void pingpong(CtrlType *ctrl, GraphType *graph, int nparts, int chain_length, float *tpwgts, float ubfactor, int toplevel)
// do batch-local search; chain_length is the search length
{
int nvtxs, nedges, moves, iter;
idxtype *w;
//float *m_adjwgt;
nedges = graph->nedges;
nvtxs = graph->nvtxs;
w = idxsmalloc(nvtxs, 0, "pingpong: weight");
Compute_Weights(ctrl, graph, w);
//m_adjwgt = fmalloc(nedges, "pingpong: normalized matrix");
//transform_matrix(ctrl, graph, w, m_adjwgt);
//printf("Chain length is %d.\n", chain_length);
moves =0;
iter =0;
//printf("Number of boundary points is %d\n", graph->nbnd);
do{
//Weighted_kernel_k_means(ctrl, graph, nparts, w, m_adjwgt, tpwgts, ubfactor);
Weighted_kernel_k_means(ctrl, graph, nparts, w, tpwgts, ubfactor);
if (chain_length>0){
//moves = local_search(ctrl, graph, nparts, chain_length, w, m_adjwgt, tpwgts, ubfactor);
moves = local_search(ctrl, graph, nparts, chain_length, w, tpwgts, ubfactor);
//printf("Number of local search moves is %d\n", moves);
//printf("Number of boundary points is %d\n", graph->nbnd);
}
iter ++;
if (iter > MAXITERATIONS)
break;
}while(moves >0) ;
if(memory_saving ==0){
remove_empty_clusters_l1(ctrl, graph, nparts, w, tpwgts, ubfactor);
if(toplevel>0)
remove_empty_clusters_l2(ctrl, graph, nparts, w, tpwgts, ubfactor);
}
free(w);
//free(m_adjwgt);
}
/*
* 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];
}
