Apache
/
commons-math
mirror of https://github.com/apache/commons-math.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

MATH-707 

Renamed "VectorialPointValuePair" to "PointVectorValuePair" and made it a
subclass of "Pair<double[], double[]>".


git-svn-id: https://svn.apache.org/repos/asf/commons/proper/math/trunk@1243310 13f79535-47bb-0310-9956-ffa450edef68
This commit is contained in:
Gilles Sadowski 2012-02-12 20:00:31 +00:00
parent 3390941cd1
commit 7921608dec
17 changed files with 186 additions and 180 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

18
src/main/java/org/apache/commons/math/optimization/BaseMultivariateVectorMultiStartOptimizer.java
View File

@ -53,7 +53,7 @@ public class BaseMultivariateVectorMultiStartOptimizer<FUNC extends Multivariate
/** Random generator for multi-start. */
private RandomVectorGenerator generator;
/** Found optima. */
private VectorialPointValuePair[] optima;
private PointVectorValuePair[] optima;
/**
* Create a multi-start optimizer from a single-start optimizer.
@ -110,7 +110,7 @@ public class BaseMultivariateVectorMultiStartOptimizer<FUNC extends Multivariate
* #optimize(int,MultivariateVectorFunction,double[],double[],double[]) optimize} has not been
* called.
*/
public VectorialPointValuePair[] getOptima() {
public PointVectorValuePair[] getOptima() {
if (optima == null) {
throw new MathIllegalStateException(LocalizedFormats.NO_OPTIMUM_COMPUTED_YET);
}
@ -128,19 +128,19 @@ public class BaseMultivariateVectorMultiStartOptimizer<FUNC extends Multivariate
}
/** {@inheritDoc} */
public ConvergenceChecker<VectorialPointValuePair> getConvergenceChecker() {
public ConvergenceChecker<PointVectorValuePair> getConvergenceChecker() {
return optimizer.getConvergenceChecker();
}
/**
* {@inheritDoc}
*/
public VectorialPointValuePair optimize(int maxEval, final FUNC f,
public PointVectorValuePair optimize(int maxEval, final FUNC f,
double[] target, double[] weights,
double[] startPoint) {
maxEvaluations = maxEval;
RuntimeException lastException = null;
optima = new VectorialPointValuePair[starts];
optima = new PointVectorValuePair[starts];
totalEvaluations = 0;
// Multi-start loop.
@ -179,9 +179,9 @@ public class BaseMultivariateVectorMultiStartOptimizer<FUNC extends Multivariate
*/
private void sortPairs(final double[] target,
final double[] weights) {
Arrays.sort(optima, new Comparator<VectorialPointValuePair>() {
public int compare(final VectorialPointValuePair o1,
final VectorialPointValuePair o2) {
Arrays.sort(optima, new Comparator<PointVectorValuePair>() {
public int compare(final PointVectorValuePair o1,
final PointVectorValuePair o2) {
if (o1 == null) {
return (o2 == null) ? 0 : 1;
} else if (o2 == null) {
@ -189,7 +189,7 @@ public class BaseMultivariateVectorMultiStartOptimizer<FUNC extends Multivariate
}
return Double.compare(weightedResidual(o1), weightedResidual(o2));
}
private double weightedResidual(final VectorialPointValuePair pv) {
private double weightedResidual(final PointVectorValuePair pv) {
final double[] value = pv.getValueRef();
double sum = 0;
for (int i = 0; i < value.length; ++i) {

4
src/main/java/org/apache/commons/math/optimization/BaseMultivariateVectorOptimizer.java
View File

@ -33,7 +33,7 @@ import org.apache.commons.math.analysis.MultivariateVectorFunction;
* @since 3.0
*/
public interface BaseMultivariateVectorOptimizer<FUNC extends MultivariateVectorFunction>
extends BaseOptimizer<VectorialPointValuePair> {
extends BaseOptimizer<PointVectorValuePair> {
/**
* Optimize an objective function.
* Optimization is considered to be a weighted least-squares minimization.
@ -54,6 +54,6 @@ public interface BaseMultivariateVectorOptimizer<FUNC extends MultivariateVector
* @throws org.apache.commons.math.exception.NullArgumentException if
* any argument is {@code null}.
*/
VectorialPointValuePair optimize(int maxEval, FUNC f, double[] target,
PointVectorValuePair optimize(int maxEval, FUNC f, double[] target,
double[] weight, double[] startPoint);
}

8
src/main/java/org/apache/commons/math/optimization/PointValuePair.java
View File

