refractoring

src/learn.jl

@@ -12,10 +12,6 @@ export learn!
 
 function learn!(m::model, modelRespond, correctAnswer=nothing)
     m.knowledgeFn[:I].learningStage = m.learningStage
-        
-    # # how many matched respond and correct answer
-    # matched = sum(isequal(modelRespond, correctAnswer)) 
-
     if correctAnswer === nothing
         correctAnswer_I = zeros(length(modelRespond))
     else
@@ -28,39 +24,6 @@ end
 """ knowledgeFn learn()
 """
 function learn!(kfn::kfn_1, correctAnswer::AbstractVector)  
-
-        #               ΔWeight          Conn. Strength
-        # case 1        no                  no          during input signal, no correct answer available, no answer
-        # case 2        no                  -           during input signal, no correct answer available, wrong answer
-        # case 3        +                   -           during input signal, correct answer available, no answer
-        # case 4        no                  -           during input signal, correct answer available, wrong answer
-        # case 5        no                  ++          during input signal, correct answer      
-        # case 6        no                  ++          after input signal, at correct timing, correct answer 
-        # case 6        +                   -           after input signal, at correct timing, no answer 
-        # case 9        no                  --          after input signal, at correct timing, wrong answer 
-        # case 7        adjust              +           after input signal, after correct timing (late), correct answer
-        # case 8                                        after input signal, after correct timing (late), no answer
-        # case 8        no                  -           after input signal, after correct timing (late), wrong answer 
-        
-        #                                    success
-    
-    if kfn.learningStage == "start_learning"
-        # reset params here instead of at the end_learning so that neuron's parameter data
-        # don't gets wiped and can be logged for visualization later
-        for n in kfn.neuronsArray
-            # epsilonRec need to be reset because it counting how many each synaptic fires and 
-            # use this info to calculate how much synaptic weight should be adjust
-            resetLearningParams!(n)
-        end
-
-        # clear variables
-        kfn.firedNeurons = Vector{Int64}()   
-        kfn.firedNeurons_t0 = Vector{Bool}()
-        kfn.firedNeurons_t1 = Vector{Bool}()
-
-        kfn.learningStage = "learning"
-    end
-
     # compute kfn error
     outs = [n.z_t1 for n in kfn.outputNeuronsArray]
     for (i, out) in enumerate(outs)