Apache Commons RNG Sampling 1.0-SNAPSHOT Reference Package org.apache.commons.rng.sampling.distribution

Return-Path: X-Original-To: archive-asf-public-internal@cust-asf2.ponee.io Delivered-To: archive-asf-public-internal@cust-asf2.ponee.io Received: from cust-asf.ponee.io (cust-asf.ponee.io [163.172.22.183]) by cust-asf2.ponee.io (Postfix) with ESMTP id 2641D200BEA for ; Mon, 12 Dec 2016 17:27:30 +0100 (CET) Received: by cust-asf.ponee.io (Postfix) id 24EAB160B1A; Mon, 12 Dec 2016 16:27:30 +0000 (UTC) Delivered-To: archive-asf-public@cust-asf.ponee.io Received: from mail.apache.org (hermes.apache.org [140.211.11.3]) by cust-asf.ponee.io (Postfix) with SMTP id 19C24160B45 for ; Mon, 12 Dec 2016 17:27:26 +0100 (CET) Received: (qmail 53284 invoked by uid 500); 12 Dec 2016 16:27:26 -0000 Mailing-List: contact notifications-help@commons.apache.org; run by ezmlm Precedence: bulk List-Help: List-Unsubscribe: List-Post: List-Id: Reply-To: dev@commons.apache.org Delivered-To: mailing list notifications@commons.apache.org Received: (qmail 53135 invoked by uid 99); 12 Dec 2016 16:27:26 -0000 Received: from Unknown (HELO svn01-us-west.apache.org) (209.188.14.144) by apache.org (qpsmtpd/0.29) with ESMTP; Mon, 12 Dec 2016 16:27:26 +0000 Received: from svn01-us-west.apache.org (localhost [127.0.0.1]) by svn01-us-west.apache.org (ASF Mail Server at svn01-us-west.apache.org) with ESMTP id CC0253A3B1A for ; Mon, 12 Dec 2016 16:27:24 +0000 (UTC) Content-Type: text/plain; charset="utf-8" MIME-Version: 1.0 Content-Transfer-Encoding: 8bit Subject: svn commit: r1002658 [24/35] - in /websites/production/commons/content/proper/commons-rng: ./ commons-rng-client-api/ commons-rng-client-api/apidocs/ commons-rng-client-api/apidocs/org/apache/commons/rng/ commons-rng-client-api/apidocs/org/apache/commo... Date: Mon, 12 Dec 2016 16:27:20 -0000 To: notifications@commons.apache.org From: erans@apache.org X-Mailer: svnmailer-1.0.9 Message-Id: <20161212162724.CC0253A3B1A@svn01-us-west.apache.org> archived-at: Mon, 12 Dec 2016 16:27:30 -0000 Modified: websites/production/commons/content/proper/commons-rng/commons-rng-sampling/xref/org/apache/commons/rng/sampling/distribution/DiscreteInverseCumulativeProbabilityFunction.html ============================================================================== --- websites/production/commons/content/proper/commons-rng/commons-rng-sampling/xref/org/apache/commons/rng/sampling/distribution/DiscreteInverseCumulativeProbabilityFunction.html (original) +++ websites/production/commons/content/proper/commons-rng/commons-rng-sampling/xref/org/apache/commons/rng/sampling/distribution/DiscreteInverseCumulativeProbabilityFunction.html Mon Dec 12 16:27:09 2016 @@ -35,8 +35,8 @@ 27 * For a random variable {@code X} distributed according to this distribution, 28 * the returned value is 29 * <ul> -30 * <li>\( \inf_{x \in \mathcal{Z}} P(X \le x) \ge p \) for \( 0 -31 * <li>\( \inf_{x \in \mathcal{Z}} P(X \le x) > 0 \) for \( p = 0 \)</li> +30 * <li>\( \inf_{x \in \mathcal{Z}} P(X \le x) \ge p \) for \( 0 \lt p \le 1 \)</li> +31 * <li>\( \inf_{x \in \mathcal{Z}} P(X \le x) \gt 0 \) for \( p = 0 \)</li> 32 * </ul> 33 * 34 * @param p Cumulative probability. Modified: websites/production/commons/content/proper/commons-rng/commons-rng-sampling/xref/org/apache/commons/rng/sampling/distribution/InternalGamma.html ============================================================================== --- websites/production/commons/content/proper/commons-rng/commons-rng-sampling/xref/org/apache/commons/rng/sampling/distribution/InternalGamma.html (original) +++ websites/production/commons/content/proper/commons-rng/commons-rng-sampling/xref/org/apache/commons/rng/sampling/distribution/InternalGamma.html Mon Dec 12 16:27:09 2016 @@ -121,7 +121,7 @@ 113 114 final double sum = lanczos(x); 115 final double tmp = x + LANCZOS_G + 0.5; -116 return ((x + 0.5) * Math.log(tmp)) - tmp + HALF_LOG_2_PI + Math.log(sum / x); +116 return (x + 0.5) * Math.log(tmp) - tmp + HALF_LOG_2_PI + Math.log(sum / x); 117 } 118 119 /** Modified: websites/production/commons/content/proper/commons-rng/commons-rng-sampling/xref/org/apache/commons/rng/sampling/distribution/InverseTransformContinuousSampler.html ============================================================================== --- websites/production/commons/content/proper/commons-rng/commons-rng-sampling/xref/org/apache/commons/rng/sampling/distribution/InverseTransformContinuousSampler.html (original) +++ websites/production/commons/content/proper/commons-rng/commons-rng-sampling/xref/org/apache/commons/rng/sampling/distribution/InverseTransformContinuousSampler.html Mon Dec 12 16:27:09 2016 @@ -31,64 +31,62 @@ 23 * <a href="https://en.wikipedia.org/wiki/Inverse_transform_sampling"> 24 * inversion method</a>. 25 * -26 * -27 * It can be used to sample any distribution that provides access to its -28 * inverse cumulative probabilty function. -29 * -30 * -31 * Example: -32 * <pre><source> -33 * import org.apache.commons.math3.distribution.RealDistribution; -34 * import org.apache.commons.math3.distribution.ChiSquaredDistribution; -35 * -36 * import org.apache.commons.rng.simple.RandomSource; -37 * import org.apache.commons.rng.sampling.ContinuousSampler; -38 * import org.apache.commons.rng.sampling.distribution.InverseTransformContinuousSampler; -39 * import org.apache.commons.rng.sampling.distribution.ContinuousInverseCumulativeProbabilityFunction; -40 * -41 * // Distribution to sample. -42 * final RealDistribution dist = new ChiSquaredDistribution(9); -43 * // Create the sampler. -44 * final ContinuousSampler chiSquareSampler = -45 * new InverseTransformContinuousSampler(RandomSource.create(RandomSource.MT), -46 * new ContinuousInverseCumulativeProbabilityFunction() { -47 * public double inverseCumulativeProbability(double p) { -48 * return dist.inverseCumulativeProbability(p); -49 * } -50 * }); -51 * -52 * // Generate random deviate. -53 * double random = chiSquareSampler.sample(); -54 * </source></pre> -55 */ -56 public class InverseTransformContinuousSampler -57 extends SamplerBase -58 implements ContinuousSampler { -59 /** Inverse cumulative probability function. */ -60 private final ContinuousInverseCumulativeProbabilityFunction function; -61 -62 /** -63 * @param rng Generator of uniformly distributed random numbers. -64 * @param function Inverse cumulative probability function. -65 */ -66 public InverseTransformContinuousSampler(UniformRandomProvider rng, -67 ContinuousInverseCumulativeProbabilityFunction function) { -68 super(rng); -69 this.function = function; -70 } -71 -72 /** {@inheritDoc} */ -73 @Override -74 public double sample() { -75 return function.inverseCumulativeProbability(nextDouble()); -76 } -77 -78 /** {@inheritDoc} */ -79 @Override -80 public String