sparse matrix implemented using vector of map - c++

Question

As I wrote in another post (closed because I found some solution) I have implement a Sparse Matrix Class , the data container is done by a vactor of map where the index of vector rappresent the index of the row of the matrix for each index therefore the vector stored a map !
here the interface:
template <typename element_type>
class SparseMatrix {
public:
template<class T>
friend SparseMatrix<T> operator+(const SparseMatrix<T>& m1 , const SparseMatrix<T>& m2 );
template<class T>
friend SparseMatrix<T> operator-(const SparseMatrix<T>& m1 , const SparseMatrix<T>& m2 );
template <class T>
friend SparseMatrix<T> operator*(const SparseMatrix<T>& m1 , const SparseMatrix<T>& m2 );
public:
// container type ;
using data_type = std::vector<std::map<std::size_t , element_type >> ;
using it_rows = typename std::map<std::size_t , element_type>::iterator ;
SparseMatrix(std::size_t rows , std::size_t cols) : rows{rows} , columns{cols}
{
data.resize(rows);
}
SparseMatrix(std::initializer_list<std::initializer_list<element_type>> l );
SparseMatrix(const std::string );
auto insert(std::size_t i , std::pair<std::size_t, element_type> p )
{
assert( i <= rows && p.first <= columns); // , "Index out of bound" );
data.at(i).insert(p);
}
auto insert(std::size_t i, std::size_t j, element_type val)
{
assert(i<=rows && j <=columns);
data.at(i)[j] = val ;
}
auto identity() noexcept ;
auto diag(const element_type& v) noexcept ;
auto print() const noexcept ;
auto dataType() const noexcept ;
auto traspose() noexcept ;
private:
std::size_t rows ;
std::size_t columns ;
data_type data ; // vector containing row element
};
In the previous post I asked about the traspose matrix .. I found this solution:
template <typename T>
auto SparseMatrix<T>::traspose() noexcept
{
//rows = columns ;
std::swap(rows,columns);
data_type newData ;
newData.resize(rows);
for(std::size_t i=0; i < columns ; i++)
{
for(std::size_t j=0 ; j < rows ; j++)
{
if(data.at(i).find(j) != data.at(i).end())
{
newData.at(j)[i] = data.at(i).at(j) ;
}
}
}
std::swap(data,newData);
}
Now i have no idea to how do compute the matrix product ... could you give me some advice ?
Ok .. thanks to suggeriment recived by #BeyelerStudios i got the poind and wrote the dot product in this way :
template <class T>
SparseMatrix<T> dot(const SparseMatrix<T>& m1 , const SparseMatrix<T>& m2 )
{
if(m1.columns != m2.rows)
{
throw std::runtime_error("Matrix sizes dosent match the dot-product");
}
SparseMatrix<T> result{m1.rows , m2.columns };
for(std::size_t i=0 ; i < result.rows ; i++)
{
for(std::size_t j=0 ; j < result.columns ; j++ )
{
for(std::size_t k=0; k< m1.columns ; k++)
{
if( m1.data.at(i).find(k) != m1.data.at(i).end() &&
m2.data.at(k).find(j) != m2.data.at(k).end() )
{
result.data.at(i)[j] += (m1.data.at(i).at(k) * m2.data.at(k).at(j)) ;
}
}
}
}
return result ;
}
now i open one other post for asking about determinant and inverse !