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49 lines
1.7 KiB
49 lines
1.7 KiB
/**
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* Created by Alex on 10-Aug-15.
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*/
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class FloydWarshall {
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constructor(){}
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getDistances(body, nodesArray, edgesArray) {
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let D_matrix = {};
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let edges = body.edges;
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// prepare matrix with large numbers
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for (let i = 0; i < nodesArray.length; i++) {
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D_matrix[nodesArray[i]] = {};
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D_matrix[nodesArray[i]] = {};
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for (let j = 0; j < nodesArray.length; j++) {
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D_matrix[nodesArray[i]][nodesArray[j]] = (i == j ? 0 : 1e9);
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D_matrix[nodesArray[i]][nodesArray[j]] = (i == j ? 0 : 1e9);
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}
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}
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// put the weights for the edges in. This assumes unidirectionality.
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for (let i = 0; i < edgesArray.length; i++) {
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let edge = edges[edgesArray[i]];
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// edge has to be connected if it counts to the distances. If it is connected to inner clusters it will crash so we also check if it is in the D_matrix
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if (edge.connected === true && D_matrix[edge.fromId] !== undefined && D_matrix[edge.toId] !== undefined) {
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D_matrix[edge.fromId][edge.toId] = 1;
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D_matrix[edge.toId][edge.fromId] = 1;
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}
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}
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let nodeCount = nodesArray.length;
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// Adapted FloydWarshall based on unidirectionality to greatly reduce complexity.
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for (let k = 0; k < nodeCount; k++) {
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for (let i = 0; i < nodeCount-1; i++) {
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for (let j = i+1; j < nodeCount; j++) {
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D_matrix[nodesArray[i]][nodesArray[j]] = Math.min(D_matrix[nodesArray[i]][nodesArray[j]],D_matrix[nodesArray[i]][nodesArray[k]] + D_matrix[nodesArray[k]][nodesArray[j]])
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D_matrix[nodesArray[j]][nodesArray[i]] = D_matrix[nodesArray[i]][nodesArray[j]];
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}
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}
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}
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return D_matrix;
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}
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}
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export default FloydWarshall;
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