改变字典规则不香吗？
改变字典的键值规则就可以把从一本书里挑随机单词这件事轻松搞定，我真搞不懂参考答案为啥要那么折腾。在Think Python 2的第十三章里，字典的默认规则是单词是键，词频是键值。既然这道题要唯一的索引找随机单词，我把键值变成唯一序号不就完事大吉了？再来一个zip把字典的键值和键互换，random.choice()直接就到达随机单词了。我只改了生成字典的规则，耗时0.12秒，参考答案折腾了不只一点点，耗时0.42秒。之所以参考答案不修改字典规则，是因为他们要灌输python拼装模块的特性，拼装很方便，但事实证明效率不一定最高。
This algorithm works, but it is not very efficient; each time you choose a random word, it rebuilds the list, which is as big as the original book. An obvious improvement is to build the list once and then make multiple selections, but the list is still big.
An alternative is: Use keys to get a list of the words in the book. Build a list that contains the cumulative sum of the word frequencies (see Exercise 2). The last item in this list is the total number of words in the book, n. Choose a random number from 1 to n. Use a bisection search (See Exercise 10) to find the index where the random number would be inserted in the cumulative sum. Use the index to find the corresponding word in the word list.
Exercise 7: Write a program that uses this algorithm to choose a random word from the book. Solution: http://thinkpython2.com/code/analyze_book3.py.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 | import string import random from time import time def set_book(fin1): useless = string.punctuation + string.whitespace + '“' + '”' # 标点符号、换行符全部咔嚓掉 d = {} i = 1 for line in fin1: line = line.replace('-', ' ') # 有-的单词全部一分为二，这样真的好吗？ for word in line.split(): word = word.strip(useless) word = word.lower() if word not in d: d[word] = i # 录入字典的时候键值就是序号 i += 1 # d[word] = d.get(word, 0) + 1 # 反正我不算词频，这个没必要了 return d fin1 = open('emma.txt', encoding='utf-8') start = time() book1 = set_book(fin1) book2 = dict(zip(book1.values(), book1.keys())) # 键和键值互换，序号成了唯一索引号 print('100 random words in book') for i in range(100): if i > 1 and i%8 == 0: print() print(random.choice(book2), end=' ') # 索引号找词，想多快有多快 print() end = time() print(end - start) # 100 random words in book # solicit laughing preserve inebriety elton's unimpeded effusions unselfish # intimate connect native judges charities travel informs colours # enigmas bragge case greensward cox's particularly unexampled promise # prone greensward dignity maps fourth christmas creature maximum # graver mildest pleasant corrected increased named partridge marks # following kept gloom conjecturing parlour inheriting say consulting # magnified abundant produces sons malt add unenforceability beautifully # richly striking confuse greatness asleep steps humility upon # already paper delight liberties confide appendages undecided male # prophecies esteem unadorned likelihood shopping deeply unbiased horrors # man's dumplings business chapter shakespeare sees counsels attentive # silenced ventured singular double mean waltzes requisite checks # unattended qualified blessed surmises # 0.12100672721862793 |
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