源码

#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 << "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:");

    system("pause");
    return 0;
}

运行结果

Armadillo version: 12.8.0 (Cortisol Injector)
A.n_rows: 2
A.n_cols: 3
A:
            0            0            0
            0            0   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 Press any key to continue . . .