您可以使用以下方法为每个 class 设置日志可能性：

_joint_log_likelihood(self, X):

"""Compute the unnormalized posterior log probability of X

I.e. ``log P(c) + log P(x|c)`` for all rows x of X, as an array-like of

shape [n_classes, n_samples].

Input is passed to _joint_log_likelihood as-is by predict,

predict_proba and predict_log_proba.

"""

朴素贝叶斯predict_log_proba只需通过上面的函数归一化即可 .

def predict_log_proba(self, X):

"""

Return log-probability estimates for the test vector X.

Parameters

----------

X : array-like, shape = [n_samples, n_features]

Returns

-------

C : array-like, shape = [n_samples, n_classes]

Returns the log-probability of the samples for each class in

the model. The columns correspond to the classes in sorted

order, as they appear in the attribute `classes_`.

"""

jll = self._joint_log_likelihood(X)

# normalize by P(x) = P(f_1, ..., f_n)

log_prob_x = logsumexp(jll, axis=1)

return jll - np.atleast_2d(log_prob_x).T