(self, states, next_states)
return v_decrease, bound
Compute confidence intervals for the decrease along Lyapunov function.

        Parameters
        ----------
        states : np.array
            The states at which to start (could be equal to discretization).
        next_states : np.array
            The dynamics evaluated at each point on the discretization. If
            the dynamics are uncertain then next_states is a tuple with mean
            and error bounds.

        Returns
        -------
        mean : np.array
            The expected decrease in values at each grid point.
        error_bounds : np.array
            The error bounds for the decrease at each grid point
Compute confidence intervals for the decrease along Lyapunov function.
Compute confidence intervals for the decrease along Lyapunov function .
def v_decrease_confidence(self, states, next_states):
        """Compute confidence intervals for the decrease along Lyapunov function.

        Parameters
        ----------
        states : np.array
            The states at which to start (could be equal to discretization).
        next_states : np.array
            The dynamics evaluated at each point on the discretization. If
            the dynamics are uncertain then next_states is a tuple with mean
            and error bounds.

        Returns
        -------
        mean : np.array
            The expected decrease in values at each grid point.
        error_bounds : np.array
            The error bounds for the decrease at each grid point

        """
        if isinstance(next_states, Sequence):
            next_states, error_bounds = next_states
            lv = self.lipschitz_lyapunov(next_states)
            bound = tf.reduce_sum(lv * error_bounds, axis=1, keepdims=True)
        else:
            bound = tf.constant(0., dtype=config.dtype)

        v_decrease = (self.lyapunov_function(next_states)
                      - self.lyapunov_function(states))

        return v_decrease, bound
def v_decrease_confidence ( self , states , next_states ) : if isinstance ( next_states , Sequence ) : next_states , error_bounds = next_states lv = self . lipschitz_lyapunov ( next_states ) bound = tf . reduce_sum ( lv * error_bounds , axis = 1 , keepdims = True ) else : bound = tf . constant ( 0. , dtype = config . dtype ) v_decrease = ( self . lyapunov_function ( next_states ) - self . lyapunov_function ( states ) ) return v_decrease , bound