C++ matrix_t::data方法代码示例

static void applyGaussian(matrix_t& m) {
	matrix_t::array_type& arr = m.data();
	double sig = *std::max_element(arr.begin(), arr.end());

	BOOST_FOREACH(double& v, arr) {
		v = std::exp(-v * v / (2 * sig * sig));
	}

// LU factorization of a general matrix A.  
//    Computes an LU factorization of a general M-by-N matrix A using
//    partial pivoting with row interchanges. Factorization has the form
//    A = P*L*U.
//    a       (IN/OUT - matrix(M,N)) On entry, the coefficient matrix A to be factored. On exit, the factors L and U from the factorization A = P*L*U.
//    ipivot  (OUT - vector(min(M,N))) Integer vector. The row i of A was interchanged with row IPIV(i).
//    info    (OUT - int)
//   0   :  successful exit
//   < 0 :  If INFO = -i, then the i-th argument had an illegal value.
//   > 0 :  If INFO = i, then U(i,i) is exactly zero. The  factorization has been completed, but the factor U is exactly singular, and division by zero will occur if it is used to solve a system of equations.
int getrf (matrix_t& a, pivot_t& ipivot)
{
	matrix_t::value_type* _a = a.data().begin();
	int _m = int(a.size1());
	int _n = int(a.size2());
	int _lda = _m;	// minor size
	int _info;

	rawLAPACK::getrf (_m, _n,	_a, _lda, ipivot.data().begin(), _info);

	return _info;
}

// Solution to a system using LU factorization 
//   Solves a system of linear equations A*X = B with a general NxN
//   matrix A using the LU factorization computed by GETRF.
//   transa  (IN - char)  'T' for the transpose of A, 'N' otherwise.
//   a       (IN - matrix(M,N)) The factors L and U from the factorization A = P*L*U as computed by GETRF.
//   ipivot  (IN - vector(min(M,N))) Integer vector. The pivot indices from GETRF; row i of A was interchanged with row IPIV(i).
//   b       (IN/OUT - matrix(ldb,NRHS)) Matrix of same numerical type as A. On entry, the right hand side matrix B. On exit, the solution matrix X.
//
//   info    (OUT - int)
//   0   : function completed normally
//   < 0 : The ith argument, where i = abs(return value) had an illegal value.
//   > 0 : if INFO =  i,  U(i,i)  is  exactly  zero;  the  matrix is singular and its inverse could not be computed.
int getrs (char transa, matrix_t& a,
	    pivot_t& ipivot, matrix_t& b)
{
	matrix_t::value_type* _a = a.data().begin();
	int a_n = int(a.size1());
	int _lda = a_n;
	int p_n = int(ipivot.size());

	matrix_t::value_type* _b = b.data().begin();
	int b_n = int(b.size1());
	int _ldb = b_n;
	int _nrhs = int(b.size2()); /* B's size2 is the # of vectors on rhs */

	if (a_n != b_n) /*Test to see if AX=B has correct dimensions */
		return -101;
	if (p_n < a_n)     /*Check to see if ipivot is big enough */
		return -102;

	int _info;
	rawLAPACK::getrs (transa, a_n, _nrhs, _a,	_lda, ipivot.data().begin(), 
				_b, _ldb, _info);

	return _info;
} 

// QR Factorization of a MxN General Matrix A.
//    a       (IN/OUT - matrix(M,N)) On entry, the coefficient matrix A. On exit , the upper triangle and diagonal is the min(M,N) by N upper triangular matrix R.  The lower triangle, together with the tau vector, is the orthogonal matrix Q as a product of min(M,N) elementary reflectors.
//    tau     (OUT - vector (min(M,N))) Vector of the same numerical type as A. The scalar factors of the elementary reflectors.
//    info    (OUT - int)
//   0   : function completed normally
//   < 0 : The ith argument, where i = abs(return value) had an illegal value.
int geqrf (matrix_t& a, vector_t& tau)
{
	int              _m = int(a.size1());
	int              _n = int(a.size2());
	int              _lda = int(a.size1());
	int              _info;

	// make_sure tau's size is greater than or equal to min(m,n)
	if (int(tau.size()) < (_n<_m ? _n : _m) )
		return -104;

	int ldwork = _n*_n;
	vector_t dwork(ldwork);
	rawLAPACK::geqrf (_m, _n, a.data().begin(), _lda, tau.data().begin(), dwork.data().begin(), ldwork, _info);

	return _info;
}