IGL_INLINE void igl::cat(
const int dim,
const Eigen::SparseMatrix<Scalar> & A,
const Eigen::SparseMatrix<Scalar> & B,
Eigen::SparseMatrix<Scalar> & C)
{
assert(dim == 1 || dim == 2);
using namespace Eigen;
// Special case if B or A is empty
if(A.size() == 0)
{
C = B;
return;
}
if(B.size() == 0)
{
C = A;
return;
}
DynamicSparseMatrix<Scalar, RowMajor> dyn_C;
if(dim == 1)
{
assert(A.cols() == B.cols());
dyn_C.resize(A.rows()+B.rows(),A.cols());
}else if(dim == 2)
{
assert(A.rows() == B.rows());
dyn_C.resize(A.rows(),A.cols()+B.cols());
}else
{
fprintf(stderr,"cat.h: Error: Unsupported dimension %d\n",dim);
}
dyn_C.reserve(A.nonZeros()+B.nonZeros());
// Iterate over outside of A
for(int k=0; k<A.outerSize(); ++k)
{
// Iterate over inside
for(typename SparseMatrix<Scalar>::InnerIterator it (A,k); it; ++it)
{
dyn_C.coeffRef(it.row(),it.col()) += it.value();
}
}
// Iterate over outside of B
for(int k=0; k<B.outerSize(); ++k)
{
// Iterate over inside
for(typename SparseMatrix<Scalar>::InnerIterator it (B,k); it; ++it)
{
int r = (dim == 1 ? A.rows()+it.row() : it.row());
int c = (dim == 2 ? A.cols()+it.col() : it.col());
dyn_C.coeffRef(r,c) += it.value();
}
}
C = SparseMatrix<Scalar>(dyn_C);
}
Eigen::VectorXd KronProdSPMat2(
Eigen::SparseMatrix<double, Eigen::RowMajor> a0,
Eigen::SparseMatrix<double, Eigen::RowMajor> a1,
Eigen::VectorXd y) {
signal(SIGSEGV, handler); // install our handler
Eigen::VectorXd retvec;
retvec.setZero( a0.rows() * a1.rows() );
//loop rows a0
for (int row_idx0=0; row_idx0<a0.outerSize(); ++row_idx0) {
int row_offset1 = row_idx0;
row_offset1 *= a1.rows();
// loop rows a1
for (int row_idx1=0; row_idx1<a1.outerSize(); ++row_idx1) {
// loop cols a0 (non-zero elements only)
for (Eigen::SparseMatrix<double,RowMajor>::InnerIterator it0(a0,row_idx0); it0; ++it0) {
int col_offset1 = it0.index();
col_offset1 *= a1.innerSize();
double factor1 = it0.value();
for (Eigen::SparseMatrix<double,RowMajor>::InnerIterator it1(a1,row_idx1); it1; ++it1) {
retvec( row_offset1 + row_idx1 ) += factor1 * it1.value() * y( col_offset1 + it1.index() );
}
}
}
}
return retvec;
}
IGL_INLINE void igl::matlab::prepare_lhs_double(
const Eigen::SparseMatrix<Vtype> & M,
mxArray *plhs[])
{
using namespace std;
const int m = M.rows();
const int n = M.cols();
// THIS WILL NOT WORK FOR ROW-MAJOR
assert(n==M.outerSize());
const int nzmax = M.nonZeros();
plhs[0] = mxCreateSparse(m, n, nzmax, mxREAL);
mxArray * mx_data = plhs[0];
// Copy data immediately
double * pr = mxGetPr(mx_data);
mwIndex * ir = mxGetIr(mx_data);
mwIndex * jc = mxGetJc(mx_data);
// Iterate over outside
int k = 0;
for(int j=0; j<M.outerSize(); j++)
{
jc[j] = k;
// Iterate over inside
for(typename Eigen::SparseMatrix<Vtype>::InnerIterator it (M,j); it; ++it)
{
// copy (cast to double)
pr[k] = it.value();
ir[k] = it.row();
k++;
}
}
jc[M.outerSize()] = k;
}
Eigen::VectorXd KronProdSPMat4(
Eigen::SparseMatrix<double, Eigen::RowMajor> a0,
Eigen::SparseMatrix<double, Eigen::RowMajor> a1,
Eigen::SparseMatrix<double, Eigen::RowMajor> a2,
Eigen::SparseMatrix<double, Eigen::RowMajor> a3,
Eigen::VectorXd y) {
signal(SIGSEGV, handler); // install our handler
Eigen::VectorXd retvec;
retvec.setZero( a0.rows() * a1.rows() * a2.rows() * a3.rows() );
//loop rows a0
for (int row_idx0=0; row_idx0<a0.outerSize(); ++row_idx0) {
int row_offset1 = row_idx0;
row_offset1 *= a1.rows();
// loop rows a1
for (int row_idx1=0; row_idx1<a1.outerSize(); ++row_idx1) {
int row_offset2 = row_offset1 + row_idx1;
row_offset2 *= a2.rows();
// loop rows a2
for (int row_idx2=0; row_idx2<a2.outerSize(); ++row_idx2) {
int row_offset3 = row_offset2 + row_idx2;
row_offset3 *= a3.rows();
// loop rows a3
for (int row_idx3=0; row_idx3<a3.outerSize(); ++row_idx3) {
// loop cols a0 (non-zero elements only)
for (Eigen::SparseMatrix<double,RowMajor>::InnerIterator it0(a0,row_idx0); it0; ++it0) {
