def train(self, clustered_ink_data,
center_type='centroid', state_reduction=False):
super(ClassifierBeam, self).train(clustered_ink_data,
center_type=center_type,
state_reduction=state_reduction)
self.labels = sorted(set([prot.label
for prot in self._trained_prototypes]))
self.beam = BeamForward(self._trained_prototypes,
beam_width=self.beam_width,
alpha=self.beam_alpha)
class ClassifierBeam(ClassifierDTW):
"""Classifier based on Beam-search forward algorithm
Parameters
----------
min_cluster_size : int
Minimum number of examples in a cluster.
alpha : float
Alpha for DTW algorithm (used in training)
beam_width : int, None
Number of states kept in memeory at each time step.
beam_alpha : float
Alpha for the beam decoding (used in classify)
"""
def __init__(self, min_cluster_size=5, alpha=0.5,
beam_width=None, beam_alpha=0.5):
ClassifierDTW.__init__(self,
min_cluster_size=min_cluster_size,
alpha=alpha)
self.beam = None
self.beam_width = beam_width
self.beam_alpha = beam_alpha
def train(self, clustered_ink_data,
center_type='centroid', state_reduction=False):
super(ClassifierBeam, self).train(clustered_ink_data,
center_type=center_type,
state_reduction=state_reduction)
self.labels = sorted(set([prot.label
for prot in self._trained_prototypes]))
self.beam = BeamForward(self._trained_prototypes,
beam_width=self.beam_width,
alpha=self.beam_alpha)
def test(self, label_ink_data):
if len(self._trained_prototypes) == 0:
raise ValueError('No prototypes in the classifier.')
n = len(label_ink_data)
true_labels, all_ink = zip(*label_ink_data)
true_labels = np.asarray(true_labels)
predicted = map(self.classify, all_ink)
predicted = np.asarray(predicted)
accuracy = 100.0 * np.sum(true_labels == predicted) / n
return (accuracy, true_labels, predicted)
def classify(self, obs):
self.beam.reset()
for i in xrange(obs.shape[0]):
self.beam.add_point(obs[i,:])
#ll = self.beam.loglikelihood()
ll = self.beam.scores()
return self._trained_prototypes[ll.argmax()].label
def posterior(self, obs):
def _label_prob(ll):
probdict = {}
for i,prot in enumerate(self._trained_prototypes):
prob = probdict.get(prot.label, 0)
probdict[prot.label] = prob + np.exp(ll[i])
v = np.zeros(len(self.labels))
for i,label in enumerate(self.labels):
v[i] = probdict[label]
return v / v.sum()
post = np.zeros((obs.shape[0], len(self.labels)))
self.beam.reset()
for i in xrange(obs.shape[0]):
self.beam.add_point(obs[i,:])
post[i,:] = _label_prob(self.beam.loglikelihood())
return post
