Generator.integers(low, high=None, size=None, dtype=’int64’, endpoint=False) 例如: 代码语言:javascript 复制 rng=np.random.default_rng(PCG64(12345))rng.integers(2,size=10) 结果:array([1, 0, 1, 0, 0, 1, 1, 1, 1, 0]) 这里的意思是生成10个数,这是个数的取值范围在0-2之间,不...
结果:array([1, 3, 3]) 这里的意思是生成1×3的数组,并且每一位都限制了取值范围。 rng.integers([1, 5, 7], 10) 1. 结果:array([6, 6, 7]) rng.integers([1, 3, 5, 7], [[10], [20]], dtype=np.uint8) 1. 结果:array([[ 1, 4, 8, 9], [ 5, 18, 16, 12]], dtype=...
numpy.random.random_integers(low, high=None, size=None) 返回随机整数,范围区间为[low,high],包含low和high 参数:low为最小值,high为最大值,size为数组维度大小 high没有填写时,默认生成随机数的范围是[1,low] 该函数在最新的numpy版本中已被替代,建议使用randint函数 In [11]: np.random.random_integers...
Parametersloc[floatorarray_likeoffloats]分布的均值("centre")。scale[floatorarray_likeoffloats]分布的标准差(spreador"width")。size[intortupleofints,optional]输出的形状(shape)。如果给定的形式为(m,n,k),那么m*n*k个样本将被提取出来;如果其值为None(default),并且参数loc和scale均为纯量(scalars),...
numpy.random.rand(d0, d1, …, dn) Random values in a given shape. Create an array of the given shape and populate it with random samples from a uniform distribution over [0, 1) . 【例】根据指定大小产生[0,1)之间均匀分布的随机数。
import numpy.random as npr rng=npr.default_rng() size=(3,4) C=rng.uniform(4,7,size) print(f"{C=}") 1. 2. 3. 4. 5. C=array([[6.0436931 , 5.63331156, 6.11905388, 5.77916688], [5.6442441 , 5.61249485, 6.79054321, 6.7742957 ], ...
array([[4, 0, 2, 1], [3, 2, 2, 0]]) random_integers(low[, high, size]) 返回随机的整数,位于闭区间 [low, high]。 Notes To sample from N evenly spaced floating-point numbers between a and b, use: a + (b - a) * (np.random.random_integers(N) - 1) / (N - 1.) ...
用法是: numpy.random.random_integers(low,high=None,size=None) 生成闭区间[low,high]上离散均匀分布的整数值;若high=None,则取值区间变为[1,low] 用法及实现 high=None的情形 1 2 3 4 >>> np.random.random_integers(1, 6, 10) array([4, 5, 2, 3, 4, 2, 5, 4, 5, 4]) >>> ...
array([2, 3, 0]) Any of the above can be repeated with an arbitrary array-like instead of just integers. 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]) ...
import numpy as np np.random.randint(10, size=(1, 20)) You can expect to see an output of the following form (different random integers will be returned each time you run it; hence you can expect the integers in the output array to differ from the example given below). array([[1...