SEXP sccanCppHelper( NumericMatrix X, NumericMatrix Y, SEXP r_maskx, SEXP r_masky, RealType sparsenessx, RealType sparsenessy, IntType nvecs, IntType its, IntType cthreshx, IntType cthreshy, RealType z, RealType smooth, Rcpp::List initializationListx, Rcpp::List initializationListy, IntType covering, RealType ell1, IntType verbose, RealType priorWeight ) { enum { Dimension = ImageType::ImageDimension }; typename ImageType::RegionType region; typedef typename ImageType::PixelType PixelType; typedef typename ImageType::Pointer ImagePointerType; typedef double Scalar; typedef itk::ants::antsSCCANObject<ImageType, Scalar> SCCANType; typedef typename SCCANType::MatrixType vMatrix; typename SCCANType::Pointer sccanobj = SCCANType::New(); typename ImageType::Pointer maskx = Rcpp::as<ImagePointerType>( r_maskx ); typename ImageType::Pointer masky = Rcpp::as<ImagePointerType>( r_masky ); bool maskxisnull = maskx.IsNull(); bool maskyisnull = masky.IsNull(); // deal with the initializationList, if any unsigned int nImagesx = initializationListx.size(); if ( ( nImagesx > 0 ) && ( !maskxisnull ) ) { itk::ImageRegionIteratorWithIndex<ImageType> it( maskx, maskx->GetLargestPossibleRegion() ); vMatrix priorROIMatx( nImagesx , X.cols() ); priorROIMatx.fill( 0 ); for ( unsigned int i = 0; i < nImagesx; i++ ) { typename ImageType::Pointer init = Rcpp::as<ImagePointerType>( initializationListx[i] ); unsigned long ct = 0; it.GoToBegin(); while ( !it.IsAtEnd() ) { PixelType pix = it.Get(); if ( pix >= 0.5 ) { pix = init->GetPixel( it.GetIndex() ); priorROIMatx( i, ct ) = pix; ct++; } ++it; } } sccanobj->SetMatrixPriorROI( priorROIMatx ); nvecs = nImagesx; } unsigned int nImagesy = initializationListy.size(); if ( ( nImagesy > 0 ) && ( !maskyisnull ) ) { itk::ImageRegionIteratorWithIndex<ImageType> it( masky, masky->GetLargestPossibleRegion() ); vMatrix priorROIMaty( nImagesy , Y.cols() ); priorROIMaty.fill( 0 ); for ( unsigned int i = 0; i < nImagesy; i++ ) { typename ImageType::Pointer init = Rcpp::as<ImagePointerType>( initializationListy[i] ); unsigned long ct = 0; it.GoToBegin(); while ( !it.IsAtEnd() ) { PixelType pix = it.Get(); if ( pix >= 0.5 ) { pix = init->GetPixel( it.GetIndex() ); priorROIMaty( i, ct ) = pix; ct++; } ++it; } } sccanobj->SetMatrixPriorROI2( priorROIMaty ); nvecs = nImagesy; } sccanobj->SetPriorWeight( priorWeight ); sccanobj->SetLambda( priorWeight ); // cast hack from Rcpp type to sccan type std::vector<double> xdat = Rcpp::as< std::vector<double> >( X ); const double* _xdata = &xdat[0]; vMatrix vnlX( _xdata , X.cols(), X.rows() ); vnlX = vnlX.transpose(); std::vector<double> ydat = Rcpp::as< std::vector<double> >( Y ); const double* _ydata = &ydat[0]; vMatrix vnlY( _ydata , Y.cols(), Y.rows() ); vnlY = vnlY.transpose(); // cast hack done sccanobj->SetGetSmall( false ); sccanobj->SetCovering( covering ); sccanobj->SetSilent( ! verbose ); if( ell1 > 0 ) { sccanobj->SetUseL1( true ); } else { sccanobj->SetUseL1( false ); } sccanobj->SetGradStep( vnl_math_abs( ell1 ) ); sccanobj->SetMaximumNumberOfIterations( its ); sccanobj->SetRowSparseness( z ); sccanobj->SetSmoother( smooth ); if ( sparsenessx < 0 ) sccanobj->SetKeepPositiveP(false); if ( sparsenessy < 0 ) sccanobj->SetKeepPositiveQ(false); sccanobj->SetSCCANFormulation( SCCANType::PQ ); sccanobj->SetFractionNonZeroP( fabs( sparsenessx ) ); sccanobj->SetFractionNonZeroQ( fabs( sparsenessy ) ); sccanobj->SetMinClusterSizeP( cthreshx ); sccanobj->SetMinClusterSizeQ( cthreshy ); sccanobj->SetMatrixP( vnlX ); sccanobj->SetMatrixQ( vnlY ); // sccanobj->SetMatrixR( r ); // FIXME sccanobj->SetMaskImageP( maskx ); sccanobj->SetMaskImageQ( masky ); sccanobj->SparsePartialArnoldiCCA( nvecs ); // FIXME - should not copy, should map memory vMatrix solP = sccanobj->GetVariatesP(); NumericMatrix eanatMatp( solP.cols(), solP.rows() ); unsigned long rows = solP.rows(); for( unsigned long c = 0; c < solP.cols(); c++ ) { for( unsigned int r = 0; r < rows; r++ ) { eanatMatp( c, r ) = solP( r, c ); } } vMatrix solQ = sccanobj->GetVariatesQ(); NumericMatrix eanatMatq( solQ.cols(), solQ.rows() ); rows = solQ.rows(); for( unsigned long c = 0; c < solQ.cols(); c++ ) { for( unsigned int r = 0; r < rows; r++ ) { eanatMatq( c, r ) = solQ( r, c ); } } return( Rcpp::List::create( Rcpp::Named("eig1") = eanatMatp, Rcpp::Named("eig2") = eanatMatq ) ); }
