net.finmath.functions.LinearAlgebra Maven / Gradle / Ivy
/*
* (c) Copyright Christian P. Fries, Germany. Contact: [email protected].
*
* Created on 23.02.2004
*/
package net.finmath.functions;
import java.util.ArrayList;
import java.util.Collections;
import java.util.List;
import org.apache.commons.math3.linear.Array2DRowRealMatrix;
import org.apache.commons.math3.linear.ArrayRealVector;
import org.apache.commons.math3.linear.DecompositionSolver;
import org.apache.commons.math3.linear.EigenDecomposition;
import org.apache.commons.math3.linear.LUDecomposition;
import org.apache.commons.math3.linear.QRDecomposition;
import org.apache.commons.math3.linear.RealMatrix;
import org.apache.commons.math3.linear.SingularValueDecomposition;
/**
* This class implements some methods from linear algebra (e.g. solution of a linear equation, PCA).
*
* It is basically a functional wrapper using either the Colt library or Apache commons math.
*
* I am currently preferring to use Colt, due to better performance in some situations, however it allows
* to easily switch some parts to Apache commons math (this is the motivation for this class).
*
* @author Christian Fries
* @version 1.6
*/
public class LinearAlgebra {
private static boolean isEigenvalueDecompositionViaSVD = Boolean.parseBoolean(System.getProperty("net.finmath.functions.LinearAlgebra.isEigenvalueDecompositionViaSVD","false"));
private static boolean isSolverUseApacheCommonsMath;
static {
// Default value is true, in which case we will NOT use jblas
boolean isSolverUseApacheCommonsMath = Boolean.parseBoolean(System.getProperty("net.finmath.functions.LinearAlgebra.isUseApacheCommonsMath","true"));
/*
* Check if jblas is available
*/
if(!isSolverUseApacheCommonsMath) {
try {
double[] x = org.jblas.Solve.solve(new org.jblas.DoubleMatrix(2, 2, 1.0, 1.0, 0.0, 1.0), new org.jblas.DoubleMatrix(2, 1, 1.0, 1.0)).data;
// The following should not happen.
if(x[0] != 1.0 || x[1] != 0.0) isSolverUseApacheCommonsMath = true;
}
catch(java.lang.UnsatisfiedLinkError e) {
isSolverUseApacheCommonsMath = true;
}
}
LinearAlgebra.isSolverUseApacheCommonsMath = isSolverUseApacheCommonsMath;
}
/**
* Find a solution of the linear equation A x = b where
*
* - A is an n x m - matrix given as double[n][m]
* - b is an m - vector given as double[m],
* - x is an n - vector given as double[n],
*
*
* @param A The matrix (left hand side of the linear equation).
* @param b The vector (right hand of the linear equation).
* @return A solution x to A x = b.
*/
public static double[] solveLinearEquation(double[][] A, double[] b) {
if(isSolverUseApacheCommonsMath) {
Array2DRowRealMatrix matrix = new Array2DRowRealMatrix(A);
DecompositionSolver solver;
if(matrix.getColumnDimension() == matrix.getRowDimension()) {
solver = new LUDecomposition(matrix).getSolver();
}
else {
solver = new QRDecomposition(new Array2DRowRealMatrix(A)).getSolver();
}
// Using SVD - very slow
// solver = new SingularValueDecomposition(new Array2DRowRealMatrix(A)).getSolver();
return solver.solve(new Array2DRowRealMatrix(b)).getColumn(0);
}
else {
return org.jblas.Solve.solve(new org.jblas.DoubleMatrix(A), new org.jblas.DoubleMatrix(b)).data;
// For use of colt:
// cern.colt.matrix.linalg.Algebra linearAlgebra = new cern.colt.matrix.linalg.Algebra();
// return linearAlgebra.solve(new DenseDoubleMatrix2D(A), linearAlgebra.transpose(new DenseDoubleMatrix2D(new double[][] { b }))).viewColumn(0).toArray();
// For use of parallel colt:
// return new cern.colt.matrix.tdouble.algo.decomposition.DenseDoubleLUDecomposition(new cern.colt.matrix.tdouble.impl.DenseDoubleMatrix2D(A)).solve(new cern.colt.matrix.tdouble.impl.DenseDoubleMatrix1D(b)).toArray();
}
}
/**
* Returns the inverse of a given matrix.
