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using System.Collections;
using System.Collections.Generic;
using UnityEngine;
using System.Linq;
public class Pathfinder : MonoBehaviour
{
// used to store all unique states that agent has visited (holds agent and sample coords)
public static List<State> closed = new List<State>();
LogicGrid world;
public static Vector2Int agent;
public static List<Vector2Int> obstacles;
public static List<Vector2Int> samples;
public static int algorithm; // 0 = A* | 1 = dfs | 2 = bfs
public static int heuristic; // 0 = h0 | 1 = h1 | 2 = h2
public static int height;
public static int width;
public Pathfinder(LogicGrid world, Vector2Int a, List<Vector2Int> o, List<Vector2Int> s, int algo, int heu)
{
this.world = world;
height = world.GetHeight();
width = world.GetWidth();
agent = a;
obstacles = o;
samples = s;
algorithm = algo;
heuristic = heu;
printInfo();
StartWork();
}
public void printInfo()
{
Debug.Log("Agent: " + agent[0] + "," + agent[1]);
for (int i = 0; i < obstacles.Count; i++)
{
Debug.Log("Obstacle " + i + ": " + obstacles[i].x + ", " + obstacles[i].y);
}
for (int i = 0; i < samples.Count; i++)
{
Debug.Log("Sample " + i + ": " + samples[i].x + ", " + samples[i].y);
}
}
public void StartWork()
{
Node initialState = new Node(null, agent, samples, '0', 0, 0);
Stack<Node> result = new Stack<Node>();
if (algorithm == 0)
{
result = StartAStar(initialState);
}
}
public static Stack<Node> StartAStar(Node initialState)
{
Stack<Node> solution = new Stack<Node>();
Queue<Node> open = new Queue<Node>();
//PriorityQueue<Node> open = new PriorityQueue<Node>();
open.Enqueue(initialState);
int cap = 10000;
while (true && cap > 0)
{
if (!open.Any())
return solution;
Node currentNode = open.Dequeue();
// check whether we are in goal state
if (currentNode.samples.Count() == 0)
{
var orderedList = open.OrderBy(Node => Node.fn);
while (currentNode.parent != null)
{
solution.Push(currentNode);
currentNode = currentNode.parent;
}
return solution;
}
// if not, expand children and continue
List<Node> children = currentNode.expand(-1);
if (heuristic.Equals("h0"))
h0(children);
/* else if (heuristic.Equals("h1"))
h1(children);
else if (heuristic.Equals("h2"))
h2(children);*/
foreach (Node child in children)
{
open.Enqueue(child);
}
cap--;
}
}
// HEURISTICS FOR ASTAR
public static List<Node> h0(List<Node> children)
{
List<Node> sorted = new List<Node>();
for (int i = 0; i < children.Count(); i++)
{
// estimated moves left: zero
children[i].heuristic = 0;
sorted.Add(children[i]);
}
sorted = sorted.OrderBy(Node => Node.fn).ToList();
return sorted;
}
/* public static List<Node> h1(List<Node> children)
{
Queue<Node> sorted = new PriorityQueue<>(new NodeComparator());
for (int i = 0; i < children.size(); i++)
{
// estimated moves left: samples left
children.get(i).heuristic = children.get(i).samples.size();
sorted.add(children.get(i));
}
return children;
}
public static Queue<Node> h2(List<Node> children)
{
Queue<Node> sorted = new PriorityQueue<>(new NodeComparator());
if (children.size() == 0)
return sorted;
Node child;
for (int i = 0; i < children.size(); i++)
{
// estimated moves left:
// distance to nearest sample + 1 (+1 to account for sample action)
child = children.get(i);
child.heuristic = Node.distance(child.agent, child.nearestSample()) + 1;
sorted.add(child);
}
return sorted;
}*/
}
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