一个简单的实现

class NaiveFilter():

'''Filter Messages from keywords

very simple filter implementation

>>> f = NaiveFilter()

>>> f.add("sexy")

>>> f.filter("hello sexy baby")

hello **** baby

'''

def __init__(self):

self.keywords = set([])

def parse(self, path):

for keyword in open(path):

self.keywords.add(keyword.strip().decode('utf-8').lower())

def filter(self, message, repl="*"):

message = str(message).lower()

for kw in self.keywords:

message = message.replace(kw, repl)

return message

其中strip() 函数 删除附近的一些空格，解码采用utf-8的形式，然后将其转为小写。

parse()函数就是打开文件，然后从中取各个关键词，然后将其存在关键词集合中。

filter()函数是一个过滤器函数，其中将消息转化为小写，然后将关键词替换成*。、

class BSFilter:

'''Filter Messages from keywords

Use Back Sorted Mapping to reduce replacement times

>>> f = BSFilter()

>>> f.add("sexy")

>>> f.filter("hello sexy baby")

hello **** baby

'''

def __init__(self):

self.keywords = []

self.kwsets = set([])

self.bsdict = defaultdict(set)

self.pat_en = re.compile(r'^[0-9a-zA-Z]+$') # english phrase or not

def add(self, keyword):

if not isinstance(keyword, str):

keyword = keyword.decode('utf-8')

keyword = keyword.lower()

if keyword not in self.kwsets:

self.keywords.append(keyword)

self.kwsets.add(keyword)

index = len(self.keywords) - 1

for word in keyword.split():

if self.pat_en.search(word):

self.bsdict[word].add(index)

else:

for char in word:

self.bsdict[char].add(index)

def parse(self, path):

with open(path, "r") as f:

for keyword in f:

self.add(keyword.strip())

def filter(self, message, repl="*"):

if not isinstance(message, str):

message = message.decode('utf-8')

message = message.lower()

for word in message.split():

if self.pat_en.search(word):

for index in self.bsdict[word]:

message = message.replace(self.keywords[index], repl)

else:

for char in word:

for index in self.bsdict[char]:

message = message.replace(self.keywords[index], repl)

return message

在上面的实现例子中，对于搜索查找进行了优化，对于英语单词，直接进行了按词索引字典查找。对于其他语言模式，我们采用逐字符查找匹配的一种模式。

BFS：宽度优先搜索方式。

class DFAFilter():

'''Filter Messages from keywords

Use DFA to keep algorithm perform constantly

>>> f = DFAFilter()

>>> f.add("sexy")

>>> f.filter("hello sexy baby")

hello **** baby

'''

def __init__(self):

self.keyword_chains = {}

self.delimit = '\x00'

def add(self, keyword):

if not isinstance(keyword, str):

keyword = keyword.decode('utf-8')

keyword = keyword.lower()

chars = keyword.strip()

if not chars:

return

level = self.keyword_chains

for i in range(len(chars)):

if chars[i] in level:

level = level[chars[i]]

else:

if not isinstance(level, dict):

break

for j in range(i, len(chars)):

level[chars[j]] = {}

last_level, last_char = level, chars[j]

level = level[chars[j]]

last_level[last_char] = {self.delimit: 0}

break

if i == len(chars) - 1:

level[self.delimit] = 0

def parse(self, path):

with open(path,encoding='UTF-8') as f:

for keyword in f:

self.add(keyword.strip())

def filter(self, message, repl="*"):

if not isinstance(message, str):

message = message.decode('utf-8')

message = message.lower()

ret = []

start = 0

while start < len(message):

level = self.keyword_chains

step_ins = 0

for char in message[start:]:

if char in level:

step_ins += 1

if self.delimit not in level[char]:

level = level[char]

else:

ret.append(repl * step_ins)

start += step_ins - 1

break

else:

ret.append(message[start])

break

else:

ret.append(message[start])

start += 1

return ''.join(ret)

DFA即Deterministic Finite Automaton，也就是确定有穷自动机。

使用了嵌套的字典来实现。

参考

以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持我们。

时间： 2019-08-12