/*
* 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.optimization.direct;
import java.util.Arrays;
import java.util.Random;
import org.apache.commons.math3.analysis.MultivariateFunction;
import org.apache.commons.math3.exception.DimensionMismatchException;
import org.apache.commons.math3.exception.TooManyEvaluationsException;
import org.apache.commons.math3.exception.NumberIsTooLargeException;
import org.apache.commons.math3.exception.NumberIsTooSmallException;
import org.apache.commons.math3.optimization.GoalType;
import org.apache.commons.math3.optimization.PointValuePair;
import org.apache.commons.math3.optimization.InitialGuess;
import org.apache.commons.math3.optimization.SimpleBounds;
import org.apache.commons.math3.util.FastMath;
import org.junit.Assert;
import org.junit.Ignore;
import org.junit.Test;
/**
* Test for {@link BOBYQAOptimizer}.
*/
@Deprecated
public class BOBYQAOptimizerTest {
static final int DIM = 13;
@Test(expected=NumberIsTooLargeException.class)
public void testInitOutOfBounds() {
double[] startPoint = point(DIM, 3);
double[][] boundaries = boundaries(DIM, -1, 2);
doTest(new Rosen(), startPoint, boundaries,
GoalType.MINIMIZE,
1e-13, 1e-6, 2000, null);
}
@Test(expected=DimensionMismatchException.class)
public void testBoundariesDimensionMismatch() {
double[] startPoint = point(DIM, 0.5);
double[][] boundaries = boundaries(DIM + 1, -1, 2);
doTest(new Rosen(), startPoint, boundaries,
GoalType.MINIMIZE,
1e-13, 1e-6, 2000, null);
}
@Test(expected=NumberIsTooSmallException.class)
public void testProblemDimensionTooSmall() {
double[] startPoint = point(1, 0.5);
doTest(new Rosen(), startPoint, null,
GoalType.MINIMIZE,
1e-13, 1e-6, 2000, null);
}
@Test(expected=TooManyEvaluationsException.class)
public void testMaxEvaluations() {
final int lowMaxEval = 2;
double[] startPoint = point(DIM, 0.1);
double[][] boundaries = null;
doTest(new Rosen(), startPoint, boundaries,
GoalType.MINIMIZE,
1e-13, 1e-6, lowMaxEval, null);
}
@Test
public void testRosen() {
double[] startPoint = point(DIM,0.1);
double[][] boundaries = null;
PointValuePair expected = new PointValuePair(point(DIM,1.0),0.0);
doTest(new Rosen(), startPoint, boundaries,
GoalType.MINIMIZE,
1e-13, 1e-6, 2000, expected);
}
@Test
public void testMaximize() {
double[] startPoint = point(DIM,1.0);
double[][] boundaries = null;
PointValuePair expected = new PointValuePair(point(DIM,0.0),1.0);
doTest(new MinusElli(), startPoint, boundaries,
GoalType.MAXIMIZE,
2e-10, 5e-6, 1000, expected);
boundaries = boundaries(DIM,-0.3,0.3);
startPoint = point(DIM,0.1);
doTest(new MinusElli(), startPoint, boundaries,
GoalType.MAXIMIZE,
2e-10, 5e-6, 1000, expected);
}
@Test
public void testEllipse() {
double[] startPoint = point(DIM,1.0);
double[][] boundaries = null;
PointValuePair expected =
new PointValuePair(point(DIM,0.0),0.0);
doTest(new Elli(), startPoint, boundaries,
GoalType.MINIMIZE,
1e-13, 1e-6, 1000, expected);
}
@Test
public void testElliRotated() {
double[] startPoint = point(DIM,1.0);
double[][] boundaries = null;
PointValuePair expected =
new PointValuePair(point(DIM,0.0),0.0);
doTest(new ElliRotated(), startPoint, boundaries,
GoalType.MINIMIZE,
1e-12, 1e-6, 10000, expected);
}
@Test
public void testCigar() {
double[] startPoint = point(DIM,1.0);
double[][] boundaries = null;
PointValuePair expected =
new PointValuePair(point(DIM,0.0),0.0);
doTest(new Cigar(), startPoint, boundaries,
GoalType.MINIMIZE,
1e-13, 1e-6, 100, expected);
}
@Test
public void testTwoAxes() {
double[] startPoint = point(DIM,1.0);
double[][] boundaries = null;
PointValuePair expected =
new PointValuePair(point(DIM,0.0),0.0);
doTest(new TwoAxes(), startPoint, boundaries,
GoalType.MINIMIZE, 2*
