public static int GetManhattanDistance(Hero hero, Tile mine)
{
uint key = (uint)((hero.Dimensions >> Hero.PositionX) | (mine.Dimensions << (16 - Hero.PositionX)));
byte distance;
lock (locker)
{
if (!ManhattanDistance.TryGetValue(key, out distance))
{
distance = (byte)(Math.Abs(hero.X - mine.X) + Math.Abs(hero.Y - mine.Y));
ManhattanDistance[key] = distance;
}
}
return(distance);
}
public void Test_model_Evaluator_correction_averaging()
{
// arrange
var distance = new ManhattanDistance();
var classifier = new BasicClassifier(distance);
string trainingPath = Path.Combine(TestContext.DeploymentDirectory, "digits", "trainingsample.csv");
string validationPath = Path.Combine(TestContext.DeploymentDirectory, "digits", "validationsample.csv");
var trainingData = DataReader.ReadObservations(trainingPath);
var validationData = DataReader.ReadObservations(validationPath);
classifier.Train(trainingData);
// act
var result = Evaluator.Correct(validationData, classifier);
// assert
Assert.AreEqual(result, 0.934);
}
static void Main(string[] args)
{
var distance = new ManhattanDistance();
var classifier = new BasicClassifier(distance);
var trainingPath = "./data/train_truncated.csv";
var training = DataReader.ReadObservations(trainingPath);
classifier.Train(training);
var validationPath = "./data/validate_truncated.csv";
var validation = DataReader.ReadObservations(validationPath);
var correct = Evaluator.Correct(validation, classifier);
Console.WriteLine("Correctly classified {0:P2}", correct);
}
static void Main(string[] args)
{
var tiles = new TilesInWrongPlace(new BoardEqualityComparer());
var manhattan = new ManhattanDistance(new BoardEqualityComparer());
var manhattanSquare = new SquareOfManhattanDistance(new BoardEqualityComparer());
var start = new SearchNode()
{
Status = new int[9] {
0, 1, 3, 4, 2, 5, 7, 8, 6
},
Steps = 0
};
var goal = new SearchNode()
{
Status = new int[9] {
1, 2, 3, 4, 5, 6, 7, 8, 0
},
Steps = 0
};
Console.WriteLine("Missing Tiles");
var path = tiles.GetPath(start, goal);
foreach (var s in path)
{
PrintMatrix(s.Status);
}
Console.WriteLine("Manhattan");
path = manhattan.GetPath(start, goal);
foreach (var s in path)
{
PrintMatrix(s.Status);
}
Console.WriteLine("Square Manhattan");
path = manhattanSquare.GetPath(start, goal);
foreach (var s in path)
{
PrintMatrix(s.Status);
}
}
static void Main(string[] args)
{
var distance = new ManhattanDistance();
var classifier = new BasicClassifier(distance);
var trainingPath = @"C:\Users\ifeanyi\Documents\visual studio 2013\Projects\MLImageDigitizer\MLImageDigitizer\Data\trainingdata.csv";
var training = DataReader.ReadObservations(trainingPath);
classifier.Train(training);
var validationPath = @"C:\Users\ifeanyi\Documents\visual studio 2013\Projects\MLImageDigitizer\MLImageDigitizer\Data\validationdata.csv";
var validation = DataReader.ReadObservations(validationPath);
var correct = Evaluator.Correct(validation, classifier);
Console.WriteLine("Correctly classified: {0:P2}", correct);
Console.ReadLine();
}
static void Main(string[] args)
{
var distance = new ManhattanDistance();
var classifier = new BasicClassifier(distance);
var dataPath = @"/Users/espen/git/ML-book-experiments/DigitsRecognizer/data/";
var trainingPath = dataPath + "trainingsample.csv";
var trainingSet = DataReader.ReadObservations(trainingPath);
classifier.Train(trainingSet);
var validationPath = dataPath + "validationsample.csv";
var validationSet = DataReader.ReadObservations(validationPath);
var correct = Evaluator.Correct(validationSet, classifier);
Console.WriteLine($"Correctly classified: {correct:P2}");
Console.WriteLine("Press enter to exit.");
Console.ReadLine();
}
protected void ChooseTarget(Unit[] potentialTargets)
{
if (potentialTargets.Length > 0)
{
//order units by their manhattan distance from the units coord.
