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MATH-1594: Remove "Serializable".

This commit is contained in:
Gilles Sadowski 2021-12-30 17:27:48 +01:00
parent 43ebe7bc45
commit eac2e8b627
13 changed files with 13 additions and 221 deletions
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57
commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/analysis/differentiation/DerivativeStructure.java
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@ -17,7 +17,6 @@
package org.apache.commons.math4.legacy.analysis.differentiation;
import java.util.Arrays;
import java.io.Serializable;
import org.apache.commons.numbers.core.Sum;
import org.apache.commons.math4.legacy.core.Field;
@ -59,11 +58,7 @@ import org.apache.commons.math4.legacy.core.MathArrays;
* @see DSCompiler
* @since 3.1
*/
public class DerivativeStructure implements RealFieldElement<DerivativeStructure>, Serializable {
/** Serializable UID. */
private static final long serialVersionUID = 20120730L;
public class DerivativeStructure implements RealFieldElement<DerivativeStructure> {
/** Compiler for the current dimensions. */
private transient DSCompiler compiler;
@ -1205,54 +1200,4 @@ public class DerivativeStructure implements RealFieldElement<DerivativeStructure
public int hashCode() {
return 227 + 229 * getFreeParameters() + 233 * getOrder() + 239 * Arrays.hashCode(data);
}
/**
* Replace the instance with a data transfer object for serialization.
* @return data transfer object that will be serialized
*/
private Object writeReplace() {
return new DataTransferObject(compiler.getFreeParameters(), compiler.getOrder(), data);
}
/** Internal class used only for serialization. */
private static class DataTransferObject implements Serializable {
/** Serializable UID. */
private static final long serialVersionUID = 20120730L;
/** Number of variables.
* @serial
*/
private final int variables;
/** Derivation order.
* @serial
*/
private final int order;
/** Partial derivatives.
* @serial
*/
private final double[] data;
/** Simple constructor.
* @param variables number of variables
* @param order derivation order
* @param data partial derivatives
*/
DataTransferObject(final int variables, final int order, final double[] data) {
this.variables = variables;
this.order = order;
this.data = data;
}
/** Replace the deserialized data transfer object with a {@link DerivativeStructure}.
* @return replacement {@link DerivativeStructure}
*/
private Object readResolve() {
return new DerivativeStructure(variables, order, data);
}
}
}

11
commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/analysis/differentiation/FiniteDifferencesDifferentiator.java
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@ -16,8 +16,6 @@
*/
package org.apache.commons.math4.legacy.analysis.differentiation;
import java.io.Serializable;
import org.apache.commons.math4.legacy.analysis.UnivariateFunction;
import org.apache.commons.math4.legacy.analysis.UnivariateMatrixFunction;
import org.apache.commons.math4.legacy.analysis.UnivariateVectorFunction;
@ -69,12 +67,9 @@ import org.apache.commons.math4.core.jdkmath.JdkMath;
* @since 3.1
*/
public class FiniteDifferencesDifferentiator
implements UnivariateFunctionDifferentiator, UnivariateVectorFunctionDifferentiator,
UnivariateMatrixFunctionDifferentiator, Serializable {
/** Serializable UID. */
private static final long serialVersionUID = 20120917L;
implements UnivariateFunctionDifferentiator,
UnivariateVectorFunctionDifferentiator,
UnivariateMatrixFunctionDifferentiator {
/** Number of points to use. */
private final int nbPoints;

7
commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/analysis/differentiation/SparseGradient.java
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@ -16,7 +16,6 @@
*/
package org.apache.commons.math4.legacy.analysis.differentiation;
import java.io.Serializable;
import java.util.Collections;
import java.util.HashMap;
import java.util.Map;
@ -40,11 +39,7 @@ import org.apache.commons.math4.core.jdkmath.JdkMath;
*
* @since 3.3
*/
public final class SparseGradient implements RealFieldElement<SparseGradient>, Serializable {
/** Serializable UID. */
private static final long serialVersionUID = 20131025L;
public final class SparseGradient implements RealFieldElement<SparseGradient> {
/** Value of the calculation. */
private double value;

