I'm new to Eigen and I would like to create 10 mxn matrices.
For some reasons I do it with the following method:
Matrix<double, m, n*10>
Which It seems that the memory allocation will be similar to the following:
_______________________________________________________________
|M1(1,1)|M2(1,1)|...|M10(1,1)|.....|M1(1,n)|M2(1,n)|...|M10(1,n)|
| . . |
| . |
Now how it possible to create a reference matrix (means by reference and without copying data) to each of this 10 matrices?
I would recommend using the dynamically allocated matrix, as m and n might be large. Also, it appears that you assume the matrix memory is row major, when the default is column major. In the example below, I've explicitly made them row major.
You can use a series Eigen::Map<MatrixXd>s like so:
#include <Eigen/Core>
#include <iostream>
using namespace Eigen;
int main(void)
{
int m = 3;
int n = 4;
int x = 6;
typedef Matrix < double, Dynamic, Dynamic, RowMajor > ourMat;
ourMat M1(m, n * x);
M1.setConstant(9.9);
for (int i = 0; i < x; i++)
{
Eigen::Map<ourMat, 0, Stride<Dynamic, Dynamic>> m_i(M1.data() + i,
m, n,
Stride<Dynamic, Dynamic>(n*x,x));
m_i.setConstant(double(i));
std::cout << m_i << std::endl;
std::cout << M1 << "\n" << std::endl;
}
Eigen::Map<VectorXd> m_i(M1.data(), m * n * x);
std::cout << m_i.transpose() << std::endl;
return 0;
}
Related
I need to get the certain rows, when a element is a vector is one.
For an example:
std::vector<bool>index{}; //contains 6000 numbers of elements 0 and 1
Now I have a matrix mat of shape (6000,4)
How can I get the rows in a matrix mat, when the corresponding element is 1 in vector index.
mat = mat[index];
If I understand your question clearly, you may find good answer from this good reply:
Eigen3 select rows out based on column conditions
Using new feature (Eigen 3.4 or 3.3.90 development branch) and take the core code from the previous link:
#include <Eigen/Dense>
#include <iostream>
#include <vector>
using namespace Eigen;
int main() {
MatrixXd mat = MatrixXd::Random(10,5);
std::cout << "original:\n" << mat << std::endl;
std::vector<int> keep_rows;
for (int i = 0; i < mat.rows(); ++i) {
if (mat(i,mat.cols() - 1) > 0.3) {
keep_rows.push_back(i);
}
}
VectorXi keep_cols = VectorXi::LinSpaced(mat.cols(), 0,mat.cols());
MatrixXd mat_sel = mat(keep_rows, keep_cols);
std::cout << "selected:\n" << mat_sel << std::endl;
}
It uses the similar style of the Matlab:
MatrixXd mat_sel = mat(keep_rows, keep_cols);
But the columns and rows that should be kept are stored in an
Eigen::VectorXi
or in a
std::vector<int>
I use the eigen library to perform the sparse matrix operations, particularly, to fill a sparse matirx. But the rows and cols are very large in our case, which results in a long time for filling the sparse matrix. Is there any efficient way to do this (maybe by the other libraries)?
Below is the my code:
SparseMatrix mat(rows,cols);
mat.reserve(VectorXi::Constant(cols,6));
for each i,j such that v_ij != 0
mat.insert(i,j) = v_ij;
mat.makeCompressed();
The order in which a SparseMatrix is filled can make an enormous difference in computation time. To fill a SparseMatrix matrix quickly, the elements should be addressed in a sequence that corresponds to the storage order of the SparseMatrix. By default, the storage order in Eigen's SparseMatrix is column major, but it is easy to change this.
The following code demonstrates the time difference between a rowwise filling of two sparse matrices with different storage order. The square sparse matrices are relatively small and nominally identical. While the RowMajor matrix is almost instantly filled, it takes a much longer time (about 30 seconds on my desktop computer) in the case of ColMajor storage format.
