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MATH-707 

"SimpleScalarValueChecker" renamed to "SimpleValueChecker".


git-svn-id: https://svn.apache.org/repos/asf/commons/proper/math/trunk@1243366 13f79535-47bb-0310-9956-ffa450edef68
This commit is contained in:
Gilles Sadowski 2012-02-12 23:38:24 +00:00
parent 0bee543107
commit 1b8959c711
12 changed files with 36 additions and 36 deletions
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2
src/main/java/org/apache/commons/math/optimization/ConvergenceChecker.java
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@ -26,7 +26,7 @@ package org.apache.commons.math.optimization;
*
* @param <PAIR> Type of the (point, objective value) pair.
*
* @see org.apache.commons.math.optimization.SimpleScalarValueChecker
* @see org.apache.commons.math.optimization.SimpleValueChecker
* @see org.apache.commons.math.optimization.SimplePointChecker<PointValuePair>
*
* @version $Id$

6
src/main/java/org/apache/commons/math/optimization/SimpleScalarValueChecker.java → src/main/java/org/apache/commons/math/optimization/SimpleValueChecker.java
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@ -31,12 +31,12 @@ import org.apache.commons.math.util.FastMath;
* @version $Id$
* @since 3.0
*/
public class SimpleScalarValueChecker
public class SimpleValueChecker
extends AbstractConvergenceChecker<PointValuePair> {
/**
* Build an instance with default thresholds.
*/
public SimpleScalarValueChecker() {}
public SimpleValueChecker() {}
/** Build an instance with specified thresholds.
*
@ -47,7 +47,7 @@ public class SimpleScalarValueChecker
* @param relativeThreshold relative tolerance threshold
* @param absoluteThreshold absolute tolerance threshold
*/
public SimpleScalarValueChecker(final double relativeThreshold,
public SimpleValueChecker(final double relativeThreshold,
final double absoluteThreshold) {
super(relativeThreshold, absoluteThreshold);
}

6
src/main/java/org/apache/commons/math/optimization/direct/BaseAbstractMultivariateOptimizer.java
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@ -26,7 +26,7 @@ import org.apache.commons.math.optimization.BaseMultivariateOptimizer;
import org.apache.commons.math.optimization.GoalType;
import org.apache.commons.math.optimization.ConvergenceChecker;
import org.apache.commons.math.optimization.PointValuePair;
import org.apache.commons.math.optimization.SimpleScalarValueChecker;
import org.apache.commons.math.optimization.SimpleValueChecker;
/**
* Base class for implementing optimizers for multivariate scalar functions.
@ -53,11 +53,11 @@ public abstract class BaseAbstractMultivariateOptimizer<FUNC extends Multivariat
/**
* Simple constructor with default settings.
* The convergence check is set to a {@link SimpleScalarValueChecker} and
* The convergence check is set to a {@link SimpleValueChecker} and
* the allowed number of evaluations is set to {@link Integer#MAX_VALUE}.
*/
protected BaseAbstractMultivariateOptimizer() {
this(new SimpleScalarValueChecker());
this(new SimpleValueChecker());
}
/**
* @param checker Convergence checker.

2
src/main/java/org/apache/commons/math/optimization/direct/BaseAbstractMultivariateSimpleBoundsOptimizer.java
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@ -51,7 +51,7 @@ public abstract class BaseAbstractMultivariateSimpleBoundsOptimizer<FUNC extends
/**
* Simple constructor with default settings.
* The convergence checker is set to a
* {@link org.apache.commons.math.optimization.SimpleScalarValueChecker} and
* {@link org.apache.commons.math.optimization.SimpleValueChecker} and
* the allowed number of evaluations is set to {@link Integer#MAX_VALUE}.
*
* @see BaseAbstractMultivariateOptimizer#BaseAbstractMultivariateOptimizer()

4
src/main/java/org/apache/commons/math/optimization/direct/CMAESOptimizer.java
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@ -35,7 +35,7 @@ import org.apache.commons.math.optimization.ConvergenceChecker;
import org.apache.commons.math.optimization.GoalType;
import org.apache.commons.math.optimization.MultivariateOptimizer;
import org.apache.commons.math.optimization.PointValuePair;
import org.apache.commons.math.optimization.SimpleScalarValueChecker;
import org.apache.commons.math.optimization.SimpleValueChecker;
import org.apache.commons.math.random.MersenneTwister;
import org.apache.commons.math.random.RandomGenerator;
import org.apache.commons.math.util.MathArrays;
@ -280,7 +280,7 @@ public class CMAESOptimizer
RandomGenerator random, boolean generateStatistics) {
this(lambda, inputSigma, boundaries, maxIterations, stopFitness, isActiveCMA,
diagonalOnly, checkFeasableCount, random, generateStatistics,
new SimpleScalarValueChecker());
new SimpleValueChecker());
}
/**

