stopifnot(require("Rmpfr")) (doExtras <- Rmpfr:::doExtras()) options(nwarnings = 50000, width = 99) (do.pdf <- !dev.interactive(orNone = TRUE)) if(do.pdf) { pdf.options(width = 8.5, height = 6) # for all pdf plots pdf("special-fun.pdf") } ## to enhance |rel.Err| plots: {also in ~/R/Pkgs/DPQ/tests/pow-tst.R } drawEps.h <- function(p2 = -(53:51), side = 4, lty=3, lwd=2, col=adjustcolor(2, 1/2)) { abline(h = 2^p2, lty=lty, lwd=lwd, col=col) axis(side, las=2, line=-1, at = 2^p2, labels = as.expression(lapply(p2, function(p) substitute(2^E, list(E=p)))), col.axis = col, col=NA, col.ticks=NA) } mtextVersion <- function(adj = 1, col = 1) { mtext(osVersion, line=1, col=col, adj=adj) mtext(sfsmisc::shortRversion(spaces=FALSE), col=col, adj=adj) } all.eq.finite <- function(x,y, ...) { ## x = 'target' y = 'current' if(any(is.finite(y[!(fx <- is.finite(x))]))) return("current has finite values where target has not") if(any(is.finite(x[!(fy <- is.finite(y))]))) return("target has finite values where current has not") ## now they have finite values at the same locations all.equal(x[fx], y[fy], ...) } n <- 1000 head(x <- mpfr(0:n, 100) / n) stopifnot(exprs = { range(x) == 0:1 all.equal(as.numeric(j0(x)), besselJ(as.numeric(x), 0), tol = 1e-14) all.equal(as.numeric(j1(x)), besselJ(as.numeric(x), 1), tol = 1e-14) all.equal(as.numeric(y0(x)), besselY(as.numeric(x), 0), tol = 1e-14) all.equal(as.numeric(y1(x)), besselY(as.numeric(x), 1), tol = 1e-14) }) ### pnorm() -> erf() : ---------------------------------------------------------- u <- 7*x - 2 stopifnot(all.equal(pnorm(as.numeric(u)), as.numeric(pnorm(u)), tol = 1e-14)) ## systematic random input testing: set.seed(101) if(doExtras) { nSim <- 50 n2 <- 100 } else { nSim <- 10 n2 <- 64 } for(n in 1:nSim) { N <- rpois(1, lambda=n2) N3 <- N %/% 3 x <- c(rnorm(N-N3), 10*rt(N3, df=1.25))# <- some large values m <- rnorm(N, sd = 1/32) s <- rlnorm(N, sd = 1/8) cEps <- .Machine$double.eps for(LOG in c(TRUE,FALSE)) for(L.T in c(TRUE,FALSE)) { p. <- pnorm( x, m=m,sd=s, log.p=LOG, lower.tail=L.T) stopifnot(all.equal(p., pnorm(mpfr(x, precBits= 48), m=m,sd=s, log.p=LOG, lower.tail=L.T), tol = 128 * cEps)) stopifnot(all.equal(p., pnorm(mpfr(x, precBits= 60), m=m,sd=s, log.p=LOG, lower.tail=L.T), tol = 2 * cEps)) } cat(".") };cat("\n") proc.time() ## Jerry Lewis - Aug 2, 2019 ## Contrast the results of pnorm with double and mpfr inputs x <- c(1:9, 5*(2:9), 10*(5:20)) ; x <- c(-rev(x), 0, x) pdL <- pnorm(x, log.p=TRUE) pdU <- pnorm(x, log.p=TRUE, lower.tail=FALSE) stopifnot(exprs = { !is.unsorted(x) 35 %in% x x == -rev(x) # exactly pdL == rev(pdU) # even exactly, currently }) mx <- mpfr(x, precBits = 128) pmL <- pnorm(mx, log.p=TRUE) pmU <- pnorm(mx, log.p=TRUE, lower.tail=FALSE) stopifnot(exprs = { pmL < 0 # not true for 'pdL' which underflows pmL == rev(pmU) # even exactly, currently