com.opengamma.analytics.math.statistics.leastsquare.LeastSquareResults.java Source code

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Here is the source code for com.opengamma.analytics.math.statistics.leastsquare.LeastSquareResults.java

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/**
 * 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);
    }

}