Fixes #2

.gitignore

@@ -2,3 +2,6 @@
 .Rhistory
 .RData
 .Ruserdata
+**/.DS_Store
+Manifest.toml
+Project.toml

CRAN-SUBMISSION

@@ -1,3 +1,3 @@
 Version: 0.1.1
-Date: 2023-03-06 15:28:50 UTC
-SHA: 099a9eccb094b8e5fdfdf2e6cf302c1e5fea9ad8
+Date: 2023-03-08 19:09:58 UTC
+SHA: dcfcb820cd540fef83bd9b3baa471b640e88fc84

R/BayesFluxR.R

@@ -1,3 +1,15 @@
+#' How precise should values be communicated to Julia?
+#'
+#' The higher the number of zeros (digits) the more precise.
+.get_num_zeros <- function() {
+  zeros <- Sys.getenv("BFLUXR_NUM_ZEROS")
+  if (zeros == "") {
+    zeros <- 20
+  } else {
+    zeros <- as.numeric(zeros)
+  }
+  return(zeros)
+}
 
 #' Installs Julia packages if needed
 #'

R/adapter-mass.R

@@ -30,7 +30,7 @@ madapter.DiagCov <- function(adapt_steps, windowlength,
                              kappa = 0.5, epsilon = 1e-6){
 
   juliavar <- get_random_symbol()
-  juliacode <- sprintf("DiagCovMassAdapter(%i, %i; kappa = %ff0, epsilon = %ff0)",
+  juliacode <- sprintf("DiagCovMassAdapter(%i, %i; kappa = Float32(%e), epsilon = Float32(%e))",
                        adapt_steps, windowlength, kappa, epsilon)
   JuliaCall::julia_command(sprintf("%s = %s",
                                    juliavar, juliacode))
@@ -122,7 +122,7 @@ madapter.FullCov <- function(adapt_steps, windowlength,
                              kappa = 0.5, epsilon = 1e-6){
 
   juliavar <- get_random_symbol()
-  juliacode <- sprintf("FullCovMassAdapter(%i, %i; kappa = %ff0, epsilon = %ff0)",
+  juliacode <- sprintf("FullCovMassAdapter(%i, %i; kappa = Float32(%e), epsilon = Float32(%e))",
                        adapt_steps, windowlength, kappa, epsilon)
   JuliaCall::julia_command(sprintf("%s = %s",
                                    juliavar, juliacode))
@@ -172,7 +172,7 @@ madapter.RMSProp <- function(adapt_steps, lambda = 1e-5, alpha = 0.99){
   JuliaCall::julia_source(system.file("Julia/ascii-translate.jl", package = "BayesFluxR"))
 
   juliavar <- get_random_symbol()
-  juliacode <- sprintf("ascii_RMSPropMassAdapter(%i; lambda = %ff0, alpha = %ff0)",
+  juliacode <- sprintf("ascii_RMSPropMassAdapter(%i; lambda = Float32(%e), alpha = Float32(%e))",
                        adapt_steps, lambda, alpha)
 
   JuliaCall::julia_command(sprintf("%s = %s",

R/adapter-stepsize.R

@@ -24,7 +24,7 @@
 #' @export
 sadapter.Const <- function(l){
   juliavar <- get_random_symbol()
-  juliacode <- sprintf("ConstantStepsize(%ff0)", l)
+  juliacode <- sprintf("ConstantStepsize(Float32(%e))", l)
   JuliaCall::julia_command(sprintf("%s = %s",
                                    juliavar, juliacode))
 
@@ -66,8 +66,8 @@ sadapter.DualAverage <- function(adapt_steps, initial_stepsize=1.0,
                                  gamma = 0.05, t0 = 10, kappa = 0.75) {
 
   juliavar <- get_random_symbol()
-  juliacode <- sprintf("DualAveragingStepSize(%ff0; target_accept = %ff0,
-                       gamma = %ff0, t0 = %i, kappa = %ff0, adapt_steps = %i)",
+  juliacode <- sprintf("DualAveragingStepSize(Float32(%e); target_accept = Float32(%e),
+                       gamma = Float32(%e), t0 = %i, kappa = Float32(%e), adapt_steps = %i)",
                        initial_stepsize, target_accept, gamma, t0,
                        kappa, adapt_steps)
 

