ranking_svm.py 文件源码

def predict(self, X):
        """Predict ranking values for new data.

        Parameters
        ----------
        X : array, shape (n_test, n_features)
            Test data

        Returns
        -------
        y : array, shape (n_test,)
            Ranking values
        """
        n_features = X.shape[1]

        if self.n_features != n_features:
            raise ValueError("Expected %d dimensions, got %d"
                             % (self.n_features, n_features))

        K = euclidean_distances(self.X, X, squared=True)
        K /= self.denom
        np.exp(K, K)

        return np.sum(self.alpha[:, np.newaxis] * (K[:-1] - K[1:]), axis=0)