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与其他矩阵库相比,Eigen(Visit)相比,Eigen只需要拷贝所有include文件到指定位置,无需编译即可使用;此外,用法上模仿Matlab矩阵操作;
上述特点,使其具有很好的实用性。
附上测试代码,以便学习和使用。
//http://eigen.tuxfamily.org/dox/group__QuickRefPage.html #include <iostream> #include <Eigen/Dense> using namespace std; using namespace Eigen; typedef Matrix<float, 1, 3> RVector; int main() { int cnt = 0; //定义一个矩阵并赋值 MatrixXd m(2,2); m(0,0) = 3; m(1,0) = 2.5; m(0,1) = -1; m(1,1) = m(1,0) + m(0,1); cout << '[' << cnt++ << "] " << ": " << "m=" << endl; cout << m << endl; cout << "m.cols()=" << m.cols() << ", m.rows()=" << m.rows() << ", size()=" << m.size() << endl << endl; m << 1, 2, 3, 4; //先行后列 cout << '[' << cnt++ << "] " << ": " << "comma赋值,m=" << endl; cout << m << endl; cout << "m的第一行:" << m(1) << endl; //产生一个随机矩阵 MatrixXd m1 = MatrixXd::Random(3,3); //3 X 3 随机矩阵,值介于-1到1之间 cout << '[' << cnt++ << "] " << ": " << "m1 =" << endl << m1 << endl << endl; MatrixXd m2 = MatrixXd::Constant(3,3,1.2); //3 x 3 值为1.2的矩阵 cout << '[' << cnt++ << "] " << ": " << "m2 = " << endl; cout << m2 << endl << endl; m1 = (m1 + m2) * 5; cout << '[' << cnt++ << "] " << ": " << "m1 = (m1 + m2) * 5 =" << endl << m1 << endl << endl; //向量赋值与矩阵相乘---Comma-initialization VectorXd v(3); v << 1, 2, 3; cout << '[' << cnt++ << "] " << ": v" << endl << v << endl << endl; cout << '[' << cnt++ << "] " << "m1 * v =" << endl << m1 * v << endl << endl; //通过循环为向量赋值 VectorXd v1(3); for(int i = 0; i < 3; i++) v1(i) = i; cout << '[' << cnt++ << "] " << "列向量v1=" << endl << v1 << endl << endl; //行向量 RVector rv; for(int i = 0; i < 3; i++) rv(i) = i; cout << '[' << cnt++ << "] " << "行向量:" << rv << endl << endl; //矩阵重新调整大小 MatrixXd m3 = MatrixXd::Random(3, 4); cout << '[' << cnt++ << "] " << ": " << "m3 = " << endl; cout << m3 << endl << endl; cout << "m3.resize(5, 5)=" << endl; m3.resize(5, 5); //不保持原值 cout << m3 << endl << endl; MatrixXd m4= MatrixXd::Random(3, 3); cout << '[' << cnt++ << "] " << ": " << "m4 = " << endl; cout << m4 << endl; //矩阵的transpose转置 cout << "M4转置=" << endl; cout << m4.transpose() << endl; //矩阵的conjugate转置 cout << "M4共轭=" << endl; cout << m4.conjugate() << endl; //矩阵的adjoint转置 cout << "M4.adjoint=" << endl; cout << m4.adjoint() << endl << endl; //矩阵与标量的+,-,×,/运算略 //矩阵与矢量的运算 MatrixXd ma = MatrixXd::Random(2, 3); MatrixXd vb = MatrixXd::Random(3, 1); cout << '[' << cnt++ << "] " << ": " << "ma = " << endl; cout << ma << endl; cout << "vb = " << endl; cout << vb << endl; cout << "ma * vb = " << endl; cout << ma * vb << endl; //dot点乘和cross叉积略,见参考 return 0; }
[0] : m=
3 -1
2.5 1.5
m.cols()=2, m.rows()=2, size()=4
[1] : comma赋值,m=
1 2
3 4
m的第一行:3
[2] : m1 =
0.680375 0.59688 -0.329554
-0.211234 0.823295 0.536459
0.566198 -0.604897 -0.444451
[3] : m2 =
1.2 1.2 1.2
1.2 1.2 1.2
1.2 1.2 1.2
[4] : m1 = (m1 + m2) * 5 =
9.40188 8.9844 4.35223
4.94383 10.1165 8.6823
8.83099 2.97551 3.77775
[5] : v
1
2
3
[6] m1 * v =
40.4274
51.2237
26.1153
[7] 列向量v1=
0
1
2
[8] 行向量:0 1 2
[9] : m3 =
0.10794 -0.270431 0.83239 -0.716795
-0.0452059 0.0268018 0.271423 0.213938
0.257742 0.904459 0.434594 -0.967399
m3.resize(5, 5)=
6.91676e-310 0 0 0 0
6.91676e-310 0 0 0 0
2.122e-314 0 0 0 0
3.7008e-33 0 0 0 0
7.23757e-320 0 0 0 0
[10] : m4 =
-0.514226 -0.686642 -0.782382
-0.725537 -0.198111 0.997849
0.608354 -0.740419 -0.563486
M4转置=
-0.514226 -0.725537 0.608354
-0.686642 -0.198111 -0.740419
-0.782382 0.997849 -0.563486
M4共轭=
-0.514226 -0.686642 -0.782382
-0.725537 -0.198111 0.997849
0.608354 -0.740419 -0.563486
M4.adjoint=
-0.514226 -0.725537 0.608354
-0.686642 -0.198111 -0.740419
-0.782382 0.997849 -0.563486
[11] : ma =
0.0258648 0.22528 0.275105
0.678224 -0.407937 0.0485744
vb =
-0.012834
0.94555
-0.414966
ma * vb =
0.0985221
-0.414586
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原文地址:http://blog.csdn.net/miscclp/article/details/42834953