home | career | drupal | java | mac | mysql | perl | scala | uml | unix

# Java example source code file (SingularValueDecomposition.java)

This example Java source code file (SingularValueDecomposition.java) is included in the alvinalexander.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" TM.

## Java - Java tags/keywords

array2drowrealmatrix, decompositionsolver, eps, override, realmatrix, realvector, singularvaluedecomposition, solver, tiny

## The SingularValueDecomposition.java Java example source code

```/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements.  See the NOTICE file distributed with
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License.  You may obtain a copy of the License at
*
*
* Unless required by applicable law or agreed to in writing, software
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
*/
package org.apache.commons.math3.linear;

import org.apache.commons.math3.exception.NumberIsTooLargeException;
import org.apache.commons.math3.exception.util.LocalizedFormats;
import org.apache.commons.math3.util.FastMath;
import org.apache.commons.math3.util.Precision;

/**
* Calculates the compact Singular Value Decomposition of a matrix.
* <p>
* The Singular Value Decomposition of matrix A is a set of three matrices: U,
* Σ and V such that A = U × Σ × V<sup>T. Let A be
* a m × n matrix, then U is a m × p orthogonal matrix, Σ is a
* p × p diagonal matrix with positive or null elements, V is a p ×
* n orthogonal matrix (hence V<sup>T is also orthogonal) where
* p=min(m,n).
* </p>
* <p>This class is similar to the class with similar name from the
* <a href="http://math.nist.gov/javanumerics/jama/">JAMA library, with the
* following changes:</p>
* <ul>
*   <li>the {@code norm2} method which has been renamed as {@link #getNorm()
*   getNorm},</li>
*   <li>the {@code cond} method which has been renamed as {@link
*   #getConditionNumber() getConditionNumber},</li>
*   <li>the {@code rank} method which has been renamed as {@link #getRank()
*   getRank},</li>
* </ul>
* @see <a href="http://mathworld.wolfram.com/SingularValueDecomposition.html">MathWorld
* @see <a href="http://en.wikipedia.org/wiki/Singular_value_decomposition">Wikipedia
* @since 2.0 (changed to concrete class in 3.0)
*/
public class SingularValueDecomposition {
/** Relative threshold for small singular values. */
private static final double EPS = 0x1.0p-52;
/** Absolute threshold for small singular values. */
private static final double TINY = 0x1.0p-966;
/** Computed singular values. */
private final double[] singularValues;
/** max(row dimension, column dimension). */
private final int m;
/** min(row dimension, column dimension). */
private final int n;
/** Indicator for transposed matrix. */
private final boolean transposed;
/** Cached value of U matrix. */
private final RealMatrix cachedU;
/** Cached value of transposed U matrix. */
private RealMatrix cachedUt;
/** Cached value of S (diagonal) matrix. */
private RealMatrix cachedS;
/** Cached value of V matrix. */
private final RealMatrix cachedV;
/** Cached value of transposed V matrix. */
private RealMatrix cachedVt;
/**
* Tolerance value for small singular values, calculated once we have
* populated "singularValues".
**/
private final double tol;

/**
* Calculates the compact Singular Value Decomposition of the given matrix.
*
* @param matrix Matrix to decompose.
*/
public SingularValueDecomposition(final RealMatrix matrix) {
final double[][] A;

// "m" is always the largest dimension.
if (matrix.getRowDimension() < matrix.getColumnDimension()) {
transposed = true;
A = matrix.transpose().getData();
m = matrix.getColumnDimension();
n = matrix.getRowDimension();
} else {
transposed = false;
A = matrix.getData();
m = matrix.getRowDimension();
n = matrix.getColumnDimension();
}

singularValues = new double[n];
final double[][] U = new double[m][n];
final double[][] V = new double[n][n];
final double[] e = new double[n];
final double[] work = new double[m];
// Reduce A to bidiagonal form, storing the diagonal elements
// in s and the super-diagonal elements in e.
final int nct = FastMath.min(m - 1, n);
final int nrt = FastMath.max(0, n - 2);
for (int k = 0; k < FastMath.max(nct, nrt); k++) {
if (k < nct) {
// Compute the transformation for the k-th column and
// place the k-th diagonal in s[k].
