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A fast and easy to use dense matrix linear algebra library written in Java.
/*
* Copyright (c) 2009-2012, Peter Abeles. All Rights Reserved.
*
* This file is part of Efficient Java Matrix Library (EJML).
*
* EJML is free software: you can redistribute it and/or modify
* it under the terms of the GNU Lesser General Public License as
* published by the Free Software Foundation, either version 3
* of the License, or (at your option) any later version.
*
* EJML 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 Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public
* License along with EJML. If not, see
* Adjusts the matrices so that the singular values are in descending order.
*
*
*
* In most implementations of SVD the singular values are automatically arranged in in descending
* order. In EJML this is not the case since it is often not needed and some computations can
* be saved by not doing that.
*
*
* @param U Matrix. Modified.
* @param tranU is U transposed or not.
* @param W Diagonal matrix with singular values. Modified.
* @param V Matrix. Modified.
* @param tranV is V transposed or not.
*/
// TODO the number of copies can probably be reduced here
public static void descendingOrder( DenseMatrix64F U , boolean tranU ,
DenseMatrix64F W ,
DenseMatrix64F V , boolean tranV )
{
int numSingular = Math.min(W.numRows,W.numCols);
checkSvdMatrixSize(U, tranU, W, V, tranV);
for( int i = 0; i < numSingular; i++ ) {
double bigValue=-1;
int bigIndex=-1;
// find the smallest singular value in the submatrix
for( int j = i; j < numSingular; j++ ) {
double v = W.get(j,j);
if( v > bigValue ) {
bigValue = v;
bigIndex = j;
}
}
// only swap if the current index is not the smallest
if( bigIndex == i)
continue;
if( bigIndex == -1 ) {
// there is at least one uncountable singular value. just stop here
break;
}
double tmp = W.get(i,i);
W.set(i,i,bigValue);
W.set(bigIndex,bigIndex,tmp);
if( V != null ) {
swapRowOrCol(V, tranV, i, bigIndex);
}
if( U != null ) {
swapRowOrCol(U, tranU, i, bigIndex);
}
}
}
/**
*
* Similar to {@link #descendingOrder(org.ejml.data.DenseMatrix64F, boolean, org.ejml.data.DenseMatrix64F, org.ejml.data.DenseMatrix64F, boolean)}
* but takes in an array of singular values instead.
*
*
* @param U Matrix. Modified.
* @param tranU is U transposed or not.
* @param singularValues Array of singular values. Modified.
* @param numSingularValues Number of elements in singularValues array
* @param V Matrix. Modified.
* @param tranV is V transposed or not.
*/
public static void descendingOrder( DenseMatrix64F U , boolean tranU ,
double singularValues[] ,
int numSingularValues ,
DenseMatrix64F V , boolean tranV )
{
// checkSvdMatrixSize(U, tranU, W, V, tranV);
for( int i = 0; i < numSingularValues; i++ ) {
double bigValue=-1;
int bigIndex=-1;
// find the smallest singular value in the submatrix
for( int j = i; j < numSingularValues; j++ ) {
double v = singularValues[j];
if( v > bigValue ) {
bigValue = v;
bigIndex = j;
}
}
// only swap if the current index is not the smallest
if( bigIndex == i)
continue;
if( bigIndex == -1 ) {
// there is at least one uncountable singular value. just stop here
break;
}
double tmp = singularValues[i];
singularValues[i] = bigValue;
singularValues[bigIndex] = tmp;
if( V != null ) {
swapRowOrCol(V, tranV, i, bigIndex);
}
if( U != null ) {
swapRowOrCol(U, tranU, i, bigIndex);
}
}
}
/**
* Checks to see if all the provided matrices are the expected size for an SVD. If an error is encountered
* then an exception is thrown. This automatically handles compact and non-compact formats
*/
public static void checkSvdMatrixSize(DenseMatrix64F U, boolean tranU, DenseMatrix64F W, DenseMatrix64F V, boolean tranV ) {
int numSingular = Math.min(W.numRows,W.numCols);
boolean compact = W.numRows == W.numCols;
if( compact ) {
if( U != null ) {
if( tranU && U.numRows != numSingular )
throw new IllegalArgumentException("Unexpected size of matrix U");
else if( !tranU && U.numCols != numSingular )
throw new IllegalArgumentException("Unexpected size of matrix U");
}
if( V != null ) {
if( tranV && V.numRows != numSingular )
throw new IllegalArgumentException("Unexpected size of matrix V");
else if( !tranV && V.numCols != numSingular )
throw new IllegalArgumentException("Unexpected size of matrix V");
}
} else {
if( U != null && U.numRows != U.numCols )
throw new IllegalArgumentException("Unexpected size of matrix U");
if( V != null && V.numRows != V.numCols )
throw new IllegalArgumentException("Unexpected size of matrix V");
if( U != null && U.numRows != W.numRows )
throw new IllegalArgumentException("Unexpected size of W");
if( V != null && V.numRows != W.numCols )
throw new IllegalArgumentException("Unexpected size of W");
}
}
private static void swapRowOrCol(DenseMatrix64F M, boolean tran, int i, int bigIndex) {
double tmp;
if( tran ) {
// swap the rows
for( int col = 0; col < M.numCols; col++ ) {
tmp = M.get(i,col);
M.set(i,col,M.get(bigIndex,col));
M.set(bigIndex,col,tmp);
}
} else {
// swap the columns
for( int row = 0; row < M.numRows; row++ ) {
tmp = M.get(row,i);
M.set(row,i,M.get(row,bigIndex));
M.set(row,bigIndex,tmp);
}
}
}
/**
*
* Returns the null-space from the singular value decomposition. The null space is a set of non-zero vectors that
* when multiplied by the original matrix return zero.
*
*
*
* The null space is found by extracting the columns in V that are associated singular values less than
* or equal to the threshold. In some situations a non-compact SVD is required.
*
*
* @param svd A precomputed decomposition. Not modified.
* @param nullSpace Storage for null space. Will be reshaped as needed. Modified.
* @param tol Threshold for selecting singular values. Try UtilEjml.EPS.
* @return The null space.
*/
public static DenseMatrix64F nullSpace( SingularValueDecomposition svd ,
DenseMatrix64F nullSpace , double tol )
{
int N = svd.numberOfSingularValues();
double s[] = svd.getSingularValues();
DenseMatrix64F V = svd.getV(null,true);
if( V.numRows != svd.numCols() ) {
throw new IllegalArgumentException("Can't compute the null space using a compact SVD for a matrix of this size.");
}
// first determine the size of the null space
int numVectors = svd.numCols()-N;
for( int i = 0; i < N; i++ ) {
if( s[i] <= tol ) {
numVectors++;
}
}
// declare output data
if( nullSpace == null ) {
nullSpace = new DenseMatrix64F(numVectors,svd.numCols());
} else {
nullSpace.reshape(numVectors,svd.numCols());
}
// now extract the vectors
int count = 0;
for( int i = 0; i < N; i++ ) {
if( s[i] <= tol ) {
CommonOps.extract(V, i,i+1,0, V.numCols,nullSpace,count++,0);
}
}
for( int i = N; i < svd.numCols(); i++ ) {
CommonOps.extract(V, i,i+1,0, V.numCols,nullSpace,count++,0);
}
CommonOps.transpose(nullSpace);
return nullSpace;
}
/**
*
* The vector associated will the smallest singular value is returned as the null space
* of the decomposed system. A right null space is returned if 'isRight' is set to true,
* and a left null space if false.
*
*
* @param svd A precomputed decomposition. Not modified.
* @param isRight true for right null space and false for left null space. Right is more commonly used.
* @param nullVector Optional storage for a vector for the null space. Modified.
* @return Vector in V associated with smallest singular value..
*/
public static DenseMatrix64F nullVector( SingularValueDecomposition svd ,
boolean isRight ,
DenseMatrix64F nullVector )
{
int N = svd.numberOfSingularValues();
double s[] = svd.getSingularValues();
DenseMatrix64F A = isRight ? svd.getV(null,true) : svd.getU(null,false);
if( isRight ) {
if( A.numRows != svd.numCols() ) {
throw new IllegalArgumentException("Can't compute the null space using a compact SVD for a matrix of this size.");
}
if( nullVector == null ) {
nullVector = new DenseMatrix64F(svd.numCols(),1);
}
} else {
if( A.numCols != svd.numRows() ) {
throw new IllegalArgumentException("Can't compute the null space using a compact SVD for a matrix of this size.");
}
if( nullVector == null ) {
nullVector = new DenseMatrix64F(svd.numRows(),1);
}
}
int smallestIndex = -1;
if( isRight && svd.numCols() > svd.numRows() )
smallestIndex = svd.numCols()-1;
else if( !isRight && svd.numCols() < svd.numRows() )
smallestIndex = svd.numRows()-1;
else {
// find the smallest singular value
double smallestValue = Double.MAX_VALUE;
for( int i = 0; i < N; i++ ) {
if( s[i] < smallestValue ) {
smallestValue = s[i];
smallestIndex = i;
}
}
}
// extract the null space
if( isRight )
SpecializedOps.subvector(A,smallestIndex,0,A.numRows,true,0,nullVector);
else
SpecializedOps.subvector(A,0,smallestIndex,A.numRows,false,0,nullVector);
return nullVector;
}
/**
* Extracts the rank of a matrix using a preexisting decomposition.
*
* @param svd A precomputed decomposition. Not modified.
* @param threshold Tolerance used to determine of a singular value is singular.
* @return The rank of the decomposed matrix.
*/
public static int rank( SingularValueDecomposition svd , double threshold ) {
int numRank=0;
double w[]= svd.getSingularValues();
int N = svd.numberOfSingularValues();
for( int j = 0; j < N; j++ ) {
if( w[j] > threshold)
numRank++;
}
return numRank;
}
/**
* Extracts the nullity of a matrix using a preexisting decomposition.
*
* @param svd A precomputed decomposition. Not modified.
* @param threshold Tolerance used to determine of a singular value is singular.
* @return The nullity of the decomposed matrix.
*/
public static int nullity( SingularValueDecomposition svd , double threshold ) {
int ret = 0;
double w[]= svd.getSingularValues();
int N = svd.numberOfSingularValues();
int numCol = svd.numCols();
for( int j = 0; j < N; j++ ) {
if( w[j] <= threshold) ret++;
}
return ret + numCol-N;
}
}