//Function to summarize results from BeQTL and return a dataframe
// [[Rcpp::export]]
Rcpp::DataFrame SumRes(const arma::mat & cormat, const arma::mat & errmat, const Rcpp::DataFrame SnpDF, const Rcpp::DataFrame Genedf, const int samplesize, const double tcutoff){
Rcpp::Rcout<<"Generating Tmat"<<std::endl;
arma::mat tmat = sqrt(samplesize-2)*(cormat/(sqrt(1-square(cormat))));
Rcpp::Rcout<<"Finding strong t"<<std::endl;
arma::uvec goods = find(abs(tmat)>tcutoff);
Rcpp::Rcout<<"Generating matrix index"<<std::endl;
arma::umat goodmat = Ind(tmat.n_rows,goods);
Rcpp::Rcout<<"Subsetting tmat"<<std::endl;
Rcpp::Rcout<<"This many good results"<<goodmat.n_rows<<std::endl;
arma::vec tvec = tmat(goods);
Rcpp::Rcout<<"Subsetting Errmat"<<std::endl;
arma::vec errvec = errmat(goods);
Rcpp::Rcout<<"Generating SNP and Gene lists"<<std::endl;
Rcpp::IntegerVector GoodGenes = Rcpp::wrap(arma::conv_to<arma::ivec>::from(goodmat.col(0)));
Rcpp::IntegerVector GoodSNPs = Rcpp::wrap(arma::conv_to<arma::ivec>::from(goodmat.col(1)));
//Subset SNP anno
Rcpp::CharacterVector SNPnames = SnpDF["rsid"];
SNPnames = SNPnames[GoodSNPs];
arma::ivec SNPchr = Rcpp::as<arma::ivec>(SnpDF["Chrom"]);
SNPchr = SNPchr(goodmat.col(1));
arma::ivec SNPpos = Rcpp::as<arma::ivec>(SnpDF["Pos"]);
SNPpos = SNPpos(goodmat.col(1));
//Subset Geneanno
Rcpp::CharacterVector GeneNames = Genedf["Symbol"];
GeneNames = GeneNames[GoodGenes];
arma::ivec Genechr = Rcpp::as<arma::ivec>(Genedf["Chrom"]);
Genechr = Genechr(goodmat.col(0));
arma::ivec Genestart = Rcpp::as<arma::ivec>(Genedf["Start"]);
Genestart = Genestart(goodmat.col(0));
arma::ivec Genestop = Rcpp::as<arma::ivec>(Genedf["Stop"]);
Genestop = Genestop(goodmat.col(0));
arma::ivec CisDist(GoodGenes.length());
Rcpp::Rcout<<"Calculating Cisdist"<<std::endl;
CisDist = arma::min(arma::join_cols(abs(Genestop-SNPpos),abs(Genestart-SNPpos)),1);
Rcpp::Rcout<<"CisDist Calculated"<<std::endl;
CisDist.elem(find(Genechr!=SNPchr)).fill(-1);
return Rcpp::DataFrame::create(Rcpp::Named("SNP")=SNPnames,
Rcpp::Named("Gene")=GeneNames,
Rcpp::Named("t-stat")=Rcpp::wrap(tvec),
Rcpp::Named("err")=Rcpp::wrap(errvec),
Rcpp::Named("CisDist")=Rcpp::wrap(CisDist));
}
//==============================================================================
Ifpack_DropFilter::Ifpack_DropFilter(const Teuchos::RefCountPtr<Epetra_RowMatrix>& Matrix,
double DropTol) :
A_(Matrix),
DropTol_(DropTol),
MaxNumEntries_(0),
MaxNumEntriesA_(0),
NumNonzeros_(0)
{
// use this filter only on serial matrices
if (A_->Comm().NumProc() != 1) {
cerr << "Ifpack_DropFilter can be used with Comm().NumProc() == 1" << endl;
cerr << "only. This class is a tool for Ifpack_AdditiveSchwarz," << endl;
