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. 如果是int，则生成随机样本，如同a是np.arange(n)

size : int or tuple of ints, optional

输出的形状。如果给定形状是例如(m, n, k)，则抽取m * n * k个样本。Default is None, in which case a single value is returned.

replace：boolean，可选

采样是否有重复

p：1-D array-like，可选

The probabilities associated with each entry in a. 如果没有给出样本，则假设在a中的所有条目均匀分布。

Returns:

samples：1-D字符串，形状(size,)

The generated random samples

引发：

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

另见

randint, shuffle, permutation

Examples

从np.arange(5)生成大小为3的均匀随机样本：

>>> np.random.choice(5, 3)
array([0, 3, 4])
>>> #This is equivalent to np.random.randint(0,5,3)

从np.arange(5)生成大小为3的非均匀随机样本：

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

从np.arange(5)生成大小为3的均匀随机样本，没有重复：

>>> np.random.choice(5, 3, replace=False)
array([3,1,0])
>>> #This is equivalent to np.random.permutation(np.arange(5))[:3]

从np.arange(5)生成大小为3的非均匀随机样本，没有重复：

>>> np.random.choice(5, 3, replace=False, p=[0.1, 0, 0.3, 0.6, 0])
array([2, 3, 0])

上述任何一个例子都可以使用一个类似数组的对象重新运行，而不是只是用整数。For instance:

>>> 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')