本文整理汇总了Java中org.apache.commons.math3.exception.util.LocalizedFormats.MEAN属性的典型用法代码示例。如果您正苦于以下问题：Java LocalizedFormats.MEAN属性的具体用法？Java LocalizedFormats.MEAN怎么用？Java LocalizedFormats.MEAN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.math3.exception.util.LocalizedFormats的用法示例。
在下文中一共展示了LocalizedFormats.MEAN属性的2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: PoissonDistribution
/**
* Creates a new Poisson distribution with specified mean, convergence
* criterion and maximum number of iterations.
*
* @param rng Random number generator.
* @param p Poisson mean.
* @param epsilon Convergence criterion for cumulative probabilities.
* @param maxIterations the maximum number of iterations for cumulative
* probabilities.
* @throws NotStrictlyPositiveException if {@code p <= 0}.
* @since 3.1
*/
public PoissonDistribution(RandomGenerator rng,
double p,
double epsilon,
int maxIterations)
throws NotStrictlyPositiveException {
super(rng);
if (p <= 0) {
throw new NotStrictlyPositiveException(LocalizedFormats.MEAN, p);
}
mean = p;
this.epsilon = epsilon;
this.maxIterations = maxIterations;
// Use the same RNG instance as the parent class.
normal = new NormalDistribution(rng, p, FastMath.sqrt(p),
NormalDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY);
exponential = new ExponentialDistribution(rng, 1,
ExponentialDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY);
}
示例2: ExponentialDistribution
/**
* Creates an exponential distribution.
*
* @param rng Random number generator.
* @param mean Mean of this distribution.
* @param inverseCumAccuracy Maximum absolute error in inverse
* cumulative probability estimates (defaults to
* {@link #DEFAULT_INVERSE_ABSOLUTE_ACCURACY}).
* @throws NotStrictlyPositiveException if {@code mean <= 0}.
* @since 3.1
*/
public ExponentialDistribution(RandomGenerator rng,
double mean,
double inverseCumAccuracy)
throws NotStrictlyPositiveException {
super(rng);
if (mean <= 0) {
throw new NotStrictlyPositiveException(LocalizedFormats.MEAN, mean);
}
this.mean = mean;
logMean = FastMath.log(mean);
solverAbsoluteAccuracy = inverseCumAccuracy;
}