IEnumerable <Unit> iter = potentialTargets.OrderBy(unit => ManhattanDistance.GetDistance(cubeCoordinate, unit.CubeCoordinate));
//choose the lowest distance (first item)
Unit closestTarget = iter.First();
//choose the closest unit as the target if
// 1. unit currently has no target
// OR 2. target has died
// OR 3. current target is further away than the closest potential target
if (target == null ||
!target.IsAlive() ||
ManhattanDistance.GetDistance(cubeCoordinate, closestTarget.CubeCoordinate)
< ManhattanDistance.GetDistance(cubeCoordinate, target.CubeCoordinate))
{
target = iter.First();
}
}
}
static void Main(string[] args)
{
var distance = new ManhattanDistance();
var classifier = new BasicClassifier(distance);
//@:写路径字符串可以让字符串不需要转义，否则需要写两个\\
//读取训练集
var trainingPath = @"D:\SojS\machine-learning\machine-learning-projects-for-dot-net-developers\chapter-1\DigitsRecognizer\Data\trainingsample.csv";
var training = DataReader.ReadObservations(trainingPath);
//训练基本分类器
classifier.Train(training);
//读取验证集
var validationPath = @"D:\SojS\machine-learning\machine-learning-projects-for-dot-net-developers\chapter-1\DigitsRecognizer\Data\validationsample.csv";
var validation = DataReader.ReadObservations(validationPath);
//验证分类器
var correct = Evaluator.Correct(validation, classifier);
Console.WriteLine("{0:P2}", correct);
Console.ReadLine();
}
public int CompareTo(object obj)
{
return(ManhattanDistance.CompareTo(((GraphVertex)obj).ManhattanDistance));
}
/// <summary>
/// Called on awake.
/// </summary>
protected sealed override void OnAwake()
{
//Determine the cost and path smoothing providers to use
ISmoothPaths pathSmoother;
var pathSmoothProvider = this.As <IPathSmootherFactory>();
if (pathSmoothProvider == null)
{
pathSmoother = new PathSmoother();
}
else
{
pathSmoother = pathSmoothProvider.CreateSmoother();
}
IMoveCost moveCostProvider;
var moveCostProviderFactory = this.As <IMoveCostFactory>();
if (moveCostProviderFactory == null)
{
if (this.moveCost == CostProviderType.Custom)
{
this.moveCost = CostProviderType.Diagonal;
}
switch (this.moveCost)
{
case CostProviderType.Euclidean:
{
moveCostProvider = new EuclideanDistance(10);
break;
}
case CostProviderType.Cardinal:
{
moveCostProvider = new CardinalDistance(10);
break;
}
case CostProviderType.Manhattan:
{
moveCostProvider = new ManhattanDistance(10);
break;
}
default:
{
moveCostProvider = new DiagonalDistance(10);
break;
}
}
}
else
{
this.moveCost = CostProviderType.Custom;
moveCostProvider = moveCostProviderFactory.CreateMoveCostProvider();
}
//Setup the pathing engine to use
IPathingEngine engine;
if (this.engineType == PathingEngineType.Astar)
{
engine = new PathingAStar(this.initialHeapSize, moveCostProvider, GameServices.cellCostStrategy, pathSmoother);
}
else
{
engine = new PathingJumpPointSearch(this.initialHeapSize, moveCostProvider, GameServices.cellCostStrategy, pathSmoother);
}
_pathService = new PathService(engine, new ThreadFactory(), this.useThreadPoolForAsyncOperations);
_pathService.runAsync = this.runAsync;
GameServices.pathService = _pathService;
}
private void Init()
{
if (unit == null)
{
Debug.LogWarning("The Path Finder Visualizer must have a unit set.");
return;
}
_unit = this.unit.GetUnitFacade();
var g = GridManager.instance.GetGrid(_unit.position);
_cellSize = g.cellSize;
_gizmoSize = new Vector3(_cellSize, 0.2f, _cellSize);
IMoveCost moveCostProvider;
if (this.moveCost == CostProviderType.Custom)
{
var moveCostProviderFactory = this.As <IMoveCostFactory>();
if (moveCostProviderFactory == null)
{
moveCostProvider = new DiagonalDistance(10);
Debug.LogWarning("Path Service Component: Cost Provider set to Custom but no Cost Provider Factory found!.");
}
else
{
moveCostProvider = moveCostProviderFactory.CreateMoveCostProvider();
}
}
else
{
switch (this.moveCost)
{
case CostProviderType.Euclidean:
{
moveCostProvider = new EuclideanDistance(10);
