d = euclideanDistance(x[best], y[best], x[j], y[j])
h = np.exp((-1*(d**2))/(2*(S**2)))
board[j].setWeight(board[j].getWeight() + N*h*(trX[i]-board[j].getWeight()))
cost = difWeight(prev, board)
print("Cost: {}".format(cost))
costV.append(cost)
prev = deepcopy(board)
bestNeurons = []
for i in range(len(trX)):
index = getBest(distance(trX[i], board))
bestNeurons.append((x[index], y[index]))
return bestNeurons, costV
trX, labels = Persistence.getTrxPaises()
# #Parameters
neuronNumber = 20 #neuronNumber^2
nIterations = 100
S = neuronNumber/2
N = 0.001
N_limit = 0.01
S_limit = 1
T1 = nIterations*len(trX)/math.log(S)
T2 = T1
board = initBoard(neuronNumber*neuronNumber, len(trX[0]))
bestNeurons, costV = train(board, trX)
Graphic.showCost(costV)
Graphic.showBoard(bestNeurons, labels, neuronNumber)