// Compute 2-norm of k-th column without under/overflow.
singularValues[k] = 0;
for (int i = k; i < m; i++) {
singularValues[k] = FastMath.hypot(singularValues[k], A[i][k]);
}
if (singularValues[k] != 0) {
if (A[k][k] < 0) {
singularValues[k] = -singularValues[k];
}
for (int i = k; i < m; i++) {
A[i][k] /= singularValues[k];
}
A[k][k] += 1;
}
singularValues[k] = -singularValues[k];
}
for (int j = k + 1; j < n; j++) {
if (k < nct &&
singularValues[k] != 0) {
// Apply the transformation.
double t = 0;
for (int i = k; i < m; i++) {
t += A[i][k] * A[i][j];
}
t = -t / A[k][k];
for (int i = k; i < m; i++) {
A[i][j] += t * A[i][k];
}
}
// Place the k-th row of A into e for the
// subsequent calculation of the row transformation.
e[j] = A[k][j];
}
if (k < nct) {
// Place the transformation in U for subsequent back
// multiplication.
for (int i = k; i < m; i++) {
U[i][k] = A[i][k];
}
}
if (k < nrt) {
// Compute the k-th row transformation and place the
// k-th super-diagonal in e[k].
// Compute 2-norm without under/overflow.
e[k] = 0;
for (int i = k + 1; i < n; i++) {
e[k] = FastMath.hypot(e[k], e[i]);
}
if (e[k] != 0) {
if (e[k + 1] < 0) {
e[k] = -e[k];
}
for (int i = k + 1; i < n; i++) {
e[i] /= e[k];
}
e[k + 1] += 1;
}
e[k] = -e[k];
if (k + 1 < m &&
e[k] != 0) {
// Apply the transformation.
for (int i = k + 1; i < m; i++) {
work[i] = 0;
}
for (int j = k + 1; j < n; j++) {
for (int i = k + 1; i < m; i++) {
work[i] += e[j] * A[i][j];
}
}
for (int j = k + 1; j < n; j++) {
final double t = -e[j] / e[k + 1];
for (int i = k + 1; i < m; i++) {
A[i][j] += t * work[i];
}
}
}

// Place the transformation in V for subsequent
// back multiplication.
for (int i = k + 1; i < n; i++) {
V[i][k] = e[i];
}
}
}
// Set up the final bidiagonal matrix or order p.
int p = n;
if (nct < n) {
singularValues[nct] = A[nct][nct];
}
if (m < p) {
singularValues[p - 1] = 0;
}
if (nrt + 1 < p) {
e[nrt] = A[nrt][p - 1];
}
e[p - 1] = 0;

// Generate U.
for (int j = nct; j < n; j++) {
for (int i = 0; i < m; i++) {
U[i][j] = 0;
}
U[j][j] = 1;
}
for (int k = nct - 1; k >= 0; k--) {
if (singularValues[k] != 0) {
for (int j = k + 1; j < n; j++) {
double t = 0;
for (int i = k; i < m; i++) {
t += U[i][k] * U[i][j];
}
t = -t / U[k][k];
for (int i = k; i < m; i++) {
U[i][j] += t * U[i][k];
}
}
for (int i = k; i < m; i++) {
U[i][k] = -U[i][k];
}
U[k][k] = 1 + U[k][k];
for (int i = 0; i < k - 1; i++) {
U[i][k] = 0;
}
} else {
for (int i = 0; i < m; i++) {
U[i][k] = 0;
}
U[k][k] = 1;
}
}

// Generate V.
for (int k = n - 1; k >= 0; k--) {
if (k < nrt &&
e[k] != 0) {
for (int j = k + 1; j < n; j++) {
double t = 0;
for (int i = k + 1; i < n; i++) {
t += V[i][k] * V[i][j];
}
t = -t / V[k + 1][k];
for (int i = k + 1; i < n; i++) {
V[i][j] += t * V[i][k];
}
}
}
for (int i = 0; i < n; i++) {
V[i][k] = 0;
}
V[k][k] = 1;
}

// Main iteration loop for the singular values.
final int pp = p - 1;
while (p > 0) {
int k;
int kase;
// Here is where a test for too many iterations would go.
// This section of the program inspects for
// negligible elements in the s and e arrays.  On
// completion the variables kase and k are set as follows.
// kase = 1     if s(p) and e[k-1] are negligible and k<p
// kase = 2     if s(k) is negligible and k<p
// kase = 3     if e[k-1] is negligible, k<p, and
//              s(k), ..., s(p) are not negligible (qr step).
