return po1[0]
def anomaly_check(self, X1, ths_mult=None):
if self.nEmissionDim == 1: X_test = np.array([X1])
else: X_test = self.convert_sequence(X1, emission=False)
try:
final_ts_obj = ghmm.EmissionSequence(self.F, X_test[0].tolist())
logp = self.ml.loglikelihood(final_ts_obj)
except:
if self.verbose:
print "Too different input profile that cannot be expressed by emission matrix"
return -1, 0.0 # error
try:
post = np.array(self.ml.posterior(final_ts_obj))
except:
if self.verbose:
print "Unexpected profile!! GHMM cannot handle too low probability. Underflow?"
return True, 0.0 # anomaly
n = len(np.squeeze(X1))
# Find the best posterior distribution
min_index, min_dist = self.findBestPosteriorDistribution(post[n - 1])
if (type(ths_mult) == list or type(ths_mult) == np.ndarray
or type(ths_mult) == tuple) and len(ths_mult) > 1:
err = logp - (self.ll_mu[min_index] +
ths_mult[min_index] * self.ll_std[min_index])
else:
err = logp - (self.ll_mu[min_index] +
ths_mult * self.ll_std[min_index])
if err < self.anomaly_offset: return True, err
else: return False, err
def learn_likelihoods_progress(i, n, m, A, B, pi, F, X_train, nEmissionDim, g_mu, g_sig, nState):
if nEmissionDim >= 2:
ml = ghmm.HMMFromMatrices(F, ghmm.MultivariateGaussianDistribution(F), A, B, pi)
else:
ml = ghmm.HMMFromMatrices(F, ghmm.GaussianDistribution(F), A, B, pi)
l_likelihood_mean = 0.0
l_likelihood_mean2 = 0.0
l_statePosterior = np.zeros(nState)
for j in xrange(n):
g_post = np.zeros(nState)
g_lhood = 0.0
g_lhood2 = 0.0
prop_sum = 0.0
for k in xrange(1, m):
final_ts_obj = ghmm.EmissionSequence(F, X_train[j][:k*nEmissionDim])
logp = ml.loglikelihoods(final_ts_obj)[0]
# print 'Log likelihood:', logp
post = np.array(ml.posterior(final_ts_obj))
k_prop = norm(loc=g_mu, scale=g_sig).pdf(k)
g_post += post[k-1] * k_prop
g_lhood += logp * k_prop
g_lhood2 += logp * logp * k_prop
prop_sum += k_prop
l_statePosterior += g_post / prop_sum / float(n)
l_likelihood_mean += g_lhood / prop_sum / float(n)
l_likelihood_mean2 += g_lhood2 / prop_sum / float(n)
return i, l_statePosterior, l_likelihood_mean, np.sqrt(l_likelihood_mean2 - l_likelihood_mean**2)
def train(self):
# This tells GHMM every possible value that it will be seeing
alphabet = ghmm.Alphabet(list(set(self.events)))
alphaLen = len(alphabet)
# Initiaize the probabilities of transitioning from each state to each other
# state. There is probably a better way to do this, but this is nice and simple.
trans_prob = 1.0 / (alphaLen)
trans = [[trans_prob for row in range(alphaLen)]
for col in range(alphaLen)]
# Initialize the probabilities of seeing each output from each state.
# Again, there is probably a better way to do this, but this is simple.
emiss_prob = 1.0 / (alphaLen)
emiss = [[emiss_prob for row in range(alphaLen)]
for col in range(alphaLen)]
# Some grease to get GHMM to work
pi = [1.0 / alphaLen] * alphaLen
# The sequence of musical events gathered from the music
train_seq = ghmm.EmissionSequence(alphabet, self.events)
# Generate the model of the data
m = ghmm.HMMFromMatrices(alphabet, ghmm.DiscreteDistribution(alphabet),
trans, emiss, pi)
# Train the model based on the training sequence
m.baumWelch(train_seq)
return (m, alphabet)
def expLikelihoods(self, X, ths_mult=None):
if self.nEmissionDim == 1: X_test = np.array([X[0]])
else: X_test = self.convert_sequence(X, emission=False)
try:
final_ts_obj = ghmm.EmissionSequence(self.F, X_test[0].tolist())
logp = self.ml.loglikelihood(final_ts_obj)
except:
print "Too different input profile that cannot be expressed by emission matrix"
return -1, 0.0 # error
try:
post = np.array(self.ml.posterior(final_ts_obj))
except:
print "Unexpected profile!! GHMM cannot handle too low probability. Underflow?"
