VSCode中使用armadillo+openblas的详细步骤

armadillo是C++中非常好用的一个线性代数运算库，有着和matlab非常相似的语法，可以非常方便的将matlab代码转移到C++中。

但是单纯的armadillo库，运算性能比较有限，如果加上开源的openblas，会极大提高矩阵运算性能。百度了下，基本上都是在VS2019中使用armadillo和openblas的教程，我大多数时候写的都是一些小项目，用VS未免有些杀鸡用牛刀的感觉，还是VSCode，好看又好用。百度了下，还没有看到相关的VSCode配置armadillo+openblas的教程，摸索了一下，总结经验如下：

第一步：openblas官网下载编译好的版本，网址：https://github.com/xianyi/OpenBLAS/releases，解压到任意盘，最好不要有中文路径，避免出错。

根据自己的情况，下载64位或32位版本。

第二步：下载armadillo，只需要其中的include文件夹即可，然后放在项目文件夹下，用vscode打开该项目文件夹。

armadillo网址：https://arma.sourceforge.net/download.html

第三步：下载的部分基本结束了，下面主要就是配置VScode中的几个json文件：

这个是c_cpp_properties.json文件的配置，很好理解，主要就是include和openblas的路径配置。

然后是settins.json的配置，不好截图，我直接粘贴：

    "code-runner.executorMap": {
        
        "c": "cd $dir && gcc '$fileName' -o '$fileNameWithoutExt.exe' -Wall -g -O2 -static-libgcc -std=c11 -fexec-charset=UTF-8 && &'$dir$fileNameWithoutExt'",

        "cpp": "cd $dir && g++ $fileName -o $fileNameWithoutExt  -I C:/Users/Administrator/Desktop/cpp2/include/armadillo_bit -DARMA_DONT_USE_WRAPPER  F:/OpenBLAS-0.3.21-x64/lib/libopenblas.lib -I C:/Users/Administrator/Desktop/cpp2/include && $dir$fileNameWithoutExt"
    },

上面这段代码，主要是第二段的cpp设置，两个-I开头的是include文件夹的路径，和include中加上armadillo_bit的路径，中间那段，前边固定，后边是你下载的编译好的openblas对应的/lib/libopenblas.lib，静态链接库文件。

主要就是这两个文件的配置。

然后是测试代码：

#include <iostream>
#include <armadillo>

using namespace std;
using namespace arma;

// Armadillo documentation is available at:
// http://arma.sourceforge.net/docs.html

// NOTE: the C++11 "auto" keyword is not recommended for use with Armadillo objects and functions

int
main(int argc, char** argv)
  {
  //cout << amradillo -v << endl;
  cout << "Armadillo version: " << arma_version::as_string() << endl;
  
  // construct a matrix according to given size and form of element initialisation
  mat A(2,3,fill::zeros);
  
  // .n_rows and .n_cols are read only
  cout << "A.n_rows: " << A.n_rows << endl;
  cout << "A.n_cols: " << A.n_cols << endl;
  
  A(1,2) = 456.0;  // access an element (indexing starts at 0)
  A.print("A:");
  
  A = 5.0;         // scalars are treated as a 1x1 matrix
  A.print("A:");
  
  A.set_size(4,5); // change the size (data is not preserved)
  
  A.fill(5.0);     // set all elements to a specific value
  A.print("A:");
  
  A = { { 0.165300, 0.454037, 0.995795, 0.124098, 0.047084 },
        { 0.688782, 0.036549, 0.552848, 0.937664, 0.866401 },
        { 0.348740, 0.479388, 0.506228, 0.145673, 0.491547 },
        { 0.148678, 0.682258, 0.571154, 0.874724, 0.444632 },
        { 0.245726, 0.595218, 0.409327, 0.367827, 0.385736 } };
        
  A.print("A:");
  
  // determinant
  cout << "det(A): " << det(A) << endl;
  
  // inverse
  cout << "inv(A): " << endl << inv(A) << endl;
  
  // save matrix as a text file
  A.save("A.txt", raw_ascii);
  
  // load from file
  mat B;
  B.load("A.txt");
  
  // submatrices
  cout << "B( span(0,2), span(3,4) ):" << endl << B( span(0,2), span(3,4) ) << endl;
  
  cout << "B( 0,3, size(3,2) ):" << endl << B( 0,3, size(3,2) ) << endl;
  
  cout << "B.row(0): " << endl << B.row(0) << endl;
  
  cout << "B.col(1): " << endl << B.col(1) << endl;
  
  // transpose
  cout << "B.t(): " << endl << B.t() << endl;
  
  // maximum from each column (traverse along rows)
  cout << "max(B): " << endl << max(B) << endl;
  
  // maximum from each row (traverse along columns)
  cout << "max(B,1): " << endl << max(B,1) << endl;
  
  // maximum value in B
  cout << "max(max(B)) = " << max(max(B)) << endl;
  
  // sum of each column (traverse along rows)
  cout << "sum(B): " << endl << sum(B) << endl;
  
  // sum of each row (traverse along columns)
  cout << "sum(B,1) =" << endl << sum(B,1) << endl;
  
  // sum of all elements
  cout << "accu(B): " << accu(B) << endl;
  
  // trace = sum along diagonal
  cout << "trace(B): " << trace(B) << endl;
  
  // generate the identity matrix
  mat C = eye<mat>(4,4);
  
  // random matrix with values uniformly distributed in the [0,1] interval
  mat D = randu<mat>(4,4);
  D.print("D:");
  
  // row vectors are treated like a matrix with one row
  rowvec r = { 0.59119, 0.77321, 0.60275, 0.35887, 0.51683 };
  r.print("r:");
  
  // column vectors are treated like a matrix with one column
  vec q = { 0.14333, 0.59478, 0.14481, 0.58558, 0.60809 };
  q.print("q:");
  
  // convert matrix to vector; data in matrices is stored column-by-column
  vec v = vectorise(A);
  v.print("v:");
  
  // dot or inner product
  cout << "as_scalar(r*q): " << as_scalar(r*q) << endl;
  
  // outer product
  cout << "q*r: " << endl << q*r << endl;
  
  // multiply-and-accumulate operation (no temporary matrices are created)
  cout << "accu(A % B) = " << accu(A % B) << endl;
  
  // example of a compound operation
  B += 2.0 * A.t();
  B.print("B:");
  
  // imat specifies an integer matrix
  imat AA = { { 1, 2, 3 },
              { 4, 5, 6 },
              { 7, 8, 9 } };
  
  imat BB = { { 3, 2, 1 }, 
              { 6, 5, 4 },
              { 9, 8, 7 } };
  
  // comparison of matrices (element-wise); output of a relational operator is a umat
  umat ZZ = (AA >= BB);
  ZZ.print("ZZ:");
  
  // cubes ("3D matrices")
  cube Q( B.n_rows, B.n_cols, 2 );
  
  Q.slice(0) = B;
  Q.slice(1) = 2.0 * B;
  
  Q.print("Q:");
  
  // 2D field of matrices; 3D fields are also supported
  field<mat> F(4,3); 
  
  for(uword col=0; col < F.n_cols; ++col)
  for(uword row=0; row < F.n_rows; ++row)
    {
    F(row,col) = randu<mat>(2,3);  // each element in field<mat> is a matrix
    }
  
  F.print("F:");
  getchar();
  return 0;
  }

正常运行，大功告成！