8
src/main/java/org/apache/commons/math/optimization/direct/SimplexOptimizer.java
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@ -24,7 +24,7 @@ import org.apache.commons.math.exception.NullArgumentException;
import org.apache.commons.math.optimization.GoalType;
import org.apache.commons.math.optimization.ConvergenceChecker;
import org.apache.commons.math.optimization.PointValuePair;
import org.apache.commons.math.optimization.SimpleScalarValueChecker;
import org.apache.commons.math.optimization.SimpleValueChecker;
import org.apache.commons.math.optimization.MultivariateOptimizer;
/**
@ -90,11 +90,11 @@ public class SimplexOptimizer
private AbstractSimplex simplex;
/**
* Constructor using a default {@link SimpleScalarValueChecker convergence
* Constructor using a default {@link SimpleValueChecker convergence
* checker}.
*/
public SimplexOptimizer() {
this(new SimpleScalarValueChecker());
this(new SimpleValueChecker());
}
/**
@ -109,7 +109,7 @@ public class SimplexOptimizer
* @param abs Absolute threshold.
*/
public SimplexOptimizer(double rel, double abs) {
this(new SimpleScalarValueChecker(rel, abs));
this(new SimpleValueChecker(rel, abs));
}
/**

4
src/main/java/org/apache/commons/math/optimization/general/AbstractScalarDifferentiableOptimizer.java
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@ -44,8 +44,8 @@ public abstract class AbstractScalarDifferentiableOptimizer
/**
* Simple constructor with default settings.
* The convergence check is set to a
* {@link org.apache.commons.math.optimization.SimpleScalarValueChecker
* SimpleScalarValueChecker}.
* {@link org.apache.commons.math.optimization.SimpleValueChecker
* SimpleValueChecker}.
*/
protected AbstractScalarDifferentiableOptimizer() {}
/**

6
src/main/java/org/apache/commons/math/optimization/general/NonLinearConjugateGradientOptimizer.java
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@ -24,7 +24,7 @@ import org.apache.commons.math.analysis.solvers.UnivariateSolver;
import org.apache.commons.math.exception.util.LocalizedFormats;
import org.apache.commons.math.optimization.GoalType;
import org.apache.commons.math.optimization.PointValuePair;
import org.apache.commons.math.optimization.SimpleScalarValueChecker;
import org.apache.commons.math.optimization.SimpleValueChecker;
import org.apache.commons.math.optimization.ConvergenceChecker;
import org.apache.commons.math.util.FastMath;
@ -54,7 +54,7 @@ public class NonLinearConjugateGradientOptimizer
private double[] point;
/**
* Constructor with default {@link SimpleScalarValueChecker checker},
* Constructor with default {@link SimpleValueChecker checker},
* {@link BrentSolver line search solver} and
* {@link IdentityPreconditioner preconditioner}.
*
@ -64,7 +64,7 @@ public class NonLinearConjugateGradientOptimizer
*/
public NonLinearConjugateGradientOptimizer(final ConjugateGradientFormula updateFormula) {
this(updateFormula,
new SimpleScalarValueChecker());
new SimpleValueChecker());
}
/**

2
src/test/java/org/apache/commons/math/optimization/DifferentiableMultivariateMultiStartOptimizerTest.java
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@ -45,7 +45,7 @@ public class DifferentiableMultivariateMultiStartOptimizerTest {
circle.addPoint( 45.0, 97.0);
NonLinearConjugateGradientOptimizer underlying =
new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE,
new SimpleScalarValueChecker(1.0e-10, 1.0e-10));
new SimpleValueChecker(1.0e-10, 1.0e-10));
JDKRandomGenerator g = new JDKRandomGenerator();
g.setSeed(753289573253l);
RandomVectorGenerator generator =