int col_offset1 = it0.index();
col_offset1 *= a1.innerSize();
double factor1 = it0.value();
// loop cols a1
for (Eigen::SparseMatrix<double,RowMajor>::InnerIterator it1(a1,row_idx1); it1; ++it1) {
int col_offset2 = col_offset1 + it1.index();
col_offset2 *= a2.innerSize();
double factor2 = factor1 * it1.value();
//loop cols a2
for (Eigen::SparseMatrix<double,RowMajor>::InnerIterator it2(a2,row_idx2); it2; ++it2) {
int col_offset3 = col_offset2 + it2.index();
col_offset3 *= a3.innerSize();
double factor3 = factor2 * it2.value();
for (Eigen::SparseMatrix<double,RowMajor>::InnerIterator it3(a3,row_idx3); it3; ++it3) {
retvec( row_offset3 + row_idx3 ) += factor3 * it3.value() * y( col_offset3 + it3.index() );
}
}
}
}
}
}
}
}
return retvec;
}
IGL_INLINE void igl::find(
const Eigen::SparseMatrix<T>& X,
Eigen::DenseBase<DerivedI> & I,
Eigen::DenseBase<DerivedJ> & J,
Eigen::DenseBase<DerivedV> & V)
{
// Resize outputs to fit nonzeros
I.derived().resize(X.nonZeros(),1);
J.derived().resize(X.nonZeros(),1);
V.derived().resize(X.nonZeros(),1);
int i = 0;
// Iterate over outside
for(int k=0; k<X.outerSize(); ++k)
{
// Iterate over inside
for(typename Eigen::SparseMatrix<T>::InnerIterator it (X,k); it; ++it)
{
V(i) = it.value();
I(i) = it.row();
J(i) = it.col();
i++;
}
}
}
void ProbitNoise::evalModel(Eigen::SparseMatrix<double> & Ytest, const int n, Eigen::VectorXd & predictions, Eigen::VectorXd & predictions_var, const Eigen::MatrixXd &cols, const Eigen::MatrixXd &rows, double mean_rating) {
const unsigned N = Ytest.nonZeros();
Eigen::VectorXd pred(N);
Eigen::VectorXd test(N);
// #pragma omp parallel for schedule(dynamic,8) reduction(+:se, se_avg) <- dark magic :)
for (int k = 0; k < Ytest.outerSize(); ++k) {
int idx = Ytest.outerIndexPtr()[k];
for (Eigen::SparseMatrix<double>::InnerIterator it(Ytest,k); it; ++it) {
pred[idx] = nCDF(cols.col(it.col()).dot(rows.col(it.row())));
test[idx] = it.value();
// https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Online_algorithm
double pred_avg;
if (n == 0) {
pred_avg = pred[idx];
} else {
double delta = pred[idx] - predictions[idx];
pred_avg = (predictions[idx] + delta / (n + 1));
predictions_var[idx] += delta * (pred[idx] - pred_avg);
}
predictions[idx++] = pred_avg;
}
}
auc_test_onesample = auc(pred,test);
auc_test = auc(predictions, test);
}
void ApplyConstraints(Eigen::SparseMatrix<float>& K, const std::vector<Constraint>& constraints)
{
std::vector<int> indicesToConstraint;
for (std::vector<Constraint>::const_iterator it = constraints.begin(); it != constraints.end(); ++it)
{
if (it->type & Constraint::UX)
{
indicesToConstraint.push_back(2 * it->node + 0);
}
if (it->type & Constraint::UY)
{
indicesToConstraint.push_back(2 * it->node + 1);
}
}
for (int k = 0; k < K.outerSize(); ++k)
{
for (Eigen::SparseMatrix<float>::InnerIterator it(K, k); it; ++it)
{
for (std::vector<int>::iterator idit = indicesToConstraint.begin(); idit != indicesToConstraint.end(); ++idit)
{
SetConstraints(it, *idit);
}
}
}
}
IGL_INLINE void igl::slice_into(
const Eigen::SparseMatrix<T>& X,
const Eigen::Matrix<int,Eigen::Dynamic,1> & R,
const Eigen::Matrix<int,Eigen::Dynamic,1> & C,
Eigen::SparseMatrix<T>& Y)
{
int xm = X.rows();
int xn = X.cols();
assert(R.size() == xm);
assert(C.size() == xn);
#ifndef NDEBUG
int ym = Y.size();
int yn = Y.size();
assert(R.minCoeff() >= 0);
assert(R.maxCoeff() < ym);
assert(C.minCoeff() >= 0);
assert(C.maxCoeff() < yn);
#endif
// create temporary dynamic sparse matrix
Eigen::DynamicSparseMatrix<T, Eigen::RowMajor> dyn_Y(Y);
// Iterate over outside
for(int k=0; k<X.outerSize(); ++k)
{
// Iterate over inside
for(typename Eigen::SparseMatrix<T>::InnerIterator it (X,k); it; ++it)
{
dyn_Y.coeffRef(R(it.row()),C(it.col())) = it.value();
}
}
Y = Eigen::SparseMatrix<T>(dyn_Y);
}
