/************************************************************************* Copyright (c) 2005-2007, Sergey Bochkanov (ALGLIB project). >>> SOURCE LICENSE >>> This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation (www.fsf.org); either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. A copy of the GNU General Public License is available at http://www.fsf.org/licensing/licenses >>> END OF LICENSE >>> *************************************************************************/ using System; namespace alglib { public class svd { /************************************************************************* Singular value decomposition of a rectangular matrix. The algorithm calculates the singular value decomposition of a matrix of size MxN: A = U * S * V^T The algorithm finds the singular values and, optionally, matrices U and V^T. The algorithm can find both first min(M,N) columns of matrix U and rows of matrix V^T (singular vectors), and matrices U and V^T wholly (of sizes MxM and NxN respectively). Take into account that the subroutine does not return matrix V but V^T. Input parameters: A - matrix to be decomposed. Array whose indexes range within [0..M-1, 0..N-1]. M - number of rows in matrix A. N - number of columns in matrix A. UNeeded - 0, 1 or 2. See the description of the parameter U. VTNeeded - 0, 1 or 2. See the description of the parameter VT. AdditionalMemory - If the parameter: * equals 0, the algorithm doesn’t use additional memory (lower requirements, lower performance). * equals 1, the algorithm uses additional memory of size min(M,N)*min(M,N) of real numbers. It often speeds up the algorithm. * equals 2, the algorithm uses additional memory of size M*min(M,N) of real numbers. It allows to get a maximum performance. The recommended value of the parameter is 2. Output parameters: W - contains singular values in descending order. U - if UNeeded=0, U isn't changed, the left singular vectors are not calculated. if Uneeded=1, U contains left singular vectors (first min(M,N) columns of matrix U). Array whose indexes range within [0..M-1, 0..Min(M,N)-1]. if UNeeded=2, U contains matrix U wholly. Array whose indexes range within [0..M-1, 0..M-1]. VT - if VTNeeded=0, VT isn’t changed, the right singular vectors are not calculated. if VTNeeded=1, VT contains right singular vectors (first min(M,N) rows of matrix V^T). Array whose indexes range within [0..min(M,N)-1, 0..N-1]. if VTNeeded=2, VT contains matrix V^T wholly. Array whose indexes range within [0..N-1, 0..N-1]. -- ALGLIB -- Copyright 2005 by Bochkanov Sergey *************************************************************************/ public static bool rmatrixsvd(double[,] a, int m, int n, int uneeded, int vtneeded, int additionalmemory, ref double[] w, ref double[,] u, ref double[,] vt) { bool result = new bool(); double[] tauq = new double[0]; double[] taup = new double[0]; double[] tau = new double[0]; double[] e = new double[0]; double[] work = new double[0]; double[,] t2 = new double[0,0]; bool isupper = new bool(); int minmn = 0; int ncu = 0; int nrvt = 0; int nru = 0; int ncvt = 0; int i = 0; int j = 0; a = (double[,])a.Clone(); result = true; if( m==0 | n==0 ) { return result; } System.Diagnostics.Debug.Assert(uneeded>=0 & uneeded<=2, "SVDDecomposition: wrong parameters!"); System.Diagnostics.Debug.Assert(vtneeded>=0 & vtneeded<=2, "SVDDecomposition: wrong parameters!"); System.Diagnostics.Debug.Assert(additionalmemory>=0 & additionalmemory<=2, "SVDDecomposition: wrong parameters!"); // // initialize // minmn = Math.Min(m, n); w = new double[minmn+1]; ncu = 0; nru = 0; if( uneeded==1 ) { nru = m; ncu = minmn; u = new double[nru-1+1, ncu-1+1]; } if( uneeded==2 ) { nru = m; ncu = m; u = new double[nru-1+1, ncu-1+1]; } nrvt = 0; ncvt = 0; if( vtneeded==1 ) { nrvt = minmn; ncvt = n; vt = new double[nrvt-1+1, ncvt-1+1]; } if( vtneeded==2 ) { nrvt = n; ncvt = n; vt = new double[nrvt-1+1, ncvt-1+1]; } // // M much larger than N // Use bidiagonal reduction with QR-decomposition // if( (double)(m)>(double)(1.6*n) ) { if( uneeded==0 ) { // // No left singular vectors to be computed // ortfac.rmatrixqr(ref a, m, n, ref tau); for(i=0; i<=n-1; i++) { for(j=0; j<=i-1; j++) { a[i,j] = 0; } } ortfac.rmatrixbd(ref a, n, n, ref tauq, ref taup); ortfac.rmatrixbdunpackpt(ref a, n, n, ref taup, nrvt, ref vt); ortfac.rmatrixbdunpackdiagonals(ref a, n, n, ref isupper, ref w, ref e); result = bdsvd.rmatrixbdsvd(ref w, e, n, isupper, false, ref u, 0, ref a, 0, ref vt, ncvt); return result; } else { // // Left singular vectors (may be full matrix