Lesson15 Reductions

最新推荐文章于 2024-08-13 06:00:00 发布

seamanj

最新推荐文章于 2024-08-13 06:00:00 发布

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分类专栏： Eigen

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本文介绍了Eigen库中矩阵的基本运算方法，包括求和、乘积、平均值、最小值、最大值及迹等操作，并通过一个2x2矩阵的具体实例展示了这些函数的应用。

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In Eigen, a reduction is a function taking a matrix or array, and returning a single scalar value. One of the most used reductions is .sum() , returning the sum of all the coefficients inside a given matrix or array.

Example:	Output:
#include <iostream> #include <Eigen/Dense> using namespace std; int main() { Eigen::Matrix2d mat; mat << 1, 2, 3, 4; cout << "Here is mat.sum(): " << mat. sum() << endl; cout << "Here is mat.prod(): " << mat. prod() << endl; cout << "Here is mat.mean(): " << mat. mean() << endl; cout << "Here is mat.minCoeff(): " << mat. minCoeff() << endl; cout << "Here is mat.maxCoeff(): " << mat. maxCoeff() << endl; cout << "Here is mat.trace(): " << mat. trace() << endl; }	Here is mat.sum(): 10 Here is mat.prod(): 24 Here is mat.mean(): 2.5 Here is mat.minCoeff(): 1 Here is mat.maxCoeff(): 4 Here is mat.trace(): 5

Example:

Output:

 
      #include <iostream> 
     
      #include <Eigen/Dense> 
     
      using namespace std; 
     
      int main() 
     
       { 
     
      Eigen::Matrix2d mat; 
     
       mat << 1, 2, 
     
       3, 4; 
     
       cout << 
      "Here is mat.sum(): " << mat. 
      sum() << endl; 
     
       cout << 
      "Here is mat.prod(): " << mat. 
      prod() << endl; 
     
       cout << 
      "Here is mat.mean(): " << mat. 
      mean() << endl; 
     
       cout << 
      "Here is mat.minCoeff(): " << mat. 
      minCoeff() << endl; 
     
       cout << 
      "Here is mat.maxCoeff(): " << mat. 
      maxCoeff() << endl; 
     
       cout << 
      "Here is mat.trace(): " << mat. 
      trace() << endl; 
     
       }

Here is mat.sum():       10
Here is mat.prod():      24
Here is mat.mean():      2.5
Here is mat.minCoeff():  1
Here is mat.maxCoeff():  4
Here is mat.trace():     5

The trace of a matrix, as returned by the function trace(), is the sum of the diagonal coefficients and can equivalently be computed a.diagonal().sum().