SEXP addNeighborhoodToImageHelper(
SEXP r_antsimage,
SEXP r_center,
SEXP r_rad,
SEXP r_vec)
{
typedef typename ImageType::Pointer ImagePointerType;
const unsigned int ImageDimension = ImageType::ImageDimension;
typedef float PixelType;
typename ImageType::Pointer image =
Rcpp::as< ImagePointerType >( r_antsimage );
Rcpp::NumericVector center( r_center );
Rcpp::NumericVector rad( r_rad );
Rcpp::NumericVector intvec( r_vec );
if ( center.size() != ImageDimension )
Rcpp::stop("addNeighborhoodToImageHelper dim error.");
typename itk::NeighborhoodIterator<ImageType>::SizeType nSize;
typename ImageType::IndexType ind;
ind.Fill( 0 );
for ( unsigned int i=0; i<ImageDimension; i++ )
{
nSize[i] = rad[i];
ind[i] = center[i]; // R coords to ITK
}
itk::NeighborhoodIterator<ImageType> nit( nSize, image,
image->GetLargestPossibleRegion() ) ;
// for each location in nitSearch, compute the correlation
// of the intvec with the nit neighborhood
nit.SetLocation( ind );
for( unsigned int i = 0; i < nit.Size(); i++ )
{
typename ImageType::IndexType ind2 = nit.GetIndex(i);
PixelType lval = image->GetPixel( ind2 );
image->SetPixel( ind2, lval + intvec[i] );
}
return 0;
}
bool zzhang::CBPDualSolver::Init() {
srand(456);
clock_t init_start = clock();
std::vector<std::vector<int> > TNodeToEclusterIdx(m_NodeSize);
std::vector<int> HasSuperSet(m_EClusterVec.size());
std::map<std::pair<int, int>, bool> IsVisited;
for (int i = 0; i < m_EClusterVec.size(); i++) {
HasSuperSet[i] = 0;
for (int j = 0; j < m_EClusterVec[i].size(); j++) {
TNodeToEclusterIdx[m_EClusterVec[i][j]].push_back(i);
}
}
for (int i = 0; i < m_NodeSize; i++) {
for (int j = 0; j < TNodeToEclusterIdx[i].size(); j++) {
int cj = TNodeToEclusterIdx[i][j];
for (int k = j + 1; k < TNodeToEclusterIdx[i].size(); k++) {
int ck = TNodeToEclusterIdx[i][k];
if (cj == ck)
continue;
std::pair<int, int> currentjk(cj, ck);
if (IsVisited.find(currentjk) != IsVisited.end())
continue;
IsVisited[currentjk] = true;
IsVisited[std::pair<int, int>(ck, cj)] = true;
if (m_EClusterSet[cj].size() == m_EClusterSet[ck].size())
continue;
if (m_EClusterSet[cj].size() < m_EClusterSet[ck].size()) {
int temp = cj;
cj = ck;
ck = temp;
}
if (std::includes(m_EClusterSet[cj].begin(),
m_EClusterSet[cj].end(), m_EClusterSet[ck].begin(),
m_EClusterSet[ck].end())) {
HasSuperSet[ck] = 1;
}
}
}
}
IsVisited.clear();
int maxsize = HasSuperSet.size();
for (int i = 0; i < maxsize; i++) {
if (HasSuperSet[i]) {
m_SubEClusters[i].clear();
continue;
}
for (int ni = 0; ni < m_EClusterVec[i].size(); ni++) {
int nidx = m_EClusterVec[i][ni];
for (int k = 0; k < TNodeToEclusterIdx[nidx].size(); k++) {
int cj = i, ck = TNodeToEclusterIdx[nidx][k];
if (cj == ck)
continue;
if (HasSuperSet[ck])
continue;
std::pair<int, int> currentjk(cj, ck);
if (IsVisited.find(currentjk) != IsVisited.end())
continue;
IsVisited[currentjk] = true;
IsVisited[std::pair<int, int>(ck, cj)] = true;
std::set<int> intersection;
std::set_intersection(m_EClusterSet[cj].begin(),
m_EClusterSet[cj].end(), m_EClusterSet[ck].begin(),
