Java 图形算法是纵向的,而不是水平的
我尝试实现一个*搜索的迭代深化。 问题是他走得很协调,不像我想的那样 当我声明一个节点时,我还写了他所在的级别(adancime) 所以我的逻辑是,如果(他得到的级别与作业中的级别(adancime)相同)是可以的,那么试试那里 我的图表如下: 来源:S目的地:G 他应该走的道路是:S B D H F H F。。。。(他应该找不到节点)因为他总是在某个点上得到最低值H或F 这是我的密码:Java 图形算法是纵向的,而不是水平的,java,graph,artificial-intelligence,iterative-deepening,Java,Graph,Artificial Intelligence,Iterative Deepening,我尝试实现一个*搜索的迭代深化。 问题是他走得很协调,不像我想的那样 当我声明一个节点时,我还写了他所在的级别(adancime) 所以我的逻辑是,如果(他得到的级别与作业中的级别(adancime)相同)是可以的,那么试试那里 我的图表如下: 来源:S目的地:G 他应该走的道路是:S B D H F H F。。。。(他应该找不到节点)因为他总是在某个点上得到最低值H或F 这是我的密码: package com.ida.algorithm; import java.util.Priority
package com.ida.algorithm;
import java.util.PriorityQueue;
import java.util.HashSet;
import java.util.Set;
import java.util.List;
import java.util.Comparator;
import java.util.ArrayList;
import java.util.Collections;
public class ItDeepAStar {
public static void main(String[] args){
Node s = new Node("S", 12, 0);
Node a = new Node("A", 5, 1);
Node b = new Node("B", 5, 1);
Node c = new Node("C", 5, 2);
Node d = new Node("D", 2, 2);
Node e = new Node("E", 2, 3);
Node f = new Node("F", 1, 4);
Node h = new Node("H", 1, 3);
Node g = new Node("G", 0, 2);
s.adjacencies = new Edge[]{
new Edge(b, 8),
new Edge(a, 10)
};
b.adjacencies = new Edge[]{
new Edge(d, 8),
new Edge(g, 16)
};
d.adjacencies = new Edge[]{
new Edge(g, 3),
new Edge(h, 1)
};
h.adjacencies = new Edge[]{
new Edge(f, 1)
};
a.adjacencies = new Edge[]{
new Edge(g, 10),
new Edge(c, 2)
};
c.adjacencies = new Edge[]{
new Edge(e, 3)
};
e.adjacencies = new Edge[]{
new Edge(g, 2)
};
AstarSearch(s, g);
List<Node> path = printPath(g);
System.out.println("Path: " + path);
}
public static List<Node> printPath(Node target){
List<Node> path = new ArrayList<Node>();
for(Node node = target; node != null; node = node.parent){
path.add(node);
}
Collections.reverse(path);
return path;
}
public static void AstarSearch(Node source, Node goal){
Set<Node> explored = new HashSet<Node>();
int level = 0;
PriorityQueue<Node> queue = new PriorityQueue<Node>(8, new Comparator<Node>(){
//override compare method
public int compare(Node i, Node j){
if(i.f_scores > j.f_scores){
return 1;
}
else if (i.f_scores < j.f_scores){
return -1;
}
else{
return 0;
}
}
});
//cost from start
source.g_scores = 0;
queue.add(source);
boolean found = false;
while((!queue.isEmpty()) && (!found)){
//the node in having the lowest f_score value
Node current = queue.poll();
explored.add(current);
//goal found
if(current.value.equals(goal.value)){
found = true;
}
//check every child of current node
for(Edge o : current.adjacencies){
Node child = o.target;
double cost = o.cost;
double temp_g_scores = current.g_scores + cost;
double temp_f_scores = temp_g_scores + child.h_scores;
level++;
/*if child node has been evaluated and
the newer f_score is higher, skip*/
if((explored.contains(child)) && (temp_f_scores >= child.f_scores) ) {
level--;
continue;
}
/*else if child node is not in queue or
newer f_score is lower*/
else if((!queue.contains(child)) || (temp_f_scores < child.f_scores) && level == child.adancime){
child.parent = current;
child.g_scores = temp_g_scores;
child.f_scores = temp_f_scores;
if(queue.contains(child)){
queue.remove(child);
level--;
}
queue.add(child);
level++;
}
}
}
}
}
class Node{
public int adancime;
public final String value;
public double g_scores;
public final double h_scores;
public double f_scores = 0;
public Edge[] adjacencies = new Edge[]{};
public Node parent;
public Node(String val, double hVal, int adaincime){
value = val;
h_scores = hVal;
this.adancime = adaincime;
}
public String toString(){
return value;
}
}
class Edge{
public final double cost;
public final Node target;
public Edge[] adjacencies = new Edge[]{};
public Edge(Node targetNode, double costVal){
target = targetNode;
cost = costVal;
}
}
package com.ida.algorithm;
导入java.util.PriorityQueue;
导入java.util.HashSet;
导入java.util.Set;
导入java.util.List;
导入java.util.Comparator;
导入java.util.ArrayList;
导入java.util.Collections;
公共级ItDeepAStar{
公共静态void main(字符串[]args){
节点s=新节点(“s”,12,0);
节点a=新节点(“a”,5,1);
节点b=新节点(“b”,5,1);
节点c=新节点(“c”,5,2);
节点d=新节点(“d”,2,2);
节点e=新节点(“e”,2,3);
节点f=新节点(“f”,1,4);
节点h=新节点(“h”,1,3);
节点g=新节点(“g”,0,2);
s、 邻接=新边[]{
新边(b,8),
新边(a,10)
};
b、 邻接=新边[]{
新边(d,8),
新边(g,16)
};
d、 邻接=新边[]{
新边(g,3),
新边(h,1)
};
h、 邻接=新边[]{
新边(f,1)
};
a、 邻接=新边[]{
新边(g,10),
新边(c,2)
};
c、 邻接=新边[]{
新边(e,3)
};
e、 邻接=新边[]{
新边(g,2)
};
AstarSearch(s,g);
列表路径=打印路径(g);
System.out.println(“路径:+Path”);
}
公共静态列表打印路径(节点目标){
列表路径=新的ArrayList();
for(Node=target;Node!=null;Node=Node.parent){
添加(节点);
}
集合。反向(路径);
返回路径;
}
公共静态void AstarSearch(节点源、节点目标){
Set explored=新的HashSet();
智力水平=0;
PriorityQueue队列=新的PriorityQueue(8,新的比较器(){
//覆盖比较法
公共整数比较(节点i、节点j){
如果(i.f_分数>j.f_分数){
返回1;
}
否则如果(i.f_分数=子项f_分数)){
级别--;
继续;
}
/*如果子节点不在队列中或
较新的f_分数较低*/
如果(!queue.contains(child))| |(temp_f_分数