Marshal a limited subset of J arrays into Repa arrays.

Tutorial

Suppose we wish to perform linear regression. In J we could do:

xs := 1 2 3
ys := 2 4 6

reg_result =: ys %. xs ^/ i.2

To do this with Haskell data:

do
   jenv <- jinit libLinux

   let hsArr0 = R.fromListUnboxed (R.ix1 3) [1.0,2.0,3.0]
       hsArr1 = R.fromListUnboxed (R.ix1 3) [2.0,4.0,6.0]
       jArr0 = JDoubleArr $ R.copyS $ R.map (realToFrac :: Double -> CDouble) hsArr0
       jArr1 = JDoubleArr $ R.copyS $ R.map (realToFrac :: Double -> CDouble) hsArr1

   setJData jenv "xs" jArr0
   setJData jenv "ys" jArr1

   bsDispatch jenv "reg_result =: ys %. xs ^/ i.2"

   JDoubleArr res <- getJData jenv "reg_result"
   R.toList res

There are three steps to do the calculation, plus one to get a J environment.

1. Use jinit with the appropriate file path for your platform
2. Marshal Haskell values and send them to the J environment. To do so, we use setJData, which takes a JData containing a repa array or a string.
3. Perform calculations within the J environment. Here, we use bsDispatch to compute some results and assign them within J
4. Marshal J values back to Haskell. We use getJData again.

Since marshaling data between J and Haskell is expensive, it's best to do as much computation as possible in J.

FFI

If you want to marshal data yourself, say to use a Vector, look at JEnv.