Added @since tags.
git-svn-id: https://svn.apache.org/repos/asf/commons/proper/math/trunk@925812 13f79535-47bb-0310-9956-ffa450edef68
This commit is contained in:
Phil Steitz
parent
332f3909cc
commit
8cb2563a2c
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@ -47,7 +47,7 @@ public class LoessInterpolator
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/** Default value of the number of robustness iterations. */
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public static final int DEFAULT_ROBUSTNESS_ITERS = 2;
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/**
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/**
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* Default value for accuracy.
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* @since 2.1
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*/
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@ -32,10 +32,15 @@ import org.apache.commons.math.analysis.UnivariateRealFunction;
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*/
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public class BrentSolver extends UnivariateRealSolverImpl {
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/** Default absolute accuracy */
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/**
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* Default absolute accuracy
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* @since 2.1
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*/
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public static final double DEFAULT_ABSOLUTE_ACCURACY = 1E-6;
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/** Default maximum number of iterations */
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/** Default maximum number of iterations
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* @since 2.1
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*/
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public static final int DEFAULT_MAXIMUM_ITERATIONS = 100;
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/** Error message for non-bracketing interval. */
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@ -71,6 +76,7 @@ public class BrentSolver extends UnivariateRealSolverImpl {
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* Construct a solver with the given absolute accuracy.
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*
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* @param absoluteAccuracy lower bound for absolute accuracy of solutions returned by the solver
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* @since 2.1
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*/
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public BrentSolver(double absoluteAccuracy) {
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super(DEFAULT_MAXIMUM_ITERATIONS, absoluteAccuracy);
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@ -81,6 +87,7 @@ public class BrentSolver extends UnivariateRealSolverImpl {
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*
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* @param maximumIterations maximum number of iterations
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* @param absoluteAccuracy lower bound for absolute accuracy of solutions returned by the solver
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* @since 2.1
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*/
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public BrentSolver(int maximumIterations, double absoluteAccuracy) {
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super(maximumIterations, absoluteAccuracy);
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@ -40,7 +40,10 @@ public abstract class AbstractContinuousDistribution
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/** Serializable version identifier */
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private static final long serialVersionUID = -38038050983108802L;
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/** Solver absolute accuracy for inverse cum computation */
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/**
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* Solver absolute accuracy for inverse cum computation
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* @since 2.1
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*/
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private double solverAbsoluteAccuracy = BrentSolver.DEFAULT_ABSOLUTE_ACCURACY;
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/**
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@ -55,6 +58,7 @@ public abstract class AbstractContinuousDistribution
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* @param x The point at which the density should be computed.
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* @return The pdf at point x.
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* @throws MathRuntimeException if the specialized class hasn't implemented this function
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* @since 2.1
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*/
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public double density(double x) throws MathRuntimeException {
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throw new MathRuntimeException(new UnsupportedOperationException(),
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@ -166,6 +170,7 @@ public abstract class AbstractContinuousDistribution
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* Returns the solver absolute accuracy for inverse cum computation.
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*
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* @return the maximum absolute error in inverse cumulative probability estimates
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* @since 2.1
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*/
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protected double getSolverAbsoluteAccuracy() {
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return solverAbsoluteAccuracy;
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@ -114,6 +114,7 @@ public class BetaDistributionImpl
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*
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* @param x The point at which the density should be computed.
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* @return The pdf at point x.
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* @since 2.1
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*/
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public double density(double x) {
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recomputeZ();
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@ -89,6 +89,7 @@ public class CauchyDistributionImpl extends AbstractContinuousDistribution
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*
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* @param x The point at which the density should be computed.
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* @return The pdf at point x.
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* @since 2.1
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*/
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@Override
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public double density(double x) {
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@ -29,7 +29,10 @@ public class ChiSquaredDistributionImpl
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extends AbstractContinuousDistribution
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implements ChiSquaredDistribution, Serializable {
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/** Default inverse cumulative probability accuracy */
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/**
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* Default inverse cumulative probability accuracy
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* @since 2.1
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*/
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public static final double DEFAULT_INVERSE_ABSOLUTE_ACCURACY = 1e-9;
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/** Serializable version identifier */
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@ -71,6 +74,7 @@ public class ChiSquaredDistributionImpl
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* @param df degrees of freedom.