// inserts the sparse matrix 'ins' into the sparse matrix 'original' in the place given by 'row' and 'col' integers
void insertSparseBlock(const Eigen::SparseMatrix<Scalar>& ins, Eigen::SparseMatrix<Scalar>& original, const unsigned int& row, const unsigned int& col)
{
for (int k=0; k<ins.outerSize(); ++k)
for (Eigen::SparseMatrix<Scalar>::InnerIterator iti(ins,k); iti; ++iti)
original.coeffRef(iti.row() + row, iti.col() + col) = iti.value();
original.makeCompressed();
}
double maxAbsCoeff(const Eigen::SparseMatrix<T> &mat) {
double maxval;
int count = 0;
for (int k = 0; k < mat.outerSize(); ++k) {
for (typename Eigen::SparseMatrix<T>::InnerIterator it(mat, k); it; ++it) {
maxval = count == 0 ? std::abs(it.value()) : std::max(std::abs(it.value()), maxval);
}
}
return maxval;
}
IGL_INLINE void igl::components(
const Eigen::SparseMatrix<AScalar> & A,
Eigen::PlainObjectBase<DerivedC> & C,
Eigen::PlainObjectBase<Derivedcounts> & counts)
{
using namespace Eigen;
using namespace std;
assert(A.rows() == A.cols() && "A should be square.");
const size_t n = A.rows();
Array<bool,Dynamic,1> seen = Array<bool,Dynamic,1>::Zero(n,1);
C.resize(n,1);
typename DerivedC::Scalar id = 0;
vector<typename Derivedcounts::Scalar> vcounts;
// breadth first search
for(int k=0; k<A.outerSize(); ++k)
{
if(seen(k))
{
continue;
}
queue<int> Q;
Q.push(k);
vcounts.push_back(0);
while(!Q.empty())
{
const int f = Q.front();
Q.pop();
if(seen(f))
{
continue;
}
seen(f) = true;
C(f,0) = id;
vcounts[id]++;
// Iterate over inside
for(typename SparseMatrix<AScalar>::InnerIterator it (A,f); it; ++it)
{
const int g = it.index();
if(!seen(g) && it.value())
{
Q.push(g);
}
}
}
id++;
}
assert((size_t) id == vcounts.size());
const size_t ncc = vcounts.size();
assert((size_t)C.maxCoeff()+1 == ncc);
counts.resize(ncc,1);
for(size_t i = 0;i<ncc;i++)
{
counts(i) = vcounts[i];
}
}
IGL_INLINE void igl::repdiag(
const Eigen::SparseMatrix<T>& A,
const int d,
Eigen::SparseMatrix<T>& B)
{
using namespace std;
using namespace Eigen;
int m = A.rows();
int n = A.cols();
vector<Triplet<T> > IJV;
IJV.reserve(A.nonZeros()*d);
// Loop outer level
for (int k=0; k<A.outerSize(); ++k)
{
// loop inner level
for (typename Eigen::SparseMatrix<T>::InnerIterator it(A,k); it; ++it)
{
for(int i = 0;i<d;i++)
{
IJV.push_back(Triplet<T>(i*m+it.row(),i*n+it.col(),it.value()));
}
}
}
B.resize(m*d,n*d);
B.setFromTriplets(IJV.begin(),IJV.end());
// Q: Why is this **Very** slow?
//int m = A.rows();
//int n = A.cols();
//B.resize(m*d,n*d);
//// Reserve enough space for new non zeros
//B.reserve(d*A.nonZeros());
//// loop over reps
//for(int i=0;i<d;i++)
//{
// // Loop outer level
// for (int k=0; k<A.outerSize(); ++k)
// {
// // loop inner level
// for (typename Eigen::SparseMatrix<T>::InnerIterator it(A,k); it; ++it)
// {
// B.insert(i*m+it.row(),i*n+it.col()) = it.value();
// }
// }
//}
//B.makeCompressed();
}
IGL_INLINE void igl::adjacency_matrix(
const Eigen::PlainObjectBase<DerivedF> & F,
Eigen::SparseMatrix<T>& A)
{
using namespace std;
using namespace Eigen;
typedef typename DerivedF::Scalar Index;
typedef Triplet<T> IJV;
vector<IJV > ijv;
ijv.reserve(F.size()*2);
// Loop over faces
for(int i = 0;i<F.rows();i++)
{
// Loop over this face
for(int j = 0;j<F.cols();j++)
{
// Get indices of edge: s --> d
Index s = F(i,j);
Index d = F(i,(j+1)%F.cols());
ijv.push_back(IJV(s,d,1));
ijv.push_back(IJV(d,s,1));
}
}
const Index n = F.maxCoeff()+1;
A.resize(n,n);
switch(F.cols())
{
case 3:
A.reserve(6*(F.maxCoeff()+1));
break;
case 4:
A.reserve(26*(F.maxCoeff()+1));
break;
}
A.setFromTriplets(ijv.begin(),ijv.end());
// Force all non-zeros to be one
// Iterate over outside
for(int k=0; k<A.outerSize(); ++k)
{
// Iterate over inside
for(typename Eigen::SparseMatrix<T>::InnerIterator it (A,k); it; ++it)
{
assert(it.value() != 0);
A.coeffRef(it.row(),it.col()) = 1;
}
}
}
IGL_INLINE void igl::harwell_boeing(
const Eigen::SparseMatrix<Scalar> & A,