m_EClusterSet[ck].end(),
std::inserter(intersection, intersection.begin()));
if (m_EClusterIdx.find(intersection) != m_EClusterIdx.end())
continue;
std::vector<int> intvec(intersection.begin(),
intersection.end());
AddECluster(intvec, -1);
//std::cout << "Added New intersection\n";
for (int j = 0; j < intvec.size(); j++) {
TNodeToEclusterIdx[intvec[j]].push_back(
m_EClusterVec.size() - 1);
}
HasSuperSet.push_back(1);
}
}
}
IsVisited.clear();
for (int i = 0; i < m_EClusterSet.size(); i++) {
if (HasSuperSet[i])
continue;
std::map<int, bool> isvisited;
for (int ni = 0; ni < m_EClusterSet[i].size(); ni++) {
int nidx = m_EClusterVec[i][ni];
for (int k = 0; k < TNodeToEclusterIdx[nidx].size(); k++) {
int cj = i, ck = TNodeToEclusterIdx[nidx][k];
if (cj == ck)
continue;
if (isvisited.find(ck) != isvisited.end())
continue;
isvisited[ck] = true;
if (m_EClusterSet[cj].size() == m_EClusterSet[ck].size())
continue;
if (std::includes(m_EClusterSet[cj].begin(),
m_EClusterSet[cj].end(), m_EClusterSet[ck].begin(),
m_EClusterSet[ck].end())) {
m_SubEClusters[cj].push_back(ck);
}
}
}
}
clock_t time_explore_graph_end = clock();
// printf("Explore Graph Structure Spends: %f seconds\n",
// (time_explore_graph_end - init_start) * 1.0 / CLOCKS_PER_SEC);
CacheIndex();
m_Potentials.clear();
m_LocalMaximum = std::vector<int>(m_EClusterVec.size());
m_DecodedSolution = boost::shared_array<int>(new int[m_NodeSize]);
m_NodeToECluIdx = TNodeToEclusterIdx;
clock_t cache_idx_cvt = clock();
printf("Cache index converting table spends: %f seconds\n",
(cache_idx_cvt - time_explore_graph_end) * 1.0 / CLOCKS_PER_SEC);
return true;
}
void SceneModeler::computeIndexSequence(){
setProgressText("compute index sequence");
rvector rawData(m_ptrData->getTrace());
int menge = 0;
for(unsigned int i = 0;i < this->classes->size(); i++){
if((*this->classes)[i].size() != 0){ menge += 1; }
}
this->classTraces = new vector< Trace* >;
this->classData = new vector< vector <double>* >(menge);
this->transitionDataClasses = new vector< int >(Ub(this->m_ptrData->getTrace())+1);
this->numStates = new int[this->numClasses];
this->classMap = new int[this->numClasses];
unsigned int count = 0;
/*creating vectors containing:
-a map that compensates the empty classes(this->classMap)
-the specific class for each datapoint(this->transitionDataClasses)*/
for(unsigned int i = 0;i < this->classes->size(); i++){
if((*this->classes)[i].size() != 0){
this->classMap[i] = i-count;
(*this->classData)[this->classMap[i]] = new vector<double>;
for(unsigned int n = 0; n < (*this->classes)[i].size(); n++){
int ende = ((unsigned int) (*this->classes)[i][n] != this->sceneLimits->size()-1) ? ((int) (*this->sceneLimits)[((int) (*this->classes)[i][n])+1]) -1 : Ub(this->m_ptrData->getTrace());;
for(int x = (*this->sceneLimits)[(*this->classes)[i][n]]; x <= ende; x++){
(*this->classData)[this->classMap[i]]->push_back(_double(rawData[x]));
(*this->transitionDataClasses)[x] = i;
}
}
double max = 0;
double min = 0;
