maths

Value Members

1. object ArrayOps extends ArrayOps

2. object Digamma

3. object FactorialCache

4. object LogGamma

5. object Probit

6. def almostEquals(d1: Double, d2: Double, epsilon: Double = 0.000001): Boolean

   Numbers that are closer than this are considered equal

7. def beta(a: Double, b: Double): Double

8. def cosh(a: Double): Double

9. def digamma(x: Double): Double

10. def entropy(p: Array[Double]): Double

    Return the entropy of the normalized distribution p.

    Return the entropy of the normalized distribution p. The log is w.r.t. base 2.

11. def factorial(n: Int): Double

12. def gamma(x: Double): Double

13. def jensenShannonDivergence(p1: Array[Double], p2: Array[Double]): Double

    Returns the Jensen-Shannon divergence.

14. def klDivergence(p1: Array[Double], p2: Array[Double]): Double

    Returns the KL divergence, K(p1 || p2).

    Returns the KL divergence, K(p1 || p2). The log is w.r.t. base 2.

    *Note*: If any value in p2 is 0.0 then the KL-divergence is infinite.

15. val log2: Double

16. def logBeta(a: Double, b: Double): Double

17. def logBinom(x: Int, n: Int, p: Double): Double

    Computes p(x;n,p) where x~B(n,p)

18. def logFactorial(n: Int): Double

19. def logGamma(xa: Double): Double

20. def logit(p: Double): Double

21. def nextBeta(alpha: Double, beta: Double)(implicit r: Random): Double

    Return a random double drawn from a Beta distribution with mean a/(a+b) and variance ab/((a+b+1)(a+b)^2).

22. def nextBitSet(size: Int, p: Double)(implicit r: Random): BitSet

    Return a random BitSet with "size" bits, each having probability p of being true.

23. def nextBoolean(p: Double)(implicit r: Random): Boolean

    Return a random boolean, with probability p of being true.

24. def nextBoolean(implicit r: Random): Boolean

    Return a random boolean, equally likely to be true or false.

25. def nextChiSq(df: Int, lambda: Double)(implicit r: Random): Double

    Return a random double drawn from an Chi-squared distribution with mean df+lambda and variance 2*df.

26. def nextChiSq(df: Int)(implicit r: Random): Double

    Return a random double drawn from an Chi-squared distribution with mean df and variance 2*df.

27. def nextChiSq(implicit r: Random): Double

    Return a random double drawn from an Chi-squarted distribution with mean 1 and variance 2.

    Return a random double drawn from an Chi-squarted distribution with mean 1 and variance 2. Equivalent to nextChiSq(1)

28. def nextDiscrete(a: Array[Double], sum: Double)(implicit r: Random): Int

    draw a single sample from (unnormalized) multinomial "a", with normalizing factor "sum".

29. def nextDiscrete(a: Array[Double])(implicit r: Random): Int

    Draw a single sample from multinomial "a".

    Draw a single sample from multinomial "a". Assumes that the elements of "a" already sum to 1.0.

30. def nextExp(beta: Double, lambda: Double)(implicit r: Random): Double

    Return a random double drawn from an Exponential distribution with mean beta+lambda and variance beta^2.

31. def nextExp(beta: Double)(implicit r: Random): Double

    Return a random double drawn from an Exponential distribution with mean beta and variance beta^2.

32. def nextExp(implicit r: Random): Double

    Return a random double drawn from an Exponential distribution with mean 1 and variance 1.

33. def nextGamma(alpha: Double, beta: Double, lambda: Double)(implicit r: Random): Double

    Return a random double drawn from a Gamma distribution with mean alpha*beta+lamba and variance alpha*beta^2.

34. def nextGamma(alpha: Double, beta: Double)(implicit r: Random): Double

    Return a random double drawn from a Gamma distribution with mean alpha*beta and variance alpha*beta^2.

35. def nextGamma(alpha: Double)(implicit r: Random): Double

    Return a random double drawn from a Gamma distribution with mean alpha and variance 1.

    Return a random double drawn from a Gamma distribution with mean alpha and variance 1.0.

36. def nextGamma(implicit r: Random): Double

    Return a random double drawn from a Gamma distribution with mean 1.

    Return a random double drawn from a Gamma distribution with mean 1.0 and variance 1.0.

37. def nextGaussian(mean: Double, s2: Double)(implicit r: Random): Double

    Return a random double drawn from a Gaussian distribution with mean m and variance s2.

38. def nextGaussian(implicit r: Random): Double

    Return a random double drawn from a Gaussian distribution with mean 0 and variance 1.

39. def nextPoisson(implicit r: Random): Double

    Return nextPoisson(1).

40. def nextPoisson(lambda: Double)(implicit r: Random): Double

    Return random integer from Poission with parameter lambda.

    Return random integer from Poission with parameter lambda. The mean of this distribution is lambda. The variance is lambda.

41. final def nextUniform(implicit r: Random): Double

    Return a random double in the range 0 to 1, inclusive, uniformly sampled from that range.

    Return a random double in the range 0 to 1, inclusive, uniformly sampled from that range. The mean of this distribution is 0.5. The variance is 1/12.

    Annotations

    @inline()

42. def numCombinations(n: Int, r: Int): Double

43. def numPermutations(n: Int, r: Int): Double

44. def pbinom(x: Int, n: Int, p: Double): Double

    Vastly inefficient O(x) method to compute cdf of B(n,p)

45. def poly(coeff: Array[Double], x: Double): Double

46. def probit(p: Double): Double

47. def sampleMean(ds: DoubleSeq): Double

48. def sampleVariance(ds: DoubleSeq, mean: Double): Double

    Returns the unbiased sample variance.

    Returns the unbiased sample variance. See http://en.wikipedia.org/wiki/Variance#Population_variance_and_sample_variance

49. def sampleVariance(ds: DoubleSeq): Double

50. def sigmoid(beta: Double): Double

51. def sigmoid_rev(sig: Double): Double

52. def subtractLogProb(a: Double, b: Double): Double

53. def sumLogProb(a: Double, b: Double): Double

    Returns the sum of two doubles expressed in log space, that is,

       sumLogProb = log (e^a + e^b)
                  = log e^a(1 + e^(b-a))
                  = a + log (1 + e^(b-a))
    

    Returns the sum of two doubles expressed in log space, that is,

       sumLogProb = log (e^a + e^b)
                  = log e^a(1 + e^(b-a))
                  = a + log (1 + e^(b-a))
    

    By exponentiating b-a, we obtain better numerical precision than we would if we calculated e^a or e^b directly.

54. def sumLogProbs(vals: DoubleSeq): Double

55. def sumLogProbs(vals: Array[Double]): Double

56. def tanh(a: Double): Double