#include <iostream>
#include <Eigen/Dense>
#include <Eigen/SparseCore>
#include <random>
using namespace Eigen;
typedef SparseMatrix<double, RowMajor> SpMat_RM;
typedef SparseMatrix<double, ColMajor> SpMat_CM;
// compile with -std=c++11 -O3
int main() {
const int n = 1e4;
const int nnzpr = 50;
std::random_device rd;
std::mt19937 gen(rd());
std::uniform_int_distribution<> randInt(0, n-1);
SpMat_RM m_RM(n,n);
m_RM.reserve(n);
SpMat_CM m_CM(n,n);
m_CM.reserve(n);
std::cout << "Row-wise filling of [" << n << " x " << n << "] sparse matrix (RowMajor) ..." << std::flush;
for (int i = 0; i < n; ++i) {
for (int j = 0; j < nnzpr; ++j) {
int col = randInt(gen);
double val = 1. ; // v_ij
m_RM.coeffRef(i,col) = val ;
}
}
m_RM.makeCompressed();
std::cout << "done." << std::endl;
std::cout << "Row-wise filling of [" << n << " x " << n << "] sparse matrix (ColMajor) ..." << std::flush;
for (int i = 0; i < n; ++i) {
for (int j = 0; j < nnzpr; ++j) {
int col = randInt(gen);
double val = 1.; // v_ij
m_CM.coeffRef(i,col) = val ;
}
}
m_CM.makeCompressed();
std::cout << "done." << std::endl;
}
I am trying to get an understanding of how to work with matrices in C++. The code at the bottom is supposed to take an input matrix and return the places where there are 0s. However, I am getting the following errors:
matrix.cpp:47:3: error: no matching function for call to 'make_zero' make_zero(i,j,l);
^~~~~~~~~
matrix.cpp:8:6: note: candidate function not viable: no known conversion from 'double [i][j]' to
'double (*)[col]' for 3rd argument
void make_zero(int row, int col, double matrix[row][col])
^
1 error generated.
when I try to run the following code:
// Matrix
#include <iostream>
#include <stdio.h>
using namespace std;
void make_zero(int row, int col, double matrix[row][col])
{
int k,l;
for(k=0;k<row;k++)
for(l=0;l<col;l++)
{
if(matrix[k][l]==0)
printf("%d %d\n",k,l);
}
}
int main ()
{
int i = 0,j = 0;
cout << "Enter no of rows of the matrix";
cin >> i;
cout << "Enter no of columns of the matrix";
cin >> j;
double l[i][j];
int p = 0, q = 0;
while (p < i) {
while (q < j) {
cout << "Enter the" << p + 1 << "*" << q + 1 << "entry";
cin >> l[p][q];
q = q + 1;
}
p = p + 1;
q = 0;
}
cout << l << "\n";
make_zero(i,j,l);
}
Any help would be appreciated. Thanks.
There are a bunch of ways to do this with pointers. The most common is
void make_zero(int row, int col, double ** matrix)
defines a pointer (usually rows) to a pointer (usually columns). Unfortunately
double l[i][j];
does not define a pointer to a pointer. If this syntax is supported by the compiler, and the compiler is not required to allow arrays of variable length, it most likely defines a pointer to a 1D array (double l[i*j];) and hides the indexing arithmetic used to convert the array to two dimensions. Anyway, it can't be passed to a double ** because it isn't a double **
Trying to pass as an array is troublesome
void make_zero(int row, int col, double matrix[][NUMBER_OF_COLUMNS])
The number of columns in the array must be known to perform the indexing arithmetic and be provided to any functions called with it. This means that number of columns cannot be changed at run time because the indexing used by the function will be rendered invalid.
Getting around this would require changes to the compiler that will drive it further and further from the C++ standard. A bad idea since there are a number of simple ways around calling functions with multi dimensional arrays. Most depend on arrays of arrays or std::vectors of std::vectors.
And when it comes to these solutions, as far as I'm concerned, the best is don't. I'm not going to cover them.
None of the arrays representing a dimension are guaranteed to be anywhere close to the others in memory, and this limits the CPU's ability to read and cache. Without caching and being able to look ahead, a modern CPU is at a serious performance disadvantage. (Read for more information: Why is it faster to process a sorted array than an unsorted array?)
