The Collection of classic codes with Python(updated in 2018.09.14)

版权声明：本文为博主原创文章，未经博主允许不得转载。 https://blog.csdn.net/github_36326955/article/details/81981563

1. 概率化编程

def add_unknown_words(word_vecs, vocab, min_df=1, k=300):
    """
    For words that occur in at least min_df documents, 
    create a separate word vector.
    same variance as pre-trained ones
    """
    for word in vocab:
        if word not in word_vecs and vocab[word] >= min_df:
            word_vecs[word] = np.random.uniform(-0.25,0.25,k)
            print(word)

def pro(pro):
  if random.random()<=pro:
    pass

2. 定义一段弃用的代码

有时候有些函数功能我们打算弃用，但是担心版本不兼容，为例保持借口的兼容性，可以仿照下面的代码进行编程

 @deprecated("Method will be removed in 4.0.0, use self.wv.__getitem__() instead")
    def __getitem__(self, words):
        """
        Deprecated. Use self.wv.__getitem__() instead.
        Refer to the documentation for `gensim.models.keyedvectors.Word2VecKeyedVectors.__getitem__`
        """
        return self.wv.__getitem__(words)

3. 计算文件行数

def count_lines(f):
    if path.isfile(f):  # Test whether a path is a regular file
        num_lines = sum(1 for line in open(f))
        """
        上面这行代码相当于：
            a=[1 for line in open(f)] # a=[1,1,1,1,1,...,1]
            num_lines = sum(a)
        """
        return num_lines
    else:
        return 0

4. 计算一组文件里单词的词频

#! /usr/bin/env python
# -*- coding: utf-8 -*-
from concurrent.futures import ProcessPoolExecutor
from collections import Counter


def count_words(file):
    c = Counter()
    with open(file, 'r') as f:
        for l in f:
            words = l.strip().split()
            c.update(words)
    return c


def count_textfiles(files, workers=1):
    c = Counter()
    with ProcessPoolExecutor(max_workers=workers) as executor:
        for c_ in executor.map(count_words, files):
            c.update(c_)
    return c

5. 代码计时

from time import time
t0 = time()
your code here
t1 = time()
print('make_directed: added missing edges {}s'.format(t1 - t0))