1e-13, 1e-6, 100, expected);
}
@Test
public void testCigTab() {
double[] startPoint = point(DIM,1.0);
double[][] boundaries = null;
PointValuePair expected =
new PointValuePair(point(DIM,0.0),0.0);
doTest(new CigTab(), startPoint, boundaries,
GoalType.MINIMIZE,
1e-13, 5e-5, 100, expected);
}
@Test
public void testSphere() {
double[] startPoint = point(DIM,1.0);
double[][] boundaries = null;
PointValuePair expected =
new PointValuePair(point(DIM,0.0),0.0);
doTest(new Sphere(), startPoint, boundaries,
GoalType.MINIMIZE,
1e-13, 1e-6, 100, expected);
}
@Test
public void testTablet() {
double[] startPoint = point(DIM,1.0);
double[][] boundaries = null;
PointValuePair expected =
new PointValuePair(point(DIM,0.0),0.0);
doTest(new Tablet(), startPoint, boundaries,
GoalType.MINIMIZE,
1e-13, 1e-6, 100, expected);
}
@Test
public void testDiffPow() {
double[] startPoint = point(DIM/2,1.0);
double[][] boundaries = null;
PointValuePair expected =
new PointValuePair(point(DIM/2,0.0),0.0);
doTest(new DiffPow(), startPoint, boundaries,
GoalType.MINIMIZE,
1e-8, 1e-1, 12000, expected);
}
@Test
public void testSsDiffPow() {
double[] startPoint = point(DIM/2,1.0);
double[][] boundaries = null;
PointValuePair expected =
new PointValuePair(point(DIM/2,0.0),0.0);
doTest(new SsDiffPow(), startPoint, boundaries,
GoalType.MINIMIZE,
1e-2, 1.3e-1, 50000, expected);
}
@Test
public void testAckley() {
double[] startPoint = point(DIM,0.1);
double[][] boundaries = null;
PointValuePair expected =
new PointValuePair(point(DIM,0.0),0.0);
doTest(new Ackley(), startPoint, boundaries,
GoalType.MINIMIZE,
1e-7, 1e-5, 1000, expected);
}
@Test
public void testRastrigin() {
double[] startPoint = point(DIM,1.0);
double[][] boundaries = null;
PointValuePair expected =
new PointValuePair(point(DIM,0.0),0.0);
doTest(new Rastrigin(), startPoint, boundaries,
GoalType.MINIMIZE,
1e-13, 1e-6, 1000, expected);
}
@Test
public void testConstrainedRosen() {
double[] startPoint = point(DIM,0.1);
double[][] boundaries = boundaries(DIM,-1,2);
PointValuePair expected =
new PointValuePair(point(DIM,1.0),0.0);
doTest(new Rosen(), startPoint, boundaries,
GoalType.MINIMIZE,
1e-13, 1e-6, 2000, expected);
}
// See MATH-728
// TODO: this test is temporarily disabled for 3.2 release as a bug in Cobertura
// makes it run for several hours before completing
@Ignore @Test
public void testConstrainedRosenWithMoreInterpolationPoints() {
final double[] startPoint = point(DIM, 0.1);
final double[][] boundaries = boundaries(DIM, -1, 2);
final PointValuePair expected = new PointValuePair(point(DIM, 1.0), 0.0);
// This should have been 78 because in the code the hard limit is
// said to be
// ((DIM + 1) * (DIM + 2)) / 2 - (2 * DIM + 1)
// i.e. 78 in this case, but the test fails for 48, 59, 62, 63, 64,
// 65, 66, ...
final int maxAdditionalPoints = 47;
for (int num = 1; num <= maxAdditionalPoints; num++) {
doTest(new Rosen(), startPoint, boundaries,
GoalType.MINIMIZE,
1e-12, 1e-6, 2000,
num,
expected,
"num=" + num);
}
}
/**
* @param func Function to optimize.
* @param startPoint Starting point.
* @param boundaries Upper / lower point limit.
* @param goal Minimization or maximization.
* @param fTol Tolerance relative error on the objective function.
* @param pointTol Tolerance for checking that the optimum is correct.
* @param maxEvaluations Maximum number of evaluations.
* @param expected Expected point / value.
*/
private void doTest(MultivariateFunction func,
double[] startPoint,
double[][] boundaries,
GoalType goal,
double fTol,
double pointTol,
int maxEvaluations,
PointValuePair expected) {
doTest(func,
startPoint,
boundaries,
goal,
fTol,
pointTol,
maxEvaluations,
0,
expected,
"");
}
/**
* @param func Function to optimize.