U) to be computed // ortfac.rmatrixqr(ref a, m, n, ref tau); ortfac.rmatrixqrunpackq(ref a, m, n, ref tau, ncu, ref u); for(i=0; i<=n-1; i++) { for(j=0; j<=i-1; j++) { a[i,j] = 0; } } ortfac.rmatrixbd(ref a, n, n, ref tauq, ref taup); ortfac.rmatrixbdunpackpt(ref a, n, n, ref taup, nrvt, ref vt); ortfac.rmatrixbdunpackdiagonals(ref a, n, n, ref isupper, ref w, ref e); if( additionalmemory<1 ) { // // No additional memory can be used // ortfac.rmatrixbdmultiplybyq(ref a, n, n, ref tauq, ref u, m, n, true, false); result = bdsvd.rmatrixbdsvd(ref w, e, n, isupper, false, ref u, m, ref a, 0, ref vt, ncvt); } else { // // Large U. Transforming intermediate matrix T2 // work = new double[Math.Max(m, n)+1]; ortfac.rmatrixbdunpackq(ref a, n, n, ref tauq, n, ref t2); blas.copymatrix(ref u, 0, m-1, 0, n-1, ref a, 0, m-1, 0, n-1); blas.inplacetranspose(ref t2, 0, n-1, 0, n-1, ref work); result = bdsvd.rmatrixbdsvd(ref w, e, n, isupper, false, ref u, 0, ref t2, n, ref vt, ncvt); blas.matrixmatrixmultiply(ref a, 0, m-1, 0, n-1, false, ref t2, 0, n-1, 0, n-1, true, 1.0, ref u, 0, m-1, 0, n-1, 0.0, ref work); } return result; } } // // N much larger than M // Use bidiagonal reduction with LQ-decomposition // if( (double)(n)>(double)(1.6*m) ) { if( vtneeded==0 ) { // // No right singular vectors to be computed // ortfac.rmatrixlq(ref a, m, n, ref tau); for(i=0; i<=m-1; i++) { for(j=i+1; j<=m-1; j++) { a[i,j] = 0; } } ortfac.rmatrixbd(ref a, m, m, ref tauq, ref taup); ortfac.rmatrixbdunpackq(ref a, m, m, ref tauq, ncu, ref u); ortfac.rmatrixbdunpackdiagonals(ref a, m, m, ref isupper, ref w, ref e); work = new double[m+1]; blas.inplacetranspose(ref u, 0, nru-1, 0, ncu-1, ref work); result = bdsvd.rmatrixbdsvd(ref w, e, m, isupper, false, ref a, 0, ref u, nru, ref vt, 0); blas.inplacetranspose(ref u, 0, nru-1, 0, ncu-1, ref work); return result; } else { // // Right singular vectors (may be full matrix VT) to be computed // ortfac.rmatrixlq(ref a, m, n, ref tau); ortfac.rmatrixlqunpackq(ref a, m, n, ref tau, nrvt, ref vt); for(i=0; i<=m-1; i++) { for(j=i+1; j<=m-1; j++) { a[i,j] = 0; } } ortfac.rmatrixbd(ref a, m, m, ref tauq, ref taup); ortfac.rmatrixbdunpackq(ref a, m, m, ref tauq, ncu, ref u); ortfac.rmatrixbdunpackdiagonals(ref a, m, m, ref isupper, ref w, ref e); work = new double[Math.Max(m, n)+1]; blas.inplacetranspose(ref u, 0, nru-1, 0, ncu-1, ref work); if( additionalmemory<1 ) { // // No additional memory available // ortfac.rmatrixbdmultiplybyp(ref a, m, m, ref taup, ref vt, m, n, false, true); result = bdsvd.rmatrixbdsvd(ref w, e, m, isupper, false, ref a, 0, ref u, nru, ref vt, n); } else { // // Large VT. Transforming intermediate matrix T2 // ortfac.rmatrixbdunpackpt(ref a, m, m, ref taup, m, ref t2); result = bdsvd.rmatrixbdsvd(ref w, e, m, isupper, false, ref a, 0, ref u, nru, ref t2, m); blas.copymatrix(ref vt, 0, m-1, 0, n-1, ref a, 0, m-1, 0, n-1); blas.matrixmatrixmultiply(ref t2, 0, m-1, 0, m-1, false, ref a, 0, m-1, 0, n-1, false, 1.0, ref vt, 0, m-1, 0, n-1, 0.0, ref work); } blas.inplacetranspose(ref u, 0, nru-1, 0, ncu-1, ref work); return result; } } // // M<=N // We can use inplace transposition of U to get rid of columnwise operations // if( m<=n ) { ortfac.rmatrixbd(ref a, m, n, ref tauq, ref taup); ortfac.rmatrixbdunpackq(ref a, m, n, ref tauq, ncu, ref u); ortfac.rmatrixbdunpackpt(ref a, m, n, ref taup, nrvt, ref vt); ortfac.rmatrixbdunpackdiagonals(ref a, m, n, ref isupper, ref w, ref e); work = new double[m+1]; blas.inplacetranspose(ref u, 0, nru-1, 0, ncu-1, ref work); result = bdsvd.rmatrixbdsvd(ref w, e, minmn, isupper, false, ref a, 0, ref u, nru, ref vt, ncvt); blas.inplacetranspose(ref u, 0, nru-1, 0, ncu-1, ref work); return result; } // // Simple bidiagonal reduction // ortfac.rmatrixbd(ref a, m, n, ref tauq, ref taup); ortfac.rmatrixbdunpackq(ref a, m, n, ref tauq, ncu, ref u); ortfac.rmatrixbdunpackpt(ref a, m, n, ref taup, nrvt, ref vt); ortfac.rmatrixbdunpackdiagonals(ref a, m, n, ref isupper, ref w, ref e); if( additionalmemory<2 | uneeded==0 ) { // // We cant use additional memory or there is no need in such operations // result = bdsvd.rmatrixbdsvd(ref w, e, minmn, isupper, false, ref u, nru, ref a, 0, ref vt, ncvt); } else { // // We can use additional memory // t2 = new double[minmn-1+1, m-1+1]; blas.copyandtranspose(ref u, 0, m-1, 0, minmn-1, ref t2, 0, minmn-1, 0, m-1); result = bdsvd.rmatrixbdsvd(ref w, e, minmn, isupper, false, ref u, 0, ref t2, m, ref vt, ncvt); blas.copyandtranspose(ref t2, 0, minmn-1, 0, m-1, ref u, 0, m-1, 0, minmn-1); } return result; } } }