Title: ThinkPython 双语学编程 Chapter 13
Date: 2015-12-31
Category: ThinkPython
Tag: 双语,Python,ThinkPython
At this point you have learned about Python’s core data structures, and you have seen some of the algorithms that use them. If you would like to know more about algorithms, this might be a good time to read Chapter 13. But you don’t have to read it before you go on; you can read it whenever you are interested.
This chapter presents a case study with exercises that let you think about choosing data structures and practice using them.
到现在位置，你已经学过 Python 中最核心的数据结构了，也学过了一些与之对应的各种算法了。如果你想要对算法进行深入的了解，就可以来读一下第十三章。但不读这一章也可以继续；无论什么时候读都可以，感兴趣了就来看即可。
本章通过一个案例和一些练习，来讲解一下如何选择和使用数据结构。
As usual, you should at least attempt the exercises before you read my solutions.
跟往常一样，你最起码也得先自己尝试做一下这些练习，然后再看参考答案。
Write a program that reads a file, breaks each line into words, strips whitespace and punctuation from the words, and converts them to lowercase.
写一个读取文件的程序，把每一行拆分成一个个词，去掉空白和标点符号，然后把所有单词都转换成小写字母的。
Hint: The string module provides a string named whitespace, which contains space, tab, newline, etc., and punctuation which contains the punctuation characters. Let’s see if we can make Python swear:
提示：字符串模块 string 提供了一个名为 whitespace 的字符串，包含了空格、跳表符、另起一行等等，然后还有个 punctuation 模块，包含了各种标点符号的字符。咱们可以试试让 Python 把标点符号都给显示一下：
>>> import string
>>>string.punctuation
'!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~' Also, you might consider using the string methods strip, replace and translate.
另外你也可以试试字符串模块的其他方法，比如 strip、replace 以及 translate。
Go to [Project Gutenberg] (http://gutenberg.org) and download your favorite out-of-copyright book in plain text format.
访问[古登堡计划网站] (http://gutenberg.org)，然后下载一个你最喜欢的公有领域的书，要下载纯文本格式的哈。，然后下载一个你最喜欢的公有领域的书，要下载纯文本格式的哈。)
Modify your program from the previous exercise to read the book you downloaded, skip over the header information at the beginning of the file, and process the rest of the words as before.
修改一下刚才上一个练习你写的程序，让这个程序能读取你下载的这本书，跳过文件开头部分的信息，继续以上个练习中的方式来处理一下整本书的正文。
Then modify the program to count the total number of words in the book, and the number of times each word is used.
然后再修改一下程序，让程序能统计一下整本书的总单词数目，以及每个单词出现的次数。
Print the number of different words used in the book. Compare different books by different authors, written in different eras. Which author uses the most extensive vocabulary?
输出一下这本书中不重复的单词的个数。对比一下不同作者、不同地域的书籍。哪个作者的词汇量最丰富？
Modify the program from the previous exercise to print the 20 most frequently-used words in the book.
再接着修改程序，输出一下每本书中最频繁出现的20个词。
Modify the previous program to read a word list (see Section 9.1) and then print all the words in the book that are not in the word list. How many of them are typos? How many of them are common words that should be in the word list, and how many of them are really obscure?
接着修改，让程序能读取一个单词列表（参考9.1），然后输出一下所有包含在书中，但不包含于单词列表中的单词。看看这些单词中有多少是排版错误的？有多少是本应被单词列表包含的常用单词？有多少是很晦涩艰深的罕见词汇？
Given the same inputs, most computer programs generate the same outputs every time, so they are said to be deterministic. Determinism is usually a good thing, since we expect the same calculation to yield the same result. For some applications, though, we want the computer to be unpredictable. Games are an obvious example, but there are more.
输入相同的情况下，大多数计算机程序每次都会给出相同的输出，这也叫做确定性。确定性通常是一件好事，因为我们都希望同样的运算产生同样的结构。但有时候为了一些特定用途，咱们就需要让计算机能有不可预测性。比如游戏等等，有很多很多这样的情景。
Making a program truly nondeterministic turns out to be difficult, but there are ways to make it at least seem nondeterministic. One of them is to use algorithms that generate pseudorandom numbers. Pseudorandom numbers are not truly random because they are generated by a deterministic computation, but just by looking at the numbers it is all but impossible to distinguish them from random.
