mean_functions.py 文件源码

def __call__(self, X):
        ind = tf.gather(tf.transpose(X), tf.shape(X)[1]-1)  # ind = X[:,-1]
        ind = tf.cast(ind, tf.int32)
        X = tf.transpose(tf.gather(tf.transpose(X), tf.range(0, tf.shape(X)[1]-1)))  # X = X[:,:-1]

        # split up X into chunks corresponding to the relevant likelihoods
        x_list = tf.dynamic_partition(X, ind, self.num_meanfunctions)
        # apply the likelihood-function to each section of the data
        results = [m(x) for x, m in zip(x_list, self.meanfunction_list)]
        # stitch the results back together
        partitions = tf.dynamic_partition(tf.range(0, tf.size(ind)), ind, self.num_meanfunctions)
        return tf.dynamic_stitch(partitions, results)