/*
 * 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
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package org.apache.commons.math3.optimization;


import org.apache.commons.math3.analysis.differentiation.MultivariateDifferentiableFunction;
import org.apache.commons.math3.geometry.euclidean.twod.Vector2D;
import org.apache.commons.math3.optimization.general.CircleScalar;
import org.apache.commons.math3.optimization.general.ConjugateGradientFormula;
import org.apache.commons.math3.optimization.general.NonLinearConjugateGradientOptimizer;
import org.apache.commons.math3.random.GaussianRandomGenerator;
import org.apache.commons.math3.random.JDKRandomGenerator;
import org.apache.commons.math3.random.RandomVectorGenerator;
import org.apache.commons.math3.random.UncorrelatedRandomVectorGenerator;
import org.junit.Assert;
import org.junit.Test;

@Deprecated
public class MultivariateDifferentiableMultiStartOptimizerTest {

    @Test
    public void testCircleFitting() {
        CircleScalar circle = new CircleScalar();
        circle.addPoint( 30.0,  68.0);
        circle.addPoint( 50.0,  -6.0);
        circle.addPoint(110.0, -20.0);
        circle.addPoint( 35.0,  15.0);
        circle.addPoint( 45.0,  97.0);
        // TODO: the wrapper around NonLinearConjugateGradientOptimizer is a temporary hack for
        // version 3.1 of the library. It should be removed when NonLinearConjugateGradientOptimizer
        // will officially be declared as implementing MultivariateDifferentiableOptimizer
        MultivariateDifferentiableOptimizer underlying =
                new MultivariateDifferentiableOptimizer() {

            private final NonLinearConjugateGradientOptimizer cg =
                    new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE,
                                                            new SimpleValueChecker(1.0e-10, 1.0e-10));
            public PointValuePair optimize(int maxEval,
                                           MultivariateDifferentiableFunction f,
                                           GoalType goalType,
                                           double[] startPoint) {
                return cg.optimize(maxEval, f, goalType, startPoint);
            }

            public int getMaxEvaluations() {
                return cg.getMaxEvaluations();
            }

            public int getEvaluations() {
                return cg.getEvaluations();
            }

            public ConvergenceChecker<PointValuePair> getConvergenceChecker() {
                return cg.getConvergenceChecker();
            }
        };
        JDKRandomGenerator g = new JDKRandomGenerator();
        g.setSeed(753289573253l);
        RandomVectorGenerator generator =
            new UncorrelatedRandomVectorGenerator(new double[] { 50.0, 50.0 }, new double[] { 10.0, 10.0 },
                                                  new GaussianRandomGenerator(g));
        MultivariateDifferentiableMultiStartOptimizer optimizer =
            new MultivariateDifferentiableMultiStartOptimizer(underlying, 10, generator);
        PointValuePair optimum =
            optimizer.optimize(200, circle, GoalType.MINIMIZE, new double[] { 98.680, 47.345 });
        Assert.assertEquals(200, optimizer.getMaxEvaluations());
        PointValuePair[] optima = optimizer.getOptima();
        for (PointValuePair o : optima) {
            Vector2D center = new Vector2D(o.getPointRef()[0], o.getPointRef()[1]);
            Assert.assertEquals(69.960161753, circle.getRadius(center), 1.0e-8);
            Assert.assertEquals(96.075902096, center.getX(), 1.0e-8);
            Assert.assertEquals(48.135167894, center.getY(), 1.0e-8);
        }
        Assert.assertTrue(optimizer.getEvaluations() > 70);
        Assert.assertTrue(optimizer.getEvaluations() < 90);
        Assert.assertEquals(3.1267527, optimum.getValue(), 1.0e-8);
    }

}