locw.jl

"""
    locw(Xtrain, Ytrain, X; listnn, listw = nothing, fun, verbose = false, 
        kwargs...)
Compute predictions for a given kNN model.
* `Xtrain` : Training X-data.
* `Ytrain` : Training Y-data.
* `X` : X-data (m observations) to predict.
Keyword arguments:
* `listnn` : List (vector) of m vectors of indexes.
* `listw` : List (vector) of m vectors of weights.
* `fun` : Function computing the model on 
    the m neighborhoods.
* `verbose` : Boolean. If `true`, fitting information
    are printed.
* `kwargs` : Keywords arguments to pass in function `fun`.
    Each argument must have length = 1 (not be a collection).

Each component i of `listnn` and `listw` contains the indexes 
and weights, respectively, of the nearest neighbors of x_i in Xtrain. 
The sizes of the neighborhood for i = 1,...,m can be different.
"""
function locw(Xtrain, Ytrain, X; listnn, listw = nothing, fun, verbose = false, 
        kwargs...)
    m = nro(X)
    q = nco(Ytrain)
    pred = similar(Ytrain, m, q)
    Threads.@threads for i = 1:m
    #@inbounds for i = 1:m
        verbose ? print(i, " ") : nothing
        s = listnn[i]
        length(s) == 1 ? s = (s:s) : nothing
        zYtrain = Ytrain[s, :]
        ## For discrimination, 
        ## case where all the neighbors have the same class
        if q == 1 && length(unique(zYtrain)) == 1
            pred[i, :] .= zYtrain[1]
        ## End
        else
            if isnothing(listw)
                fm = fun(Xtrain[s, :],  zYtrain; kwargs...)
            else
                fm = fun(Xtrain[s, :], zYtrain, mweight(listw[i]); kwargs...)
            end
            pred[i, :] = predict(fm, vrow(X, i:i)).pred
        end
    end
    verbose ? println() : nothing    
    (pred = pred,)
end