然而，想让一个程序真正变成不可预测的，也是很难的，但好在有办法能让程序看上去不太确定。其中一种方法就是通过算法来产生假随机数。假随机数，顾名思义就知道不是真正随机的了，因为它们是通过一种确定性的运算来得到的，但这些数字看上去是随机的，很难与真正的随机数区分。
（译者注：这里大家很容易一带而过，而不去探究到底怎样能确定是真随机数。实际上随机数是否能得到以及是否存在会影响哲学判断，可知论和不可知论等等。那么就建议大家思考和搜索一下，随机数算法产生的随机数和真正随机数有什么本质的区别，以及是否有办法得到真正的随机数。如果有，如何得到呢？）
The random module provides functions that generate pseudorandom numbers (which I will simply call “random” from here on).
random 模块提供了生成假随机数的函数（从这里开始，咱们就用随机数来简称假随机数了哈）。
The function random returns a random float between 0.0 and 1.0 (including 0.0 but not 1.0). Each time you call random, you get the next number in a long series. To see a sample, run this loop:
函数 random 返回一个在0.0到1.0的前闭后开区间（就是包括0.0但不包括1.0，这个特性在 Python 随处都是，比如序列的索引等等）的随机数。每次调用 random，就会得到一个很长的数列中的下一个数。如下这个循环就是一个例子了：
import random for i in range(10):
x = random.random()
print(x)The function randint takes parameters low and high and returns an integer between low and high (including both).
randint函数接收两个参数作为下界和上界，然后返回一个二者之间的整数，这个整数可以是下界或者上界。
>>> random.randint(5, 10)
5
>>> random.randint(5, 10)
9To choose an element from a sequence at random, you can use choice:
choice 函数可以用来从一个序列中随机选出一个元素：
>>> t = [1, 2, 3]
>>> random.choice(t)
2
>>> random.choice(t)
3The random module also provides functions to generate random values from continuous distributions including Gaussian, exponential, gamma, and a few more.
random 模块还提供了其他一些函数，可以计算某些连续分布的随机值，比如Gaussian高斯分布, exponential指数分布, gamma γ分布等等。
Write a function named choose_from_hist that takes a histogram as defined in Section 11.2 and returns a random value from the histogram, chosen with probability in proportion to frequency. For example, for this histogram:
写一个名为 choose_from_hist 的函数，用这个函数来处理一下11.2中定义的那个histogram函数，从histogram 的值当中随机选择一个，这个选择的概率按照比例来定。比如下面这个histogram：
>>> t = ['a', 'a', 'b']
>>> hist = histogram(t)
>>> hist
{'a': 2, 'b': 1}your function should return ’a’ with probability 2/3 and ’b’ with probability 1/3.
你的函数就应该返回a 的概率为2/3，返回b 的概率为1/3
You should attempt the previous exercises before you go on. You can download my solution from Here.
你得先把前面的练习作一下，然后再继续哈。可以从这里下载我的样例代码。
You will also need This.
此外还要下载这个。
Here is a program that reads a file and builds a histogram of the words in the file:
下面这个程序先读取一个文件，然后对该文件中的词频进行了统计：
import string
def process_file(filename):
hist = dict()
fp = open(filename)
for line in fp:
process_line(line, hist)
return hist
def process_line(line, hist):
line = line.replace('-', ' ')
for word in line.split():
word = word.strip(string.punctuation + string.whitespace)
word = word.lower()
hist[word] = hist.get(word, 0) + 1
hist = process_file('emma.txt')This program reads emma.txt, which contains the text of Emma by Jane Austen.
上面这个程序读取的是 emma.txt 这个文件，该文件是简奥斯汀的小说《艾玛》。
process_file loops through the lines of the file, passing them one at a time to process_line. The histogram hist is being used as an accumulator.
process_file这个函数遍历整个文件，逐行读取，然后把每行的内容发给process_line函数。词频统计函数 hist 在该程序中是一个累加器。
process_line uses the string method replace to replace hyphens with spaces before using split to break the line into a list of strings. It traverses the list of words and uses strip and lower to remove punctuation and convert to lower case. (It is a shorthand to say that strings are “converted”; remember that string are immutable, so methods like strip and lower return new strings.)
process_line使用字符串的方法 replace把各种连字符都用空格替换，然后用 split 方法把整行打散成一个字符串列表。程序遍历整个单词列表，然后用 strip 和 lower 这两个方法移除了标点符号，并且把所有字母都转换成小写的。（一定要记住，这里说的『转换』是图方便而已，实际上并不能转换，要记住字符串是不可以修改的，strip 和 lower 这些方法都是返回了新的字符串，一定要记得！）
Finally, process_line updates the histogram by creating a new item or incrementing an existing one.
To count the total number of words in the file, we can add up the frequencies in the histogram:
最终，process_line 函数通过建立新项或者累加已有项，对字频统计 histogram 进行了更新。
要计算整个文件中的单词总数，就可以把 histogram 中的所有频数加到一起就可以了：
def total_words(hist):
return sum(hist.values())The number of different words is just the number of items in the dictionary:
不重复的单词的数目也就是字典中项的个数了：
def different_words(hist):
return len(hist)Here is some code to print the results:
输出结果的代码如下：
print('Total number of words:', total_words(hist))
print('Number of different words:', different_words(hist))And the results:
结果如下所示：
Total number of words: 161080
Number of different words: 7214To find the most common words, we can make a list of tuples, where each tuple contains a word and its frequency, and sort it.
The following function takes a histogram and returns a list of word-frequency tuples:
要找到最常用的词，可以做一个元组列表，每一个元组包含一个单词和该单词出现的次数，然后整理一下这个列表，就可以了。
下面的函数就接收了词频统计结果，然后返回一个『单词-次数』元组组成的列表：
def most_common(hist):
t = []
for key, value in hist.items():
t.append((value, key))
t.sort(reverse=True)
return tIn each tuple, the frequency appears first, so the resulting list is sorted by frequency. Here is a loop that prints the ten most common words:
这些元组中，要先考虑词频，返回的列表因此根据词频来排序。下面是一个输出最常用单词的循环体：
t = most_common(hist)
print('The most common words are:')
for freq, word in t[:10]:
print(word, freq, sep='\t')I use the keyword argument sep to tell print to use a tab character as a “separator”, rather than a space, so the second column is lined up. Here are the results from Emma:
此处用了关键词 sep 来让 print 输出的时候以一个tab跳表符来作为分隔，而不是一个空格，这样第二列就会对齐。下面就是对《艾玛》这本小说的统计结果：
(译者注：这个效果在 Python 下很明显，此处 markdown 我刚开始熟悉，不清楚咋实现。)
The most common words are:
to 5242
the 5205
and 4897
of 4295
i 3191
a 3130
it 2529
her 2483
was 2400
she 2364This code can be simplified using the key parameter of the sort function. If you are curious, you can read about it at Here.
如果使用 sort 函数的 key 参数，上面的代码还可以进一步简化。如果你好奇的话，可以进一步阅读一下说明
We have seen built-in functions and methods that take optional arguments. It is possible to write programmer-defined functions with optional arguments, too. For example, here is a function that prints the most common words in a histogram:
咱们已经看过好多有可选参数的内置函数和方法了。实际上咱们自己也可以写，写这种有可选参数的自定义函数。比如下面就是一个根据词频数据来统计最常用单词的函数：
def print_most_common(hist, num=10):
t = most_common(hist)
print('The most common words are:')
for freq, word in t[:num]:
print(word, freq, sep='\t')The first parameter is required; the second is optional. The default value of num is 10.
If you only provide one argument:
上面这个函数中，第一个参数是必须输入的；而第二个参数就是可选的了。第二个参数 num 的默认值是10.
如果只提供第一个参数：
print_most_common(hist)num gets the default value. If you provide two arguments:
这样 num 就用默认值了。如果提供两个参数：
print_most_common(hist, 20)num gets the value of the argument instead. In other words, the optional argument overrides the default value.
If a function has both required and optional parameters, all the required parameters have to come first, followed by the optional ones.
这样 num 就用参数值来赋值了。换句话说，可选参数可以覆盖默认值。
如果一个函数同时含有必需参数和可选参数，就必须在定义函数的时候，把必需参数全都放到前面，而可选的参数要放到后面。
Finding the words from the book that are not in the word list from words.txt is a problem you might recognize as set subtraction; that is, we want to find all the words from one set (the words in the book) that are not in the other (the words in the list).
有的单词存在于书当中，但没有包含在文件 words.txt 的单词列表中，找这些单词就有点难了，你估计已经意识到了，这是一种集合的减法；也就是要从一个集合（也就是书）中所有不被另一个集合（也就是单词列表）包含的单词。
subtract takes dictionaries d1 and d2 and returns a new dictionary that contains all the keys from d1 that are not in d2. Since we don’t really care about the values, we set them all to None.
