推荐|np.random.choice用法中文和样例详解

 
 官方介绍 

 
 http://docs.scipy.org/doc/numpy-dev/reference/generated/numpy.random.choice.html 

 
 https://docs.python.org/3/library/random.html 

 
 numpy.random.choice 
  
   
   numpy.random. 
   choice 
   ( 
   a,  
   size=None,  
   replace=True,  
   p=None 
   ) 
    
   
  Generates a random sample from a given 1-D array
New in version 1.7.0.
Parameters:a : 1-D array-like or int
If an ndarray, a random sample is generated from its elements. If an int, the random sample is generated as if a was np.arange(n)
size : int or tuple of ints, optional
Output shape. If the given shape is, e.g., (m, n, k), then m * n * k samples are drawn. Default is None, in which case a single value is returned.
replace : boolean, optional
Whether the sample is with or without replacement
p : 1-D array-like, optional
The probabilities associated with each entry in a. If not given the sample assumes a uniform distribution over all entries in a.
Returns:samples : 1-D ndarray, shape (size,)
The generated random samples
Raises:ValueError
If a is an int and less than zero, if a or p are not 1-dimensional, if a is an array-like of size 0, if p is not a vector of probabilities, if a and p have different lengths, or if replace=False and the sample size is greater than the population size
 
  
 
 

Parameters:	a : 1-D array-like or int If an ndarray, a random sample is generated from its elements. If an int, the random sample is generated as if a was np.arange(n) size : int or tuple of ints, optional Output shape. If the given shape is, e.g., `(m, n, k)`, then `m * n * k` samples are drawn. Default is None, in which case a single value is returned. replace : boolean, optional Whether the sample is with or without replacement p : 1-D array-like, optional The probabilities associated with each entry in a. If not given the sample assumes a uniform distribution over all entries in a.
Returns:	samples : 1-D ndarray, shape (size,) The generated random samples
Raises:	ValueError If a is an int and less than zero, if a or p are not 1-dimensional, if a is an array-like of size 0, if p is not a vector of probabilities, if a and p have different lengths, or if replace=False and the sample size is greater than the population size

 
 np.random.choice 

 
 RandomState.choice(a, size=None, replace=True, p=None) 

  a : 1-D array-like or int    If an ndarray, a random sample is generated from its elements.  
 
    如果是ndarray数组，随机样本在该数组获取（取数据元素），    If an int, the random sample is generated as if a was np.arange(n) 

  如果是整型数据随机样本生成类似np.arange(n)     
 
size : int or tuple of ints, optional  
 
    大小：整型或 
 整型元组中元素个数，可选 

  replace : boolean, optional 
 
    替换：布尔型，可选    Whether the sample is with or without replacement 
 
    样本是否有重复值（False，没有；True，有；默认：True） 

  见例子3 

  p : 1-D array-like, optional 
 
    1维数组，可选 

      The probabilities associated with each entry in a. If not given the sample assumes a uniform distribution over all entries in a. 
 
和a里的每个输入联系，如果没有该参数，默认 
 假设 
 a里的每个输入等概率出现。（和a中元素一一对应，表示该元素出现的概率，概率大的，出现较多）

      见例子2 

 
 实例 

 
 例子1： np.arange(5) 中产生一个size为3的随机采样: 

 
 >>> 
  np.random.choice( 
 5 
 ,  
 3 
 ) 

 
 array([ 
 0 
 ,  
 3 
 ,  
 4 
 ]) 

 
 >>> 
   
 #This is equivalent to np.random.randint(0,5,3)

 
 例子2：从 np.arange(5) 中产生一个非标准的 size为 3的随机采样: 

 
 >>> 
  np.random.choice( 
 5 
 ,  
 3 
 , p 
 = 
 [ 
 0.1 
 ,  
 0 
 ,  
 0.3 
 ,  
 0.6 
 ,  
 0 
 ]) 

 
 array([ 
 3 
 ,  
 3 
 ,  
 0 
 ]) 

 
 说明：3出现概率最大，所以array([3, 3, 0])中，3最多 

a：	0	1	2	3	4
p:	0.1	0	0.3	0.6	0

 
 例子3： np.arange(5) 产生一个标准分布、size为 3、没有重复替换的随机采样： 

 
 >>> 
  np.random.choice( 
 5 
 ,  
 3 
 , replace 
 = 
 False 
 ) 

 
 array([ 
 3 
 , 
 1 
 , 
 0 
 ]) 

 
 >>> 
   
 #This is equivalent to np.random.permutation(np.arange(5))[:3]

 
 例子4：也可以这样，不必一定是整型数字： 

 
 >>> 
  aa_milne_arr  
 = 
  [ 
 'pooh' 
 ,  
 'rabbit' 
 ,  
 'piglet' 
 ,  
 'Christopher' 
 ] 

 
 >>> 
  np.random.choice(aa_milne_arr,  
 5 
 , p 
 = 
 [ 
 0.5 
 ,  
 0.1 
 ,  
 0.1 
 ,  
 0.3 
 ]) 

 
 array([ 
 'pooh' 
 ,  
 'pooh' 
 ,  
 'pooh' 
 ,  
 'Christopher' 
 ,  
 'piglet' 
 ], dtype 
 = 
 '|S11' 
 ) 

 
 例子5：实际使用中，首先创建一个mask变量，然后通过mask来对需要采样的数据进行采样： 

 
 ... mask  
 = 
  np.random.choice(split_size, batch_size)  

 
 captions  
 = 
  data[ 
 '%s_captions' 
   
 % 
  split][mask]  

 
 image_idxs  
 = 
  data[ 
 '%s_image_idxs' 
   
 % 
  split][mask]... 

  参考： 

https://oldpan.me/archives/python-numpy-choice

文章知识点与官方知识档案匹配，可进一步学习相关知识

Python入门技能树科学计算基础软件包NumPyNumPy概述416686 人正在系统学习中

numpy.random.choice

评论记录：