for i in range(0, 1000):
#Gerando funcao aleatoria
func = Function()
# func.set(1, 0)
x0 = uniform(-1,1)
y0 = uniform(-1,1)
x1 = uniform(-1,1)
y1 = uniform(-1,1)
func.buildFromPoints( x0, y0, x1, y1)
# func._print()
#Gerando pontos aleatorios com base na funcao
X = generatePoints(100)
y = generateY(func, X)
#Treinando modelo com perceptron
perc = Perceptron()
perc.train(X, y)
# print( 'Iterations: ', perc.iterations)
iterations.append(perc.iterations)
#Plotando dados na amostra de treinamento
xs = [ x[0] for x in X]
ys = [ x[1] for x in X]
errors = []
for iter in range(0, 1000):
func = Function()
x0 = uniform(-1, 1)
y0 = uniform(-1, 1)
x1 = uniform(-1, 1)
y1 = uniform(-1, 1)
func.buildFromPoints(x0, y0, x1, y1)
# func._print()
#Gerando pontos aleatorios com base na funcao
X = generatePoints(100)
X_with_x0 = [[1] + x for x in X] ##adicionando bias
y = generateNoise(generateY(func, X))
# print(y)
perc = PocketPLA()
perc.train(X, y, MAX_ITERATIONS=50)
#gerando dados fora da amostra de treino
X = generatePoints(1000)
X_with_x0 = [[1] + x for x in X] ##adicionando bias
y = generateY(func, X) #generateNoise( generateY(func, X) )
# plot(X,y,perc,func)
#Calculando Erro Fora da amostra
errorCount = 0