下面的代码中定义的 subtrac t这个函数，接收两个字典 d1和 d2，然后返回一个新字典，这个新字典包含所有 d1中包含而 d2中不包含的键。键值就无所谓了，就都设置为空即可。
def subtract(d1, d2):
res = dict()
for key in d1:
if key not in d2:
res[key] = None
return resTo find the words in the book that are not in words.txt, we can use process_file to build a histogram for words.txt, and then subtract:
要找到书中含有而words.txt 中不含有的单词，就可以用 process_file 函数来建立一个 words.txt 的词频统计，然后用 subtract 函数来相减：
words = process_file('words.txt')
diff = subtract(hist, words)
print("Words in the book that aren't in the word list:")
for word in diff.keys():
print(word, end=' ')Here are some of the results from Emma:
下面依然还是对《艾玛》得到的结果：
Words in the book that aren't in the word list:
rencontre
jane's
blanche
woodhouses
disingenuousness
friend's
venice
apartment
...Some of these words are names and possessives. Others, like “rencontre”, are no longer in common use. But a few are common words that should really be in the list!
这些单词有的是名字或者所有格之类的。另外的一些，比如『rencontre』，都是现在不怎么常用的了。不过也确实有一些单词是挺常用的，挺应该被列表所包含的！
Python provides a data structure called set that provides many common set operations. You can read about them in Section 19.5, or read the documentation at Here.
Python 提供了一个数据结构叫 set（集合），该类型提供了很多常见的集合运算。可以在19.5阅读一下，或者阅读一下这里的官方文档。
Write a program that uses set subtraction to find words in the book that are not in the word list. Solution.
写一个程序吧，用集合的减法，来找一下书中包含而列表中不包含的单词吧。 样例代码。
To choose a random word from the histogram, the simplest algorithm is to build a list with multiple copies of each word, according to the observed frequency, and then choose from the list:
要从词频数据中选一个随机的单词，最简单的算法就是根据已知的单词频率来将每个单词复制相对应的个数的副本，然后组建成一个列表，从列表中选择单词：
def random_word(h):
t = []
for word, freq in h.items():
t.extend([word] * freq)
return random.choice(t)The expression [word] * freq creates a list with freq copies of the string word. The extend method is similar to append except that the argument is a sequence.
上面代码中的[word] * freq表达式建立了一个列表，列表中字符串单词的出现次数即其原来的词频数。extend 方法和 append 方法相似，区别是前者的参数是一个序列，而后者是单独的元素。
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.
上面这个算法确实能用，但效率实在不怎么好；每次选择随机单词的时候，程序都要重建列表，这个列表就和源书一样大了。很显然，一次性建立列表，而多次使用该列表来进行选择，这样能有明显改善，但列表依然还是很大。
An alternative is:
备选的思路如下：
Use keys to get a list of the words in the book.
用键来存储书中单词的列表。
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.
再建立一个列表，该列表包含所有词频的累加总和（参考练习2）。该列表的最后一个元素是书中所有单词的总数 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.
选择一个1到 n 之间的随机数。使用折半法搜索（参考练习10），找到随机数在累计总和中所在位置的索引值。
Use the index to find the corresponding word in the word list.
用该索引值来找到单词列表中对应的单词。
Write a program that uses this algorithm to choose a random word from the book. Solution.
写一个程序，用上面说的算法来从一本书中随机挑选单词。样例代码。
If you choose words from the book at random, you can get a sense of the vocabulary, but you probably won’t get a sentence:
如果让你从一本书中随机挑选一些单词，这些单词都能理解，但估计难以成为一句话：
this the small regard harriet which knightley's it most thingsA series of random words seldom makes sense because there is no relationship between successive words. For example, in a real sentence you would expect an article like “the” to be followed by an adjective or a noun, and probably not a verb or adverb.
一系列随机次很少能组成整句的话，因为这些单词连接起来并没有什么关系。例如，成句的话中，冠词 the 后面应该是跟着形容词或者名词，而不应该是动词或者副词。
One way to measure these kinds of relationships is Markov analysis, which characterizes, for a given sequence of words, the probability of the words that might come next. For example, the song Eric, the Half a Bee begins:
衡量单词之间关系的一种方法就是马科夫分析法，这一方法就是：对给定的单词序列，分析一个词跟着另一个词后面出现的概率。比如，Eric, the Half a Bee这首歌的开头：
Half a bee, philosophically,
Must, ipso facto, half not be.
But half the bee has got to be
Vis a vis, its entity. D’you see?