2
src/test/java/org/apache/commons/math/optimization/MultivariateMultiStartOptimizerTest.java
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@ -33,7 +33,7 @@ public class MultivariateMultiStartOptimizerTest {
public void testRosenbrock() {
Rosenbrock rosenbrock = new Rosenbrock();
SimplexOptimizer underlying
= new SimplexOptimizer(new SimpleScalarValueChecker(-1, 1.0e-3));
= new SimplexOptimizer(new SimpleValueChecker(-1, 1.0e-3));
NelderMeadSimplex simplex = new NelderMeadSimplex(new double[][] {
{ -1.2, 1.0 }, { 0.9, 1.2 } , { 3.5, -2.3 }
});

4
src/test/java/org/apache/commons/math/optimization/direct/SimplexOptimizerMultiDirectionalTest.java
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@ -20,7 +20,7 @@ package org.apache.commons.math.optimization.direct;
import org.apache.commons.math.analysis.MultivariateFunction;
import org.apache.commons.math.optimization.GoalType;
import org.apache.commons.math.optimization.PointValuePair;
import org.apache.commons.math.optimization.SimpleScalarValueChecker;
import org.apache.commons.math.optimization.SimpleValueChecker;
import org.apache.commons.math.util.FastMath;
import org.junit.Assert;
import org.junit.Test;
@ -73,7 +73,7 @@ public class SimplexOptimizerMultiDirectionalTest {
@Test
public void testMaximize2() {
SimplexOptimizer optimizer = new SimplexOptimizer(new SimpleScalarValueChecker(1e-15, 1e-30));
SimplexOptimizer optimizer = new SimplexOptimizer(new SimpleValueChecker(1e-15, 1e-30));
optimizer.setSimplex(new MultiDirectionalSimplex(new double[] { 0.2, 0.2 }));
final FourExtrema fourExtrema = new FourExtrema();

