本文整理汇总了C++中Mat::baseInterface方法的典型用法代码示例。如果您正苦于以下问题：C++ Mat::baseInterface方法的具体用法？C++ Mat::baseInterface怎么用？C++ Mat::baseInterface使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类Mat的用法示例。
在下文中一共展示了Mat::baseInterface方法的4个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C++代码示例。
示例1: volumeIntegration
double volumeIntegration(Mat<> f)
{
double sumf = f.baseInterface().sum();
return sumf/1000*comdata.deltax*comdata.deltay*comdata.deltaz;
}示例2: volumeIntegration
//
// volumeIntegration
//
double GeometricFlow::volumeIntegration(const Mat<>& f)
{
double sumf = f.baseInterface().sum();
return sumf/1000 * p_comdata.deltax() * p_comdata.deltay() * p_comdata.deltaz();
}示例3: pbsolver
//.........这里部分代码省略.........
eps2(i,j,k) = (eps(i,j+1,k) + eps(i,j,k))/2.0;
eps3(i,j,k) = (eps(i,j,k+1) + eps(i,j,k))/2.0;
}
}
}
std::vector <Eigen::Triplet<double> > tripletList;
tripletList.reserve(nx*ny*nz);
Eigen::VectorXd phi_flat(nx*ny*nz);
size_t n = nx*ny*nz;
for (size_t i = 1; i <= nx; ++i) {
for (size_t j = 1; j <= ny; ++j) {
for(size_t k = 1; k <= nz; ++k) {
size_t ijk = (i-1)*nz*ny + (j-1)*nz + k-1;
if (i==1 || i==nx || j==1 || j==ny || k==1 || k==nz) {
tripletList.push_back(Eigen::Triplet<double>(ijk, ijk, 1.0));
} else {
double f = -( (eps1(i,j,k) + eps1(i-1,j,k))/dx/dx
+ (eps2(i,j,k) + eps2(i,j-1,k))/dy/dy
+ (eps3(i,j,k) + eps3(i,j,k-1))/dz/dz );
tripletList.push_back(Eigen::Triplet<double>(ijk, ijk, f));
double weit[6];
weit[0] = eps1(i-1,j,k)/dx/dx;
weit[1] = eps2(i,j-1,k)/dy/dy;
weit[2] = eps3(i,j,k-1)/dz/dz;
weit[3] = eps3(i,j,k)/dz/dz;
weit[4] = eps2(i,j,k)/dy/dy;
weit[5] = eps1(i,j,k)/dx/dx;
assert(ijk > nz*ny);
size_t jj = ijk - nz*ny;
tripletList.push_back(Eigen::Triplet<double>(ijk, jj,
weit[0]));
assert(ijk > nz);
jj = ijk - nz;
tripletList.push_back(Eigen::Triplet<double>(ijk, jj,
weit[1]));
assert(ijk > 1);
jj = ijk - 1;
tripletList.push_back(Eigen::Triplet<double>(ijk, jj,
weit[2]));
assert(ijk + 1 < n);
jj = ijk + 1;
tripletList.push_back(Eigen::Triplet<double>(ijk, jj,
weit[3]));
assert(ijk + nz < n);
jj = ijk + nz;
tripletList.push_back(Eigen::Triplet<double>(ijk, jj,
weit[4]));
assert(ijk + nz*ny < n);
jj = ijk + nz*ny;
tripletList.push_back(Eigen::Triplet<double>(ijk, jj,
weit[5]));
}
phi_flat(ijk) = phi(i,j,k);
}
}
}
Eigen::SparseMatrix<double> A(n, n);
A.setFromTriplets(tripletList.begin(), tripletList.end());
A.makeCompressed();
//std::cout << "A: " << A << std::endl; ??
//
// bi conjugate gradient stabilized solver for sparse square problems.
// http://en.wikipedia.org/wiki/Biconjugate_gradient_method
//
Eigen::BiCGSTAB<Eigen::SparseMatrix<double>, Eigen::IdentityPreconditioner> solver(A);
solver.setMaxIterations(iter);
solver.setTolerance(tol);
// KTS Note -- I remember being here writing the unit tests, and thinking
// that this may have had something to do with the difference in the elec
// energies. I don't remember the details, but it's something to do with
// the Eigen solver being different from what is in the Fortran code.
