我有一组整数值，我想把它们设置成Weibull分布，然后得到最佳拟合参数。然后利用最佳拟合参数，绘制数据的直方图和Weibull分布的pdf。这是我用过的密码。在from jtlHandler import *

import warnings

import numpy as np

import pandas as pd

import scipy.stats as st

import matplotlib

matplotlib.use('Agg')

import matplotlib.pyplot as plt

def get_pdf(latencies):

a = np.array(latencies)

ag = st.gaussian_kde(a)

ak = np.linspace(np.min(a), np.max(a), len(a))

agv = ag(ak)

plt.plot(ak,agv)

plt.show()

return (ak,agv)

def fit_to_distribution(distribution, data):

params = distribution.fit(data)

# Return MLEs for shape (if applicable), location, and scale parameters from data.

#

# MLE stands for Maximum Likelihood Estimate. Starting estimates for the fit are given by input arguments; for any arguments not provided with starting estimates, self._fitstart(data) is called to generate such.

return params

def make_distribution_pdf(dist, params, end):

arg = params[:-2]

loc = params[-2]

scale = params[-1]

# Build PDF and turn into pandas Series

x = np.linspace(0, end, end)

y = dist.pdf(x, loc=loc, scale=scale, *arg)

pdf = pd.Series(y, x)

return pdf

latencies = getLatencyList("filename")

latencies = latencies[int(9*(len(latencies)/10)):len(latencies)]

data = pd.Series(latencies)

params = fit_to_distribution(st.weibull_max, data)

print("Parameters for the fit: "+str(params))

# Make PDF

pdf = make_distribution_pdf(st.weibull_max, params, max(latencies))

# Display

plt.figure()

ax = pdf.plot(lw=2, label='PDF', legend=True)

data.plot(kind='hist', bins=200, normed=True, alpha=0.5, label='Data',

legend=True, ax=ax)

ax.set_title('Weibull distribution')

ax.set_xlabel('Latnecy')

ax.set_ylabel('Frequency')

plt.savefig("image.png")

这是结果图。

如图所示，威布尔近似与原始数据分布并不相似。在

我怎样才能得到数据的最佳威布尔近似值？在