R/distributions.R

@@ -21,7 +21,7 @@
 #'
 #' @export
 Gamma <- function(shape=2.0, scale=2.0){
-  juliacode <- sprintf("Gamma(%f, %f)", shape, scale)
+  juliacode <- sprintf("Gamma(%e, %e)", shape, scale)
   symbol <- get_random_symbol()
   JuliaCall::julia_command(sprintf("%s = %s;", symbol, juliacode))
   out <- list(juliavar = symbol, juliacode = juliacode)
@@ -50,7 +50,7 @@ Gamma <- function(shape=2.0, scale=2.0){
 #' }
 #' @export
 InverseGamma <- function(shape=2.0, scale=2.0){
-  juliacode <- sprintf("InverseGamma(%f, %f)", shape, scale)
+  juliacode <- sprintf("InverseGamma(%e, %e)", shape, scale)
   symbol <- get_random_symbol()
   JuliaCall::julia_command(sprintf("%s = %s;", symbol, juliacode))
   out <- list(juliavar = symbol, juliacode = juliacode)
@@ -78,7 +78,7 @@ InverseGamma <- function(shape=2.0, scale=2.0){
 #'
 #' @export
 Normal <- function(mu=0, sigma=1){
-  juliacode <- sprintf("Normal(%f, %f)", mu, sigma)
+  juliacode <- sprintf("Normal(%e, %e)", mu, sigma)
   symbol <- get_random_symbol()
   JuliaCall::julia_command(sprintf("%s = %s;", symbol, juliacode))
   out <- list(juliavar = symbol, juliacode = juliacode)
@@ -106,7 +106,7 @@ Normal <- function(mu=0, sigma=1){
 #'
 #' @export
 Truncated <- function(dist, lower, upper){
-  juliacode <- sprintf("Truncated(%s, %f, %f)", dist$juliacode, lower, upper)
+  juliacode <- sprintf("Truncated(%s, %e, %e)", dist$juliacode, lower, upper)
   symbol <- get_random_symbol()
   JuliaCall::julia_command(sprintf("%s = %s;", symbol, juliacode))
   out <- list(juliavar = symbol, juliacode = juliacode)

R/likelihoods.R

@@ -71,7 +71,7 @@ likelihood.feedforward_normal <- function(chain, sig_prior){
 #'
 #' @export
 likelihood.feedforward_tdist <- function(chain, sig_prior, nu=30){
-  juliacode <- sprintf("BayesFlux.FeedforwardTDist(%s, %s, %ff0)",
+  juliacode <- sprintf("BayesFlux.FeedforwardTDist(%s, %s, Float32(%e))",
                        chain$nc, sig_prior$juliavar, nu)
   symbol <- get_random_symbol()
   JuliaCall::julia_command(sprintf("%s = %s;", symbol, juliacode))
@@ -146,7 +146,7 @@ likelihood.seqtoone_normal <- function(chain, sig_prior){
 #'
 #' @export
 likelihood.seqtoone_tdist <- function(chain, sig_prior, nu = 30){
-  juliacode <- sprintf("BayesFlux.SeqToOneTDist(%s, %s, %ff0)",
+  juliacode <- sprintf("BayesFlux.SeqToOneTDist(%s, %s, Float32(%e))",
                        chain$nc, sig_prior$juliavar, nu)
   sym <- get_random_symbol()
   JuliaCall::julia_command(sprintf("%s = %s;", sym, juliacode))

R/mcmc.R

@@ -34,7 +34,7 @@ sampler.SGLD <- function(stepsize_a = 0.1, stepsize_b = 0,
   JuliaCall::julia_source(system.file("Julia/ascii-translate.jl", package = "BayesFluxR"))
 
   juliavar <- get_random_symbol()
-  juliacode <- sprintf("ascii_SGLD(; stepsize_a = %ff0, stepsize_b = %ff0, stepsize_gamma = %ff0, min_stepsize = Float32(%s))",
+  juliacode <- sprintf("ascii_SGLD(; stepsize_a = Float32(%e), stepsize_b = Float32(%e), stepsize_gamma = Float32(%e), min_stepsize = Float32(%s))",
                        stepsize_a, stepsize_b, stepsize_gamma, min_stepsize)
   JuliaCall::julia_command(sprintf("%s = %s;",
                                    juliavar, juliacode))
@@ -82,7 +82,7 @@ sampler.AdaptiveMH <- function(bnn, t0, sd, eps=1e-6){
                                    sym.C0, sym.C0))
 
   juliavar <- get_random_symbol()
-  juliacode <- sprintf("AdaptiveMH(%s, %i, %ff0, %ff0)",
+  juliacode <- sprintf("AdaptiveMH(%s, %i, Float32(%e), Float32(%e))",
                        sym.C0, t0, sd, eps)
   JuliaCall::julia_command(sprintf("%s = %s;",
                                    juliavar, juliacode))
@@ -130,7 +130,7 @@ sampler.GGMC <- function(beta = 0.1, l = 1.0,
   JuliaCall::julia_source(system.file("Julia/ascii-translate.jl", package = "BayesFluxR"))
 
   juliavar <- get_random_symbol()
-  juliacode <- sprintf("ascii_GGMC(; beta = %ff0, l = %ff0, sadapter = %s, madapter = %s, steps = %i)",
+  juliacode <- sprintf("ascii_GGMC(; beta = Float32(%e), l = Float32(%e), sadapter = %s, madapter = %s, steps = %i)",
                        beta, l, sadapter$juliavar, madapter$juliavar, steps)
 
