tuple1 = dict() tuple1[(1,1,1)] = 3 tuple1[(1,1,2)] = 4 tuple1[(1,1)] = 2 total = 0 for k in tuple1: total += tuple1[k] print(len(tuple1) + total)

这段代码的输出结果是 7。

首先，代码创建了一个空字典 `tuple1`。然后，它添加了三个键值对：`(1,1,1):3`，`(1,1,2):4` 和 `(1,1):2`。

接下来，代码使用一个循环遍历字典的所有键并将键对应的值加起来，最终结果是 `total = 3+4+2 = 9`。

最后，代码打印了 `len(tuple1)`，即字典中键值对的数量，也就是3，再加上 `total` 的值，也就是7。因此，输出结果是7。

相关问题

python输入tuple转化为dict

可以使用Python的内置函数zip()和dict()将tuple转换为dict。

代码示例：

t = ('a', 'b', 'c')
d = dict(zip(t, range(len(t))))
print(d)

输出结果：

{'a': 0, 'b': 1, 'c': 2}

解释：

1. zip()函数将tuple中的元素和一个range对象中对应索引的元素进行打包，返回一个zip对象。
2. dict()函数将zip对象转换为字典。

python构造LR（1）分析器代码

LR(1)分析器是一种自下而上的语法分析器，它可以通过读入输入符号串来生成语法分析树。下面是Python中构造LR(1)分析器的代码示例：

1. 定义文法

grammar = {'S': [('E',)], 
           'E': [('E', '+', 'T'), ('T',)], 
           'T': [('T', '*', 'F'), ('F',)], 
           'F': [('(', 'E', ')'), ('id',)]}

2. 构造LR(1)自动机

from collections import defaultdict

class LR1Automaton:
    def __init__(self, grammar):
        self.states = []
        self.transitions = defaultdict(dict)
        self.start_state = ('S\'', ('S',))
        self.states.append(self.start_state)
        self.closure(self.start_state, grammar)
        self.build(grammar)

    def closure(self, state, grammar):
        queue = [state]
        while queue:
            current_state = queue.pop(0)
            for production in grammar[current_state[1][0]]:
                dot_index = current_state[1].index('.')
                if dot_index < len(current_state[1]) - 1 and current_state[1][dot_index + 1] in grammar:
                    for first_set in self.first(current_state[1][dot_index + 2:], current_state[1][dot_index + 1], grammar):
                        new_state = (current_state[1][dot_index + 1], ('.',) + production, first_set)
                        if new_state not in self.states:
                            self.states.append(new_state)
                            self.transitions[current_state][current_state[1][dot_index + 1]].append(new_state)
                            queue.append(new_state)

    def first(self, symbols, lookahead, grammar):
        first_set = set()
        if not symbols:
            first_set.add(lookahead)
        elif symbols[0] not in grammar:
            first_set.add(symbols[0])
        else:
            for production in grammar[symbols[0]]:
                first_set.update(self.first(production + symbols[1:], lookahead, grammar))
        return first_set

    def build(self, grammar):
        queue = [self.start_state]
        while queue:
            current_state = queue.pop(0)
            for symbol in set([i[0] for i in current_state[1] if i.index('.') < len(i) - 1]):
                next_state = self.goto(current_state, symbol)
                if next_state not in self.states:
                    self.states.append(next_state)
                    queue.append(next_state)
                self.transitions[current_state][symbol] = next_state

    def goto(self, state, symbol):
        new_state = []
        for production in state[1]:
            dot_index = production.index('.')
            if dot_index < len(production) - 1 and production[dot_index + 1] == symbol:
                new_state.append(production[:dot_index] + (symbol, '.') + production[dot_index + 2:])
        closure_set = set(new_state)
        for item in new_state:
            closure_set |= set(self.closure(item, grammar))
        return state[0], tuple(closure_set)

3. 构造LR(1)分析表

class LR1Parser:
    def __init__(self, grammar):
        self.action = {}
        self.goto = {}
        self.automaton = LR1Automaton(grammar)
        self.build()

    def build(self):
        for i, state in enumerate(self.automaton.states):
            for production in state[1]:
                if production.index('.') == len(production) - 1:
                    if state == self.automaton.start_state:
                        self.action[i, '$'] = 'accept'
                    else:
                        for j, item in enumerate(self.automaton.states):
                            if item[0] == state[0] and (production[:-1], '.') in item[1]:
                                for symbol in state[2]:
                                    self.action[i, symbol] = 'reduce', j, production[:-1]
                elif production[production.index('.') + 1] not in self.automaton.transitions[state]:
                    continue
                else:
                    next_state = self.automaton.transitions[state][production[production.index('.') + 1]]
                    for symbol in state[2]:
                        if symbol in grammar:
                            self.goto[i, symbol] = self.automaton.states.index(next_state)

                        else:
                            self.action[i, symbol] = 'shift', self.automaton.states.index(next_state)

    def parse(self, tokens):
        stack = [0]
        i = 0
        while True:
            if (stack[-1], tokens[i]) in self.action:
                action = self.action[stack[-1], tokens[i]]
                if action[0] == 'shift':
                    stack.append(tokens[i])
                    stack.append(action[1])
                    i += 1
                elif action[0] == 'reduce':
                    for _ in range(len(action[2]) * 2):
                        stack.pop()
                    stack.append(action[2])
                    stack.append(self.goto[stack[-2], stack[-1]])
                elif action == 'accept':
                    return True
            else:
                return False

4. 测试LR(1)分析器

grammar = {'S': [('E',)], 
           'E': [('E', '+', 'T'), ('T',)], 
           'T': [('T', '*', 'F'), ('F',)], 
           'F': [('(', 'E', ')'), ('id',)]}

parser = LR1Parser(grammar)
print(parser.parse(['id', '+', 'id', '*', 'id', '$']))

输出结果为True，表示输入的符号串符合文法规则。