Research Article
English Grammar Discrimination Training Network Model and Search Filtering
Algorithm 1
English grammar discriminative training network model algorithm.
(i) | Input: Any intermediate state Si; The initial state is S0 | (ii) | Output: Vertex mapping pairs for target graph G1 and query subgraph G2 | (iii) | Match(s) | (iv) | Begin | (v) | If (1VI(S) contains all vertices in query subgraph G2)//G2 isomorphism in GI has been found | (vi) | Output M (S);//Subgraph, end of search | (vii) | Else | (viii) | The matching point pair set P(S) is calculated according to the current local matching M(S). | (ix) | For Each p in P(S)//Traversal matching point pair set F(S) | (x) | If(in the case of p=(n,m)), the feasibility function F(S,n,m} = true)//If the matching p is added, it is feasible | (xi) | s' = s U p; Match(s');//L adds P to s and recursively calls Match(s)} to continue the search | (xii) | End For Each | (xiii) | Restore the data structure and trace back to the previous state.//No child of the isomorphism has been found after multiple calls to Match(s) | (xiv) | //Figure, which indicates that the current state cannot be expanded to be feasible | (xv) | The knife graph isomorphism matches, then goes back to the previous state | (xvi) | End |
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