PCL eigen introduction and simple use

Eigen It can be used for Linear Algebra 、 matrix 、 Vector operation, etc C++ library .
Related code operations are as follows ：

//#include "stdafx.h"// The original blog used this , But my computer can't find , So replace... With the following three lines .
#include <stdio.h>
#include "stdlib.h"
#include <tchar.h>
 
#include <iostream>
 
#include <Eigen/Dense>
// Ten 、eigen Use 
using namespace std;
using namespace Eigen;
template <typename T>
static void matrix_mul_matrix(T* p1, int iRow1, int iCol1, T* p2, int iRow2, int iCol2, T* p3)
{
    
	if (iRow1 != iRow2) return;
 
	// Column priority 
	//Eigen::Map< Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic> > map1(p1, iRow1, iCol1);
	//Eigen::Map< Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic> > map2(p2, iRow2, iCol2);
	//Eigen::Map< Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic> > map3(p3, iCol1, iCol2);
 
	// Line first 
	Eigen::Map< Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor> > map1(p1, iRow1, iCol1);
	Eigen::Map< Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor> > map2(p2, iRow2, iCol2);
	Eigen::Map< Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor> > map3(p3, iCol1, iCol2);
 
	map3 = map1 * map2;
}
 
int main(int argc, char* argv[])
{
    
	//1.  Definition of matrix 
	cout << "1.  Definition of matrix " << endl;
	Eigen::MatrixXd m(2, 2);
	Eigen::Vector3d vec3d;
	Eigen::Vector4d vec4d(1.0, 2.0, 3.0, 4.0);
 
	//2.  Dynamic matrix 、 Static matrix 
	cout << "2.  Dynamic matrix 、 Static matrix " << endl;
	Eigen::MatrixXd matrixXd;
	Eigen::Matrix3d matrix3d;
 
	//3.  Access to matrix elements 
	cout << "3.  Access to matrix elements " << endl;
	m(0, 0) = 1;
	m(0, 1) = 2;
	m(1, 0) = m(0, 0) + 3;
	m(1, 1) = m(0, 0) * m(0, 1);
	std::cout << m << std::endl << std::endl;
 
	//4.  Set the elements of the matrix 
	cout << "4.  Set the elements of the matrix " << endl;
	m << -1.5, 2.4,
		6.7, 2.0;
	std::cout << m << std::endl << std::endl;
	int row = 4;
	int col = 5;
	Eigen::MatrixXf matrixXf(row, col);
	matrixXf << 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20;
	std::cout << matrixXf << std::endl << std::endl;
	matrixXf << Eigen::MatrixXf::Identity(row, col);//Identity Unit matrix means ：Identity matrix
	std::cout << matrixXf << std::endl << std::endl;
 
	//5.  Reset matrix size 
	cout << "5.  Reset matrix size " << endl;
	Eigen::MatrixXd matrixXd1(3, 3);
	m = matrixXd1;
	std::cout << m.rows() << " " << m.cols() << std::endl << std::endl;
 
	//6.  Matrix operations 
	cout << "6.  Matrix operations " << endl;
	m << 1, 2, 7,
		3, 4, 8,
		5, 6, 9;
	std::cout << m << std::endl;
	matrixXd1 = Eigen::Matrix3d::Random();
	m += matrixXd1;
	std::cout << m << std::endl << std::endl;
	m *= 2;
	std::cout << m << std::endl << std::endl;
	std::cout << -m << std::endl << std::endl;
	std::cout << m << std::endl << std::endl;
 
	//7.  Transpose matrix 、 Conjugate matrix 、 Adjoint matrix 
	cout << "7.  Transpose matrix 、 Conjugate matrix 、 Adjoint matrix " << endl;
	std::cout << m.transpose() << std::endl << std::endl;
	std::cout << m.conjugate() << std::endl << std::endl;
	std::cout << m.adjoint() << std::endl << std::endl;
	std::cout << m << std::endl << std::endl;
	m.transposeInPlace();
	std::cout << m << std::endl << std::endl;
 
	//8.  matrix multiplication 、 Matrix vector multiplication 
	cout << "8.  matrix multiplication 、 Matrix vector multiplication " << endl;
	std::cout << m*m << std::endl << std::endl;
	vec3d = Eigen::Vector3d(1, 2, 3);
	std::cout << m * vec3d << std::endl << std::endl;
	std::cout << vec3d.transpose()*m << std::endl << std::endl;
 
	//9.  Block operation of matrix 
	cout << "9.  Block operation of matrix " << endl;
	std::cout << m << std::endl << std::endl;
	std::cout << m.block(1, 1, 2, 2) << std::endl << std::endl;
	std::cout << m.block<1, 2>(0, 0) << std::endl << std::endl;
	std::cout << m.col(1) << std::endl << std::endl;
	std::cout << m.row(0) << std::endl << std::endl;
 
	//10.  Block operation of vector 
	cout << "10.  Block operation of vector " << endl;
	Eigen::ArrayXf arrayXf(10);
	arrayXf << 1, 2, 3, 4, 5, 6, 7, 8, 9, 10;
	std::cout << vec3d << std::endl << std::endl;
	std::cout << arrayXf << std::endl << std::endl;
	std::cout << arrayXf.head(5) << std::endl << std::endl;
	std::cout << arrayXf.tail(4) * 2 << std::endl << std::endl;
 
	//11.  Solve the eigenvalues and eigenvectors of the matrix 
	cout << "11.  Solve the eigenvalues and eigenvectors of the matrix " << endl;
	Eigen::Matrix2f matrix2f;
	matrix2f << 1, 2, 3, 4;
	Eigen::SelfAdjointEigenSolver<Eigen::Matrix2f> eigenSolver(matrix2f);// Form is this form , In case of high-dimensional problems, replace them with high-dimensional ones matrix2f.
	if (eigenSolver.info() == Eigen::Success) {
    
		std::cout << eigenSolver.eigenvalues() << std::endl << std::endl;
		std::cout << eigenSolver.eigenvectors() << std::endl << std::endl;
	}
 
	//12.  class Map And the use of dynamic matrix 
	cout << "12.  class Map And the use of dynamic matrix " << endl;
	int array1[4] = {
     1, 2, 3, 4 };
	int array2[4] = {
     5, 6, 7, 8 };
	int array3[4] = {
     0, 0, 0, 0 };
	matrix_mul_matrix(array1, 2, 2, array2, 2, 2, array3);
	for (int i = 0; i < 4; i++)
		std::cout << array3[i] << std::endl;
 
	return 0;
}