ranking_svm.py 文件源码

def fit(self, X):
        """Fit ranking SVM.

        Parameters
        ----------
        X : array, shape (n_samples, n_features)
            Training data, sorted, highest rank first
        """
        self.n_samples, self.n_features = X.shape
        self.n_alpha = self.n_samples - 1
        self.X = X

        if self.n_samples < 2:
            raise ValueError("Expected at least 2 training samples, got %d"
                             % self.n_samples)

        random_state = check_random_state(self.random_state)
        n_iter = self.n_iter
        if n_iter < 0:
            n_iter = int(50000 * np.sqrt(self.n_features))

        K = euclidean_distances(self.X, squared=True)

        # Average distance between training data
        sigma = np.sqrt(K).sum() / ((self.n_samples - 1) * self.n_samples)
        sigma *= self.c_sigma
        self.denom = -np.maximum(2.0 * sigma ** 2, MACHINE_EPSILON)

        K /= self.denom
        np.exp(K, K)

        # Constraint violation cost
        Ci = np.linspace(self.n_alpha, 1, self.n_alpha) ** self.c_pow
        Ci *= 10 ** self.c_base

        # Optimize alpha parameters
        self.alpha = optimize(Ci, K, 1.0, n_iter, random_state)

        return self