But can a bee be said to be
Or not to be an entire bee
When half the bee is not a bee
Due to some ancient injury?In this text, the phrase “half the” is always followed by the word “bee”, but the phrase “the bee” might be followed by either “has” or “is”.
在上面的文本中，『half the』这个词组后面总是跟着『bee』，但词组『the bee』后面可以是 『has』，也可以是 『is』。
The result of Markov analysis is a mapping from each prefix (like “half the” and “the bee”) to all possible suffixes (like “has” and “is”).
马科夫分析的结果是从每个前缀（比如『half the』和『the bee』）到所有可能的后缀（比如『has』和『is』）的映射。
Given this mapping, you can generate a random text by starting with any prefix and choosing at random from the possible suffixes. Next, you can combine the end of the prefix and the new suffix to form the next prefix, and repeat.
有了这一映射，你就可以制造随机文本了，用任意的前缀开头，然后从可能的后缀中随机选一个。下一次就把前缀的末尾和新的后缀结合起来，作为新的前缀，然后重复上面的步骤。
For example, if you start with the prefix “Half a”, then the next word has to be “bee”, because the prefix only appears once in the text. The next prefix is “a bee”, so the next suffix might be “philosophically”, “be” or “due”.
In this example the length of the prefix is always two, but you can do Markov analysis with any prefix length.
例如，你用前缀『Half a』来开始，那接下来的就必须是『bee』了，因为这个前缀只在文本中出现了一次。接下来，第二次了，前缀就变成了『a bee』了，所以接下来的后缀可以是『philosophically』, 『be』或者『due』。
在这个例子中，前缀的长度总是两个单词，但你可以以任意长度的前缀来进行马科夫分析。
Markov analysis:
马科夫分析：
Write a program to read a text from a file and perform Markov analysis. The result should be a dictionary that maps from prefixes to a collection of possible suffixes. The collection might be a list, tuple, or dictionary; it is up to you to make an appropriate choice. You can test your program with prefix length two, but you should write the program in a way that makes it easy to try other lengths.
写一个程序，读取文件中的文本，然后进行马科夫分析。结果应该是一个字典，从前缀到一个可能的后缀组成的序列的映射。这个序列可以是列表，元组，也可以是字典；你自己来选择合适的类型来写就好。你可以用两个单词长度的前缀来测试你的程序，但应该让程序能够兼容其他长度的前缀。
Add a function to the previous program to generate random text based on the Markov analysis. Here is an example from Emma with prefix length 2:
在上面的程序中添加一个函数，基于马科夫分析来生成随机文本。下面是用《艾玛》使用两个单词长度的前缀来生成的一个随机文本样例：
He was very clever, be it sweetness or be angry, ashamed or only amused, at such a stroke. She had never thought of Hannah till you were never meant for me?" "I cannot make speeches, Emma:" he soon cut it all himself.For this example, I left the punctuation attached to the words. The result is almost syntactically correct, but not quite. Semantically, it almost makes sense, but not quite.
这个例子中，我保留了单词中连接的标点符号。得到的结果在语法上基本是正确的，但也还差点。语义上，这些单词连起来也还能有一些意义，但也不咋对劲。
What happens if you increase the prefix length? Does the random text make more sense?
如果增加前缀的单词长度会怎么样？随机文本是不是读起来更通顺呢？
一旦你的程序能用了，你可以试试混搭一下：如果你把两本以上的书合并起来，生成的随机文本就会以很有趣的方式从多种来源混合单词和短语来生成随机文本。
Credit: This case study is based on an example from Kernighan and Pike, The Practice of Programming, Addison-Wesley, 1999.
引用：这个案例研究是基于Kernighan 和 Pike 在1999年由Addison-Wesley出版社出版的《The Practice of Programming》一书中的一个例子。
You should attempt this exercise before you go on; then you can can download my solution from Here. You will also need This, the txt file of Emma.
你应该自己独立尝试一下这些练习，然后再继续；然后你可以下载我的样例代码。另外你可能需要下载 《艾玛》这本书的文本文件。
Using Markov analysis to generate random text is fun, but there is also a point to this exercise: data structure selection. In your solution to the previous exercises, you had to choose:
使用马科夫分析来生成随机文本挺有意思的，但这个练习还有另外一个要点：数据结构的选择。在前面这些练习中，你必须要选择以下内容：
How to represent the prefixes.
如何表示前缀。
How to represent the collection of possible suffixes.
如何表示可能后缀的集合。
How to represent the mapping from each prefix to the collection of possible suffixes.
