Replace "CMAESOptimizer.Sigma" with "Sigma".
This commit is contained in:
Gilles Sadowski
parent
336811dff5
commit
d81b5e921e
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@ -22,7 +22,6 @@ import java.util.Arrays;
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import java.util.List;
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import org.apache.commons.math4.legacy.exception.DimensionMismatchException;
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import org.apache.commons.math4.legacy.exception.NotPositiveException;
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import org.apache.commons.math4.legacy.exception.NotStrictlyPositiveException;
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import org.apache.commons.math4.legacy.exception.OutOfRangeException;
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import org.apache.commons.math4.legacy.exception.TooManyEvaluationsException;
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@ -35,6 +34,7 @@ import org.apache.commons.math4.legacy.optim.OptimizationData;
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import org.apache.commons.math4.legacy.optim.PointValuePair;
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import org.apache.commons.math4.legacy.optim.nonlinear.scalar.GoalType;
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import org.apache.commons.math4.legacy.optim.nonlinear.scalar.PopulationSize;
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import org.apache.commons.math4.legacy.optim.nonlinear.scalar.Sigma;
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import org.apache.commons.math4.legacy.optim.nonlinear.scalar.MultivariateOptimizer;
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import org.apache.commons.rng.UniformRandomProvider;
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import org.apache.commons.statistics.distribution.ContinuousDistribution;
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@ -78,14 +78,6 @@ import org.apache.commons.math4.core.jdkmath.JdkMath;
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* <li><a href="http://en.wikipedia.org/wiki/CMA-ES">Wikipedia</a></li>
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* </ul>
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*
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* <p>
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* The {@link PopulationSize number of offsprings} is the primary strategy
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* parameter. In the absence of better clues, a good default could be an integer
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* close to {@code 4 + 3 ln(n)}, where {@code n} is the number of optimized
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* parameters. Increasing the population size improves global search properties
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* at the expense of speed (which in general decreases at most linearly with
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* increasing population size).
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*
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* @since 3.0
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*/
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public class CMAESOptimizer
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@ -278,44 +270,6 @@ public class CMAESOptimizer
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return statisticsDHistory;
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}
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/**
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* Input sigma values.
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* They define the initial coordinate-wise standard deviations for
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* sampling new search points around the initial guess.
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* It is suggested to set them to the estimated distance from the
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* initial to the desired optimum.
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* Small values induce the search to be more local (and very small
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* values are more likely to find a local optimum close to the initial
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* guess).
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* Too small values might however lead to early termination.
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*/
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public static class Sigma implements OptimizationData {
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/** Sigma values. */
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private final double[] sigma;
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/**
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* @param s Sigma values.
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* @throws NotPositiveException if any of the array entries is smaller
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* than zero.
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*/
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public Sigma(double[] s) {
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for (int i = 0; i < s.length; i++) {
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if (s[i] < 0) {
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throw new NotPositiveException(s[i]);
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}
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}
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sigma = s.clone();
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}
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/**
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* @return the sigma values.
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*/
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public double[] getSigma() {
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return sigma.clone();
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}
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}
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/**
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* {@inheritDoc}
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*
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@ -323,14 +277,32 @@ public class CMAESOptimizer
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* {@link MultivariateOptimizer#parseOptimizationData(OptimizationData[])
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* MultivariateOptimizer}, this method will register the following data:
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* <ul>
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* <li>{@link Sigma}</li>
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* <li>{@link PopulationSize}</li>
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* <li>
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* {@link Sigma} values define the initial coordinate-wise standard
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* deviations for sampling new search points around the initial guess.
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* It is suggested to set them to the estimated distance from the
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* initial to the desired optimum.
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* Small values induce the search to be more local (and very small
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* values are more likely to find a local optimum close to the initial
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* guess).
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* Too small values might however lead to early termination.
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* </li>
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* <li>
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* {@link PopulationSize} is the number of offsprings and the primary
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* strategy parameter.
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* In the absence of better clues, a good default could be an integer
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* close to {@code 4 + 3 ln(n)}, where {@code n} is the number of
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* optimized parameters.
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* Increasing the population size improves global search properties at
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* the expense of speed (which in general decreases at most linearly
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* with increasing population size).
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* </li>
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* </ul>
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* @return {@inheritDoc}
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* @throws TooManyEvaluationsException if the maximal number of
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* evaluations is exceeded.
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* @throws DimensionMismatchException if the initial guess, target, and weight
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* arguments have inconsistent dimensions.
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* @throws TooManyEvaluationsException if the maximal number of evaluations
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* is exceeded.
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* @throws DimensionMismatchException if the initial guess, target, and
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* weight arguments have inconsistent dimensions.