*
* @param matrix A matrix given as double[n][n].
* @return The inverse of the given matrix.
*/
public static double[][] invert(double[][] matrix) {
if(isSolverUseApacheCommonsMath) {
// Use LU from common math
LUDecomposition lu = new LUDecomposition(new Array2DRowRealMatrix(matrix));
double[][] matrixInverse = lu.getSolver().getInverse().getData();
return matrixInverse;
}
else {
return org.jblas.Solve.pinv(new org.jblas.DoubleMatrix(matrix)).toArray2();
}
}
/**
* Find a solution of the linear equation A x = b where
*
* - A is an symmetric n x n - matrix given as double[n][n]
* - b is an n - vector given as double[n],
* - x is an n - vector given as double[n],
*
*
* @param matrix The matrix A (left hand side of the linear equation).
* @param vector The vector b (right hand of the linear equation).
* @return A solution x to A x = b.
*/
public static double[] solveLinearEquationSymmetric(double[][] matrix, double[] vector) {
if(isSolverUseApacheCommonsMath) {
DecompositionSolver solver = new LUDecomposition(new Array2DRowRealMatrix(matrix)).getSolver();
return solver.solve(new Array2DRowRealMatrix(vector)).getColumn(0);
}
else {
return org.jblas.Solve.solveSymmetric(new org.jblas.DoubleMatrix(matrix), new org.jblas.DoubleMatrix(vector)).data;
/* To use the linear algebra package colt from cern.
cern.colt.matrix.linalg.Algebra linearAlgebra = new cern.colt.matrix.linalg.Algebra();
double[] x = linearAlgebra.solve(new DenseDoubleMatrix2D(A), linearAlgebra.transpose(new DenseDoubleMatrix2D(new double[][] { b }))).viewColumn(0).toArray();
return x;
*/
}
}
/**
* Find a solution of the linear equation A x = b in the least square sense where
*
* - A is an n x m - matrix given as double[n][m]
* - b is an m - vector given as double[m],
* - x is an n - vector given as double[n],
*
*
* @param matrix The matrix A (left hand side of the linear equation).
* @param vector The vector b (right hand of the linear equation).
* @return A solution x to A x = b.
*/
public static double[] solveLinearEquationLeastSquare(double[][] matrix, double[] vector) {
// We use the linear algebra package apache commons math
DecompositionSolver solver = new SingularValueDecomposition(new Array2DRowRealMatrix(matrix, false)).getSolver();
return solver.solve(new ArrayRealVector(vector)).toArray();
}
/**
* Returns the matrix of the n Eigenvectors corresponding to the first n largest Eigenvalues of a correlation matrix.
* These Eigenvectors can also be interpreted as "principal components" (i.e., the method implements the PCA).
*
* @param correlationMatrix The given correlation matrix.
* @param numberOfFactors The requested number of factors (eigenvectors).
* @return Matrix of n Eigenvectors (columns) (matrix is given as double[n][numberOfFactors], where n is the number of rows of the correlationMatrix.
*/
public static double[][] getFactorMatrix(double[][] correlationMatrix, int numberOfFactors) {
return getFactorMatrixUsingCommonsMath(correlationMatrix, numberOfFactors);
}
/**
* Returns a correlation matrix which has rank < n and for which the first n factors agree with the factors of correlationMatrix.
*
* @param correlationMatrix The given correlation matrix.
* @param numberOfFactors The requested number of factors (Eigenvectors).
* @return Factor reduced correlation matrix.
*/
public static double[][] factorReduction(double[][] correlationMatrix, int numberOfFactors) {
return factorReductionUsingCommonsMath(correlationMatrix, numberOfFactors);
}
/**
* Returns the matrix of the n Eigenvectors corresponding to the first n largest Eigenvalues of a correlation matrix.
* These eigenvectors can also be interpreted as "principal components" (i.e., the method implements the PCA).
*
* @param correlationMatrix The given correlation matrix.
* @param numberOfFactors The requested number of factors (Eigenvectors).
* @return Matrix of n Eigenvectors (columns) (matrix is given as double[n][numberOfFactors], where n is the number of rows of the correlationMatrix.
