本文整理汇总了Java中org.apache.commons.math.util.MathUtils.TWO_PI属性的典型用法代码示例。如果您正苦于以下问题：Java MathUtils.TWO_PI属性的具体用法？Java MathUtils.TWO_PI怎么用？Java MathUtils.TWO_PI使用的例子？那么, 这里精选的属性代码示例或许可以为您提供帮助。您也可以进一步了解该属性所在类org.apache.commons.math.util.MathUtils的用法示例。
在下文中一共展示了MathUtils.TWO_PI属性的3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: logBinomialProbability
/**
* Compute the PMF for a binomial distribution using the saddle point
* expansion.
*
* @param x the value at which the probability is evaluated.
* @param n the number of trials.
* @param p the probability of success.
* @param q the probability of failure (1 - p).
* @return log(p(x)).
*/
static double logBinomialProbability(int x, int n, double p, double q) {
double ret;
if (x == 0) {
if (p < 0.1) {
ret = -getDeviancePart(n, n * q) - n * p;
} else {
ret = n * Math.log(q);
}
} else if (x == n) {
if (q < 0.1) {
ret = -getDeviancePart(n, n * p) - n * q;
} else {
ret = n * Math.log(p);
}
} else {
ret = getStirlingError(n) - getStirlingError(x) -
getStirlingError(n - x) - getDeviancePart(x, n * p) -
getDeviancePart(n - x, n * q);
double f = (MathUtils.TWO_PI * x * (n - x)) / n;
ret = -0.5 * Math.log(f) + ret;
}
return ret;
}
示例2: logBinomialProbability
/**
* Compute the PMF for a binomial distribution using the saddle point
* expansion.
*
* @param x the value at which the probability is evaluated.
* @param n the number of trials.
* @param p the probability of success.
* @param q the probability of failure (1 - p).
* @return log(p(x)).
*/
static double logBinomialProbability(int x, int n, double p, double q) {
double ret;
if (x == 0) {
if (p < 0.1) {
ret = -getDeviancePart(n, n * q) - n * p;
} else {
ret = n * Math.log(q);
}
} else if (x == n) {
if (q < 0.1) {
ret = -getDeviancePart(n, n * p) - n * q;
} else {
ret = n * Math.log(p);
}
} else {
ret = getStirlingError(n) - getStirlingError(x) -
getStirlingError(n - x) - getDeviancePart(x, n * p) -
getDeviancePart(n - x, n * q);
double f = (MathUtils.TWO_PI * x * (n - x)) / n;
ret = -0.5 * Math.log(f) + ret;
}
return ret;
}
示例3: logBinomialProbability
/**
* Compute the PMF for a binomial distribution using the saddle point
* expansion.
*
* @param x the value at which the probability is evaluated.
* @param n the number of trials.
* @param p the probability of success.
* @param q the probability of failure (1 - p).
* @return log(p(x)).
*/
static double logBinomialProbability(int x, int n, double p, double q) {
double ret;
if (x == 0) {
if (p < 0.1) {
ret = -getDeviancePart(n, n * q) - n * p;
} else {
ret = n * FastMath.log(q);
}
} else if (x == n) {
if (q < 0.1) {
ret = -getDeviancePart(n, n * p) - n * q;
} else {
ret = n * FastMath.log(p);
}
} else {
ret = getStirlingError(n) - getStirlingError(x) -
getStirlingError(n - x) - getDeviancePart(x, n * p) -
getDeviancePart(n - x, n * q);
double f = (MathUtils.TWO_PI * x * (n - x)) / n;
ret = -0.5 * FastMath.log(f) + ret;
}
return ret;
}