return 1.0, 0.0 # anomaly
n = len(np.squeeze(X[0]))
# Find the best posterior distribution
min_index, min_dist = self.findBestPosteriorDistribution(post[n-1])
# print 'Computing anomaly'
# print logp
# print self.ll_mu[min_index]
# print self.ll_std[min_index]
# print 'logp:', logp, 'll_mu', self.ll_mu[min_index], 'll_std', self.ll_std[min_index], 'mult_std', ths_mult*self.ll_std[min_index]
if (type(ths_mult) == list or type(ths_mult) == np.ndarray or type(ths_mult) == tuple) and len(ths_mult)>1:
## print min_index, self.ll_mu[min_index], self.ll_std[min_index], ths_mult[min_index], " = ", (self.ll_mu[min_index] + ths_mult[min_index]*self.ll_std[min_index])
return self.ll_mu[min_index] + ths_mult[min_index]*self.ll_std[min_index]
else:
return self.ll_mu[min_index] + ths_mult*self.ll_std[min_index]
def predict2(self, X, x1, x2):
X = np.squeeze(X)
X_test = X.tolist()
n = len(X_test)
mu_l = np.zeros(2)
cov_l = np.zeros(4)
final_ts_obj = ghmm.EmissionSequence(self.F, X_test + [x1, x2])
try:
(alpha, scale) = self.ml.forward(final_ts_obj)
except:
print "No alpha is available !!"
sys.exit()
alpha = np.array(alpha)
for j in xrange(self.nState):
[[mu1, mu2], [cov11, cov12, cov21, cov22]] = self.B[j]
mu_l[0] = x1
mu_l[1] += alpha[n / self.nEmissionDim, j] * (mu2 + cov21 / cov11 *
(x1 - mu1))
## cov_l[0] += (cov11)*(total**2)
## cov_l[1] += (cov12)*(total**2)
## cov_l[2] += (cov21)*(total**2)
## cov_l[3] += (cov22)*(total**2)
return mu_l, cov_l
def test(self, model, ts_obj):
# Find Viterbi Path
final_ts_obj = ghmm.EmissionSequence(self.F,ts_obj.tolist())
path_obj = model.viterbi(final_ts_obj)
return path_obj
def _predict_next(self):
"""@todo: Docstring for _predict_next.
:returns: @todo
"""
a_init = normalize_stoch_map(np.random.rand(self._n_hid, self._n_hid))
b_init = normalize_stoch_map(
np.random.rand(self._n_hid, self._n_sym**2))
pi_init = normalize_stoch_map(np.random.rand(self._n_hid))
hmm = gh.HMMFromMatrices(self._alphab,
gh.DiscreteDistribution(self._alphab), a_init,
b_init, pi_init)
obs = gh.EmissionSequence(self._alphab, self._memory)
hmm.baumWelch(obs)
alpha = hmm.forward(obs)[0][-1]
trans = hmm.asMatrices()[0]
alpha = np.dot(alpha, trans)
next_moves_dist = np.zeros(self._n_sym**2)
for i in range(self._n_hid):
next_moves_dist += np.asarray(hmm.getEmission(i)) * alpha[i]
next_moves_dist = next_moves_dist[self._conversion_array]
next_move = np.argmax(np.sum(next_moves_dist, axis=0))
return np.where(self._rules[next_move] == -1)[0][0]
def test_forward_against_ghmm(self):
from .util import ghmm_from_discrete_hmm
import ghmm
hmm = self.new_hmm(random_transitions=True, random_emissions=True)
hmm_reference = ghmm_from_discrete_hmm(hmm)
observation_size = 10
observed = np.random.choice(range(self.nSymbols),
size=observation_size).tolist()
seq = ghmm.EmissionSequence(hmm_reference.emissionDomain, observed)
forward, scale = 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))
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", forward
print "Scale:\n", scale
assert np.allclose(forward, forward_reference)
assert np.allclose(scale, scale_reference)
def conditional_prob(self, x):
'''
Input
@ x: dim x length
Output
@ A list of conditional probabilities P(x_t|x_s,lambda)
Only single sample works
'''
from scipy.stats import norm, entropy
# logp from all features
X_test = util.convert_sequence2(x, emission=False)
X_test = np.squeeze(X_test)
final_ts_obj = ghmm.EmissionSequence(self.F, X_test.tolist())
logp_all = self.ml.loglikelihood(final_ts_obj)
