def test_3(steps):
weights = 1
print "Target converse {0}, {1} steps".format(weights, steps)
neuron = Neuron(weights, sigm, sigmp, error)
errors = []
for i in range(steps):
inputs = [random.random() for r in range(weights)]
target = 1.0 - inputs[0]
neuron.learn_1(inputs, target)
errors.append(neuron.last_error)
print report(errors)
def test_1(steps):
weights = 3
print "Linear combination of weights {0}, {1} steps".format(weights, steps)
neuron = Neuron(weights, sigm, sigmp, error)
errors = []
for i in range(steps):
inputs = [random.random() for r in range(weights)]
target = 2*inputs[0] + 0.3*inputs[1] - 0.7*inputs[2]
neuron.learn_1(inputs, target)
errors.append(neuron.last_error)
print report(errors)
def test_5(steps):
weights = 40
print "Target sqrt(avg) {0}, {1} steps".format(weights, steps)
neuron = Neuron(weights, sigm, sigmp, error)
errors = []
for i in range(steps):
inputs = [random.random() for r in range(weights)]
avg = sum(inputs)/len(inputs)
target = math.sqrt(avg)
neuron.learn_1(inputs, target)
errors.append(neuron.last_error)
print report(errors)
def test_4(steps):
weights = 40
print "Target max - min {0}, {1} steps".format(weights, steps)
neuron = Neuron(weights, sigm, sigmp, error)
errors = []
for i in range(steps):
inputs = [random.random() for r in range(weights)]
imax = max(inputs)
imin = min(inputs)
target = imax - imin
neuron.learn_1(inputs, target)
errors.append(neuron.last_error)
print report(errors)