all.equal(pmL, pdL, tol=4e-16) # 'tol=0' shows 4.46e-17 }) ## some explorations : dlp <- diff(log(-pmL))/diff(x) n <- length(x) x.1 <- (x[-1] + x[-n])/2 plot(x.1, dlp, type="b", ylab = "d/dx log(-pnorm(., log=TRUE))"); mtextVersion() plot(x.1[-1], diff(dlp)/diff(x.1), type="b", ylab = "d^2/dx^2 log(-pnorm(., log=TRUE))") stopifnot(exprs = { -1 < (d2 <- diff(dlp)/diff(x.1)) d2 < 0 diff(d2) < 0 }) x.3 <- x.1[-c(1L,n-1L)] plot(x.3, -diff(d2)/ diff(x.1)[-1], type="o", log="y") ### Riemann's Zeta function: ---------------------------------------------------- ## -- integer arguments -- stopifnot(all(mpfrIs0(zeta(-2*(1:100))))) k.neg <- 2*(-100:0) - 1 Z.neg <- zeta(k.neg) plot(k.neg, abs(as.numeric(Z.neg)), type = "l", log="y") Pi <- Const("pi", 128L) ## confirm published value of Euler's gamma to 100 digits pub.g <- paste("0.5772156649", "0153286060", "6512090082", "4024310421", "5933593992", "3598805767", "2348848677", "2677766467", "0936947063", "2917467495", sep="") ## almost = our.g <- Const("gamma", log2(10) * 100) # 100 digits (ff.g <- .mpfr2str(our.g)) M <- function(x) mpfr(x, 128L) stopifnot(all.equal(zeta( 0), -1/2, tol = 2^-100) , all.equal(zeta(-1), -1/M(12), tol = 2^-100) , all.equal(zeta(-3), 1/M(120), tol = 2^-100) ## positive ones : , all.equal(zeta(2), Pi^2/6, tol = 2^-100) , all.equal(zeta(4), Pi^4/90, tol = 2^-100) , all.equal(zeta(6), Pi^6/945, tol = 2^-100) ) ### Exponential Integral Ei(.) curve(Ei, 0,5, n=5001) if(mpfrVersion() >= "3") { ## only available since MPFR 3.0.0 ### Airy function Ai(.) curve(Ai, -10, 5, n=5001); abline(h=0,v=0, col="gray", lty=3) } ### Utilities hypot(), atan2() : -------------------------------------------------------------- ## ======= TODO! ======== ## beta(), lbeta() ## --------------- ## The simplistic "slow" versions: B <- function(a,b) { a <- as(a, "mpfr"); b <- as(b, "mpfr"); gamma(a)*gamma(b) / gamma(a+b) } lB <- function(a,b) { a <- as(a, "mpfr"); b <- as(b, "mpfr"); lgamma(a)+lgamma(b) - lgamma(a+b) } ## For partly *integer* arguments Bi1 <- function(a,b) 1/(a*chooseMpfr(a+b-1, a)) # a must be integer >= 0 Bi2 <- function(a,b) 1/(b*chooseMpfr(a+b-1, b)) # b must be integer >= 0 x <- 1:10 + 0 ; (b10 <- mpfr(x, 128L)) stopifnot(all.equal( B(1,b10), 1/x), all.equal( B(2,b10), 1/(x*(x+1))), all.equal( beta(1,b10), 1/x), all.equal( beta(2,b10), 1/(x*(x+1))), TRUE) if(do.pdf) { dev.off(); pdf("special-fun-beta.pdf") } x <- -10:10 + 0; X <- mpfr(x, 128L) stopifnot(exprs = { Bi1(1,X) == (B1x <- Bi2(X,1)) Bi1(2,X) == (B2x <- Bi2(X,2)) Bi1(3,X) == (B3x <- Bi2(X,3)) all.equal(B1x, 1/x, tol= 4e-16) all.equal(B2x, 1/(x*(x+1)), tol= 8e-16) all.equal(B3x, 2/(x*(x+1)*(x+2)), tol=16e-16) ## these the "poles" are all odd i.e. result in { +Inf / -Inf / NaN} ## are all "ok" {e.g. 1/(x*(x+1)) gives (-Inf, Inf) for x = -1:0 } all.eq.finite(beta(1,X), 1/x) all.eq.finite(beta(X,2), 