def __call__(self):
hmmdata = dict()
for (name, tracks, probs, objids, coords) in \
self.dtable.iterby(self.ecopts.sortby, True):
if tracks is probs is None:
hmmdata[name] = None
continue
labelmapper = LabelMapper(np.unique(tracks),
self.classdef.class_names.keys())
# np.unique -> sorted ndarray
idx = labelmapper.index_from_classdef(np.unique(tracks))
idx.sort()
# no prediction probabilities available
if probs is not None:
probs = probs[:, :, idx]
est = self._get_estimator(probs, labelmapper.label2index(tracks))
est.constrain(self.hmmc(est, labelmapper))
# ugly sklearn
hmm_ = MultinomialHMM(n_components=est.nstates)
hmm_.startprob_ = est.startprob
hmm_.transmat_ = est.trans
hmm_.emissionprob_ = est.emis
tracks2 = []
for track in labelmapper.label2index(tracks):
tracks2.append(hmm_.predict(track))
tracks2 = labelmapper.index2labels(np.array(tracks2, dtype=int))
bucket = HmmBucket(tracks,
tracks2,
est.startprob,
est.emis,
est.trans,
self.dtable.groups(self.ecopts.sortby, name),
tracks.shape[0],
objids,
coords,
self.ecopts.timelapse)
hmmdata[name] = bucket
return hmmdata
def __init__(self, states, estimator, tracks):
# tracks have been mapped to array indices already
super(HMMBaumWelchEstimator, self).__init__(states)
self._trans = estimator.trans
self._emis = estimator.emis
self._startprob = estimator.startprob
# the initialisation is essential!
hmm_ = MultinomialHMM(n_components=estimator.nstates,
transmat=estimator.trans,
startprob=estimator.startprob,
n_iter=1000,
init_params="")
hmm_.emissionprob_ = estimator.emis
hmm_.fit(tracks)
self._trans = hmm_.transmat_
self._emis = hmm_.emissionprob_
self._startprob = hmm_.startprob_
def __init__(self, states, estimator, tracks):
# tracks have been mapped to array indices already
super(HMMBaumWelchEstimator, self).__init__(states)
self._trans = estimator.trans
self._emis = estimator.emis
self._startprob = estimator.startprob
# the initialisation is essential!
hmm_ = MultinomialHMM(n_components=estimator.nstates,
transmat=estimator.trans,
startprob=estimator.startprob,
n_iter=1000,
init_params="")
hmm_.emissionprob_ = estimator.emis
hmm_.fit(tracks)
self._trans = hmm_.transmat_
self._emis = hmm_.emissionprob_
self._startprob = hmm_.startprob_
def __call__(self):
hmmdata = dict()
for (name, tracks, probs, objids, coords) in \
self.dtable.iterby(self.ecopts.sortby, True):
if tracks is probs is None:
hmmdata[name] = None
continue
labelmapper = LabelMapper(np.unique(tracks),
self.classdef.class_names.keys())
# np.unique -> sorted ndarray
idx = labelmapper.index_from_classdef(np.unique(tracks))
idx.sort()
# no prediction probabilities available
if probs is not None:
probs = probs[:, :, idx]
est = self._get_estimator(probs, labelmapper.label2index(tracks))
est.constrain(self.hmmc(est, labelmapper))
# ugly sklearn
hmm_ = MultinomialHMM(n_components=est.nstates)
hmm_.startprob_ = est.startprob
hmm_.transmat_ = est.trans
hmm_.emissionprob_ = est.emis
tracks2 = []
for track in labelmapper.label2index(tracks):
tracks2.append(hmm_.predict(track))
tracks2 = labelmapper.index2labels(np.array(tracks2, dtype=int))
bucket = HmmBucket(tracks, tracks2, est.startprob, est.emis,
est.trans,
self.dtable.groups(self.ecopts.sortby,
name), tracks.shape[0],
objids, coords, self.ecopts.timelapse)
hmmdata[name] = bucket
return hmmdata