# feature-wise conditional probability
cond_prob = []
for i in xrange(self.nEmissionDim): # per feature
B = copy.copy(self.B)
for j in xrange(self.nState):
B[j][0] = [b for idx, b in enumerate(B[j][0]) if idx != i]
B_arr = copy.copy(B[j][1])
B_arr = np.array(B_arr).reshape(
(self.nEmissionDim, self.nEmissionDim))
B_arr = np.delete(B_arr, (i), axis=0)
B_arr = np.delete(B_arr, (i), axis=1)
B[j][1] = B_arr.flatten().tolist()
ml_src = ghmm.HMMFromMatrices(self.F, ghmm.MultivariateGaussianDistribution(self.F), \
self.A, B, self.pi)
# logp from remains
X_test = util.convert_sequence2([ x[j] for j in xrange(len(x)) if j != i ], \
emission=False)
X_test = np.squeeze(X_test)
final_ts_obj = ghmm.EmissionSequence(self.F, X_test.tolist())
logp_src = ml_src.loglikelihood(final_ts_obj)
cond_prob.append(logp_all - logp_src)
if np.isnan(cond_prob[-1]) or np.isinf(cond_prob[-1]):
print "NaN in conditional probabilities: ", np.shape(x)
return None
return np.array(cond_prob)
def _sequence_from_matrix(m):
# Conversion happens as follows: data is a n x m matrix, where n is the number of
# samples and m is the number of features per sample. Multivariate data in ghmm is
# represented as a single list, where the samples are unrolled. Hence the resulting
# data has the following structure: [x_11, x_12, x_13, x21, x22, x23, ...] where m = 3.
# Source: http://sourceforge.net/p/ghmm/mailman/message/20578788/
unrolled = m.ravel().tolist()
seq = impl.EmissionSequence(impl.Float(), unrolled)
return seq
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 decodeHMM(m, hmmStates):
''' Decode HMM. '''
print >> sys.stderr, printTime(), "Decode HMM for each chromosome."
sigma = ghmm.IntegerRange(0, 2)
for chrom in hmmStates:
print >> sys.stderr, printTime(), chrom
state, score = m.viterbi(
ghmm.EmissionSequence(sigma, hmmStates[chrom]))
hmmStates[chrom] = state
print >> sys.stderr, printTime(), "Decode HMM finished."
print >> sys.stderr
def predict(self, X):
'''
Input
@ X: dimension x sample x length #samples x known steps
Output
@ observation distribution: mu, var #samples x 1 [list]
'''
# sample x some length
X_test = util.convert_sequence(X, emission=False)
mu_l = []
cov_l = []
for i in xrange(len(X_test)):
# Past profile
final_ts_obj = ghmm.EmissionSequence(self.F, X_test[i].tolist())
try:
# alpha: X_test length y #latent States at the moment t when state i is ended
# test_profile_length x number_of_hidden_state
(alpha, scale) = self.ml.forward(final_ts_obj)
alpha = np.array(alpha)
scale = np.array(scale)
except:
print "No alpha is available !!"
sys.exit()
## continue
f = lambda x: round(x, 12)
for j in range(len(alpha)):
alpha[j] = map(f, alpha[j])
alpha[-1] = map(f, alpha[-1])
n = len(X_test[i])
t_mu = np.zeros(self.nEmissionDim)
t_cov = np.zeros(self.nEmissionDim * self.nEmissionDim)
t_sum = 0.0
for j in xrange(self.nState): # N+1
total = np.sum(
self.A[:, j] *
alpha[n / self.nEmissionDim - 1, :]) #* scaling_factor
[mu, cov] = self.B[j]
t_mu += np.array(mu) * total
t_cov += np.array(cov) * (total**2)
t_sum += total
mu_l.append(t_mu.tolist())
cov_l.append(t_cov.tolist())
return mu_l, cov_l
def predict(self, X):
X = np.squeeze(X)
X_test = X.tolist()
mu_l = np.zeros(3)
cov_l = np.zeros(9)
print self.F
final_ts_obj = ghmm.EmissionSequence(self.F,
X_test) # is it neccessary?
try:
# alpha: X_test length y # latent States at the moment t when state i is ended
# test_profile_length x number_of_hidden_state
(alpha, scale) = self.ml.forward(final_ts_obj)
alpha = np.array(alpha)
except:
print "No alpha is available !!"
