kernels.py 文件源码

def K(self, X, X2=None, presliced=False):
        if not presliced:
            X, X2 = self._slice(X, X2)
        if X2 is None:
            X2 = X

        # Introduce dummy dimension so we can use broadcasting
        f = tf.expand_dims(X, 1)  # now N x 1 x D
        f2 = tf.expand_dims(X2, 0)  # now 1 x M x D

        r = np.pi * (f - f2) / self.period
        r = tf.reduce_sum(tf.square(tf.sin(r) / self.lengthscales), 2)

        return self.variance * tf.exp(-0.5 * r)