RandomWrapper

Value Members

1. final def !=(arg0: Any): Boolean

   

   Definition Classes

   Any

2. final def ##(): Int

   

   Definition Classes

   Any

3. final def ==(arg0: Any): Boolean

   

   Definition Classes

   Any

4. final def asInstanceOf[T0]: T0

   

   Definition Classes

   Any

5. def choice[A](xs: TraversableOnce[A]): A

   

6. def getClass(): Class[_ <: AnyVal]

   

   Definition Classes

   AnyVal → Any

7. final def isInstanceOf[T0]: Boolean

   

   Definition Classes

   Any

8. def nextBytes(count: Int): Array[Byte]

   

   Creates an array of random bytes.

9. def nextDouble(startInclusive: Double, endInclusive: Double): Double

   

   Returns a random double within the specified range.

10. def nextExponential(lambda: Double): Double

    

    Exponential distribution.

    Exponential distribution. lambda is 1.0 divided by the desired mean. It should be nonzero. Returned values range from 0 to positive infinity if lambda is positive, and from negative infinity to 0 if lambda is negative.

11. def nextFloat(startInclusive: Float, endInclusive: Float): Float

    

    Returns a random float within the specified range.

12. def nextGaussian(mu: Double, sigma: Double): Double

    

    Gaussian distribution.

    Gaussian distribution. mu is the mean, and sigma is the standard deviation.

13. def nextInt(startInclusive: Int, endExclusive: Int): Int

    

    Returns a random integer within the specified range.

14. def nextLogNormal(mu: Double, sigma: Double): Double

    

    Log normal distribution.

    Log normal distribution. If you take the natural logarithm of this distribution, you’ll get a normal distribution with mean mu and standard deviation sigma. mu can have any value, and sigma must be greater than zero.

15. def nextLong(startInclusive: Long, endExclusive: Long): Long

    

    Returns a random long within the specified range.

16. def nextPareto(alpha: Double): Double

    

    Pareto distribution.

    Pareto distribution. alpha is the shape parameter.

17. def nextWeibull(alpha: Double, beta: Double): Double

    

    Weibull distribution.

    Weibull distribution. alpha is the scale parameter and beta is the shape parameter.

18. val random: Random

    

19. def randomAlphabetic(count: Int): String

    

20. def randomAlphanumeric(count: Int): String

    

21. def randomAscii(count: Int): String

    

22. def randomNumeric(count: Int): String

    

23. def randomString(count: Int, chars: String): String

    

24. def randomString(count: Int, chars: Array[Char]): String

    

25. def randomString(count: Int): String

    

26. def sampleWithReplacement[A, CC[X] <: TraversableOnce[X]](xs: CC[A], k: Int)(implicit cbf: CanBuildFrom[CC[A], A, CC[A]]): CC[A]

    

27. def sampleWithoutReplacement[A, CC[X] <: TraversableOnce[X]](xs: CC[A], k: Int)(implicit cbf: CanBuildFrom[CC[A], A, CC[A]]): CC[A]

    

28. def toString(): String

    

    Definition Classes

    Any