using Random, LinearAlgebra, StatsFuns
#Random.seed!(23)

# Activation function: Sigmoid function
Sigmoid(x) = 1.0 ./ (1.0 .+ exp(-x))
∇Sigmoid(x) = Sigmoid(x)*(1 - Sigmoid(x))

#Softmax(T, k) = exp(T[k])/sum([exp(T[l]) for l=1:length(T)])

Tanh(x) = tanh(x)
∇Tanh(x) = sech(x)

# Training function
function TrainMLN(Xin, Y, M, γ)
	printlm("da completare")
end

#Xxor = [0 0; 0 1; 1 0; 1 1]
#Yxor = [-1.0, 1, 1, -1.0]
#println("START LEARNING OR")
#TrainMLN(Xxor, Yxor, 3, 0.9)

L = [1 1 1; 1 0 1; 1 1 0; 1 0 0; 0 1 1; 0 0 1; 0 1 0; 0 0 0]
Y = [1, 1, 1, 1, -1, 1, -1, -1]
TrainMLN(L, Y, 5, 0.1)