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<p>我有以下代码,试图最小化一个日志似然函数。</p>
<pre><code>#!/usr/bin/python
import math
import random
import numpy as np
from scipy.optimize import minimize
def loglikelihood(params, data):
(mu, alpha, beta) = params
tlist = np.array(data)
r = np.zeros(len(tlist))
for i in xrange(1,len(tlist)):
r[i] = math.exp(-beta*(tlist[i]-tlist[i-1]))*(1+r[i-1])
loglik = -tlist[-1]*mu
loglik = loglik+alpha/beta*sum(np.exp(-beta*(tlist[-1]-tlist))-1)
loglik = loglik+np.sum(np.log(mu+alpha*r))
return -loglik
atimes = [ 148.98894201, 149.70253172, 151.13717804, 160.35968355,
160.98322609, 161.21331798, 163.60755544, 163.68994973,
164.26131871, 228.79436067]
a= 0.01
alpha = 0.5
beta = 0.6
print loglikelihood((a, alpha, beta), atimes)
res = minimize(loglikelihood, (0.01, 0.1,0.1), method = 'BFGS',args = (atimes,))
print res
</code></pre>
<p>它给了我</p>
<pre><code>28.3136498357
./test.py:17: RuntimeWarning: invalid value encountered in log
loglik = loglik+np.sum(np.log(mu+alpha*r))
status: 2
success: False
njev: 14
nfev: 72
hess_inv: array([[1, 0, 0],
[0, 1, 0],
[0, 0, 1]])
fun: 32.131359359964378
x: array([ 0.01, 0.1 , 0.1 ])
message: 'Desired error not necessarily achieved due to precision loss.'
jac: array([ -2.8051672 , 13.06962156, -48.97879982])
</code></pre>
<p>注意,它根本没能优化参数,最小值32大于28,这就是a=0.01,alpha=0.5,beta=0.6得到的结果。有可能通过选择更好的初始猜测可以避免这个问题,但如果是这样,我如何能自动做到这一点?</p>