CRSSparsity reshape(const CRSSparsity& a, int n, int m){
casadi_assert_message(a.numel() == n*m,
"reshape: number of elements must remain the same." << endl <<
"Input argument has shape " << a.size1() << " x " << a.size2() << " = " << a.numel() << ", while you request a reshape to " <<
n << " x " << m << " = " << n*m
);
// our strategy is: (col,rowind) -> (col,row) -> modulus calculus -> (col_new, row_new) -> sp_NZ
std::vector<int> row = a.getRow();
const std::vector<int> &col = a.col();
std::vector<int> row_new(a.size());
std::vector<int> col_new(a.size());
// int i=0;int j=0; int z =0;
for(int k=0; k<a.size(); ++k){
int i = row[k];
int j = col[k];
int z = j+i*a.size2();
row_new[k] = z/m;
col_new[k] = z%m;
}
return sp_triplet(n,m,row_new,col_new);
}
CRSSparsity lowerSparsity(const CRSSparsity& a, bool includeDiagonal) {
const std::vector<int> & col= a.col();
std::vector<int> row = a.getRow();
std::vector<int> new_col; // new col
std::vector<int> new_row; // new row
int offset = (includeDiagonal ? 1 : 0);
// Find size
int n=0;
for (int k=0;k<row.size();k++) n+= row[k] + offset > col[k];
new_col.resize(n);
new_row.resize(n);
// populate return vector
int cnt=0;
for (int k=0;k<row.size();k++) {
if (row[k] + offset > col[k]) {
new_col[cnt]=col[k];
new_row[cnt]=row[k];
cnt++;
}
}
return sp_triplet(a.size1(), a.size2(), new_row, new_col);
}
std::vector<int> getNZDense(const CRSSparsity &sp) {
std::vector<int> ret(sp.size());
std::vector<int> row = sp.getRow();
const std::vector<int> &col = sp.col();
int s2 = sp.size2();
for(int k=0;k<sp.size();k++) {
ret[k] = col[k]+row[k]*s2;
}
return ret;
}
std::vector<int> lowerNZ(const CRSSparsity& a) {
const std::vector<int> & col= a.col();
std::vector<int> row = a.getRow();
// Return vector
std::vector<int> ret;
// Find size of return vector
int n=0;
for (int k=0;k<row.size();k++) n+= (row[k] >= col[k]);
ret.resize(n);
// populate return vector
int cnt=0;
for (int k=0;k<row.size();k++) {
if (row[k] >= col[k]) ret[cnt++]=k;
}
return ret;
}
