refractoring

src/readout.jl

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+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
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+end # module
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