pep_models.py 文件源码

def init_hypers(self, x_train=None, key_suffix=''):
        """Summary

        Args:
            x_train (None, optional): Description
            key_suffix (str, optional): Description

        Returns:
            TYPE: Description
        """
        # dict to hold hypers, inducing points and parameters of q(U)
        N = self.N
        M = self.M
        Din = self.Din
        Dout = self.Dout

        if x_train is None:
            ls = np.log(np.ones((Din, )) + 0.1 * np.random.rand(Din, ))
            sf = np.log(np.array([1]))
            zu = np.tile(np.linspace(-1, 1, M).reshape((M, 1)), (1, Din))
        else:
            if N < 10000:
                centroids, label = kmeans2(x_train, M, minit='points')
            else:
                randind = np.random.permutation(N)
                centroids = x_train[randind[0:M], :]
            zu = centroids

            if N < 10000:
                X1 = np.copy(x_train)
            else:
                randind = np.random.permutation(N)
                X1 = X[randind[:5000], :]

            x_dist = cdist(X1, X1, 'euclidean')
            triu_ind = np.triu_indices(N)
            ls = np.zeros((Din, ))
            d2imed = np.median(x_dist[triu_ind])
            for i in range(Din):
                ls[i] = np.log(d2imed + 1e-16)
            sf = np.log(np.array([0.5]))

        params = dict()
        params['sf' + key_suffix] = sf
        params['ls' + key_suffix] = ls
        params['zu' + key_suffix] = zu

        return params