a
是形状。您尝试过的仅适用于loc = 0
. 首先,我们从两个示例开始,shape (or a
) = 10 和 scale = 5,第二个 d1plus50 与第一个相差 50,您可以看到由 loc 决定的移位:
from scipy.stats import gamma
import matplotlib.pyplot as plt
d1 = gamma.rvs(a = 10, scale=5,size=1000,random_state=99)
plt.hist(d1,bins=50,label='loc=0,shape=10,scale=5',density=True)
d1plus50 = gamma.rvs(a = 10, loc= 50,scale=5,size=1000,random_state=99)
plt.hist(d1plus50,bins=50,label='loc=50,shape=10,scale=5',density=True)
plt.legend(loc='upper right')
因此,您有 3 个参数可以从数据中估计,一种方法是使用gamma.fit,我们将其应用于 loc=0 的模拟分布:
xlin = np.linspace(0,160,50)
fit_shape, fit_loc, fit_scale=gamma.fit(d1)
print([fit_shape, fit_loc, fit_scale])
[11.135335235456457, -1.9431969603988053, 4.693776771991816]
plt.hist(d1,bins=50,label='loc=0,shape=10,scale=5',density=True)
plt.plot(xlin,gamma.pdf(xlin,a=fit_shape,loc = fit_loc, scale = fit_scale)
如果我们对我们用 loc 模拟的分布进行此操作,您可以看到 loc 被正确估计,以及形状和比例:
fit_shape, fit_loc, fit_scale=gamma.fit(d1plus50)
print([fit_shape, fit_loc, fit_scale])
[11.135287555530564, 48.05688649976989, 4.693789434095116]
plt.hist(d1plus50,bins=50,label='loc=0,shape=10,scale=5',density=True)
plt.plot(xlin,gamma.pdf(xlin,a=fit_shape,loc = fit_loc, scale = fit_scale))