使用sklearn GMM计算概率

import numpy as np
from sklearn.mixture import GMM

training_data = np.hstack((
    np.random.normal(500,100,2000).reshape(-1,1),np.random.normal(500,))

# train the classifier and get max score
g = GMM(n_components=1)
g.fit(training_data)
scores = g.score(training_data)
max_score = np.amax(scores)

# create a candidate data point and calculate the probability
# it belongs to the training population
candidate_data = np.array([[490,450]])
candidate_score = g.score(candidate_data)

从这一点开始,我不知道该怎么做.我正在阅读我必须规范化对数概率,以便获得候选数据点属于总体的概率.会是这样的……

candidate_probability = (np.exp(candidate_score)/np.exp(max_score)) * 100

print candidate_probability
>>> [ 87.81751913]

这个数字似乎并不合理,但我真的离开了我的舒适区,所以我想我会问.谢谢！