7
commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/analysis/interpolation/DividedDifferenceInterpolator.java
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@ -16,8 +16,6 @@
*/
package org.apache.commons.math4.legacy.analysis.interpolation;
import java.io.Serializable;
import org.apache.commons.math4.legacy.analysis.polynomials.PolynomialFunctionLagrangeForm;
import org.apache.commons.math4.legacy.analysis.polynomials.PolynomialFunctionNewtonForm;
import org.apache.commons.math4.legacy.exception.DimensionMismatchException;
@ -37,10 +35,7 @@ import org.apache.commons.math4.legacy.exception.NumberIsTooSmallException;
* @since 1.2
*/
public class DividedDifferenceInterpolator
implements UnivariateInterpolator, Serializable {
/** serializable version identifier. */
private static final long serialVersionUID = 107049519551235069L;
implements UnivariateInterpolator {
/**
* Compute an interpolating function for the dataset.
*

5
commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/analysis/interpolation/LoessInterpolator.java
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@ -16,7 +16,6 @@
*/
package org.apache.commons.math4.legacy.analysis.interpolation;
import java.io.Serializable;
import java.util.Arrays;
import org.apache.commons.math4.legacy.analysis.polynomials.PolynomialSplineFunction;
@ -47,7 +46,7 @@ import org.apache.commons.math4.legacy.core.MathArrays;
* @since 2.0
*/
public class LoessInterpolator
implements UnivariateInterpolator, Serializable {
implements UnivariateInterpolator {
/** Default value of the bandwidth parameter. */
public static final double DEFAULT_BANDWIDTH = 0.3;
/** Default value of the number of robustness iterations. */
@ -57,8 +56,6 @@ public class LoessInterpolator
* @since 2.1
*/
public static final double DEFAULT_ACCURACY = 1e-12;
/** serializable version identifier. */
private static final long serialVersionUID = 5204927143605193821L;
/**
* The bandwidth parameter: when computing the loess fit at
* a particular point, this fraction of source points closest

9
commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/analysis/interpolation/NevilleInterpolator.java
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@ -16,8 +16,6 @@
*/
package org.apache.commons.math4.legacy.analysis.interpolation;
import java.io.Serializable;
import org.apache.commons.math4.legacy.analysis.polynomials.PolynomialFunctionLagrangeForm;
import org.apache.commons.math4.legacy.exception.DimensionMismatchException;
import org.apache.commons.math4.legacy.exception.NonMonotonicSequenceException;
@ -34,12 +32,7 @@ import org.apache.commons.math4.legacy.exception.NumberIsTooSmallException;
*
* @since 1.2
*/
public class NevilleInterpolator implements UnivariateInterpolator,
Serializable {
/** serializable version identifier. */
static final long serialVersionUID = 3003707660147873733L;
public class NevilleInterpolator implements UnivariateInterpolator {
/**
* Computes an interpolating function for the data set.
*

7
commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/analysis/polynomials/PolynomialFunction.java
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@ -16,7 +16,6 @@
*/
package org.apache.commons.math4.legacy.analysis.polynomials;
import java.io.Serializable;
import java.util.Arrays;
import org.apache.commons.math4.legacy.analysis.ParametricUnivariateFunction;
@ -34,11 +33,7 @@ import org.apache.commons.math4.core.jdkmath.JdkMath;
* is used to evaluate the function.</p>
*
*/
public class PolynomialFunction implements UnivariateDifferentiableFunction, Serializable {
/**
* Serialization identifier.
*/
private static final long serialVersionUID = -7726511984200295583L;
public class PolynomialFunction implements UnivariateDifferentiableFunction {
/**
* The coefficients of the polynomial, ordered by degree -- i.e.,
* coefficients[0] is the constant term and coefficients[n] is the

