import random

class Connection:
    def __init__(self, in_, out_):
        self.weight = random.uniform(-1.0, 1.0) 
        self.in_ = in_
        self.out_ = out_
        self.value = 0.0

    def transfer(self, value:float):
        self.value = value

    def adjust(self, delta:float, learning_rate:float):
        old_weight = self.weight
        
        gradient = delta * self.value
        self.weight += learning_rate * gradient

        if self.in_ is None:
            return

        error_for_prev = delta * old_weight
        self.in_.adjust(error_for_prev, learning_rate)