* @param startPoint Starting point.
* @param boundaries Upper / lower point limit.
* @param goal Minimization or maximization.
* @param fTol Tolerance relative error on the objective function.
* @param pointTol Tolerance for checking that the optimum is correct.
* @param maxEvaluations Maximum number of evaluations.
* @param additionalInterpolationPoints Number of interpolation to used
* in addition to the default (2 * dim + 1).
* @param expected Expected point / value.
*/
private void doTest(MultivariateFunction func,
double[] startPoint,
double[][] boundaries,
GoalType goal,
double fTol,
double pointTol,
int maxEvaluations,
int additionalInterpolationPoints,
PointValuePair expected,
String assertMsg) {
// System.out.println(func.getClass().getName() + " BEGIN"); // XXX
int dim = startPoint.length;
// MultivariateOptimizer optim =
// new PowellOptimizer(1e-13, FastMath.ulp(1d));
// PointValuePair result = optim.optimize(100000, func, goal, startPoint);
final double[] lB = boundaries == null ? null : boundaries[0];
final double[] uB = boundaries == null ? null : boundaries[1];
final int numIterpolationPoints = 2 * dim + 1 + additionalInterpolationPoints;
BOBYQAOptimizer optim = new BOBYQAOptimizer(numIterpolationPoints);
PointValuePair result = boundaries == null ?
optim.optimize(maxEvaluations, func, goal,
new InitialGuess(startPoint)) :
optim.optimize(maxEvaluations, func, goal,
new InitialGuess(startPoint),
new SimpleBounds(lB, uB));
// System.out.println(func.getClass().getName() + " = "
// + optim.getEvaluations() + " f(");
// for (double x: result.getPoint()) System.out.print(x + " ");
// System.out.println(") = " + result.getValue());
Assert.assertEquals(assertMsg, expected.getValue(), result.getValue(), fTol);
for (int i = 0; i < dim; i++) {
Assert.assertEquals(expected.getPoint()[i],
result.getPoint()[i], pointTol);
}
// System.out.println(func.getClass().getName() + " END"); // XXX
}
private static double[] point(int n, double value) {
double[] ds = new double[n];
Arrays.fill(ds, value);
return ds;
}
private static double[][] boundaries(int dim,
double lower, double upper) {
double[][] boundaries = new double[2][dim];
for (int i = 0; i < dim; i++)
boundaries[0][i] = lower;
for (int i = 0; i < dim; i++)
boundaries[1][i] = upper;
return boundaries;
}
private static class Sphere implements MultivariateFunction {
public double value(double[] x) {
double f = 0;
for (int i = 0; i < x.length; ++i)
f += x[i] * x[i];
return f;
}
}
private static class Cigar implements MultivariateFunction {
private double factor;
Cigar() {
this(1e3);
}
Cigar(double axisratio) {
factor = axisratio * axisratio;
}
public double value(double[] x) {
double f = x[0] * x[0];
for (int i = 1; i < x.length; ++i)
f += factor * x[i] * x[i];
return f;
}
}
private static class Tablet implements MultivariateFunction {
private double factor;
Tablet() {
this(1e3);
}
Tablet(double axisratio) {
factor = axisratio * axisratio;
}
public double value(double[] x) {
double f = factor * x[0] * x[0];
for (int i = 1; i < x.length; ++i)
f += x[i] * x[i];
return f;
}
}
private static class CigTab implements MultivariateFunction {
private double factor;
CigTab() {
this(1e4);
}
CigTab(double axisratio) {
factor = axisratio;
}
public double value(double[] x) {
int end = x.length - 1;
double f = x[0] * x[0] / factor + factor * x[end] * x[end];
for (int i = 1; i < end; ++i)
f += x[i] * x[i];
return f;
}
}
private static class TwoAxes implements MultivariateFunction {
private double factor;
TwoAxes() {
this(1e6);
}
TwoAxes(double axisratio) {
factor = axisratio * axisratio;
}
public double value(double[] x) {
double f = 0;
for (int i = 0; i < x.length; ++i)
f += (i < x.length / 2 ? factor : 1) * x[i] * x[i];
return f;
}
}
private static class ElliRotated implements MultivariateFunction {
