public void TC_IStateOrConditions()
{
var problem = new Planner.SAS.Problem(new SASInputData(GetFilePath("TC_Gripper.sas")));
var heuristic = new FFHeuristic(problem);
IStateOrConditions state = problem.InitialState;
IStateOrConditions state2 = new Planner.SAS.State(1, 1, 1, 1, 0, 4, 4);
Assert.IsTrue(CollectionsEquality.Equals(problem.GetSuccessors((IState)state), state.DetermineTransitions(problem)));
Assert.IsFalse(state.DetermineGoalNode(problem));
Assert.IsTrue(state2.DetermineGoalNode(problem));
Assert.AreEqual(heuristic.GetValue((IState)state), state.DetermineHeuristicValue(heuristic));
IStateOrConditions conditions = problem.GoalConditions;
IStateOrConditions conditions2 = new Planner.SAS.Conditions(new Planner.SAS.Assignment(5, 4), new Planner.SAS.Assignment(6, 4));
Assert.IsTrue(CollectionsEquality.Equals(problem.GetPredecessors((IConditions)conditions), conditions.DetermineTransitions(problem)));
Assert.IsFalse(conditions.DetermineGoalNode(problem));
Assert.IsTrue(conditions2.DetermineGoalNode(problem));
Assert.AreEqual(heuristic.GetValue((IConditions)conditions), conditions.DetermineHeuristicValue(heuristic));
IStateOrConditions relativeState = problem.GoalConditions.GetCorrespondingRelativeStates(problem).First();
IStateOrConditions relativeState2 = new Planner.SAS.RelativeState(-1, -1, -1, -1, -1, 4, 4);
Assert.IsTrue(CollectionsEquality.Equals(problem.GetPredecessors((IRelativeState)relativeState), relativeState.DetermineTransitions(problem)));
Assert.IsFalse(relativeState.DetermineGoalNode(problem));
Assert.IsTrue(relativeState2.DetermineGoalNode(problem));
Assert.AreEqual(heuristic.GetValue((IRelativeState)relativeState), relativeState.DetermineHeuristicValue(heuristic));
}
public NNHeuristic(Problem p)
{
this.heur = new FFHeuristic(p);
var features = FeaturesCalculator.generateFeaturesFromProblem(p);
features.Add(0); //feature dependent on the state;
nnInputs = features.Select(d => (float)d).ToList();
}
public static double computeFFHeuristic(string stateString)
{
var splitted = stateString.Split('_');
string domainName = splitted[0];
string problemName = splitted[1];
string stateAsString = splitted[2];
string sasProblemPath = Path.Combine(SAS_all_WithoutAxioms, domainName, problemName);
Problem p = new Problem(sasProblemPath, false);
IState state = State.Parse(stateAsString);
FFHeuristic h = new FFHeuristic(p);
return(h.GetValue(state));
}
public void TC_FFHeuristic()
{
var sasProblem = new Planner.SAS.Problem(new SASInputData(GetFilePath("TC_Gripper.sas")));
var pddlProblem = new Planner.PDDL.Problem(new PDDLInputData(GetFilePath("TC_Gripper_D.pddl"), GetFilePath("TC_Gripper_P.pddl")));
var heuristic = new FFHeuristic(sasProblem);
Assert.AreEqual(9, heuristic.GetValue(sasProblem.GetInitialState()));
Assert.AreEqual(9, heuristic.GetValue(sasProblem.GetGoalConditions()));
Assert.AreEqual(9, heuristic.GetValue(sasProblem.GetGoalConditions().GetCorrespondingRelativeStates(sasProblem).First()));
Assert.AreEqual("FF Heuristic", heuristic.GetName());
Assert.AreEqual(3, heuristic.GetCallsCount());
var heuristic2 = new FFHeuristic(pddlProblem);
Assert.AreEqual(5, heuristic2.GetValue(pddlProblem.GetInitialState()));
Assert.AreEqual(5, heuristic2.GetValue(pddlProblem.GetGoalConditions()));
Assert.AreEqual(5, heuristic2.GetValue(pddlProblem.GetGoalConditions().GetCorrespondingRelativeStates(pddlProblem).First()));
Assert.AreEqual("FF Heuristic", heuristic2.GetName());
Assert.AreEqual(3, heuristic2.GetCallsCount());
}
public static void runPlanningExperiments(string domainsFolder, TimeSpan timeLimit, int param)
{
Logger logger = new Logger();
List <SearchResults> allResults = new List <SearchResults>();
//try
//{
//the number of computes that participate on this job. Computation is distributed among them.