int & num_rows,
std::vector<Scalar> & V,
std::vector<Index> & R,
std::vector<Index> & C)
{
num_rows = A.rows();
int num_cols = A.cols();
int nnz = A.nonZeros();
V.resize(nnz);
R.resize(nnz);
C.resize(num_cols+1);
// Assumes outersize is columns
assert(A.cols() == A.outerSize());
int column_pointer = 0;
int i = 0;
int k = 0;
// Iterate over outside
for(; k<A.outerSize(); ++k)
{
C[k] = column_pointer;
// Iterate over inside
for(typename Eigen::SparseMatrix<Scalar>::InnerIterator it (A,k); it; ++it)
{
V[i] = it.value();
R[i] = it.row();
i++;
// Also increment column pointer
column_pointer++;
}
}
// by convention C[num_cols] = nnz
C[k] = column_pointer;
}
Eigen::SparseMatrix<Type> kronecker(Eigen::SparseMatrix<Type> x,
Eigen::SparseMatrix<Type> y){
typedef Eigen::Triplet<Type> T;
typedef typename Eigen::SparseMatrix<Type>::InnerIterator Iterator;
std::vector<T> tripletList;
int n1=x.rows(),n2=x.cols(),n3=y.rows(),n4=y.cols();
int i,j,k,l;
// Loop over nonzeros of x
for (int cx=0; cx<x.outerSize(); cx++)
for (Iterator itx(x,cx); itx; ++itx)
// Loop over nonzeros of y
for (int cy=0; cy<y.outerSize(); cy++)
for (Iterator ity(y,cy); ity; ++ity)
{
i=itx.row();
j=itx.col();
k=ity.row();
l=ity.col();
tripletList.push_back(T(i*n3+k,j*n4+l, itx.value()*ity.value() ));
}
Eigen::SparseMatrix<Type> mat(n1*n3,n2*n4);
mat.setFromTriplets(tripletList.begin(), tripletList.end());
return mat;
}
IGL_INLINE void igl::full(
const Eigen::SparseMatrix<T> & A,
Eigen::PlainObjectBase<DerivedB>& B)
{
assert(false && "Obsolete. Just call B = Matrix(A)");
using namespace Eigen;
B = PlainObjectBase<DerivedB >::Zero(A.rows(),A.cols());
// Iterate over outside
for(int k=0; k<A.outerSize(); ++k)
{
// Iterate over inside
for(typename SparseMatrix<T>::InnerIterator it (A,k); it; ++it)
{
B(it.row(),it.col()) = it.value();
}
}
}
void Dirichlet_LIMSolver3D::prepareProblemData(std::vector<int>& hessRowIdx, std::vector<int>& hessColIdx)
{
const int numNodes = mesh->InitalVertices->rows();
const int numTets = mesh->Tetrahedra->rows();
Eigen::SparseMatrix<double> tempL;
Eigen::SparseMatrix<double> M;
igl::massmatrix(*mesh->InitalVertices,*mesh->Tetrahedra,igl::MASSMATRIX_TYPE_BARYCENTRIC, M);
igl::cotmatrix(*mesh->InitalVertices, *mesh->Tetrahedra,tempL);
tempL *= -1;
// Create L matrix
L.resize(numVariables,numVariables);
std::vector<Eigen::Triplet<double> > triplets;
for (int k=0;k<tempL.outerSize();++k)
{
for (Eigen::SparseMatrix<double>::InnerIterator it(tempL,k);it;++it)
{
int row = 3*it.row();
int col = 3*it.col();
triplets.push_back(Eigen::Triplet<double>(row,col,it.value()));
triplets.push_back(Eigen::Triplet<double>(row+1,col+1,it.value()));
triplets.push_back(Eigen::Triplet<double>(row+2,col+2,it.value()));
}
}
L.setFromTriplets(triplets.begin(), triplets.end());
TetrahedronVertexIdx.resize(12,mesh->Tetrahedra->rows());
for (int k=0;k<L.outerSize();++k)
{
for (Eigen::SparseMatrix<double>::InnerIterator it(L,k);it;++it)
{
int row = it.row();
int col = it.col();
// std::sort for upper triangule matrix
if(row <= col)
{
hessRowIdx.push_back(row);
hessColIdx.push_back(col);
}
}
}
}
inline std::unique_ptr<giss::VectorSparseMatrix> Eigen_to_giss(
Eigen::SparseMatrix<double> const &mat)
{
std::unique_ptr<giss::VectorSparseMatrix> ret(
new giss::VectorSparseMatrix(giss::SparseDescr(
mat.rows(), mat.cols())));
int nz = mat.nonZeros();
ret->reserve(nz);
for (int k=0; k<mat.outerSize(); ++k) {
for (Eigen::SparseMatrix<double>::InnerIterator it(mat,k); it; ++it) {
ret->set(it.row(), it.col(), it.value(), SparseMatrix::DuplicatePolicy::ADD);
}
}
return ret;
}
//// AdaptiveGaussianNoise ////
void AdaptiveGaussianNoise::init(const Eigen::SparseMatrix<double> &train, double mean_value) {
double se = 0.0;
#pragma omp parallel for schedule(dynamic, 4) reduction(+:se)
for (int k = 0; k < train.outerSize(); ++k) {
for (SparseMatrix<double>::InnerIterator it(train,k); it; ++it) {
se += square(it.value() - mean_value);
}
}
var_total = se / train.nonZeros();
if (var_total <= 0.0 || std::isnan(var_total)) {
// if var cannot be computed using 1.0
var_total = 1.0;
}
// Var(noise) = Var(total) / (SN + 1)
alpha = (sn_init + 1.0) / var_total;
alpha_max = (sn_max + 1.0) / var_total;
}
void Dirichlet_LIMSolver2D::prepareProblemData(std::vector<int>& hessRowIdx, std::vector<int>& hessColIdx)
{
const int numNodes = mesh->InitalVertices->rows();
const int numTriangles = mesh->Triangles->rows();
// create sparse laplacian matrix L
Eigen::SparseMatrix<double> tempL;
igl::cotmatrix(*mesh->InitalVertices, *mesh->Triangles,tempL);
tempL *= -1;
// Create L matrix
L.resize(numVariables,numVariables);
std::vector<Eigen::Triplet<double> > triplets;
for (int k=0;k<tempL.outerSize();++k)
{
for (Eigen::SparseMatrix<double>::InnerIterator it(tempL,k);it;++it)
{
int row = 2*it.row();
int col = 2*it.col();
triplets.push_back(Eigen::Triplet<double>(row,col,it.value()));
triplets.push_back(Eigen::Triplet<double>(row+1,col+1,it.value()));
}
}
L.setFromTriplets(triplets.begin(), triplets.end());
for (int k=0;k<L.outerSize();++k)
{
for (Eigen::SparseMatrix<double>::InnerIterator it(L,k);it;++it)
{
int row = it.row();
int col = it.col();
// std::sort for upper triangule matrix
if(row <= col)
{
hessRowIdx.push_back(row);
hessColIdx.push_back(col);
}
}
}
}
void compare(const Physika::SparseMatrix<mytype> &a, const Eigen::SparseMatrix<mytype> &b)
{
/*if (a.nonZeros() != b.nonZeros())
{
cout<<"a.nonzeros:"<<a.nonZeros()<<endl;
cout << "b.nonZeros:" << b.nonZeros() << endl;
cout << "correctness bad!" << endl;
return;
}*/
std::vector<Physika::Trituple<mytype>> v;
bool correctness = true;
for(unsigned int i = 0;i<a.rows();++i)
{
v = a.getRowElements(i);
for(unsigned int j=0;j<v.size();++j)
{
int row = v[j].row(), col = v[j].col();
mytype value = v[j].value();
if(b.coeff(row,col) != value)
{
cout<<"eror: "<<row<<' '<<col<<" value: psm "<<value<<" "<<b.coeff(row,col)<<endl;
correctness = false;
break;
}
}
}
for (unsigned int i = 0; i < b.outerSize();++i)
for (Eigen::SparseMatrix<mytype>::InnerIterator it(b, i); it; ++it)
{
if (it.value() != a(it.row(), it.col()))
{
cout << "eror: " << it.row() << ' ' << it.col() << " value: psm " << a(it.row(),it.col()) << " " << it.value() << endl;
correctness = false;
break;
}
}
if(correctness) cout<<"correctness OK!"<<endl;
else cout<<"correctness bad!"<<endl;
}
IGL_INLINE void igl::components(
const Eigen::SparseMatrix<AScalar> & A,
Eigen::PlainObjectBase<DerivedC> & C)
{
assert(A.rows() == A.cols());
using namespace Eigen;
// THIS IS DENSE:
//boost::adjacency_matrix<boost::undirectedS> bA(A.rows());
boost::adjacency_list<boost::vecS,boost::vecS,boost::undirectedS> bA(A.rows());
for(int j=0; j<A.outerSize();j++)
{
// Iterate over inside
for(typename SparseMatrix<AScalar>::InnerIterator it (A,j); it; ++it)
{
if(0 != it.value())
{
boost::add_edge(it.row(),it.col(),bA);
}
}
}
C.resize(A.rows(),1);
boost::connected_components(bA,C.data());
}
inline void igl::PlanarizerShapeUp<DerivedV, DerivedF>::assembleQ()
{
std::vector<Eigen::Triplet<typename DerivedV::Scalar> > tripletList;
// assemble the Ni matrix
assembleNi();
for (int fi = 0; fi< numF; fi++)
{
Eigen::SparseMatrix<typename DerivedV::Scalar > Sfi;
assembleSelector(fi, Sfi);
// the final matrix per face
Eigen::SparseMatrix<typename DerivedV::Scalar > Qi = weightsSqrt(fi)*Ni*Sfi;
// put it in the correct block of Q
// todo: this can be made faster by omitting the selector matrix
for (int k=0; k<Qi.outerSize(); ++k)
for (typename Eigen::SparseMatrix<typename DerivedV::Scalar >::InnerIterator it(Qi,k); it; ++it)
{
typename DerivedV::Scalar val = it.value();
int row = it.row();
int col = it.col();
tripletList.push_back(Eigen::Triplet<typename DerivedV::Scalar>(row+3*ni*fi,col,val));
