public AStar(Game game, Grid grid, IHeuristic heuristic)
: base(game)
{
this.grid = grid;
Heuristic = heuristic;
IsActive = false;
}
public override bool Find(IMap grid, IHeuristic h)
{
Clear();
var sqrt2 = Sqrt(2);
var cutOff = H(h, grid.StartNode, grid.EndNode);
Dictionary <int, Node> route;
Tuple <Node, double> t;
OnStart(BuildArgs(0, grid));
var k = 0;
for (k = 0; true; k++)
{
route = new Dictionary <int, Node>();
t = Search(h, grid, grid.StartNode, 0, cutOff, route, 0, grid.EndNode, k);
if (t == null || t.Item2 == double.PositiveInfinity)
{
OnEnd(BuildArgs(k, grid, false));
return(false);
}
if (t.Item1 != null)
{
var lis = route.OrderByDescending(e => e.Key).Select(e => e.Value).ToList();
for (int i = 1; i < lis.Count; i++)
{
lis[i - 1].ParentNode = lis[i];
}
OnEnd(BuildArgs(k, grid, true));
return(true);
}
cutOff = t.Item2;
}
// OnEnd(BuildArgs(0, false));
// return false;
}
/// <summary>
/// Gets the full search results of the planning search.
/// </summary>
/// <param name="includingSolutionPlan">Include the solution plan?</param>
/// <returns>Search results.</returns>
public SearchResults GetSearchResults(bool includingSolutionPlan = true)
{
IProblem problem = Problem as IProblem;
IHeuristic heuristic = Heuristic as IHeuristic;
SearchResults results = new SearchResults
{
ResultStatus = ResultStatus,
DomainName = (problem != null) ? problem.GetDomainName() : "",
ProblemName = Problem.GetProblemName(),
Algorithm = GetName(),
Heuristic = Heuristic.GetName(),
SearchTime = GetSearchTime(),
ClosedNodes = GetClosedNodesCount(),
OpenNodes = GetOpenNodesCount(),
MaxGValue = MaxGValue,
BestHeuristicValue = heuristic?.GetStatistics().BestHeuristicValue ?? double.MaxValue,
AverageHeuristicValue = heuristic?.GetStatistics().AverageHeuristicValue ?? double.MaxValue,
SolutionPlan = null,
SolutionCost = GetSolutionCost()
};
if (includingSolutionPlan)
{
results.SolutionPlan = GetSolutionPlan();
}
return(results);
}
public Solver(Graph g, IHeuristic heuristic, IPriorityQueue <PriorityQueueItem> queue)
{
_heuristic = heuristic;
graph = g;
queue.Clear();
priorityQueue = queue;
}
public Search_AStar(SparseGraph <TNode, TEdge> Graph, IHeuristic <SparseGraph <TNode, TEdge> > Heuristic, int Source, int Target)
{
this.Graph = Graph;
this.bFound = false;
this.Heuristic = Heuristic;
SourceNodeIndex = Source;
TargetNodeIndex = Target;
int ActiveNodeCount = Graph.ActiveNodeCount();
int NodeCount = Graph.NodeCount();
ShortestPathTree = new List <TEdge>(NodeCount);
SearchFrontier = new List <TEdge>(NodeCount);
CostToThisNode = new List <double>(NodeCount);
GCosts = new List <double>(NodeCount);
FCosts = new List <double>(NodeCount);
// not sure i need to initialize these...nt);
for (int i = 0; i < NodeCount; i++)
{
ShortestPathTree.Insert(i, null);
SearchFrontier.Insert(i, null);
CostToThisNode.Insert(i, 0);
FCosts.Insert(i, 0);
GCosts.Insert(i, 0);
}
TimeSlicedQ = new IndexedPriorityQueueLow <double>(FCosts, Graph.NodeCount());
TimeSlicedQ.Insert(SourceNodeIndex);
}
public NodeArrayAStarPathFinding(WorldModel WM, IHeuristic heuristic) : base(WM,null,null,heuristic)
{
this.NodeRecordArray = new NodeRecordArray(WM);
this.lstars = new List<NodeRecord>();
this.Open = this.NodeRecordArray;
this.Closed = this.NodeRecordArray;
}
public MapNpc(IItemProvider?itemProvider, ILogger logger, IHeuristic distanceCalculator)
{
_itemProvider = itemProvider;
_logger = logger;
Requests = new Subject <RequestData>();
_distanceCalculator = distanceCalculator;
}
public NodeArrayAStarPathFinding(WorldModel WM, IHeuristic heuristic) : base(WM, null, null, heuristic)
{
this.NodeRecordArray = new NodeRecordArray(WM);
this.lstars = new List <NodeRecord>();
this.Open = this.NodeRecordArray;
