test_conditionals.py 文件源码

def prepare(self):
        num_latent = 2
        num_data = 3
        k = gpflow.kernels.Matern32(1) + gpflow.kernels.White(1)
        k.white.variance = 0.01
        X = tf.placeholder(settings.float_type)
        mu = tf.placeholder(settings.float_type)
        Xs = tf.placeholder(settings.float_type)
        sqrt = tf.placeholder(settings.float_type, shape=[num_data, num_latent])

        rng = np.random.RandomState(0)
        X_data = rng.randn(num_data, 1)
        mu_data = rng.randn(num_data, num_latent)
        sqrt_data = rng.randn(num_data, num_latent)
        Xs_data = rng.randn(50, 1)

        feed_dict = {X: X_data, Xs: Xs_data, mu: mu_data, sqrt: sqrt_data}
        k.compile()

        #the chols are diagonal matrices, with the same entries as the diag representation.
        chol = tf.stack([tf.diag(sqrt[:, i]) for i in range(num_latent)])
        chol = tf.transpose(chol, perm=[1, 2, 0])
        return Xs, X, k, mu, sqrt, chol, feed_dict