Armadillo在ubuntu下的安装与测试

Armadillo安装前需要先安装依赖库： OpenBLAS and LAPACK

一、安装

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

方法一

sudo apt-get install libarmadillo-dev

方法二
先下载，手动编译：

http://arma.sourceforge.net/download.html

cmake .
make
sudo make install

二、测试

源代码

#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<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;
    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<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 is a matrix
        }

    F.print("F:");

    return 0;
}

CMakeKist的写法

cmake_minimum_required(VERSION 3.10)
project(ArmadilloTest)

set(CMAKE_CXX_STANDARD 11)

add_executable(ArmadilloTest main.cpp)
target_link_libraries(ArmadilloTest -larmadillo)

运行结果：

A.n_rows: 2
A.n_cols: 3
A:
  1.7786e-322  1.9763e-323  1.2015e-318
  1.7786e-322  9.3207e-314   4.5600e+02
A:
   5.0000
A:
   5.0000   5.0000   5.0000   5.0000   5.0000
   5.0000   5.0000   5.0000   5.0000   5.0000
   5.0000   5.0000   5.0000   5.0000   5.0000
   5.0000   5.0000   5.0000   5.0000   5.0000
A:
   0.1653   0.4540   0.9958   0.1241   0.0471
   0.6888   0.0365   0.5528   0.9377   0.8664
   0.3487   0.4794   0.5062   0.1457   0.4915
   0.1487   0.6823   0.5712   0.8747   0.4446
   0.2457   0.5952   0.4093   0.3678   0.3857
det(A): -0.0246018
inv(A): 
    1.2916    2.0000   -7.4695   -6.0752   11.8714
   -0.1011   -0.4619   -1.5556   -0.9830    4.1651
    0.8976   -0.1524    1.9191    1.2554   -3.6600
    0.1869    0.6267   -2.6662    0.1198    1.8289
   -1.7976   -0.9973    7.6647    3.9404   -9.2573

B( span(0,2), span(3,4) ):
   0.1241   0.0471
   0.9377   0.8664
   0.1457   0.4915

B( 0,3, size(3,2) ):
   0.1241   0.0471
   0.9377   0.8664
   0.1457   0.4915

B.row(0): 
   0.1653   0.4540   0.9958   0.1241   0.0471

B.col(1): 
   0.4540
   0.0365
   0.4794
   0.6823
   0.5952

B.t(): 
   0.1653   0.6888   0.3487   0.1487   0.2457
   0.4540   0.0365   0.4794   0.6823   0.5952
   0.9958   0.5528   0.5062   0.5712   0.4093
   0.1241   0.9377   0.1457   0.8747   0.3678
   0.0471   0.8664   0.4915   0.4446   0.3857

max(B): 
   0.6888   0.6823   0.9958   0.9377   0.8664

max(B,1): 
   0.9958
   0.9377
   0.5062
   0.8747
   0.5952

max(max(B)) = 0.995795
sum(B): 
   1.5972   2.2474   3.0354   2.4500   2.2354

sum(B,1) =
   1.7863
   3.0822
   1.9716
   2.7214
   2.0038

accu(B): 11.5654
trace(B): 1.96854
D:
   0.7868   0.0193   0.5206   0.1400
   0.2505   0.4049   0.3447   0.5439
   0.7107   0.2513   0.2742   0.5219
   0.9467   0.0227   0.5610   0.8571
r:
   0.5912   0.7732   0.6028   0.3589   0.5168
q:
   0.1433
   0.5948
   0.1448
   0.5856
   0.6081
v:
   0.1653
   0.6888
   0.3487
   0.1487
   0.2457
   0.4540
   0.0365
   0.4794
   0.6823
   0.5952
   0.9958
   0.5528
   0.5062
   0.5712
   0.4093
   0.1241
   0.9377
   0.1457
   0.8747
   0.3678
   0.0471
   0.8664
   0.4915
   0.4446
   0.3857
as_scalar(r*q): 1.15634
q*r: 
   0.0847   0.1108   0.0864   0.0514   0.0741
   0.3516   0.4599   0.3585   0.2134   0.3074
   0.0856   0.1120   0.0873   0.0520   0.0748
   0.3462   0.4528   0.3530   0.2101   0.3026
   0.3595   0.4702   0.3665   0.2182   0.3143

accu(A % B) = 7.16744
B:
   0.4959   1.8316   1.6933   0.4215   0.5385
   1.5969   0.1096   1.5116   2.3022   2.0568
   2.3403   1.5851   1.5187   1.2880   1.3102
   0.3969   2.5576   0.8625   2.6242   1.1803
   0.3399   2.3280   1.3924   1.2571   1.1572
ZZ:
        0        1        1
        0        1        1
        0        1        1
Q:
[cube slice 0]
   0.4959   1.8316   1.6933   0.4215   0.5385
   1.5969   0.1096   1.5116   2.3022   2.0568
   2.3403   1.5851   1.5187   1.2880   1.3102
   0.3969   2.5576   0.8625   2.6242   1.1803
   0.3399   2.3280   1.3924   1.2571   1.1572
        
[cube slice 1]
   0.9918   3.6632   3.3865   0.8429   1.0771
   3.1937   0.2193   3.0232   4.6044   4.1137
   4.6807   3.1702   3.0374   2.5760   2.6204
   0.7937   5.1152   1.7250   5.2483   2.3606
   0.6798   4.6560   2.7848   2.5142   2.3144
        
F:
[field column 0]
   0.4998   0.7443   0.2393
   0.4194   0.2492   0.3201

   0.9105   0.2455   0.7159
   0.1648   0.1983   0.9678

   0.7694   0.4599   0.7770
   0.0807   0.2573   0.5839

   0.9503   0.3223   0.2564
   0.4381   0.5324   0.0455


[field column 1]
   0.5050   0.0912   0.0309
   0.6962   0.9071   0.1520

   0.9815   0.2988   0.4810
   0.6204   0.3613   0.2978

   0.2852   0.6289   0.7139
   0.9242   0.7550   0.7228

   0.0698   0.0889   0.4238
   0.4868   0.7596   0.5970


[field column 2]
   0.0864   0.6238   0.2254
   0.2730   0.2221   0.4341

   0.9873   0.8532   0.8364
   0.2110   0.2841   0.3667

   0.9351   0.4909   0.3621
   0.8599   0.0221   0.7364

   0.5194   0.0290   0.1122
   0.4230   0.9092   0.9802