this.Closed = this.NodeRecordArray;
}
// Permet de selectionner une heuristique pour un solver
private void HeuristicButton_Click(object sender, EventArgs e)
{
// Enlève le focus du bouton précedent
if (_selectedHeuristic.GetType() == typeof(Manhattan))
{
ButtonUnsetFocus(heuristicThreeButton);
}
else if (_selectedHeuristic.GetType() == typeof(PLC))
{
ButtonUnsetFocus(heuristicOneButton);
}
else
{
ButtonUnsetFocus(heuristicTwoButton);
}
// Ajoute l'heuristique choisie comme courante et met le bouton en Focus
Button button = (Button)sender;
if (button == heuristicOneButton)
{
_selectedHeuristic = new PLC();
}
else if (button == heuristicTwoButton)
{
_selectedHeuristic = new PLCC();
}
else
{
_selectedHeuristic = new Manhattan();
}
ButtonSetFocus(button);
}
public void CopyWithNewReferences_CreatesDeepCopy()
{
var puzzle = new PuzzleWithPossibleValues(new int?[][] {
new int?[] { 1, null /* 4 */, null /* 3 */, 2 },
new int?[] { null /* 2 */, null /* 3 */, null /* 1 */, 4 },
new int?[] { null /* 4 */, null /* 1 */, null /* 2 */, 3 },
new int?[] { 3, 2, null /* 4 */, 1 }
});
var ruleKeeper = new StandardRuleKeeper();
Assert.True(ruleKeeper.TryInit(puzzle));
var heuristic = new UniqueInColumnHeuristic(
(IMissingColumnValuesTracker)ruleKeeper.GetRules()[0]);
Assert.True(heuristic.TryInitFor(puzzle));
var puzzleCopy = new PuzzleWithPossibleValues(puzzle);
var ruleKeeperCopy = (StandardRuleKeeper)ruleKeeper.CopyWithNewReferences(
puzzleCopy);
IHeuristic heuristicCopy = heuristic.CopyWithNewReferences(
puzzleCopy, ruleKeeperCopy.GetRules());
var coord = new Coordinate(0, 1);
BitVector originalPossibleValues = puzzle.GetPossibleValues(coord);
Assert.Equal(originalPossibleValues, puzzleCopy.GetPossibleValues(coord));
heuristicCopy.UpdateAll();
Assert.Equal(originalPossibleValues, puzzle.GetPossibleValues(coord));
Assert.NotEqual(originalPossibleValues, puzzleCopy.GetPossibleValues(coord));
}
public AStarModified(IGraph graph, IEndPoints endPoints, IStepRule stepRule, IHeuristic function)
: base(graph, endPoints, stepRule, function)
{
PercentOfFarthestVerticesToDelete
= CalculatePercentOfFarthestVerticesToDelete;
deletedVertices = new Queue <IVertex>();
}
public SearchResult GreedyBestFirstSearch(IHeuristic heuristic)
{
NodeComparator comparator = (node1, node2) => node1.HHat.CompareTo(node2.HHat);
return(GenericSearch(_initialState, _goalState, "Greedy Best First Search",
comparator, (state1, state2) => 1, heuristic));
}
public Node(Point point, Point endLocation, IHeuristic heuristic, int max)
{
Location = point;
_heuristic = heuristic;
H = _heuristic.GetTraversalCost(Location, endLocation);
G = max;
}
public SearchResult AStarSearch(IHeuristic heuristic)
{
NodeComparator comparator = (node1, node2) => node1.FHat.CompareTo(node2.FHat);
return(GenericSearch(_initialState, _goalState, "AStar",
comparator, (state1, state2) => 1, heuristic));
}
public PLCC()
{
// On utilise nos heuristiques P, LC et C prédéfini
_h1 = new Manhattan();
_h2 = new LinearConflict();
_h3 = new CornerConflict();
}
public global::PathfindingAlgorithms.Algorithms.Astar.Astar ConstructorTest(IHeuristic heuristic, IAdjacement adjacement)
{
global::PathfindingAlgorithms.Algorithms.Astar.Astar target
= new global::PathfindingAlgorithms.Algorithms.Astar.Astar
(heuristic, adjacement);
return target;
// TODO: добавление проверочных утверждений в метод AstarTest.ConstructorTest(IHeuristic, IAdjacement)
}
public AStarPathfinding(NavMeshPathGraph graph, IOpenSet open, IClosedSet closed, IHeuristic heuristic)
{
this.NavMeshGraph = graph;
this.Open = open;
this.Closed = closed;
this.InProgress = false;
this.Heuristic = heuristic;
}
/// <summary>
/// Gets the minimum-weight path to a goal using the A* algorithm.
