void MathUtilities::createDiagonalMatrix( const scalar& c, SparseMatrixsc& D )
{
assert( D.rows() == D.cols() );
D.reserve( VectorXi::Constant( D.cols(), 1 ) );
for( int i = 0; i < D.cols(); ++i ) { D.insert(i,i) = c; }
D.makeCompressed();
}
// TODO: Use utility class here
void MathUtilities::deserialize( SparseMatrixsc& A, std::istream& stm )
{
assert( stm.good() );
VectorXi col_ptr;
VectorXi row_ind;
VectorXs val;
// Read the number of rows in the matrix
int rows{ -1 };
stm.read((char*)&rows,sizeof(int));
assert( rows >= 0 );
// Read the number of columns in the matrix
int cols{ -1 };
stm.read((char*)&cols,sizeof(int));
assert( cols >= 0 );
// Read the column pointer array
col_ptr.resize(cols+1);
stm.read((char*)col_ptr.data(),col_ptr.size()*sizeof(int));
// Read the number of nonzeros in the array
int nnz{ -1 };
stm.read((char*)&nnz,sizeof(int));
assert( nnz >= 0 );
// Read the row-index array
row_ind.resize(nnz);
stm.read((char*)row_ind.data(),row_ind.size()*sizeof(int));
// Read the value array
val.resize(nnz);
stm.read((char*)val.data(),val.size()*sizeof(scalar));
A.resize(rows,cols);
A.reserve(nnz);
for( int col = 0; col < cols; ++col )
{
A.startVec(col);
for( int curel = col_ptr(col); curel < col_ptr(col+1); ++curel )
{
int row = row_ind(curel);
scalar curval = val(curel);
A.insertBack(row,col) = curval;
}
}
A.finalize();
}
static void reserveSpaceInBasisMatrix( const int nsamples, const std::vector<std::unique_ptr<Constraint>>& K, SparseMatrixsc& D )
{
assert( D.cols() % nsamples == 0 );
const int ncons{ D.cols() / nsamples };
VectorXi column_nonzeros{ D.cols() };
std::vector<std::unique_ptr<Constraint>>::const_iterator itr{ K.begin() };
for( int con_idx = 0; con_idx < ncons; ++con_idx )
{
for( int smpl_num = 0; smpl_num < nsamples; ++smpl_num )
{
column_nonzeros( con_idx * nsamples + smpl_num ) = (*itr)->frictionStencilSize();
}
++itr;
}
assert( itr == K.end() );
D.reserve( column_nonzeros );
}
void FrictionOperatorUtilities::formLinearFrictionDiskConstraint( const int num_samples, SparseMatrixsc& E )
{
{
const VectorXi column_nonzeros{ VectorXi::Constant( E.cols(), num_samples ) };
E.reserve( column_nonzeros );
}
// For each column
for( int col = 0; col < E.cols(); ++col )
{
for( int samplenum = 0; samplenum < num_samples; ++samplenum )
{
// Note the negative for QL
E.insert( num_samples * col + samplenum, col ) = 1.0;
}
}
E.makeCompressed();
assert( E.nonZeros() == E.cols() * num_samples );
assert( E.sum() == E.nonZeros() );
}
// TODO: Despecialize from smooth
void FrictionOperator::formGeneralizedSmoothFrictionBasis( const unsigned ndofs, const unsigned ncons, const VectorXs& q, const std::vector<std::unique_ptr<Constraint>>& K, const MatrixXXsc& bases, SparseMatrixsc& D )
{
assert( ncons == K.size() );
const unsigned nambientdims{ static_cast<unsigned>( bases.rows() ) };
const unsigned nsamples{ nambientdims - 1 };
D.resize( ndofs, nsamples * ncons );
auto itr = K.cbegin();
{
VectorXi column_nonzeros( D.cols() );
for( unsigned collision_number = 0; collision_number < ncons; ++collision_number )
{
for( unsigned sample_number = 0; sample_number < nsamples; ++sample_number )
{
assert( nsamples * collision_number + sample_number < column_nonzeros.size() );
column_nonzeros( nsamples * collision_number + sample_number ) = (*itr)->frictionStencilSize();
}
++itr;
}
assert( ( column_nonzeros.array() > 0 ).all() );
assert( itr == K.cend() );
D.reserve( column_nonzeros );
}
itr = K.cbegin();
for( unsigned collision_number = 0; collision_number < ncons; ++collision_number )
{
for( unsigned sample_number = 0; sample_number < nsamples; ++sample_number )
{
const unsigned current_column{ nsamples * collision_number + sample_number };
const VectorXs current_sample{ bases.col( nambientdims * collision_number + sample_number + 1 ) };
assert( fabs( current_sample.dot( bases.col( nambientdims * collision_number ) ) ) <= 1.0e-6 );
(*itr)->computeGeneralizedFrictionGivenTangentSample( q, current_sample, current_column, D );
}
++itr;
}
assert( itr == K.cend() );
D.prune( []( const Eigen::Index& row, const Eigen::Index& col, const scalar& value ) { return value != 0.0; } );
assert( D.innerNonZeroPtr() == nullptr );
}
// TODO: Pull the outerIndexPtr arithmetic into a helper function
void MathUtilities::extractColumns( const SparseMatrixsc& A0, const std::vector<unsigned>& cols, SparseMatrixsc& A1 )
{
const unsigned ncols_to_extract{ static_cast<unsigned>( cols.size() ) };
assert( ncols_to_extract <= static_cast<unsigned>( A0.cols() ) );
#ifndef NDEBUG
for( unsigned i = 0; i < ncols_to_extract; ++i )
{
assert( cols[i] < unsigned( A0.cols() ) );
}
#endif
// Compute the number of nonzeros in each column of the new matrix
VectorXi column_nonzeros{ ncols_to_extract };
for( unsigned i = 0; i < ncols_to_extract; ++i )
{
column_nonzeros( i ) = A0.outerIndexPtr()[cols[i]+1] - A0.outerIndexPtr()[cols[i]];
}
// Resize A1 and reserve space
A1.resize( A0.rows(), ncols_to_extract );
A1.reserve( column_nonzeros );
// Copy the data over, column by column
for( unsigned cur_col = 0; cur_col < ncols_to_extract; ++cur_col )
{
for( SparseMatrixsc::InnerIterator it( A0, cols[ cur_col ] ); it; ++it )
{
A1.insert( it.row(), cur_col ) = it.value();
}
}
A1.makeCompressed();
#ifndef NDEBUG
for( int i = 0 ; i < A1.cols(); ++i )
{
assert( ( A1.outerIndexPtr()[i+1] - A1.outerIndexPtr()[i] ) == column_nonzeros( i ) );
}
#endif
}
