倾斜的正态分布

根据Wikipedia的描述，

from scipy import linspacefrom scipy import pi,sqrt,expfrom scipy.special import erffrom pylab import plot,showdef pdf(x):    return 1/sqrt(2*pi) * exp(-x**2/2)def cdf(x):    return (1 + erf(x/sqrt(2))) / 2def skew(x,e=0,w=1,a=0):    t = (x-e) / w    return 2 / w * pdf(t) * cdf(a*t)    # You can of course use the scipy.stats.norm versions    # return 2 * norm.pdf(t) * norm.cdf(a*t)n = 2**10e = 1.0 # locationw = 2.0 # scalex = linspace(-10,10,n)for a in range(-3,4):    p = skew(x,e,w,a)    plot(x,p)show()

如果你想找到一个数据集的使用规模，位置和形状参数

scipy.optimize.leastsq

e=1.0

w=2.0

a=1.0

fzz = skew(x,e,w,a) + norm.rvs(0,0.04,size=n) # fuzzy datadef optm(l,x):    return skew(x,l[0],l[1],l[2]) - fzzprint leastsq(optm,[0.5,0.5,0.5],(x,))

应该给你类似的东西，

(array([ 1.05206154,  1.96929465,  0.94590444]), 1)