MATH-1443: Depend on "Commons Statistics". Simplify classes that remain in "Commons Math".
Project: http://git-wip-us.apache.org/repos/asf/commons-math/repo Commit: http://git-wip-us.apache.org/repos/asf/commons-math/commit/b1a8299a Tree: http://git-wip-us.apache.org/repos/asf/commons-math/tree/b1a8299a Diff: http://git-wip-us.apache.org/repos/asf/commons-math/diff/b1a8299a Branch: refs/heads/master Commit: b1a8299ad223c3b2552f6d03e51c2ecba5df46ed Parents: d2359ce Author: Gilles <er...@apache.org> Authored: Thu Jan 25 19:20:03 2018 +0100 Committer: Gilles <er...@apache.org> Committed: Thu Jan 25 19:20:03 2018 +0100 ---------------------------------------------------------------------- .../distribution/AbstractRealDistribution.java | 11 +- .../distribution/EmpiricalDistribution.java | 9 +- .../math4/distribution/RealDistribution.java | 157 +------------------ .../AbstractRealDistributionTest.java | 12 +- .../distribution/EmpiricalDistributionTest.java | 12 +- .../RealDistributionAbstractTest.java | 4 +- 6 files changed, 26 insertions(+), 179 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/commons-math/blob/b1a8299a/src/main/java/org/apache/commons/math4/distribution/AbstractRealDistribution.java ---------------------------------------------------------------------- diff --git a/src/main/java/org/apache/commons/math4/distribution/AbstractRealDistribution.java b/src/main/java/org/apache/commons/math4/distribution/AbstractRealDistribution.java index c00039a..f4b0eac 100644 --- a/src/main/java/org/apache/commons/math4/distribution/AbstractRealDistribution.java +++ b/src/main/java/org/apache/commons/math4/distribution/AbstractRealDistribution.java @@ -46,7 +46,8 @@ import org.apache.commons.math4.util.FastMath; * @since 3.0 */ public abstract class AbstractRealDistribution - implements RealDistribution, Serializable { + implements RealDistribution, + Serializable { /** Default absolute accuracy for inverse cumulative computation. */ public static final double SOLVER_DEFAULT_ABSOLUTE_ACCURACY = 1e-6; /** Serializable version identifier */ @@ -131,8 +132,8 @@ public abstract class AbstractRealDistribution return upperBound; } - final double mu = getNumericalMean(); - final double sig = FastMath.sqrt(getNumericalVariance()); + final double mu = getMean(); + final double sig = FastMath.sqrt(getVariance()); final boolean chebyshevApplies; chebyshevApplies = !(Double.isInfinite(mu) || Double.isNaN(mu) || Double.isInfinite(sig) || Double.isNaN(sig)); @@ -245,8 +246,8 @@ public abstract class AbstractRealDistribution /**{@inheritDoc} */ @Override - public Sampler createSampler(final UniformRandomProvider rng) { - return new RealDistribution.Sampler() { + public ContinuousDistribution.Sampler createSampler(final UniformRandomProvider rng) { + return new ContinuousDistribution.Sampler() { /** * Inversion method distribution sampler. */ http://git-wip-us.apache.org/repos/asf/commons-math/blob/b1a8299a/src/main/java/org/apache/commons/math4/distribution/EmpiricalDistribution.java ---------------------------------------------------------------------- diff --git a/src/main/java/org/apache/commons/math4/distribution/EmpiricalDistribution.java b/src/main/java/org/apache/commons/math4/distribution/EmpiricalDistribution.java index 97f6561..82357e9 100644 --- a/src/main/java/org/apache/commons/math4/distribution/EmpiricalDistribution.java +++ b/src/main/java/org/apache/commons/math4/distribution/EmpiricalDistribution.java @@ -100,7 +100,8 @@ import org.apache.commons.math4.util.MathUtils; * </ul> * */ -public class EmpiricalDistribution extends AbstractRealDistribution { +public class EmpiricalDistribution extends AbstractRealDistribution + implements ContinuousDistribution { /** Default bin count */ public static final int DEFAULT_BIN_COUNT = 1000; @@ -623,7 +624,7 @@ public class EmpiricalDistribution extends AbstractRealDistribution { * @since 3.1 */ @Override - public double getNumericalMean() { + public double getMean() { return sampleStats.getMean(); } @@ -632,7 +633,7 @@ public class EmpiricalDistribution extends AbstractRealDistribution { * @since 3.1 */ @Override - public double getNumericalVariance() { + public double getVariance() { return sampleStats.getVariance(); } @@ -665,7 +666,7 @@ public class EmpiricalDistribution extends AbstractRealDistribution { /**{@inheritDoc} */ @Override - public