toString() { -81 return function.toString() + " (inverse method) [" + super.toString() + "]"; -82 } -83 } +26 * It can be used to sample any distribution that provides access to its +27 * inverse cumulative probabilty function. +28 * +29 * Example: +30 * <pre><code> +31 * import org.apache.commons.math3.distribution.RealDistribution; +32 * import org.apache.commons.math3.distribution.ChiSquaredDistribution; +33 * +34 * import org.apache.commons.rng.simple.RandomSource; +35 * import org.apache.commons.rng.sampling.distribution.ContinuousSampler; +36 * import org.apache.commons.rng.sampling.distribution.InverseTransformContinuousSampler; +37 * import org.apache.commons.rng.sampling.distribution.ContinuousInverseCumulativeProbabilityFunction; +38 * +39 * // Distribution to sample. +40 * final RealDistribution dist = new ChiSquaredDistribution(9); +41 * // Create the sampler. +42 * final ContinuousSampler chiSquareSampler = +43 * new InverseTransformContinuousSampler(RandomSource.create(RandomSource.MT), +44 * new ContinuousInverseCumulativeProbabilityFunction() { +45 * public double inverseCumulativeProbability(double p) { +46 * return dist.inverseCumulativeProbability(p); +47 * } +48 * }); +49 * +50 * // Generate random deviate. +51 * double random = chiSquareSampler.sample(); +52 * </code></pre> +53 */ +54 public class InverseTransformContinuousSampler +55 extends SamplerBase +56 implements ContinuousSampler { +57 /** Inverse cumulative probability function. */ +58 private final ContinuousInverseCumulativeProbabilityFunction function; +59 +60 /** +61 * @param rng Generator of uniformly distributed random numbers. +62 * @param function Inverse cumulative probability function. +63 */ +64 public InverseTransformContinuousSampler(UniformRandomProvider rng, +65 ContinuousInverseCumulativeProbabilityFunction function) { +66 super(rng); +67 this.function = function; +68 } +69 +70 /** {@inheritDoc} */ +71 @Override +72 public double sample() { +73 return function.inverseCumulativeProbability(nextDouble()); +74 } +75 +76 /** {@inheritDoc} */ +77 @Override +78 public String toString() { +79 return function.toString() + " (inverse method) [" + super.toString() + "]"; +80 } +81 }

Modified: websites/production/commons/content/proper/commons-rng/commons-rng-sampling/xref/org/apache/commons/rng/sampling/distribution/InverseTransformParetoSampler.html ============================================================================== --- websites/production/commons/content/proper/commons-rng/commons-rng-sampling/xref/org/apache/commons/rng/sampling/distribution/InverseTransformParetoSampler.html (original) +++ websites/production/commons/content/proper/commons-rng/commons-rng-sampling/xref/org/apache/commons/rng/sampling/distribution/InverseTransformParetoSampler.html Mon Dec 12 16:27:09 2016 @@ -43,8 +43,8 @@ 35 * @param shape Shape of the distribution. 36 */ 37 public InverseTransformParetoSampler(UniformRandomProvider rng, -38 double scale, -39 double shape) { +38 double scale, +39 double shape) { 40 super(rng); 41 this.scale = scale; 42 this.shape = shape; Modified: websites/production/commons/content/proper/commons-rng/commons-rng-sampling/xref/org/apache/commons/rng/sampling/distribution/PoissonSampler.html ============================================================================== --- websites/production/commons/content/proper/commons-rng/commons-rng-sampling/xref/org/apache/commons/rng/sampling/distribution/PoissonSampler.html (original) +++ websites/production/commons/content/proper/commons-rng/commons-rng-sampling/xref/org/apache/commons/rng/sampling/distribution/PoissonSampler.html Mon Dec 12 16:27:09 2016 @@ -74,119 +74,121 @@ 66 67 gaussian = new BoxMullerGaussianSampler(rng, 0, 1); 68 exponential = new AhrensDieterExponentialSampler(rng, 1); -69 factorialLog = InternalUtils.FactorialLog.create(); -70 } -71 -72 /** {@inheritDoc} */ -73 @Override -74 public int sample() { -75 return (int) Math.min(nextPoisson(mean), Integer.MAX_VALUE); -76 } -77 -78 /** {@inheritDoc} */ -79 @Override -80 public String toString() { -81 return "Poisson deviate [" + super.toString() + "]"; -82 } -83 -84 /** -85 * @param meanPoisson Mean. -86 * @return the next sample. -87 */ -88 private long nextPoisson(double meanPoisson) { -89 if (meanPoisson < PIVOT) { -90 double p = Math.exp(-meanPoisson); -91 long n = 0; -92 double r = 1; -93 -94 while (n < 1000 * meanPoisson) { -95 r *= nextDouble(); -96 if (r >= p) { -97 n++; -98 } else { -99 break; -100 } -101 } -102 return n; -103 } else { -104 final double lambda = Math.floor(meanPoisson); -105 final double lambdaFractional = meanPoisson - lambda; -106 final double logLambda = Math.log(lambda); -107 final double logLambdaFactorial = factorialLog((int) lambda); -108 final long y2 = lambdaFractional < Double.MIN_VALUE ? 0 : nextPoisson(lambdaFractional); -109 final double delta = Math.sqrt(lambda * Math.log(32 * lambda / Math.PI + 1)); -110 final double halfDelta = delta / 2; -111 final double twolpd = 2 * lambda + delta; -112 final double a1 = Math.sqrt(Math.PI * twolpd) * Math.exp(1 / (8 * lambda)); -113 final double a2 = (twolpd / delta) * Math.exp(-delta * (1 + delta) / twolpd); -114 final double aSum = a1 + a2 + 1; -115 final double p1 = a1 / aSum; -116 final double p2 = a2 / aSum; -117 final double c1 = 1 / (8 * lambda); -118 -119 double x = 0; -120 double y = 0; -121 double v = 0; -122 int a = 0; -123 double t = 0; -124 double qr = 0; -125 double qa = 0; -126 while (true) { -127 final double u = nextDouble(); -128 if (u <= p1) { -129 final double n = gaussian.sample(); -130 x = n * Math.sqrt(lambda + halfDelta) - 0.5d; -131 if (x > delta || x < -lambda) { -132 continue; -133 } -134 y = x < 0 ? Math.floor(x) : Math.ceil(x); -135 final double e = exponential.sample(); -136 v = -e - (n * n / 2) + c1; -137 } else { -138 if (u > p1 + p2) { -139 y = lambda; -140 break; -141 } else { -142 x = delta + (twolpd / delta) * exponential.sample(); -143 y = Math.ceil(x); -144 v = -exponential.sample() - delta * (x + 1) / twolpd; -145 } -146 } -147 a = x < 0 ? 1 : 0; -148 t = y * (y + 1) / (2 * lambda); -149 if (v < -t && a == 0) { -150 y = lambda + y; -151 break; -152 } -153 qr = t * ((2 * y + 1) / (6 * lambda) - 1); -154 qa = qr - (t * t) / (3 * (lambda + a * (y + 1))); -155 if (v < qa) { -156 y = lambda + y; -157 break; -158 } -159 if (v > qr) { -160 continue; -161 } -162 if (v < y * logLambda - factorialLog((int) (y + lambda)) + logLambdaFactorial) { -163 y = lambda + y; -164 break; -165 } -166 } -167 return y2 + (long) y; -168 } -169 } -170 -171 /** -172 * Compute the natural logarithm of the factorial of {@code n}. -173 * -174 * @param n Argument. -175 * @return {@code log(n!)