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* @param inverseCumAccuracy the maximum absolute error in inverse cumulative probability estimates
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* (defaults to {@link #DEFAULT_INVERSE_ABSOLUTE_ACCURACY})
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* @since 2.1
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*/
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public ChiSquaredDistributionImpl(double df, double inverseCumAccuracy) {
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super();
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@ -120,6 +124,7 @@ public class ChiSquaredDistributionImpl
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*
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* @param x The point at which the density should be computed.
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* @return The pdf at point x.
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* @since 2.1
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*/
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@Override
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public double density(double x) {
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@ -257,6 +262,7 @@ public class ChiSquaredDistributionImpl
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* inverse cumulative probabilities.
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*
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* @return the solver absolute accuracy
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* @since 2.1
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*/
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@Override
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protected double getSolverAbsoluteAccuracy() {
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@ -91,6 +91,7 @@ public class ExponentialDistributionImpl extends AbstractContinuousDistribution
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*
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* @param x The point at which the density should be computed.
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* @return The pdf at point x.
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* @since 2.1
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*/
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@Override
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public double density(double x) {
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@ -32,7 +32,10 @@ public class FDistributionImpl
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extends AbstractContinuousDistribution
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implements FDistribution, Serializable {
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/** Default inverse cumulative probability accuracy */
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/**
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* Default inverse cumulative probability accurac
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* @since 2.1
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*/
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public static final double DEFAULT_INVERSE_ABSOLUTE_ACCURACY = 1e-9;
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/** Message for non positive degrees of freddom. */
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@ -67,6 +70,7 @@ public class FDistributionImpl
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* @param denominatorDegreesOfFreedom the denominator degrees of freedom.
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* @param inverseCumAccuracy the maximum absolute error in inverse cumulative probability estimates
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* (defaults to {@link #DEFAULT_INVERSE_ABSOLUTE_ACCURACY})
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* @since 2.1
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*/
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public FDistributionImpl(double numeratorDegreesOfFreedom, double denominatorDegreesOfFreedom,
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double inverseCumAccuracy) {
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@ -81,6 +85,7 @@ public class FDistributionImpl
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*
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* @param x The point at which the density should be computed.
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* @return The pdf at point x.
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* @since 2.1
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*/
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@Override
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public double density(double x) {
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@ -269,6 +274,7 @@ public class FDistributionImpl
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* inverse cumulative probabilities.
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*
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* @return the solver absolute accuracy
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* @since 2.1
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*/
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@Override
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protected double getSolverAbsoluteAccuracy() {
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@ -30,7 +30,10 @@ import org.apache.commons.math.special.Gamma;
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public class GammaDistributionImpl extends AbstractContinuousDistribution
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implements GammaDistribution, Serializable {
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/** Default inverse cumulative probability accuracy */
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/**
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* Default inverse cumulative probability accuracy
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* @since 2.1
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*/
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public static final double DEFAULT_INVERSE_ABSOLUTE_ACCURACY = 1e-9;
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/** Serializable version identifier */
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@ -60,6 +63,7 @@ public class GammaDistributionImpl extends AbstractContinuousDistribution
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* @param beta the scale parameter.
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* @param inverseCumAccuracy the maximum absolute error in inverse cumulative probability estimates
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* (defaults to {@link #DEFAULT_INVERSE_ABSOLUTE_ACCURACY})
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* @since 2.1
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*/
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public GammaDistributionImpl(double alpha, double beta, double inverseCumAccuracy) {
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super();
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@ -285,6 +289,7 @@ public class GammaDistributionImpl extends AbstractContinuousDistribution
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* inverse cumulative probabilities.
