Perceptron AND/OR gate

threshold = 0.5 # any value
learning_rate = 0.1 # any value
weights = [1, 1, 1] # x0 = 1 is constant, x1, x2 any value

# AND GATE
training_set = [((1, 0, 0), 0), ((1, 0, 1), 0), ((1, 1, 0), 0), ((1, 1, 1), 1)]

# OR GATE
#training_set = [((1, 0, 0), 0), ((1, 0, 1), 1), ((1, 1, 0), 1), ((1, 1, 1), 1)]

def dot_product(values, weights):
return sum(value * weight for value, weight in zip(values, weights))

while True:
    print('-' * 60)
    error_count = 0
    for input_vector, desired_output in training_set:
        result = dot_product(input_vector, weights) > threshold
        error = desired_output - result
        if error != 0:
            error_count += 1
            for index, value in enumerate(input_vector):
                weights[index] += learning_rate * error * value
    if error_count == 0:
        break

print(str(weights[1]) + ", " + str(weights[2])) # w1 & w2
print(threshold-weights[0]) # threshold