Question

我检查了EES，感觉应该是正确的，但是输出的数字太奇怪了。我希望有一个人可以帮助我。我对Python和统计资料非常陌生。我不知道这是否正常，但是使用matlab的相同数据结果似乎还可以。

# -*- coding: utf-8 -*-
import matplotlib.pyplot as plt
import scipy.stats as st
import seaborn as sns
import numpy as np

data = [ 40, 21, 14, 9, 14, 32, 28, 42, 33, 30, 40, 14, 11, 32,
  53, 163,  28,  13,  14,  15,  16,  15,  12,   9,  12,  21,  41,  24,
  12,  26,  14,  36,  22,   4,  36,  13,  71,  10,  30,  17,  89,  31,
  30,  13,  37,  22,  30,  32,  31,  20,  19,  53,  18,  30,  31,  11,
  39,  37,  12,  26,   9,   9,]

sns.set()
DISTRIBUTIONS = [st.norm,st.gamma,st.weibull_min,st.burr,st.beta]
bins_=20

def cal_sse(fitting,data,bins):
    y, x = np.histogram(data , bins=bins)
    bin_width = x[1]-x[0]
    x_mid = (x + np.roll(x, -1))[:-1] / 2.0
    N = len(data)
    params = fitting.fit(data)
    arg = params[:-2]
    loc = params[-2]
    scale = params[-1]
    pdf = fitting.pdf(x_mid, loc=loc, scale=scale, *arg)
    pdf_scaled = pdf * bin_width * N
    sse = np.sum((y - pdf_scaled)**2)
    if fitting == st.weibull_min:
        print(y,x)
        print('------------------')
        print(pdf_scaled )
    return sse

def draw_fitting(data,bins,fittings):
    for fit in range(len(fittings)):
        if fit == 0:
            sns.distplot(data,bins=bins, kde=False, fit=fittings[fit],fit_kws={"color": "g", "linewidth": 3,"dashes":[6, 2]})
            print(cal_sse(fittings[fit],data,bins))
        else:
            sns.distplot(data,bins=bins, kde=False ,fit=fittings[fit],hist_kws={"alpha":0})
            print(cal_sse(fittings[fit],data,bins))

draw_fitting(data,bins_,DISTRIBUTIONS)
plt.show()

the result

奇怪的历史拟合使用scipy gamma和weibull_min

0 个答案: