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  • 9.普利姆算法(加点法)求最小生成树(JavaScript版)

    普利姆算法(加点法)求最小生成树

    <!DOCTYPE html>
    <html lang="en">
    
    <head>
        <meta charset="UTF-8">
        <meta name="viewport" content="width=device-width, initial-scale=1.0">
        <title>Document</title>
    </head>
    
    <body>
        <script>
            function Node(value) {
                this.value = value;
                this.neighbor = [];
                this.distance = [];
            }
    
            var nodeA = new Node("a");
            var nodeB = new Node("b");
            var nodeC = new Node("c");
            var nodeD = new Node("d");
            var nodeE = new Node("e");
            var nodeF = new Node("f");
            var nodeG = new Node("g");
            var nodeH = new Node("h");
    
            //存放所有节点的数组
            var pointSet = [nodeA, nodeB, nodeC, nodeD, nodeE, nodeF, nodeG, nodeH];
    
            var max = Number.POSITIVE_INFINITY; //无穷大
    
            var distance = [ //点与点之间的距离
                //   a    b    c    d    e    f    g    h
                [0, 1, 2, max, max, max, max, max], //a
                [1, 0, max, 3, max, 5, max, max], //b
                [2, max, 0, 4, max, max, 7, max], //c
                [max, 3, 4, 0, 6, max, max, max], //d
                [max, max, max, 6, 0, 8, 9, max], //e
                [max, 5, max, max, 8, 0, max, 10], //f
                [max, max, 7, max, 9, max, 0, 11], //g
                [max, max, max, max, max, 10, 11, 0] //h
            ];
    
            //prim算法
            function prim(pointSet, distance, start) {
                var nowPointSet = [];
                nowPointSet.push(start);//将开始节点放入已连接数组中
                while (true) {
                    //通过已连接节点,找到和它们相连接开销最小的节点
                    var minDistanceNode = getMinDistanceNode(pointSet, distance, nowPointSet);
                    nowPointSet.push(minDistanceNode);//将开销最小的节点加入已连接数组中
                    if(nowPointSet.length == pointSet.length) break;//所有节点都连接,跳出循环
                }
                console.log(nowPointSet);   
            }
    
            function getMinDistanceNode(pointSet, distance, nowPointSet) {
                for (var i = 0; i < nowPointSet.length; i++) { //遍历已连接的点
                    var pointIndex = getIndex(nowPointSet[i].value);//获取已连接节点在pointSet中的索引值
                    var pointDistance = distance[pointIndex];//通过pointIndex找到该连接节点对应所有边的开销
                    var minDistance = max;//最小距离默认为max
                    var fromNode = null;//起始节点
                    var endNode = null;//终止节点
                    for (var j = 0; j < pointDistance.length; j++) { //遍历所有边的开销
                        if (nowPointSet.indexOf(pointSet[j]) < 0 && pointDistance[j] <
                            minDistance) { //最小距离连接的节点不能在nowPointSet中 && 要小于minDistance
                            minDistance = pointDistance[j];
                            fromNode = nowPointSet[i];
                            endNode = pointSet[j];
                        }
                    }
                }
    
                fromNode.neighbor.push(endNode);//起始节点 将开销最小的节点加入
                fromNode.distance.push({//起始节点 将开销最小的节点的值和距离加入
                    from: fromNode.value,
                    to: endNode.value,
                    distance: minDistance
                });
                endNode.neighbor.push(fromNode);
                endNode.distance.push({
                    from: fromNode.value,
                    to: endNode.value,
                    distance: minDistance
                });
                         
                return endNode;//返回开销最小的节点
            }
    
            function getIndex(str) {//获取索引值
                for (var i = 0; i < pointSet.length; i++) {
                    if (str == pointSet[i].value) {
                        return i;
                    }
                }
    
                return -1;
            }
    
            prim(pointSet, distance, pointSet[2]);
        </script>
    </body>
    
    </html>
    普利姆算法
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  • 原文地址:https://www.cnblogs.com/lanshanxiao/p/13195607.html
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