diff --git a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/distribution/EmpiricalDistribution.java b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/distribution/EmpiricalDistribution.java index cca2c1900..8058ea50a 100644 --- a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/distribution/EmpiricalDistribution.java +++ b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/distribution/EmpiricalDistribution.java @@ -551,8 +551,8 @@ public final class EmpiricalDistribution extends AbstractRealDistribution stats.getVariance() == 0) { return new ConstantContinuousDistribution(stats.getMean()); } else { - return new NormalDistribution(stats.getMean(), - stats.getStandardDeviation()); + return NormalDistribution.of(stats.getMean(), + stats.getStandardDeviation()); } }; } diff --git a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/distribution/MultivariateNormalDistribution.java b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/distribution/MultivariateNormalDistribution.java index 6a72dbb85..b216816e5 100644 --- a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/distribution/MultivariateNormalDistribution.java +++ b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/distribution/MultivariateNormalDistribution.java @@ -181,7 +181,7 @@ public class MultivariateNormalDistribution public MultivariateRealDistribution.Sampler createSampler(final UniformRandomProvider rng) { return new MultivariateRealDistribution.Sampler() { /** Normal distribution. */ - private final ContinuousDistribution.Sampler gauss = new NormalDistribution(0, 1).createSampler(rng); + private final ContinuousDistribution.Sampler gauss = NormalDistribution.of(0, 1).createSampler(rng); /** {@inheritDoc} */ @Override diff --git a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/optim/nonlinear/scalar/noderiv/CMAESOptimizer.java b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/optim/nonlinear/scalar/noderiv/CMAESOptimizer.java index 83b036e5d..eb92f7b41 100644 --- a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/optim/nonlinear/scalar/noderiv/CMAESOptimizer.java +++ b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/optim/nonlinear/scalar/noderiv/CMAESOptimizer.java @@ -246,7 +246,7 @@ public class CMAESOptimizer this.isActiveCMA = isActiveCMA; this.diagonalOnly = diagonalOnly; this.checkFeasableCount = Math.max(0, checkFeasableCount); - this.random = new NormalDistribution(0, 1).createSampler(rng); + this.random = NormalDistribution.of(0, 1).createSampler(rng); this.generateStatistics = generateStatistics; } diff --git a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/correlation/PearsonsCorrelation.java b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/correlation/PearsonsCorrelation.java index 34c2f6d78..eda73a64d 100644 --- a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/correlation/PearsonsCorrelation.java +++ b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/correlation/PearsonsCorrelation.java @@ -192,7 +192,7 @@ public class PearsonsCorrelation { * @throws NullPointerException if this instance was created with no data. */ public RealMatrix getCorrelationPValues() { - TDistribution tDistribution = new TDistribution(nObs - 2); + TDistribution tDistribution = TDistribution.of(nObs - 2); int nVars = correlationMatrix.getColumnDimension(); double[][] out = new double[nVars][nVars]; for (int i = 0; i < nVars; i++) { diff --git a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/BinomialTest.java b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/BinomialTest.java index 011b7aa31..80650c9b4 100644 --- a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/BinomialTest.java +++ b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/BinomialTest.java @@ -119,7 +119,7 @@ public class BinomialTest { throw new NullArgumentException(); } - final BinomialDistribution distribution = new BinomialDistribution(numberOfTrials, probability); + final BinomialDistribution distribution = BinomialDistribution.of(numberOfTrials, probability); switch (alternativeHypothesis) { case GREATER_THAN: return 1 - distribution.cumulativeProbability(numberOfSuccesses - 1); diff --git a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/ChiSquareTest.java b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/ChiSquareTest.java index 92f43a0a1..e695d3fac 100644 --- a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/ChiSquareTest.java +++ b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/ChiSquareTest.java @@ -157,7 +157,7 @@ public class ChiSquareTest { // pass a null rng to avoid unneeded overhead as we will not sample from this distribution final ChiSquaredDistribution distribution = - new ChiSquaredDistribution(expected.length - 1.0); + ChiSquaredDistribution.of(expected.length - 1.0); return 1.0 - distribution.cumulativeProbability(chiSquare(expected, observed)); } @@ -332,7 +332,7 @@ public class ChiSquareTest { checkArray(counts); double df = ((double) counts.length -1) * ((double) counts[0].length - 1); // pass a null rng to avoid unneeded overhead as we will not sample from this distribution - final ChiSquaredDistribution distribution = new ChiSquaredDistribution(df); + final ChiSquaredDistribution distribution = ChiSquaredDistribution.of(df); return 1 - distribution.cumulativeProbability(chiSquare(counts)); } @@ -536,7 +536,7 @@ public class ChiSquareTest { // pass a null rng to avoid unneeded overhead as we will not sample from this distribution final ChiSquaredDistribution distribution = - new ChiSquaredDistribution((double) observed1.length - 1); + ChiSquaredDistribution.of((double) observed1.length - 1); return 1 - distribution.cumulativeProbability( chiSquareDataSetsComparison(observed1, observed2)); diff --git a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/GTest.java b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/GTest.java index baaefb3ae..6dddb4a1e 100644 --- a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/GTest.java +++ b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/GTest.java @@ -154,7 +154,7 @@ public class GTest { // pass a null rng to avoid unneeded overhead as we will not sample from this distribution final ChiSquaredDistribution distribution = - new ChiSquaredDistribution(expected.length - 1.0); + ChiSquaredDistribution.of(expected.length - 1.0); return 1.0 - distribution.cumulativeProbability(g(expected, observed)); } @@ -185,7 +185,7 @@ public class GTest { // pass a null rng to avoid unneeded overhead as we will not sample from this distribution final ChiSquaredDistribution distribution = - new ChiSquaredDistribution(expected.length - 2.0); + ChiSquaredDistribution.of(expected.length - 2.0); return 1.0 - distribution.cumulativeProbability(g(expected, observed)); } @@ -475,7 +475,7 @@ public class GTest { // pass a null rng to avoid unneeded overhead as we will not sample from this distribution final ChiSquaredDistribution distribution = - new ChiSquaredDistribution((double) observed1.length - 1); + ChiSquaredDistribution.of((double) observed1.length - 1); return 1 - distribution.cumulativeProbability( gDataSetsComparison(observed1, observed2)); } diff --git a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/MannWhitneyUTest.java b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/MannWhitneyUTest.java index be1456368..2f1cdd306 100644 --- a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/MannWhitneyUTest.java +++ b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/MannWhitneyUTest.java @@ -181,7 +181,7 @@ public class MannWhitneyUTest { // No try-catch or advertised exception because args are valid // pass a null rng to avoid unneeded overhead as we will not sample from this distribution - final NormalDistribution standardNormal = new NormalDistribution(0, 1); + final NormalDistribution standardNormal = NormalDistribution.of(0, 1); return 2 * standardNormal.cumulativeProbability(z); } diff --git a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/OneWayAnova.java b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/OneWayAnova.java index e76cb2095..d47742797 100644 --- a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/OneWayAnova.java +++ b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/OneWayAnova.java @@ -126,7 +126,7 @@ public class OneWayAnova { final AnovaStats a = anovaStats(categoryData); // No try-catch or advertised exception because args are valid // pass a null rng to avoid unneeded overhead as we will not sample from this distribution - final FDistribution fdist = new FDistribution(a.dfbg, a.dfwg); + final FDistribution fdist = FDistribution.of(a.dfbg, a.dfwg); return 1.0 - fdist.cumulativeProbability(a.f); } @@ -168,7 +168,7 @@ public class OneWayAnova { final AnovaStats a = anovaStats(categoryData, allowOneElementData); // pass a