fix_gnb_proba

Author: agramfort

sklearn/naive_bayes.py

@@ -170,7 +170,7 @@ def _joint_log_likelihood(self, X):
         joint_log_likelihood = []
         for i in range(np.size(self.classes_)):
             jointi = np.log(self.class_prior_[i])
-            n_ij = - 0.5 * np.sum(np.log(np.pi * self.sigma_[i, :]))
+            n_ij = - 0.5 * np.sum(np.log(2. * np.pi * self.sigma_[i, :]))
             n_ij -= 0.5 * np.sum(((X - self.theta_[i, :]) ** 2) /
                                  (self.sigma_[i, :]), 1)
             joint_log_likelihood.append(jointi + n_ij)