NFA确定化的Python代码

确定化有限自动机（Deterministic Finite Automaton, DFA）是将非确定型有限自动机（Non-Deterministic Finite Automaton, NFA）转换为等价的确定型自动机的过程。这个过程通常通过构造一个状态图并应用算法来实现，如Hopcroft's Minimization Algorithm。

下面是一个简单的Python代码示例，展示如何使用字典数据结构模拟NFA到DFA的转换过程：

from collections import defaultdict

def nfa_to_dfa(nfa):
    # 初始化DFA
    dfa = {'q0': set()}

    # 模拟状态转换函数
    def transition(state, input_char):
        transitions = nfa.get((state, input_char), None)
        if not transitions:
            return None
        return {df_state for df_state in transitions}

    # 添加起点和接受状态
    dfa['q0'] |= {nfa['start']}
    accepting_states = {state for state, accept in nfa.items() if accept}
    
    while True:
        new_dfa = {}
        for df_state, inputs in dfa.items():
            for input_char in inputs:
                next_states = transition(df_state, input_char)
                if not next_states:
                    continue
                for ns in next_states:
                    if ns not in new_dfa:
                        new_dfa[ns] = set()
                    new_dfa[ns] |= {input_char}
        
        # 检查是否有新的状态添加
        if len(new_dfa) == len(dfa):
            break
        else:
            dfa = new_dfa

    final_dfa = {'qF': {input_char for state, chars in dfa.items() if 'qF' in state and chars}}
    dfa.update(final_dfa)

    return dfa

# 使用示例
nfa_example = {
    ('q0', 'a'): {'q1', 'q2'},
    ('q0', 'b'): {'q3'},
    ('q1', 'a'): {'q4'},
    ('q1', 'b'): {'q1'},
    ('q2', 'a'): {'q5'},
    ('q2', 'b'): {'q2'},
    ('q3', 'a'): {'q6'},
    ('q3', 'b'): {'q3'},
    ('q4', 'a'): {'q7'},
    ('q4', 'b'): {'q4'},
    ('q5', 'a'): {'q8'},
    ('q5', 'b'): {'q5'},
    ('q6', 'a'): {'q9'},
    ('q6', 'b'): {'q6'},
    ('q7', 'a'): {'qF'},
    ('q7', 'b'): {'q7'},
    ('q8', 'a'): {'qF'},
    ('q8', 'b'): {'q8'},
    ('q9', 'a'): {'qF'},
    ('q9', 'b'): {'q9'},
    ('qF', 'a'): {},
    ('qF', 'b'): {},
    'start': 'q0'
}

determined_dfa = nfa_to_dfa(nfa_example)

在这个例子中，`nfa_to_dfa`函数接收一个表示NFA的字典，它模拟了状态转移规则，并最终返回一个DFA的状态机器模型。请注意这只是一个简化版本，实际应用中可能需要更复杂的处理逻辑。