@ -23,6 +23,7 @@ import org.apache.commons.math.util.Pair;
* This class holds a point and the value of an objective function at
* that point.
*
* @see PointVectorValuePair
* @see org.apache.commons.math.analysis.MultivariateFunction
* @version $Id$
* @since 3.0
@ -31,8 +32,8 @@ public class PointValuePair extends Pair<double[], Double> {
/**
* Builds a point/objective function value pair.
*
* @param point Point coordinates (this instance will store
* a copy of the array, not the array passed as argument).
* @param point Point coordinates. This instance will store
* a copy of the array, not the array passed as argument.
* @param value Value of the objective function at the point.
*/
public PointValuePair(final double[] point,
@ -63,7 +64,8 @@ public class PointValuePair extends Pair<double[], Double> {
* @return a copy of the stored point.
*/
public double[] getPoint() {
return getKey().clone();
final double[] p = getKey();
return p == null ? null : p.clone();
}
/**

104
src/main/java/org/apache/commons/math/optimization/PointVectorValuePair.java Normal file
View File

@ -0,0 +1,104 @@
/*
* 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 org.apache.commons.math.util.Pair;
/**
* This class holds a point and the vectorial value of an objective function at
* that point.
*
* @see PointValuePair
* @see org.apache.commons.math.analysis.MultivariateVectorFunction
* @version $Id$
* @since 3.0
*/
public class PointVectorValuePair extends Pair<double[], double[]> {
/**
* Builds a point/objective function value pair.
*
* @param point Point coordinates. This instance will store
* a copy of the array, not the array passed as argument.
* @param value Value of the objective function at the point.
*/
public PointVectorValuePair(final double[] point,
final double[] value) {
this(point, value, true);
}
/**
* Build a point/objective function value pair.
*
* @param point Point coordinates.
* @param value Value of the objective function at the point.
* @param copyArray if {@code true}, the input arrays will be copied,
* otherwise they will be referenced.
*/
public PointVectorValuePair(final double[] point,
final double[] value,
final boolean copyArray) {
super(copyArray ?
((point == null) ? null :
point.clone()) :
point,
copyArray ?
((value == null) ? null :
value.clone()) :
value);
}
/**
* Gets the point.
*
* @return a copy of the stored point.
*/
public double[] getPoint() {
final double[] p = getKey();
return p == null ? null : p.clone();
}
/**
* Gets a reference to the point.
*
* @return a reference to the internal array storing the point.
*/
public double[] getPointRef() {
return getKey();
}
/**
* Gets the value of the objective function.
*
* @return a copy of the stored value of the objective function.
*/
@Override
public double[] getValue() {
final double[] v = super.getValue();
return v == null ? null : v.clone();
}
/**
* Gets a reference to the value of the objective function.
*
* @return a reference to the internal array storing the value of
* the objective function.
*/
public double[] getValueRef() {
return super.getValue();
}
}

6
src/main/java/org/apache/commons/math/optimization/SimpleVectorialPointChecker.java
View File

@ -30,7 +30,7 @@ package org.apache.commons.math.optimization;
* @since 3.0
*/
public class SimpleVectorialPointChecker
extends AbstractConvergenceChecker<VectorialPointValuePair> {
extends AbstractConvergenceChecker<PointVectorValuePair> {
/**
* Build an instance with default threshold.
*/
@ -69,8 +69,8 @@ public class SimpleVectorialPointChecker
*/
@Override
public boolean converged(final int iteration,
final VectorialPointValuePair previous,
final VectorialPointValuePair current) {
final PointVectorValuePair previous,
final PointVectorValuePair current) {
final double[] p = previous.getPointRef();
final double[] c = current.getPointRef();
for (int i = 0; i < p.length; ++i) {

6
src/main/java/org/apache/commons/math/optimization/SimpleVectorialValueChecker.java
View File

@ -32,7 +32,7 @@ import org.apache.commons.math.util.FastMath;
* @since 3.0
*/
public class SimpleVectorialValueChecker
extends AbstractConvergenceChecker<VectorialPointValuePair> {
extends AbstractConvergenceChecker<PointVectorValuePair> {
/**
* Build an instance with default thresholds.
*/
@ -71,8 +71,8 @@ public class SimpleVectorialValueChecker
*/
@Override
public boolean converged(final int iteration,
final VectorialPointValuePair previous,
final VectorialPointValuePair current) {
final PointVectorValuePair previous,
final PointVectorValuePair current) {
final double[] p = previous.getValueRef();
final double[] c = current.getValueRef();
for (int i = 0; i < p.length; ++i) {

2
src/main/java/org/apache/commons/math/optimization/VectorialConvergenceChecker.java
View File

