public IEnumerable <double> ResolveStrains(TriaxialTest _traxialTest, double[] pointsOfIntrest)
{
var list = new List <double>();
foreach (var point in pointsOfIntrest)
{
_estimator.EstimatorData = _baseData.GetFilteredDataByStressRelative();
list.Add(_estimator.Estimate(point));
}
return(list);
}
public List <ActionBase> SolvePart(State state)
{
//Console.Out.WriteLine("START PART");
var bestEstimation = double.MinValue;
List <ActionBase> bestSolution = null;
for (int i = 0; i < tryCount; i++)
{
var clone = state;//.Clone();
var undoes = new List <Action>();
var solution = SolveStep(clone, undoes);
var estimation = estimator.Estimate(clone, state.SingleWorker);
undoes.Reverse();
foreach (var undo in undoes)
{
undo();
}
//Console.Out.Write($" {estimation} {solution.Format()}");
if (estimation > bestEstimation)
{
bestEstimation = estimation;
bestSolution = solution;
//Console.Out.WriteLine(" -- better");
}
// else
// Console.Out.WriteLine();
}
return(bestSolution);
}
private (ActionBase action, double score) EstimateAction(ActionBase action, State state)
{
var undo = state.Apply(action);
var score = estimator.Estimate(state, state.SingleWorker);
undo();
return(action, score);
}
private void ApplyRecursion(TreeNode <Block> node, int recDepth)
{
if (recDepth == _depth - 1)
{
_currentEstimator = _estimators.Last();
}
else
{
_currentEstimator = _estimators.First();
}
var nextTurns = getNextTurns(node.GameState);
if (nextTurns.Count == 0)
{
node.Score = _currentEstimator.Estimate(node.GameState);
return;
}
if (recDepth == _depth)
{
return;
}
if (node.Children == null)
{
node.Children = new List <TreeNode <Block> >();
foreach (var(block, score) in nextTurns)
{
var currentGameState = node.GameState.copy();
currentGameState.deleteBlock(block);
node.Children.Add(new TreeNode <Block>(score, null, node, block,
currentGameState));
}
}
foreach (var child in node.Children)
{
ApplyRecursion(child, recDepth + 1);
}
}
public static List <IRecommendedItem> GetTopItems(int howMany,
IEnumerator <long> possibleItemIDs,
IDRescorer rescorer,
IEstimator <long> estimator)
{
//Preconditions.checkArgument(possibleItemIDs != null, "possibleItemIDs is null");
//Preconditions.checkArgument(estimator != null, "estimator is null");
var topItems = new SortedSet <IRecommendedItem>(ByValueRecommendedItemComparator.getReverseInstance());
bool full = false;
double lowestTopValue = Double.NegativeInfinity;
while (possibleItemIDs.MoveNext())
{
long itemID = possibleItemIDs.Current;
if (rescorer == null || !rescorer.isFiltered(itemID))
{
double preference;
try {
preference = estimator.Estimate(itemID);
} catch (NoSuchItemException nsie) {
continue;
}
double rescoredPref = rescorer == null ? preference : rescorer.rescore(itemID, preference);
if (!Double.IsNaN(rescoredPref) && (!full || rescoredPref > lowestTopValue))
{
topItems.Add(new GenericRecommendedItem(itemID, (float)rescoredPref));
if (full)
{
topItems.Remove(topItems.Min);
}
else if (topItems.Count > howMany)
{
full = true;
topItems.Remove(topItems.Min); // topItems.poll();
}
lowestTopValue = topItems.Min.GetValue();
}
}
}
int size = topItems.Count;
if (size == 0)
{
return(new List <IRecommendedItem>());
}
List <IRecommendedItem> result = new List <IRecommendedItem>(size);
result.AddRange(topItems);
result.Reverse();
//Collections.sort(result, ByValueRecommendedItemComparator.getInstance());
return(result);
}
public static long[] GetTopUsers(int howMany,
IEnumerator <long> allUserIDs,
IDRescorer rescorer,
IEstimator <long> estimator)
{
var topUsers = new SortedSet <SimilarUser>();
bool full = false;
double lowestTopValue = Double.NegativeInfinity;
while (allUserIDs.MoveNext())
{
long userID = allUserIDs.Current;
