/*
 * 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.math.optimization;

import static org.junit.Assert.assertEquals;
import static org.junit.Assert.assertTrue;

import org.apache.commons.math.ConvergenceException;
import org.apache.commons.math.FunctionEvaluationException;
import org.apache.commons.math.analysis.MultivariateRealFunction;
import org.apache.commons.math.optimization.direct.NelderMead;
import org.apache.commons.math.random.GaussianRandomGenerator;
import org.apache.commons.math.random.JDKRandomGenerator;
import org.apache.commons.math.random.RandomVectorGenerator;
import org.apache.commons.math.random.UncorrelatedRandomVectorGenerator;
import org.junit.Test;

public class MultiStartMultivariateRealOptimizerTest {

	@Test
	public void testRosenbrock() throws FunctionEvaluationException, ConvergenceException {

		Rosenbrock rosenbrock = new Rosenbrock();
		NelderMead underlying = new NelderMead();
		underlying.setStartConfiguration(new double[][] { { -1.2, 1.0 }, { 0.9, 1.2 }, { 3.5, -2.3 } });
		JDKRandomGenerator g = new JDKRandomGenerator();
		g.setSeed(16069223052l);
		RandomVectorGenerator generator = new UncorrelatedRandomVectorGenerator(2, new GaussianRandomGenerator(g));
		MultiStartMultivariateRealOptimizer optimizer = new MultiStartMultivariateRealOptimizer(underlying, 10,
				generator);
		optimizer.setConvergenceChecker(new SimpleScalarValueChecker(-1, 1.0e-3));
		optimizer.setMaxIterations(100);
		RealPointValuePair optimum = optimizer.optimize(rosenbrock, GoalType.MINIMIZE, new double[] { -1.2, 1.0 });

		assertEquals(rosenbrock.getCount(), optimizer.getEvaluations());
		assertTrue(optimizer.getEvaluations() > 20);
		assertTrue(optimizer.getEvaluations() < 250);
		assertTrue(optimum.getValue() < 8.0e-4);

	}

	private static class Rosenbrock implements MultivariateRealFunction {

		private int count;

		public Rosenbrock() {
			count = 0;
		}

		public double value(double[] x) throws FunctionEvaluationException {
			++count;
			double a = x[1] - x[0] * x[0];
			double b = 1.0 - x[0];
			return 100 * a * a + b * b;
		}

		public int getCount() {
			return count;
		}

	}

}