1/(x*(x+1))) all.eq.finite(beta(3,X), 2/(x*(x+1)*(x+2)), tol=16e-16) }) ## (a,b) *both* integer, one negative: for(i in (-20):(-1)) { cat(i,":\n") a <- mpfr(i, 99) i1 <- i+1 b. <- seq_len(-i1) Bab <- beta(a, b.) stopifnot(is.nan(beta(a, (i1:0))), is.nan(lbeta(a, (i1:0))), all.equal(Bab, Bi2(a, b.), tol=1e-20), all.equal(lbeta(a, b.), log(abs(Bab)), tol=1e-20), allow.logical0 = TRUE) } ## (a,b) all positive c10 <- b10 + 0.25 for(a in c(0.1, 1, 1.5, 2, 20)) { stopifnot(all.equal( B(a,b10), (bb <- beta(a, b10))), all.equal(lB(a,b10), (lb <- lbeta(a, b10))), all.equal(lb, log(bb)), all.equal( B(a,c10), (bb <- beta(a, c10))), all.equal(lB(a,c10), (lb <- lbeta(a, c10))), all.equal(lb, log(bb)), TRUE) } ## However, the speedup is *not* much (50%) when applied to vectors: stopifnot(validObject(xx <- outer(b10, runif(20))), dim(xx) == c(length(b10), 20), validObject(vx <- as(xx, "mpfr")), class(vx) == "mpfr", is.null(dim(vx))) C1 <- replicate(10, system.time(bb <<- beta(vx, vx+2))) C2 <- replicate(10, system.time(b2 <<- B(vx, vx+2))) summary(1000*C1[1,]) ## 80.3 {cmath-5, 2009} summary(1000*C2[1,]) ## 125.1 { " } stopifnot(all.equal(bb, b2)) ## and for a single number, the speedup is a factor 3: x1 <- vx[1]; x2 <- x1+2 system.time(for(i in 1:100) bb <- beta(x1, x2))# .27 system.time(for(i in 1:100) b2 <- B(x1, x2))# .83 ## a+b is integer <= 0, but a and b are not integer: a <- b <- .5 + -10:10 ab <- data.matrix(expand.grid(a=a, b=b, KEEP.OUT.ATTRS=FALSE)) ab <- mpfr(ab[rowSums(ab) <= 0, ], precBits = 128) stopifnot( beta(ab[,"a"], ab[,"b"]) == 0, lbeta(ab[,"a"], ab[,"b"]) == -Inf) ## was NaN in Rmpfr <= 0.5-2 stopifnot(all.equal(6 * beta(mpfr(1:3,99), -3.), c(-2,1,-2), tol=1e-20)) ## add more checks, notably for b (> 0) above and below the "large_b" in ## ../src/utils.c : bb <- beta(mpfr(1:23, 128), -23) stopifnot(all.equal(bb, Bi1(1:23, -23), tol=1e-7)) # Bi1() does not get high prec for small b ## can be written via rationals: N / D : bn <- c(330, -360, 468, -728, 1365, -3120, 8840, -31824, 151164, -1007760, 10581480, -232792560) bn <- c(rev(bn[-1]), bn) bd <- 24* as.bigz(2 * 3 * 5 * 7 * 11) * 13 * 17 * 19 * 23 stopifnot(all.equal(bb, as(bn/bd,"mpfr"), tol=0)) stopifnot(all.equal(6 * beta(mpfr(1:3, 99), -3.), c(-2,1,-2), tol=1e-20), all.equal( lbeta(mpfr(1:3, 128), -3.), log(mpfr(c( 2,1, 2), 128) / 6), tol=1e-20)) ## add more checks, notably for b (> 0) above and below the "large_b" in ## ../src/utils.c : bb <- beta(mpfr(1:23, 128), -23) stopifnot(all.equal(bb, Bi1(1:23, -23), tol=1e-7)) # Bi1() does not get high prec for small b ## can be written via rationals: N / D : bn <- c(330, -360, 468, -728, 1365, -3120, 8840, -31824, 151164, -1007760, 10581480, -232792560) bn <- c(rev(bn[-1]), bn) bd <- 24* as.bigz(2 * 3 * 5 * 7 * 11) * 13 * 17 * 19 * 23 stopifnot(all.equal(bb, as(bn/bd,"mpfr"), tol=0)) ## 2) add