@ -50,6 +50,6 @@ public interface VectorialConvergenceChecker {
* @param current point from current iteration
* @return true if the algorithm is considered to have converged
*/
boolean converged(int iteration, VectorialPointValuePair previous, VectorialPointValuePair current);
boolean converged(int iteration, PointVectorValuePair previous, PointVectorValuePair current);
}

100
src/main/java/org/apache/commons/math/optimization/VectorialPointValuePair.java
View File

@ -1,100 +0,0 @@
/*
* 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 java.io.Serializable;
/**
* This class holds a point and the vectorial value of an objective function at this point.
* <p>This is a simple immutable container.</p>
* @see PointValuePair
* @see org.apache.commons.math.analysis.MultivariateVectorFunction
* @version $Id$
* @since 2.0
*/
public class VectorialPointValuePair implements Serializable {
/** Serializable version identifier. */
private static final long serialVersionUID = 1003888396256744753L;
/** Point coordinates. */
private final double[] point;
/** Vectorial value of the objective function at the point. */
private final double[] value;
/** Build a point/objective function value pair.
* @param point point coordinates (the built instance will store
* a copy of the array, not the array passed as argument)
* @param value value of an objective function at the point
*/
public VectorialPointValuePair(final double[] point, final double[] value) {
this.point = (point == null) ? null : point.clone();
this.value = (value == null) ? null : value.clone();
}
/** Build a point/objective function value pair.
* @param point point coordinates (the built instance will store
* a copy of the array, not the array passed as argument)
* @param value value of an objective function at the point
* @param copyArray if true, the input arrays will be copied, otherwise
* they will be referenced
*/
public VectorialPointValuePair(final double[] point, final double[] value,
final boolean copyArray) {
this.point = copyArray ?
((point == null) ? null : point.clone()) :
point;
this.value = copyArray ?
((value == null) ? null : value.clone()) :
value;
}
/** Get the point.
* @return a copy of the stored point
*/
public double[] getPoint() {
return (point == null) ? null : point.clone();
}
/** Get a reference to the point.
* <p>This method is provided as a convenience to avoid copying
* the array, the elements of the array should <em>not</em> be modified.</p>
* @return a reference to the internal array storing the point
*/
public double[] getPointRef() {
return point;
}
/** Get the value of the objective function.
* @return a copy of the stored value of the objective function
*/
public double[] getValue() {
return (value == null) ? null : value.clone();
}
/** Get a reference to the value of the objective function.
* <p>This method is provided as a convenience to avoid copying
* the array, the elements of the array should <em>not</em> be modified.</p>
* @return a reference to the internal array storing the value of the objective function
*/
public double[] getValueRef() {
return value;
}
}

12
src/main/java/org/apache/commons/math/optimization/direct/BaseAbstractMultivariateVectorOptimizer.java
View File

@ -25,7 +25,7 @@ import org.apache.commons.math.exception.NullArgumentException;
import org.apache.commons.math.analysis.MultivariateVectorFunction;
import org.apache.commons.math.optimization.BaseMultivariateVectorOptimizer;
import org.apache.commons.math.optimization.ConvergenceChecker;
import org.apache.commons.math.optimization.VectorialPointValuePair;
import org.apache.commons.math.optimization.PointVectorValuePair;
import org.apache.commons.math.optimization.SimpleVectorialValueChecker;
/**
@ -43,7 +43,7 @@ public abstract class BaseAbstractMultivariateVectorOptimizer<FUNC extends Multi
/** Evaluations counter. */
protected final Incrementor evaluations = new Incrementor();
/** Convergence checker. */
private ConvergenceChecker<VectorialPointValuePair> checker;
private ConvergenceChecker<PointVectorValuePair> checker;
/** Target value for the objective functions at optimum. */
private double[] target;
/** Weight for the least squares cost computation. */
@ -64,7 +64,7 @@ public abstract class BaseAbstractMultivariateVectorOptimizer<FUNC extends Multi
/**
* @param checker Convergence checker.
*/
protected BaseAbstractMultivariateVectorOptimizer(ConvergenceChecker<VectorialPointValuePair> checker) {
protected BaseAbstractMultivariateVectorOptimizer(ConvergenceChecker<PointVectorValuePair> checker) {
this.checker = checker;
}
@ -79,7 +79,7 @@ public abstract class BaseAbstractMultivariateVectorOptimizer<FUNC extends Multi
}
/** {@inheritDoc} */
public ConvergenceChecker<VectorialPointValuePair> getConvergenceChecker() {
public ConvergenceChecker<PointVectorValuePair> getConvergenceChecker() {
return checker;
}
@ -101,7 +101,7 @@ public abstract class BaseAbstractMultivariateVectorOptimizer<FUNC extends Multi
}
/** {@inheritDoc} */
public VectorialPointValuePair optimize(int maxEval, FUNC f, double[] t, double[] w,
public PointVectorValuePair optimize(int maxEval, FUNC f, double[] t, double[] w,
double[] startPoint) {
// Checks.
if (f == null) {
@ -147,7 +147,7 @@ public abstract class BaseAbstractMultivariateVectorOptimizer<FUNC extends Multi
* @return the point/value pair giving the optimal value for the
* objective function.
*/
protected abstract VectorialPointValuePair doOptimize();
protected abstract PointVectorValuePair doOptimize();
/**
* @return a reference to the {@link #target array}.

