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Fixed error in CtHMM baum welch and maximum likelihood where tA /= pt should be 0 not NaN. #8

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Fixed error in CtHMM baum welch and maximum likelihood where tA /= pt should be 0 not NaN. #8

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bdc0b7f
fixed the iteration print counter and cases where 0 / 0 should just b…
Jan 3, 2019
c37acb4
removed value error messages, which are not an issue now that we some…
cmaclell Jan 3, 2019
d239b65
removed commented out lines.
cmaclell Jan 3, 2019
1f3c112
removed all the printing
cmaclell Jan 3, 2019
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24 changes: 7 additions & 17 deletions hmms/cthmm.pyx
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Original file line number Diff line number Diff line change
Expand Up @@ -739,7 +739,7 @@ cdef class CtHMM(hmm.HMM):

for it in range( iterations ):

print("it",it)
# print("it",it)

self._prepare_matrices_pt( t_seqs )

Expand Down Expand Up @@ -814,23 +814,18 @@ cdef class CtHMM(hmm.HMM):

if i == j:

tA /= self._pt[ ix ]
tA = numpy.divide(tA, self._pt[ ix ], out=numpy.zeros_like(tA), where=tA!=0)
tau[i] += numpy.exp( self.log_sum( (ksi_sum[ix] + numpy.log( tA ) ).flatten() ) ) #tau is not in log prob anymore.

else:
tA *= self._q[i,j]
tA /= self._pt[ ix ]
tA = numpy.divide(tA, self._pt[ ix ], out=numpy.zeros_like(tA), where=tA!=0)
eta[i,j] += numpy.exp( self.log_sum( (ksi_sum[ix] + numpy.log( tA ) ).flatten() ) ) #eta is not in log prob anymore.

#Update parameter Q:

self._q = ( eta.T / tau ).T

self._q = numpy.nan_to_num(self._q) # nan can appear, when some of the states is not reachable

if sum( self._q.flatten() ) == 0:
raise ValueError("Parameter error! Matrix Q can't contain unreachable states.")

for i in range( s_num ):
self._q[i,i] = - numpy.sum( self._q[i,:] )

Expand Down Expand Up @@ -934,8 +929,7 @@ cdef class CtHMM(hmm.HMM):

for it in range( iterations ):

print("iteration ", i+1, "/", iterations )

# print("iteration ", it+1, "/", iterations )

self._prepare_matrices_pt( times )

Expand Down Expand Up @@ -1033,14 +1027,15 @@ cdef class CtHMM(hmm.HMM):

if i == j:

tA /= self._pt[ ix ]
tA = numpy.divide(tA, self._pt[ ix ], out=numpy.zeros_like(tA), where=tA!=0)

tau[i] += numpy.exp( self.log_sum( (ksi_sum[ix] + numpy.log( tA ) ).flatten() ) ) #tau is not in log prob anymore.


else:
tA *= self._q[i,j]
tA /= self._pt[ ix ]
tA = numpy.divide(tA, self._pt[ ix ], out=numpy.zeros_like(tA), where=tA!=0)

eta[i,j] += numpy.exp( self.log_sum( (ksi_sum[ix] + numpy.log( tA ) ).flatten() ) ) #eta is not in log prob anymore.

#Update parameters:
Expand All @@ -1052,13 +1047,8 @@ cdef class CtHMM(hmm.HMM):
#jump rates matrice
self._q = ( eta.T / tau ).T



self._q = numpy.nan_to_num(self._q) # nan can appear, when some of the states is not reachable

if sum( self._q.flatten() ) == 0:
raise ValueError("Parameter error! Matrix Q can't contain unreachable states.")

for i in range( s_num ):
self._q[i,i] = - numpy.sum( self._q[i,:] )

Expand Down