In Non-Contradiction/autodiffr: Automatic Differentiation in R through 'Julia'

knitr::opts_chunk$set(echo = TRUE)

require(autodiffr)
ad_setup() # to ensure it is established

ores <- function(x){
    x # Function will be the parameters. ofn is sum of squares
}

logit <- function(x) exp(x) / (1 + exp(x))

ofn <- function(x){
    res <- ores(x) # returns a vector of residual values
    sum(logit(res) ^ 2)
}

## Now try to generate the gradient function
ogr <- makeGradFunc(ofn)

system.time(ogr(runif(100)))

system.time(ogr(runif(100)))

ogr1 <- makeGradFunc(ofn, x = runif(100))

system.time(ogr1(runif(100)))

system.time(ogr1(runif(100)))

ogr2 <- makeGradFunc(ofn, x = runif(100), use_tape = TRUE)

system.time(ogr2(runif(100)))

system.time(ogr2(runif(100)))

Non-Contradiction/autodiffr documentation built on May 10, 2019, 8:04 a.m.