def main():
file_add = sys.argv[1]
tst, val, tst_lbl, val_lbl = get_data(file_add)
vec_size = len(tst[0])
neuron = Perceptron(vec_size, 0.1)
# train
has_err = True
iters = 0
cnt_err = 0
while has_err:
has_err = False
cnt_err = 0
iters += 1
for x, y in zip(tst, tst_lbl):
p = neuron.input(x)
# print('input:', x)
# print('out:', p)
# print('expected:', y)
b = neuron.feedback(p, y, x)
if b:
cnt_err += 1
has_err = b or has_err
print(f"iteration {iters} finished (err: {cnt_err})")
print(f"learned weights: {neuron.w} bias: {neuron.b}")
# evaluate
count = len(val)
if count > 0:
cnt_err = 0
for x, y in zip(val, val_lbl):
p = neuron.input(x)
if p != y:
cnt_err += 1
ratio = cnt_err / count
print(f"err: {cnt_err}/{count} = {ratio:.2f}")
if vec_size == 2:
fig = plt.figure()
ax = fig.add_subplot(111)
plot_data(tst, tst_lbl, ax)
x0 = np.min(tst, 0)[0]
x1 = np.max(tst, 0)[0]
plot_line(neuron.w, neuron.b, ax, x0, x1)
plt.show()
