org.apache.spark.streamdm.utils

Statistics

object Statistics

Class implementing some distributions, tests, etc. The code is mostly adapted from the CERN Jet Java libraries:

Copyright 2001 University of Waikato Copyright 1999 CERN - European Organization for Nuclear Research. Permission to use, copy, modify, distribute and sell this software and its documentation for any purpose is hereby granted without fee, provided that the above copyright notice appear in all copies and that both that copyright notice and this permission notice appear in supporting documentation. CERN and the University of Waikato make no representations about the suitability of this software for any purpose. It is provided "as is" without expressed or implied warranty.

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$Revision: 5619 $

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

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  3. final def ##(): Int

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  6. def FProbability(F: Double, df1: Int, df2: Int): Double

    Computes probability of F-ratio.

    Computes probability of F-ratio.

    F

    the F-ratio

    df1

    the first number of degrees of freedom

    df2

    the second number of degrees of freedom

    returns

    the probability of the F-ratio.

  7. val LOGPI: Double

  8. val MACHEP: Double

    Some constants

  9. val MAXGAM: Double

  10. val MAXLOG: Double

  11. val MINLOG: Double

  12. val P0: Array[Double]

  13. val P1: Array[Double]

  14. val P2: Array[Double]

  15. val Q0: Array[Double]

  16. val Q1: Array[Double]

  17. val Q2: Array[Double]

  18. val SQRTH: Double

  19. val SQTPI: Double

  20. final def asInstanceOf[T0]: T0

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  21. val big: Double

  22. val biginv: Double

  23. def binomialStandardError(p: Double, n: Int): Double

    Computes standard error for observed values of a binomial random variable.

    Computes standard error for observed values of a binomial random variable.

    p

    the probability of success

    n

    the size of the sample

    returns

    the standard error

  24. def chiSquaredProbability(x: Double, v: Double): Double

    Returns chi-squared probability for given value and degrees of freedom.

    Returns chi-squared probability for given value and degrees of freedom. (The probability that the chi-squared variate will be greater than x for the given degrees of freedom.)

    x

    the value

    v

    the number of degrees of freedom

    returns

    the chi-squared probability

  25. def clone(): AnyRef

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  26. final def eq(arg0: AnyRef): Boolean

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  27. def equals(arg0: Any): Boolean

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  28. def errorFunction(x: Double): Double

    Returns the error function of the normal distribution.

    Returns the error function of the normal distribution. The integral is

                              x
                               -
                    2         | |          2
      erf(x)  =  --------     |    exp( - t  ) dt.
                 sqrt(pi)   | |
                             -
                              0
    
    Implementation: For 0 <= |x| < 1, erf(x) = x * P4(x**2)/Q5(x**2); otherwise erf(x) = 1 - erfc(x).

    Code adapted from the <A HREF="http://www.sci.usq.edu.au/staff/leighb/graph/Top.html"> Java 2D Graph Package 2.4</A>, which in turn is a port from the <A HREF="http://people.ne.mediaone.net/moshier/index.html#Cephes">Cephes 2.2</A> Math Library (C).

  29. def errorFunctionComplemented(a: Double): Double

    Returns the complementary Error function of the normal distribution.

    Returns the complementary Error function of the normal distribution.

     1 - erf(x) =
    
                              inf.
                                -
                     2         | |          2
      erfc(x)  =  --------     |    exp( - t  ) dt
                  sqrt(pi)   | |
                              -
                               x
    
    Implementation: For small x, erfc(x) = 1 - erf(x); otherwise rational approximations are computed.

    Code adapted from the <A HREF="http://www.sci.usq.edu.au/staff/leighb/graph/Top.html"> Java 2D Graph Package 2.4</A>, which in turn is a port from the <A HREF="http://people.ne.mediaone.net/moshier/index.html#Cephes">Cephes 2.2</A> Math Library (C).

    a

    the argument to the function.

  30. def finalize(): Unit

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  31. def gamma(what: Double): Double

    Returns the Gamma function of the argument.

  32. final def getClass(): Class[_]

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  33. def hashCode(): Int

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  34. def incompleteBeta(aa: Double, bb: Double, xx: Double): Double

    Returns the Incomplete Beta Function evaluated from zero to xx.

    Returns the Incomplete Beta Function evaluated from zero to xx.

    aa

    the alpha parameter of the beta distribution.

    bb

    the beta parameter of the beta distribution.

    xx

    the integration end point.

  35. def incompleteBetaFraction1(a: Double, b: Double, x: Double): Double

    Continued fraction expansion #1 for incomplete beta integral.