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*
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* @return the solver absolute accuracy
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* @since 2.1
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*/
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@Override
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protected double getSolverAbsoluteAccuracy() {
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@ -33,7 +33,10 @@ import org.apache.commons.math.special.Erf;
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public class NormalDistributionImpl extends AbstractContinuousDistribution
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implements NormalDistribution, Serializable {
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/** Default inverse cumulative probability accuracy */
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/**
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* Default inverse cumulative probability accuracy
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* @since 2.1
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*/
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public static final double DEFAULT_INVERSE_ABSOLUTE_ACCURACY = 1e-9;
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/** Serializable version identifier */
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@ -67,6 +70,7 @@ public class NormalDistributionImpl extends AbstractContinuousDistribution
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* @param mean mean for this distribution
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* @param sd standard deviation for this distribution
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* @param inverseCumAccuracy inverse cumulative probability accuracy
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* @since 2.1
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*/
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public NormalDistributionImpl(double mean, double sd, double inverseCumAccuracy) {
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super();
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@ -156,6 +160,7 @@ public class NormalDistributionImpl extends AbstractContinuousDistribution
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*
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* @param x The point at which the density should be computed.
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* @return The pdf at point x.
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* @since 2.1
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*/
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public double density(double x) {
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double x0 = x - mean;
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@ -190,6 +195,7 @@ public class NormalDistributionImpl extends AbstractContinuousDistribution
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* inverse cumulative probabilities.
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*
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* @return the solver absolute accuracy
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* @since 2.1
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*/
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@Override
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protected double getSolverAbsoluteAccuracy() {
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@ -33,11 +33,13 @@ public class PoissonDistributionImpl extends AbstractIntegerDistribution
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/**
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* Default maximum number of iterations for cumulative probability calculations.
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* @since 2.1
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*/
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public static final int DEFAULT_MAX_ITERATIONS = 10000000;
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/**
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* Default convergence criterion
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* Default convergence criterion.
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* @since 2.1
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*/
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public static final double DEFAULT_EPSILON = 1E-12;
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@ -83,6 +85,7 @@ public class PoissonDistributionImpl extends AbstractIntegerDistribution
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* @param p the Poisson mean
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* @param epsilon the convergence criteria for cumulative probabilites
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* @param maxIterations the maximum number of iterations for cumulative probabilites
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* @since 2.1
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*/
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public PoissonDistributionImpl(double p, double epsilon, int maxIterations) {
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setMean(p);
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@ -95,6 +98,7 @@ public class PoissonDistributionImpl extends AbstractIntegerDistribution
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*
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* @param p the Poisson mean
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* @param epsilon the convergence criteria for cumulative probabilites
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* @since 2.1
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*/
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public PoissonDistributionImpl(double p, double epsilon) {
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setMean(p);
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@ -106,6 +110,7 @@ public class PoissonDistributionImpl extends AbstractIntegerDistribution
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*
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* @param p the Poisson mean
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* @param maxIterations the maximum number of iterations for cumulative probabilites
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* @since 2.1
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*/
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public PoissonDistributionImpl(double p, int maxIterations) {
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setMean(p);
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@ -33,7 +33,10 @@ public class TDistributionImpl
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extends AbstractContinuousDistribution
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implements TDistribution, Serializable {
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/** Default inverse cumulative probability accuracy */
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/**
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* Default inverse cumulative probability accuracy
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* @since 2.1
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*/
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public static final double DEFAULT_INVERSE_ABSOLUTE_ACCURACY = 1e-9;
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/** Serializable version identifier */
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@ -52,6 +55,7 @@ public class TDistributionImpl
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* @param degreesOfFreedom the degrees of freedom.
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* @param inverseCumAccuracy the maximum absolute error in inverse cumulative probability estimates
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* (defaults to {@link #DEFAULT_INVERSE_ABSOLUTE_ACCURACY})
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* @since 2.1
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*/
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public TDistributionImpl(double degreesOfFreedom, double inverseCumAccuracy) {
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super();
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@ -102,6 +106,7 @@ public class TDistributionImpl
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*
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* @param x The point at which the density should be computed.
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* @return The pdf at point x.
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* @since 2.1
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*/
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@Override
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public double density(double x) {
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@ -210,6 +215,7 @@ public class TDistributionImpl
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* inverse cumulative probabilities.