So what you want is a 1 D array, and those are easy to pass around. The indexing math is also easy, row number * size of column + column number, but you need to pass at least the size of the column around. Rather than scatter the book-keeping around like this:
void make_zero(int row, int col, std::vector<double> matrix)
make a wrapper class like this:
class Matrix
{
private:
std::vector<double> myArray;
size_t nrRows;
size_t nrColumns;
public:
Matrix(size_t rows, size_t columns) :
myArray(rows * columns), // allocate vector to store matrix.
nrRows(rows),
nrColumns(columns)
{
}
size_t getNrRows() const
{
return nrRows;
}
size_t getNrColumns() const
{
return nrColumns;
}
// gets value at row, column and returns a reference so caller can
// modify the value
double& operator()(size_t row, size_t column)
{
// note: No sanity check for row >= nrRows or column > nrColumns
return myArray[row * nrColumns + column];
}
// gets value at row, column and returns a copy so caller cannot
// change the contents of the Matrix
double operator()(size_t row, size_t column) const
{
return myArray[row * nrColumns + column];
}
};
Using the vector gets around a number of common pointer-to-array problems by managing its own memory. No destructor is required and Matrix can be copied and moved without requiring special handling because vector performs all that heavy lifting for us.
And as a usage example, let's make a function that prints the matrix out:
std::ostream & operator<<(std::ostream & out, const Matrix & in)
{
for (size_t i = 0; i < in.getNrRows(); i++)
{
for (size_t j = 0; j < in.getNrColumns(); j++)
{
out << in(i,j) << ' ';
}
out << "\n";
}
return out;
}
And modifying OP's main function to use Matrix we get:
int main()
{
int i = 0, j = 0;
cout << "Enter no of rows of the matrix";
cin >> i;
cout << "Enter no of columns of the matrix";
cin >> j;
Matrix matrix(i,j);
int p = 0, q = 0;
while (p < i)
{
while (q < j)
{
cout << "Enter the" << p + 1 << "*" << q + 1 << "entry";
cin >> matrix(p,q);
q = q + 1;
}
p = p + 1;
q = 0;
}
cout << matrix << "\n";
make_zero(matrix);
}
void make_zero(int row, int col, double ** matrix)
Note, that you need to pass also size of the matrix separately.
Also you can use
std::vector<std::vector<double> >
instead and pass this object by reference, pointer, or just make a copy.
Actually, it works, but your problem in this line also:
double l[i][j];
i, j is unknown during the compile time.
You have 2 ways.
1) dynamically allocate the memory
2) use std::vector<std::vector<double> >. Default constructor already sets zero values. But you can do it manually like this:
#include <iostream>
#include <vector>
void make_zero(std::vector<std::vector<double> > & to_zero) {
for (int i = 0; i < to_zero.size(); ++i) {
for (int j = 0; j < to_zero[i].size(); ++j) {
to_zero[i][j] = 0;
}
}
}
void print_double_vector(const std::vector<std::vector<double> > & to_print) {
for (int i = 0; i < to_print.size(); ++i) {
for (int j = 0; j < to_print[i].size(); ++j) {
std::cout << to_print[i][j] << " ";
}
std::cout << std::endl;
}
std::cout << std::endl;
}
int main() {
// your code goes here
int n, m;
std::cin >> n >> m;
std::vector<std::vector<double> > d(n, std::vector<double>(m));
print_double_vector(d);
make_zero(d);
print_double_vector(d);
return 0;
}
http://ideone.com/0X53Yj
I need to write a program that computes values in a matrix, first sequentially, then in parallel using openCL.
It is the same thing as I already did in regular C (using MPI)
I want to make simple functions to initializeMatrix and printMatrix and the likes.
In C i used to do this very simply :
// Matrix initialization
void initMatrix(size_t M, size_t N, double (*matrix)[M][N][2])
{
int i, j;
for (j = 0; j < N; ++j)
{
for (i = 0; i < M; ++i)
{
(*matrix)[i][j][0] = (double)(( i * ( M - i - 1 ) ) * ( j * ( N - j - 1 ) ));
(*matrix)[i][j][1] = (*matrix)[i][j][0];
}
}
printf("Matrix has been initialized\n");
}
I saw this gets me errors in C++, as the compiler wants to know at COMPILE TIME the sizes of arrays (the M and N sizes are passed as arguments to program, therefore I can't know at compile time).