}
}
Q.resize(3*ni*numF,3*numV);
Q.setFromTriplets(tripletList.begin(), tripletList.end());
// the actual lhs matrix is Q'*Q
// prefactor that matrix
solver.compute(Q.transpose()*Q);
if(solver.info()!=Eigen::Success)
{
std::cerr << "Cholesky failed - PlanarizerShapeUp.cpp" << std::endl;
assert(0);
}
}
IGL_INLINE void igl::sum(
const Eigen::SparseMatrix<T>& X,
const int dim,
Eigen::SparseVector<T>& S)
{
// dim must be 2 or 1
assert(dim == 1 || dim == 2);
// Get size of input
int m = X.rows();
int n = X.cols();
// resize output
if(dim==1)
{
S = Eigen::SparseVector<T>(n);
}else
{
S = Eigen::SparseVector<T>(m);
}
// Iterate over outside
for(int k=0; k<X.outerSize(); ++k)
{
// Iterate over inside
for(typename Eigen::SparseMatrix<T>::InnerIterator it (X,k); it; ++it)
{
if(dim == 1)
{
S.coeffRef(it.col()) += it.value();
}else
{
S.coeffRef(it.row()) += it.value();
}
}
}
}
void AdaptiveGaussianNoise::update(const Eigen::SparseMatrix<double> &train, double mean_value, std::vector< std::unique_ptr<Eigen::MatrixXd> > & samples)
{
double sumsq = 0.0;
MatrixXd & U = *samples[0];
MatrixXd & V = *samples[1];
#pragma omp parallel for schedule(dynamic, 4) reduction(+:sumsq)
for (int j = 0; j < train.outerSize(); j++) {
auto Vj = V.col(j);
for (SparseMatrix<double>::InnerIterator it(train, j); it; ++it) {
double Yhat = Vj.dot( U.col(it.row()) ) + mean_value;
sumsq += square(Yhat - it.value());
}
}
// (a0, b0) correspond to a prior of 1 sample of noise with full variance
double a0 = 0.5;
double b0 = 0.5 * var_total;
double aN = a0 + train.nonZeros() / 2.0;
double bN = b0 + sumsq / 2.0;
alpha = rgamma(aN, 1.0 / bN);
if (alpha > alpha_max) {
alpha = alpha_max;
}
}
void LSConformal_LIMSolver2D::prepareProblemData(std::vector<int>& hessRowIdx, std::vector<int>& hessColIdx)
{
const int numNodes = mesh->InitalVertices->rows();
const int numTriangles = mesh->Triangles->rows();
// create sparse laplacian matrix L
Eigen::SparseMatrix<double> tempL;//(numNodes,numNodes);
igl::cotmatrix(*mesh->InitalVertices, *mesh->Triangles,tempL);
tempL *= -1;
// Create L matrix
L.resize(numVariables,numVariables);
std::vector<Eigen::Triplet<double> > triplets;
for (int k=0;k<tempL.outerSize();++k)
{
for (Eigen::SparseMatrix<double>::InnerIterator it(tempL,k);it;++it)
{
int row = 2*it.row();
int col = 2*it.col();
triplets.push_back(Eigen::Triplet<double>(row,col,it.value()));
triplets.push_back(Eigen::Triplet<double>(row+1,col+1,it.value()));
}
}
L.setFromTriplets(triplets.begin(), triplets.end());
// Create A matrix
int numBorderVertices = mesh->BorderVertices->rows();
Eigen::SparseMatrix<double> B, C, BNonZero, CNonZero;
A.resize(2*numNodes,2*numNodes);
B.resize(2*numBorderVertices, 2*numNodes);
C.resize(2*numBorderVertices, 2*numNodes);
std::vector<Eigen::Triplet<double> > tripletsB, tripletsC;
for(int i=0,j=numBorderVertices-1;i<mesh->BorderVertices->rows();j=i,i++)
{
int rIdx = 2*i;
int iIdx = 2*mesh->BorderVertices->coeff(i);
int jIdx = 2*mesh->BorderVertices->coeff(j);
tripletsB.push_back(Eigen::Triplet<double>(rIdx,jIdx,1));
tripletsB.push_back(Eigen::Triplet<double>(rIdx+1,jIdx+1,-1));
tripletsC.push_back(Eigen::Triplet<double>(rIdx,iIdx+1,1));
tripletsC.push_back(Eigen::Triplet<double>(rIdx+1,iIdx,1));
}
B.setFromTriplets(tripletsB.begin(), tripletsB.end());
C.setFromTriplets(tripletsC.begin(), tripletsC.end());
A = B.transpose()*C;
Eigen::SparseMatrix<double> AT = A.transpose();
L = L - 0.5*(A+AT);
for (int k=0;k<L.outerSize();++k)
{
for (Eigen::SparseMatrix<double>::InnerIterator it(L,k);it;++it)
{
int row = it.row();
int col = it.col();
// std::sort for upper triangule matrix
if(row <= col)
{
hessRowIdx.push_back(row);
hessColIdx.push_back(col);
}
}
}
}
IGL_INLINE void igl::slice(
const Eigen::SparseMatrix<TX>& X,
const Eigen::Matrix<int,Eigen::Dynamic,1> & R,