f = lambda x: round(x, 12)
for i in range(len(alpha)):
alpha[i] = map(f, alpha[i])
alpha[-1] = map(f, alpha[-1])
n = len(X_test)
pred_numerator = 0.0
for j in xrange(self.nState): # N+1
total = np.sum(
self.A[:, j] *
alpha[n / self.nEmissionDim - 1, :]) #* scaling_factor
[[mu1, mu2, mu3],
[cov11, cov12, cov13, cov21, cov22, cov23, cov31, cov32,
cov33]] = self.B[j]
## print mu1, mu2, cov11, cov12, cov21, cov22, total
pred_numerator += total
mu_l[0] += mu1 * total
mu_l[1] += mu2 * total
mu_l[2] += mu3 * total
cov_l[0] += cov11 * (total**2)
cov_l[1] += cov12 * (total**2)
cov_l[2] += cov13 * (total**2)
cov_l[3] += cov21 * (total**2)
cov_l[4] += cov22 * (total**2)
cov_l[5] += cov23 * (total**2)
cov_l[6] += cov31 * (total**2)
cov_l[7] += cov32 * (total**2)
cov_l[8] += cov33 * (total**2)
return mu_l, cov_l
def loglikelihood(self, X):
X = np.squeeze(X)
X_test = X.tolist()
final_ts_obj = ghmm.EmissionSequence(self.F, X_test)
try:
p = self.ml.loglikelihood(final_ts_obj)
except:
if self.verbose: print 'Likelihood error!!!!'
sys.exit()
return p
def _removeOutliers(models, trainData, outliers):
needTrain = False
for i in range(len(models)):
mean = 0
variance = 0
#Calculate model mean
for tmp in trainData[i]:
eSeq = ghmm.EmissionSequence(ghmm.Float(), tmp)
a = abs(models[i].loglikelihood(eSeq))
mean += a
try:
mean /= (len(trainData[i]) * 1.0)
except:
continue
#Calculate the model variance
for tmp in trainData[i]:
eSeq = ghmm.EmissionSequence(ghmm.Float(), tmp)
v = abs(models[i].loglikelihood(eSeq))
variance += (mean - v)**2
variance /= (len(trainData[i]) * 1.0)
std = variance**0.5
for tmp in trainData[i]:
eSeq = ghmm.EmissionSequence(ghmm.Float(), tmp)
v = abs(models[i].loglikelihood(eSeq))
if (v - mean) > (2 * std):
trainData[i].remove(tmp)
outliers.append(tmp)
needTrain = True
if needTrain:
models = _trainModels(trainData, models)
def computeLikelihood(idx, A, B, pi, F, X, nEmissionDim, nState, startIdx=1, \
bPosterior=False, converted_X=False, cov_type='full'):
'''
This function will be deprecated. Please, use computeLikelihoods.
'''
if nEmissionDim >= 2:
ml = ghmm.HMMFromMatrices(F, ghmm.MultivariateGaussianDistribution(F),
A, B, pi)
if cov_type == 'diag' or cov_type.find('diag') >= 0:
ml.setDiagonalCovariance(1)
else:
ml = ghmm.HMMFromMatrices(F, ghmm.GaussianDistribution(F), A, B, pi)
if converted_X is False:
X_test = util.convert_sequence(X, emission=False)
X_test = np.squeeze(X_test)
X_test = X_test.tolist()
else:
X_test = X
l_idx = []
l_likelihood = []
l_posterior = []
for i in xrange(startIdx, len(X_test) / nEmissionDim):
final_ts_obj = ghmm.EmissionSequence(F, X_test[:i * nEmissionDim])
try:
logp = ml.loglikelihood(final_ts_obj)
if bPosterior: post = np.array(ml.posterior(final_ts_obj))
except:
print "Unexpected profile!! GHMM cannot handle too low probability. Underflow?"
l_idx.append(i)
l_likelihood.append(-100000000)
if bPosterior:
if len(l_posterior) == 0: l_posterior.append(list(pi))
else: l_posterior.append(l_posterior[-1])
## return False, False # anomaly
continue
l_idx.append(i)
l_likelihood.append(logp)
if bPosterior: l_posterior.append(post[i - 1])
if bPosterior:
return idx, l_idx, l_likelihood, l_posterior
else:
return idx, l_idx, l_likelihood
def anomaly_check(self, X1, X2=None, X3=None, ths_mult=None):
if self.nEmissionDim == 1: X_test = np.array([X1])
else: X_test = self.convert_sequence(X1, X2, X3, emission=False)
try:
final_ts_obj = ghmm.EmissionSequence(self.F, X_test[0].tolist())
logp = self.ml.loglikelihood(final_ts_obj)
except:
print "Too different input profile that cannot be expressed by emission matrix"
return -1, 0.0 # error
try:
post = np.array(self.ml.posterior(final_ts_obj))
except:
print "Unexpected profile!! GHMM cannot handle too low probability. Underflow?"
