#include #include 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 main2(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:"); // 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; }