4
src/main/java/org/apache/commons/math/optimization/fitting/CurveFitter.java
View File

@ -24,7 +24,7 @@ import org.apache.commons.math.analysis.DifferentiableMultivariateVectorFunction
import org.apache.commons.math.analysis.ParametricUnivariateFunction;
import org.apache.commons.math.analysis.MultivariateMatrixFunction;
import org.apache.commons.math.optimization.DifferentiableMultivariateVectorOptimizer;
import org.apache.commons.math.optimization.VectorialPointValuePair;
import org.apache.commons.math.optimization.PointVectorValuePair;
/** Fitter for parametric univariate real functions y = f(x).
* <p>When a univariate real function y = f(x) does depend on some
@ -154,7 +154,7 @@ public class CurveFitter {
}
// perform the fit
VectorialPointValuePair optimum =
PointVectorValuePair optimum =
optimizer.optimize(maxEval, new TheoreticalValuesFunction(f),
target, weights, initialGuess);

6
src/main/java/org/apache/commons/math/optimization/general/AbstractLeastSquaresOptimizer.java
View File

@ -27,7 +27,7 @@ import org.apache.commons.math.linear.DecompositionSolver;
import org.apache.commons.math.linear.MatrixUtils;
import org.apache.commons.math.optimization.ConvergenceChecker;
import org.apache.commons.math.optimization.DifferentiableMultivariateVectorOptimizer;
import org.apache.commons.math.optimization.VectorialPointValuePair;
import org.apache.commons.math.optimization.PointVectorValuePair;
import org.apache.commons.math.optimization.direct.BaseAbstractMultivariateVectorOptimizer;
import org.apache.commons.math.util.FastMath;
@ -89,7 +89,7 @@ public abstract class AbstractLeastSquaresOptimizer
/**
* @param checker Convergence checker.
*/
protected AbstractLeastSquaresOptimizer(ConvergenceChecker<VectorialPointValuePair> checker) {
protected AbstractLeastSquaresOptimizer(ConvergenceChecker<PointVectorValuePair> checker) {
super(checker);
}
@ -252,7 +252,7 @@ public abstract class AbstractLeastSquaresOptimizer
/** {@inheritDoc} */
@Override
public VectorialPointValuePair optimize(int maxEval,
public PointVectorValuePair optimize(int maxEval,
final DifferentiableMultivariateVectorFunction f,
final double[] target, final double[] weights,
final double[] startPoint) {

16
src/main/java/org/apache/commons/math/optimization/general/GaussNewtonOptimizer.java
View File

@ -28,7 +28,7 @@ import org.apache.commons.math.linear.RealMatrix;
import org.apache.commons.math.linear.SingularMatrixException;
import org.apache.commons.math.optimization.ConvergenceChecker;
import org.apache.commons.math.optimization.SimpleVectorialValueChecker;
import org.apache.commons.math.optimization.VectorialPointValuePair;
import org.apache.commons.math.optimization.PointVectorValuePair;
/**
* Gauss-Newton least-squares solver.
@ -64,7 +64,7 @@ public class GaussNewtonOptimizer extends AbstractLeastSquaresOptimizer {
*
* @param checker Convergence checker.
*/
public GaussNewtonOptimizer(ConvergenceChecker<VectorialPointValuePair> checker) {
public GaussNewtonOptimizer(ConvergenceChecker<PointVectorValuePair> checker) {
this(true, checker);
}
@ -88,29 +88,29 @@ public class GaussNewtonOptimizer extends AbstractLeastSquaresOptimizer {
* @param checker Convergence checker.
*/
public GaussNewtonOptimizer(final boolean useLU,
ConvergenceChecker<VectorialPointValuePair> checker) {
ConvergenceChecker<PointVectorValuePair> checker) {
super(checker);
this.useLU = useLU;
}
/** {@inheritDoc} */
@Override
public VectorialPointValuePair doOptimize() {
public PointVectorValuePair doOptimize() {
final ConvergenceChecker<VectorialPointValuePair> checker
final ConvergenceChecker<PointVectorValuePair> checker
= getConvergenceChecker();
// iterate until convergence is reached
VectorialPointValuePair current = null;
PointVectorValuePair current = null;
int iter = 0;
for (boolean converged = false; !converged;) {
++iter;
// evaluate the objective function and its jacobian
VectorialPointValuePair previous = current;
PointVectorValuePair previous = current;
updateResidualsAndCost();
updateJacobian();
current = new VectorialPointValuePair(point, objective);
current = new PointVectorValuePair(point, objective);
final double[] targetValues = getTargetRef();
final double[] residualsWeights = getWeightRef();

