本文整理汇总了Java中org.apache.commons.math.special.Gamma.regularizedGammaQ方法的典型用法代码示例。如果您正苦于以下问题：Java Gamma.regularizedGammaQ方法的具体用法？Java Gamma.regularizedGammaQ怎么用？Java Gamma.regularizedGammaQ使用的例子？那么恭喜您, 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.math.special.Gamma的用法示例。
在下文中一共展示了Gamma.regularizedGammaQ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: cumulativeProbability
import org.apache.commons.math.special.Gamma; //导入方法依赖的package包/类
/**
* The probability distribution function P(X <= x) for a Poisson distribution.
*
* @param x the value at which the PDF is evaluated.
* @return Poisson distribution function evaluated at x
* @throws MathException if the cumulative probability can not be
* computed due to convergence or other numerical errors.
*/
public double cumulativeProbability(int x) throws MathException {
if (x < 0) {
return 0;
}
if (x == Integer.MAX_VALUE) {
return 1;
}
return Gamma.regularizedGammaQ((double)x + 1, mean,
1E-12, Integer.MAX_VALUE);
}
示例2: cumulativeProbability
import org.apache.commons.math.special.Gamma; //导入方法依赖的package包/类
/**
* The probability distribution function P(X <= x) for a Poisson
* distribution.
*
* @param x the value at which the PDF is evaluated.
* @return Poisson distribution function evaluated at x
* @throws MathException if the cumulative probability can not be computed
* due to convergence or other numerical errors.
*/
@Override
public double cumulativeProbability(int x) throws MathException {
if (x < 0) {
return 0;
}
if (x == Integer.MAX_VALUE) {
return 1;
}
return Gamma.regularizedGammaQ((double) x + 1, mean, epsilon, maxIterations);
}
示例3: cumulativeProbability
import org.apache.commons.math.special.Gamma; //导入方法依赖的package包/类
/**
* The probability distribution function P(X <= x) for a Poisson
* distribution.
*
* @param x the value at which the PDF is evaluated.
* @return Poisson distribution function evaluated at x
* @throws MathException if the cumulative probability can not be computed
* due to convergence or other numerical errors.
*/
@Override
public double cumulativeProbability(int x) throws MathException {
if (x < 0) {
return 0;
}
if (x == Integer.MAX_VALUE) {
return 1;
}
return Gamma.regularizedGammaQ((double) x + 1, mean, 1E-12,
Integer.MAX_VALUE);
}
示例4: cumulativeProbability
import org.apache.commons.math.special.Gamma; //导入方法依赖的package包/类
/**
* The probability distribution function {@code P(X <= x)} for a Poisson
* distribution.
*
* @param x Value at which the PDF is evaluated.
* @return the Poisson distribution function evaluated at {@code x}.
* @throws MathException if the cumulative probability cannot be computed
* due to convergence or other numerical errors.
*/
@Override
public double cumulativeProbability(int x) throws MathException {
if (x < 0) {
return 0;
}
if (x == Integer.MAX_VALUE) {
return 1;
}
return Gamma.regularizedGammaQ((double) x + 1, mean, epsilon, maxIterations);
}
示例5: cumulativeProbability
import org.apache.commons.math.special.Gamma; //导入方法依赖的package包/类
/**
* The probability distribution function P(X <= x) for a Poisson
* distribution.
*
* @param x the value at which the PDF is evaluated.
* @return Poisson distribution function evaluated at x
* @throws MathException if the cumulative probability can not be computed
* due to convergence or other numerical errors.
*/
@Override
public double cumulativeProbability(int x) throws MathException {
if (x < 0) {
return 0;
}
if (x == Integer.MAX_VALUE) {
return 1;
}
return Gamma.regularizedGammaQ((double) x + 1, mean, epsilon, maxIterations);
}
示例6: cumulativeProbability
import org.apache.commons.math.special.Gamma; //导入方法依赖的package包/类
/**
* The probability distribution function P(X <= x) for a Poisson distribution.
*
* @param x the value at which the PDF is evaluated.
* @return Poisson distribution function evaluated at x
* @throws MathException if the cumulative probability can not be
* computed due to convergence or other numerical errors.
*/
@Override
public double cumulativeProbability(int x) throws MathException {
if (x < 0) {
return 0;
}
if (x == Integer.MAX_VALUE) {
return 1;
}
return Gamma.regularizedGammaQ((double)x + 1, mean,
1E-12, Integer.MAX_VALUE);
}