/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* 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
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.commons.math3.fitting.leastsquares;
import org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem.Evaluation;
import org.apache.commons.math3.linear.ArrayRealVector;
import org.apache.commons.math3.linear.DecompositionSolver;
import org.apache.commons.math3.linear.QRDecomposition;
import org.apache.commons.math3.linear.RealMatrix;
import org.apache.commons.math3.linear.RealVector;
import org.apache.commons.math3.util.FastMath;
/**
* An implementation of {@link Evaluation} that is designed for extension. All of the
* methods implemented here use the methods that are left unimplemented.
* <p/>
* TODO cache results?
*
* @since 3.3
*/
public abstract class AbstractEvaluation implements Evaluation {
/** number of observations */
private final int observationSize;
/**
* Constructor.
*
* @param observationSize the number of observation. Needed for {@link
* #getRMS()}.
*/
AbstractEvaluation(final int observationSize) {
this.observationSize = observationSize;
}
/** {@inheritDoc} */
public RealMatrix getCovariances(double threshold) {
// Set up the Jacobian.
final RealMatrix j = this.getJacobian();
// Compute transpose(J)J.
final RealMatrix jTj = j.transpose().multiply(j);
// Compute the covariances matrix.
final DecompositionSolver solver
= new QRDecomposition(jTj, threshold).getSolver();
return solver.getInverse();
}
/** {@inheritDoc} */
public RealVector getSigma(double covarianceSingularityThreshold) {
final RealMatrix cov = this.getCovariances(covarianceSingularityThreshold);
final int nC = cov.getColumnDimension();
final RealVector sig = new ArrayRealVector(nC);
for (int i = 0; i < nC; ++i) {
sig.setEntry(i, FastMath.sqrt(cov.getEntry(i,i)));
}
return sig;
}
/** {@inheritDoc} */
public double getRMS() {
final double cost = this.getCost();
return FastMath.sqrt(cost * cost / this.observationSize);
}
/** {@inheritDoc} */
public double getCost() {
final ArrayRealVector r = new ArrayRealVector(this.getResiduals());
return FastMath.sqrt(r.dotProduct(r));
}
}