null rng to avoid unneeded overhead as we will not sample from this distribution - final FDistribution fdist = new FDistribution(a.dfbg, a.dfwg); + final FDistribution fdist = FDistribution.of(a.dfbg, a.dfwg); return 1.0 - fdist.cumulativeProbability(a.f); } diff --git a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/TTest.java b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/TTest.java index 9854538cd..be4dc9094 100644 --- a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/TTest.java +++ b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/TTest.java @@ -1057,7 +1057,7 @@ public class TTest { final double t = AccurateMath.abs(t(m, mu, v, n)); // pass a null rng to avoid unneeded overhead as we will not sample from this distribution - final TDistribution distribution = new TDistribution(n - 1); + final TDistribution distribution = TDistribution.of(n - 1); return 2.0 * distribution.cumulativeProbability(-t); } @@ -1087,7 +1087,7 @@ public class TTest { final double t = AccurateMath.abs(t(m1, m2, v1, v2, n1, n2)); final double degreesOfFreedom = df(v1, v2, n1, n2); // pass a null rng to avoid unneeded overhead as we will not sample from this distribution - final TDistribution distribution = new TDistribution(degreesOfFreedom); + final TDistribution distribution = TDistribution.of(degreesOfFreedom); return 2.0 * distribution.cumulativeProbability(-t); } @@ -1117,7 +1117,7 @@ public class TTest { final double t = AccurateMath.abs(homoscedasticT(m1, m2, v1, v2, n1, n2)); final double degreesOfFreedom = n1 + n2 - 2; // pass a null rng to avoid unneeded overhead as we will not sample from this distribution - final TDistribution distribution = new TDistribution(degreesOfFreedom); + final TDistribution distribution = TDistribution.of(degreesOfFreedom); return 2.0 * distribution.cumulativeProbability(-t); } diff --git a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/WilcoxonSignedRankTest.java b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/WilcoxonSignedRankTest.java index 8d7141d78..0d1fdd46f 100644 --- a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/WilcoxonSignedRankTest.java +++ b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/inference/WilcoxonSignedRankTest.java @@ -253,7 +253,7 @@ public class WilcoxonSignedRankTest { // No try-catch or advertised exception because args are valid // pass a null rng to avoid unneeded overhead as we will not sample from this distribution - final NormalDistribution standardNormal = new NormalDistribution(0, 1); + final NormalDistribution standardNormal = NormalDistribution.of(0, 1); return 2*standardNormal.cumulativeProbability(z); } diff --git a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/interval/AgrestiCoullInterval.java b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/interval/AgrestiCoullInterval.java index 3ddb4cb60..2e60415d1 100644 --- a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/interval/AgrestiCoullInterval.java +++ b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/interval/AgrestiCoullInterval.java @@ -34,7 +34,7 @@ public class AgrestiCoullInterval implements BinomialConfidenceInterval { public ConfidenceInterval createInterval(int numberOfTrials, int numberOfSuccesses, double confidenceLevel) { IntervalUtils.checkParameters(numberOfTrials, numberOfSuccesses, confidenceLevel); final double alpha = (1.0 - confidenceLevel) / 2; - final NormalDistribution normalDistribution = new NormalDistribution(0, 1); + final NormalDistribution normalDistribution = NormalDistribution.of(0, 1); final double z = normalDistribution.inverseCumulativeProbability(1 - alpha); final double zSquared = AccurateMath.pow(z, 2); final double modifiedNumberOfTrials = numberOfTrials + zSquared; diff --git a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/interval/ClopperPearsonInterval.java b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/interval/ClopperPearsonInterval.java index 20f491391..a28522cdc 100644 --- a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/interval/ClopperPearsonInterval.java +++ b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/interval/ClopperPearsonInterval.java @@ -40,16 +40,16 @@ public class ClopperPearsonInterval implements BinomialConfidenceInterval { final double alpha = 0.5 * (1 - confidenceLevel); if (numberOfSuccesses > 0) { - final FDistribution distributionLowerBound = new FDistribution(2.0 * (numberOfTrials - numberOfSuccesses + 1), - 2.0 * numberOfSuccesses); + final FDistribution distributionLowerBound = FDistribution.of(2.0 * (numberOfTrials - numberOfSuccesses + 1), + 2.0 * numberOfSuccesses); final double fValueLowerBound = distributionLowerBound.inverseCumulativeProbability(1 - alpha); lowerBound = numberOfSuccesses / (numberOfSuccesses + (numberOfTrials - numberOfSuccesses + 1) * fValueLowerBound); } if (numberOfSuccesses < numberOfTrials) { - final FDistribution distributionUpperBound = new FDistribution(2.0 * (numberOfSuccesses + 1), - 2.0 * (numberOfTrials - numberOfSuccesses)); + final FDistribution distributionUpperBound = FDistribution.of(2.0 * (numberOfSuccesses + 1), + 2.0 * (numberOfTrials - numberOfSuccesses)); final double fValueUpperBound = distributionUpperBound.inverseCumulativeProbability(1 - alpha); upperBound = (numberOfSuccesses + 1) * fValueUpperBound / (numberOfTrials - numberOfSuccesses + (numberOfSuccesses + 1) * fValueUpperBound); diff --git a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/interval/NormalApproximationInterval.java b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/interval/NormalApproximationInterval.java index 75533e4a7..c9e48321b 100644 --- a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/interval/NormalApproximationInterval.java +++ b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/interval/NormalApproximationInterval.java @@ -36,7 +36,7 @@ public class NormalApproximationInterval implements BinomialConfidenceInterval { IntervalUtils.checkParameters(numberOfTrials, numberOfSuccesses, confidenceLevel); final double mean = (double) numberOfSuccesses / (double) numberOfTrials; final double alpha = (1.0 - confidenceLevel) / 2; - final NormalDistribution normalDistribution = new NormalDistribution(0, 1); + final NormalDistribution normalDistribution = NormalDistribution.of(0, 1); final double difference = normalDistribution.inverseCumulativeProbability(1 - alpha) * AccurateMath.sqrt(1.0 / numberOfTrials * mean * (1 - mean)); return new ConfidenceInterval(mean - difference, mean + difference, confidenceLevel); diff --git a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/interval/WilsonScoreInterval.java b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/interval/WilsonScoreInterval.java index 713f46e60..0fa89ecc5 100644 --- a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/interval/WilsonScoreInterval.java +++ b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/interval/WilsonScoreInterval.java @@ -32,7 +32,7 @@ public class WilsonScoreInterval implements BinomialConfidenceInterval { public ConfidenceInterval createInterval(int numberOfTrials, int numberOfSuccesses, double confidenceLevel) { IntervalUtils.checkParameters(numberOfTrials, numberOfSuccesses, confidenceLevel); final double alpha = (1 - confidenceLevel) / 2; - final NormalDistribution normalDistribution = new NormalDistribution(0, 1); + final NormalDistribution normalDistribution = NormalDistribution.of(0, 1); final double z = normalDistribution.inverseCumulativeProbability(1 - alpha); final double zSquared = z * z; final double oneOverNumTrials = 1d / numberOfTrials; diff --git a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/regression/SimpleRegression.java b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/regression/SimpleRegression.java index c4b18dd3b..981f14d85 100644 --- a/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/regression/SimpleRegression.java +++ b/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/stat/regression/SimpleRegression.java @@ -698,7 +698,7 @@ public class SimpleRegression implements Serializable, UpdatingMultipleLinearReg alpha, 0, 1); } // No advertised NotStrictlyPositiveException here - will return NaN above - TDistribution distribution = new TDistribution(n - 2d); + TDistribution distribution = TDistribution.of(n - 2d); return getSlopeStdErr() * distribution.inverseCumulativeProbability(1d - alpha / 2d); } @@ -730,7 +730,7 @@ public class SimpleRegression implements Serializable, UpdatingMultipleLinearReg return Double.NaN; } // No advertised NotStrictlyPositiveException here - will return NaN above - TDistribution distribution = new TDistribution(n - 2d); + TDistribution distribution = TDistribution.of(n - 2d); return 2d * (1.0 - distribution.cumulativeProbability( AccurateMath.abs(getSlope()) / getSlopeStdErr())); }