RealDistribution.Sampler createSampler(final UniformRandomProvider rng) { + public ContinuousDistribution.Sampler createSampler(final UniformRandomProvider rng) { if (!loaded) { throw new MathIllegalStateException(LocalizedFormats.DISTRIBUTION_NOT_LOADED); } http://git-wip-us.apache.org/repos/asf/commons-math/blob/b1a8299a/src/main/java/org/apache/commons/math4/distribution/RealDistribution.java ---------------------------------------------------------------------- diff --git a/src/main/java/org/apache/commons/math4/distribution/RealDistribution.java b/src/main/java/org/apache/commons/math4/distribution/RealDistribution.java index 45511e1..225f817 100644 --- a/src/main/java/org/apache/commons/math4/distribution/RealDistribution.java +++ b/src/main/java/org/apache/commons/math4/distribution/RealDistribution.java @@ -26,159 +26,4 @@ import org.apache.commons.rng.UniformRandomProvider; * * @since 3.0 */ -public interface RealDistribution { - /** - * For a random variable {@code X} whose values are distributed according - * to this distribution, this method returns {@code P(X = x)}. In other - * words, this method represents the probability mass function (PMF) - * for the distribution. - * - * @param x the point at which the PMF is evaluated - * @return the value of the probability mass function at point {@code x} - */ - double probability(double x); - - /** - * For a random variable {@code X} whose values are distributed according - * to this distribution, this method returns {@code P(x0 < X <= x1)}. - * - * @param x0 the exclusive lower bound - * @param x1 the inclusive upper bound - * @return the probability that a random variable with this distribution - * takes a value between {@code x0} and {@code x1}, - * excluding the lower and including the upper endpoint - * @throws NumberIsTooLargeException if {@code x0 > x1} - * - * @since 4.0, was previously named cumulativeProbability - */ - double probability(double x0, double x1) throws NumberIsTooLargeException; - - /** - * Returns the probability density function (PDF) of this distribution - * evaluated at the specified point {@code x}. In general, the PDF is - * the derivative of the {@link #cumulativeProbability(double) CDF}. - * If the derivative does not exist at {@code x}, then an appropriate - * replacement should be returned, e.g. {@code Double.POSITIVE_INFINITY}, - * {@code Double.NaN}, or the limit inferior or limit superior of the - * difference quotient. - * - * @param x the point at which the PDF is evaluated - * @return the value of the probability density function at point {@code x} - */ - double density(double x); - - /** - * Returns the natural logarithm of the probability density function - * (PDF) of this distribution evaluated at the specified point {@code x}. - * In general, the PDF is the derivative of the {@link #cumulativeProbability(double) CDF}. - * If the derivative does not exist at {@code x}, then an appropriate replacement - * should be returned, e.g. {@code Double.POSITIVE_INFINITY}, {@code Double.NaN}, - * or the limit inferior or limit superior of the difference quotient. Note that - * due to the floating point precision and under/overflow issues, this method will - * for some distributions be more precise and faster than computing the logarithm of - * {@link #density(double)}. - * - * @param x the point at which the PDF is evaluated - * @return the logarithm of the value of the probability density function at point {@code x} - * @since 4.0 - */ - double logDensity(double x); - - /** - * For a random variable {@code X} whose values are distributed according - * to this distribution, this method returns {@code P(X <= x)}. In other - * words, this method represents the (cumulative) distribution function - * (CDF) for this distribution. - * - * @param x the point at which the CDF is evaluated - * @return the probability that a random variable with this - * distribution takes a value less than or equal to {@code x} - */ - double cumulativeProbability(double x); - - /** - * Computes the quantile function of this distribution. For a random - * variable {@code X} distributed according to this distribution, the - * returned value is - * <ul> - * <li>{@code inf{x in R | P(X<=x) >= p}} for {@code 0 < p <= 1},</li> - * <li>{@code inf{x in R | P(X<=x) > 0}} for {@code p = 0}.