如何表示每个前缀与对应的后缀集合之间的映射。
The last one is easy: a dictionary is the obvious choice for a mapping from keys to corresponding values.
最后一个最简单了：明显就应该用字典了，这样来把每个键映射到对应的多个值。
For the prefixes, the most obvious options are string, list of strings, or tuple of strings.
前缀的选择，明显可以使用字符串、字符串列表，或者字符串元组。
For the suffixes, one option is a list; another is a histogram (dictionary).
后缀的先泽，要么是用列表，要么就用咱们之前写过的词频函数 histogram（这个也是个字典）。
How should you choose? The first step is to think about the operations you will need to implement for each data structure. For the prefixes, we need to be able to remove words from the beginning and add to the end. For example, if the current prefix is “Half a”, and the next word is “bee”, you need to be able to form the next prefix, “a bee”.
该咋选呢？第一步就是想一下，每种数据结构都需要用到哪些运算。比如对前缀来说，咱们就得能删掉头部，然后在尾部添加新词。例如，加入现在的前缀是『Half a』，接下来的单词是『bee』，就得能够组成下一个前缀，也就是『a bee』。
Your first choice might be a list, since it is easy to add and remove elements, but we also need to be able to use the prefixes as keys in a dictionary, so that rules out lists. With tuples, you can’t append or remove, but you can use the addition operator to form a new tuple:
你的首选估计就是列表了，因为列表很容易增加和剔除元素，但我们还需要能用前缀做字典中的键，所以列表就不合格了。那就剩元组了，元组没法添加和删除元素，但可以用加法运算符来建立新的元组。
def shift(prefix, word):
return prefix[1:] + (word,)shift takes a tuple of words, prefix, and a string, word, and forms a new tuple that has all the words in prefix except the first, and word added to the end.
上面这个 shift 函数，接收一个单词的元组，也就是前缀，然后还接收一个字符串，也就是单词了，然后形成一个新的元组，就是把原有的前缀去掉头部，用新单词拼接到尾部。
For the collection of suffixes, the operations we need to perform include adding a new suffix (or increasing the frequency of an existing one), and choosing a random suffix.
对后缀的集合来说，我们需要进行的运算包括添加新的后缀（或者增加一个已有后缀的频次），然后选择一个随机的后缀。
Adding a new suffix is equally easy for the list implementation or the histogram. Choosing a random element from a list is easy; choosing from a histogram is harder to do efficiently (see Exercise 7).
添加新后缀，无论是用列表还是用词频字典，实现起来都一样容易。不过从列表中选择一个随机元素很容易；但从词频字典中选择随机元素实现起来就不太有效率了（参考练习7）。
So far we have been talking mostly about ease of implementation, but there are other factors to consider in choosing data structures. One is run time. Sometimes there is a theoretical reason to expect one data structure to be faster than other; for example, I mentioned that the in operator is faster for dictionaries than for lists, at least when the number of elements is large.
目前为止，我们说完了实现难度，但对数据结构的选择还要考虑一些其他的因素。比如运行时间。有时候要考虑理论上的原因来考虑，最好的数据结构要比其他的快；例如我之前提到了 in 运算符在字典中要比列表中速度快，最起码当元素数量增多的时候会体现出来。
But often you don’t know ahead of time which implementation will be faster. One option is to implement both of them and see which is better. This approach is called benchmarking. A practical alternative is to choose the data structure that is easiest to implement, and then see if it is fast enough for the intended application. If so, there is no need to go on. If not, there are tools, like the profile module, that can identify the places in a program that take the most time.
但一般情况下，咱们不能提前知道哪一种实现方法的速度更快。所以就可以两种都写出来，然后运行对比一下，看看到底哪个快。这种方法就叫对比测试。另外一种方法是选一个实现起来最简单的数据结构，然后看看运行速度是不是符合问题的要求。如果可以，就不用再改进了。如果速度不够快，就亏用到一些工具，比如 profile 模块，来判断程序的哪些部分消耗了最多的运行时间。
The other factor to consider is storage space. For example, using a histogram for the collection of suffixes might take less space because you only have to store each word once, no matter how many times it appears in the text. In some cases, saving space can also make your program run faster, and in the extreme, your program might not run at all if you run out of memory. But for many applications, space is a secondary consideration after run time.