} -176 * @throws IllegalArgumentException if {@code n < 0}. -177 */ -178 private double factorialLog(int n) { -179 return factorialLog.value(n); -180 } -181 } +69 factorialLog = mean < PIVOT ? +70 null : // Not used. +71 InternalUtils.FactorialLog.create().withCache((int) Math.min(mean, 2 * PIVOT)); +72 } +73 +74 /** {@inheritDoc} */ +75 @Override +76 public int sample() { +77 return (int) Math.min(nextPoisson(mean), Integer.MAX_VALUE); +78 } +79 +80 /** {@inheritDoc} */ +81 @Override +82 public String toString() { +83 return "Poisson deviate [" + super.toString() + "]"; +84 } +85 +86 /** +87 * @param meanPoisson Mean. +88 * @return the next sample. +89 */ +90 private long nextPoisson(double meanPoisson) { +91 if (meanPoisson < PIVOT) { +92 double p = Math.exp(-meanPoisson); +93 long n = 0; +94 double r = 1; +95 +96 while (n < 1000 * meanPoisson) { +97 r *= nextDouble(); +98 if (r >= p) { +99 n++; +100 } else { +101 break; +102 } +103 } +104 return n; +105 } else { +106 final double lambda = Math.floor(meanPoisson); +107 final double lambdaFractional = meanPoisson - lambda; +108 final double logLambda = Math.log(lambda); +109 final double logLambdaFactorial = factorialLog((int) lambda); +110 final long y2 = lambdaFractional < Double.MIN_VALUE ? 0 : nextPoisson(lambdaFractional); +111 final double delta = Math.sqrt(lambda * Math.log(32 * lambda / Math.PI + 1)); +112 final double halfDelta = delta / 2; +113 final double twolpd = 2 * lambda + delta; +114 final double a1 = Math.sqrt(Math.PI * twolpd) * Math.exp(1 / (8 * lambda)); +115 final double a2 = (twolpd / delta) * Math.exp(-delta * (1 + delta) / twolpd); +116 final double aSum = a1 + a2 + 1; +117 final double p1 = a1 / aSum; +118 final double p2 = a2 / aSum; +119 final double c1 = 1 / (8 * lambda); +120 +121 double x = 0; +122 double y = 0; +123 double v = 0; +124 int a = 0; +125 double t = 0; +126 double qr = 0; +127 double qa = 0; +128 while (true) { +129 final double u = nextDouble(); +130 if (u <= p1) { +131 final double n = gaussian.sample(); +132 x = n * Math.sqrt(lambda + halfDelta) - 0.5d; +133 if (x > delta || x < -lambda) { +134 continue; +135 } +136 y = x < 0 ? Math.floor(x) : Math.ceil(x); +137 final double e = exponential.sample(); +138 v = -e - 0.5 * n * n + c1; +139 } else { +140 if (u > p1 + p2) { +141 y = lambda; +142 break; +143 } else { +144 x = delta + (twolpd / delta) * exponential.sample(); +145 y = Math.ceil(x); +146 v = -exponential.sample() - delta * (x + 1) / twolpd; +147 } +148 } +149 a = x < 0 ? 1 : 0; +150 t = y * (y + 1) / (2 * lambda); +151 if (v < -t && a == 0) { +152 y = lambda + y; +153 break; +154 } +155 qr = t * ((2 * y + 1) / (6 * lambda) - 1); +156 qa = qr - (t * t) / (3 * (lambda + a * (y + 1))); +157 if (v < qa) { +158 y = lambda + y; +159 break; +160 } +161 if (v > qr) { +162 continue; +163 } +164 if (v < y * logLambda - factorialLog((int) (y + lambda)) + logLambdaFactorial) { +165 y = lambda + y; +166 break; +167 } +168 } +169 return y2 + (long) y; +170 } +171 } +172 +173 /** +174 * Compute the natural logarithm of the factorial of {@code n}. +175 * +176 * @param n Argument. +177 * @return {@code log(n!)} +178 * @throws IllegalArgumentException if {@code n < 0}. +179 */ +180 private double factorialLog(int n) { +181 return factorialLog.value(n); +182 } +183 }

Modified: websites/production/commons/content/proper/commons-rng/commons-rng-sampling/xref/org/apache/commons/rng/sampling/distribution/RejectionInversionZipfSampler.html ============================================================================== --- websites/production/commons/content/proper/commons-rng/commons-rng-sampling/xref/org/apache/commons/rng/sampling/distribution/RejectionInversionZipfSampler.html (original) +++ websites/production/commons/content/proper/commons-rng/commons-rng-sampling/xref/org/apache/commons/rng/sampling/distribution/RejectionInversionZipfSampler.html Mon Dec 12 16:27:09 2016 @@ -77,7 +77,7 @@ 69 * <blockquote> 70 * Wolfgang HÃ¶rmann and Gerhard Derflinger. 71 * "Rejection-inversion to generate variates from monotone discrete -72 * distributions", +72 * distributions", 73 * ACM Transactions on Modeling and Computer Simulation (TOMACS) 6.3 (1996): 169-184. 74 * </blockquote> 75 */ Modified: websites/production/commons/content/proper/commons-rng/commons-rng-sampling/xref/org/apache/commons/rng/sampling/distribution/package-frame.html ============================================================================== --- websites/production/commons/content/proper/commons-rng/commons-rng-sampling/xref/org/apache/commons/rng/sampling/distribution/package-frame.html (original) +++ websites/production/commons/content/proper/commons-rng/commons-rng-sampling/xref/org/apache/commons/rng/sampling/distribution/package-frame.html Mon Dec 12 16:27:09 2016 @@ -3,7 +3,7 @@ - Apache Commons RNG Sampling 1.0-SNAPSHOT Reference Package org.apache.commons.rng.sampling.distribution + Apache Commons RNG Sampling 1.0 Reference Package org.apache.commons.rng.sampling.distribution Modified: websites/production/commons/content/proper/commons-rng/commons-rng-sampling/xref/org/apache/commons/rng/sampling/distribution/package-summary.html ============================================================================== --- websites/production/commons/content/proper/commons-rng/commons-rng-sampling/xref/org/apache/commons/rng/sampling/distribution/package-summary.html (original) +++ websites/production/commons/content/proper/commons-rng/commons-rng-sampling/xref/org/apache/commons/rng/sampling/distribution/package-summary.html Mon Dec 12 16:27:09 2016 @@ -3,7 +3,7 @@ - Apache Commons RNG Sampling 1.0-SNAPSHOT Reference Package org.apache.commons.rng.sampling.distribution + Apache Commons RNG Sampling 1.0 Reference Package org.apache.commons.rng.sampling.distribution Modified: websites/production/commons/content/proper/commons-rng/commons-rng-sampling/xref/org/apache/commons/rng/sampling/package-frame.html ============================================================================== --- websites/production/commons/content/proper/commons-rng/commons-rng-sampling/xref/org/apache/commons/rng/sampling/package-frame.html (original) +++ websites/production/commons/content/proper/commons-rng/commons-rng-sampling/xref/org/apache/commons/rng/sampling/package-frame.html Mon Dec 12 16:27:09 2016 @@ -3,7 +3,7 @@ - Apache Commons RNG Sampling 1.0-SNAPSHOT Reference Package org.apache.commons.rng.sampling + Apache Commons RNG Sampling 1.0 Reference Package org.apache.commons.rng.sampling Modified: websites/production/commons/content/proper/commons-rng/commons-rng-sampling/xref/org/apache/commons/rng/sampling/package-summary.html ============================================================================== --- websites/production/commons/content/proper/commons-rng/commons-rng-sampling/xref/org/apache/commons/rng/sampling/package-summary.html (original) +++ websites/production/commons/content/proper/commons-rng/commons-rng-sampling/xref/org/apache/commons/rng/sampling/package-summary.html Mon Dec 12 16:27:09 2016 @@ -3,7 +3,7 @@ - Apache Commons RNG Sampling 1.0-SNAPSHOT Reference Package org.apache.commons.rng.sampling + Apache Commons RNG Sampling 1.0 Reference Package org.apache.commons.rng.sampling Modified: websites/production/commons/content/proper/commons-rng/commons-rng-sampling/xref/overview-frame.html ============================================================================== --- websites/production/commons/content/proper/commons-rng/commons-rng-sampling/xref/overview-frame.html (original) +++ websites/production/commons/content/proper/commons-rng/commons-rng-sampling/xref/overview-frame.html Mon Dec 12 16:27:09 2016 @@ -3,7 +3,7 @@ - Apache Commons RNG Sampling 1.0-SNAPSHOT Reference + Apache Commons RNG Sampling 1.0 Reference Modified: websites/production/commons/content/proper/commons-rng/commons-rng-sampling/xref/overview-summary.html ============================================================================== --- websites/production/commons/content/proper/commons-rng/commons-rng-sampling/xref/overview-summary.html (original) +++ websites/production/commons/content/proper/commons-rng/commons-rng-sampling/xref/overview-summary.html Mon Dec 12 16:27:09 2016 @@ -3,7 +3,7 @@ - Apache Commons RNG Sampling 1.0-SNAPSHOT Reference + Apache Commons RNG Sampling 1.0 Reference @@ -24,7 +24,7 @@ -

Apache Commons RNG Sampling 1.0-SNAPSHOT Reference

Apache Commons RNG Sampling 1.0 Reference

Modified: websites/production/commons/content/proper/commons-rng/commons-rng-simple/apidocs/allclasses-frame.html ============================================================================== --- websites/production/commons/content/proper/commons-rng/commons-rng-simple/apidocs/allclasses-frame.html (original) +++ websites/production/commons/content/proper/commons-rng/commons-rng-simple/apidocs/allclasses-frame.html Mon Dec 12 16:27:09 2016 @@ -4,7 +4,7 @@ -All Classes (Apache Commons RNG Simple 1.0-SNAPSHOT API) +All Classes (Apache Commons RNG Simple 1.0 API) Modified: websites/production/commons/content/proper/commons-rng/commons-rng-simple/apidocs/allclasses-noframe.html ============================================================================== --- websites/production/commons/content/proper/commons-rng/commons-rng-simple/apidocs/allclasses-noframe.html (original) +++ websites/production/commons/content/proper/commons-rng/commons-rng-simple/apidocs/allclasses-noframe.html Mon Dec 12 16:27:09 2016 @@ -4,7 +4,7 @@ -All Classes (Apache Commons RNG Simple 1.0-SNAPSHOT API) +All Classes (Apache Commons RNG Simple 1.0 API) Modified: websites/production/commons/content/proper/commons-rng/commons-rng-simple/apidocs/constant-values.html ============================================================================== --- websites/production/commons/content/proper/commons-rng/commons-rng-simple/apidocs/constant-values.html (original) +++ websites/production/commons/content/proper/commons-rng/commons-rng-simple/apidocs/constant-values.html Mon Dec 12 16:27:09 2016 @@ -4,7 +4,7 @@ -Constant Field Values (Apache Commons RNG Simple 1.0-SNAPSHOT API) +Constant Field Values (Apache Commons RNG Simple 1.0 API) @@ -12,7 +12,7 @@ @@ -12,7 +12,7 @@ @@ -12,7 +12,7 @@ @@ -12,7 +12,7 @@ @@ -12,7 +12,7 @@ @@ -12,7 +12,7 @@