/// </summary>
/// <typeparam name="TNode">The type of a node.</typeparam>
/// <typeparam name="TKey">The type of a node key.</typeparam>
/// <param name="sources">The set of nodes from which to start the search, weighted by their initial cost.</param>
/// <param name="descriptor">The object describing how to navigate the weighted graph.</param>
/// <param name="goal">The goal predicate.</param>
/// <param name="heuristic">The heuristic function.</param>
/// <returns>The minimum-weight path, or null if none exists.</returns>
public static IWeighted <IEnumerable <TNode> > AStar <TNode, TKey>(
this IEnumerable <IWeighted <TNode> > sources,
IWeightedGraphDescriptor <TNode, TKey> descriptor,
Func <TNode, bool> goal,
IHeuristic <TNode> heuristic)
{
return(AStar(sources, descriptor.Key, descriptor.Next, goal, heuristic));
}
public AStarPathfinding(WorldModel WM, IOpenSet open, IClosedSet closed, IHeuristic heuristic) : base(WM)
{
this.Open = open;
this.Closed = closed;
this.NodesPerSearch = uint.MaxValue; //by default we process all nodes in a single request
this.InProgress = false;
this.Heuristic = heuristic;
}
public AStarPathfinding(WorldModel WM, IOpenSet open, IClosedSet closed, IHeuristic heuristic) : base(WM)
{
this.Open = open;
this.Closed = closed;
this.NodesPerSearch = uint.MaxValue; //by default we process all nodes in a single request
this.InProgress = false;
this.Heuristic = heuristic;
}
public NodeArrayAStarPathFinding(NavMeshPathGraph graph, IHeuristic heuristic) : base(graph, null, null, heuristic)
{
//do not change this
var nodes = this.GetNodesHack(graph);
this.NodeRecordArray = new NodeRecordArray(nodes);
this.Open = this.NodeRecordArray;
this.Closed = this.NodeRecordArray;
}
public NodeArrayAStarPathFinding(NavMeshPathGraph graph, IHeuristic heuristic) : base(graph, null, null, heuristic)
{
//do not change this
var nodes = this.GetNodesHack(graph);
this.NodeRecordArray = new NodeRecordArray(nodes);
this.Open = this.NodeRecordArray;
this.Closed = this.NodeRecordArray;
}
public AStarPathfinding(NavMeshPathGraph graph, IOpenSet open, IClosedSet closed, IHeuristic heuristic)
{
this.NavMeshGraph = graph;
this.Open = open;
this.Closed = closed;
this.NodesPerFrame = uint.MaxValue; //by default we process all nodes in a single request
this.InProgress = false;
this.Heuristic = heuristic;
}
/// <summary>
/// For the heuristic function h(n), returns the relaxed heuristic function h'(n) = (1 + amount) * h(n).
/// </summary>
/// <typeparam name="TNode">The type of a node.</typeparam>
/// <param name="heuristic">The heuristic function.</param>
/// <param name="amount">The amount to relax by.</param>
/// <param name="maintainConsistency">
/// Should the consistency of the heuristic be maintained?
/// Defaults to true since this is usually the desired behavior, even though theoretically this is not always the case.
