0936d8c20b
Added docs for math/distributions (about 75% finished) Started docs for math/array (very incomplete)
115 lines
4.0 KiB
Racket
115 lines
4.0 KiB
Racket
#lang typed/racket/base
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Generate Gamma samples using various algorithms
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For 0 <= k < 1:
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J H Ahrens and U Dieter.
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Computer Methods for Sampling from Gamma, Beta, Poisson and Binomial Distributions.
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Computing, 1974, vol 12, pp 223--246.
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For 3 <= k < 1e10:
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Pandu R Tadikamalla.
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Computer Generation of Gamma Random Variables--II.
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Communications of the ACM, May 1978, vol 21, issue 5, pp 419--422.
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For others: sum of Gamma and Exponential variables, Normal approximation.
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|#
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(require "../../../flonum.rkt"
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"../../../vector.rkt"
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"../../unsafe.rkt"
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"normal-random.rkt")
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(provide flgamma-sample)
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(: flgamma-sample-small (Flonum Flonum Natural -> FlVector))
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;; Ahrens and Dieter's rejection method
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;; Good for 0.0 <= k < 1.0
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(define (flgamma-sample-small k s n)
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(cond
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[(fl= k 0.0) (make-flvector n 0.0)]
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[else
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(define e (fl+ 1.0 (fl* k (flexp -1.0))))
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(define k-1 (fl- k 1.0))
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(define 1/k (fl/ 1.0 k))
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(build-flvector
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n (λ (_)
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(let loop ()
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(define p (fl* e (random)))
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(define q (fllog (random)))
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(cond [(p . fl>= . 1.0)
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(define x (- (fllog (fl/ (fl- e p) k))))
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(cond [(q . fl<= . (fl* k-1 (fllog x))) (fl* s x)]
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[else (loop)])]
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[else
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(define x (flexpt p 1/k))
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(cond [(q . fl<= . (- x)) (fl* s x)]
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[else (loop)])]))))]))
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(: flgamma-sample-1-2 (Flonum Flonum Natural -> FlVector))
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;; Sum of Gamma and Exponential rvs
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;; Good for 1.0 <= k < 2.0
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(define (flgamma-sample-1-2 k s n)
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(define xs (flgamma-sample-small (fl- k 1.0) s n))
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(for ([i (in-range n)])
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(define x (unsafe-flvector-ref xs i))
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(unsafe-flvector-set! xs i (fl- x (fl* s (fllog (random))))))
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xs)
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(: flgamma-sample-2-3 (Flonum Flonum Natural -> FlVector))
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;; Sum of Gamma and two Exponential rvs
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;; Good for 2.0 <= k < 3.0
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(define (flgamma-sample-2-3 k s n)
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(define xs (flgamma-sample-small (fl- k 2.0) s n))
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(for ([i (in-range n)])
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(define x (unsafe-flvector-ref xs i))
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(unsafe-flvector-set! xs i (fl- x (fl* s (fl+ (fllog (random)) (fllog (random)))))))
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xs)
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(: flgamma-sample-large (Flonum Flonum Natural -> FlVector))
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;; Tadikamalla's rejection method (Laplacian candidate)
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;; Good for 1.0 <= k < huge (where "huge" causes the floating-point ops to behave badly)
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;; Faster than the other methods for large k when k >= 3 or so (Laplacian left tail generates too
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;; many negative candidates, which are rejected, when k < 3)
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(define (flgamma-sample-large k s n)
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(define A (fl- k 1.0))
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(define B (fl+ 0.5 (fl* 0.5 (flsqrt (fl- (fl* 4.0 k) 3.0)))))
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(define C (fl/ (fl* A (fl+ 1.0 B)) B))
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(define D (fl/ (fl- B 1.0) (fl* A B)))
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(build-flvector
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n (λ (_)
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(let loop ()
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(define lx (flmax -max.0 (fllog (random))))
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(define x (fl+ A (fl* B (if ((random) . fl< . 0.5) (- lx) lx))))
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(cond [(x . fl< . 0.0)
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(loop)]
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[((fllog (random)) . fl<= . (fl+ (fl- (fl- (fl* A (fllog (fl* D x))) x) lx) C))
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(fl* s x)]
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[else
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(loop)])))))
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(: flgamma-sample-huge (Flonum Flonum Natural -> FlVector))
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;; Normal approximation
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;; Good for 1e10 <= k <= +inf.0
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(define (flgamma-sample-huge k s n)
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(cond [(fl= k +inf.0) (build-flvector n (λ (_) +inf.0))]
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[else
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(define xs (flnormal-sample k (flsqrt k) n))
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(for ([i (in-range n)])
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(define x (unsafe-flvector-ref xs i))
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(unsafe-flvector-set! xs i (flmax 0.0 (fl* s x))))
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xs]))
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(: flgamma-sample (Flonum Flonum Integer -> FlVector))
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(define (flgamma-sample k s n)
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(cond [(n . < . 0) (raise-argument-error 'flgamma-sample "Natural" 2 k s n)]
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[(k . fl>= . 1e10) (flgamma-sample-huge k s n)]
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[(k . fl>= . 3.0) (flgamma-sample-large k s n)]
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[(k . fl>= . 2.0) (flgamma-sample-2-3 k s n)]
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[(k . fl>= . 1.0) (flgamma-sample-1-2 k s n)]
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[(k . fl>= . 0.0) (flgamma-sample-small k s n)]
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[else (build-flvector n (λ (_) +nan.0))]))
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