/**
* Copyright (C) 2009 - present by OpenGamma Inc. and the OpenGamma group of companies
*
* Please see distribution for license.
*/
package com.opengamma.analytics.math.statistics.leastsquare;
import org.apache.commons.lang.NotImplementedException;
import org.apache.commons.lang.ObjectUtils;
import org.apache.commons.lang.Validate;
import com.opengamma.analytics.math.matrix.DoubleMatrix1D;
import com.opengamma.analytics.math.matrix.DoubleMatrix2D;
/**
* Container for the results of a least square (minimum chi-square) fit, where some model (with a set of parameters), is calibrated
* to a data set.
*/
public class LeastSquareResults {
@Override
public String toString() {
return "LeastSquareResults [chiSq=" + _chiSq + ", fit parameters=" + _parameters.toString() + ", covariance=" + _covariance.toString() + "]";
}
private final double _chiSq;
private final DoubleMatrix1D _parameters;
private final DoubleMatrix2D _covariance;
private final DoubleMatrix2D _inverseJacobian;
public LeastSquareResults(LeastSquareResults from) {
this(from._chiSq, from._parameters, from._covariance, from._inverseJacobian);
}
public LeastSquareResults(final double chiSq, final DoubleMatrix1D parameters, final DoubleMatrix2D covariance) {
this(chiSq, parameters, covariance, null);
}
public LeastSquareResults(final double chiSq, final DoubleMatrix1D parameters, final DoubleMatrix2D covariance, final DoubleMatrix2D inverseJacobian) {
Validate.isTrue(chiSq >= 0, "chi square < 0");
Validate.notNull(parameters, "parameters");
Validate.notNull(covariance, "covariance");
final int n = parameters.getNumberOfElements();
Validate.isTrue(covariance.getNumberOfColumns() == n, "covariance matrix not square");
Validate.isTrue(covariance.getNumberOfRows() == n, "covariance matrix wrong size");
//TODO test size of inverse Jacobian
_chiSq = chiSq;
_parameters = parameters;
_covariance = covariance;
_inverseJacobian = inverseJacobian;
}
/**
* Gets the Chi-square of the fit
* @return the chiSq
*/
public double getChiSq() {
return _chiSq;
}
/**
* Gets the value of the fitting parameters, when the chi-squared is minimised
* @return the parameters
*/
public DoubleMatrix1D getFitParameters() {
return _parameters;
}
/**
* Gets the estimated covariance matrix of the standard errors in the fitting parameters. <b>Note</b> only in the case of
* normally distributed errors, does this have any meaning full mathematical interpretation (See NR third edition, p812-816)
* @return the formal covariance matrix
*/
public DoubleMatrix2D getCovariance() {
return _covariance;
}
/**
* This a matrix where the i,jth element is the (infinitesimal) sensitivity of the ith fitting parameter to the jth data
* point (NOT the model point), when the fitting parameter are such that the chi-squared is minimised. So it is a type of (inverse)
* Jacobian, but should not be confused with the model jacobian (sensitivity of model data points, to parameters) or its inverse.
* @return a matrix
*/
public DoubleMatrix2D getFittingParameterSensitivityToData() {
if (_inverseJacobian == null) {
throw new NotImplementedException("The inverse Jacobian was not set");
}
return _inverseJacobian;
}
@Override
public int hashCode() {
final int prime = 31;
int result = 1;
long temp;
temp = Double.doubleToLongBits(_chiSq);
result = prime * result + (int) (temp ^ (temp >>> 32));
result = prime * result + _covariance.hashCode();
result = prime * result + _parameters.hashCode();
result = prime * result + (_inverseJacobian == null ? 0 : _inverseJacobian.hashCode());
return result;
}
@Override
public boolean equals(final Object obj) {
if (this == obj) {
return true;
}
if (obj == null) {
return false;
}
if (getClass() != obj.getClass()) {
return false;
}
final LeastSquareResults other = (LeastSquareResults) obj;
if (Double.doubleToLongBits(_chiSq) != Double.doubleToLongBits(other._chiSq)) {
return false;
}
if (!ObjectUtils.equals(_covariance, other._covariance)) {
return false;
}
if (!ObjectUtils.equals(_inverseJacobian, other._inverseJacobian)) {
return false;
}
return ObjectUtils.equals(_parameters, other._parameters);
}
}