/// </param>
/// <returns>The relaxed heuristic function.</returns>
public static IHeuristic <TNode> Relax <TNode>(this IHeuristic <TNode> heuristic, double amount, bool maintainConsistency)
{
if (amount < 0)
{
throw new ArgumentOutOfRangeException("amount", "Must be nonnegative.");
}
amount += 1;
return(Create <TNode>(x => amount * heuristic.Evaluate(x), maintainConsistency && heuristic.IsConsistent));
}
//maybe hold a list with all final operations- usefull for get next operation.
public Strips(Board board)
{
this.board = board;
var goalState = FindGoalStatePredicates();
stack.Push(goalState);
heuristics = new Heuristic();
}
public GraphGroup(int size, IHeuristic heuristic)
{
Size = size;
AllowedValues = Enumerable.Repeat(true, size).ToArray();
mutable = Enumerable.Repeat(true, size).ToArray();
Nodes = new List <GraphNode>();
Filled = Nodes.Count(a => a.Starting);
ranker = heuristic;
}
public AStarPathfinding(NavMeshPathGraph graph, IOpenSet open, IClosedSet closed, IHeuristic heuristic)
{
this.NavMeshGraph = graph;
this.Open = open;
this.Closed = closed;
this.NodesPerSearch = uint.MaxValue; //by default we process all nodes in a single request
this.InProgress = false;
this.Heuristic = heuristic;
}
static TestDomainData[] GenTest(IHeuristic heuristic, int numTasks = 100, int numSteps = Global.MAXSTEPS, bool writeCSV = true, bool computeCost = true)
{
TestDomainData[] data = new TestDomainData[numTasks];
string csvWritePath = string.Format(Global.ROOTPATH, Global.TESTTASKFN);
CSVWriter csv = new CSVWriter(csvWritePath, ',');
for (int i = 0; i < numTasks; i++)
{
DomainContainer domainContainer = new DomainContainer(heuristic, numSteps);
ISearchDomain domain = domainContainer.Domain;
IState initState = domain.Initial();
string initStateStr = string.Join(" ", initState.Arr);
int cost = 0;
long generated = 0;
if (computeCost == true)
{
if (Global.DOMAINTYPE == typeof(BlocksWorld.BlocksWorld) && heuristic == null)
{
cost = PostBlocksWorld(initState, domain.Goal());
}
else
{
IDAStar <ISearchDomain> planner = new IDAStar <ISearchDomain>(domain);
List <IState> plan = planner.Search(initState);
cost = (plan.Count - 1);
generated = planner.Generated;
}
}
data[i] = new TestDomainData(initState.Arr, cost, generated);
csv.Add(initStateStr);
csv.Add(cost.ToString());
csv.Add(generated.ToString());
csv.EndLine();
}
if (writeCSV == true)
{
csv.Write();
}
return(data);
}
public MapNpc(IItemGenerationService?itemProvider, ILogger logger, IHeuristic distanceCalculator)
{
_itemProvider = itemProvider;
_logger = logger;
Requests = new Dictionary <Type, Subject <RequestData> >()
{
[typeof(INrunEventHandler)] = new Subject <RequestData>()
};
_distanceCalculator = distanceCalculator;
}
public SecureNodeArrayAStarPathFinding(AutonomousCharacter character, NavMeshPathGraph graph, IHeuristic heuristic)
: base(graph, null, null, heuristic)
{
//do not change this
var nodes = this.GetNodesHack(graph);
this.NodeRecordArray = new NodeRecordArray(nodes);
this.Open = this.NodeRecordArray;
this.Closed = this.NodeRecordArray;
this.character = character;
}
public Solver(uint[,] board, IHeuristic heuristic, Func <int, uint[, ]> generateTarget, bool useGreedy = false)
{
Heuristic = heuristic;
GenerateTarget = generateTarget;
UseGreedy = useGreedy;
Solution = new List <Board>();
StopWatch = new Stopwatch();
Solve(board);
}
public Astar(IHeuristic heuristic, IAdjacement adjacement)
{
if (heuristic == null)
throw new ArgumentNullException("heuristic");
if (adjacement == null)
throw new ArgumentNullException("adjacement");
this.heuristic = heuristic;
this.adjacement = adjacement;
}
public override bool Find(IMap grid, IHeuristic heuristic)
{
Clear();
var sqrt2 = Sqrt(2);
grid.StartNode.Cost = 0;
grid.EndNode.Cost = 0;
grid.AddInOpenList(grid.StartNode);
var step = 0;
OnStart(BuildArgs(step, grid));
while (grid.OpenListCount() != 0)
{
var node = grid.PopOpenList();
grid.AddInClosedList(node);
if (node == grid.EndNode)
{
//_endNode = node;