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*
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* @return the solver absolute accuracy
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* @since 2.1
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*/
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@Override
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protected double getSolverAbsoluteAccuracy() {
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@ -31,7 +31,10 @@ import org.apache.commons.math.MathRuntimeException;
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public class WeibullDistributionImpl extends AbstractContinuousDistribution
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implements WeibullDistribution, Serializable {
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/** Default inverse cumulative probability accuracy */
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/**
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* Default inverse cumulative probability accuracy
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* @since 2.1
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*/
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public static final double DEFAULT_INVERSE_ABSOLUTE_ACCURACY = 1e-9;
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/** Serializable version identifier */
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@ -63,6 +66,7 @@ public class WeibullDistributionImpl extends AbstractContinuousDistribution
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* @param beta the scale parameter.
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* @param inverseCumAccuracy the maximum absolute error in inverse cumulative probability estimates
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* (defaults to {@link #DEFAULT_INVERSE_ABSOLUTE_ACCURACY})
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* @since 2.1
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*/
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public WeibullDistributionImpl(double alpha, double beta, double inverseCumAccuracy){
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super();
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@ -107,6 +111,7 @@ public class WeibullDistributionImpl extends AbstractContinuousDistribution
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*
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* @param x The point at which the density should be computed.
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* @return The pdf at point x.
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* @since 2.1
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*/
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@Override
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public double density(double x) {
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@ -246,6 +251,7 @@ public class WeibullDistributionImpl extends AbstractContinuousDistribution
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* inverse cumulative probabilities.
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*
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* @return the solver absolute accuracy
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* @since 2.1
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*/
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@Override
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protected double getSolverAbsoluteAccuracy() {
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@ -219,6 +219,7 @@ public class GeneticAlgorithm {
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* reach {@link StoppingCondition} in the last run.
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*
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* @return number of generations evolved
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* @since 2.1
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*/
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public int getGenerationsEvolved() {
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return generationsEvolved;
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@ -200,6 +200,7 @@ public class OpenMapRealVector extends AbstractRealVector implements SparseRealV
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* Determine if this value is within epsilon of zero.
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* @param value The value to test
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* @return <code>true</code> if this value is within epsilon to zero, <code>false</code> otherwise
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* @since 2.1
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*/
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protected boolean isDefaultValue(double value) {
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return Math.abs(value) < epsilon;
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@ -279,7 +280,10 @@ public class OpenMapRealVector extends AbstractRealVector implements SparseRealV
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return res;
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}
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/** {@inheritDoc} */
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/**
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* {@inheritDoc}
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* @since 2.1
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*/
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@Override
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public OpenMapRealVector copy() {
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return new OpenMapRealVector(this);
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@ -63,10 +63,16 @@ public abstract class AbstractLeastSquaresOptimizer implements DifferentiableMul
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/** Number of rows of the jacobian matrix. */
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protected int rows;
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/** Target value for the objective functions at optimum. */
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/**
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* Target value for the objective functions at optimum.
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* @since 2.1
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*/
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protected double[] targetValues;
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/** Weight for the least squares cost computation. */
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/**
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* Weight for the least squares cost computation.
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* @since 2.1
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*/
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protected double[] residualsWeights;
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/** Current point. */
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@ -45,7 +45,10 @@ public abstract class AbstractScalarDifferentiableOptimizer
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/** Convergence checker. */
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protected RealConvergenceChecker checker;
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/** Type of optimization. */
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/**
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* Type of optimization.
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* @since 2.1
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*/
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protected GoalType goal;
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/** Current point set. */
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@ -37,16 +37,28 @@ public abstract class AbstractLinearOptimizer implements LinearOptimizer {
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/** Default maximal number of iterations allowed. */
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public static final int DEFAULT_MAX_ITERATIONS = 100;
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/** Linear objective function. */
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/**
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* Linear objective function.
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* @since 2.1
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*/
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protected LinearObjectiveFunction function;
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/** Linear constraints. */
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/**
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* Linear constraints.
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* @since 2.1
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*/
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protected Collection<LinearConstraint> linearConstraints;
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/** Type of optimization goal: either {@link GoalType#MAXIMIZE} or {@link GoalType#MINIMIZE}. */
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/**
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* Type of optimization goal: either {@link GoalType#MAXIMIZE} or {@link GoalType#MINIMIZE}.