此外还要考虑的一个因素就是存储空间了。比如用一个词频字典作为后缀集合就可能省一些存储空间，因为无论这些单词在稳重出现了多少次，在该字典中每个单词只存储一次。有的情况下，节省空间也能让你的程序运行更快，此外在一些极端情况下，比如内存耗尽了，你的程序就根本无法运行了。不过对大多数应用来说，都是优先考虑运行时间，存储空间只是次要因素了。
One final thought: in this discussion, I have implied that we should use one data structure for both analysis and generation. But since these are separate phases, it would also be possible to use one structure for analysis and then convert to another structure for generation. This would be a net win if the time saved during generation exceeded the time spent in conversion.
最后再考虑一下：上文中，我已经暗示了，咱们选择某种数据结构，要兼顾分析和生成。但这二者是分开的步骤，所以也可以分析的时候用一种数据结构，而生成的时候再转换成另外一种结构。只要生成时候节省的时间胜过转换所花费的时间，相权衡之下依然是划算的。
When you are debugging a program, and especially if you are working on a hard bug, there are five things to try:
调试一个程序的时候，尤其是遇到特别严峻的问题的时候，有以下五个步骤一定要做好：
阅读代码：
Examine your code, read it back to yourself, and check that it says what you meant to say.
好好检查代码，多读几次，好好看看代码所表述的内容是不是跟你的设想相一致。
运行程序：
Experiment by making changes and running different versions. Often if you display the right thing at the right place in the program, the problem becomes obvious, but sometimes you have to build scaffolding.
做一些修改，然后运行各个版本来对比实验一下。通常来说，只要你在程序对应的位置加上输出，问题就能比较明确了，不过有时候你还是得搭建一些脚手架代码来帮忙找错误。
反复思考：
Take some time to think! What kind of error is it: syntax, runtime, or semantic? What information can you get from the error messages, or from the output of the program? What kind of error could cause the problem you’re seeing? What did you change last, before the problem appeared?
多花点时间去思考！想下到底是什么类型的错误：语法，运行，还是语义错误？从错误信息以及程序的输出能得到什么信息？想想哪种错误能引起你所看到的问题？问题出现之前的那一次你做了什么修改？
小黄鸭调试法：
If you explain the problem to someone else, you sometimes find the answer before you finish asking the question. Often you don’t need the other person; you could just talk to a rubber duck. And that’s the origin of the well-known strategy called rubber duck debugging. I am not making this up; see Here.
如果你对另外一个人解释问题，你有时候就能在问完问题之前就找到答案。通常你根本不用找另外一个人；就根一个橡胶鸭子说就可以了。这就是很著名的所谓小黄鸭调试法的起源了。我可不是瞎编的哈；看这里的解释。
以退为进：
At some point, the best thing to do is back off, undoing recent changes, until you get back to a program that works and that you understand. Then you can start rebuilding.
有时候，最佳的策略反而就是后撤，取消最近的修改，一直到程序恢复工作，并且你能清楚理解。然后再重头来改进。
Beginning programmers sometimes get stuck on one of these activities and forget the others. Each activity comes with its own failure mode.
新手程序员经常会在上面这些步骤中的某一项上卡壳，然后忘了其他的步骤。上面的每一步都有各自的失灵情况。
For example, reading your code might help if the problem is a typographical error, but not if the problem is a conceptual misunderstanding. If you don’t understand what your program does, you can read it 100 times and never see the error, because the error is in your head.
比如，错误很典型的情况下，阅读代码也许有效，但如果错误是概念上误解导致的，这就没啥用了。如果你不理解你程序的功能，你就算读上一百测也找不到错误，因为是你脑中的理解有错误。
Running experiments can help, especially if you run small, simple tests. But if you run experiments without thinking or reading your code, you might fall into a pattern I call “random walk programming”, which is the process of making random changes until the program does the right thing. Needless to say, random walk programming can take a long time.
在你进行小规模的简单测试的时候，进行试验会有用。但如果不思考和阅读代码，你就可以陷入到我称之为『随机走路编程』的陷阱中，这种过程就是随机做一些修改，一直到程序工作位置。毋庸置疑，这种随机修改肯定得浪费好多时间的。
You have to take time to think. Debugging is like an experimental science. You should have at least one hypothesis about what the problem is. If there are two or more possibilities, try to think of a test that would eliminate one of them.
最重要的就是思考，一定要花时间去思考。调试就像是一种实验科学。你至少应该对问题的本质有一种假设。如果有两种或者两种以上的可能性，就要设计个测试，来逐个排除可能性。
But even the best debugging techniques will fail if there are too many errors, or if the code you are trying to fix is too big and complicated. Sometimes the best option is to retreat, simplifying the program until you get to something that works and that you understand.