return 1.0, 0.0 # anomaly
n = len(np.squeeze(X1))
# Find the best posterior distribution
min_dist = 100000000
min_index = 0
print 'Loglikelihood', logp
print 'Last posterior', post[n - 1]
print 'Computing entropies'
for j in xrange(self.nGaussian):
dist = entropy(post[n - 1], self.l_statePosterior[j])
print 'Index:', j, 'Entropy:', dist
if min_dist > dist:
min_index = j
min_dist = dist
print 'Computing anomaly'
print logp
print self.ll_mu[min_index]
print self.ll_std[min_index]
if (type(ths_mult) == list or type(ths_mult) == np.ndarray
or type(ths_mult) == tuple) and len(ths_mult) > 1:
err = logp - (self.ll_mu[min_index] +
ths_mult[min_index] * self.ll_std[min_index])
else:
err = logp - (self.ll_mu[min_index] +
ths_mult * self.ll_std[min_index])
print 'Error', err
if err < 0.0: return 1.0, 0.0 # anomaly
else: return 0.0, err # normal
def trainHMM(hmmState):
''' Train HMM with the given chromosome. '''
print >> sys.stderr, printTime(), "Train HMM with one chromosome."
T = [[0.9, 0.1], [0.1, 0.9]]
e1 = [0.1, 0.9]
e0 = [0.9, 0.1]
E = [e0, e1]
pi = [0.9, 0.1] # initial 10% are peak?
sigma = ghmm.IntegerRange(0, 2) # 0, 1
m = ghmm.HMMFromMatrices(sigma, ghmm.DiscreteDistribution(sigma), T, E,
pi)
m.baumWelch(ghmm.EmissionSequence(sigma, hmmState))
print >> sys.stderr, printTime(), "Train HMM finished."
print >> sys.stderr
return m
def state_clustering(self, X1, X2, X3, X4):
n,m = np.shape(X1)
print n,m
x = np.arange(0., float(m))*(1./43.)
state_mat = np.zeros((self.nState, m*n))
likelihood_mat = np.zeros((1, m*n))
count = 0
for i in xrange(n):
for j in xrange(1,m):
x_test1 = X1[i:i+1,:j]
x_test2 = X2[i:i+1,:j]
x_test3 = X3[i:i+1,:j]
x_test4 = X4[i:i+1,:j]
X_test = self.convert_sequence(x_test1, x_test2, x_test3, x_test4, emission=False)
final_ts_obj = ghmm.EmissionSequence(self.F, X_test[0].tolist())
## path,_ = self.ml.viterbi(final_ts_obj)
post = self.ml.posterior(final_ts_obj)
logp = self.ml.loglikelihood(final_ts_obj)
state_mat[:, count] = np.array(post[j-1])
likelihood_mat[0,count] = logp
count += 1
# k-means
init_center = np.eye(self.nState, self.nState)
self.km = KMeans(self.nState, init=init_center)
idx_list = self.km.fit_predict(state_mat.transpose())
# mean and variance of likelihoods
l = []
for i in xrange(self.nState):
l.append([])
for i, idx in enumerate(idx_list):
l[idx].append(likelihood_mat[0][i])
l_mean = []
l_std = []
for i in xrange(self.nState):
l_mean.append( np.mean(l[i]) )
l_std.append( np.std(l[i]) )
return l_mean, l_std
def viterbi(self, hmm, obj):
hmm_ = self._get_hmm(hmm)
if isinstance(obj, SequenceSet):
obj = [array_flatten(s[:]) for s in obj]
obj = ghmm.SequenceSet(DOMAIN, obj)
res = hmm_.viterbi(obj)
# ghmm returns a scalar even though a sequence set was passed
# if length == 1 but we want an array
if len(obj) == 1:
res = [[res[0]], [res[1]]]
else:
obj = ghmm.EmissionSequence(DOMAIN, array_flatten(obj[:]))
res = hmm_.viterbi(obj)
return res
def dist(self, data):
"""dist(pattern)
Calculate the distance between piece of data and the model.
This is an absurd distance function as the closest distance is
the greatest value of this function. Also the function can be
greater than or less than zero.
"""
eSeq = ghmm.EmissionSequence(ghmm.Float(), data)
tmp = self.model.loglikelihood(eSeq)
try:
tmp/1
except Exception, e:
print "In exception"
tmp = -1000