/*
* Does a "null" fit to the data, all estimates same for all snps
*
* @param Contrast - contrast values for each snp
* @param Strength - strength values for each snp
* @param zG - weight for each snp
* @param Predictor - predictor function for this snp
* @param Fitted - fitted values
*/
void Dummy_Fit(const std::vector<double> &Contrast,
const std::vector<double> &Strength,
const std::vector<double> &zG,
std::vector<double> &Predictor,
std::vector<double> &Fitted)
{
// fit Contrast to covariates with weights zG
// returning predicted values mG
// currently uses no covariates at all and just takes means
double sum, denom,tmp;
sum=denom=0;
for (unsigned int i=0; i<Contrast.size(); i++)
{
sum += Contrast[i]*zG[i];
denom += zG[i];
}
tmp = sum/denom;
Predictor.resize(4);
Predictor[0]=tmp;
Predictor[1]=Predictor[2]=Predictor[3]=0;
FillVector(Fitted,tmp);
}
void DynamicHouseUpdateCallback::operator()( osg::Node* node, osg::NodeVisitor* nv )
{
m_pGroup = dynamic_cast< osg::Group* >( node );
if ( m_pGroup )
{
//вернуть ссылку на массив видимых патчей
const std::vector< dataPatch > &data_vis = VisiblePatchArray::Instance().GetVisibleArray512();
//очистить данные с предыдущего шага
ClearPrevStep();
for ( int i = 0 ; i < data_vis.size() ; ++i )
{
unsigned char ucR , ucG;
PatchInfo::Instance().Convert( data_vis[ i ].m_iX ,
data_vis[ i ].m_iY , &ucR , &ucG );
//обратиться к патчу по индексу
binPatch &_binPatch = PatchInfo::Instance().GetBinPatch( ucR , ucG );
//перебрать все содержимое патча и добавить его в m_mHause
ParsePatch( _binPatch , data_vis[ i ].m_iX , data_vis[ i ].m_iY );
}
//заполнить вектор
FillVector();
if ( !m_mHause.empty() )
{
std::cout << m_mHause.begin()->second.size() << " ";
}
}
// first update subgraph to make sure objects are all moved into position
traverse(node,nv);
}
/*
* Do EM repeatedly to fit normalization functions for each genotype
*
* @param Contrast - contrast values for each snp
* @param Strength - strength values for each snp
* @param max_iterations - don't go to infinity
* @param log_lik_convergence - stopped making progress
* @param NP - output prediction functions for each genotype
*/
void EMContrast(const std::vector<double> &Contrast,
const std::vector<double> &Strength,
const int max_iterations,
const double log_lik_convergence,
NormalizationPredictor &NP)
{
// this is the only routine that needs newmat
// so I can isolate it!
vector<double> mu;
vector<double> sigma;
vector<double> probs;
vector<double> wAA,wAB,wBB;
vector<double> mAA,mAB,mBB;
vector<double> zAA,zAB,zBB;
vector<double> lik_per_snp;
double last_log_lik,log_lik_diff,curr_log_lik;
unsigned int n_iterations,nSNP;
InitializeEMVars(mu,sigma,probs,Contrast);
NP.TargetCenter.resize(3);
NP.TargetCenter[0]=-.66;
NP.TargetCenter[1]=0;
NP.TargetCenter[2]= .66;
NP.CenterCovariates.resize(3);
NP.CenterCovariates[0] = 0;
NP.CenterCovariates[1] = 0;
NP.CenterCovariates[2] = 0;
nSNP = Contrast.size();
mAA.resize(nSNP);
