Predicting Spread Probability of Learning-Effect Computer Virus

Algorithm 5

BAT-2.

(i)	Input: A 1-lag temporal vector T reordered to in decreasing of the coordinate values.
(ii)	Output: All feasible temporal vectors respective to T.
(iii)	STEP T0. Let Y be a zero vector with n coordinates represented the node infected time, vector index k = 1, istop = 1 if TARGET = 0, and istop = 0 if TARGET >0.
(iv)	STEP T1. Let coordinate index i = (n − 1).
(v)	STEP T2. If Y (i) <  (i), let Y (i) = Y (i) + 1, and execute 1-lag_Temporal_Vector(X).
(vi)	STEP T3. If Y (u) < Y (v) and (u) <  (v) for all nodes u and v in V, Y is infeasible and go STEP S1. Otherwise, let k = k + 1 and go STEP S1.
(vii)	STEP T4. If i = istop, halt and Y1, Y2, …, Yk are all feasible temporal vectors generated from T.
(viii)	STEP T5. Let X (i) = 0, i = (i − 1), and go to STEP T2.