</li> - * </ul> - * - * @param p the cumulative probability - * @return the smallest {@code p}-quantile of this distribution - * (largest 0-quantile for {@code p = 0}) - * @throws OutOfRangeException if {@code p < 0} or {@code p > 1} - */ - double inverseCumulativeProbability(double p) throws OutOfRangeException; - - /** - * Use this method to get the numerical value of the mean of this - * distribution. - * - * @return the mean or {@code Double.NaN} if it is not defined - */ - double getNumericalMean(); - - /** - * Use this method to get the numerical value of the variance of this - * distribution. - * - * @return the variance (possibly {@code Double.POSITIVE_INFINITY} as - * for certain cases in {@link TDistribution}) or {@code Double.NaN} if it - * is not defined - */ - double getNumericalVariance(); - - /** - * Access the lower bound of the support. This method must return the same - * value as {@code inverseCumulativeProbability(0)}. In other words, this - * method must return - * <p>{@code inf {x in R | P(X <= x) > 0}}.</p> - * - * @return lower bound of the support (might be - * {@code Double.NEGATIVE_INFINITY}) - */ - double getSupportLowerBound(); - - /** - * Access the upper bound of the support. This method must return the same - * value as {@code inverseCumulativeProbability(1)}. In other words, this - * method must return - * <p>{@code inf {x in R | P(X <= x) = 1}}.</p> - * - * @return upper bound of the support (might be - * {@code Double.POSITIVE_INFINITY}) - */ - double getSupportUpperBound(); - - /** - * Use this method to get information about whether the support is connected, - * i.e. whether all values between the lower and upper bound of the support - * are included in the support. - * - * @return whether the support is connected or not - */ - boolean isSupportConnected(); - - /** - * Creates a sampler. - * - * @param rng Generator of uniformly distributed numbers. - * @return a sampler that produces random numbers according this - * distribution. - */ - Sampler createSampler(UniformRandomProvider rng); - - /** - * Sampling functionality. - */ - interface Sampler extends ContinuousDistribution.Sampler { - /** - * Generates a random value sampled from this distribution. - * - * @return a random value. - */ - double sample(); - } -} +public interface RealDistribution extends ContinuousDistribution {} http://git-wip-us.apache.org/repos/asf/commons-math/blob/b1a8299a/src/test/java/org/apache/commons/math4/distribution/AbstractRealDistributionTest.java ---------------------------------------------------------------------- diff --git a/src/test/java/org/apache/commons/math4/distribution/AbstractRealDistributionTest.java b/src/test/java/org/apache/commons/math4/distribution/AbstractRealDistributionTest.java index 60645c2..396dc4a 100644 --- a/src/test/java/org/apache/commons/math4/distribution/AbstractRealDistributionTest.java +++ b/src/test/java/org/apache/commons/math4/distribution/AbstractRealDistributionTest.java @@ -69,13 +69,13 @@ public class AbstractRealDistributionTest { } @Override - public double getNumericalMean() { + public double getMean() { return ((x0 + x1) * p12 + (x2 + x3) * (1.0 - p12)) / 2.0; } @Override - public double getNumericalVariance() { - final double meanX = getNumericalMean(); + public double getVariance() { + final double meanX = getMean(); final double meanX2; meanX2 = ((x0 * x0 + x0 * x1 + x1 * x1) * p12 + (x2 * x2 + x2 * x3 + x3 * x3) @@ -155,7 +155,7 @@ public class AbstractRealDistributionTest { } @Override - public double getNumericalMean() { + public double getMean() { final UnivariateFunction f = new UnivariateFunction() { @Override @@ -168,8 +168,8 @@ public class AbstractRealDistributionTest { } @Override - public double getNumericalVariance() { - final double meanX = getNumericalMean(); + public double getVariance() { + final double meanX = getMean(); final UnivariateFunction f = new UnivariateFunction() { @Override http://git-wip-us.apache.org/repos/asf/commons-math/blob/b1a8299a/src/test/java/org/apache/commons/math4/distribution/EmpiricalDistributionTest.java ---------------------------------------------------------------------- diff --git a/src/test/java/org/apache/commons/math4/distribution/EmpiricalDistributionTest.java b/src/test/java/org/apache/commons/math4/distribution/EmpiricalDistributionTest.java index c3c0071..299af66 100644 --- a/src/test/java/org/apache/commons/math4/distribution/EmpiricalDistributionTest.java +++ b/src/test/java/org/apache/commons/math4/distribution/EmpiricalDistributionTest.java @@ -270,7 +270,7 @@ public final class EmpiricalDistributionTest extends RealDistributionAbstractTes private void tstGen(double tolerance)throws Exception { empiricalDistribution.load(url); - RealDistribution.Sampler sampler + ContinuousDistribution.Sampler sampler = empiricalDistribution.createSampler(RandomSource.create(RandomSource.WELL_19937_C, 1000)); SummaryStatistics stats = new SummaryStatistics(); for (int i = 1; i < 1000; i++) { @@ -282,7 +282,7 @@ public final class EmpiricalDistributionTest extends RealDistributionAbstractTes private void tstDoubleGen(double tolerance)throws Exception { empiricalDistribution2.load(dataArray); - RealDistribution.Sampler sampler + ContinuousDistribution.Sampler sampler = empiricalDistribution2.createSampler(RandomSource.create(RandomSource.WELL_19937_C, 1000)); SummaryStatistics stats = new SummaryStatistics(); for (int i = 1; i < 1000; i++) { @@ -413,7 +413,7 @@ public final class EmpiricalDistributionTest extends RealDistributionAbstractTes } EmpiricalDistribution dist = new EmpiricalDistribution(10); dist.load(data); - RealDistribution.Sampler sampler + ContinuousDistribution.Sampler sampler = dist.createSampler(RandomSource.create(RandomSource.WELL_19937_C, 1000)); for (int i = 0; i < 1000; i++) { final double dev = sampler.sample(); @@ -430,7 +430,7 @@ public final class EmpiricalDistributionTest extends RealDistributionAbstractTes final double[] data = {0, 0, 1, 1}; EmpiricalDistribution dist = new EmpiricalDistribution(2); dist.load(data); - RealDistribution.Sampler sampler + ContinuousDistribution.Sampler sampler = dist.createSampler(RandomSource.create(RandomSource.WELL_19937_C, 1000)); for (int i = 0; i < 1000; i++) { final double dev = sampler.sample(); @@ -473,7 +473,7 @@ public final class EmpiricalDistributionTest extends RealDistributionAbstractTes final EmpiricalDistribution dist = new ConstantKernelEmpiricalDistribution(5); final double[] data = {1d,2d,3d, 4d,5d,6d, 7d,8d,9d, 10d,11d,12d, 13d,14d,15d}; dist.load(data); - RealDistribution.Sampler sampler + ContinuousDistribution.Sampler sampler = dist.createSampler(RandomSource.create(RandomSource.WELL_19937_C, 1000)); // Bin masses concentrated on 2, 5, 8, 11, 14 <- effectively discrete uniform distribution over these double[] values = {2d, 5d, 8d, 11d, 14d}; @@ -501,7 +501,7 @@ public final class EmpiricalDistributionTest extends RealDistributionAbstractTes final EmpiricalDistribution dist = new UniformKernelEmpiricalDistribution(5); final double[] data = {1d,2d,3d, 4d,5d,6d, 7d,8d,9d, 10d,11d,12d, 13d,14d,15d}; dist.load(data); - RealDistribution.Sampler sampler + ContinuousDistribution.Sampler sampler = dist.createSampler(RandomSource.create(RandomSource.WELL_19937_C, 1000)); // Kernels are uniform distributions on [1,3], [4,6], [7,9], [10,12], [13,15] final double bounds[] = {3d, 6d, 9d, 12d}; http://git-wip-us.apache.org/repos/asf/commons-math/blob/b1a8299a/src/test/java/org/apache/commons/math4/distribution/RealDistributionAbstractTest.java ---------------------------------------------------------------------- diff --git a/src/test/java/org/apache/commons/math4/distribution/RealDistributionAbstractTest.java b/src/test/java/org/apache/commons/math4/distribution/RealDistributionAbstractTest.java index 8d7ac9f..ea6d392 100644 --- a/src/test/java/org/apache/commons/math4/distribution/RealDistributionAbstractTest.java +++ b/src/test/java/org/apache/commons/math4/distribution/RealDistributionAbstractTest.java @@ -391,11 +391,11 @@ public abstract class RealDistributionAbstractTest { // generator, using a fixed seed for deterministic results. final long seed = 123; RandomSource source = RandomSource.WELL_512_A; - RealDistribution.Sampler origSampler = distribution.createSampler(RandomSource.create(source, seed)); + ContinuousDistribution.Sampler origSampler = distribution.createSampler(RandomSource.create(source, seed)); // Clone the distribution. final RealDistribution cloned = deepClone(); - RealDistribution.Sampler clonedSampler = cloned.createSampler(RandomSource.create(source, seed)); + ContinuousDistribution.Sampler clonedSampler = cloned.createSampler(RandomSource.create(source, seed)); // Make sure they still produce the same samples. Assert.assertEquals(origSampler.sample(),
