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1 parent dfc7c5c commit f4eced3Copy full SHA for f4eced3
sklearn/naive_bayes.py
@@ -171,9 +171,9 @@ def _joint_log_likelihood(self, X):
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joint_log_likelihood = []
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for i in xrange(np.size(self._classes)):
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jointi = np.log(self.class_prior_[i])
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- n_ij = - 0.5 * np.sum(np.log(np.pi * self.sigma[i, :]))
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- n_ij -= 0.5 * np.sum(((X - self.theta[i, :]) ** 2) / \
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- (self.sigma[i, :]), 1)
+ 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)
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joint_log_likelihood.append(jointi + n_ij)
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joint_log_likelihood = np.array(joint_log_likelihood).T
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return joint_log_likelihood
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