private Basis B = new Basis();
private double factor;
ElliRotated() {
this(1e3);
}
ElliRotated(double axisratio) {
factor = axisratio * axisratio;
}
public double value(double[] x) {
double f = 0;
x = B.Rotate(x);
for (int i = 0; i < x.length; ++i)
f += FastMath.pow(factor, i / (x.length - 1.)) * x[i] * x[i];
return f;
}
}
private static class Elli implements MultivariateFunction {
private double factor;
Elli() {
this(1e3);
}
Elli(double axisratio) {
factor = axisratio * axisratio;
}
public double value(double[] x) {
double f = 0;
for (int i = 0; i < x.length; ++i)
f += FastMath.pow(factor, i / (x.length - 1.)) * x[i] * x[i];
return f;
}
}
private static class MinusElli implements MultivariateFunction {
private final Elli elli = new Elli();
public double value(double[] x) {
return 1.0 - elli.value(x);
}
}
private static class DiffPow implements MultivariateFunction {
// private int fcount = 0;
public double value(double[] x) {
double f = 0;
for (int i = 0; i < x.length; ++i)
f += FastMath.pow(FastMath.abs(x[i]), 2. + 10 * (double) i
/ (x.length - 1.));
// System.out.print("" + (fcount++) + ") ");
// for (int i = 0; i < x.length; i++)
// System.out.print(x[i] + " ");
// System.out.println(" = " + f);
return f;
}
}
private static class SsDiffPow implements MultivariateFunction {
public double value(double[] x) {
double f = FastMath.pow(new DiffPow().value(x), 0.25);
return f;
}
}
private static class Rosen implements MultivariateFunction {
public double value(double[] x) {
double f = 0;
for (int i = 0; i < x.length - 1; ++i)
f += 1e2 * (x[i] * x[i] - x[i + 1]) * (x[i] * x[i] - x[i + 1])
+ (x[i] - 1.) * (x[i] - 1.);
return f;
}
}
private static class Ackley implements MultivariateFunction {
private double axisratio;
Ackley(double axra) {
axisratio = axra;
}
public Ackley() {
this(1);
}
public double value(double[] x) {
double f = 0;
double res2 = 0;
double fac = 0;
for (int i = 0; i < x.length; ++i) {
fac = FastMath.pow(axisratio, (i - 1.) / (x.length - 1.));
f += fac * fac * x[i] * x[i];
res2 += FastMath.cos(2. * FastMath.PI * fac * x[i]);
}
f = (20. - 20. * FastMath.exp(-0.2 * FastMath.sqrt(f / x.length))
+ FastMath.exp(1.) - FastMath.exp(res2 / x.length));
return f;
}
}
private static class Rastrigin implements MultivariateFunction {
private double axisratio;
private double amplitude;
Rastrigin() {
this(1, 10);
}
Rastrigin(double axisratio, double amplitude) {
this.axisratio = axisratio;
this.amplitude = amplitude;
}
public double value(double[] x) {
double f = 0;
double fac;
for (int i = 0; i < x.length; ++i) {
fac = FastMath.pow(axisratio, (i - 1.) / (x.length - 1.));
if (i == 0 && x[i] < 0)
fac *= 1.;
f += fac * fac * x[i] * x[i] + amplitude
* (1. - FastMath.cos(2. * FastMath.PI * fac * x[i]));
}
return f;
}
}
private static class Basis {
double[][] basis;
Random rand = new Random(2); // use not always the same basis
double[] Rotate(double[] x) {
GenBasis(x.length);
double[] y = new double[x.length];
for (int i = 0; i < x.length; ++i) {
y[i] = 0;
for (int j = 0; j < x.length; ++j)
y[i] += basis[i][j] * x[j];
}
return y;
}
void GenBasis(int DIM) {
if (basis != null ? basis.length == DIM : false)
return;
double sp;
int i, j, k;
/* generate orthogonal basis */
basis = new double[DIM][DIM];
for (i = 0; i < DIM; ++i) {
/* sample components gaussian */
for (j = 0; j < DIM; ++j)
basis[i][j] = rand.nextGaussian();
/* substract projection of previous vectors */
for (j = i - 1; j >= 0; --j) {
for (sp = 0., k = 0; k < DIM; ++k)
sp += basis[i][k] * basis[j][k]; /* scalar product */
for (k = 0; k < DIM; ++k)
basis[i][k] -= sp * basis[j][k]; /* substract */
}
/* normalize */
for (sp = 0., k = 0; k < DIM; ++k)
sp += basis[i][k] * basis[i][k]; /* squared norm */
for (k = 0; k < DIM; ++k)
basis[i][k] /= FastMath.sqrt(sp);
}
}
}
}