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

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