From 1caffe80a93dd0d6fe8880c1f6b9817f7d742d59 Mon Sep 17 00:00:00 2001 From: Michel Vincent <vm.michel@gmail.com> Date: Mon, 26 Apr 2010 07:21:58 +0000 Subject: [PATCH] Fix a bug in the computation of the log lokelihood git-svn-id: https://scikit-learn.svn.sourceforge.net/svnroot/scikit-learn/trunk@707 22fbfee3-77ab-4535-9bad-27d1bd3bc7d8 --- scikits/learn/naive_bayes.py | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/scikits/learn/naive_bayes.py b/scikits/learn/naive_bayes.py index a6c34574ee..887e158bef 100644 --- a/scikits/learn/naive_bayes.py +++ b/scikits/learn/naive_bayes.py @@ -75,11 +75,10 @@ class GNB(object): joint_log_likelihood = [] for i in range(np.size(self.unique_y)): jointi = np.log(self.proba_y[i]) - n_ij = np.sum(-0.5 * np.log(np.pi * self.sigma[i,:])) - n_ij = n_ij * np.ones(np.size(X, 0)) - n_ij -= np.sum((X - self.theta[i,:])**2, 1) - n_ij += np.sum(2 * self.sigma[i,:]) * np.ones(np.size(X, 0)) - joint_log_likelihood.append(jointi + n_ij) + n_ij = - 0.5 * np.sum(np.log(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) joint_log_likelihood = np.array(joint_log_likelihood).T proba = np.exp(joint_log_likelihood) proba = proba / np.sum(proba,1)[:,np.newaxis] -- GitLab