def neg_log_likelihood(scale):
total = 0.0
for x, count in counter.iteritems():
total += (math.log(scale) + x / scale) * count
return total
这里有一个程序来尝试一下。在
import scipy.stats
import scipy.optimize
import math
import collections
def fit1(counter):
def neg_log_likelihood(scale):
total = 0.0
for x, count in counter.iteritems():
total += (math.log(scale) + x / scale) * count
return total
optimize_result = scipy.optimize.minimize(neg_log_likelihood, [1.0])
if not optimize_result.success:
raise Exception(optimize_result.message)
return optimize_result.x[0]
def fit2(counter):
data = []
# Create an array where each key is repeated as many times
# as the value of the counter.
for x, count in counter.iteritems():
data += [x] * count
fit_result = scipy.stats.expon.fit(data, floc = 0)
return fit_result[-1]
def test():
c = collections.Counter()
c[1] = 193260
c[2] = 51794
c[3] = 19112
c[4] = 9250
c[5] = 6486
print "fit1 'scale' is %f " % fit1(c)
print "fit2 'scale' is %f " % fit2(c)
test()
看起来像scipy.stats.expon.fit基本上是一个小包装scipy.optimize.minimize.最小化,它首先创建一个函数来计算负对数似然,然后使用scipy.optimize.minimize.最小化以适应pdf参数。在
所以,我认为你需要做的是编写你自己的函数来计算counter对象的neg log可能性,然后调用scipy.optimize.minimize.最小化你自己。在
更具体地说,scipy在这里定义了expon'scale'参数 http://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.expon.html
因此,pdf是:
所以,取两边的对数,我们得到:
^{pr2}$因此,您的counter object中所有内容的负对数似然性为:
这里有一个程序来尝试一下。在
输出如下:
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