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* @since 2.1
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*/
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protected GoalType goal;
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/** Whether to restrict the variables to non-negative values. */
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/**
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* Whether to restrict the variables to non-negative values.
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* @since 2.1
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*/
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protected boolean nonNegative;
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/** Maximal number of iterations allowed. */
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@ -436,6 +436,7 @@ public class EmpiricalDistributionImpl implements Serializable, EmpiricalDistrib
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* bounds now returned by {@link #getGeneratorUpperBounds()}.</p>
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*
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* @return array of bin upper bounds
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* @since 2.1
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*/
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public double[] getUpperBounds() {
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double[] binUpperBounds = new double[binCount];
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@ -21,6 +21,7 @@ package org.apache.commons.math.random;
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/**
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* Generate random vectors isotropically located on the surface of a sphere.
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*
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* @since 2.1
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* @version $Revision$ $Date$
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*/
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@ -99,7 +99,10 @@ public class EuclideanIntegerPoint implements Clusterable<EuclideanIntegerPoint>
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return hashCode;
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}
|
||||
|
||||
/** {@inheritDoc} */
|
||||
/**
|
||||
* {@inheritDoc}
|
||||
* @since 2.1
|
||||
*/
|
||||
@Override
|
||||
public String toString() {
|
||||
final StringBuffer buff = new StringBuffer("(");
|
||||
|
|
|
|||
|
|
@ -192,6 +192,7 @@ public class BigReal implements FieldElement<BigReal>, Comparable<BigReal>, Seri
|
|||
* Gets the rounding mode for division operations
|
||||
* The default is {@code RoundingMode.HALF_UP}
|
||||
* @return the rounding mode.
|
||||
* @since 2.1
|
||||
*/
|
||||
public RoundingMode getRoundingMode() {
|
||||
return roundingMode;
|
||||
|
|
@ -200,6 +201,7 @@ public class BigReal implements FieldElement<BigReal>, Comparable<BigReal>, Seri
|
|||
/***
|
||||
* Sets the rounding mode for decimal divisions.
|
||||
* @param roundingMode rounding mode for decimal divisions
|
||||
* @since 2.1
|
||||
*/
|
||||
public void setRoundingMode(RoundingMode roundingMode) {
|
||||
this.roundingMode = roundingMode;
|
||||
|
|
@ -209,6 +211,7 @@ public class BigReal implements FieldElement<BigReal>, Comparable<BigReal>, Seri
|
|||
* Sets the scale for division operations.
|
||||
* The default is 64
|
||||
* @return the scale
|
||||
* @since 2.1
|
||||
*/
|
||||
public int getScale() {
|
||||
return scale;
|
||||
|
|
@ -217,6 +220,7 @@ public class BigReal implements FieldElement<BigReal>, Comparable<BigReal>, Seri
|
|||
/***
|
||||
* Sets the scale for division operations.
|
||||
* @param scale scale for division operations
|
||||
* @since 2.1
|
||||
*/
|
||||
public void setScale(int scale) {
|
||||
this.scale = scale;
|
||||
|
|
|
|||
|
|
@ -38,7 +38,10 @@ public final class MathUtils {
|
|||
*/
|
||||
public static final double SAFE_MIN = 0x1.0p-1022;
|
||||
|
||||
/** 2 π. */
|
||||
/**
|
||||
* 2 π.
|
||||
* @since 2.1
|
||||
*/
|
||||
public static final double TWO_PI = 2 * Math.PI;
|
||||
|
||||
/** -1.0 cast as a byte. */
|
||||
|
|
@ -1141,6 +1144,7 @@ public final class MathUtils {
|
|||
* @return normalized array
|
||||
* @throws ArithmeticException if the input array contains infinite elements or sums to zero
|
||||
* @throws IllegalArgumentException if the target sum is infinite or NaN
|
||||
* @since 2.1
|
||||
*/
|
||||
public static double[] normalizeArray(double[] values, double normalizedSum)
|
||||
throws ArithmeticException, IllegalArgumentException {
|
||||
|
|
|
|||
Loading…
Reference in New Issue