const Eigen::Matrix<int,Eigen::Dynamic,1> & C,
Eigen::SparseMatrix<TY>& Y)
{
#if 1
int xm = X.rows();
int xn = X.cols();
int ym = R.size();
int yn = C.size();
// special case when R or C is empty
if(ym == 0 || yn == 0)
{
Y.resize(ym,yn);
return;
}
assert(R.minCoeff() >= 0);
assert(R.maxCoeff() < xm);
assert(C.minCoeff() >= 0);
assert(C.maxCoeff() < xn);
// Build reindexing maps for columns and rows, -1 means not in map
std::vector<std::vector<int> > RI;
RI.resize(xm);
for(int i = 0;i<ym;i++)
{
RI[R(i)].push_back(i);
}
std::vector<std::vector<int> > CI;
CI.resize(xn);
// initialize to -1
for(int i = 0;i<yn;i++)
{
CI[C(i)].push_back(i);
}
// Resize output
Eigen::DynamicSparseMatrix<TY, Eigen::RowMajor> dyn_Y(ym,yn);
// Take a guess at the number of nonzeros (this assumes uniform distribution
// not banded or heavily diagonal)
dyn_Y.reserve((X.nonZeros()/(X.rows()*X.cols())) * (ym*yn));
// Iterate over outside
for(int k=0; k<X.outerSize(); ++k)
{
// Iterate over inside
for(typename Eigen::SparseMatrix<TX>::InnerIterator it (X,k); it; ++it)
{
std::vector<int>::iterator rit, cit;
for(rit = RI[it.row()].begin();rit != RI[it.row()].end(); rit++)
{
for(cit = CI[it.col()].begin();cit != CI[it.col()].end(); cit++)
{
dyn_Y.coeffRef(*rit,*cit) = it.value();
}
}
}
}
Y = Eigen::SparseMatrix<TY>(dyn_Y);
#else
// Alec: This is _not_ valid for arbitrary R,C since they don't necessary
// representation a strict permutation of the rows and columns: rows or
// columns could be removed or replicated. The removal of rows seems to be
// handled here (although it's not clear if there is a performance gain when
// the #removals >> #remains). If this is sufficiently faster than the
// correct code above, one could test whether all entries in R and C are
// unique and apply the permutation version if appropriate.
//
int xm = X.rows();
int xn = X.cols();
int ym = R.size();
int yn = C.size();
// special case when R or C is empty
if(ym == 0 || yn == 0)
{
Y.resize(ym,yn);
return;
}
assert(R.minCoeff() >= 0);
assert(R.maxCoeff() < xm);
assert(C.minCoeff() >= 0);
assert(C.maxCoeff() < xn);
// initialize row and col permutation vectors
Eigen::VectorXi rowIndexVec = Eigen::VectorXi::LinSpaced(xm,0,xm-1);
Eigen::VectorXi rowPermVec = Eigen::VectorXi::LinSpaced(xm,0,xm-1);
for(int i=0;i<ym;i++)
{
int pos = rowIndexVec.coeffRef(R(i));
if(pos != i)
{
int& val = rowPermVec.coeffRef(i);
std::swap(rowIndexVec.coeffRef(val),rowIndexVec.coeffRef(R(i)));
std::swap(rowPermVec.coeffRef(i),rowPermVec.coeffRef(pos));
}
}
Eigen::PermutationMatrix<Eigen::Dynamic,Eigen::Dynamic,int> rowPerm(rowIndexVec);
Eigen::VectorXi colIndexVec = Eigen::VectorXi::LinSpaced(xn,0,xn-1);
Eigen::VectorXi colPermVec = Eigen::VectorXi::LinSpaced(xn,0,xn-1);
for(int i=0;i<yn;i++)
{
int pos = colIndexVec.coeffRef(C(i));
if(pos != i)
{
int& val = colPermVec.coeffRef(i);
std::swap(colIndexVec.coeffRef(val),colIndexVec.coeffRef(C(i)));
std::swap(colPermVec.coeffRef(i),colPermVec.coeffRef(pos));
}
}
Eigen::PermutationMatrix<Eigen::Dynamic,Eigen::Dynamic,int> colPerm(colPermVec);
Eigen::SparseMatrix<T> M = (rowPerm * X);
Y = (M * colPerm).block(0,0,ym,yn);
#endif
}
IGL_INLINE void igl::orientable_patches(
const Eigen::PlainObjectBase<DerivedF> & F,
Eigen::PlainObjectBase<DerivedC> & C,
Eigen::SparseMatrix<AScalar> & A)
{
using namespace Eigen;
using namespace std;
// simplex size
assert(F.cols() == 3);
// List of all "half"-edges: 3*#F by 2
Matrix<typename DerivedF::Scalar, Dynamic, 2> allE,sortallE,uE;
allE.resize(F.rows()*3,2);
Matrix<int,Dynamic,2> IX;
VectorXi IA,IC;
allE.block(0*F.rows(),0,F.rows(),1) = F.col(1);
allE.block(0*F.rows(),1,F.rows(),1) = F.col(2);
allE.block(1*F.rows(),0,F.rows(),1) = F.col(2);
allE.block(1*F.rows(),1,F.rows(),1) = F.col(0);
allE.block(2*F.rows(),0,F.rows(),1) = F.col(0);
allE.block(2*F.rows(),1,F.rows(),1) = F.col(1);
// Sort each row