mAB.resize(nSNP);
mBB.resize(nSNP);
wAA.resize(nSNP);
wAB.resize(nSNP);
wBB.resize(nSNP);
zAA.resize(nSNP);
zAB.resize(nSNP);
zBB.resize(nSNP);
lik_per_snp.resize(nSNP);
FillVector(mBB,mu[0]);
FillVector(mAB,mu[1]);
FillVector(mAA,mu[2]);
Compute_Weights(wAA,mAA,sigma[2],Contrast);
Compute_Weights(wAB,mAB,sigma[1],Contrast);
Compute_Weights(wBB,mBB,sigma[0],Contrast);
last_log_lik = -1000000;
log_lik_diff = log_lik_convergence+1;
n_iterations = 0;
while (log_lik_diff > log_lik_convergence && n_iterations < max_iterations){
n_iterations += 1;
// E step
// update probs for each group
// relative genotype probability per SNP
// relative likelihood steps
LikelihoodPerSNP(lik_per_snp,wAA,wAB,wBB,probs);
//cout<< "likpersnp\t";
//td(lik_per_snp);
GenotypePerSNP(zAA,wAA,lik_per_snp,probs[2]);
GenotypePerSNP(zAB,wAB,lik_per_snp,probs[1]);
GenotypePerSNP(zBB,wBB,lik_per_snp,probs[0]);
/*td(zAA);
td(zAB);
td(zBB);*/
probs[0] = compute_mean(zBB);
probs[1] = compute_mean(zAB);
probs[2] = compute_mean(zAA);
//cout << "probs:\t";
//td(probs);
curr_log_lik = ComputeLL(lik_per_snp);
//cout << "curr_lik:\t" << curr_log_lik << endl;
log_lik_diff = curr_log_lik-last_log_lik;
last_log_lik = curr_log_lik;
// done with E step
// now do the big M step
// do magic: generate predictors for each group
// how to do magic: SVD of of design matrix outside of loop
// do magic within loop
// fitAA = predictor weighted by zAA
// mAA = current predictions for this
// fitAB = predictor weighted by zAB
// mAB = current predictions for this
// fitBB = predictor weighted by zBB
// mBB = current predictions for this genotype by snp
//Dummy_Fit(Contrast,mAA,zAA);
Dummy_Fit(Contrast,Strength,zAB,NP.fitAB,mAB);
//Dummy_Fit(Contrast,mBB,zBB);
// try the real one
FitWeightedCubic(Contrast,Strength,zAA,NP.fitAA,mAA);
//FitWeightedCubic(Contrast,Strength,zAB,NP.fitAB,mAB);
FitWeightedCubic(Contrast,Strength,zBB,NP.fitBB,mBB);
/*td(zAA);
td(mAA);
td(zAB);
td(mAB);
td(zBB);
td(mBB);*/
sigma[0] = WeightedSigma(Contrast,mBB,zBB);
sigma[1] = WeightedSigma(Contrast,mAB,zAB);
sigma[2] = WeightedSigma(Contrast,mAA,zAA);
MinSigma(sigma, .001);
Compute_Weights(wAA,mAA,sigma[2],Contrast);
Compute_Weights(wAB,mAB,sigma[1],Contrast);
Compute_Weights(wBB,mBB,sigma[0],Contrast);
HardShell(Contrast,wAA,wBB);
/*td(wAA);
td(wAB);
td(wBB);
cout << "sigma\t";
td(sigma);*/
}
/*cout << "Fitted values" << endl;
unsigned int dummy;
for (dummy=0; dummy<Contrast.size(); dummy++)
{
cout << dummy << "\t";
cout << Contrast[dummy] << "\t";
cout << Strength[dummy] << "\t";
cout << mAA[dummy] << "\t";
cout << mAB[dummy] << "\t";
cout << mBB[dummy] << "\t";
cout << zAA[dummy] << "\t";
cout << zAB[dummy] << "\t";
cout << zBB[dummy] << "\t";
cout << endl;
}*/
//td(Contrast);
//td(mAA);
//td(mAB);
//td(mBB);
// transfer useful data out of program
// need: predictor vectors fAA, fAB, fBB
// need: sigma
NP.sigma.resize(3);
NP.sigma[0]=sigma[0];
NP.sigma[1]=sigma[1];
NP.sigma[2]=sigma[2];
}
