Building and using HMM

3 Building and using HMM

This part is based on data file seq_hmm.fa and Lproportion file lprop2.

We want to compute partitions on a set of sequences from a HMM, and compute proportions from the resulting partitions.

We load the sequences and the HMM.
```
import lsequence
ls=lsequence.Lsequence()
ls.read_nf('seq_hmm.fa')
import lcompte
lpr=lcompte.Lproportion(fic="lprop2")
print lpr
```
We notice that in these proportions there is no transition proportion for the sequences beginning.

We compute a Lpartition with Viterbi algorithm, and print the number of segments of each partition

import lpartition
lpa=lpartition.Lpartition()
lpa.add_Lseq(ls)
import lexique  
lx=lexique.Lexique()
lx.read_Lprop(lpr)
lpa.viterbi(lx)
for i in lpa:
    print len(i[1]),

We build a new Lcompte of 2-length words from the segmentations.
```
lc=lcompte.Lcompte()
lc.read_Lpart(lpa,2)
print lc
```

We compute a new HMM from the 1|1-proportions on the latter Lcompte.

lpr2=lcompte.Lproportion()
lpr2.read_Lcompte(lc.rstrip(),lprior=1,lpost=1)

We compute the Kullback-Leibler divergence form lpr to lpr2, using MC simulation on 100 sequences of length 5000.
```
lpr.KL_MC(lpr2,100,5000)
```

On the studied sequences, we compute new partitions with this new HMM with FB algorithm.

lx2=lexique.Lexique()
lx2.read_Lprop(lpr2)
lpa2=lpartition.Lpartition()
lpa2.add_Lseq(lpa.Lseq())
lpa2.fb(lx2)

And so on.