sort(allE,2,true,sortallE,IX);
//IC(i) tells us where to find sortallE(i,:) in uE:
// so that sortallE(i,:) = uE(IC(i),:)
unique_rows(sortallE,uE,IA,IC);
// uE2FT(e,f) = 1 means face f is adjacent to unique edge e
vector<Triplet<AScalar> > uE2FTijv(IC.rows());
for(int e = 0;e<IC.rows();e++)
{
uE2FTijv[e] = Triplet<AScalar>(e%F.rows(),IC(e),1);
}
SparseMatrix<AScalar> uE2FT(F.rows(),uE.rows());
uE2FT.setFromTriplets(uE2FTijv.begin(),uE2FTijv.end());
// kill non-manifold edges
for(int j=0; j<(int)uE2FT.outerSize();j++)
{
int degree = 0;
for(typename SparseMatrix<AScalar>::InnerIterator it (uE2FT,j); it; ++it)
{
degree++;
}
// Iterate over inside
if(degree > 2)
{
for(typename SparseMatrix<AScalar>::InnerIterator it (uE2FT,j); it; ++it)
{
uE2FT.coeffRef(it.row(),it.col()) = 0;
}
}
}
// Face-face Adjacency matrix
SparseMatrix<AScalar> uE2F;
uE2F = uE2FT.transpose().eval();
A = uE2FT*uE2F;
// All ones
for(int j=0; j<A.outerSize();j++)
{
// Iterate over inside
for(typename SparseMatrix<AScalar>::InnerIterator it (A,j); it; ++it)
{
if(it.value() > 1)
{
A.coeffRef(it.row(),it.col()) = 1;
}
}
}
//% Connected components are patches
//%C = components(A); % alternative to graphconncomp from matlab_bgl
//[~,C] = graphconncomp(A);
// graph connected components using boost
components(A,C);
}
void Poisson_LIMSolver2D::prepareProblemData(std::vector<int>& hessRowIdx, std::vector<int>& hessColIdx)
{
const int numNodes = mesh->InitalVertices->rows();
// create sparse gradient operator matrix
Eigen::SparseMatrix<double> tempG;
Eigen::VectorXd dAreas,dAreasTemp;
Eigen::Matrix<double,Eigen::Dynamic,Eigen::Dynamic> vertices(*mesh->DeformedVertices);
Eigen::Matrix<int,Eigen::Dynamic,Eigen::Dynamic> faces(*mesh->Triangles);
igl::grad(vertices,faces,tempG);
// Only get x and y derivatives of elements as z is zero
int newRowSize = 2.0/3.0*tempG.rows();
std::vector<Eigen::Triplet<double> > triplets;
for (int k=0;k<tempG.outerSize();++k)
{
for (Eigen::SparseMatrix<double>::InnerIterator it(tempG,k);it;++it)
{
int row = it.row();
int col = it.col();
if(row < newRowSize)
{
triplets.push_back(Eigen::Triplet<double>(row,col,it.value()));
}
}
}
tempG.setZero();
tempG.resize(newRowSize,tempG.cols());
tempG.setFromTriplets(triplets.begin(), triplets.end());
// Extend gradient operator matrix for x and y scalar function
triplets.clear();
G.resize(newRowSize*2,tempG.cols()*2);
for (int k=0;k<tempG.outerSize();++k)
{
for (Eigen::SparseMatrix<double>::InnerIterator it(tempG,k);it;++it)
{
int row = it.row()*2;
int col = it.col()*2;
triplets.push_back(Eigen::Triplet<double>(row,col,it.value()));
triplets.push_back(Eigen::Triplet<double>(row+1,col+1,it.value()));
}
}
G.setFromTriplets(triplets.begin(), triplets.end());
// Compute area weights
Eigen::SparseMatrix<double> M;
igl::doublearea(vertices,faces,dAreas);
triplets.clear();
M.resize(dAreas.rows()*4,dAreas.rows()*4);
for(int r=0;r<dAreas.rows();r++)
{
int id = 4*r;
triplets.push_back(Eigen::Triplet<double>(id,id,dAreas(r)));
triplets.push_back(Eigen::Triplet<double>(id+1,id+1,dAreas(r)));
triplets.push_back(Eigen::Triplet<double>(id+2,id+2,dAreas(r)));
triplets.push_back(Eigen::Triplet<double>(id+3,id+3,dAreas(r)));
}
M.setFromTriplets(triplets.begin(),triplets.end());
// Compute laplacian
L = 0.5*G.transpose()*M*G;
for (int k=0;k<L.outerSize();++k)
{
for (Eigen::SparseMatrix<double>::InnerIterator it(L,k);it;++it)
{
int row = it.row();
int col = it.col();
// std::sort for upper triangule matrix
if(row <= col)
{
hessRowIdx.push_back(row);
hessColIdx.push_back(col);
}
}
}
GTb = 0.5*G.transpose()*M*b;
constantEnergyPart = b.transpose()*b;
}
void exportTriplets(const char* filename) {ofstream f; f.open(filename);
for (int k=0; k<A.outerSize(); ++k)
for (Eigen::SparseMatrix<double>::InnerIterator it(A,k); it; ++it) f<< it.row()<<" "<< it.col()<<" "<<it.value()<<endl; f.close();};
