/*
* 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.RealMatrix;
import org.apache.commons.math3.linear.RealVector;
import org.apache.commons.math3.optim.ConvergenceChecker;
import org.junit.Assert;
import org.junit.Test;
/** Unit tests for {@link EvaluationRmsChecker}. */
public class EvaluationRmsCheckerTest {
/** check {@link ConvergenceChecker#converged(int, Object, Object)}. */
@Test
public void testConverged() {
//setup
ConvergenceChecker<Evaluation> checker = new EvaluationRmsChecker(0.1, 1);
Evaluation e200 = mockEvaluation(200);
Evaluation e1 = mockEvaluation(1);
//action + verify
//just matches rel tol
Assert.assertEquals(true, checker.converged(0, e200, mockEvaluation(210)));
//just matches abs tol
Assert.assertEquals(true, checker.converged(0, e1, mockEvaluation(1.9)));
//matches both
Assert.assertEquals(true, checker.converged(0, e1, mockEvaluation(1.01)));
//matches neither
Assert.assertEquals(false, checker.converged(0, e200, mockEvaluation(300)));
}
/**
* Create a mock {@link Evaluation}.
*
* @param rms the evaluation's rms.
* @return a new mock evaluation.
*/
private static Evaluation mockEvaluation(final double rms) {
return new Evaluation() {
public RealMatrix getCovariances(double threshold) {
return null;
}
public RealVector getSigma(double covarianceSingularityThreshold) {
return null;
}
public double getRMS() {
return rms;
}
public RealMatrix getJacobian() {
return null;
}
public double getCost() {
return 0;
}
public RealVector getResiduals() {
return null;
}
public RealVector getPoint() {
return null;
}
};
}
}