ubuntu linux armadillo线性代数库安装

一些依赖的安装

在官网下的安装包里面有readme，其实按照readme安装是没问题的，但是里面没有具体的指令，这里写一下

sudo apt-get install libopenblas-dev
sudo apt-get install liblapack-dev
sudo apt-get install libarpack2-dev
sudo apt-get install libsuperlu-dev

四个依赖库

然后

cmake .

make

sudo make install

然后就编译一下他的example

g++ example1.cpp -o example1 -O2 -larmadillo

这里贴一下代码，功能很丰富，mat计算，vector计算都有

#include 
#include 

using namespace std;
using namespace arma;

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

int
main(int argc, char** argv)
  {
  cout << "Armadillo version: " << arma_version::as_string() << endl;
  
  mat A(2,3);  // directly specify the matrix size (elements are uninitialised)
  
  cout << "A.n_rows: " << A.n_rows << endl;  // .n_rows and .n_cols are read only
  cout << "A.n_cols: " << A.n_cols << endl;
  
  A(1,2) = 456.0;  // directly 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 particular value
  A.print("A:");
  
  // endr indicates "end of row"
  A << 0.165300 << 0.454037 << 0.995795 << 0.124098 << 0.047084 << endr
    << 0.688782 << 0.036549 << 0.552848 << 0.937664 << 0.866401 << endr
    << 0.348740 << 0.479388 << 0.506228 << 0.145673 << 0.491547 << endr
    << 0.148678 << 0.682258 << 0.571154 << 0.874724 << 0.444632 << endr
    << 0.245726 << 0.595218 << 0.409327 << 0.367827 << 0.385736 << endr;
  
  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(4,4);
  
  // random matrix with values uniformly distributed in the [0,1] interval
  mat D = randu(4,4);
  D.print("D:");
  
  // row vectors are treated like a matrix with one row
  rowvec r;
  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;
  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;
  imat BB;
  
  AA << 1 << 2 << 3 << endr << 4 << 5 << 6 << endr << 7 << 8 << 9;
  BB << 3 << 2 << 1 << endr << 6 << 5 << 4 << endr << 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 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(2,3);  // each element in field is a matrix
    }
  
  F.print("F:");
  
  return 0;
  }