// phi_flat = solver.solveWithGuess(bgf.baseInterface(), phi_flat);
// ERJ Note -- Fortran solver:
// http://sdphca.ucsd.edu/slatec_top/source/dsluom.f (DSLUOM is the
// Incomplete LU Orthomin Sparse Iterative Ax=b Solver.)
phi_flat = solver.solve(bgf.baseInterface());
//std::cout << "phi_flat: " << phi_flat << std::endl;
for(size_t i = 1; i <= nx; ++i) {
for(size_t j = 1; j <= ny; ++j) {
for(size_t k = 1; k <= nz; ++k) {
size_t ijk = (i-1)*nz*ny + (j-1)*nz + k-1;
phi(i,j,k) = phi_flat(ijk);
}
}
}
}示例4: pbsolver
void pbsolver(Mat<>& eps, Mat<>& phi, Mat<>& bgf, double dcel, double tol, int iter){
size_t nx = eps.nx(), ny = eps.ny(), nz = eps.nz();
double dx = comdata.deltax, dy = comdata.deltay, dz = comdata.deltaz;
Mat<> eps1(nx,ny,nz), eps2(nx,ny,nz), eps3(nx,ny,nz);
for(size_t i=1; i<nx; ++i){
for(size_t j=1; j<ny; ++j){
for(size_t k=1; k<nz; ++k){
eps1(i,j,k) = (eps(i+1,j,k) + eps(i,j,k))/2.0;
eps2(i,j,k) = (eps(i,j+1,k) + eps(i,j,k))/2.0;
eps3(i,j,k) = (eps(i,j,k+1) + eps(i,j,k))/2.0;
}}}
std::vector< Eigen::Triplet<double> > tripletList;
tripletList.reserve(nx*ny*nz);
Eigen::VectorXd phi_flat(nx*ny*nz);
size_t n = nx*ny*nz;
for(size_t i=1; i<=nx; ++i){
for(size_t j=1; j<=ny; ++j){
for(size_t k=1; k<=nz; ++k){
size_t ijk = (i-1)*nz*ny + (j-1)*nz + k-1;
if(i==1 || i==nx || j==1 || j==ny || k==1 || k==nz){
tripletList.push_back( Eigen::Triplet<double>(ijk, ijk, 1.0) );
}else{
double f = -( (eps1(i,j,k) + eps1(i-1,j,k))/dx/dx
+ (eps2(i,j,k) + eps2(i,j-1,k))/dy/dy
+ (eps3(i,j,k) + eps3(i,j,k-1))/dz/dz );
tripletList.push_back( Eigen::Triplet<double>(ijk, ijk, f) );
double weit[6];
weit[0] = eps1(i-1,j,k)/dx/dx;
weit[1] = eps2(i,j-1,k)/dy/dy;
weit[2] = eps3(i,j,k-1)/dz/dz;
weit[3] = eps3(i,j,k)/dz/dz;
weit[4] = eps2(i,j,k)/dy/dy;
weit[5] = eps1(i,j,k)/dx/dx;
size_t jj = ijk - nz*ny;
if(jj>=0){ tripletList.push_back( Eigen::Triplet<double>(ijk, jj, weit[0]) ); }
jj = ijk - nz;
if(jj>=0){ tripletList.push_back( Eigen::Triplet<double>(ijk, jj, weit[1]) ); }
jj = ijk - 1;
if(jj>=0){ tripletList.push_back( Eigen::Triplet<double>(ijk, jj, weit[2]) ); }
jj = ijk + 1;
if(jj<n){ tripletList.push_back( Eigen::Triplet<double>(ijk, jj, weit[3]) ); }
jj = ijk + nz;
if(jj<n){ tripletList.push_back( Eigen::Triplet<double>(ijk, jj, weit[4]) ); }
jj = ijk + nz*ny;
if(jj<n){ tripletList.push_back( Eigen::Triplet<double>(ijk, jj, weit[5]) ); }
}
phi_flat(ijk) = phi(i,j,k);
}}}
Eigen::SparseMatrix<double> A(n, n);
A.setFromTriplets(tripletList.begin(), tripletList.end());
A.makeCompressed();
Eigen::BiCGSTAB<Eigen::SparseMatrix<double>, Eigen::IdentityPreconditioner > solver(A);
solver.setMaxIterations(iter);
solver.setTolerance(tol);
phi_flat = solver.solveWithGuess(bgf.baseInterface(), phi_flat);
for(size_t i=1; i<=nx; ++i){
for(size_t j=1; j<=ny; ++j){
for(size_t k=1; k<=nz; ++k){
size_t ijk = (i-1)*nz*ny + (j-1)*nz + k-1;
phi(i,j,k) = phi_flat(ijk);
}}}
}