python 3 imports

pyhsmm/internals/hmm_states.py

@@ -605,20 +605,20 @@ class HMMStatesEigen(HMMStatesPython):
 
     @staticmethod
     def _messages_backwards_log(trans_matrix,log_likelihoods):
-        from hmm_messages_interface import messages_backwards_log
+        from pyhsmm.internals.hmm_messages_interface import messages_backwards_log
         return messages_backwards_log(
                 trans_matrix,log_likelihoods,
                 np.empty_like(log_likelihoods))
 
     @staticmethod
     def _messages_forwards_log(trans_matrix,init_state_distn,log_likelihoods):
-        from hmm_messages_interface import messages_forwards_log
+        from pyhsmm.internals.hmm_messages_interface import messages_forwards_log
         return messages_forwards_log(trans_matrix,log_likelihoods,
                 init_state_distn,np.empty_like(log_likelihoods))
 
     @staticmethod
     def _messages_forwards_normalized(trans_matrix,init_state_distn,log_likelihoods):
-        from hmm_messages_interface import messages_forwards_normalized
+        from pyhsmm.internals.hmm_messages_interface import messages_forwards_normalized
         return messages_forwards_normalized(trans_matrix,log_likelihoods,
                 init_state_distn,np.empty_like(log_likelihoods))
 
@@ -640,19 +640,19 @@ class HMMStatesEigen(HMMStatesPython):
 
     @staticmethod
     def _sample_forwards_log(betal,trans_matrix,init_state_distn,log_likelihoods):
-        from hmm_messages_interface import sample_forwards_log
+        from pyhsmm.internals.hmm_messages_interface import sample_forwards_log
         return sample_forwards_log(trans_matrix,log_likelihoods,
                 init_state_distn,betal,np.empty(log_likelihoods.shape[0],dtype='int32'))
 
     @staticmethod
     def _sample_backwards_normalized(alphan,trans_matrix_transpose):
-        from hmm_messages_interface import sample_backwards_normalized
+        from pyhsmm.internals.hmm_messages_interface import sample_backwards_normalized
         return sample_backwards_normalized(trans_matrix_transpose,alphan,
                 np.empty(alphan.shape[0],dtype='int32'))
 
     @staticmethod
     def _resample_multiple(states_list):
-        from hmm_messages_interface import resample_normalized_multiple
+        from pyhsmm.internals.hmm_messages_interface import resample_normalized_multiple
         if len(states_list) > 0:
             loglikes = resample_normalized_multiple(
                     states_list[0].trans_matrix,states_list[0].pi_0,
@@ -666,7 +666,7 @@ class HMMStatesEigen(HMMStatesPython):
     def _expected_statistics_from_messages(
             trans_potential,likelihood_log_potential,alphal,betal,
             expected_states=None,expected_transcounts=None):
-        from hmm_messages_interface import expected_statistics_log
+        from pyhsmm.internals.hmm_messages_interface import expected_statistics_log
         expected_states = np.zeros_like(alphal) \
                 if expected_states is None else expected_states
         expected_transcounts = np.zeros_like(trans_potential) \
@@ -678,7 +678,7 @@ class HMMStatesEigen(HMMStatesPython):
     ### Vitberbi
 
     def Viterbi(self):
-        from hmm_messages_interface import viterbi
+        from pyhsmm.internals.hmm_messages_interface import viterbi
         self.stateseq = viterbi(self.trans_matrix,self.aBl,self.pi_0,
                 np.empty(self.aBl.shape[0],dtype='int32'))
 

pyhsmm/internals/hsmm_states.py

@@ -500,7 +500,7 @@ class HSMMStatesEigen(HSMMStatesPython):
     def messages_backwards(self):
         # NOTE: np.maximum calls are because the C++ code doesn't do
         # np.logaddexp(-inf,-inf) = -inf, it likes nans instead
-        from hsmm_messages_interface import messages_backwards_log
+        from pyhsmm.internals.hsmm_messages_interface import messages_backwards_log
         betal, betastarl = messages_backwards_log(
                 np.maximum(self.trans_matrix,1e-50),self.aBl,np.maximum(self.aDl,-1000000),
                 self.aDsl,np.empty_like(self.aBl),np.empty_like(self.aBl),
@@ -519,7 +519,7 @@ class HSMMStatesEigen(HSMMStatesPython):
         return super(HSMMStatesEigen,self).messages_backwards()
 
     def sample_forwards(self,betal,betastarl):
-        from hsmm_messages_interface import sample_forwards_log
+        from pyhsmm.internals.hsmm_messages_interface import sample_forwards_log
         if self.left_censoring:
             raise NotImplementedError
         caBl = np.vstack((np.zeros(betal.shape[1]),np.cumsum(self.aBl[:-1],axis=0)))
@@ -533,7 +533,7 @@ class HSMMStatesEigen(HSMMStatesPython):
 
     @staticmethod
     def _resample_multiple(states_list):
-        from hsmm_messages_interface import resample_log_multiple
+        from pyhsmm.internals.hsmm_messages_interface import resample_log_multiple
         if len(states_list) > 0:
             Ts = [s.T for s in states_list]
             longest = np.argmax(Ts)

pyhsmm/util/plot.py

 from __future__ import division
 import numpy as np
 from matplotlib import pyplot as plt
-from stats import cov
+
+from pyhsmm.util.stats import cov
 
 def plot_gaussian_2D(mu, lmbda, color='b', centermarker=True,label='',alpha=1.,ax=None,artists=None):
     '''