然而一旦错误特别多了，再好的调试技术也不管用的，程序太大太复杂也会容易有类似情况。所以有时候最好的方法就是以退为进，简化一下程序，直到能工作了，并且你能理解整个程序了为止。
Beginning programmers are often reluctant to retreat because they can’t stand to delete a line of code (even if it’s wrong). If it makes you feel better, copy your program into another file before you start stripping it down. Then you can copy the pieces back one at a time.
新手程序员经常不愿意后撤，因为他们不情愿删掉一行代码（哪怕是错误的代码）。可以这样，复制一下整个代码到另外一个文件中做个备份，然后再删减，这样是不是感觉好些。然后你可以再复制回来的。
Finding a hard bug requires reading, running, ruminating, and sometimes retreating. If you get stuck on one of these activities, try the others.
找到一个困难问题的解决方法，需要阅读、测试、分析，有时候还要后撤。如果你在某一步骤中卡住了，试试其他方法。
deterministic:
Pertaining to a program that does the same thing each time it runs, given the same inputs.
确定性：给定同样的输出，程序每次运行结果都相同。
pseudorandom:
Pertaining to a sequence of numbers that appears to be random, but is generated by a deterministic program.
假随机数：一段数字序列中的数，看上去似乎是随机的，但实际上也是由确定的算法来生成的。
default value:
The value given to an optional parameter if no argument is provided.
默认值：如果不对可选参数进行赋值的话，该参数会用默认设置的值。
override:
To replace a default value with an argument.
覆盖：用户在调用函数的时候给可选参数提供了参数，这个参数就覆盖掉默认值。
benchmarking:
The process of choosing between data structures by implementing alternatives and testing them on a sample of the possible inputs.
对比测试：
rubber duck debugging:
Debugging by explaining your problem to an inanimate object such as a rubber duck. Articulating the problem can help you solve it, even if the rubber duck doesn’t know Python.
小黄鸭调试法：对一个无生命的对象来解释你的问题，比如小黄鸭之类的，这样来调试。描述清楚问题很有助于解决问题，所以虽然小黄鸭并不会理解 Python 也不要紧。
The “rank” of a word is its position in a list of words sorted by frequency: the most common word has rank 1, the second most common has rank 2, etc.
单词的『排名』就是在一个单词列表中，按照出现频率而排的位置：最常见的单词就排名第一了，第二常见的就排第二，依此类推。
[Zipf’s law](http://en.wikipedia.org/wiki/Zipf's_law) describes a relationship between the ranks and frequencies of words in natural languages . Specifically, it predicts that the frequency, f, of the word with rank r is:
[Zipf定律](http://en.wikipedia.org/wiki/Zipf's_law) 描述了自然语言中排名和频率的关系。该定律预言了排名 r 与词频 f 之间的关系如下：
$$ f = cr^{−s} $$
where s and c are parameters that depend on the language and the text. If you take the logarithm of both sides of this equation, you get:
这里的 s 和 c 都是参数，依据语言和文本而定。如果对等式两边同时取对数，得到如下公式：
$$ \log f = \log c − s*\log r $$
（译者注：Zipf定律是美国学者G.K.齐普夫提出的。可以表述为：在自然语言的语料库里，一个单词出现的频率与它在频率表里的排名成反比。）
So if you plot log f versus log r, you should get a straight line with slope −s and intercept log c.
因此如果你将 log f 和 log r 进行二维坐标系投点，就应该得到一条直线，斜率是-s，截距是 log c。
Write a program that reads a text from a file, counts word frequencies, and prints one line for each word, in descending order of frequency, with log f and log r.
写一个程序，从一个文件中读取文本，统计单词频率，然后每个单词一行来输出，按照词频的降序，同时输出一下 log f 和 log r。
Use the graphing program of your choice to plot the results and check whether they form a straight line. Can you estimate the value of s?
选一种投图程序，把结果进行投图，然后检查一下是否为一条直线。
能否估计一下 s 的值呢？
Solution. To run my solution, you need the plotting module matplotlib. If you installed Anaconda, you already have matplotlib; otherwise you might have to install it.
样例代码。要运行刚刚这个代码的话，你需要有投图模块 matplotlib。如果你安装了 Anaconda，就已经有 matplotlib 了；或者你就可能需要安装一下了。
（译者注：matplotlib 的安装方法有很多，比如 pip install matplotlib 或者 easy_install -U matplotlib）