class MixedLogisticRegressionEM(MixedLogisticRegression):
"""
Class of EM algorithm
"""
def __init__(self, input_for_decision_layer, c, n_in_for_decision_layer, n_out,
p_membership=None, input_for_hidden_layer=None, n_in_for_hidden_layer=None):
MixedLogisticRegression.__init__(self, input_for_decision_layer, c, n_in_for_decision_layer, n_out,
p_membership, input_for_hidden_layer, n_in_for_hidden_layer)
self.hiddenLayer2 = HiddenLayer(c, p_membership, input_for_hidden_layer, n_in_for_hidden_layer)
self.decisionLayer2 = DecisionLayer(c, input_for_decision_layer, n_in_for_decision_layer, n_out)
self.params = self.hiddenLayer2.params + self.decisionLayer2.params # parameter for updating
def negative_expected_log_likelihood(self, y):
"""
:param y:
:return:
"""
lp_membership = T.log(self.hiddenLayer2.p_membership)
lp_decision = T.log(self.decisionLayer2.conditionalLikelihood(y))
p_conditionalMembership = self.conditionalMembershipProbability(y) # inherited from MixedLogisticRegression
return -T.sum(p_conditionalMembership * (lp_membership + lp_decision))
def m_step(self):
# Pylearn2 may be helpful
# Reference: http://deeplearning.net/software/pylearn2/index.html#
pass
def __init__(self, input_for_decision_layer, c, n_in_for_decision_layer, n_out,
p_membership=None, input_for_hidden_layer=None, n_in_for_hidden_layer=None):
self.hiddenLayer = HiddenLayer(c, p_membership,
input_for_hidden_layer, n_in_for_hidden_layer)
self.decisionLayer = DecisionLayer(c, input_for_decision_layer,
n_in_for_decision_layer, n_out)
self.params = self.hiddenLayer.params + self.decisionLayer.params
def __init__(self, input_for_decision_layer, c, n_in_for_decision_layer, n_out,
p_membership=None, input_for_hidden_layer=None, n_in_for_hidden_layer=None):
MixedLogisticRegression.__init__(self, input_for_decision_layer, c, n_in_for_decision_layer, n_out,
p_membership, input_for_hidden_layer, n_in_for_hidden_layer)
self.hiddenLayer2 = HiddenLayer(c, p_membership, input_for_hidden_layer, n_in_for_hidden_layer)
self.decisionLayer2 = DecisionLayer(c, input_for_decision_layer, n_in_for_decision_layer, n_out)
self.params = self.hiddenLayer2.params + self.decisionLayer2.params # parameter for updating
class MixedLogisticRegression(object):
def __init__(self, input_for_decision_layer, c, n_in_for_decision_layer, n_out,
p_membership=None, input_for_hidden_layer=None, n_in_for_hidden_layer=None):
self.hiddenLayer = HiddenLayer(c, p_membership,
input_for_hidden_layer, n_in_for_hidden_layer)
self.decisionLayer = DecisionLayer(c, input_for_decision_layer,
n_in_for_decision_layer, n_out)
self.params = self.hiddenLayer.params + self.decisionLayer.params
def conditionalMembershipProbability(self, y):
conditionalLikelihood = self.decisionLayer.conditionalLikelihood(y)
p_membership = self.hiddenLayer.p_membership
# if self.hiddenLayer.isHierarchical:
# p_membership = self.hiddenLayer.p_membership
# else:
# p_membership = self.hiddenLayer.params
jointProbabilities = conditionalLikelihood * p_membership
return (jointProbabilities.T / T.sum(jointProbabilities, axis=1)).T
def negative_expected_log_likelihood(self, y):
"""
Minimization target for M-step
:param y:
:return:
"""
lp_membership = T.log(self.hiddenLayer.p_membership)
lp_decision = T.log(self.decisionLayer.conditionalLikelihood(y))
p_conditionalMembership = self.conditionalMembershipProbability(y)
return -T.sum(p_conditionalMembership * (lp_membership + lp_decision))
def negative_log_likelihood(self, y):
p_membership = self.hiddenLayer.p_membership
p_decision = self.decisionLayer.conditionalLikelihood(y)
return -T.sum(T.log(T.sum(p_decision * p_membership, axis=1)))
