random.
logistic
从物流配送中抽取样本。
从具有指定参数、loc(位置或平均值,也包括中位数)和scale(>0)的逻辑分布中提取样本。
注解
新代码应该使用 logistic A方法 default_rng() 请参阅 快速启动 .
default_rng()
分布的参数。默认值为0。
分布的参数。必须为非负。默认值为1。
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 loc 和 scale 都是标量。否则, np.broadcast(loc, scale).size 取样。
(m, n, k)
m * n * k
None
loc
scale
np.broadcast(loc, scale).size
从参数化物流分布中提取样本。
参见
scipy.stats.logistic
概率密度函数、分布或累积密度函数等。
Generator.logistic
应该用于新代码。
笔记
逻辑分布的概率密度是
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在哪里? =位置和 =比例尺。
在极端值问题中,逻辑分布可以作为Gumbel分布的混合,在流行病学中,以及由国际象棋联合会(FID)使用,在ELO排名系统中,假设每个玩家的表现是一个逻辑分布的随机变量。
工具书类
Reiss,R.-D.和Thomas M.(2001),“保险、金融、水文和其他领域的极值统计分析”,Birkhauser Verlag,巴塞尔,第132-133页。
物流配送〉,选自MathWorld——Wolfram网络资源。http://mathworld.wolfram.com/logisticdistribution.html
维基百科,“物流配送”,https://en.wikipedia.org/wiki/logistic_distribution
实例
从分发中抽取样本:
>>> loc, scale = 10, 1 >>> s = np.random.logistic(loc, scale, 10000) >>> import matplotlib.pyplot as plt >>> count, bins, ignored = plt.hist(s, bins=50)
#按分布绘制
>>> def logist(x, loc, scale): ... return np.exp((loc-x)/scale)/(scale*(1+np.exp((loc-x)/scale))**2) >>> lgst_val = logist(bins, loc, scale) >>> plt.plot(bins, lgst_val * count.max() / lgst_val.max()) >>> plt.show()