方法
random.RandomState.
noncentral_chisquare
从非中心卡方分布中提取样本。
非中心的 分布是广义的 分布。
注解
新代码应该使用 noncentral_chisquare A方法 default_rng() 请参阅 快速启动 .
default_rng()
自由度必须大于0。
在 1.10.0 版更改: 早期的numpy版本需要dfnum>1。
非中心性,必须是非负的。
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 df 和 nonc 都是标量。否则, np.broadcast(df, nonc).size 取样。
(m, n, k)
m * n * k
None
df
nonc
np.broadcast(df, nonc).size
从参数化非中心卡方分布中提取样本。
参见
Generator.noncentral_chisquare
应该用于新代码。
笔记
非中心卡方分布的概率密度函数是
System Message: WARNING/2 (P(x;df,nonc)=\sum^{\infty}{i=0})
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在哪里? 是具有Q自由度的卡方。
工具书类
维基百科,“非中心卡方分布”https://en.wikipedia.org/wiki/noncentral_chi-squared_distribution
实例
从分布中绘制值并绘制柱状图
>>> import matplotlib.pyplot as plt >>> values = plt.hist(np.random.noncentral_chisquare(3, 20, 100000), ... bins=200, density=True) >>> plt.show()
从非中心性很小的非中心角砾中提取值,并与角砾进行比较。
>>> plt.figure() >>> values = plt.hist(np.random.noncentral_chisquare(3, .0000001, 100000), ... bins=np.arange(0., 25, .1), density=True) >>> values2 = plt.hist(np.random.chisquare(3, 100000), ... bins=np.arange(0., 25, .1), density=True) >>> plt.plot(values[1][0:-1], values[0]-values2[0], 'ob') >>> plt.show()
演示非中心性的大值如何导致更对称的分布。
>>> plt.figure() >>> values = plt.hist(np.random.noncentral_chisquare(3, 20, 100000), ... bins=200, density=True) >>> plt.show()