请写一段Python代码，实现输入DFA输出最小化的DFA

在Python中，我们可以使用状态压缩（State Minimization）技术来优化有限自动机（Deterministic Finite Automaton，DFA）。首先，我们需要创建一个基础的DFA类，然后添加一些方法来合并等价的状态。以下是简化的示例：

class DFAState:
    def __init__(self, value=None, transitions={}):
        self.value = value
        self.transitions = transitions

class DFA:
    def __init__(self, states, initial_state, accepting_states):
        self.states = states
        self.initial_state = initial_state
        self.accepting_states = accepting_states

    def minimize(self):
        marked = set()
        eq_classes = {}
        for state in self.states:
            if state not in marked:
                new_class = DFAState(len(eq_classes) + 1)
                eq_classes[new_class] = {state}
                marked.add(state)
                for symbol, next_state in state.transitions.items():
                    if next_state not in marked:
                        eq_classes[new_class].add(next_state)
                        marked.add(next_state)

        minimized_dfa = DFA(
            [eq_classes[state] for state in eq_classes],
            eq_classes[self.initial_state],
            [state for class_ in eq_classes.values() if any(state in acc for acc in self.accepting_states) for state in class_]
        )

        return minimized_dfa

# 使用示例：
states = [
    DFAState('A', {'a': 'B', 'b': 'C'}),
    DFAState('B', {'a': 'A', 'b': 'D'}),
    DFAState('C', {'a': 'E', 'b': 'F'}),
    DFAState('D', {'a': 'D', 'b': 'F'}),
    DFAState('E'),
    DFAState('F')
]

initial_state = states[0]
accepting_states = [states[-2], states[-1]]

d = DFA(states, initial_state, accepting_states)
minimized_dfa = d.minimize()

在这个例子中，`minimize` 方法会遍历所有未标记的状态，并将它们归入等价类。等价类由共享相同输入映射的状态组成。最后返回的是一个由等价类组成的最小化DFA。