org.apache.commons.math3.special.Gamma.logGamma()方法的使用及代码示例

x33g5p2x  于2022-01-20 转载在 其他  
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本文整理了Java中org.apache.commons.math3.special.Gamma.logGamma()方法的一些代码示例，展示了Gamma.logGamma()的具体用法。这些代码示例主要来源于Github/Stackoverflow/Maven等平台，是从一些精选项目中提取出来的代码，具有较强的参考意义，能在一定程度帮忙到你。Gamma.logGamma()方法的具体详情如下：
包路径：org.apache.commons.math3.special.Gamma
类名称：Gamma
方法名：logGamma

Gamma.logGamma介绍

[英]Returns the value of log Γ(x) for x > 0.

For x ≤ 8, the implementation is based on the double precision implementation in the NSWC Library of Mathematics Subroutines, DGAMLN. For x > 8, the implementation is based on

代码示例

代码示例来源：origin: org.apache.commons/commons-math3

  1. /** Recompute the normalization factor. */
  2. private void recomputeZ() {
  3.   if (Double.isNaN(z)) {
  4.     z = Gamma.logGamma(alpha) + Gamma.logGamma(beta) - Gamma.logGamma(alpha + beta);
  5.   }
  6. }

代码示例来源：origin: org.apache.commons/commons-math3

  1. /**
  2.  * Creates a t distribution.
  3.  *
  4.  * @param rng Random number generator.
  5.  * @param degreesOfFreedom Degrees of freedom.
  6.  * @param inverseCumAccuracy the maximum absolute error in inverse
  7.  * cumulative probability estimates
  8.  * (defaults to {@link #DEFAULT_INVERSE_ABSOLUTE_ACCURACY}).
  9.  * @throws NotStrictlyPositiveException if {@code degreesOfFreedom <= 0}
  10.  * @since 3.1
  11.  */
  12. public TDistribution(RandomGenerator rng,
  13.            double degreesOfFreedom,
  14.            double inverseCumAccuracy)
  15.   throws NotStrictlyPositiveException {
  16.   super(rng);
  17.   if (degreesOfFreedom <= 0) {
  18.     throw new NotStrictlyPositiveException(LocalizedFormats.DEGREES_OF_FREEDOM,
  19.                         degreesOfFreedom);
  20.   }
  21.   this.degreesOfFreedom = degreesOfFreedom;
  22.   solverAbsoluteAccuracy = inverseCumAccuracy;
  23.   final double n = degreesOfFreedom;
  24.   final double nPlus1Over2 = (n + 1) / 2;
  25.   factor = Gamma.logGamma(nPlus1Over2) -
  26.        0.5 * (FastMath.log(FastMath.PI) + FastMath.log(n)) -
  27.        Gamma.logGamma(n / 2);
  28. }

代码示例来源：origin: org.apache.commons/commons-math3

  1. /**
  2.  * used by {@link #getNumericalMean()}
  3.  *
  4.  * @return the mean of this distribution
  5.  */
  6. protected double calculateNumericalMean() {
  7.   final double sh = getShape();
  8.   final double sc = getScale();
  9.   return sc * FastMath.exp(Gamma.logGamma(1 + (1 / sh)));
  10. }

代码示例来源：origin: deeplearning4j/nd4j

  1. ret = EXACT_STIRLING_ERRORS[(int) z2];
  2. } else {
  3.   ret = Gamma.logGamma(z + 1.0) - (z + 0.5) * FastMath.log(z) + z - HALF_LOG_2_PI;

代码示例来源：origin: org.apache.commons/commons-math3

  1. ret = EXACT_STIRLING_ERRORS[(int) z2];
  2. } else {
  3.   ret = Gamma.logGamma(z + 1.0) - (z + 0.5) * FastMath.log(z) +
  4.      z - HALF_LOG_2_PI;

代码示例来源：origin: org.apache.commons/commons-math3

  1. /**
  2.  * used by {@link #getNumericalVariance()}
  3.  *
  4.  * @return the variance of this distribution
  5.  */
  6. protected double calculateNumericalVariance() {
  7.   final double sh = getShape();
  8.   final double sc = getScale();
  9.   final double mn = getNumericalMean();
  10.   return (sc * sc) * FastMath.exp(Gamma.logGamma(1 + (2 / sh))) -
  11.       (mn * mn);
  12. }

代码示例来源：origin: org.apache.commons/commons-math3

  1. ret = FastMath.exp(-x + (a * FastMath.log(x)) - logGamma(a)) * ret;

代码示例来源：origin: org.apache.commons/commons-math3

  1. ret = 1.0;
  2. } else {
  3.   ret = FastMath.exp(-x + (a * FastMath.log(x)) - logGamma(a)) * sum;