65
commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/descriptive/rank/PSquarePercentile.java
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@ -16,9 +16,6 @@
*/
package org.apache.commons.math4.legacy.stat.descriptive.rank;
import java.io.IOException;
import java.io.ObjectInputStream;
import java.io.Serializable;
import java.text.DecimalFormat;
import java.util.ArrayList;
import java.util.Arrays;
@ -53,7 +50,7 @@ import org.apache.commons.math4.legacy.stat.descriptive.StorelessUnivariateStati
* {@link Percentile} should be used.</p>
*/
public class PSquarePercentile extends AbstractStorelessUnivariateStatistic
implements StorelessUnivariateStatistic, Serializable {
implements StorelessUnivariateStatistic {
/**
* The maximum array size used for psquare algorithm.
@ -66,11 +63,6 @@ public class PSquarePercentile extends AbstractStorelessUnivariateStatistic
*/
private static final double DEFAULT_QUANTILE_DESIRED = 50d;
/**
* Serial ID.
*/
private static final long serialVersionUID = 20150412L;
/**
* A decimal formatter for print convenience.
*/
@ -311,12 +303,7 @@ public class PSquarePercentile extends AbstractStorelessUnivariateStatistic
* Markers is an encapsulation of the five markers/buckets as indicated in
* the original works.
*/
private static final class Markers implements PSquareMarkers, Serializable {
/**
* Serial version id.
*/
private static final long serialVersionUID = 1L;
private static final class Markers implements PSquareMarkers {
/** Low marker index. */
private static final int LOW = 2;
@ -524,25 +511,6 @@ public class PSquarePercentile extends AbstractStorelessUnivariateStatistic
}
}
/**
* Sets previous and next markers after default read is done.
*
* @param anInputStream the input stream to be deserialized
* @throws ClassNotFoundException thrown when a desired class not found
* @throws IOException thrown due to any io errors
*/
private void readObject(ObjectInputStream anInputStream)
throws ClassNotFoundException, IOException {
// always perform the default de-serialization first
anInputStream.defaultReadObject();
// Build links
for (int i = 1; i < PSQUARE_CONSTANT; i++) {
markerArray[i].previous(markerArray[i - 1]).next(markerArray[i + 1]).index(i);
}
markerArray[0].previous(markerArray[0]).next(markerArray[1]).index(0);
markerArray[5].previous(markerArray[4]).next(markerArray[5]).index(5);
}
/**
* Return marker height given index.
*
@ -591,13 +559,7 @@ public class PSquarePercentile extends AbstractStorelessUnivariateStatistic
/**
* The class modeling the attributes of the marker of the P-square algorithm.
*/
private static final class Marker implements Serializable {
/**
* Serial Version ID.
*/
private static final long serialVersionUID = -3575879478288538431L;
private static final class Marker {
/**
* The marker index which is just a serial number for the marker in the
* marker array of 5+1.
@ -832,20 +794,6 @@ public class PSquarePercentile extends AbstractStorelessUnivariateStatistic
desiredMarkerIncrement, desiredMarkerPosition, previous.index, next.index});
}
/**
* Read Object to deserialize.
*
* @param anInstream Stream Object data
* @throws IOException thrown for IO Errors
* @throws ClassNotFoundException thrown for class not being found
*/
private void readObject(ObjectInputStream anInstream)
throws ClassNotFoundException, IOException {
anInstream.defaultReadObject();
previous=next=this;
linear = new LinearInterpolator();
}
/**
* Copy this instance.
*
@ -877,12 +825,7 @@ public class PSquarePercentile extends AbstractStorelessUnivariateStatistic
*
* @param <E> type for fixed capacity list
*/
private static class FixedCapacityList<E> extends ArrayList<E> implements Serializable {
/**
* Serialization Version Id.
*/
private static final long serialVersionUID = 2283952083075725479L;
private static class FixedCapacityList<E> extends ArrayList<E> {
/**
* Capacity of the list.
*/

