refine javadoc
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XenoAmess
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@ -57,14 +57,14 @@ public class DividedDifferenceInterpolator
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throws DimensionMismatchException,
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NumberIsTooSmallException,
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NonMonotonicSequenceException {
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/**
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/*
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* a[] and c[] are defined in the general formula of Newton form:
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* p(x) = a[0] + a[1](x-c[0]) + a[2](x-c[0])(x-c[1]) + ... +
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* a[n](x-c[0])(x-c[1])...(x-c[n-1])
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*/
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PolynomialFunctionLagrangeForm.verifyInterpolationArray(x, y, true);
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/**
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/*
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* When used for interpolation, the Newton form formula becomes
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* p(x) = f[x0] + f[x0,x1](x-x0) + f[x0,x1,x2](x-x0)(x-x1) + ... +
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* f[x0,x1,...,x[n-1]](x-x0)(x-x1)...(x-x[n-2])
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@ -1122,7 +1122,7 @@ public class Dfp implements RealFieldElement<Dfp> {
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result = result.add(a);
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}
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/** If exactly equal to 1/2 and odd then increment */
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/* If exactly equal to 1/2 and odd then increment */
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if (a.equals(half) && result.exp > 0 && (result.mant[mant.length-result.exp]&1) != 0) {
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a = newInstance(getOne());
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a.sign = sign;
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@ -41,7 +41,7 @@ public abstract class AbstractEvaluation implements Evaluation {
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* Constructor.
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*
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* @param observationSize the number of observations.
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* Needed for {@link #getRMS()} and {@link #getReducedChiSquare()}.
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* Needed for {@link #getRMS()} and {@link #getReducedChiSquare(int)}.
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*/
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AbstractEvaluation(final int observationSize) {
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this.observationSize = observationSize;
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@ -1209,7 +1209,7 @@ public abstract class RealVector {
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* @return an unmodifiable view of {@code v}.
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*/
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public static RealVector unmodifiableRealVector(final RealVector v) {
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/**
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/*
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* This anonymous class is an implementation of {@link RealVector}
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* with read-only access.
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* It wraps any {@link RealVector}, and exposes all methods which
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@ -229,7 +229,7 @@ public class NeuronString implements Serializable {
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/**
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* Custom serialization.
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*
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* @return the {@link Neuron} for which this instance is the proxy.
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* @return the {@link NeuronString} for which this instance is the proxy.
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*/
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private Object readResolve() {
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return new NeuronString(wrap,
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@ -22,6 +22,7 @@ import org.apache.commons.math4.analysis.function.Logit;
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import org.apache.commons.math4.analysis.function.Sigmoid;
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import org.apache.commons.math4.exception.DimensionMismatchException;
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import org.apache.commons.math4.exception.NumberIsTooSmallException;
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import org.apache.commons.math4.optim.OptimizationData;
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import org.apache.commons.math4.util.FastMath;
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import org.apache.commons.math4.util.MathUtils;
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@ -52,7 +53,7 @@ import org.apache.commons.math4.util.MathUtils;
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* user is responsible for converting his bounded point to unbounded by calling
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* {@link #boundedToUnbounded(double[])} before providing them to the optimizer.
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* For the same reason, the point returned by the {@link
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* org.apache.commons.math4.optim.BaseMultivariateOptimizer#optimize(OptimizationData[])}
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* org.apache.commons.math4.optim.BaseMultivariateOptimizer#optimize(OptimizationData...)}
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* method is unbounded. So to convert this point to bounded, users must call
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* {@link #unboundedToBounded(double[])} by themselves!</p>
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* <p>
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@ -108,7 +108,7 @@ public class NonLinearConjugateGradientOptimizer
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* find an interval that brackets the optimum in order to perform the
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* line search.
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*
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* @see LineSearch#LineSearch(MultivariateOptimizer,double,double,double)
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* @see LineSearch#LineSearch(org.apache.commons.math4.optim.nonlinear.scalar.MultivariateOptimizer,double,double,double)
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* @since 3.3
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*/
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public NonLinearConjugateGradientOptimizer(final Formula updateFormula,
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@ -136,7 +136,7 @@ public class NonLinearConjugateGradientOptimizer
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* find an interval that brackets the optimum in order to perform the
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* line search.
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*
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* @see LineSearch#LineSearch(MultivariateOptimizer,double,double,double)
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* @see LineSearch#LineSearch(org.apache.commons.math4.optim.nonlinear.scalar.MultivariateOptimizer, double, double, double)
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* @since 3.3
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*/
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public NonLinearConjugateGradientOptimizer(final Formula updateFormula,
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@ -43,7 +43,7 @@ import org.apache.commons.math4.util.FastMath;
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* Line search is performed by the {@link LineSearch} class.