break;
}
case CostProviderType.Cardinal:
{
moveCostProvider = new CardinalDistance(10);
break;
}
case CostProviderType.Manhattan:
{
moveCostProvider = new ManhattanDistance(10);
break;
}
default:
{
moveCostProvider = new DiagonalDistance(10);
break;
}
}
}
var preProcessors = this.GetComponents(typeof(IRequestPreProcessor)).Cast <IRequestPreProcessor>().OrderByDescending(p => p.priority).ToArray();
//Setup the pathing engine to use
if (this.engineType == PathingEngineType.Astar)
{
_engine = new VisualizedAStar(this.initialHeapSize, moveCostProvider, GameServices.cellCostStrategy, new PathSmoother(), preProcessors);
}
else
{
_engine = new VisualizedJPS(this.initialHeapSize, moveCostProvider, GameServices.cellCostStrategy, new PathSmoother(), preProcessors);
}
var tmp = new BasicPathRequest
{
requester = this,
requesterProperties = _unit,
pathFinderOptions = _unit.pathFinderOptions
};
if (_req != null)
{
tmp.from = _req.from;
tmp.to = _req.to;
}
_req = tmp;
}
public void Setup()
{
ed = new EuclideanDistance();
md = new ManhattanDistance();
}
/// <summary>
/// display a GUI and generate the WEKA based clusterer
/// </summary>
/// <param name="InstancesList">list of the weka instance</param>
/// <returns>weka clusterer</returns>
public Clusterer BuildClusterer(cParamAlgo ClusteringAlgo, cExtendedTable Input)
{
this.InputTable = Input;
foreach (var item in Input)
{
this.ListDescriptors.Add(item.Name);
}
cListValuesParam Parameters = ClusteringAlgo.GetListValuesParam();
Clusterer ClustererToReturn = null;
Instances ListInstancesWithoutClasses = CreateInstancesWithoutClass(Input);
#region EM
if (ClusteringAlgo.Name == "EM")
{
ClustererToReturn = new EM();
if (Parameters.ListCheckValues.Get("checkBoxAutomatedClassNum").Value)
((EM)ClustererToReturn).setNumClusters(-1);
else
((EM)ClustererToReturn).setNumClusters((int)Parameters.ListDoubleValues.Get("numericUpDownNumClasses").Value);
((EM)ClustererToReturn).setMaxIterations((int)Parameters.ListDoubleValues.Get("numericUpDownMaxIterations").Value);
((EM)ClustererToReturn).setMinStdDev((double)Parameters.ListDoubleValues.Get("numericUpDownMinStdev").Value);
((EM)ClustererToReturn).setSeed((int)Parameters.ListDoubleValues.Get("numericUpDownSeedNumber").Value);
ClustererToReturn.buildClusterer(ListInstancesWithoutClasses);
this.NumberOfClusters = ClustererToReturn.numberOfClusters();
}
#endregion
#region K Means
else if (ClusteringAlgo.Name == "K-Means")
{
ClustererToReturn = new SimpleKMeans();
((SimpleKMeans)ClustererToReturn).setNumClusters((int)Parameters.ListDoubleValues.Get("numericUpDownNumClasses").Value);
((SimpleKMeans)ClustererToReturn).setSeed((int)Parameters.ListDoubleValues.Get("numericUpDownSeedNumber").Value);
string DistanceType = (string)Parameters.ListTextValues.Get("comboBoxDistance").Value;
if (DistanceType == "Euclidean")
{
EuclideanDistance ED = new EuclideanDistance();
ED.setDontNormalize(!(bool)Parameters.ListCheckValues.Get("checkBoxNormalize").Value);
((SimpleKMeans)ClustererToReturn).setDistanceFunction(ED);
}
else if (DistanceType == "Manhattan")
{
ManhattanDistance MD = new ManhattanDistance();
MD.setDontNormalize(!(bool)Parameters.ListCheckValues.Get("checkBoxNormalize").Value);
((SimpleKMeans)ClustererToReturn).setDistanceFunction(MD);
}
else return null;
ClustererToReturn.buildClusterer(ListInstancesWithoutClasses);
this.NumberOfClusters = ClustererToReturn.numberOfClusters();
}
#endregion
//#region K Means++
//else if (ClusteringAlgo.Name == "K-Means++")
//{
// ClustererToReturn = new SimpleKMeans();
// ((SimpleKMeans)ClustererToReturn).setNumClusters((int)Parameters.ListDoubleValues.Get("numericUpDownNumClasses").Value);
// ((SimpleKMeans)ClustererToReturn).setSeed((int)Parameters.ListDoubleValues.Get("numericUpDownSeedNumber").Value);
// string DistanceType = (string)Parameters.ListTextValues.Get("comboBoxDistance").Value;