OnEnd(BuildArgs(step, grid, true));
return(true);
}
var neighbors = grid.GetNeighbors(node);
for (var i = 0; i < neighbors.Count; ++i)
{
var neighbor = neighbors[i];
if (grid.IsClosed(neighbor))
{
continue;
}
var x = neighbor.X;
var y = neighbor.Y;
// get the distance between current node and the neighbor
// and calculate the next g score
var ng = node.G + ((x - node.X == 0 || y - node.Y == 0) ? 1 : sqrt2);
// check if the neighbor has not been inspected yet, or
// can be reached with smaller cost from the current node
if (!grid.IsOpen(neighbor) || ng < neighbor.G)
{
neighbor.G = ng;
neighbor.H = Weight * CalcH(heuristic, Abs(x - grid.EndNode.X), Abs(y - grid.EndNode.Y));
neighbor.Cost = neighbor.G + neighbor.H;
neighbor.ParentNode = node;
if (!grid.IsOpen(neighbor))
{
grid.PushInOpenList(neighbor);
}
}
}
grid.OrderOpenList(e => e.Cost);
OnStep(BuildArgs(step++, grid));
}
OnEnd(BuildArgs(step, grid, false));
return(false);
}
public SearchResult(Node resultNode, int nodesGen, int nodesPrevGen, int nodesOnFrontier,
int nodesOnExplored, string algName, IHeuristic heuristic)
{
ResultNode = resultNode;
NodesGen = nodesGen;
NodesPrevGen = nodesPrevGen;
NodesOnFrontier = nodesOnFrontier;
NodesOnExplored = nodesOnExplored;
AlgName = algName;
HeuristicName = heuristic?.Name;
}
public SearchResult(SearchNode solution, int nodesGen, int nodesPrevGen, int nodesOnOpen,
int nodesOnClosed, string algName, IHeuristic heuristic)
{
Solution = solution;
NodesGenerated = nodesGen;
NodesPrevGenerated = nodesPrevGen;
NodesOnOpenList = nodesOnOpen;
NodesOnClosedList = nodesOnClosed;
AlgorithmName = algName;
HeuristicName = heuristic == null ? null : heuristic.Name;
}
public Problem(
StateType startState,
List <Operator <StateType> > operators,
IGoalStateChecker <StateType> goalStateChecker,
IHeuristic <StateType> heuristic = null)
{
this.StartState = startState;
this.Operators = operators;
this.GoalStateChecker = goalStateChecker;
this.Heuristic = heuristic;
}
public AStar(Graph <TModel> graph, int source, int target, IHeuristic <TModel> heuristic)
{
this.graph = graph;
this.ShortestPathTree = new Edge[graph.Count()];
this.searchEdges = new Edge[graph.Count()];
this.gCosts = new double[graph.Count()];
this.fCosts = new double[graph.Count()];
this.source = source;
this.target = target;
this.Search(heuristic);
}
static int _runFinder(IFinder finder, IHeuristic h, IMap map, bool window, bool noshow, int blocksize, int sleep)
{
if (!noshow)
{
if (sleep > 0)
{
finder.SleepUITimeInMs = sleep;
}
var viewer =
window ?
ViewerFactory.GetOpenGlViewerImplementation(blocksize)
: ViewerFactory.GetConsoleViewerImplementation();
viewer.SetFinder(finder);
viewer.Run(map, h);
}
else
{
if (finder.Find(map, h))
{
var path = map.GetPath();
AbstractViewer.ShowEndLog(finder, path, new Pathfinder.Abstraction.FinderEventArgs
{
Finded = true,
GridMap = map,
ExpandedNodesCount = map.GetMaxExpandedNodes(),
PassedTimeInMs = finder.GetProcessedTime(),
Step = 0
});
var result = FileTool.GetTextRepresentation(map, false, path);
if (!window)
{
Console.WriteLine(result);
}
else
{
MapViewWindow.OpenGlWindow(result, blocksize);
}
}
else
{
Console.WriteLine("Cant find a path");
}
}
return(0);
}
public Agent(LogarithmicGrid startingGrid, ISearcherFactory searcherFactory, IHeuristic heuristic)
{
this.searcherFactory = searcherFactory;
this.searchTree = new SearchTree(heuristic, startingGrid);
this.Timings.Add(1, Duration.Zero);
if (startingGrid.Flatten().Any(i => i == 2))
{
this.Timings.Add(2, Duration.Zero);
}
}
public static global::PathfindingAlgorithms.Algorithms.Astar.Astar Create(IHeuristic heuristic_iHeuristic, IAdjacement adjacement_iAdjacement)
{
global::PathfindingAlgorithms.Algorithms.Astar.Astar astar
= new global::PathfindingAlgorithms.Algorithms.Astar.Astar
(heuristic_iHeuristic, adjacement_iAdjacement);
return astar;
// TODO: Edit factory method of Astar
// This method should be able to configure the object in all possible ways.