for(unsigned int n = 0; n < (*this->classData)[this->classMap[i]]->size();n++){
min = (n == 0 || min > (*(*this->classData)[this->classMap[i]])[n]) ? (*(*this->classData)[this->classMap[i]])[n] : min;
max = (max < (*(*this->classData)[this->classMap[i]])[n]) ? (*(*this->classData)[this->classMap[i]])[n] : max;
}
rvector dump(0,(*this->classData)[this->classMap[i]]->size()-1);
for(unsigned int n=0;n< (*this->classData)[this->classMap[i]]->size();n++){
dump[n] = (*(*this->classData)[this->classMap[i]])[n];
}
this->classTraces->push_back(new Trace(dump));
if( _double(this->classTraces->back()->getStandardDeviation()) == 0 ){
this->numStates[i] = 1;
}
else{
this->numStates[i] = (int) floor((max - min) / _double(this->classTraces->back()->getStandardDeviation()));
}
}
else{
this->numStates[i] = 0;
this->classMap[i] = -1;
count++;
}
setProgressValue(41+ (int)( (double) (20.0 / this->classes->size()) * i));
}
/*creating vector containing empty states*/
this->strippedStates = new vector< vector <int>* >(this->classes->size());
for(unsigned int i=0; i < this->classes->size();i++){
(*this->strippedStates)[i] = new vector<int>;
if(this->classMap[i] != -1){
intvector intvec(0,Ub((*this->classTraces)[this->classMap[i]]->getTrace()));
for(int c=0;c <= Ub(intvec);c++){
intvec[c] = this->getStateFromTrace((*(*this->classTraces)[this->classMap[i]]),(*this->classTraces)[this->classMap[i]]->getTrace()[c]);
}
rmatrix* tmp = new rmatrix(AbstractDiscreteSSMPModeler::computeTransition(intvec));
for(int n=0;n < this->numStates[i];n++){
real sum=0;
for(int x=0;x < this->numStates[i];x++){
sum += (*tmp)[cxsc::Row(n)][x];
}
if(((int) cxsc::_double(sum)) != 1){ (*this->strippedStates)[this->classMap[i]]->push_back(n); }
}
delete tmp;
}
setProgressValue(61 + (int)( (double) (20.0 / this->classes->size()) * i));
}
/*creating intvector containing states for each datapoint*/
this->transitionDataStates = new intvector(0,Ub(m_ptrData->getTrace()));
for(int i=0; i <= Ub(m_ptrData->getTrace());i++){
int stateBuff=0;
for(int n=0;n < (*this->transitionDataClasses)[i];n++){
stateBuff += this->numStates[n]-(*this->strippedStates)[n]->size();
}
(*this->transitionDataStates)[i] = (stateBuff+ this->getStateFromTrace( ((*(*this->classTraces)[this->classMap[(*this->transitionDataClasses)[i]]])) , m_ptrData->getTrace()[i] ) );
setProgressValue(81 + (int)( (double) ( 15.0 / Ub( m_ptrData->getTrace() )) * i));
}
}
SEXP jointLabelFusionNeighborhoodSearchHelper(
SEXP r_intvec,
SEXP r_center,
unsigned int rad,
unsigned int radSearch,
SEXP r_antsimage,
SEXP r_antsimageseg)
{
unsigned int segval = 0;
typedef typename ImageType::Pointer ImagePointerType;
const unsigned int ImageDimension = ImageType::ImageDimension;
typedef float PixelType;
typename ImageType::Pointer image =
Rcpp::as< ImagePointerType >( r_antsimage );
typename ImageType::Pointer imageseg =
Rcpp::as< ImagePointerType >( r_antsimageseg );
Rcpp::NumericVector intvec( r_intvec );
Rcpp::NumericVector outvec =
Rcpp::NumericVector( intvec.size(), Rcpp::NumericVector::get_na() );
Rcpp::NumericVector bestvec =
