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 choice[A](xs: Array[A]): A

   

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

   

   Definition Classes

   AnyVal → Any

8. final def isInstanceOf[T0]: Boolean

   

   Definition Classes

   Any

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

   

   Creates an array of random bytes.

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

    

    Returns a random double within the specified range.

11. 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.

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

    

    Returns a random float within the specified range.

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

    

    Gaussian distribution.

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

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

    

    Returns a random integer within the specified range.

15. 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.

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

    

    Returns a random long within the specified range.

17. def nextNormal(mu: Double, sigma: Double): Double

    

18. def nextPareto(alpha: Double): Double

    

    Pareto distribution.

    Pareto distribution. alpha is the shape parameter.

19. def nextTriangular(low: Double, high: Double, mode: Double): Double

    

    Triangular distribution.

    Triangular distribution. Continuous distribution bounded by given lower and upper limits, and having a given mode value in-between.

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

    

    Weibull distribution.

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

21. val random: Random

    

22. def randomAlphabetic(count: Int): String

    

23. def randomAlphanumeric(count: Int): String

    

24. def randomAscii(count: Int): String

    

25. def randomNumeric(count: Int): String

    

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

    

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

    

28. def randomString(count: Int): String

    

29. def sample[A, CC[X] <: TraversableOnce[X]](xs: CC[A], k: Int, withReplacement: Boolean = false)(implicit cbf: CanBuildFrom[CC[A], A, CC[A]]): CC[A]

    

30. def sample[A](xs: Array[A], k: Int, withReplacement: Boolean)(implicit arg0: ClassTag[A]): Array[A]

    

31. def sample[A](xs: Array[A], k: Int)(implicit arg0: ClassTag[A]): Array[A]

    

32. def shuffleArray[A](xs: Array[A])(implicit arg0: ClassTag[A]): Array[A]

    

33. def toString(): String

    

    Definition Classes

    Any