def main():
w = np.matrix([0., 0.])
index = 0
while True:
if (w.dot(paitentData[index].T) > 0) != ( hasCancerData[index] > 0):
w += paitentData[index] * hasCancerData[index]
index = 0
continue
index += 1
if index >= len(hasCancerData):
break
print w.tolist()[0]
#### Do the training here ###
mlt.plot_prec(paitentData.tolist(), hasCancerData, w.tolist()[0])
def main():
nn = NeuNet.NeuralNetwork([2, 10, 1])
nn.fit(xor_data, xor_expected)
mlt.plot_nn(xor_data, xor_expected, nn)
def main():
nn = NeuNet.NeuralNetwork([2, 10, 1])
nn.fit(xor_data, xor_expected)
mlt.plot_nn(xor_data, xor_expected, nn)
