本文整理汇总了Java中org.apache.commons.math3.special.Gamma.lanczos方法的典型用法代码示例。如果您正苦于以下问题：Java Gamma.lanczos方法的具体用法？Java Gamma.lanczos怎么用？Java Gamma.lanczos使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类org.apache.commons.math3.special.Gamma的用法示例。
在下文中一共展示了Gamma.lanczos方法的6个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Java代码示例。
示例1: logGamma
import org.apache.commons.math3.special.Gamma; //导入方法依赖的package包/类
public static double logGamma(double x) {
/*
* This is a copy of
* double Gamma.logGamma(double)
* prior to MATH-849
*/
double ret;
if (Double.isNaN(x) || (x <= 0.0)) {
ret = Double.NaN;
} else {
double sum = Gamma.lanczos(x);
double tmp = x + Gamma.LANCZOS_G + .5;
ret = ((x + .5) * FastMath.log(tmp)) - tmp +
HALF_LOG_2_PI + FastMath.log(sum / x);
}
return ret;
}
示例2: CustomGammaDistribution
import org.apache.commons.math3.special.Gamma; //导入方法依赖的package包/类
/**
* Creates a Gamma distribution.
*
* @param rng
* Random number generator.
* @param shape
* the shape parameter
* @param scale
* the scale parameter
* @param inverseCumAccuracy
* the maximum absolute error in inverse
* cumulative probability estimates (defaults to {@link #DEFAULT_INVERSE_ABSOLUTE_ACCURACY}).
* @throws NotStrictlyPositiveException
* if {@code shape <= 0} or {@code scale <= 0}.
* @since 3.1
*/
public CustomGammaDistribution(RandomGenerator rng, double shape, double scale, double inverseCumAccuracy)
throws NotStrictlyPositiveException
{
super(rng);
if (shape <= 0)
{
throw new NotStrictlyPositiveException(LocalizedFormats.SHAPE, shape);
}
if (scale <= 0)
{
throw new NotStrictlyPositiveException(LocalizedFormats.SCALE, scale);
}
this.shape = shape;
this.scale = scale;
this.solverAbsoluteAccuracy = inverseCumAccuracy;
this.shiftedShape = shape + Gamma.LANCZOS_G + 0.5;
final double aux = FastMath.E / (2.0 * FastMath.PI * shiftedShape);
this.densityPrefactor2 = shape * FastMath.sqrt(aux) / Gamma.lanczos(shape);
this.densityPrefactor1 = this.densityPrefactor2 / scale * FastMath.pow(shiftedShape, -shape) *
FastMath.exp(shape + Gamma.LANCZOS_G);
this.minY = shape + Gamma.LANCZOS_G - FastMath.log(Double.MAX_VALUE);
this.maxLogY = FastMath.log(Double.MAX_VALUE) / (shape - 1.0);
}
示例3: GammaDistribution
import org.apache.commons.math3.special.Gamma; //导入方法依赖的package包/类
/**
* Creates a Gamma distribution.
*
* @param rng Random number generator.
* @param shape the shape parameter
* @param scale the scale parameter
* @param inverseCumAccuracy the maximum absolute error in inverse
* cumulative probability estimates (defaults to
* {@link #DEFAULT_INVERSE_ABSOLUTE_ACCURACY}).
* @throws NotStrictlyPositiveException if {@code shape <= 0} or
* {@code scale <= 0}.
* @since 3.1
*/
public GammaDistribution(RandomGenerator rng,
double shape,
double scale,
double inverseCumAccuracy)
throws NotStrictlyPositiveException {
super(rng);
if (shape <= 0) {
throw new NotStrictlyPositiveException(LocalizedFormats.SHAPE, shape);
}
if (scale <= 0) {
throw new NotStrictlyPositiveException(LocalizedFormats.SCALE, scale);
}
this.shape = shape;
this.scale = scale;
this.solverAbsoluteAccuracy = inverseCumAccuracy;
this.shiftedShape = shape + Gamma.LANCZOS_G + 0.5;
final double aux = FastMath.E / (2.0 * FastMath.PI * shiftedShape);
this.densityPrefactor2 = shape * FastMath.sqrt(aux) / Gamma.lanczos(shape);
this.logDensityPrefactor2 = FastMath.log(shape) + 0.5 * FastMath.log(aux) -
FastMath.log(Gamma.lanczos(shape));
this.densityPrefactor1 = this.densityPrefactor2 / scale *
FastMath.pow(shiftedShape, -shape) *
FastMath.exp(shape + Gamma.LANCZOS_G);
this.logDensityPrefactor1 = this.logDensityPrefactor2 - FastMath.log(scale) -
FastMath.log(shiftedShape) * shape +
shape + Gamma.LANCZOS_G;
this.minY = shape + Gamma.LANCZOS_G - FastMath.log(Double.MAX_VALUE);
this.maxLogY = FastMath.log(Double.MAX_VALUE) / (shape - 1.0);
}
示例4: GammaDistribution
import org.apache.commons.math3.special.Gamma; //导入方法依赖的package包/类
/**
* Creates a Gamma distribution.