SEXP eigenanatomyCppHelper( NumericMatrix X, SEXP r_mask, RealType sparseness, IntType nvecs, IntType its, IntType cthresh, RealType z, RealType smooth, // NumericMatrix initializationMatrix, Rcpp::List initializationList, IntType covering, RealType ell1, IntType verbose, IntType powerit, RealType priorWeight, IntType useMaxBasedThresh ) { enum { Dimension = ImageType::ImageDimension }; typename ImageType::RegionType region; typedef typename ImageType::PixelType PixelType; typedef typename ImageType::Pointer ImagePointerType; typedef double Scalar; typedef itk::ants::antsSCCANObject<ImageType, Scalar> SCCANType; typedef typename SCCANType::MatrixType vMatrix; typename SCCANType::Pointer sccanobj = SCCANType::New(); typename ImageType::Pointer mask = Rcpp::as<ImagePointerType>( r_mask ); bool maskisnull = mask.IsNull(); // deal with the initializationList, if any unsigned int nImages = initializationList.size(); if ( ( nImages > 0 ) && ( !maskisnull ) ) { itk::ImageRegionIteratorWithIndex<ImageType> it( mask, mask->GetLargestPossibleRegion() ); vMatrix priorROIMat( nImages , X.cols() ); priorROIMat.fill( 0 ); for ( unsigned int i = 0; i < nImages; i++ ) { typename ImageType::Pointer init = Rcpp::as<ImagePointerType>( initializationList[i] ); unsigned long ct = 0; it.GoToBegin(); while ( !it.IsAtEnd() ) { PixelType pix = it.Get(); if ( pix >= 0.5 ) { pix = init->GetPixel( it.GetIndex() ); priorROIMat( i, ct ) = pix; ct++; } ++it; } } sccanobj->SetMatrixPriorROI( priorROIMat ); nvecs = nImages; } sccanobj->SetPriorWeight( priorWeight ); sccanobj->SetLambda( priorWeight ); sccanobj->SetMaxBasedThresholding( useMaxBasedThresh ); // cast hack from Rcpp type to sccan type std::vector<double> xdat = Rcpp::as< std::vector<double> >( X ); const double* _xdata = &xdat[0]; vMatrix vnlX( _xdata , X.cols(), X.rows() ); vnlX = vnlX.transpose(); sccanobj->SetGetSmall( false ); vMatrix priorROIMat; // sccanobj->SetMatrixPriorROI( priorROIMat); // sccanobj->SetMatrixPriorROI2( priorROIMat ); sccanobj->SetCovering( covering ); sccanobj->SetSilent( ! verbose ); if( ell1 > 0 ) { sccanobj->SetUseL1( true ); } else { sccanobj->SetUseL1( false ); } sccanobj->SetGradStep( std::abs( ell1 ) ); sccanobj->SetMaximumNumberOfIterations( its ); sccanobj->SetRowSparseness( z ); sccanobj->SetSmoother( smooth ); if ( sparseness < 0 ) sccanobj->SetKeepPositiveP(false); sccanobj->SetSCCANFormulation( SCCANType::PQ ); sccanobj->SetFractionNonZeroP( fabs( sparseness ) ); sccanobj->SetMinClusterSizeP( cthresh ); sccanobj->SetMatrixP( vnlX ); // sccanobj->SetMatrixR( r ); // FIXME sccanobj->SetMaskImageP( mask ); RealType truecorr = 0; if( powerit == 1 ) { truecorr = sccanobj->SparseReconHome( nvecs ); } else if ( priorWeight > 1.e-12 ) truecorr = sccanobj->SparseReconPrior( nvecs, true ); else truecorr = sccanobj->SparseRecon(nvecs); /* else if( powerit != 0 ) { truecorr = sccanobj->SparseArnoldiSVD(nvecs); } else if( svd_option == 4 ) { truecorr = sccanobj->NetworkDecomposition( nvecs ); } else if( svd_option == 5 ) { truecorr = sccanobj->LASSO( nvecs ); } else if( svd_option == 2 ) { truecorr = sccanobj->CGSPCA(nvecs); // cgspca } else if( svd_option == 6 ) { truecorr = sccanobj->SparseRecon(nvecs); // sparse (default) } else if( svd_option == 7 ) { // sccanobj->SetPriorScaleMat( priorScaleMat); sccanobj->SetMatrixPriorROI( priorROIMat); sccanobj->SetFlagForSort(); sccanobj->SetLambda(sccanparser->Convert<double>( option->GetFunction( 0 )->GetParameter( 3 ) ) ); truecorr = sccanobj->SparseReconPrior(nvecs, true); // Prior } else { truecorr = sccanobj->SparseArnoldiSVDGreedy( nvecs ); // sparse (default) } */ // solutions should be much smaller so may not be a big deal to copy // FIXME - should not copy, should map memory vMatrix solV = sccanobj->GetVariatesP(); NumericMatrix eanatMat( solV.cols(), solV.rows() ); unsigned long rows = solV.rows(); for( unsigned long c = 0; c < solV.cols(); c++ ) { for( unsigned int r = 0; r < rows; r++ ) { eanatMat( c, r ) = solV( r, c ); } } vMatrix solU = sccanobj->GetMatrixU(); NumericMatrix eanatMatU( solU.rows(), solU.cols() ); rows = solU.rows(); for( unsigned long c = 0; c < solU.cols(); c++ ) { for( unsigned int r = 0; r < rows; r++ ) { eanatMatU( r, c) = solU( r, c); } } return( Rcpp::List::create( Rcpp::Named("eigenanatomyimages") = eanatMat, Rcpp::Named("umatrix") = eanatMatU, Rcpp::Named("varex") = truecorr ) ); }