18
src/main/java/org/apache/commons/math/optimization/general/LevenbergMarquardtOptimizer.java
View File

@ -20,7 +20,7 @@ import java.util.Arrays;
import org.apache.commons.math.exception.ConvergenceException;
import org.apache.commons.math.exception.util.LocalizedFormats;
import org.apache.commons.math.optimization.VectorialPointValuePair;
import org.apache.commons.math.optimization.PointVectorValuePair;
import org.apache.commons.math.optimization.ConvergenceChecker;
import org.apache.commons.math.util.Precision;
import org.apache.commons.math.util.FastMath;
@ -166,7 +166,7 @@ public class LevenbergMarquardtOptimizer extends AbstractLeastSquaresOptimizer {
*
* @param checker Convergence checker.
*/
public LevenbergMarquardtOptimizer(ConvergenceChecker<VectorialPointValuePair> checker) {
public LevenbergMarquardtOptimizer(ConvergenceChecker<PointVectorValuePair> checker) {
this(100, checker, 1e-10, 1e-10, 1e-10, Precision.SAFE_MIN);
}
@ -193,7 +193,7 @@ public class LevenbergMarquardtOptimizer extends AbstractLeastSquaresOptimizer {
* of the matrix is reduced.
*/
public LevenbergMarquardtOptimizer(double initialStepBoundFactor,
ConvergenceChecker<VectorialPointValuePair> checker,
ConvergenceChecker<PointVectorValuePair> checker,
double costRelativeTolerance,
double parRelativeTolerance,
double orthoTolerance,
@ -269,7 +269,7 @@ public class LevenbergMarquardtOptimizer extends AbstractLeastSquaresOptimizer {
/** {@inheritDoc} */
@Override
protected VectorialPointValuePair doOptimize() {
protected PointVectorValuePair doOptimize() {
// arrays shared with the other private methods
solvedCols = FastMath.min(rows, cols);
diagR = new double[cols];
@ -296,9 +296,9 @@ public class LevenbergMarquardtOptimizer extends AbstractLeastSquaresOptimizer {
// outer loop
lmPar = 0;
boolean firstIteration = true;
VectorialPointValuePair current = new VectorialPointValuePair(point, objective);
PointVectorValuePair current = new PointVectorValuePair(point, objective);
int iter = 0;
final ConvergenceChecker<VectorialPointValuePair> checker = getConvergenceChecker();
final ConvergenceChecker<PointVectorValuePair> checker = getConvergenceChecker();
while (true) {
++iter;
@ -307,7 +307,7 @@ public class LevenbergMarquardtOptimizer extends AbstractLeastSquaresOptimizer {
}
// compute the Q.R. decomposition of the jacobian matrix
VectorialPointValuePair previous = current;
PointVectorValuePair previous = current;
updateJacobian();
qrDecomposition();
@ -357,7 +357,7 @@ public class LevenbergMarquardtOptimizer extends AbstractLeastSquaresOptimizer {
if (maxCosine <= orthoTolerance) {
// convergence has been reached
updateResidualsAndCost();
current = new VectorialPointValuePair(point, objective);
current = new PointVectorValuePair(point, objective);
return current;
}
@ -457,7 +457,7 @@ public class LevenbergMarquardtOptimizer extends AbstractLeastSquaresOptimizer {
xNorm += xK * xK;
}
xNorm = FastMath.sqrt(xNorm);
current = new VectorialPointValuePair(point, objective);
current = new PointVectorValuePair(point, objective);
// tests for convergence.
if (checker != null) {

4
src/test/java/org/apache/commons/math/optimization/DifferentiableMultivariateVectorMultiStartOptimizerTest.java
View File