// kase = 4     if e(p-1) is negligible (convergence).
for (k = p - 2; k >= 0; k--) {
final double threshold
= TINY + EPS * (FastMath.abs(singularValues[k]) +
FastMath.abs(singularValues[k + 1]));

// the following condition is written this way in order
// to break out of the loop when NaN occurs, writing it
// as "if (FastMath.abs(e[k]) <= threshold)" would loop
// indefinitely in case of NaNs because comparison on NaNs
// always return false, regardless of what is checked
// see issue MATH-947
if (!(FastMath.abs(e[k]) > threshold)) {
e[k] = 0;
break;
}

}

if (k == p - 2) {
kase = 4;
} else {
int ks;
for (ks = p - 1; ks >= k; ks--) {
if (ks == k) {
break;
}
final double t = (ks != p ? FastMath.abs(e[ks]) : 0) +
(ks != k + 1 ? FastMath.abs(e[ks - 1]) : 0);
if (FastMath.abs(singularValues[ks]) <= TINY + EPS * t) {
singularValues[ks] = 0;
break;
}
}
if (ks == k) {
kase = 3;
} else if (ks == p - 1) {
kase = 1;
} else {
kase = 2;
k = ks;
}
}
k++;
// Perform the task indicated by kase.
switch (kase) {
// Deflate negligible s(p).
case 1: {
double f = e[p - 2];
e[p - 2] = 0;
for (int j = p - 2; j >= k; j--) {
double t = FastMath.hypot(singularValues[j], f);
final double cs = singularValues[j] / t;
final double sn = f / t;
singularValues[j] = t;
if (j != k) {
f = -sn * e[j - 1];
e[j - 1] = cs * e[j - 1];
}

for (int i = 0; i < n; i++) {
t = cs * V[i][j] + sn * V[i][p - 1];
V[i][p - 1] = -sn * V[i][j] + cs * V[i][p - 1];
V[i][j] = t;
}
}
}
break;
// Split at negligible s(k).
case 2: {
double f = e[k - 1];
e[k - 1] = 0;
for (int j = k; j < p; j++) {
double t = FastMath.hypot(singularValues[j], f);
final double cs = singularValues[j] / t;
final double sn = f / t;
singularValues[j] = t;
f = -sn * e[j];
e[j] = cs * e[j];

for (int i = 0; i < m; i++) {
t = cs * U[i][j] + sn * U[i][k - 1];
U[i][k - 1] = -sn * U[i][j] + cs * U[i][k - 1];
U[i][j] = t;
}
}
}
break;
// Perform one qr step.
case 3: {
// Calculate the shift.
final double maxPm1Pm2 = FastMath.max(FastMath.abs(singularValues[p - 1]),
FastMath.abs(singularValues[p - 2]));
final double scale = FastMath.max(FastMath.max(FastMath.max(maxPm1Pm2,
FastMath.abs(e[p - 2])),
FastMath.abs(singularValues[k])),
FastMath.abs(e[k]));
final double sp = singularValues[p - 1] / scale;
final double spm1 = singularValues[p - 2] / scale;
final double epm1 = e[p - 2] / scale;
final double sk = singularValues[k] / scale;
final double ek = e[k] / scale;
final double b = ((spm1 + sp) * (spm1 - sp) + epm1 * epm1) / 2.0;
final double c = (sp * epm1) * (sp * epm1);
double shift = 0;
if (b != 0 ||
c != 0) {
shift = FastMath.sqrt(b * b + c);
if (b < 0) {
shift = -shift;
}
shift = c / (b + shift);
}
double f = (sk + sp) * (sk - sp) + shift;
double g = sk * ek;
// Chase zeros.
for (int j = k; j < p - 1; j++) {
double t = FastMath.hypot(f, g);
double cs = f / t;
double sn = g / t;
if (j != k) {
e[j - 1] = t;
}
f = cs * singularValues[j] + sn * e[j];
e[j] = cs * e[j] - sn * singularValues[j];
g = sn * singularValues[j + 1];
singularValues[j + 1] = cs * singularValues[j + 1];

for (int i = 0; i < n; i++) {
t = cs * V[i][j] + sn * V[i][j + 1];
V[i][j + 1] = -sn * V[i][j] + cs * V[i][j + 1];
V[i][j] = t;
}
t = FastMath.hypot(f, g);
cs = f / t;
sn = g / t;
singularValues[j] = t;
f = cs * e[j] + sn * singularValues[j + 1];
singularValues[j + 1] = -sn * e[j] + cs * singularValues[j + 1];
g = sn * e[j + 1];
e[j + 1] = cs * e[j + 1];
if (j < m - 1) {
for (int i = 0; i < m; i++) {
t = cs * U[i][j] + sn * U[i][j + 1];
U[i][j + 1] = -sn * U[i][j] + cs * U[i][j + 1];
U[i][j] = t;
}
}
}
e[p - 2] = f;
}
break;
// Convergence.