if (rescorer != null && rescorer.isFiltered(userID))
{
continue;
}
double similarity;
try {
similarity = estimator.Estimate(userID);
} catch (NoSuchUserException nsue) {
continue;
}
double rescoredSimilarity = rescorer == null ? similarity : rescorer.rescore(userID, similarity);
if (!Double.IsNaN(rescoredSimilarity) && (!full || rescoredSimilarity > lowestTopValue))
{
topUsers.Add(new SimilarUser(userID, rescoredSimilarity));
if (full)
{
topUsers.Remove(topUsers.Max); // topUsers.poll();
}
else if (topUsers.Count > howMany)
{
full = true;
topUsers.Remove(topUsers.Max); // topUsers.poll();
}
lowestTopValue = topUsers.Max.getSimilarity();
}
}
int size = topUsers.Count;
if (size == 0)
{
return(NO_IDS);
}
List <SimilarUser> sorted = new List <SimilarUser>(size);
return(topUsers.Select(s => s.getUserID()).ToArray());
}
private Block getNextTurn(GameState curGameState)
{
var initialLegals = curGameState.legals();
Block bestTurn = null;
var bestTurnScore = double.MinValue;
foreach (var block in initialLegals)
{
var mutableGameState = curGameState.copy();
mutableGameState.deleteBlock(block);
var curScore = _estimator.Estimate(mutableGameState);
if (!(curScore > bestTurnScore))
{
continue;
}
bestTurn = block;
bestTurnScore = curScore;
}
return(bestTurn);
}
public List <ActionBase> SolvePart(State state, List <List <ActionBase> > partialSolution)
{
var bestEstimation = double.MinValue;
List <ActionBase> bestSolution = null;
var usedClonings = partialSolution.Sum(x => x.Count(c => c is UseCloning));
var useCloningsLocal = partialSolution.Count == 0 &&
state.CloningCount - usedClonings > 0 &&
state.Boosters.Any(b => b.Type == BoosterType.MysteriousPoint && b.Position == state.Workers[0].Position); //only first worker can use cloning
var usedWheels = partialSolution.Sum(x => x.Count(c => c is UseFastWheels));
var useWheelsLocal = useWheels && (state.FastWheelsCount - usedWheels) > 0;
var usedDrill = partialSolution.Sum(x => x.Count(c => c is UseDrill));
var useDrillLocal = useDrill && (state.DrillCount - usedDrill) > 0;
foreach (var chain2 in chains)
{
var chain = chain2.ToList();
if (useCloningsLocal)
{
chain.Insert(0, new UseCloning());
chain.RemoveAt(chain.Count - 1);
}
if (useWheelsLocal)
{
chain.Insert(0, new UseFastWheels());
chain.RemoveAt(chain.Count - 1);
}
if (useDrillLocal)
{
chain.Insert(0, new UseDrill());
chain.RemoveAt(chain.Count - 1);
}
var solution = new List <ActionBase>();
var undos = new List <Action>();
for (var c = 0; c < chain.Count; c++)
{
var action = chain[c];
if (action is Move moveAction)
{
var worker = state.Workers[partialSolution.Count];
var nextPosition = worker.Position + moveAction.Shift;
if (!nextPosition.Inside(state.Map) || (state.Map[nextPosition] == CellState.Obstacle && worker.DrillTimeLeft <= 1))
{
break;
}
}
undos.Add(state.Apply(
state
.Workers
.Select(
(w, i) => (w, i < partialSolution.Count ? partialSolution[i][c]
: i == partialSolution.Count ? action
: new Wait()))
.ToList()));
solution.Add(action);
if (state.UnwrappedLeft == 0)
{
break;
}
}
while (solution.Count < depth)
{
var wait = new Wait();
undos.Add(state.Apply(
state
.Workers
.Select((w, i) => (w, i < partialSolution.Count ? partialSolution[i][solution.Count] : wait))
.ToList()));
solution.Add(wait);
}
var estimation = estimator.Estimate(state, state.Workers[partialSolution.Count]);
//Console.Out.Write($" w{partialSolution.Count} {estimation} {solution.Format()}");
if (estimation > bestEstimation)
{
bestEstimation = estimation;
bestSolution = solution;
//Console.Out.WriteLine(" -- better");
}
/*else
* Console.Out.WriteLine();*/
undos.Reverse();
foreach (var undo in undos)
{
undo();
}
}
return(bestSolution);
}