Rcpp::NumericVector( intvec.size(), Rcpp::NumericVector::get_na() );
Rcpp::NumericVector outsegvec =
Rcpp::NumericVector( intvec.size(), Rcpp::NumericVector::get_na() );
Rcpp::NumericVector bestsegvec =
Rcpp::NumericVector( intvec.size(), Rcpp::NumericVector::get_na() );
Rcpp::NumericVector center( r_center );
if ( center.size() != ImageDimension )
Rcpp::stop("jointLabelFusionNeighborhoodSearchHelper dim error.");
typename itk::NeighborhoodIterator<ImageType>::SizeType nSize;
typename itk::NeighborhoodIterator<ImageType>::SizeType nSizeSearch;
typename ImageType::IndexType ind;
ind.Fill( 0 );
for ( unsigned int i=0; i<ImageDimension; i++ )
{
nSize[i] = rad;
nSizeSearch[i] = radSearch;
ind[i] = center[i]; // R coords to ITK
}
itk::NeighborhoodIterator<ImageType> nit( nSize, image,
image->GetLargestPossibleRegion() ) ;
itk::NeighborhoodIterator<ImageType> nitSearch( nSizeSearch, image,
image->GetLargestPossibleRegion() ) ;
// for each location in nitSearch, compute the correlation
// of the intvec with the nit neighborhood
nitSearch.SetLocation( ind );
PixelType bestcor = 1.e11;
PixelType bestsd = 0;
PixelType bestmean = 0;
for( unsigned int i = 0; i < nitSearch.Size(); i++ )
{
typename ImageType::IndexType ind2 = nitSearch.GetIndex(i);
nit.SetLocation( ind2 );
PixelType outmean = 0;
PixelType outsd = 0;
PixelType inmean = 0;
PixelType insd = 0;
for ( unsigned int i=0; i < intvec.size(); i++ ) {
PixelType pix = image->GetPixel( nit.GetIndex(i) );
outvec[i] = pix;
outsegvec[i] = imageseg->GetPixel( nit.GetIndex(i) );
outmean += pix;
inmean += intvec[i];
}
outmean /= ( static_cast<PixelType>(intvec.size()) );
inmean /= ( static_cast<PixelType>(intvec.size()) );
for ( unsigned int i=0; i < intvec.size(); i++ ) {
// should use recursive formula in above loop
outsd += ( outvec[i] - outmean ) * ( outvec[i] - outmean );
insd += ( intvec[i] - inmean ) * ( intvec[i] - inmean );
}
outsd = sqrt( outsd );
insd = sqrt( insd );
PixelType sum_uv = 0;
PixelType sum_psearch = 0;
PixelType ssq_psearch = 0;
unsigned int n = intvec.size();
for(unsigned int i = 0; i < n; i++)
{
PixelType v = intvec[i];
PixelType u = outvec[i];
sum_psearch += u;
ssq_psearch += u * u;
sum_uv += u * v;
}
PixelType var_u_unnorm = ssq_psearch - sum_psearch * sum_psearch / n;
if(var_u_unnorm < 1.0e-6)
var_u_unnorm = 1.0e-6;
PixelType locor = 0;
if ( sum_uv > 0 )
locor = ( -1.0 * (sum_uv * sum_uv) / var_u_unnorm );
else
locor = ( sum_uv * sum_uv ) / var_u_unnorm;
// - (\Sum u_i v_i)^2 / z, where z = sigma_v^2 * (n-1)
// locor = locor / ( insd * outsd );
if ( locor < bestcor )
{
segval = imageseg->GetPixel( ind2 );
for ( unsigned int i=0; i < intvec.size(); i++ ) {
bestvec[i] = outvec[i];
bestsegvec[i] = outsegvec[i];
}
bestcor = locor;
bestsd = outsd;
bestmean = outmean;
}
}
return Rcpp::List::create( Rcpp::Named("segval") = segval,
Rcpp::Named("values") = bestvec,
Rcpp::Named("bestmean") = bestmean,
Rcpp::Named("bestsd") = bestsd,
Rcpp::Named("bestcor") = bestcor,
Rcpp::Named("bestsegvec") = bestsegvec );
}