default: {
// Make the singular values positive.
if (singularValues[k] <= 0) {
singularValues[k] = singularValues[k] < 0 ? -singularValues[k] : 0;

for (int i = 0; i <= pp; i++) {
V[i][k] = -V[i][k];
}
}
// Order the singular values.
while (k < pp) {
if (singularValues[k] >= singularValues[k + 1]) {
break;
}
double t = singularValues[k];
singularValues[k] = singularValues[k + 1];
singularValues[k + 1] = t;
if (k < n - 1) {
for (int i = 0; i < n; i++) {
t = V[i][k + 1];
V[i][k + 1] = V[i][k];
V[i][k] = t;
}
}
if (k < m - 1) {
for (int i = 0; i < m; i++) {
t = U[i][k + 1];
U[i][k + 1] = U[i][k];
U[i][k] = t;
}
}
k++;
}
p--;
}
break;
}
}

// Set the small value tolerance used to calculate rank and pseudo-inverse
tol = FastMath.max(m * singularValues[0] * EPS,
FastMath.sqrt(Precision.SAFE_MIN));

if (!transposed) {
cachedU = MatrixUtils.createRealMatrix(U);
cachedV = MatrixUtils.createRealMatrix(V);
} else {
cachedU = MatrixUtils.createRealMatrix(V);
cachedV = MatrixUtils.createRealMatrix(U);
}
}

/**
* Returns the matrix U of the decomposition.
* <p>U is an orthogonal matrix, i.e. its transpose is also its inverse.
* @return the U matrix
* @see #getUT()
*/
public RealMatrix getU() {
// return the cached matrix
return cachedU;

}

/**
* Returns the transpose of the matrix U of the decomposition.
* <p>U is an orthogonal matrix, i.e. its transpose is also its inverse.
* @return the U matrix (or null if decomposed matrix is singular)
* @see #getU()
*/
public RealMatrix getUT() {
if (cachedUt == null) {
cachedUt = getU().transpose();
}
// return the cached matrix
return cachedUt;
}

/**
* Returns the diagonal matrix Σ of the decomposition.
* <p>Σ is a diagonal matrix. The singular values are provided in
* non-increasing order, for compatibility with Jama.</p>
* @return the Σ matrix
*/
public RealMatrix getS() {
if (cachedS == null) {
// cache the matrix for subsequent calls
cachedS = MatrixUtils.createRealDiagonalMatrix(singularValues);
}
return cachedS;
}

/**
* Returns the diagonal elements of the matrix Σ of the decomposition.
* <p>The singular values are provided in non-increasing order, for
* compatibility with Jama.</p>
* @return the diagonal elements of the Σ matrix
*/
public double[] getSingularValues() {
return singularValues.clone();
}

/**
* Returns the matrix V of the decomposition.
* <p>V is an orthogonal matrix, i.e. its transpose is also its inverse.
* @return the V matrix (or null if decomposed matrix is singular)
* @see #getVT()
*/
public RealMatrix getV() {
// return the cached matrix
return cachedV;
}

/**
* Returns the transpose of the matrix V of the decomposition.
* <p>V is an orthogonal matrix, i.e. its transpose is also its inverse.
* @return the V matrix (or null if decomposed matrix is singular)
* @see #getV()
*/
public RealMatrix getVT() {
if (cachedVt == null) {
cachedVt = getV().transpose();
}
// return the cached matrix
return cachedVt;
}

/**
* Returns the n × n covariance matrix.
* <p>The covariance matrix is V × J × VT
* where J is the diagonal matrix of the inverse of the squares of
* the singular values.</p>
* @param minSingularValue value below which singular values are ignored
* (a 0 or negative value implies all singular value will be used)
* @return covariance matrix
* @exception IllegalArgumentException if minSingularValue is larger than
* the largest singular value, meaning all singular values are ignored
*/
public RealMatrix getCovariance(final double minSingularValue) {
// get the number of singular values to consider
final int p = singularValues.length;
int dimension = 0;
while (dimension < p &&
singularValues[dimension] >= minSingularValue) {
++dimension;
}

if (dimension == 0) {
throw new NumberIsTooLargeException(LocalizedFormats.TOO_LARGE_CUTOFF_SINGULAR_VALUE,
minSingularValue, singularValues[0], true);
}

final double[][] data = new double[dimension][p];
getVT().walkInOptimizedOrder(new DefaultRealMatrixPreservingVisitor() {
/** {@inheritDoc} */
@Override
public void visit(final int row, final int column,
final double value) {
data[row][column] = value / singularValues[row];
}
}, 0, dimension - 1, 0, p - 1);

RealMatrix jv = new Array2DRowRealMatrix(data, false);
return jv.transpose().multiply(jv);
}

/**
* Returns the L<sub>2 norm of the matrix.