*
* @param rng Random number generator.
* @param shape the shape parameter
* @param scale the scale parameter
* @param inverseCumAccuracy the maximum absolute error in inverse
* cumulative probability estimates (defaults to
* {@link #DEFAULT_INVERSE_ABSOLUTE_ACCURACY}).
* @throws NotStrictlyPositiveException if {@code shape <= 0} or
* {@code scale <= 0}.
* @since 3.1
*/
public GammaDistribution(RandomGenerator rng,
double shape,
double scale,
double inverseCumAccuracy)
throws NotStrictlyPositiveException {
super(rng);
if (shape <= 0) {
throw new NotStrictlyPositiveException(LocalizedFormats.SHAPE, shape);
}
if (scale <= 0) {
throw new NotStrictlyPositiveException(LocalizedFormats.SCALE, scale);
}
this.shape = shape;
this.scale = scale;
this.solverAbsoluteAccuracy = inverseCumAccuracy;
this.shiftedShape = shape + Gamma.LANCZOS_G + 0.5;
final double aux = FastMath.E / (2.0 * FastMath.PI * shiftedShape);
this.densityPrefactor2 = shape * FastMath.sqrt(aux) / Gamma.lanczos(shape);
this.densityPrefactor1 = this.densityPrefactor2 / scale *
FastMath.pow(shiftedShape, -shape) *
FastMath.exp(shape + Gamma.LANCZOS_G);
this.minY = shape + Gamma.LANCZOS_G - FastMath.log(Double.MAX_VALUE);
this.maxLogY = FastMath.log(Double.MAX_VALUE) / (shape - 1.0);
}
示例5: logGamma
import org.apache.commons.math3.special.Gamma; //导入方法依赖的package包/类
/**
* Copied from Apache Commons FastMath. Removed support for NaN and x < 0.5.
* <p>
* Returns the value of log Γ(x) for x > 0.
* </p>
* <p>
* For x ≤ 8, the implementation is based on the double precision
* implementation in the <em>NSWC Library of Mathematics Subroutines</em>,
* {@code DGAMLN}. For x > 8, the implementation is based on
* </p>
* <ul>
* <li><a href="http://mathworld.wolfram.com/GammaFunction.html">Gamma
* Function</a>, equation (28).</li>
* <li><a href="http://mathworld.wolfram.com/LanczosApproximation.html">
* Lanczos Approximation</a>, equations (1) through (5).</li>
* <li><a href="http://my.fit.edu/~gabdo/gamma.txt">Paul Godfrey, A note on
* the computation of the convergent Lanczos complex Gamma
* approximation</a></li>
* </ul>
*
* @param x
* Argument.
* @return the value of {@code log(Gamma(x))}, {@code Double.NaN} if
* {@code x <= 0.0}.
*/
private static double logGamma(double x)
{
// if (Double.isNaN(x) || (x <= 0.0))
// {
// return Double.NaN;
// }
// else if (x < 0.5)
// {
// return Gamma.logGamma1p(x) - Math.log(x);
// }
// else
if (x <= 2.5)
{
return Gamma.logGamma1p((x - 0.5) - 0.5);
}
else if (x <= 8.0)
{
final int n = (int) FastMath.floor(x - 1.5);
double prod = 1.0;
for (int i = 1; i <= n; i++)
{
prod *= x - i;
}
return Gamma.logGamma1p(x - (n + 1)) + Math.log(prod);
}
else
{
double sum = Gamma.lanczos(x);
double tmp = x + Gamma.LANCZOS_G + .5;
return ((x + .5) * Math.log(tmp)) - tmp + HALF_LOG_2_PI + Math.log(sum / x);
}
}
示例6: init
import org.apache.commons.math3.special.Gamma; //导入方法依赖的package包/类
public void init() {
this.shiftedShape = shape + Gamma.LANCZOS_G + 0.5;
final double aux = FastMath.E / (2.0 * FastMath.PI * shiftedShape);
this.densityPrefactor2 = shape * FastMath.sqrt(aux) / Gamma.lanczos(shape);
this.logDensityPrefactor2 = FastMath.log(shape) + 0.5 * FastMath.log(aux) -
FastMath.log(Gamma.lanczos(shape));
this.densityPrefactor1 = this.densityPrefactor2 / scale *
FastMath.pow(shiftedShape, -shape) *
FastMath.exp(shape + Gamma.LANCZOS_G);
this.logDensityPrefactor1 = this.logDensityPrefactor2 - FastMath.log(scale) -
FastMath.log(shiftedShape) * shape +
shape + Gamma.LANCZOS_G;
this.minY = shape + Gamma.LANCZOS_G - FastMath.log(Double.MAX_VALUE);
this.maxLogY = FastMath.log(Double.MAX_VALUE) / (shape - 1.0);
}