代码示例来源：origin: org.apache.commons/commons-math3

  1. (Gamma.logGamma(ared) +
  2.        logGammaMinusLogGammaSum(ared, b));
  3. } else {
  4.         (Gamma.logGamma(ared) +
  5.         (Gamma.logGamma(bred) -
  6.         logGammaSum(ared, bred)));
  7.   } else {
  8.     return FastMath.log(prod1) +
  9.         Gamma.logGamma(ared) +
  10.         logGammaMinusLogGammaSum(ared, b);
  11.         (Gamma.logGamma(a) +
  12.         (Gamma.logGamma(bred) -
  13.          logGammaSum(a, bred)));
  14.   } else {
  15.     return Gamma.logGamma(a) +
  16.         logGammaMinusLogGammaSum(a, b);
  17.   return Gamma.logGamma(a) +
  18.       Gamma.logGamma(b) -
  19.       logGammaSum(a, b);
  20.   return Gamma.logGamma(a) +
  21.       logGammaMinusLogGammaSum(a, b);
  22. } else {

代码示例来源：origin: lenskit/lenskit

  1. pLL = (rating == 0) ? (-eThetaBeta) : Math.log(1 - Math.exp(-eThetaBeta));
  2. } else {
  3.   pLL = rating * Math.log(eThetaBeta) - eThetaBeta - Gamma.logGamma(rating + 1);

代码示例来源：origin: improbable-research/keanu

  1. @Override
  2. public DoubleTensor logGammaInPlace() {
  3.   value = Gamma.logGamma(value);
  4.   return this;
  5. }

代码示例来源：origin: geogebra/geogebra

  1. /** Recompute the normalization factor. */
  2. private void recomputeZ() {
  3.   if (Double.isNaN(z)) {
  4.     z = Gamma.logGamma(alpha) + Gamma.logGamma(beta) - Gamma.logGamma(alpha + beta);
  5.   }
  6. }

代码示例来源：origin: org.apache.mahout/mahout-mrlegacy

  1. @Override
  2. public double logP(double betaIJ) {
  3.  return Gamma.logGamma((df + 1.0) / 2.0)
  4.    - Math.log(df * Math.PI)
  5.    - Gamma.logGamma(df / 2.0)
  6.    - (df + 1.0) / 2.0 * Math.log1p(betaIJ * betaIJ);
  7. }

代码示例来源：origin: org.apache.mahout/mahout-mr

  1. @Override
  2. public double logP(double betaIJ) {
  3.  return Gamma.logGamma((df + 1.0) / 2.0)
  4.    - Math.log(df * Math.PI)
  5.    - Gamma.logGamma(df / 2.0)
  6.    - (df + 1.0) / 2.0 * Math.log1p(betaIJ * betaIJ);
  7. }

代码示例来源：origin: OpenGamma/Strata

  1. @Override
  2. public double applyAsDouble(double x) {
  3.  if (x > 0.0) {
  4.   return Math.exp(Gamma.logGamma(x));
  5.  }
  6.  return Math.PI / Math.sin(Math.PI * x) / applyAsDouble(1 - x);
  7. }

代码示例来源：origin: OpenGamma/Strata

  1. @Override
  2. public Double apply(Double x) {
  3.  ArgChecker.isTrue(x > 0, "x must be greater than zero");
  4.  return Gamma.logGamma(x);
  5. }

代码示例来源：origin: lenskit/lenskit

  1. pLL = (rating == 0) ? (-eThetaBeta) : Math.log(1 - Math.exp(-eThetaBeta));
  2. } else {
  3.   pLL = rating * Math.log(eThetaBeta) - eThetaBeta - Gamma.logGamma(rating + 1);

代码示例来源：origin: geogebra/geogebra

  1. /**
  2.  * used by {@link #getNumericalMean()}
  3.  *
  4.  * @return the mean of this distribution
  5.  */
  6. protected double calculateNumericalMean() {
  7.   final double sh = getShape();
  8.   final double sc = getScale();
  9.   return sc * Math.exp(Gamma.logGamma(1 + (1 / sh)));
  10. }

代码示例来源：origin: improbable-research/keanu

  1. @Test
  2. public void logGammaMatrixVertexValues() {
  3.   operatesOn2x2MatrixVertexValues(
  4.     new double[]{0.0, 0.1, 0.2, 0.3},
  5.     new double[]{Gamma.logGamma(0.0), Gamma.logGamma(0.1), Gamma.logGamma(0.2), Gamma.logGamma(0.3)},
  6.     DoubleVertex::logGamma
  7.   );
  8. }

代码示例来源：origin: geogebra/geogebra

  1. /**
  2.  * used by {@link #getNumericalVariance()}
  3.  *
  4.  * @return the variance of this distribution
  5.  */
  6. protected double calculateNumericalVariance() {
  7.   final double sh = getShape();
  8.   final double sc = getScale();
  9.   final double mn = getNumericalMean();
  10.   return (sc * sc) * Math.exp(Gamma.logGamma(1 + (2 / sh))) -
  11.       (mn * mn);
  12. }

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