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* <br>
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* Constraints are not supported: the call to
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* {@link #optimize(OptimizationData[]) optimize} will throw
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* {@link #optimize(org.apache.commons.math4.optim.OptimizationData...)} will throw
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* {@link MathUnsupportedOperationException} if bounds are passed to it.
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* In order to impose simple constraints, the objective function must be
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* wrapped in an adapter like
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@ -288,7 +288,7 @@ public class PowellOptimizer
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/**
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* @throws MathUnsupportedOperationException if bounds were passed to the
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* {@link #optimize(OptimizationData[]) optimize} method.
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* {@link #optimize(org.apache.commons.math4.optim.OptimizationData[]) optimize} method.
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*/
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private void checkParameters() {
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if (getLowerBound() != null ||
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@ -48,9 +48,7 @@ public class Covariance {
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/** covariance matrix */
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private final RealMatrix covarianceMatrix;
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/**
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* Create an empty covariance matrix.
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*/
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/* Create an empty covariance matrix. */
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/** Number of observations (length of covariate vectors) */
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private final int n;
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@ -61,7 +61,7 @@ public class Median extends Percentile implements Serializable {
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}
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/**
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* Constructs a Median with the specific {@link EstimationType}, {@link NaNStrategy} and {@link PivotingStrategy}.
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* Constructs a Median with the specific {@link EstimationType}, {@link NaNStrategy} and {@link KthSelector}.
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*
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* @param estimationType one of the percentile {@link EstimationType estimation types}
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* @param nanStrategy one of {@link NaNStrategy} to handle with NaNs
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@ -258,8 +258,8 @@ public class PerfTestUtils {
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/**
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* Timing and report (to standard output).
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* This method calls {@link #timeAndReport(String,int,int,boolean,RunTest[])
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* timeAndReport(title, 1000, 10000, false, methods)}.
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* This method calls {@link #timeAndReport(String,int,int,int,boolean,RunTest[])
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* timeAndReport(title, 45, 1000, 10000, false, methods)}.
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*
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* @param title Title of the test (for the report).
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* @param methods Codes being timed.
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@ -287,7 +287,7 @@ public final class TricubicInterpolatingFunctionTest {
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* @param d2fdxdy Second partial cross-derivative w.r.t x and y of the function to test.
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* @param d2fdxdz Second partial cross-derivative w.r.t x and z of the function to test.
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* @param d2fdydz Second partial cross-derivative w.r.t y and z of the function to test.
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* @param d3fdxdy Third partial cross-derivative w.r.t x, y and z of the function to test.
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* @param d3fdxdydz Third partial cross-derivative w.r.t x, y and z of the function to test.
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* @param meanRelativeTolerance Allowed average error (mean error on all interpolated values).
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* @param maxRelativeTolerance Allowed error on each interpolated value.
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* @param onDataMaxAbsoluteTolerance Allowed error on a data point.
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@ -67,9 +67,6 @@ import org.junit.Test;
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* where possible, and the source of the reference data and/or validation
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* should be documented in the test cases. A framework for validating
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* distribution data against R is included in the /src/test/R source tree.
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* <p>
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* See {@link NormalDistributionTest} and {@link ChiSquaredDistributionTest}
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* for examples.
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*
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*/
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public abstract class RealDistributionAbstractTest {
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@ -27,7 +27,7 @@ import org.junit.Test;
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import org.junit.runner.RunWith;
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/**
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* Test cases for the {@link RandomUtils#DataGenerator} class.
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* Test cases for the {@link RandomUtils.DataGenerator} class.
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*/
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@RunWith(RetryRunner.class)
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public abstract class RandomUtilsDataGeneratorAbstractTest {
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@ -19,7 +19,7 @@ package org.apache.commons.math4.random;
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import java.util.Random;
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/**
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* Test cases for the {@link RandomUtils#DataGenerator} class, using
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* Test cases for the {@link RandomUtils.DataGenerator} class, using
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* {@link Random} as the underlying source of randomness.
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*/
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public class RandomUtilsDataGeneratorJDKRandomTest
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@ -24,7 +24,7 @@ import org.junit.Assert;
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import org.junit.Test;
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/**
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* Tests for {@link IntegerSequence} and {@link IntegerSequence#Incrementor}.
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* Tests for {@link IntegerSequence} and {@link IntegerSequence.Incrementor}.
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*/
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public class IntegerSequenceTest {
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@Test
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@ -501,8 +501,8 @@ public class MathArraysTest {
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Assert.assertEquals(125, x3[0], FastMath.ulp(1d));
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}
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@Test
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/** Example in javadoc */
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@Test
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public void testSortInPlaceExample() {
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final double[] x = {3, 1, 2};
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final double[] y = {1, 2, 3};
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