9
commons-math-legacy/src/test/java/org/apache/commons/math4/legacy/analysis/differentiation/DerivativeStructureTest.java
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@ -1565,15 +1565,6 @@ public class DerivativeStructureTest extends ExtendedFieldElementAbstractTest<De
}
}
@Test
public void testSerialization() {
DerivativeStructure a = new DerivativeStructure(3, 2, 0, 1.3);
DerivativeStructure b = (DerivativeStructure) TestUtils.serializeAndRecover(a);
Assert.assertEquals(a.getFreeParameters(), b.getFreeParameters());
Assert.assertEquals(a.getOrder(), b.getOrder());
checkEquals(a, b, 1.0e-15);
}
private void checkF0F1(DerivativeStructure ds, double value, double...derivatives) {
// check dimension

10
commons-math-legacy/src/test/java/org/apache/commons/math4/legacy/analysis/differentiation/FiniteDifferencesDifferentiatorTest.java
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@ -47,16 +47,6 @@ public class FiniteDifferencesDifferentiatorTest {
new FiniteDifferencesDifferentiator(3, 0.0);
}
@Test
public void testSerialization() {
FiniteDifferencesDifferentiator differentiator =
new FiniteDifferencesDifferentiator(3, 1.0e-3);
FiniteDifferencesDifferentiator recovered =
(FiniteDifferencesDifferentiator) TestUtils.serializeAndRecover(differentiator);
Assert.assertEquals(differentiator.getNbPoints(), recovered.getNbPoints());
Assert.assertEquals(differentiator.getStepSize(), recovered.getStepSize(), 1.0e-15);
}
@Test
public void testConstant() {
FiniteDifferencesDifferentiator differentiator =

7
commons-math-legacy/src/test/java/org/apache/commons/math4/legacy/analysis/differentiation/SparseGradientTest.java
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@ -1107,13 +1107,6 @@ public class SparseGradientTest extends ExtendedFieldElementAbstractTest<SparseG
}
}
@Test
public void testSerialization() {
SparseGradient a = SparseGradient.createVariable(0, 1.3);
SparseGradient b = (SparseGradient) TestUtils.serializeAndRecover(a);
Assert.assertEquals(a, b);
}
private void checkF0F1(SparseGradient sg, double value, double...derivatives) {
// check value

6
commons-math-legacy/src/test/java/org/apache/commons/math4/legacy/analysis/polynomials/PolynomialFunctionTest.java
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@ -216,12 +216,6 @@ public final class PolynomialFunctionTest {
}
}
@Test
public void testSerial() {
PolynomialFunction p2 = new PolynomialFunction(new double[] { 3, 2, 1 });
Assert.assertEquals(p2, TestUtils.serializeAndRecover(p2));
}
/**
* tests the firstDerivative function by comparison
*

34
commons-math-legacy/src/test/java/org/apache/commons/math4/legacy/stat/descriptive/StorelessUnivariateStatisticAbstractTest.java
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@ -75,33 +75,6 @@ public abstract class StorelessUnivariateStatisticAbstractTest
Assert.assertTrue(Double.isNaN(statistic.getResult()));
}
@Test
public void testSerialization() {
StorelessUnivariateStatistic statistic =
(StorelessUnivariateStatistic) getUnivariateStatistic();
TestUtils.checkSerializedEquality(statistic);
statistic.clear();
for (int i = 0; i < testArray.length; i++) {
statistic.increment(testArray[i]);
if(i % 5 == 0) {
statistic = (StorelessUnivariateStatistic)TestUtils.serializeAndRecover(statistic);
}
}
TestUtils.checkSerializedEquality(statistic);
Assert.assertEquals(expectedValue(), statistic.getResult(), getTolerance());
statistic.clear();
checkClearValue(statistic);
}
@Test
public void testEqualsAndHashCode() {
StorelessUnivariateStatistic statistic =
@ -210,13 +183,6 @@ public abstract class StorelessUnivariateStatisticAbstractTest
Assert.assertEquals(master, replica);
}
@Test
public void testSerial() {
StorelessUnivariateStatistic s =
(StorelessUnivariateStatistic) getUnivariateStatistic();
Assert.assertEquals(s, TestUtils.serializeAndRecover(s));
}
/**
* Make sure that evaluate(double[]) does not alter the internal state.
*/