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A comprehensive collection of matrix data structures, linear solvers, least squares methods, eigenvalue, and singular value decompositions.

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package no.uib.cipr.matrix;

import com.github.fommil.netlib.LAPACK;

import java.util.BitSet;

/**
 * Matrix that represents a permutation of another matrix's rows / columns.
 * 

* NOTE: the transpose of a permutation matrix is its inverse. * * @author Sam Halliday */ public class PermutationMatrix extends AbstractMatrix { /** * The sequential row permutations to perform, e.g. (2, 3, 3) means: permute * row 1 with row 2, then permute row 2 with row 3, then permute row 3 with * row 3 (i.e. do nothing). *

* Using this factory will ensure that LAPACK optimisations are available * for multiplication operations. * * @param pivots * using fortran (1-indexed) notation. */ public static PermutationMatrix fromPartialPivots(int pivots[]) { int[] permutations = new int[pivots.length]; for (int i = 0; i < pivots.length; i++) { permutations[i] = i; } for (int i = 0; i < pivots.length; i++) { int j = pivots[i] - 1; if (j == i) continue; int tmp = permutations[i]; permutations[i] = permutations[j]; permutations[j] = tmp; } return new PermutationMatrix(permutations, pivots); } private int[] permutations, pivots; private boolean transposed; // the instantaneous permutations to perform (zero-indexed) // http://en.wikipedia.org/wiki/Permutation_matrix public PermutationMatrix(int permutations[]) { this(permutations, null); } // permutations - instantaneous (zero-indexed) // pivots - sequential (fortran-indexed) private PermutationMatrix(int permutations[], int pivots[]) { super(permutations.length, permutations.length); this.permutations = permutations; BitSet bitset = new BitSet(); for (int i : permutations) { if (bitset.get(i)) throw new IllegalArgumentException("non-unique permutations: " + i); bitset.set(i); } this.pivots = pivots; } @Override public double get(int row, int column) { if (!transposed && permutations[row] == column) return 1; if (transposed && permutations[column] == row) return 1; return 0; } @Override public Matrix transpose() { transposed = !transposed; return this; } @Override public Matrix mult(Matrix B, Matrix C) { if (C instanceof DenseMatrix) return mult(B, (DenseMatrix) C); return super.mult(B, C); } public Matrix mult(Matrix B, DenseMatrix C) { if (pivots == null) return super.mult(B, C); checkMultAdd(B, C); C.set(B); LAPACK.getInstance().dlaswp(C.numColumns(), C.getData(), Matrices.ld(C.numRows()), 1, pivots.length, pivots, transposed ? -1 : 1); return C; } @Override public Matrix transAmult(Matrix B, Matrix C) { if (C instanceof DenseMatrix) return transAmult(B, (DenseMatrix) C); return super.transAmult(B, C); } public Matrix transAmult(Matrix B, DenseMatrix C) { if (pivots == null) return super.transAmult(B, C); checkTransAmultAdd(B, C); C.set(B); LAPACK.getInstance().dlaswp(C.numColumns(), C.getData(), Matrices.ld(C.numRows()), 1, pivots.length, pivots, transposed ? 1 : -1); return C; } }





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