discriminant_analysis.py 文件源码

def predict_proba(self, X):
        """Estimate probability.

        Parameters
        ----------
        X : array-like, shape (n_samples, n_features)
            Input data.

        Returns
        -------
        C : array, shape (n_samples, n_classes)
            Estimated probabilities.
        """
        prob = self.decision_function(X)
        prob *= -1
        np.exp(prob, prob)
        prob += 1
        np.reciprocal(prob, prob)
        if len(self.classes_) == 2:  # binary case
            return np.column_stack([1 - prob, prob])
        else:
            # OvR normalization, like LibLinear's predict_probability
            prob /= prob.sum(axis=1).reshape((prob.shape[0], -1))
            return prob