* <p>The L2 norm is max(|A × u|2 /
* |u|<sub>2), where |.|2 denotes the vectorial 2-norm
* (i.e. the traditional euclidian norm).</p>
* @return norm
*/
public double getNorm() {
return singularValues[0];
}

/**
* Return the condition number of the matrix.
* @return condition number of the matrix
*/
public double getConditionNumber() {
return singularValues[0] / singularValues[n - 1];
}

/**
* Computes the inverse of the condition number.
* In cases of rank deficiency, the {@link #getConditionNumber() condition
* number} will become undefined.
*
* @return the inverse of the condition number.
*/
public double getInverseConditionNumber() {
return singularValues[n - 1] / singularValues[0];
}

/**
* Return the effective numerical matrix rank.
* <p>The effective numerical rank is the number of non-negligible
* singular values. The threshold used to identify non-negligible
* terms is max(m,n) × ulp(s<sub>1) where ulp(s1)
* is the least significant bit of the largest singular value.</p>
* @return effective numerical matrix rank
*/
public int getRank() {
int r = 0;
for (int i = 0; i < singularValues.length; i++) {
if (singularValues[i] > tol) {
r++;
}
}
return r;
}

/**
* Get a solver for finding the A × X = B solution in least square sense.
* @return a solver
*/
public DecompositionSolver getSolver() {
return new Solver(singularValues, getUT(), getV(), getRank() == m, tol);
}

/** Specialized solver. */
private static class Solver implements DecompositionSolver {
/** Pseudo-inverse of the initial matrix. */
private final RealMatrix pseudoInverse;
/** Singularity indicator. */
private boolean nonSingular;

/**
* Build a solver from decomposed matrix.
*
* @param singularValues Singular values.
* @param uT U<sup>T matrix of the decomposition.
* @param v V matrix of the decomposition.
* @param nonSingular Singularity indicator.
* @param tol tolerance for singular values
*/
private Solver(final double[] singularValues, final RealMatrix uT,
final RealMatrix v, final boolean nonSingular, final double tol) {
final double[][] suT = uT.getData();
for (int i = 0; i < singularValues.length; ++i) {
final double a;
if (singularValues[i] > tol) {
a = 1 / singularValues[i];
} else {
a = 0;
}
final double[] suTi = suT[i];
for (int j = 0; j < suTi.length; ++j) {
suTi[j] *= a;
}
}
pseudoInverse = v.multiply(new Array2DRowRealMatrix(suT, false));
this.nonSingular = nonSingular;
}

/**
* Solve the linear equation A × X = B in least square sense.
* <p>
* The m×n matrix A may not be square, the solution X is such that
* ||A × X - B|| is minimal.
* </p>
* @param b Right-hand side of the equation A × X = B
* @return a vector X that minimizes the two norm of A × X - B
* @throws org.apache.commons.math3.exception.DimensionMismatchException
* if the matrices dimensions do not match.
*/
public RealVector solve(final RealVector b) {
return pseudoInverse.operate(b);
}

/**
* Solve the linear equation A × X = B in least square sense.
* <p>
* The m×n matrix A may not be square, the solution X is such that
* ||A × X - B|| is minimal.
* </p>
*
* @param b Right-hand side of the equation A × X = B
* @return a matrix X that minimizes the two norm of A × X - B
* @throws org.apache.commons.math3.exception.DimensionMismatchException
* if the matrices dimensions do not match.
*/
public RealMatrix solve(final RealMatrix b) {
return pseudoInverse.multiply(b);
}

/**
* Check if the decomposed matrix is non-singular.
*
* @return {@code true} if the decomposed matrix is non-singular.
*/
public boolean isNonSingular() {
return nonSingular;
}

/**
* Get the pseudo-inverse of the decomposed matrix.
*
* @return the inverse matrix.
*/
public RealMatrix getInverse() {
return pseudoInverse;
}
}
}
```

## Other Java examples (source code examples)

Here is a short list of links related to this Java SingularValueDecomposition.java source code file: