Basic Neural Network AGK Tier 1

SetWindowSize(1024, 768, 0)
global Sum as float
Sum = 0
global Error as float
Error = 0
global Actual_Result as float
Actual_Result = 0
global Desired_Result as float
Desired_Result = 1
global Learning_Rate as float
Learning_Rate = 0.1
global Trials as integer
Trials = 10


global Inputs as float[4]

Inputs[1] = 0
Inputs[2] = 1
Inputs[3] = 0
Inputs[4] = 0

global Weights as float[4]

Weights[1] = 0
Weights[2] = 0
Weights[3] = 0
Weights[4] = 0

global Results as float[4]

Results[1] = 0
Results[2] = 0
Results[3] = 0
Results[4] = 0

function NeuralNetwork()
	Sum = 0
	for x = 1 to 4
		Results[x] = 0
		Results[x] = Inputs[x] * Weights[x]
		Sum = Results[x] + Sum
	next x
endfunction

function Evaluate_NeuralNetwork()
	for i = 1 to 4
		Actual_Result = Results[i] + Actual_Result
	next i
	Error = Desired_Result - Actual_Result
endfunction

function Learn_NeuralNetwork()
	for j = 1 to 4
		if Inputs[j] > 0
			Weights[j] = Weights[j] + Learning_Rate
		endif
	next j
endfunction

function Train_NeuralNetwork()
	while Trials > 0
		NeuralNetwork()
		Learn_NeuralNetwork()
		Trials = Trials - 1
	endwhile

	while Trials = 0
		NeuralNetwork()
		Evaluate_NeuralNetwork()
		Trials = Trials - 1
	endwhile
endfunction

do
	Train_NeuralNetwork()
	printC("Neural network output: ")
	print(Sum)
	printC("Error: ")
	print(Error)
	print("")
	Print("Inputs: ")
	for i = 1 to 4
		Printc(i)
		printc(": ")
		Print(Inputs[i])
	next i

	Print("")
	Print("Weights: ")
	for i = 1 to 4
		Printc(i)
		printc(": ")
		print(Weights[i])
	next i

	Print("")
	Print("Results: ")
	for i = 1 to 4
		Printc(i)
		printc(": ")
		print(Results[i])
	next i

	Print("")
	Print("Actual Result: ")
	Printc("1: ")
	Print(Actual_Result)

	Print("")
	Print("Desired Result: ")
	Printc("1: ")
	Print(Desired_Result)
	sync()
loop