How do I do this in C++?
I am considering using Vectors, but I'm not sure if it's a good idea since I will have to use the OpenCL library
You can pass the array by reference/const reference via a template:
#include <iostream>
#include <cstddef> // for std::size_t
template <typename T, int M, int N, int P>
void f(T (&arr)[M][N][P]) // accepts 3-D arrays of arbitrary types
{
std::cout << "Size: " << M << " x " << N << " x " << P;
}
int main()
{
const std::size_t M = 2;
const std::size_t N = 3;
double arr[M][N][4];
f(arr);
}
How to extract a block from a Eigen::SparseMatrix<double>. It seems there aren't the methods I used for the dense ones.
‘class Eigen::SparseMatrix<double>’ has no member named ‘topLeftCorner’
‘class Eigen::SparseMatrix<double>’ has no member named ‘block’
There is a way to extract a block as a Eigen::SparseMatrix<double> ?
I made this function to extract blocks from a Eigen::SparseMatrix<double,ColMaior>
typedef Triplet<double> Tri;
SparseMatrix<double> sparseBlock(SparseMatrix<double,ColMajor> M,
int ibegin, int jbegin, int icount, int jcount){
//only for ColMajor Sparse Matrix
assert(ibegin+icount <= M.rows());
assert(jbegin+jcount <= M.cols());
int Mj,Mi,i,j,currOuterIndex,nextOuterIndex;
vector<Tri> tripletList;
tripletList.reserve(M.nonZeros());
for(j=0; j<jcount; j++){
Mj=j+jbegin;
currOuterIndex = M.outerIndexPtr()[Mj];
nextOuterIndex = M.outerIndexPtr()[Mj+1];
for(int a = currOuterIndex; a<nextOuterIndex; a++){
Mi=M.innerIndexPtr()[a];
if(Mi < ibegin) continue;
if(Mi >= ibegin + icount) break;
i=Mi-ibegin;
tripletList.push_back(Tri(i,j,M.valuePtr()[a]));
}
}
SparseMatrix<double> matS(icount,jcount);
matS.setFromTriplets(tripletList.begin(), tripletList.end());
return matS;
}
And these if the sub-matrix is in one of the four corners:
SparseMatrix<double> sparseTopLeftBlock(SparseMatrix<double> M,
int icount, int jcount){
return sparseBlock(M,0,0,icount,jcount);
}
SparseMatrix<double> sparseTopRightBlock(SparseMatrix<double> M,
int icount, int jcount){
return sparseBlock(M,0,M.cols()-jcount,icount,jcount);
}
SparseMatrix<double> sparseBottomLeftBlock(SparseMatrix<double> M,
int icount, int jcount){
return sparseBlock(M,M.rows()-icount,0,icount,jcount);
}
SparseMatrix<double> sparseBottomRightBlock(SparseMatrix<double> M,
int icount, int jcount){
return sparseBlock(M,M.rows()-icount,M.cols()-jcount,icount,jcount);
}
This is now supported in Eigen 3.2.2 Docs (though maybe earlier versions support it too).
#include <iostream>
#include <Eigen/Dense>
#include <Eigen/Sparse>
using namespace Eigen;
int main()
{
MatrixXd silly(6, 3);
silly << 0, 1, 2,
0, 3, 0,
2, 0, 0,
3, 2, 1,
0, 1, 0,
2, 0, 0;
SparseMatrix<double, RowMajor> sparse_silly = silly.sparseView();
std::cout <<"Whole Matrix" << std::endl;
std::cout << sparse_silly << std::endl;
std::cout << "block of matrix" << std::endl;
std::cout << sparse_silly.block(1,1,3,2) << std::endl;
return 0;
}
There is very sparse support (sorry, no pun intended) for submatrices in sparse matrices. Effectively you can only access a continuous set of rows for row-major, and columns for column major. The reason for that is not that the matrices could be empty, but rather that the indexing scheme is somewhat more complicated than with dense matrices. With dense matrices you only need an additional stride number in order to support sub-matrix support.