  36. def incompleteBetaFraction2(a: Double, b: Double, x: Double): Double

    Continued fraction expansion #2 for incomplete beta integral.

  37. def incompleteGamma(a: Double, x: Double): Double

    Returns the Incomplete Gamma function.

    Returns the Incomplete Gamma function.

    a

    the parameter of the gamma distribution.

    x

    the integration end point.

  38. def incompleteGammaComplement(a: Double, x: Double): Double

    Returns the Complemented Incomplete Gamma function.

    Returns the Complemented Incomplete Gamma function.

    a

    the parameter of the gamma distribution.

    x

    the integration start point.

  39. final def isInstanceOf[T0]: Boolean

    Definition Classes
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  40. def lnGamma(what: Double): Double

    Returns natural logarithm of gamma function.

    Returns natural logarithm of gamma function.

    returns

    natural logarithm of gamma function

  41. final def ne(arg0: AnyRef): Boolean

    Definition Classes
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  42. def normalInverse(y0: Double): Double

    Returns the value, x, for which the area under the Normal (Gaussian) probability density function (integrated from minus infinity to x) is equal to the argument y (assumes mean is zero, variance is one).

    Returns the value, x, for which the area under the Normal (Gaussian) probability density function (integrated from minus infinity to x) is equal to the argument y (assumes mean is zero, variance is one).

    For small arguments 0 < y < exp(-2), the program computes z = sqrt( -2.0 * log(y) ); then the approximation is x = z - log(z)/z - (1/z) P(1/z) / Q(1/z). There are two rational functions P/Q, one for 0 < y < exp(-32) and the other for y up to exp(-2). For larger arguments, w = y - 0.5, and x/sqrt(2pi) = w + w**3 R(w**2)/S(w**2)).

    y0

    the area under the normal pdf

    returns

    the z-value

  43. def normalProbability(a: Double): Double

    Returns the area under the Normal (Gaussian) probability density function, integrated from minus infinity to x (assumes mean is zero, variance is one).

    Returns the area under the Normal (Gaussian) probability density function, integrated from minus infinity to x (assumes mean is zero, variance is one).

                               x
                                -
                      1        | |          2
     normal(x)  = ---------    |    exp( - t /2 ) dt
                  sqrt(2pi)  | |
                              -
                             -inf.
    
                =  ( 1 + erf(z) ) / 2
                =  erfc(z) / 2
    
    where z = x/sqrt(2). Computation is via the functions errorFunction and errorFunctionComplement.

    a

    the z-value

    returns

    the probability of the z value according to the normal pdf

  44. final def notify(): Unit

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  45. final def notifyAll(): Unit

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  46. def p1evl(x: Double, coef: Array[Double], N: Int): Double

    Evaluates the given polynomial of degree N at x.

    Evaluates the given polynomial of degree N at x. Evaluates polynomial when coefficient of N is 1.0. Otherwise same as polevl().

                        2          N
    y  =  C  + C x + C x  +...+ C x
           0    1     2          N
    
    Coefficients are stored in reverse order:
    
    coef[0] = C  , ..., coef[N] = C  .
               N                   0
    
    The function p1evl() assumes that coef[N] = 1.0 and is omitted from the array. Its calling arguments are otherwise the same as polevl().

    In the interest of speed, there are no checks for out of bounds arithmetic.

    x

    argument to the polynomial.

    coef

    the coefficients of the polynomial.

    N

    the degree of the polynomial.

  47. def polevl(x: Double, coef: Array[Double], N: Int): Double

    Evaluates the given polynomial of degree N at x.

    Evaluates the given polynomial of degree N at x.

                        2          N
    y  =  C  + C x + C x  +...+ C x
           0    1     2          N
    
    Coefficients are stored in reverse order:
    
    coef[0] = C  , ..., coef[N] = C  .
               N                   0
    
    In the interest of speed, there are no checks for out of bounds arithmetic.

    x

    argument to the polynomial.

    coef

    the coefficients of the polynomial.

    N

    the degree of the polynomial.

  48. def powerSeries(a: Double, b: Double, x: Double): Double

    Power series for incomplete beta integral.

    Power series for incomplete beta integral. Use when b*x is small and x not too close to 1.

  49. def stirlingFormula(x: Double): Double

    Returns the Gamma function computed by Stirling's formula.

    Returns the Gamma function computed by Stirling's formula. The polynomial STIR is valid for 33 <= x <= 172.

  50. final def synchronized[T0](arg0: ⇒ T0): T0

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  52. final def wait(): Unit

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