int numberOfComputes = 2;
//if set to true, problem file that don't have histogram computed will be skipped. Otherwise all problems will be processed.
bool onlyWhenHistogramExists = false;
Problem d;
AStarSearch ast;
//HillClimbingSearch ast;
var directories = Directory.EnumerateDirectories(domainsFolder);
allResults = new List <SearchResults>();
foreach (var directory in directories)
{
var files = Directory.EnumerateFiles(directory).ToList();
foreach (var SASFile in files)
{
int indexOfFile = files.IndexOf(SASFile);
if (indexOfFile % numberOfComputes != param) //if (indexOfFile != param)
{
continue;
}
if (onlyWhenHistogramExists && !IsHistogramComputed(directory, SASFile))
{
continue;
}
logger.Log(" ----- new problem ----- ");
logger.Log(directory + "\\" + SASFile);
d = new Problem(SASFile, false);
Heuristic h = new FFHeuristic(d);
/*
* string histogramFolder = @"C:\Users\Ota\Documents\Visual Studio 2017\Projects\PADD\heuristicStats";
* var samples = HistogramVisualizer.Form1.getHistograms(histogramFolder);
* List<HistogramVisualizer.Histograms> selectedSamples = new List<HistogramVisualizer.Histograms>();
* foreach (var item in samples[Path.GetFileName(directory)].Values)
* {
* selectedSamples.AddRange(item);
* }
*/
//string trainedNetworkFile = Path.Combine("..", "trainedNetwork.bin");
//Heuristic h = new NNHeuristic(d, trainedNetworkFile);
//string dataFile = Path.Combine(directory, "histograms", "dataToLearn.tsv");
//Heuristic h = new FileBasedHeuristic(d, dataFile, false);
//Heuristic h = new FFHeuristic(d);
//Heuristic h = new RegHeuristic(new FFHeuristic(d));
//Heuristic h = new FileBasedHeuristic(d, dataFile, false);
//h = getHeuristicByParam(param, d);
//h = getHeuristicByParam(6, d);
ISearchHeap heapStructure = null;
//heapStructure = getHeapByParam(param);
//ISearchHeap heapStructure = new Heaps.MeasuredHeap();
heapStructure = new RedBlackTreeHeap();
//ISearchHeap heapStructure = new Heaps.FibonacciHeap1();
//ISearchHeap heapStructure = new Heaps.FibonacciHeap2();
//ISearchHeap heapStructure = new Heaps.RegularBinaryHeap();
//ISearchHeap heapStructure = new Heaps.RegularTernaryHeap();
//ISearchHeap heapStructure = new Heaps.BinomialHeap();
//ISearchHeap heapStructure = new Heaps.LeftistHeap();
ast = new AStarSearch(d, h, heapStructure);
//ast = new MultiHeuristicAStarSearch(d, h);
//ast = new MultiHeuristicAStarSearch(d, new List<Heuristic>() { h, hNN });
//ast = new IterativeDeepeningAStarSearch(d, null);
DirectoryInfo currentDirectory = new DirectoryInfo(directory);
FileInfo currentFile = new FileInfo(SASFile);
if (ast.OpenNodes is MeasuredHeap <IState> )
{
((MeasuredHeap <IState>)ast.OpenNodes).SetLoggingOutputFile(currentDirectory.Name + "_" + currentFile.Name);
}
ast.TimeLimitOfSearch = timeLimit;
ast.Start();
var searchResult = ast.GetSearchResults(false);
searchResult.DomainName = (Path.GetFileName(directory));
searchResult.ProblemName = (Path.GetFileName(SASFile));
searchResult.Heuristic = h.GetDescription();
searchResult.Algorithm = ast.GetDescription() + "+" + heapStructure.GetName();
searchResult.BestHeuristicValue = h.Statistics.BestHeuristicValue;
searchResult.AverageHeuristicValue = h.Statistics.AverageHeuristicValue;
//foreach (var item in ast.GetSolution().GetOperatorSeqIndices())
// Console.Write(item + " ");
allResults.Add(searchResult);
if (ast.OpenNodes is MeasuredHeap <IState> )
{
((MeasuredHeap <IState>)ast.OpenNodes).ClearStats();
}
logger.Log();
}
logger.Log(" ----- new domain ----- ");
}
// catch (Exception e)
// {
// using (var writer = new System.IO.StreamWriter("results" + Environment.MachineName + ".txt"))
// foreach (var item in allResults)
// {
// writer.WriteLine(item.ToString());
// }
//}
using (var writer = new System.IO.StreamWriter("results" + Environment.MachineName + ".txt"))
foreach (var item in allResults)
{
writer.WriteLine(item.ToString());
}
}