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*/
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@Override
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public PointValuePair optimize(OptimizationData... optData) {
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@ -18,7 +18,7 @@ package org.apache.commons.math4.legacy.optim.nonlinear.scalar.noderiv;
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import org.apache.commons.math4.legacy.analysis.MultivariateFunction;
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import org.apache.commons.math4.legacy.exception.DimensionMismatchException;
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import org.apache.commons.math4.legacy.exception.NotPositiveException;
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import org.apache.commons.math4.legacy.exception.NotStrictlyPositiveException;
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import org.apache.commons.math4.legacy.exception.NumberIsTooLargeException;
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import org.apache.commons.math4.legacy.exception.NumberIsTooSmallException;
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import org.apache.commons.math4.legacy.exception.OutOfRangeException;
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@ -28,6 +28,7 @@ import org.apache.commons.math4.legacy.optim.PointValuePair;
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import org.apache.commons.math4.legacy.optim.SimpleBounds;
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import org.apache.commons.math4.legacy.optim.nonlinear.scalar.GoalType;
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import org.apache.commons.math4.legacy.optim.nonlinear.scalar.PopulationSize;
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import org.apache.commons.math4.legacy.optim.nonlinear.scalar.Sigma;
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import org.apache.commons.math4.legacy.optim.nonlinear.scalar.ObjectiveFunction;
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import org.apache.commons.math4.legacy.optim.nonlinear.scalar.TestFunction;
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import org.apache.commons.rng.simple.RandomSource;
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@ -81,7 +82,7 @@ public class CMAESOptimizerTest {
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1e-13, 1e-6, 100000, expected);
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}
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@Test(expected = NotPositiveException.class)
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@Test(expected = NotStrictlyPositiveException.class)
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public void testInputSigmaNegative() {
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final int dim = 12;
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double[] startPoint = OptimTestUtils.point(dim, 0.5);
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@ -175,7 +176,7 @@ public class CMAESOptimizerTest {
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SimpleBounds.unbounded(1),
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GoalType.MINIMIZE,
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new PopulationSize(5),
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new CMAESOptimizer.Sigma(sigma),
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new Sigma(sigma),
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new InitialGuess(start)).getPoint();
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Assert.assertEquals(0, result[0], 1e-7);
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}
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@ -396,7 +397,7 @@ public class CMAESOptimizerTest {
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new ObjectiveFunction(fitnessFunction),
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GoalType.MINIMIZE,
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new PopulationSize(5),
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new CMAESOptimizer.Sigma(sigma),
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new Sigma(sigma),
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new InitialGuess(start),
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new SimpleBounds(lower, upper)).getPoint();
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Assert.assertTrue("Out of bounds (" + result[0] + " > " + upper[0] + ")",
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@ -428,7 +429,7 @@ public class CMAESOptimizerTest {
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GoalType.MINIMIZE,
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SimpleBounds.unbounded(1),
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new PopulationSize(5),
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new CMAESOptimizer.Sigma(new double[] { 1e-1 }),
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new Sigma(new double[] { 1e-1 }),
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new InitialGuess(start));
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final double resNoBound = result.getPoint()[0];
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@ -440,7 +441,7 @@ public class CMAESOptimizerTest {
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new ObjectiveFunction(fitnessFunction),
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GoalType.MINIMIZE,
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new PopulationSize(5),
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new CMAESOptimizer.Sigma(sigma),
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new Sigma(sigma),
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new InitialGuess(start),
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new SimpleBounds(lower, upper));
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final double resNearLo = result.getPoint()[0];
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@ -452,7 +453,7 @@ public class CMAESOptimizerTest {
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new ObjectiveFunction(fitnessFunction),
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GoalType.MINIMIZE,
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new PopulationSize(5),
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new CMAESOptimizer.Sigma(sigma),
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new Sigma(sigma),
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new InitialGuess(start),
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new SimpleBounds(lower, upper));
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final double resNearHi = result.getPoint()[0];
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@ -505,7 +506,7 @@ public class CMAESOptimizerTest {
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goal,
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new InitialGuess(startPoint),
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SimpleBounds.unbounded(dim),
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new CMAESOptimizer.Sigma(inSigma),
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new Sigma(inSigma),
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new PopulationSize(lambda)) :
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optim.optimize(new MaxEval(maxEvaluations),
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new ObjectiveFunction(func),
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@ -513,7 +514,7 @@ public class CMAESOptimizerTest {
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new SimpleBounds(boundaries[0],
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boundaries[1]),
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new InitialGuess(startPoint),
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new CMAESOptimizer.Sigma(inSigma),
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new Sigma(inSigma),
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new PopulationSize(lambda));
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Assert.assertEquals(expected.getValue(), result.getValue(), fTol);
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