我正在尝试在c ++程序中使用Armadillo库，我正在通过Netbeans编译Cygwin版本的g ++。 Armadillo的说明在这个主题上有点令人困惑，它们没有任何特定于我尝试使用的确切设置。我确实在stackoverflow上找到了这个Compiling c++ with Armadillo library at NeatBeans并且遵循了BMaster23对T的建议并且最初得到了以下错误：
cd 'C:\Users\Ben\Google Drive\School\BMI 8020\Assignements\Program Assignment
2\BMI8020_Assignment_2'
C:\cygwin64\bin\make.exe -f Makefile CONF=Debug
"/usr/bin/make" -f nbproject/Makefile-Debug.mk QMAKE= SUBPROJECTS= .build-
conf
make[1]: Entering directory '/cygdrive/c/Users/Ben/Google Drive/School/BMI
8020/Assignements/Program Assignment 2/BMI8020_Assignment_2'
"/usr/bin/make" -f nbproject/Makefile-Debug.mk dist/Debug/Cygwin-
Windows/bmi8020_assignment_2.exe
make[2]: Entering directory '/cygdrive/c/Users/Ben/Google Drive/School/BMI
8020/Assignements/Program Assignment 2/BMI8020_Assignment_2'
make[2]: *** No rule to make target 'lapack_win64_MT.lib', needed by
'dist/Debug/Cygwin-Windows/bmi8020_assignment_2.exe'. Stop.
make[2]: Leaving directory '/cygdrive/c/Users/Ben/Google Drive/School/BMI
8020/Assignements/Program Assignment 2/BMI8020_Assignment_2'
make[1]: *** [nbproject/Makefile-Debug.mk:59: .build-conf] Error 2
make[1]: Leaving directory '/cygdrive/c/Users/Ben/Google Drive/School/BMI
8020/Assignements/Program Assignment 2/BMI8020_Assignment_2'
make: *** [nbproject/Makefile-impl.mk:40: .build-impl] Error 2
我尝试了几个不同的东西，例如从这里下载一些不同的dll和lib文件http://icl.cs.utk.edu/lapack-for-windows/lapack/并使用它们并在附加依赖项中替换它们并得到与上面相同的错误。
有人有什么想法吗？
编辑：根据要求，我尝试运行的代码只是Armadillo的示例代码。我认为这将是有效的代码。
#include <iostream>
#include <armadillo>
using namespace arma;
using namespace std;
// 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<mat> is a matrix
}
F.print("F:");
return 0;
}