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while calculating covariance matrix in logs, i have modified the _reestimateMixtures() method in the file _ContinuousHMM.py as follows.
` numer = numpy.matrix(numpy.zeros( (self.d,self.d), dtype=self.precision))
denom = numpy.matrix(numpy.zeros( (self.d,self.d), dtype=self.precision))
for t in xrange(len(observations)):
Problem is while training, some times the numer and denom in the above method are becoming zero's. and as a result, my covariance matrix contains all the Nan values.
Can anyone please help me, how to avoid covariance matrix not to contain nan values.
The text was updated successfully, but these errors were encountered:
Hi anil,
I was trying to use this codebase for continuous HMM and I am facing the same issue.
Did you get it fixed or were you able to find a library online where a good implementation of this model is done. I have been stuck for many days. It would be great if you could help me out.
Hi,
while calculating covariance matrix in logs, i have modified the _reestimateMixtures() method in the file _ContinuousHMM.py as follows.
` numer = numpy.matrix(numpy.zeros( (self.d,self.d), dtype=self.precision))
denom = numpy.matrix(numpy.zeros( (self.d,self.d), dtype=self.precision))
for t in xrange(len(observations)):
covars_new[j][m] = numer/denom
covars_new[j][m] = covars_new[j][m] + cov_prior[j][m]`
Problem is while training, some times the numer and denom in the above method are becoming zero's. and as a result, my covariance matrix contains all the Nan values.
Can anyone please help me, how to avoid covariance matrix not to contain nan values.
The text was updated successfully, but these errors were encountered: