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random.f90
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random.f90
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!>@author
!>Netlib 2019
!>@copyright Public Domain
!>@brief
!> various routines for random number generation
!>Downloaded from Netlib, 2019,
!> with additions by Paul Connolly, University of Manchester
MODULE random
! A module for random number generation from the following distributions:
!
! Distribution Function/subroutine name
!
! Normal (Gaussian) random_normal
! Gamma random_gamma
! Chi-squared random_chisq
! Exponential random_exponential
! Weibull random_Weibull
! Beta random_beta
! t random_t
! Multivariate normal random_mvnorm
! Generalized inverse Gaussian random_inv_gauss
! Poisson random_Poisson
! Binomial random_binomial1 *
! random_binomial2 *
! Negative binomial random_neg_binomial
! von Mises random_von_Mises
! Cauchy random_Cauchy
!
! Generate a random ordering of the integers 1 .. N
! random_order
! Initialize (seed) the uniform random number generator for ANY compiler
! seed_random_number
! Lognormal - see note below.
! ** Two functions are provided for the binomial distribution.
! If the parameter values remain constant, it is recommended that the
! first function is used (random_binomial1). If one or both of the
! parameters change, use the second function (random_binomial2).
! The compilers own random number generator, SUBROUTINE RANDOM_NUMBER(r),
! is used to provide a source of uniformly distributed random numbers.
! N.B. At this stage, only one random number is generated at each call to
! one of the functions above.
! The module uses the following functions which are included here:
! bin_prob to calculate a single binomial probability
! lngamma to calculate the logarithm to base e of the gamma function
! Some of the code is adapted from Dagpunar's book:
! Dagpunar, J. 'Principles of random variate generation'
! Clarendon Press, Oxford, 1988. ISBN 0-19-852202-9
!
! In most of Dagpunar's routines, there is a test to see whether the value
! of one or two floating-point parameters has changed since the last call.
! These tests have been replaced by using a logical variable FIRST.
! This should be set to .TRUE. on the first call using new values of the
! parameters, and .FALSE. if the parameter values are the same as for the
! previous call.
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
! Lognormal distribution
! If X has a lognormal distribution, then log(X) is normally distributed.
! Here the logarithm is the natural logarithm, that is to base e, sometimes
! denoted as ln. To generate random variates from this distribution, generate
! a random deviate from the normal distribution with mean and variance equal
! to the mean and variance of the logarithms of X, then take its exponential.
! Relationship between the mean & variance of log(X) and the mean & variance
! of X, when X has a lognormal distribution.
! Let m = mean of log(X), and s^2 = variance of log(X)
! Then
! mean of X = exp(m + 0.5s^2)
! variance of X = (mean(X))^2.[exp(s^2) - 1]
! In the reverse direction (rarely used)
! variance of log(X) = log[1 + var(X)/(mean(X))^2]
! mean of log(X) = log(mean(X) - 0.5var(log(X))
! N.B. The above formulae relate to population parameters; they will only be
! approximate if applied to sample values.
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
! Version 1.13, 2 October 2000
! Changes from version 1.01
! 1. The random_order, random_Poisson & random_binomial routines have been
! replaced with more efficient routines.
! 2. A routine, seed_random_number, has been added to seed the uniform random
! number generator. This requires input of the required number of seeds
! for the particular compiler from a specified I/O unit such as a keyboard.
! 3. Made compatible with Lahey's ELF90.
! 4. Marsaglia & Tsang algorithm used for random_gamma when shape parameter > 1.
! 5. INTENT for array f corrected in random_mvnorm.
! Author: Alan Miller
! CSIRO Division of Mathematical & Information Sciences
! Private Bag 10, Clayton South MDC
! Clayton 3169, Victoria, Australia
! Phone: (+61) 3 9545-8016 Fax: (+61) 3 9545-8080
! e-mail: amiller @ bigpond.net.au
use numerics_type, only : wp
IMPLICIT NONE
REAL(wp), PRIVATE :: zero = 0.0_wp, half = 0.5_wp, one = 1.0_wp, two = 2.0_wp, &
vsmall = TINY(1.0_wp), vlarge = HUGE(1.0_wp)
PRIVATE :: integral
INTEGER, PARAMETER :: dp = SELECTED_REAL_KIND(12, 60)
CONTAINS
FUNCTION random_normal() RESULT(fn_val)
! Adapted from the following Fortran 77 code
! ALGORITHM 712, COLLECTED ALGORITHMS FROM ACM.
! THIS WORK PUBLISHED IN TRANSACTIONS ON MATHEMATICAL SOFTWARE,
! VOL. 18, NO. 4, DECEMBER, 1992, PP. 434-435.
! The function random_normal() returns a normally distributed pseudo-random
! number with zero mean and unit variance.
! The algorithm uses the ratio of uniforms method of A.J. Kinderman
! and J.F. Monahan augmented with quadratic bounding curves.
REAL(wp) :: fn_val
! Local variables
REAL(wp) :: s = 0.449871_wp, t = -0.386595_wp, a = 0.19600_wp, b = 0.25472_wp, &
r1 = 0.27597_wp, r2 = 0.27846_wp, u, v, x, y, q
! Generate P = (u,v) uniform in rectangle enclosing acceptance region
DO
CALL RANDOM_NUMBER(u)
CALL RANDOM_NUMBER(v)
v = 1.7156_wp * (v - half)
! Evaluate the quadratic form
x = u - s
y = ABS(v) - t
q = x**2 + y*(a*y - b*x)
! Accept P if inside inner ellipse
IF (q < r1) EXIT
! Reject P if outside outer ellipse
IF (q > r2) CYCLE
! Reject P if outside acceptance region
IF (v**2 < -4.0_wp*LOG(u)*u**2) EXIT
END DO
! Return ratio of P's coordinates as the normal deviate
fn_val = v/u
RETURN
END FUNCTION random_normal
FUNCTION random_gamma(s, first) RESULT(fn_val)
! Adapted from Fortran 77 code from the book:
! Dagpunar, J. 'Principles of random variate generation'
! Clarendon Press, Oxford, 1988. ISBN 0-19-852202-9
! FUNCTION GENERATES A RANDOM GAMMA VARIATE.
! CALLS EITHER random_gamma1 (S > 1.0)
! OR random_exponential (S = 1.0)
! OR random_gamma2 (S < 1.0).
! S = SHAPE PARAMETER OF DISTRIBUTION (0 < REAL).
REAL(wp), INTENT(IN) :: s
LOGICAL, INTENT(IN) :: first
REAL(wp) :: fn_val
IF (s <= zero) THEN
WRITE(*, *) 'SHAPE PARAMETER VALUE MUST BE POSITIVE'
STOP
END IF
IF (s > one) THEN
fn_val = random_gamma1(s, first)
ELSE IF (s < one) THEN
fn_val = random_gamma2(s, first)
ELSE
fn_val = random_exponential()
END IF
RETURN
END FUNCTION random_gamma
FUNCTION random_gamma1(s, first) RESULT(fn_val)
! Uses the algorithm in
! Marsaglia, G. and Tsang, W.W. (2000) `A simple method for generating
! gamma variables', Trans. om Math. Software (TOMS), vol.26(3), pp.363-372.
! Generates a random gamma deviate for shape parameter s >= 1.
REAL(wp), INTENT(IN) :: s
LOGICAL, INTENT(IN) :: first
REAL(wp) :: fn_val
! Local variables
REAL(wp), SAVE :: c, d
REAL(wp) :: u, v, x
IF (first) THEN
d = s - one/3.
c = one/SQRT(9.0_wp*d)
END IF
! Start of main loop
DO
! Generate v = (1+cx)^3 where x is random normal; repeat if v <= 0.
DO
x = random_normal()
v = (one + c*x)**3
IF (v > zero) EXIT
END DO
! Generate uniform variable U
CALL RANDOM_NUMBER(u)
IF (u < one - 0.0331_wp*x**4) THEN
fn_val = d*v
EXIT
ELSE IF (LOG(u) < half*x**2 + d*(one - v + LOG(v))) THEN
fn_val = d*v
EXIT
END IF
END DO
RETURN
END FUNCTION random_gamma1
FUNCTION random_gamma2(s, first) RESULT(fn_val)
! Adapted from Fortran 77 code from the book:
! Dagpunar, J. 'Principles of random variate generation'
! Clarendon Press, Oxford, 1988. ISBN 0-19-852202-9
! FUNCTION GENERATES A RANDOM VARIATE IN [0,INFINITY) FROM
! A GAMMA DISTRIBUTION WITH DENSITY PROPORTIONAL TO
! GAMMA2**(S-1) * EXP(-GAMMA2),
! USING A SWITCHING METHOD.
! S = SHAPE PARAMETER OF DISTRIBUTION
! (REAL < 1.0)
REAL(wp), INTENT(IN) :: s
LOGICAL, INTENT(IN) :: first
REAL(wp) :: fn_val
! Local variables
REAL(wp) :: r, x, w
REAL(wp), SAVE :: a, p, c, uf, vr, d
IF (s <= zero .OR. s >= one) THEN
WRITE(*, *) 'SHAPE PARAMETER VALUE OUTSIDE PERMITTED RANGE'
STOP
END IF
IF (first) THEN ! Initialization, if necessary
a = one - s
p = a/(a + s*EXP(-a))
IF (s < vsmall) THEN
WRITE(*, *) 'SHAPE PARAMETER VALUE TOO SMALL'
STOP
END IF
c = one/s
uf = p*(vsmall/a)**s
vr = one - vsmall
d = a*LOG(a)
END IF
DO
CALL RANDOM_NUMBER(r)
IF (r >= vr) THEN
CYCLE
ELSE IF (r > p) THEN
x = a - LOG((one - r)/(one - p))
w = a*LOG(x)-d
ELSE IF (r > uf) THEN
x = a*(r/p)**c
w = x
ELSE
fn_val = zero
RETURN
END IF
CALL RANDOM_NUMBER(r)
IF (one-r <= w .AND. r > zero) THEN
IF (r*(w + one) >= one) CYCLE
IF (-LOG(r) <= w) CYCLE
END IF
EXIT
END DO
fn_val = x
RETURN
END FUNCTION random_gamma2
FUNCTION random_chisq(ndf, first) RESULT(fn_val)
! Generates a random variate from the chi-squared distribution with
! ndf degrees of freedom
INTEGER, INTENT(IN) :: ndf
LOGICAL, INTENT(IN) :: first
REAL(wp) :: fn_val
fn_val = two * random_gamma(half*ndf, first)
RETURN
END FUNCTION random_chisq
FUNCTION random_exponential() RESULT(fn_val)
! Adapted from Fortran 77 code from the book:
! Dagpunar, J. 'Principles of random variate generation'
! Clarendon Press, Oxford, 1988. ISBN 0-19-852202-9
! FUNCTION GENERATES A RANDOM VARIATE IN [0,INFINITY) FROM
! A NEGATIVE EXPONENTIAL DlSTRIBUTION WlTH DENSITY PROPORTIONAL
! TO EXP(-random_exponential), USING INVERSION.
REAL(wp) :: fn_val
! Local variable
REAL(wp) :: r
DO
CALL RANDOM_NUMBER(r)
IF (r > zero) EXIT
END DO
fn_val = -LOG(r)
RETURN
END FUNCTION random_exponential
FUNCTION random_Weibull(a) RESULT(fn_val)
! Generates a random variate from the Weibull distribution with
! probability density:
! a
! a-1 -x
! f(x) = a.x e
REAL(wp), INTENT(IN) :: a
REAL(wp) :: fn_val
! For speed, there is no checking that a is not zero or very small.
fn_val = random_exponential() ** (one/a)
RETURN
END FUNCTION random_Weibull
FUNCTION random_beta(aa, bb, first) RESULT(fn_val)
! Adapted from Fortran 77 code from the book:
! Dagpunar, J. 'Principles of random variate generation'
! Clarendon Press, Oxford, 1988. ISBN 0-19-852202-9
! FUNCTION GENERATES A RANDOM VARIATE IN [0,1]
! FROM A BETA DISTRIBUTION WITH DENSITY
! PROPORTIONAL TO BETA**(AA-1) * (1-BETA)**(BB-1).
! USING CHENG'S LOG LOGISTIC METHOD.
! AA = SHAPE PARAMETER FROM DISTRIBUTION (0 < REAL)
! BB = SHAPE PARAMETER FROM DISTRIBUTION (0 < REAL)
REAL(wp), INTENT(IN) :: aa, bb
LOGICAL, INTENT(IN) :: first
REAL(wp) :: fn_val
! Local variables
REAL, PARAMETER :: aln4 = 1.3862944_wp
REAL(wp) :: a, b, g, r, s, x, y, z
REAL(wp), SAVE :: d, f, h, t, c
LOGICAL, SAVE :: swap
IF (aa <= zero .OR. bb <= zero) THEN
WRITE(*, *) 'IMPERMISSIBLE SHAPE PARAMETER VALUE(S)'
STOP
END IF
IF (first) THEN ! Initialization, if necessary
a = aa
b = bb
swap = b > a
IF (swap) THEN
g = b
b = a
a = g
END IF
d = a/b
f = a+b
IF (b > one) THEN
h = SQRT((two*a*b - f)/(f - two))
t = one
ELSE
h = b
t = one/(one + (a/(vlarge*b))**b)
END IF
c = a+h
END IF
DO
CALL RANDOM_NUMBER(r)
CALL RANDOM_NUMBER(x)
s = r*r*x
IF (r < vsmall .OR. s <= zero) CYCLE
IF (r < t) THEN
x = LOG(r/(one - r))/h
y = d*EXP(x)
z = c*x + f*LOG((one + d)/(one + y)) - aln4
IF (s - one > z) THEN
IF (s - s*z > one) CYCLE
IF (LOG(s) > z) CYCLE
END IF
fn_val = y/(one + y)
ELSE
IF (4.0_wp*s > (one + one/d)**f) CYCLE
fn_val = one
END IF
EXIT
END DO
IF (swap) fn_val = one - fn_val
RETURN
END FUNCTION random_beta
FUNCTION random_t(m) RESULT(fn_val)
! Adapted from Fortran 77 code from the book:
! Dagpunar, J. 'Principles of random variate generation'
! Clarendon Press, Oxford, 1988. ISBN 0-19-852202-9
! FUNCTION GENERATES A RANDOM VARIATE FROM A
! T DISTRIBUTION USING KINDERMAN AND MONAHAN'S RATIO METHOD.
! M = DEGREES OF FREEDOM OF DISTRIBUTION
! (1 <= 1NTEGER)
INTEGER, INTENT(IN) :: m
REAL(wp) :: fn_val
! Local variables
REAL(wp), SAVE :: s, c, a, f, g
REAL(wp) :: r, x, v
REAL(wp), PARAMETER :: three = 3.0_wp, four = 4.0_wp, quart = 0.25_wp, &
five = 5.0_wp, sixteen = 16.0_wp
INTEGER :: mm = 0
IF (m < 1) THEN
WRITE(*, *) 'IMPERMISSIBLE DEGREES OF FREEDOM'
STOP
END IF
IF (m /= mm) THEN ! Initialization, if necessary
s = m
c = -quart*(s + one)
a = four/(one + one/s)**c
f = sixteen/a
IF (m > 1) THEN
g = s - one
g = ((s + one)/g)**c*SQRT((s+s)/g)
ELSE
g = one
END IF
mm = m
END IF
DO
CALL RANDOM_NUMBER(r)
IF (r <= zero) CYCLE
CALL RANDOM_NUMBER(v)
x = (two*v - one)*g/r
v = x*x
IF (v > five - a*r) THEN
IF (m >= 1 .AND. r*(v + three) > f) CYCLE
IF (r > (one + v/s)**c) CYCLE
END IF
EXIT
END DO
fn_val = x
RETURN
END FUNCTION random_t
SUBROUTINE random_mvnorm(n, h, d, f, first, x, ier)
! Adapted from Fortran 77 code from the book:
! Dagpunar, J. 'Principles of random variate generation'
! Clarendon Press, Oxford, 1988. ISBN 0-19-852202-9
! N.B. An extra argument, ier, has been added to Dagpunar's routine
! SUBROUTINE GENERATES AN N VARIATE RANDOM NORMAL
! VECTOR USING A CHOLESKY DECOMPOSITION.
! ARGUMENTS:
! N = NUMBER OF VARIATES IN VECTOR
! (INPUT,INTEGER >= 1)
! H(J) = J'TH ELEMENT OF VECTOR OF MEANS
! (INPUT,REAL)
! X(J) = J'TH ELEMENT OF DELIVERED VECTOR
! (OUTPUT,REAL)
!
! D(J*(J-1)/2+I) = (I,J)'TH ELEMENT OF VARIANCE MATRIX (J> = I)
! (INPUT,REAL)
! F((J-1)*(2*N-J)/2+I) = (I,J)'TH ELEMENT OF LOWER TRIANGULAR
! DECOMPOSITION OF VARIANCE MATRIX (J <= I)
! (OUTPUT,REAL)
! FIRST = .TRUE. IF THIS IS THE FIRST CALL OF THE ROUTINE
! OR IF THE DISTRIBUTION HAS CHANGED SINCE THE LAST CALL OF THE ROUTINE.
! OTHERWISE SET TO .FALSE.
! (INPUT,LOGICAL)
! ier = 1 if the input covariance matrix is not +ve definite
! = 0 otherwise
INTEGER, INTENT(IN) :: n
REAL(wp), INTENT(IN) :: h(:), d(:) ! d(n*(n+1)/2)
REAL(wp), INTENT(IN OUT) :: f(:) ! f(n*(n+1)/2)
REAL(wp), INTENT(OUT) :: x(:)
LOGICAL, INTENT(IN) :: first
INTEGER, INTENT(OUT) :: ier
! Local variables
INTEGER :: j, i, m
REAL(wp) :: y, v
INTEGER, SAVE :: n2
IF (n < 1) THEN
WRITE(*, *) 'SIZE OF VECTOR IS NON POSITIVE'
STOP
END IF
ier = 0
IF (first) THEN ! Initialization, if necessary
n2 = 2*n
IF (d(1) < zero) THEN
ier = 1
RETURN
END IF
f(1) = SQRT(d(1))
y = one/f(1)
DO j = 2,n
f(j) = d(1+j*(j-1)/2) * y
END DO
DO i = 2,n
v = d(i*(i-1)/2+i)
DO m = 1,i-1
v = v - f((m-1)*(n2-m)/2+i)**2
END DO
IF (v < zero) THEN
ier = 1
RETURN
END IF
v = SQRT(v)
y = one/v
f((i-1)*(n2-i)/2+i) = v
DO j = i+1,n
v = d(j*(j-1)/2+i)
DO m = 1,i-1
v = v - f((m-1)*(n2-m)/2+i)*f((m-1)*(n2-m)/2 + j)
END DO ! m = 1,i-1
f((i-1)*(n2-i)/2 + j) = v*y
END DO ! j = i+1,n
END DO ! i = 2,n
END IF
x(1:n) = h(1:n)
DO j = 1,n
y = random_normal()
DO i = j,n
x(i) = x(i) + f((j-1)*(n2-j)/2 + i) * y
END DO ! i = j,n
END DO ! j = 1,n
RETURN
END SUBROUTINE random_mvnorm
FUNCTION random_inv_gauss(h, b, first) RESULT(fn_val)
! Adapted from Fortran 77 code from the book:
! Dagpunar, J. 'Principles of random variate generation'
! Clarendon Press, Oxford, 1988. ISBN 0-19-852202-9
! FUNCTION GENERATES A RANDOM VARIATE IN [0,INFINITY] FROM
! A REPARAMETERISED GENERALISED INVERSE GAUSSIAN (GIG) DISTRIBUTION
! WITH DENSITY PROPORTIONAL TO GIG**(H-1) * EXP(-0.5*B*(GIG+1/GIG))
! USING A RATIO METHOD.
! H = PARAMETER OF DISTRIBUTION (0 <= REAL)
! B = PARAMETER OF DISTRIBUTION (0 < REAL)
REAL(wp), INTENT(IN) :: h, b
LOGICAL, INTENT(IN) :: first
REAL(wp) :: fn_val
! Local variables
REAL(wp) :: ym, xm, r, w, r1, r2, x
REAL(wp), SAVE :: a, c, d, e
REAL(wp), PARAMETER :: quart = 0.25_wp
IF (h < zero .OR. b <= zero) THEN
WRITE(*, *) 'IMPERMISSIBLE DISTRIBUTION PARAMETER VALUES'
STOP
END IF
IF (first) THEN ! Initialization, if necessary
IF (h > quart*b*SQRT(vlarge)) THEN
WRITE(*, *) 'THE RATIO H:B IS TOO SMALL'
STOP
END IF
e = b*b
d = h + one
ym = (-d + SQRT(d*d + e))/b
IF (ym < vsmall) THEN
WRITE(*, *) 'THE VALUE OF B IS TOO SMALL'
STOP
END IF
d = h - one
xm = (d + SQRT(d*d + e))/b
d = half*d
e = -quart*b
r = xm + one/xm
w = xm*ym
a = w**(-half*h) * SQRT(xm/ym) * EXP(-e*(r - ym - one/ym))
IF (a < vsmall) THEN
WRITE(*, *) 'THE VALUE OF H IS TOO LARGE'
STOP
END IF
c = -d*LOG(xm) - e*r
END IF
DO
CALL RANDOM_NUMBER(r1)
IF (r1 <= zero) CYCLE
CALL RANDOM_NUMBER(r2)
x = a*r2/r1
IF (x <= zero) CYCLE
IF (LOG(r1) < d*LOG(x) + e*(x + one/x) + c) EXIT
END DO
fn_val = x
RETURN
END FUNCTION random_inv_gauss
FUNCTION random_Poisson(mu, first) RESULT(ival)
!**********************************************************************
! Translated to Fortran 90 by Alan Miller from:
! RANLIB
!
! Library of Fortran Routines for Random Number Generation
!
! Compiled and Written by:
!
! Barry W. Brown
! James Lovato
!
! Department of Biomathematics, Box 237
! The University of Texas, M.D. Anderson Cancer Center
! 1515 Holcombe Boulevard
! Houston, TX 77030
!
! This work was supported by grant CA-16672 from the National Cancer Institute.
! GENerate POIsson random deviate
! Function
! Generates a single random deviate from a Poisson distribution with mean mu.
! Arguments
! mu --> The mean of the Poisson distribution from which
! a random deviate is to be generated.
! REAL mu
! Method
! For details see:
! Ahrens, J.H. and Dieter, U.
! Computer Generation of Poisson Deviates
! From Modified Normal Distributions.
! ACM Trans. Math. Software, 8, 2
! (June 1982),163-179
! TABLES: COEFFICIENTS A0-A7 FOR STEP F. FACTORIALS FACT
! COEFFICIENTS A(K) - FOR PX = FK*V*V*SUM(A(K)*V**K)-DEL
! SEPARATION OF CASES A AND B
! .. Scalar Arguments ..
REAL(wp), INTENT(IN) :: mu
LOGICAL, INTENT(IN) :: first
INTEGER :: ival
! ..
! .. Local Scalars ..
REAL(wp) :: b1, b2, c, c0, c1, c2, c3, del, difmuk, e, fk, fx, fy, g, &
omega, px, py, t, u, v, x, xx
REAL(wp), SAVE :: s, d, p, q, p0
INTEGER :: j, k, kflag
LOGICAL, SAVE :: full_init
INTEGER, SAVE :: l, m
! ..
! .. Local Arrays ..
REAL(wp), SAVE :: pp(35)
! ..
! .. Data statements ..
REAL(wp), PARAMETER :: a0 = -.5_wp, a1 = .3333333_wp, a2 = -.2500068_wp, a3 = .2000118_wp, &
a4 = -.1661269_wp, a5 = .1421878_wp, a6 = -.1384794_wp, &
a7 = .1250060_wp
REAL(wp), PARAMETER :: fact(10) = (/ 1._wp, 1._wp, 2._wp, 6._wp, 24._wp, 120._wp, 720._wp, 5040._wp, &
40320._wp, 362880._wp /)
! ..
! .. Executable Statements ..
IF (mu > 10.0_wp) THEN
! C A S E A. (RECALCULATION OF S, D, L IF MU HAS CHANGED)
IF (first) THEN
s = SQRT(mu)
d = 6.0_wp*mu*mu
! THE POISSON PROBABILITIES PK EXCEED THE DISCRETE NORMAL
! PROBABILITIES FK WHENEVER K >= M(MU). L=IFIX(MU-1.1484)
! IS AN UPPER BOUND TO M(MU) FOR ALL MU >= 10 .
l = mu - 1.1484_wp
full_init = .false.
END IF
! STEP N. NORMAL SAMPLE - random_normal() FOR STANDARD NORMAL DEVIATE
g = mu + s*random_normal()
IF (g > 0.0_wp) THEN
ival = g
! STEP I. IMMEDIATE ACCEPTANCE IF ival IS LARGE ENOUGH
IF (ival>=l) RETURN
! STEP S. SQUEEZE ACCEPTANCE - SAMPLE U
fk = ival
difmuk = mu - fk
CALL RANDOM_NUMBER(u)
IF (d*u >= difmuk*difmuk*difmuk) RETURN
END IF
! STEP P. PREPARATIONS FOR STEPS Q AND H.
! (RECALCULATIONS OF PARAMETERS IF NECESSARY)
! .3989423=(2*PI)**(-.5) .416667E-1=1./24. .1428571=1./7.
! THE QUANTITIES B1, B2, C3, C2, C1, C0 ARE FOR THE HERMITE
! APPROXIMATIONS TO THE DISCRETE NORMAL PROBABILITIES FK.
! C=.1069/MU GUARANTEES MAJORIZATION BY THE 'HAT'-FUNCTION.
IF (.NOT. full_init) THEN
omega = .3989423_wp/s
b1 = .4166667E-1_wp/mu
b2 = .3_wp*b1*b1
c3 = .1428571_wp*b1*b2
c2 = b2 - 15._wp*c3
c1 = b1 - 6._wp*b2 + 45._wp*c3
c0 = 1._wp - b1 + 3._wp*b2 - 15._wp*c3
c = .1069_wp/mu
full_init = .true.
END IF
IF (g < 0.0_wp) GO TO 50
! 'SUBROUTINE' F IS CALLED (KFLAG=0 FOR CORRECT RETURN)
kflag = 0
GO TO 70
! STEP Q. QUOTIENT ACCEPTANCE (RARE CASE)
40 IF (fy-u*fy <= py*EXP(px-fx)) RETURN
! STEP E. EXPONENTIAL SAMPLE - random_exponential() FOR STANDARD EXPONENTIAL
! DEVIATE E AND SAMPLE T FROM THE LAPLACE 'HAT'
! (IF T <= -.6744 THEN PK < FK FOR ALL MU >= 10.)
50 e = random_exponential()
CALL RANDOM_NUMBER(u)
u = u + u - one
t = 1.8_wp + SIGN(e, u)
IF (t <= (-.6744_wp)) GO TO 50
ival = mu + s*t
fk = ival
difmuk = mu - fk
! 'SUBROUTINE' F IS CALLED (KFLAG=1 FOR CORRECT RETURN)
kflag = 1
GO TO 70
! STEP H. HAT ACCEPTANCE (E IS REPEATED ON REJECTION)
60 IF (c*ABS(u) > py*EXP(px+e) - fy*EXP(fx+e)) GO TO 50
RETURN
! STEP F. 'SUBROUTINE' F. CALCULATION OF PX, PY, FX, FY.
