numpy.random.RandomState.uniform

方法

random.RandomState.uniform(low=0.0, high=1.0, size=None)

从均匀分布中提取样品。

样品在半开放区间内均匀分布。 [low, high) (包括低,但不包括高)。换言之,给定间隔内的任何值同样可能被 uniform .

注解

新代码应该使用 uniform A方法 default_rng() 请参阅 快速启动 .

参数
low浮点数或类似浮点数的数组,可选

输出间隔的下限。生成的所有值都将大于或等于“低”。默认值为0。

high浮点数或类似浮点数的数组

输出间隔的上边界。生成的所有值都将小于或等于高。默认值为1.0。

sizeint或int的元组,可选

输出形状。如果给定的形状是,例如, (m, n, k) 然后 m * n * k 取样。如果尺寸是 None (默认),如果 lowhigh 都是标量。否则, np.broadcast(low, high).size 取样。

返回
outndarray或scalar

从参数化均匀分布中提取样本。

参见

randint

离散均匀分布,产生整数。

random_integers

闭区间上的离散均匀分布 [low, high] .

random_sample

浮体均匀分布 [0, 1) .

random

Alias random_sample .

rand

接受尺寸作为输入的便利功能,例如, rand(2,2) 将生成一个2乘2的浮动数组,均匀分布在 [0, 1) .

Generator.uniform

应该用于新代码。

笔记

均匀分布的概率密度函数是

System Message: WARNING/2 (p(x)=\frac 1 b-a)

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间隔内的任何地方 [a, b) ,其他地方为零。

什么时候? high = lowlow 将被退回。如果 high < low ,结果是官方未定义的,最终可能会产生错误,即当传递的参数满足该不等式条件时,不依赖此函数的行为。这个 high 由于公式中的浮点舍入,返回的浮点数组中可能包含limit low + (high-low) * random_sample() . 例如:

>>> x = np.float32(5*0.99999999)
>>> x
5.0

实例

从分发中抽取样本:

>>> s = np.random.uniform(-1,0,1000)

所有值都在给定的间隔内:

>>> np.all(s >= -1)
True
>>> np.all(s < 0)
True

显示样本的直方图,以及概率密度函数:

>>> import matplotlib.pyplot as plt
>>> count, bins, ignored = plt.hist(s, 15, density=True)
>>> plt.plot(bins, np.ones_like(bins), linewidth=2, color='r')
>>> plt.show()
../../../_images/numpy-random-RandomState-uniform-1.png