def adjust(self,
target_result,
calculated_result,
in_data):
error = target_result - calculated_result
for i in range(len(in_data)):
correction = error * in_data[i] *self.learning_rate
self.weights[i] += correction
def above_line(point, line_func):
x, y = point
if y > line_func(x):
return 1
else:
return 0
points = np.random.randint(1, 100, (100, 2))
p = Perceptron(2)
def lin1(x):
return x + 4