check for 'b' > maximal unsigned int {so C code uses different branch} two <- mpfr(2, 128) for(b in list(mpfr(9, 128), mpfr(5, 128)^10, two^25, two^26, two^100)) { a <- -(b+ (1:7)) stopifnot(a+b == -(1:7), # just ensuring that there was no cancellation is.finite( B <- beta(a,b)), ## was NaN .. is.finite(lB <- lbeta(a,b)), ## ditto all.equal(log(abs(B)), lB), TRUE) } ee <- c(10:145, 5*(30:59), 10*(30:39), 25*(16:30)) b <- mpfr(2, precBits = 10 + max(ee))^ee # enough precision {now "automatic"} stopifnot((b+4)-b == 4, # <==> enough precision above b == (b. <- as(as(b,"bigz"),"mpfr"))) (pp <- getPrec(b.))# shows why b. is not *identical* to b. system.time(Bb <- beta(-b-4, b))# 0.334 sec if(dev.interactive()) plot(ee, asNumeric(log(Bb)), type="o",col=2) lb <- asNumeric(log(Bb)) ## using coef(lm(lb ~ ee)) stopifnot(all.equal(lb, 3.175933 -3.46571851*ee, tol = 1e-5))# 4.254666 e-6 bb <- beta( 1:4, mpfr(2,99)) stopifnot(identical(bb, beta(mpfr(2,99), 1:4)), all.equal((2*bb)*cumsum(1:4), rep(1, 4), tol=1e-20), getPrec(bb) == 128) ##-- The d*() density functions from ../R/special-fun.R | ../man/distr-etc.Rd --- if(do.pdf) { dev.off(); pdf("special-fun-density.pdf") } dx <- 1400+ 0:10 mx <- mpfr(dx, 120) nx <- sort(c(c(-32:32)/2, 50*(-8:8))) xL <- 2^(989+(0:139)/4) # "close" to double.xmax dnbD <- dnbinom(xL, prob=1-1/4096, size=1e307, log=TRUE)# R's own iF <- -(130:140) # index of finite dnbD[] dnbx8 <- dnbinom(xL, prob=1-mpfr(2, 2^ 8)^-12, size=1e307, log=TRUE) dnbx10 <- dnbinom(xL, prob=1-mpfr(2, 2^10)^-12, size=1e307, log=TRUE) dnbx13 <- dnbinom(xL, prob=1-mpfr(2, 2^13)^-12, size=1e307, log=TRUE) stopifnot(exprs = { all.equal(dpois(dx, 1000), dpois(mx, 1000), tol = 3e-13) # 64b Lnx: 7.369e-14 all.equal(dbinom(0:16, 16, pr = 4 / 5), dbinom(0:16, 16, pr = 4/mpfr(5, 128)) -> db, tol = 5e-15)# 64b Lnx: 4.3e-16 all.equal(dnorm( -3:3, m=10, s=1/4), dnorm(mpfr(-3:3, 128), m=10, s=1/4), tol = 1e-15) # 64b Lnx: 6.45e-17 all.equal(dnorm(nx), dnorm(mpfr(nx, 99)), tol = 1e-15) all.equal(dnorm( nx, m = 4, s = 1/4), dnorm(mpfr(nx, 99), m = 4, s = 1/4), tol = 1e-15) all.equal(dnorm( nx, m = -10, s = 1/4, log=TRUE), dnorm(mpfr(nx, 99), m = -10, s = 1/4, log=TRUE), tol = 1e-15) ## t-distrib. : all.equal(dt(nx, df=3), dt(mpfr(nx, 99), df=3), tol = 1e-15) all.equal(dt( nx, df = 0.75), dt(mpfr(nx, 99), df = 0.75), tol = 1e-15) all.equal(dt( nx, df = 2.5, log=TRUE), dt(mpfr(nx, 99), df = 2.5, log=TRUE), tol = 1e-15) ## negative binomial dnbinom(): all.equal(dnbx13, dnbx10, tol = 2^-999) # see 2^-1007, but not 2^-1008 all.equal(dnbx13, dnbx8, tol = 2^-238) # see 2^-239, but not 2^-240 all.equal(dnbx10[iF], dnbD[iF], tol = 6e-16) # R's *is* accurate here (seen 2.9e-16) }) ## plot dt() "error" of R's implementation nx <- seq(-100, 100, by=1/8) dtd <- dt( nx, df= .75) dtM <- dt(mpfr(nx, 256), df= .75) if(doExtras) withAutoprint({ system.time( dtMx <- dt(mpfr(nx, 2048), df= .75) ) # 2.5 sec stopifnot(all.equal(dtMx, dtM, tol = 2^-254)) # almost all of dtM's 256 bits are correct }) relE <- asNumeric(dtd/dtM - 1) plot(relE ~ nx, type="l", col=2); mtextVersion() plot(abs(relE) ~ nx, type="l", col=2, log="y", ylim=c(5e-17, 1.5e-15)) ## ============== even smaller 'df' such that lgamma1p(df) is better than lgamma(1+df) ==== require(sfsmisc)# -> eaxis(); relErrV() u <- sort(outer(10^-(20:1), c(1,2,5))) # *not* "exact" on purpose ## .. unfinished .. exploring *when* dt() would suffer from inaccurate stirlerr() -- would it? nu <- 2^-(70:1) dt10 <- dt( 10, df=nu) dt10M <- dt(mpfr(10, 1024), df=nu) re10 <- asNumeric(relErrV(dt10M, dt10)) plot(re10 ~ nu, type="l", lwd=2, log="x", main = quote(rel.Err( dt(10, df==nu) )), xaxt="n"); eaxis(1, nintLog=20) mtextVersion() abline(h = (-1:1)*2^-53, lty=4, col=adjustcolor("blue", 1/2)) plot(abs(re10) ~ nu, type="l", lwd=2, log="xy", xlab = quote(df == nu), ylab = quote(abs(relE)), main = quote(abs(rel.Err( dt(10, df==nu) ))), xaxt="n", yaxt="n") eaxis(1, nintLog=20); eaxis(2); drawEps.h() x0 <- c(0, 10^(-5:10)) # only >= 0 should be sufficient; x0 <- c(-rev(x0),0,x0) stopifnot(!is.unsorted(nu), # just for plotting .. !is.unsorted(x0)) xnu <- expand.grid(x=x0, df=nu) dt2 <- with(xnu, dt( x, df=df)) dtM2 <- with(xnu, dt(mpfr(x, 512), df=df)) str(relE2 <- `attributes<-`(asNumeric(relErrV(dtM2, dt2)), attr(xnu, "out.attrs"))) ## consistency check that with() etc was fine: stopifnot(identical(re10, unname(relE2[which(x0 == 10), ]))) filled.contour(x=log10(1e-7+x0), y=log10(nu), z = relE2) filled.contour(x=log10(1e-7+x0), y=log10(nu), z = abs(relE2)) ## around nu = 10^-16 is the most critical place (pch <- c(1L:9L, 0L, letters, LETTERS)[1:ncol(relE2)]) matplot(x0+1e-7, relE2, type="b", log="x", main="rel.err{ dt(x, df=df) }") legend("topright", legend = paste0("df=",formatC(nu,wid=3)), ncol=7, bty="n", lwd=1, pch=pch, col=1:6, lty=1:5, cex = 0.8) abline(h = c(-4:4)*2^-53, lty=3, col="gray") matplot(nu, t(relE2), type="b", log="x", main="rel.err{ dt(x, df=df) }") legend("topright", legend = paste0("x=",formatC(x0,wid=3)), ncol=7, bty="n", lwd=1, pch=pch, col=1:6, lty=1:5, cex = 0.8) abline(h = c(-4:4)*2^-53, lty=3, col="gray") matplot(nu, pmax(abs(t(relE2)), 1e-19), type="b", log="xy", axes=FALSE, ylab = quote(abs("rel Err")), ylim = c(7e-17, max(abs(relE2))), main="|rel.err{ dt(x, df=df)}|") eaxis(1, nintLog=22) ; eaxis(2, line=-1/2); drawEps.h() legend("topright", legend = paste0("x=",formatC(x0,wid=3)), ncol=7, bty="n", lwd=1, pch=pch, col=1:6, lty=1:5, cex = 0.8) 1 ## dnbinom() -- has mode as expected, but with huge size, the scales are "off reality" .. ### ..... TODO ! ##--> >>>>>>>> ./special-fun-dgamma.R <<< (was here originally) cat('Time elapsed: ', proc.time(),'\n') # "stats" if(!interactive()) warnings()