*/
private static double[][] getFactorMatrixUsingCommonsMath(double[][] correlationMatrix, int numberOfFactors) {
/*
* Factor reduction
*/
// Create an eigen vector decomposition of the correlation matrix
double[] eigenValues;
double[][] eigenVectorMatrix;
if(isEigenvalueDecompositionViaSVD) {
SingularValueDecomposition svd = new SingularValueDecomposition(new Array2DRowRealMatrix(correlationMatrix));
eigenValues = svd.getSingularValues();
eigenVectorMatrix = svd.getV().getData();
}
else {
EigenDecomposition eigenDecomp = new EigenDecomposition(new Array2DRowRealMatrix(correlationMatrix, false));
eigenValues = eigenDecomp.getRealEigenvalues();
eigenVectorMatrix = eigenDecomp.getV().getData();
}
class EigenValueIndex implements Comparable {
private int index;
Double value;
public EigenValueIndex(int index, double value) {
this.index = index; this.value = value;
}
@Override
public int compareTo(EigenValueIndex o) { return o.value.compareTo(value); }
};
List eigenValueIndices = new ArrayList();
for(int i=0; i 0.0 ? 1.0 : -1.0; // Convention: Have first entry of eigenvector positive. This is to make results more consistent.
double eigenVectorNormSquared = 0.0;
for (int row = 0; row < eigenValues.length; row++) {
eigenVectorNormSquared += eigenVectorMatrix[row][eigenVectorIndex] * eigenVectorMatrix[row][eigenVectorIndex];
}
eigenValue = Math.max(eigenValue,0.0);
for (int row = 0; row < eigenValues.length; row++) {
factorMatrix[row][factor] = signChange * Math.sqrt(eigenValue/eigenVectorNormSquared) * eigenVectorMatrix[row][eigenVectorIndex];
}
}
return factorMatrix;
}
/**
* Returns a correlation matrix which has rank < n and for which the first n factors agree with the factors of correlationMatrix.
*
* @param correlationMatrix The given correlation matrix.
* @param numberOfFactors The requested number of factors (Eigenvectors).
* @return Factor reduced correlation matrix.
*/
public static double[][] factorReductionUsingCommonsMath(double[][] correlationMatrix, int numberOfFactors) {
// Extract factors corresponding to the largest eigenvalues
double[][] factorMatrix = getFactorMatrix(correlationMatrix, numberOfFactors);
// Renormalize rows
for (int row = 0; row < correlationMatrix.length; row++) {
double sumSquared = 0;
for (int factor = 0; factor < numberOfFactors; factor++)
sumSquared += factorMatrix[row][factor] * factorMatrix[row][factor];
if(sumSquared != 0) {
for (int factor = 0; factor < numberOfFactors; factor++)
factorMatrix[row][factor] = factorMatrix[row][factor] / Math.sqrt(sumSquared);
}
else {
// This is a rare case: The factor reduction of a completely decorrelated system to 1 factor
for (int factor = 0; factor < numberOfFactors; factor++)
factorMatrix[row][factor] = 1.0;
}
}
// Orthogonalized again
double[][] reducedCorrelationMatrix = (new Array2DRowRealMatrix(factorMatrix).multiply(new Array2DRowRealMatrix(factorMatrix).transpose())).getData();
return getFactorMatrix(reducedCorrelationMatrix, numberOfFactors);
}
/**
* Calculate the "matrix exponential" (expm).
*
* Note: The function currently requires jblas. If jblas is not availabe on your system, an exception will be thrown.
* A future version of this function may implement a fall back.
*
* @param matrix The given matrix.
* @return The exp(matrix).
*/
public double[][] exp(double[][] matrix) {
return org.jblas.MatrixFunctions.expm(new org.jblas.DoubleMatrix(matrix)).toArray2();
}
/**
* Calculate the "matrix exponential" (expm).
*
* Note: The function currently requires jblas. If jblas is not availabe on your system, an exception will be thrown.
* A future version of this function may implement a fall back.
*
* @param matrix The given matrix.
* @return The exp(matrix).
*/
public RealMatrix exp(RealMatrix matrix) {
return new Array2DRowRealMatrix(exp(matrix.getData()));
}
}
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