FITC_network.py 文件源码

def estimate_ls_temp(self, X):
        Ntrain = X.shape[0]
        if Ntrain < 10000:
            X1 = np.copy(X)
        else:
            randind = np.random.permutation(Ntrain)
            X1 = X[randind[0:(5*self.no_pseudos[0])], :]

        dist = cdist(X1, X1, 'euclidean')

        # diff = X1[:, None, :] - X1[None, :, :]
        # dist = np.sum(abs(diff), axis=2)
        D = X1.shape[1]
        N = X1.shape[0]
        triu_ind = np.triu_indices(N)
        ls = np.zeros((D, ))
        d2imed = np.median(dist[triu_ind])
        for i in range(D):
            ls[i] = np.log(d2imed + 1e-16)
        return ls