Stochastic triple getindex rule does not support vector-valued array elements

[Eemilp]

Hi,
It appears to me that there is no support for higher dimensional random walks. Is this an inherent property of the approach or is there a method of making this work?

A minimal example based on the random walk example:

using Distributions # defines several supported discrete distributions
using StochasticAD
using LinearAlgebra

function simulate_walk(probs, steps, n)
    state = [0, 0]
    for i in 1:n
        probs_here = probs(state) # transition probabilities for possible steps
        step_index = rand(Categorical(probs_here)) # which step do we take?
        step = steps[step_index] # Error happens here
        state += step
    end
    return norm(state)
end

steps = [[0, 1],[0,-1], [1, 0], [-1, 0]] # move in any direction
make_probs(p) = X -> [1 - exp(-norm(X) / norm(p)), exp(-norm(X) / norm(p)), 0, 0]

f(p, n) = simulate_walk(make_probs(p), steps, n)
@show f(50, 100) # let's run a single random walk with p = 50
@show stochastic_triple(p -> f(p, 100), 50) # let's see how a single stochastic triple looks like at p = 50

f_squared(p, n) = f(p, n)^2

samples = [derivative_estimate(p -> f_squared(p, 100), 50) for i in 1:1000] # many samples from derivative program at p = 50
derivative = mean(samples)
uncertainty = std(samples) / sqrt(1000)
println("derivative of 𝔼[f_squared] = $derivative ± $uncertainty")

results in

ERROR: LoadError: MethodError: no method matching value(::Vector{Int64})

I suppose the Base.getindex method in general_rules.jl could be modified to work with a vector of vectors but I haven't yet figured out how. Or could there be a workaround using propagate? Probably related to #117.

Thanks!

[gaurav-arya]

Hi! Indeed this is not a fundamental limitation, just a lack of generality in our getindex rule.

Let's leave this issue open until I generalize the getindex rule, but you should indeed be able to work around it via propagate: see https://gaurav-arya.github.io/StochasticAD.jl/stable/devdocs.html#StochasticAD.propagate. For your particular case, another simpler solution could be to replace steps[step_index] with a map over each dimension such that only the scalar get-index is used.

[Eemilp]

Sounds good. I had problems with using propagate, but using map to convert to a scalar index works perfectly. Thanks for the suggestion!