26
src/test/java/org/apache/commons/math/optimization/general/NonLinearConjugateGradientOptimizerTest.java
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@ -27,7 +27,7 @@ import org.apache.commons.math.linear.BlockRealMatrix;
import org.apache.commons.math.linear.RealMatrix;
import org.apache.commons.math.optimization.GoalType;
import org.apache.commons.math.optimization.PointValuePair;
import org.apache.commons.math.optimization.SimpleScalarValueChecker;
import org.apache.commons.math.optimization.SimpleValueChecker;
import org.junit.Assert;
import org.junit.Test;
@ -100,7 +100,7 @@ public class NonLinearConjugateGradientOptimizerTest {
new LinearProblem(new double[][] { { 2 } }, new double[] { 3 });
NonLinearConjugateGradientOptimizer optimizer =
new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE,
new SimpleScalarValueChecker(1e-6, 1e-6));
new SimpleValueChecker(1e-6, 1e-6));
PointValuePair optimum =
optimizer.optimize(100, problem, GoalType.MINIMIZE, new double[] { 0 });
Assert.assertEquals(1.5, optimum.getPoint()[0], 1.0e-10);
@ -115,7 +115,7 @@ public class NonLinearConjugateGradientOptimizerTest {
NonLinearConjugateGradientOptimizer optimizer =
new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE,
new SimpleScalarValueChecker(1e-6, 1e-6));
new SimpleValueChecker(1e-6, 1e-6));
PointValuePair optimum =
optimizer.optimize(100, problem, GoalType.MINIMIZE, new double[] { 0, 0 });
Assert.assertEquals(7.0, optimum.getPoint()[0], 1.0e-10);
@ -136,7 +136,7 @@ public class NonLinearConjugateGradientOptimizerTest {
}, new double[] { 0.0, 1.1, 2.2, 3.3, 4.4, 5.5 });
NonLinearConjugateGradientOptimizer optimizer =
new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE,
new SimpleScalarValueChecker(1e-6, 1e-6));
new SimpleValueChecker(1e-6, 1e-6));
PointValuePair optimum =
optimizer.optimize(100, problem, GoalType.MINIMIZE, new double[] { 0, 0, 0, 0, 0, 0 });
for (int i = 0; i < problem.target.length; ++i) {
@ -153,7 +153,7 @@ public class NonLinearConjugateGradientOptimizerTest {
}, new double[] { 1, 1, 1});
NonLinearConjugateGradientOptimizer optimizer =
new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE,
new SimpleScalarValueChecker(1e-6, 1e-6));
new SimpleValueChecker(1e-6, 1e-6));
PointValuePair optimum =
optimizer.optimize(100, problem, GoalType.MINIMIZE, new double[] { 0, 0, 0 });
Assert.assertEquals(1.0, optimum.getPoint()[0], 1.0e-10);
@ -190,7 +190,7 @@ public class NonLinearConjugateGradientOptimizerTest {
NonLinearConjugateGradientOptimizer optimizer =
new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE,
new SimpleScalarValueChecker(1e-13, 1e-13),
new SimpleValueChecker(1e-13, 1e-13),
new BrentSolver(),
preconditioner);
@ -214,7 +214,7 @@ public class NonLinearConjugateGradientOptimizerTest {
}, new double[] { 1, 1, 1 });
NonLinearConjugateGradientOptimizer optimizer =
new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE,
new SimpleScalarValueChecker(1e-6, 1e-6));
new SimpleValueChecker(1e-6, 1e-6));
PointValuePair optimum =
optimizer.optimize(100, problem, GoalType.MINIMIZE, new double[] { 0, 0, 0 });
Assert.assertTrue(optimum.getValue() > 0.5);
@ -230,7 +230,7 @@ public class NonLinearConjugateGradientOptimizerTest {
}, new double[] { 32, 23, 33, 31 });
NonLinearConjugateGradientOptimizer optimizer =
new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE,
new SimpleScalarValueChecker(1e-13, 1e-13),
new SimpleValueChecker(1e-13, 1e-13),
new BrentSolver(1e-15, 1e-15));
PointValuePair optimum1 =
optimizer.optimize(200, problem1, GoalType.MINIMIZE, new double[] { 0, 1, 2, 3 });
@ -264,7 +264,7 @@ public class NonLinearConjugateGradientOptimizerTest {
NonLinearConjugateGradientOptimizer optimizer =
new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE,
new SimpleScalarValueChecker(1e-6, 1e-6));
new SimpleValueChecker(1e-6, 1e-6));
PointValuePair optimum =
optimizer.optimize(100, problem, GoalType.MINIMIZE, new double[] { 7, 6, 5, 4 });
Assert.assertEquals(0, optimum.getValue(), 1.0e-10);
@ -282,7 +282,7 @@ public class NonLinearConjugateGradientOptimizerTest {
}, new double[] { 3.0, 12.0, -1.0, 7.0, 1.0 });
NonLinearConjugateGradientOptimizer optimizer =
new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE,
new SimpleScalarValueChecker(1e-6, 1e-6));
new SimpleValueChecker(1e-6, 1e-6));
PointValuePair optimum =
optimizer.optimize(100, problem, GoalType.MINIMIZE, new double[] { 2, 2, 2, 2, 2, 2 });
Assert.assertEquals(0, optimum.getValue(), 1.0e-10);
@ -298,7 +298,7 @@ public class NonLinearConjugateGradientOptimizerTest {
NonLinearConjugateGradientOptimizer optimizer =
new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE,
new SimpleScalarValueChecker(1e-6, 1e-6));
new SimpleValueChecker(1e-6, 1e-6));
PointValuePair optimum =
optimizer.optimize(100, problem, GoalType.MINIMIZE, new double[] { 1, 1 });
Assert.assertEquals(2.0, optimum.getPoint()[0], 1.0e-8);
@ -316,7 +316,7 @@ public class NonLinearConjugateGradientOptimizerTest {
NonLinearConjugateGradientOptimizer optimizer =
new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE,
new SimpleScalarValueChecker(1e-6, 1e-6));
new SimpleValueChecker(1e-6, 1e-6));
PointValuePair optimum =
optimizer.optimize(100, problem, GoalType.MINIMIZE, new double[] { 1, 1 });
Assert.assertTrue(optimum.getValue() > 0.1);
@ -333,7 +333,7 @@ public class NonLinearConjugateGradientOptimizerTest {
circle.addPoint( 45.0, 97.0);
NonLinearConjugateGradientOptimizer optimizer =
new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE,
new SimpleScalarValueChecker(1e-30, 1e-30),
new SimpleValueChecker(1e-30, 1e-30),
new BrentSolver(1e-15, 1e-13));
PointValuePair optimum =
optimizer.optimize(100, circle, GoalType.MINIMIZE, new double[] { 98.680, 47.345 });