Package autodiffr provides an R wrapper for Julia packages
ForwardDiff.jl and
ReverseDiff.jl through
JuliaCall to do
automatic differentiation for native R functions.
Julia is needed to use autodiffr. You can
download a generic Julia binary from
https://julialang.org/downloads/ and add it to the path. Pakcage
autodiffr is not on CRAN yet. You can get the development version of
autodiffr by
devtools::install_github("Non-Contradiction/autodiffr")Important: Note that currently Julia v0.6.x, v0.7.0 and v1.0 are
all supported by autodiffr, but to use autodiffr with Julia
v0.7/1.0, you need to get the development version of JuliaCall by:
devtools::install_github("Non-Contradiction/JuliaCall")library(autodiffr)
## Do initial setup
ad_setup()
#> Julia version 1.0.0 at location /Applications/Julia-1.0.app/Contents/Resources/julia/bin will be used.
#> Loading setup script for JuliaCall...
#> Finish loading setup script for JuliaCall.
## If you want to use a julia at a specific location, you could do the following:
## ad_setup(JULIA_HOME = "the folder that contains julia binary"),
## or you can set JULIA_HOME in command line environment or use `options(...)`
## Define a target function with vector input and scalar output
f <- function(x) sum(x^2L)
## Calculate gradient of f at [2,3] by
ad_grad(f, c(2, 3)) ## deriv(f, c(2, 3))
#> [1] 4 6
## Get a gradient function g
g <- makeGradFunc(f)
## Evaluate the gradient function g at [2,3]
g(c(2, 3))
#> [1] 4 6
## Calculate hessian of f at [2,3] by
ad_hessian(f, c(2, 3))
#> [,1] [,2]
#> [1,] 2 0
#> [2,] 0 2
## Get a hessian function h
h <- makeHessianFunc(f)
## Evaluate the hessian function h at [2,3]
h(c(2, 3))
#> [,1] [,2]
#> [1,] 2 0
#> [2,] 0 2
## Define a target function with vector input and vector output
f <- function(x) x^2
## Calculate jacobian of f at [2,3] by
ad_jacobian(f, c(2, 3))
#> [,1] [,2]
#> [1,] 4 0
#> [2,] 0 6
## Get a jacobian function j
j <- makeJacobianFunc(f)
## Evaluate the gradient function j at [2,3]
j(c(2, 3))
#> [,1] [,2]
#> [1,] 4 0
#> [2,] 0 6## Define a target function with mulitple arguments
f <- function(a = 1, b = 2, c = 3) a * b ^ 2 * c ^ 3
## Calculate gradient/derivative of f at a = 2, when b = c = 1 by
ad_grad(f, 2, b = 1, c = 1) ## deriv(f, 2, b = 1, c = 1)
#> [1] 1
## Get a gradient/derivative function g w.r.t a when b = c = 1 by
g <- makeGradFunc(f, b = 1, c = 1)
## Evaluate the gradient/derivative function g at a = 2
g(2)
#> [1] 1
## Calculate gradient/derivative of f at a = 2, b = 3, when c = 1 by
ad_grad(f, list(a = 2, b = 3), c = 1)
#> $a
#> [1] 9
#>
#> $b
#> [1] 12
## Get a gradient/derivative function g w.r.t a and b when c = 1 by
g <- makeGradFunc(f, c = 1)
## Evaluate the gradient/derivative function g at a = 2, b = 3
g(list(a = 2, b = 3))
#> $a
#> [1] 9
#>
#> $b
#> [1] 12Make sure the Julia installation is correct. autodiffr is able to
find Julia on PATH, and there are three ways for autodiffr to find
Julia not on PATH.
- Use
ad_setup(JULIA_HOME = "the folder that contains julia binary") - Use
options(JULIA_HOME = "the folder that contains julia binary") - Set
JULIA_HOMEin command line environment.
-
The GitHub Pages for this repository host the documentation for the development version of
autodiffr: https://non-contradiction.github.io/autodiffr/. -
And you are more than welcome to contact me about
autodiffrat lch34677@gmail.com or cxl508@psu.edu.
autodiffr is under active development now. Any suggestion or issue
reporting is welcome! You may report it using the link:
https://github.com/Non-Contradiction/autodiffr/issues/new. Or email me
at lch34677@gmail.com or cxl508@psu.edu.
The project autodiffr was a Google Summer of Code (GSoC) 2018 project
for the “R Project for statistical computing” and with mentors John Nash
and Hans W Borchers. Thanks a lot!