module readout

using Flux.Optimise: apply!

using Statistics, Flux, Random, LinearAlgebra
using GeneralUtils
using ..types, ..readout, ..learn, ..forward

export readout!

#------------------------------------------------------------------------------------------------100

function readout!(kfn::knowledgeFn; correctAnswer=nothing)  # correctAnswer=nothing use for inference
    # clear output to start reading
    # kfn.on_out_t0 *= 0.0    #FIXME should I clear it before RSNN readout?
    respondCount = zeros(length(kfn.on_out_t0))

    # prepare signal used to read RSNN
    readoutSignal = zeros(length(kfn.passthrough_zt0))  
    readoutSignal[1] = 1
    readoutSignal[end] = 1

    lastKfnTimeStamp = kfn.timeStamp[1]
    for t in 1:kfn.on_tauOut[1]
        # println("t $t")
        tick = lastKfnTimeStamp + t
        if t == kfn.on_tauOut[1]
            println("")
        end
        if kfn.learningStage[1] == 0   # RSNN is in inference mode, do not change marker
            # skip
        else    # RSNN is in learning mode, assign marker for commiting wChange at the end of readout window.
            marker = t == kfn.on_tauOut[1] ? 4 : kfn.learningStage[1]
        end

        # RSNN forward ----------
        singleTimeReadout, on_out_t0, softmaxRespond = kfn(readoutSignal, tick, marker, 
                                                    correctAnswer=correctAnswer)
        _, _, respondPosition = Utils.findMax(softmaxRespond)
        respondCount += respondPosition
        
        if correctAnswer !== nothing
            kfn.kfnError = [Flux.logitcrossentropy(on_out_t0, correctAnswer)]
            learn!(kfn)
        end
    end

    _, readout, _ = Utils.findMax(respondCount/kfn.on_tauOut[1])

    return readout, kfn.on_out_t0
end










end # module