void pathRecovery(const std::vector<std::vector<int> > & p, int i,int k){
if (p[i][k]!=-1) {
pathRecovery(p,i,p[i][k]);
std::cout<<p[i][k]+1<<" ";
pathRecovery(p,p[i][k],k);
}
}
void _LikelihoodFunction::RunViterbi ( _Matrix & result, const _Parameter * patternLikelihoods,
_Matrix & hmm, _Matrix& hmf,
_SimpleList * duplicateMap, const _SimpleList* scalers,
long bl )
{
long ni = hmm.GetHDim(),
siteCount = duplicateMap?duplicateMap->lLength:bl;
_Matrix temp (ni,1,false,true),
temp2 (ni,1,false,true);
_SimpleList pathRecovery (siteCount * ni, 0, 0);
if ((duplicateMap?duplicateMap->lLength:bl) > 1)
// non-trivial case (more than one site)
{
for (long site = siteCount-1; site > 0; site --)
{
for (long parentState = 0; parentState < ni; parentState ++)
{
long bestState = 0,
mi = duplicateMap?
duplicateMap->lData[site]:
site,
currentScaler = duplicateMap?
scalers->lData[mi]:
((_SimpleList*)((_List*)scalers)->lData[0])->lData[site];
_Parameter bestValue = log(patternLikelihoods[mi]*hmm.theData[parentState*ni]) + temp.theData[0];
mi += bl;
if (currentScaler)
bestValue -= currentScaler * _logLFScaler;
for (long currentState = 1; currentState < ni; currentState ++, mi += bl)
{
currentScaler = duplicateMap?
scalers->lData[mi]:
((_SimpleList*)((_List*)scalers)->lData[currentState])->lData[site];
_Parameter currentValue = log(patternLikelihoods[mi]*hmm.theData[parentState*ni + currentState]) +
temp.theData[currentState];
if (currentScaler)
currentValue -= currentScaler * _logLFScaler;
if (currentValue > bestValue)
{
bestValue = currentValue;
bestState = currentState;
}
}
temp2.theData[parentState] = bestValue;
pathRecovery.lData[site*ni + parentState] = bestState;
//if (parentState != bestState && parentState == 1)
// printf ("%d %d -> %d\n", site, parentState, bestState);
}
_Parameter* swap = temp.theData;
temp.theData = temp2.theData;
temp2.theData = swap;
}
}
else
{
for (long parentState = 0; parentState < ni; parentState ++)
{
temp.theData[parentState] = log(patternLikelihoods[parentState]) +
(duplicateMap?scalers->lData[parentState]:((_SimpleList*)((_List*)scalers)->lData[parentState])->lData[0])*_logLFScaler;
}
}
long mi = duplicateMap?duplicateMap->lData[0]:0,
bestState = 0;
_Parameter bestValue = log(patternLikelihoods [mi]*hmf.theData[0]) + temp.theData[0] +
(duplicateMap?scalers->lData[mi]:((_SimpleList*)((_List*)scalers)->lData[0])->lData[0])*_logLFScaler;
mi+=bl;
for (long initState = 1; initState < ni; initState ++, mi += bl)
{
long currentScaler = duplicateMap?scalers->lData[mi]:((_SimpleList*)((_List*)scalers)->lData[initState])->lData[0];
_Parameter currentValue = log(patternLikelihoods[mi]*hmf.theData[initState]) +
temp.theData[initState];
if (currentScaler)
currentValue -= currentScaler * _logLFScaler;
if (currentValue > bestValue)
{
bestValue = currentValue;
bestState = initState;
}
}
result.theData[0] = bestState;
for (long site = 1; site < siteCount; site++)
{
result.theData[site] = pathRecovery.lData[site*ni + (long)result.theData[site-1]];
//printf ("%d: (%g) 0-%ld 1-%ld\n", site,result.theData[site-1], pathRecovery.lData[site*ni], pathRecovery.lData[site*ni+1]);
}
}