@ -117,11 +117,11 @@ public class DifferentiableMultivariateVectorMultiStartOptimizerTest {
} catch (MathIllegalStateException ise) {
// expected
}
VectorialPointValuePair optimum =
PointVectorValuePair optimum =
optimizer.optimize(100, problem, problem.target, new double[] { 1 }, new double[] { 0 });
Assert.assertEquals(1.5, optimum.getPoint()[0], 1.0e-10);
Assert.assertEquals(3.0, optimum.getValue()[0], 1.0e-10);
VectorialPointValuePair[] optima = optimizer.getOptima();
PointVectorValuePair[] optima = optimizer.getOptima();
Assert.assertEquals(10, optima.length);
for (int i = 0; i < optima.length; ++i) {
Assert.assertEquals(1.5, optima[i].getPoint()[0], 1.0e-10);

26
src/test/java/org/apache/commons/math/optimization/general/GaussNewtonOptimizerTest.java
View File

@ -30,7 +30,7 @@ import org.apache.commons.math.analysis.MultivariateMatrixFunction;
import org.apache.commons.math.linear.BlockRealMatrix;
import org.apache.commons.math.linear.RealMatrix;
import org.apache.commons.math.optimization.SimpleVectorialValueChecker;
import org.apache.commons.math.optimization.VectorialPointValuePair;
import org.apache.commons.math.optimization.PointVectorValuePair;
import org.apache.commons.math.util.FastMath;
import org.junit.Assert;
import org.junit.Test;
@ -107,7 +107,7 @@ public class GaussNewtonOptimizerTest {
GaussNewtonOptimizer optimizer
= new GaussNewtonOptimizer(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
VectorialPointValuePair optimum =
PointVectorValuePair optimum =
optimizer.optimize(100, problem, problem.target, new double[] { 1 }, new double[] { 0 });
Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
Assert.assertEquals(1.5, optimum.getPoint()[0], 1.0e-10);
@ -124,7 +124,7 @@ public class GaussNewtonOptimizerTest {
GaussNewtonOptimizer optimizer
= new GaussNewtonOptimizer(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
VectorialPointValuePair optimum =
PointVectorValuePair optimum =
optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1 }, new double[] { 0, 0 });
Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
Assert.assertEquals(7.0, optimum.getPoint()[0], 1.0e-10);
@ -149,7 +149,7 @@ public class GaussNewtonOptimizerTest {
GaussNewtonOptimizer optimizer
= new GaussNewtonOptimizer(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
VectorialPointValuePair optimum =
PointVectorValuePair optimum =
optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1, 1, 1, 1 },
new double[] { 0, 0, 0, 0, 0, 0 });
Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
@ -170,7 +170,7 @@ public class GaussNewtonOptimizerTest {
GaussNewtonOptimizer optimizer
= new GaussNewtonOptimizer(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
VectorialPointValuePair optimum =
PointVectorValuePair optimum =
optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1 }, new double[] { 0, 0, 0 });
Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
Assert.assertEquals(1.0, optimum.getPoint()[0], 1.0e-10);
@ -194,7 +194,7 @@ public class GaussNewtonOptimizerTest {
GaussNewtonOptimizer optimizer
= new GaussNewtonOptimizer(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
VectorialPointValuePair optimum =
PointVectorValuePair optimum =
optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1, 1, 1, 1 },
new double[] { 0, 0, 0, 0, 0, 0 });
Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
@ -234,7 +234,7 @@ public class GaussNewtonOptimizerTest {
GaussNewtonOptimizer optimizer
= new GaussNewtonOptimizer(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
VectorialPointValuePair optimum1 =
PointVectorValuePair optimum1 =
optimizer.optimize(100, problem1, problem1.target, new double[] { 1, 1, 1, 1 },
new double[] { 0, 1, 2, 3 });
Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
@ -249,7 +249,7 @@ public class GaussNewtonOptimizerTest {
{ 8.00, 5.98, 9.89, 9.00 },
{ 6.99, 4.99, 9.00, 9.98 }
}, new double[] { 32, 23, 33, 31 });
VectorialPointValuePair optimum2 =
PointVectorValuePair optimum2 =
optimizer.optimize(100, problem2, problem2.target, new double[] { 1, 1, 1, 1 },
new double[] { 0, 1, 2, 3 });
Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
@ -304,7 +304,7 @@ public class GaussNewtonOptimizerTest {
GaussNewtonOptimizer optimizer
= new GaussNewtonOptimizer(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
VectorialPointValuePair optimum =
PointVectorValuePair optimum =
optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1 },
new double[] { 1, 1 });
Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
@ -336,7 +336,7 @@ public class GaussNewtonOptimizerTest {
GaussNewtonOptimizer optimizer
= new GaussNewtonOptimizer(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
VectorialPointValuePair optimum =
PointVectorValuePair optimum =
optimizer.optimize(100, problem, problem.target, new double[] { 1, 1 }, new double[] { 0, 0 });
Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
Assert.assertEquals(-1, optimum.getPoint()[0], 1.0e-10);
@ -355,7 +355,7 @@ public class GaussNewtonOptimizerTest {
GaussNewtonOptimizer optimizer
= new GaussNewtonOptimizer(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
VectorialPointValuePair optimum =
PointVectorValuePair optimum =
optimizer.optimize(100, problem, problem.target, new double[] { 1, 1 }, new double[] { 0, 0 });
Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
Assert.assertEquals(-1, optimum.getPoint()[0], 1.0e-10);
@ -395,7 +395,7 @@ public class GaussNewtonOptimizerTest {
GaussNewtonOptimizer optimizer
= new GaussNewtonOptimizer(new SimpleVectorialValueChecker(1.0e-13, 1.0e-13));
VectorialPointValuePair optimum =
PointVectorValuePair optimum =
optimizer.optimize(100, circle, new double[] { 0, 0, 0, 0, 0 },
new double[] { 1, 1, 1, 1, 1 },
new double[] { 98.680, 47.345 });
@ -439,7 +439,7 @@ public class GaussNewtonOptimizerTest {
GaussNewtonOptimizer optimizer
= new GaussNewtonOptimizer(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
VectorialPointValuePair optimum =
PointVectorValuePair optimum =
optimizer.optimize(100, circle, target, weights, new double[] { 0, 0 });
Assert.assertEquals(-0.1517383071957963, optimum.getPointRef()[0], 1.0e-6);
Assert.assertEquals(0.2074999736353867, optimum.getPointRef()[1], 1.0e-6);