   JuliaCall::julia_command(sprintf("%s = %s;",
@@ -183,7 +183,7 @@ sampler.HMC <- function(l, path_len,
                         sadapter = sadapter.DualAverage(1000),
                         madapter = madapter.FixedMassMatrix()){
   juliavar <- get_random_symbol()
-  juliacode <- sprintf("HMC(%ff0, %i; sadapter = %s, madapter = %s)",
+  juliacode <- sprintf("HMC(Float32(%e), %i; sadapter = %s, madapter = %s)",
                        l, path_len, sadapter$juliavar, madapter$juliavar)
   JuliaCall::julia_command(sprintf("%s = %s;",
                            juliavar, juliacode))
@@ -238,7 +238,7 @@ sampler.SGNHTS <- function(l, sigmaA = 1, xi = 1, mu = 1,
   JuliaCall::julia_source(system.file("Julia/ascii-translate.jl", package = "BayesFluxR"))
 
   juliavar <- get_random_symbol()
-  juliacode <- sprintf("ascii_SGNHTS(%ff0, %ff0; xi = %ff0, mu = %ff0, madapter = %s)",
+  juliacode <- sprintf("ascii_SGNHTS(Float32(%e), Float32(%e); xi = Float32(%e), mu = Float32(%e), madapter = %s)",
                        l, sigmaA, xi, mu, madapter$juliavar)
 
   JuliaCall::julia_command(sprintf("%s = %s;",

R/mode-finding.R

@@ -29,7 +29,7 @@
 #' @export
 opt.Descent <- function(eta = 0.1){
   juliavar <- get_random_symbol()
-  juliacode <- sprintf("Flux.Descent(%f)",
+  juliacode <- sprintf("Flux.Descent(%e)",
                        eta)
 
   JuliaCall::julia_command(sprintf("%s = %s;",
@@ -63,7 +63,7 @@ opt.Descent <- function(eta = 0.1){
 #' @export
 opt.ADAM <- function(eta = 0.001, beta = c(0.9, 0.999), eps = 1e-8){
   juliavar <- get_random_symbol()
-  juliacode <- sprintf("Flux.ADAM(%f, (%f, %f), %f)",
+  juliacode <- sprintf("Flux.ADAM(%e, (%e, %e), %e)",
                        eta, beta[1], beta[2], eps)
   JuliaCall::julia_command(sprintf("%s = %s;",
                                    juliavar, juliacode))
@@ -96,7 +96,7 @@ opt.ADAM <- function(eta = 0.001, beta = c(0.9, 0.999), eps = 1e-8){
 #' @export
 opt.RMSProp <- function(eta = 0.001, rho = 0.9, eps = 1e-8){
   juliavar <- get_random_symbol()
-  juliacode <- sprintf("Flux.RMSProp(%f, %f, %f)",
+  juliacode <- sprintf("Flux.RMSProp(%e, %e, %e)",
                        eta, rho, eps)
   JuliaCall::julia_command(sprintf("%s = %s;",
                                    juliavar, juliacode))

R/priors.R

@@ -31,7 +31,7 @@
 #'
 #' @export
 prior.gaussian <- function(chain, sigma){
-  juliacode <- sprintf("GaussianPrior(%s, %ff0)",
+  juliacode <- sprintf("GaussianPrior(%s, Float32(%e))",
                        chain$nc, sigma)
   juliavar <- get_random_symbol()
   JuliaCall::julia_command(sprintf("%s = %s;",
@@ -74,7 +74,7 @@ prior.gaussian <- function(chain, sigma){
 #'
 #' @export
 prior.mixturescale <- function(chain, sigma1, sigma2, pi1){
-  juliacode <- sprintf("MixtureScalePrior(%s, %ff0, %ff0, %ff0)",
+  juliacode <- sprintf("MixtureScalePrior(%s, Float32(%e), Float32(%e), Float32(%e))",
                        chain$nc, sigma1, sigma2, pi1)
   juliavar <- get_random_symbol()
   JuliaCall::julia_command(sprintf("%s = %s;",