cerr << "and it is not meant to be used otherwise." << endl;
exit(EXIT_FAILURE);
}
if ((A_->NumMyRows() != A_->NumGlobalRows()) ||
(A_->NumMyRows() != A_->NumMyCols()))
IFPACK_CHK_ERRV(-2);
NumRows_ = A_->NumMyRows();
MaxNumEntriesA_ = A_->MaxNumEntries();
NumEntries_.resize(NumRows_);
Indices_.resize(MaxNumEntriesA_);
Values_.resize(MaxNumEntriesA_);
vector<int> Ind(MaxNumEntriesA_);
vector<double> Val(MaxNumEntriesA_);
for (int i = 0 ; i < NumRows_ ; ++i) {
NumEntries_[i] = MaxNumEntriesA_;
int Nnz;
IFPACK_CHK_ERRV(ExtractMyRowCopy(i,MaxNumEntriesA_,Nnz,
&Val[0], &Ind[0]));
NumEntries_[i] = Nnz;
NumNonzeros_ += Nnz;
if (Nnz > MaxNumEntries_)
MaxNumEntries_ = Nnz;
}
}
cv::Mat PM_type::nms(const cv::Mat &boxes, float overlap)
//% Non-maximum suppression.
//% pick = nms(boxes, overlap)
//%
//% Greedily select high-scoring detections and skip detections that are
//% significantly covered by a previously selected detection.
//%
//% Return value
//% pick Indices of locally maximal detections
//%
//% Arguments
//% boxes Detection bounding boxes (see pascal_test.m)
//% overlap Overlap threshold for suppression
//% For a selected box Bi, all boxes Bj that are covered by
//% more than overlap are suppressed. Note that 'covered' is
//% is |Bi \cap Bj| / |Bj|, not the PASCAL intersection over
//% union measure.
{
if( boxes.empty() )
return boxes;
cv::Mat x1 = boxes.col(0);
cv::Mat y1 = boxes.col(1);
cv::Mat x2 = boxes.col(2);
cv::Mat y2 = boxes.col(3);
cv::Mat s = boxes.col(boxes.cols - 1);
cv::Mat area = x2 - x1 + 1;
area = area.mul(y2-y1+1);
vector<int> Ind( s.rows, 0 );
cv::Mat Idx(s.rows, 1, CV_32SC1, &Ind[0]);
sortIdx( s, Idx, CV_SORT_EVERY_COLUMN+CV_SORT_ASCENDING );
vector<int> pick;
while( !Ind.empty() ){
int last = Ind.size() - 1;
int i = Ind[last];
pick.push_back(i);
vector<int> suppress( 1, last );
for( int pos=0; pos<last; pos++ ){
int j = Ind[pos];
float xx1 = std::max(x1.at<float>(i), x1.at<float>(j));
float yy1 = std::max(y1.at<float>(i), y1.at<float>(j));
float xx2 = std::min(x2.at<float>(i), x2.at<float>(j));
float yy2 = std::min(y2.at<float>(i), y2.at<float>(j));
float w = xx2-xx1+1;
float h = yy2-yy1+1;
if( w>0 && h>0 ){
// compute overlap
float area_intersection = w * h;
float o1 = area_intersection / area.at<float>(j);
float o2 = area_intersection / area.at<float>(i);
float o = std::max(o1,o2);
if( o>overlap )
suppress.push_back(pos);
}
}
std::set<int> supp( suppress.begin(), suppress.end() );
vector<int> Ind2;
for( int i=0; i!=Ind.size(); i++ ){
if( supp.find(i)==supp.end() )
Ind2.push_back( Ind[i] );
}
Ind = Ind2;
}
cv::Mat ret(pick.size(), boxes.cols, boxes.type());
for( unsigned i=0; i<pick.size(); i++ )
boxes.row( pick[i] ).copyTo( ret.row(i) );
return ret;
}