26
src/test/java/org/apache/commons/math/optimization/general/LevenbergMarquardtOptimizerTest.java
View File

@ -34,7 +34,7 @@ import org.apache.commons.math.linear.BlockRealMatrix;
import org.apache.commons.math.linear.RealMatrix;
import org.apache.commons.math.linear.SingularMatrixException;
import org.apache.commons.math.optimization.SimpleVectorialValueChecker;
import org.apache.commons.math.optimization.VectorialPointValuePair;
import org.apache.commons.math.optimization.PointVectorValuePair;
import org.apache.commons.math.util.Precision;
import org.apache.commons.math.util.FastMath;
import org.junit.Assert;
@ -109,7 +109,7 @@ public class LevenbergMarquardtOptimizerTest {
LinearProblem problem =
new LinearProblem(new double[][] { { 2 } }, new double[] { 3 });
LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer();
VectorialPointValuePair optimum =
PointVectorValuePair optimum =
optimizer.optimize(100, problem, problem.target, new double[] { 1 }, new double[] { 0 });
Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
try {
@ -130,7 +130,7 @@ public class LevenbergMarquardtOptimizerTest {
new double[] { 4.0, 6.0, 1.0 });
LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer();
VectorialPointValuePair optimum =
PointVectorValuePair optimum =
optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1 }, new double[] { 0, 0 });
Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
Assert.assertEquals(7.0, optimum.getPoint()[0], 1.0e-10);
@ -151,7 +151,7 @@ public class LevenbergMarquardtOptimizerTest {
{ 0, 0, 0, 0, 0, 2 }
}, new double[] { 0.0, 1.1, 2.2, 3.3, 4.4, 5.5 });
LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer();
VectorialPointValuePair optimum =
PointVectorValuePair optimum =
optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1, 1, 1, 1 },
new double[] { 0, 0, 0, 0, 0, 0 });
Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
@ -169,7 +169,7 @@ public class LevenbergMarquardtOptimizerTest {
{ 0, -1, 1 }
}, new double[] { 1, 1, 1});
LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer();
VectorialPointValuePair optimum =
PointVectorValuePair optimum =
optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1 }, new double[] { 0, 0, 0 });
Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
Assert.assertEquals(1.0, optimum.getPoint()[0], 1.0e-10);
@ -190,7 +190,7 @@ public class LevenbergMarquardtOptimizerTest {
}, new double[] { 2, -9, 2, 2, 1 + epsilon * epsilon, 2});
LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer();
VectorialPointValuePair optimum =
PointVectorValuePair optimum =
optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1, 1, 1, 1 },
new double[] { 0, 0, 0, 0, 0, 0 });
Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
@ -230,7 +230,7 @@ public class LevenbergMarquardtOptimizerTest {
{ 7.0, 5.0, 9.0, 10.0 }
}, new double[] { 32, 23, 33, 31 });
LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer();
VectorialPointValuePair optimum1 =
PointVectorValuePair optimum1 =
optimizer.optimize(100, problem1, problem1.target, new double[] { 1, 1, 1, 1 },
new double[] { 0, 1, 2, 3 });
Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
@ -245,7 +245,7 @@ public class LevenbergMarquardtOptimizerTest {
{ 8.00, 5.98, 9.89, 9.00 },
{ 6.99, 4.99, 9.00, 9.98 }
}, new double[] { 32, 23, 33, 31 });
VectorialPointValuePair optimum2 =
PointVectorValuePair optimum2 =
optimizer.optimize(100, problem2, problem2.target, new double[] { 1, 1, 1, 1 },
new double[] { 0, 1, 2, 3 });
Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
@ -281,7 +281,7 @@ public class LevenbergMarquardtOptimizerTest {
}, new double[] { 3.0, 12.0, -1.0, 7.0, 1.0 });
LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer();
VectorialPointValuePair optimum =
PointVectorValuePair optimum =
optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1, 1, 1 },
new double[] { 2, 2, 2, 2, 2, 2 });
Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
@ -300,7 +300,7 @@ public class LevenbergMarquardtOptimizerTest {
}, new double[] { 3.0, 1.0, 5.0 });
LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer();
VectorialPointValuePair optimum =
PointVectorValuePair optimum =
optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1 },
new double[] { 1, 1 });
Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
@ -327,7 +327,7 @@ public class LevenbergMarquardtOptimizerTest {
new LinearProblem(new double[][] { { 1, 0 }, { 0, 1 } }, new double[] { -1, 1 });
LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer();
VectorialPointValuePair optimum =
PointVectorValuePair optimum =
optimizer.optimize(100, problem, problem.target, new double[] { 1, 1 }, new double[] { 0, 0 });
Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
Assert.assertEquals(-1, optimum.getPoint()[0], 1.0e-10);
@ -398,7 +398,7 @@ public class LevenbergMarquardtOptimizerTest {
circle.addPoint( 35.0, 15.0);
circle.addPoint( 45.0, 97.0);
LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer();
VectorialPointValuePair optimum =
PointVectorValuePair optimum =
optimizer.optimize(100, circle, new double[] { 0, 0, 0, 0, 0 }, new double[] { 1, 1, 1, 1, 1 },
new double[] { 98.680, 47.345 });
Assert.assertTrue(optimizer.getEvaluations() < 10);
@ -481,7 +481,7 @@ public class LevenbergMarquardtOptimizerTest {
}
LevenbergMarquardtOptimizer optimizer
= new LevenbergMarquardtOptimizer(new SimpleVectorialValueChecker(1.0e-8, 1.0e-8));
VectorialPointValuePair optimum =
PointVectorValuePair optimum =
optimizer.optimize(100, circle, target, weights, new double[] { -12, -12 });
Point2D.Double center = new Point2D.Double(optimum.getPointRef()[0], optimum.getPointRef()[1]);
Assert.assertTrue(optimizer.getEvaluations() < 25);

6
src/test/java/org/apache/commons/math/optimization/general/MinpackTest.java
View File

@ -24,7 +24,7 @@ import java.util.Arrays;
import org.apache.commons.math.exception.TooManyEvaluationsException;
import org.apache.commons.math.analysis.DifferentiableMultivariateVectorFunction;
import org.apache.commons.math.analysis.MultivariateMatrixFunction;
import org.apache.commons.math.optimization.VectorialPointValuePair;
import org.apache.commons.math.optimization.PointVectorValuePair;
import org.apache.commons.math.util.FastMath;
import org.junit.Assert;
import org.junit.Test;
@ -509,7 +509,7 @@ public class MinpackTest {
2.22044604926e-16);
// Assert.assertTrue(function.checkTheoreticalStartCost(optimizer.getRMS()));
try {
VectorialPointValuePair optimum =
PointVectorValuePair optimum =
optimizer.optimize(400 * (function.getN() + 1), function,
function.getTarget(), function.getWeight(),
function.getStartPoint());
@ -579,7 +579,7 @@ public class MinpackTest {
Assert.assertEquals(theoreticalMinCost, FastMath.sqrt(m) * rms, threshold);
}
public void checkTheoreticalMinParams(VectorialPointValuePair optimum) {
public void checkTheoreticalMinParams(PointVectorValuePair optimum) {
double[] params = optimum.getPointRef();
if (theoreticalMinParams != null) {
for (int i = 0; i < theoreticalMinParams.length; ++i) {