implement start learning

src/learn.jl

@@ -10,70 +10,37 @@ export learn!
 
 #------------------------------------------------------------------------------------------------100
 
-function learn!(m::model, modelRespond, correctAnswer=nothing, correctTiming=nothing)
-
-    # set all KFN
-    if m.learningStage == "start_learning"     
-        m.knowledgeFn[:I].learningStage = "start_learning"
-    elseif m.learningStage == "end_learning"
-        m.knowledgeFn[:I].learningStage = "end_learning"
-    else
-    end
-    
-    #WORKING compute error
-    # timingError = 
-
-
-    too_early = m.modelParams[:perfect_timing] - m.timeStep
-    model_error = (model_respond .- correct_answer) * too_early
-
-
-
-
-
-
-
-
-
-    model_error = Flux.logitcrossentropy(model_respond, correct_answer)
-    output_elements_error = model_respond - correct_answer
-
-    learn!(m.knowledgeFn[:I], model_error, output_elements_error)
-
-
-
+function learn!(m::model, modelRespond, correctAnswer=nothing)
+    m.knowledgeFn[:I].learningStage = m.learningStage
+        #               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
 
+    # how many matched respond and correct answer
+    matched = sum(isequal(modelRespond, correctAnswer)) 
 
-    return model_error
+    correctAnswer_I = correctAnswer # correct answer for kfn I
+    learn!(m.knowledgeFn[:I], correctAnswer_I)
+
+    # return model_error
 end
 
 
-# function learn!(m::model, raw_model_respond, correct_answer=nothing)
-#     if m.learningStage != "doing_inference"
-#         model_error = Flux.logitcrossentropy(raw_model_respond, correct_answer)
-#         output_elements_error = raw_model_respond - correct_answer
-
-#         learn!(m.knowledgeFn[:I], model_error, output_elements_error)
-#     else
-#         model_error = nothing
-#     end
-
-#     return model_error
-# end
-
-
-
-
 """ knowledgeFn learn()
 """
-function learn!(kfn::knowledgeFn, error::Union{Float64,Nothing}=nothing,
-                outputError::Union{Vector,Nothing}=nothing)
-    kfn.error = error
-    kfn.outputError = outputError
-
-    kfn.learningStage = m.learningStage
-    if m.learningStage == "start_learning"
+function learn!(kfn::kfn_1, correctAnswer=nothing)
+    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
@@ -85,6 +52,10 @@ function learn!(kfn::knowledgeFn, error::Union{Float64,Nothing}=nothing,
         # clear variables
         kfn.firedNeurons = Vector{Int64}()   
         kfn.outputs = nothing
+
+        kfn.learningStage = "learning"
+    elseif kfn.learningStage = "end_learning"
+        kfn.learningStage = "inference"
     end
 
     # Threads.@threads for n in kfn.neuronsArray