dmp_sequence.py 文件源码

def set_params(self, params):
        """Utility function: set currently optimizable parameters."""
        weights, goals, goal_vels = np.split(params, (self.n_weights,
            self.n_weights + (self.n_dmps - 1) * self.n_task_dims))
        G = np.split(goals, [i * self.n_task_dims
                             for i in range(1, self.n_dmps - 1)])
        self.weights = [w.reshape(self.n_weights_per_dmp[i], self.n_task_dims)
                        for i, w in enumerate(np.split(
                            weights, self.split_weights * self.n_task_dims)[
                                :self.n_dmps])]

        for i in range(self.n_dmps - 1):
            self.subgoals[i + 1] = G[i]
        if self.learn_goal_velocities:
            self.subgoal_velocities = np.split(
                goal_vels, [i * self.n_task_dims
                            for i in xrange(1, self.n_dmps)])