// Add as many parameters as needed,
// and assign their values to each field by using the API.
}
public NodeArrayAStarPathFinding(NavMeshPathGraph graph, IHeuristic heuristic, bool usingGoalBounding) : base(graph, null, null, heuristic)
{
//do not change this
var nodes = this.GetNodesHack(graph);
this.NodeRecordArray = new NodeRecordArray(nodes, new BoundingBox());
this.Open = this.NodeRecordArray;
this.Closed = this.NodeRecordArray;
this.UsingGoalBounding = usingGoalBounding;
this.fileReader = new FileReader();
if (usingGoalBounding)
{
fileReader.fileName = "goalBoundingInfo.xml";
NodeRecordArray = fileReader.readFile(this.NodeRecordArray);
}
}
/**
* Create a path finder
*
* @param heuristic The heuristic used to determine the search order of the map
* @param map The map to be searched
* @param maxSearchDistance The maximum depth we'll search before giving up
* @param allowDiagMovement True if the search should try diagonal movement
*/
public PathFinder(Quinoa quinoa, IHeuristic heuristic)
{
this.heuristic = heuristic;
this.quinoa = quinoa;
}
public GoalBoundingNodeArrayAStarPathFinding(NavMeshPathGraph graph, IHeuristic heuristic)
: base(graph, heuristic)
{
}
public InfluenceMapAStarPathfinding(NavMeshPathGraph graph, IHeuristic heuristic, InfluenceMap redMap, InfluenceMap greenMap)
: base(graph,heuristic)
{
this.RedMap = redMap;
this.GreenMap = greenMap;
}
public SearchTree(IHeuristic heuristic, LogarithmicGrid startingGrid)
{
this.Heuristic = heuristic;
this.rootNode = new PlayerNode(startingGrid, this, startingGrid.Sum());
}
/// <summary>
/// Generic search algorithm
/// </summary>
private static SearchResult GenericSearch(StateBase initialState, StateBase goalState, string algName, OpenListComparator comparator, CostFunc cost, IHeuristic heuristic, int? depthLimit = null)
{
var openList = new OpenList(comparator);
var closedList = new ClosedList();
var cache = new HeuristicCache(goalState, heuristic);
var nodesGenerated = 0;
var nodesPrevGenerated = 0;
// add initial node to open list
openList.Push(new SearchNode(cost, initialState, null, null, cache.Evaluate));
while (true)
{
// if nothing on open list, fail
if (openList.Count == 0)
{
return new SearchResult(null, nodesGenerated, nodesPrevGenerated, openList.Count, closedList.Count, algName, heuristic);
}
// get next node to expand
var node = openList.Pop();
closedList.Push(node);
// if at goal state, success
if (node.State.Equals(goalState))
{
return new SearchResult(node, nodesGenerated, nodesPrevGenerated, openList.Count, closedList.Count, algName, heuristic);
}
// if at depth limit, don't generate successors
if (depthLimit != null && node.Depth == depthLimit)
{
continue;
}
var daughters = node.Successors(cost, cache.Evaluate);
foreach (var daughter in daughters)
{
nodesGenerated++;
// if this state is already in open list, replace old node with new node if g-hat is better
var foundInOpen = openList.Find(daughter.State);
if (foundInOpen != null)
{
nodesPrevGenerated++;
if (daughter.Ghat < foundInOpen.Ghat)
{
openList.Replace(foundInOpen, daughter);
}
}
else
{
// else if this state is already in closed list, move from closed to open if g-hat is better
var foundInClosed = closedList.Find(daughter.State);
if (foundInClosed != null)
{
nodesPrevGenerated++;
if (daughter.Ghat < foundInClosed.Ghat)
{
openList.Push(daughter);
closedList.Remove(foundInClosed);
}
}
else
{
// else didn't find in open or closed, add to open
openList.Push(daughter);
}
}
}
}
}
/// <summary>
/// Compares H-hat values for open list
/// </summary>
public SearchResult GreedySearch(CostFunc cost, IHeuristic heuristic)
{
OpenListComparator compareNodes = (node1, node2) => node1.Hhat.CompareTo(node2.Hhat);
return GenericSearch(_initialState, _goalState, "Greedy", compareNodes, cost, heuristic);
}