! CASE ival < 10 USES FACTORIALS FROM TABLE FACT
70 IF (ival>=10) GO TO 80
px = -mu
py = mu**ival/fact(ival+1)
GO TO 110
! CASE ival >= 10 USES POLYNOMIAL APPROXIMATION
! A0-A7 FOR ACCURACY WHEN ADVISABLE
! .8333333E-1=1./12. .3989423=(2*PI)**(-.5)
80 del = .8333333E-1_wp/fk
del = del - 4.8_wp*del*del*del
v = difmuk/fk
IF (ABS(v)>0.25_wp) THEN
px = fk*LOG(one + v) - difmuk - del
ELSE
px = fk*v*v* (((((((a7*v+a6)*v+a5)*v+a4)*v+a3)*v+a2)*v+a1)*v+a0) - del
END IF
py = .3989423_wp/SQRT(fk)
110 x = (half - difmuk)/s
xx = x*x
fx = -half*xx
fy = omega* (((c3*xx + c2)*xx + c1)*xx + c0)
IF (kflag <= 0) GO TO 40
GO TO 60
!---------------------------------------------------------------------------
! C A S E B. mu < 10
! START NEW TABLE AND CALCULATE P0 IF NECESSARY
ELSE
IF (first) THEN
m = MAX(1, INT(mu))
l = 0
p = EXP(-mu)
q = p
p0 = p
END IF
! STEP U. UNIFORM SAMPLE FOR INVERSION METHOD
DO
CALL RANDOM_NUMBER(u)
ival = 0
IF (u <= p0) RETURN
! STEP T. TABLE COMPARISON UNTIL THE END PP(L) OF THE
! PP-TABLE OF CUMULATIVE POISSON PROBABILITIES
! (0.458=PP(9) FOR MU=10)
IF (l == 0) GO TO 150
j = 1
IF (u > 0.458_wp) j = MIN(l, m)
DO k = j, l
IF (u <= pp(k)) GO TO 180
END DO
IF (l == 35) CYCLE
! STEP C. CREATION OF NEW POISSON PROBABILITIES P
! AND THEIR CUMULATIVES Q=PP(K)
150 l = l + 1
DO k = l, 35
p = p*mu / k
q = q + p
pp(k) = q
IF (u <= q) GO TO 170
END DO
l = 35
END DO
170 l = k
180 ival = k
RETURN
END IF
RETURN
END FUNCTION random_Poisson
FUNCTION random_binomial1(n, p, first) RESULT(ival)
! FUNCTION GENERATES A RANDOM BINOMIAL VARIATE USING C.D.Kemp's method.
! This algorithm is suitable when many random variates are required
! with the SAME parameter values for n & p.
! P = BERNOULLI SUCCESS PROBABILITY
! (0 <= REAL <= 1)
! N = NUMBER OF BERNOULLI TRIALS
! (1 <= INTEGER)
! FIRST = .TRUE. for the first call using the current parameter values
! = .FALSE. if the values of (n,p) are unchanged from last call
! Reference: Kemp, C.D. (1986). `A modal method for generating binomial
! variables', Commun. Statist. - Theor. Meth. 15(3), 805-813.
INTEGER, INTENT(IN) :: n
REAL(wp), INTENT(IN) :: p
LOGICAL, INTENT(IN) :: first
INTEGER :: ival
! Local variables
INTEGER :: ru, rd
INTEGER, SAVE :: r0
REAL(wp) :: u, pd, pu
REAL(wp), SAVE :: odds_ratio, p_r
REAL(wp), PARAMETER :: zero = 0.0_wp, one = 1.0_wp
IF (first) THEN
r0 = (n+1)*p
p_r = bin_prob(n, p, r0)
odds_ratio = p / (one - p)
END IF
CALL RANDOM_NUMBER(u)
u = u - p_r
IF (u < zero) THEN
ival = r0
RETURN
END IF
pu = p_r
ru = r0
pd = p_r
rd = r0
DO
rd = rd - 1
IF (rd >= 0) THEN
pd = pd * (rd+1) / (odds_ratio * (n-rd))
u = u - pd
IF (u < zero) THEN
ival = rd
RETURN
END IF
END IF
ru = ru + 1
IF (ru <= n) THEN
pu = pu * (n-ru+1) * odds_ratio / ru
u = u - pu
IF (u < zero) THEN
ival = ru
RETURN
END IF
END IF
END DO
! This point should not be reached, but just in case:
ival = r0