我正在尝试在Python中实现NFA以识别单词，但是我的代码无法正常工作， [英] I am trying to implement NFA in Python to recognize words but my code doesn't work,

问题描述

我正在尝试实现一种识别单词的方法.我编写了以下代码，并尝试在纸上遵循我的代码，并使用示例输入逐步执行它，但是我找不到我的代码没有按我希望他做的那样的原因.有人看到这个缺陷吗?我看不到它，我对为什么它不起作用感到困惑.

I am trying to implement a method that recognizes words. I have written the following code and have tried to follow my code on paper and execute it step by step with example inputs, but I can't find the reason why my code is not doing what I want him to do. Does anyone see the flaw? I can't see it and I am confused on why it doesn't work.

from collections import defaultdict

class NFA:
    def __init__(self, initial, trns, final):
        self.initial = initial
        self.final = set(final)
        self.trns = defaultdict(set)
        for (src, char, tgt) in trns:
            self.trns[src, char].add(tgt)


    def recognizewords(self, strng):
        strang = [char for char in strng]
        strang.reverse()
        visited = set()
        agenda = [self.initial]
        while strang and agenda:
            currentletter = strang.pop()
            current = agenda.pop()
            visited.add(current)
            if (current, currentletter) in self.trns.keys():
                state = self.trns[(current, currentletter)]
                for st in state:
                    if strang == [] and state in self.final:
                        return True
            for i in self.trns[(current, currentletter)]:
                agenda.append(i)
        return False

exampleO = NFA(0, [(0,'o',1), (1,'k',2), (2,'i',1), (2,'!',3)], [3])
print(exampleO.recognizewords("ok!"))

它应该返回True，因为在某一时刻我的列表"strang"将为空(当我将currentletter分配给！"时)，同时3在self.final中，因为self.final为[3]对于我的对象示例O ....

It should return True, because at one point my list "strang" will be empty (when I assigned currentletter to "!") and at the same time 3 is in self.final, because self.final is [3] for my object exampleO....

推荐答案

因此，事实证明，非递归解决方案比正确执行回溯要复杂得多(它需要更多堆栈).我更改了一些变量名，对我而言，这更合逻辑.下面有两个版本.第二个修改了第一个，仅作了一些细微的更改以支持对空字符串的过渡:

So as it turns out that the non-recursive solution is a bit more complicated than what you had to do backtracking correctly (it requires more stacks). I have changed a few variable names, which for me are more logical. There are two versions below. The second one modifeds the first with just a few slight changes to support transitions on empty strings:

from collections import defaultdict

class NFA:
    def __init__(self, initial, trns, final):
        self.initial = initial
        self.final = set(final)
        self.trns = defaultdict(set)
        for (src, char, tgt) in trns:
            self.trns[src, char].add(tgt)


    def recognizewords(self, strng):
        strlen = len(strng)
        if strlen == 0:
            return self.initial in self.final
        index = 0
        next_states = [self.initial]
        next_states_stack = []
        index_stack = []
        while index < strlen:
            current_letter = strng[index]
            if next_states:
                state = next_states.pop()
                if (state, current_letter) in self.trns.keys():
                    new_next_states = self.trns[(state, current_letter)]
                    if new_next_states & self.final:
                        # did we use up all the characters?
                        return index == strlen - 1
                    next_states_stack.append(next_states)
                    index_stack.append(index)
                    next_states = list(new_next_states)
                    index += 1
            elif next_states_stack:
                next_states = next_states_stack.pop()
                index = index_stack.pop()
            else:
                return False
        return False

# ab(d|e)
exampleO = NFA(0, [(0,'a',1), (1,'b',2), (1,'b',3), (2,'d',4), (3,'e',4)], [4])
print(exampleO.recognizewords("abd"))
print(exampleO.recognizewords("abe"))

打印:

True
True

支持在空字符串上进行过渡的变体

from collections import defaultdict

class NFA_Epsilon:
    def __init__(self, initial, trns, final):
        self.initial = initial
        self.final = set(final)
        self.trns = defaultdict(set)
        self.epsilon_states = set()
        for (src, char, tgt) in trns:
            if char == '':
                self.epsilon_states.add(src)
            self.trns[src, char].add(tgt)


    def recognizewords(self, strng):
        strlen = len(strng)
        if strlen == 0:
            return self.initial in self.final
        index = 0
        next_states = [self.initial]
        next_states_stack = []
        index_stack = []
        while index < strlen:
            if next_states:
                state = next_states.pop()
                current_letter = '' if state in self.epsilon_states else strng[index]
                if (state, current_letter) in self.trns.keys():
                    new_next_states = self.trns[(state, current_letter)]
                    if new_next_states & self.final:
                        # did we use up all the characters?
                        return index == strlen - 1
                    next_states_stack.append(next_states)
                    index_stack.append(index)
                    next_states = list(new_next_states)
                    if current_letter != '':
                        index += 1
            elif next_states_stack:
                next_states = next_states_stack.pop()
                index = index_stack.pop()
            else:
                return False
        return False


# ab(cd|ef)gh
example1 = NFA_Epsilon(0, [
    (0,'a',1),
    (1,'b',2),
    (2,'',3),
    (2,'',6),
    (3,'c',4),
    (4,'d',5),
    (5,'',9),
    (6,'e',7),
    (7,'f',8),
    (8,'',9),
    (9,'g',10),
    (10,'h',11)
    ],[11])
print(example1.recognizewords('abcdgh'))
print(example1.recognizewords('abefgh'))

打印:

True
True

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