Python面向对象：深入理解继承中父类属性的初始化与传递

class A():
    def __init__(self, a=12) -> None:
        self.a = a

class B(A):
    def __init__(self) -> None:
        super().__init__() # 调用父类A的__init__方法

# 实例化A和B
instance_a = A(a=16)
instance_b = B()
print(f"instance_a.a: {instance_a.a}") # 期望输出 16
print(f"instance_b.a: {instance_b.a}") # 实际输出 12，为什么不是 16？

class A():
    def __init__(self, a=12) -> None:
        self.a = a
        print(f"A.__init__ called, self.a={self.a}")

class B(A):
    def __init__(self, a_val=None) -> None: # 接受一个参数来定制父类的a
        if a_val is not None:
            super().__init__(a=a_val) # 将参数传递给父类
        else:
            super().__init__() # 如果未提供，则使用父类默认值
        print(f"B.__init__ finished, self.a={self.a}")

# 实例化
instance_a = A(a=16)
instance_b_default = B()          # 使用A的默认值12
instance_b_custom = B(a_val=20)   # 传递20给父类a

print(f"\ninstance_a.a: {instance_a.a}")
print(f"instance_b_default.a: {instance_b_default.a}")
print(f"instance_b_custom.a: {instance_b_custom.a}")

A.__init__ called, self.a=16
A.__init__ called, self.a=12
B.__init__ finished, self.a=12
A.__init__ called, self.a=20
B.__init__ finished, self.a=20

instance_a.a: 16
instance_b_default.a: 12
instance_b_custom.a: 20

class SchedulingSystem(): # 对应原问题中的Class A
    def __init__(self, machines: list, jobs: list, capacities: dict) -> None:
        self.machines = machines
        self.jobs = jobs
        self.capacities = capacities
        print(f"SchedulingSystem initialized with machines: {self.machines}")

class AlgorithmEnvironment(): # 对应原问题中的Class B
    def __init__(self, scheduling_system_instance: SchedulingSystem) -> None:
        # AlgorithmEnvironment不再继承SchedulingSystem，而是持有它的一个实例
        self.scheduling_system = scheduling_system_instance
        print("AlgorithmEnvironment initialized.")

    def solve_problem(self):
        # AlgorithmEnvironment可以通过其持有的scheduling_system实例访问所有参数
        print(f"Algorithm is solving problem using machines: {self.scheduling_system.machines}")
        print(f"And jobs: {self.scheduling_system.jobs}")
        # ... 其他算法逻辑 ...

# 实例化调度系统
my_scheduling_system = SchedulingSystem(
    machines=['M1', 'M2'],
    jobs=['J1', 'J2', 'J3'],
    capacities={'M1': 10, 'M2': 15}
)

# 实例化算法环境，并将调度系统实例传入
my_algorithm_env = AlgorithmEnvironment(scheduling_system_instance=my_scheduling_system)

# 算法环境现在可以访问调度系统的参数
print(f"\nAlgorithm environment sees machines: {my_algorithm_env.scheduling_system.machines}")
my_algorithm_env.solve_problem()

SchedulingSystem initialized with machines: ['M1', 'M2']
AlgorithmEnvironment initialized.

Algorithm environment sees machines: ['M1', 'M2']
Algorithm is solving problem using machines: ['M1', 'M2']
And jobs: ['J1', 'J2', 'J3']