class numpy.random.RandomState(seed=None)
RandomState 是一个基于Mersenne Twister算法的伪随机数生成类
RandomState 包含很多生成 概率分布的伪随机数 的方法。
如果指定seed值，那么每次生成的随机数都是一样的。即对于某一个伪随机数发生器，只要该种子相同，产生的随机数序列就是相同的。
numpy.random.RandomState.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).
rand()函数产生 [0,1)间的均匀分布的指定维度的 伪随机数
Parameters:
d0, d1, …, dn : int, optional
The dimensions of the returned array, should all be positive. If no argument is given a single Python float is returned.
Returns:
out : ndarray, shape (d0, d1, ..., dn)
Random values.
numpy.random.RandomState.uniform(low=0.0, high=1.0, size=None)
Draw samples from a uniform distribution.
Samples are uniformly distributed over the half-open interval [low, high) (includes low, but excludes high). In other words, any value within the given interval is equally likely to be drawn by uniform.
uniform()函数产生 [low,high)间的 均匀分布的指定维度的 伪随机数
Parameters:
low : float or array_like of floats, optional
Lower boundary of the output interval. All values generated will be greater than or equal to low. The default value is 0.
high : float or array_like of floats
Upper boundary of the output interval. All values generated will be less than high. The default value is 1.0.
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.
If size is None (default), a single value is returned if low and high are both scalars. Otherwise, np.broadcast(low, high).size samples are drawn.
Returns:
out : ndarray or scalar
Drawn samples from the parameterized uniform distribution.
有时候我们需要自己模拟构造 输入数据(矩阵)，那么这种随机数的生成是一种很好的方式。
1 # -*- coding: utf-8 -*-
2 """
3 Created on Tue May 29 12:14:11 2018
4
5 @author: Frank
6 """
7
8 import numpy as np
9
10 #基于seed产生随机数
11 rng = np.random.RandomState(seed)
12 print(type(rng))
13
14 #生成[0,1)间的 32行2列矩阵
15 X=rng.rand(32, 2)
16 print("X.type{}".format(type(X)))
17 print(X)
18
19 #生成[0,1)间的 一个随机数
20 a1 = rng.rand()
21 print("a1.type{}".format(type(a1)))
22 print(a1)
23
24 #生成[0,1)间的 一个包含两个元素的随机数组
25 a2 = rng.rand(2)
26 print("a2.type{}".format(type(a2)))
27 print(a2)
28
29 #生成[1,2)间的随机浮点数
30 X1 = rng.uniform(1,2)
31 print("X1.type{}".format(type(X1)))
32 print(X1)
33
34 #生成[1,2)间的随机数,一维数组且仅含1个数
35 X2 = rng.uniform(1,2,1)
36 print("X2.type{}".format(type(X2)))
37 print(X2)
38
39 #生成[1,2)间的随机数,一维数组且仅含2个数
40 X3 = rng.uniform(1,2,2)
41 print("X3.type{}".format(type(X3)))
42 print(X3)
43
44 #生成[1,2)间的随机数,2行3列矩阵
45 X4 = rng.uniform(1,2,(2,3))
46 print("X4.type{}".format(type(X4)))
47 print(X4)
原文地址：https://www.cnblogs.com/black-mamba/p/9104546.html