// if (DistanceType == "Euclidean")
// {
// EuclideanDistance ED = new EuclideanDistance();
// ED.setDontNormalize(!(bool)Parameters.ListCheckValues.Get("checkBoxNormalize").Value);
// ((SimpleKMeans)ClustererToReturn).setDistanceFunction(ED);
// }
// else if (DistanceType == "Manhattan")
// {
// ManhattanDistance MD = new ManhattanDistance();
// MD.setDontNormalize(!(bool)Parameters.ListCheckValues.Get("checkBoxNormalize").Value);
// ((SimpleKMeans)ClustererToReturn).setDistanceFunction(MD);
// }
// else return null;
// ClustererToReturn.buildClusterer(ListInstancesWithoutClasses);
// this.NumberOfClusters = ClustererToReturn.numberOfClusters();
//}
//#endregion
#region hierarchical
else if (ClusteringAlgo.Name == "Hierarchical")
{
ClustererToReturn = new weka.clusterers.HierarchicalClusterer();
string OptionDistance = " -N " + (int)Parameters.ListDoubleValues.Get("numericUpDownNumClasses").Value;
string DistanceType = (string)Parameters.ListTextValues.Get("comboBoxDistance").Value;
OptionDistance += " -A \"weka.core.";
switch (DistanceType)
{
case "Euclidean":
OptionDistance += "EuclideanDistance";
break;
case "Manhattan":
OptionDistance += "ManhattanDistance";
break;
case "Chebyshev":
OptionDistance += "ChebyshevDistance";
break;
default:
break;
}
if (!(bool)Parameters.ListCheckValues.Get("checkBoxNormalize").Value)
OptionDistance += " -D";
OptionDistance += " -R ";
OptionDistance += "first-last\"";
string WekaOption = "-L " + (string)Parameters.ListTextValues.Get("comboBoxLinkType").Value + OptionDistance;
((HierarchicalClusterer)ClustererToReturn).setOptions(weka.core.Utils.splitOptions(WekaOption));
ClustererToReturn.buildClusterer(ListInstancesWithoutClasses);
this.NumberOfClusters = ClustererToReturn.numberOfClusters();
}
#endregion
#region Farthest First
else if (ClusteringAlgo.Name == "FarthestFirst")
{
ClustererToReturn = new weka.clusterers.FarthestFirst();
((FarthestFirst)ClustererToReturn).setNumClusters((int)Parameters.ListDoubleValues.Get("numericUpDownNumClasses").Value);
((FarthestFirst)ClustererToReturn).setSeed((int)Parameters.ListDoubleValues.Get("numericUpDownSeedNumber").Value);
ClustererToReturn.buildClusterer(ListInstancesWithoutClasses);
this.NumberOfClusters = ClustererToReturn.numberOfClusters();
}
#endregion
#region CobWeb
else if (ClusteringAlgo.Name == "CobWeb")
{
ClustererToReturn = new weka.clusterers.Cobweb();
((Cobweb)ClustererToReturn).setSeed((int)Parameters.ListDoubleValues.Get("numericUpDownSeedNumber").Value);
((Cobweb)ClustererToReturn).setAcuity((double)Parameters.ListDoubleValues.Get("numericUpDownAcuity").Value);
((Cobweb)ClustererToReturn).setCutoff((double)Parameters.ListDoubleValues.Get("numericUpDownCutOff").Value);
ClustererToReturn.buildClusterer(ListInstancesWithoutClasses);
this.NumberOfClusters = ClustererToReturn.numberOfClusters();
}
#endregion
#region Manual
else if (ClusteringAlgo.Name == "Manual")
{
string DescriptorName = (string)Parameters.ListTextValues.Get("comboBoxForDescriptorManualClustering").Value;
// this.Classes = new double[ListInstancesWithoutClasses.numInstances()];
for (int IdxPt = 0; IdxPt < this.Classes.Count / 2; IdxPt++)
{
this.Classes[IdxPt] = 2;
}
this.NumberOfClusters = 2;
// break;
//int IdxDesc = -1;
//foreach (string item in this.ListDescriptors)
//{
// IdxDesc++;
// if (item == DescriptorName) break;
//}
//int Idx=0;
//foreach (Instance item in ListInstancesWithoutClasses)
//{
// this.Classes.Add(((int)item.value(IdxDesc)) % cGlobalInfo.ListCellularPhenotypes.Count);
//}
//// re - ordonner les valeurs du discripteur afin que les classes se suivent sans laisser de classe vide !!
//this.NumberOfClusters = cGlobalInfo.ListCellularPhenotypes.Count;
}
#endregion
else
{
System.Windows.Forms.MessageBox.Show("Clustering method not implemented !", "Error", MessageBoxButtons.OK, MessageBoxIcon.Error);
return null;
}
return ClustererToReturn;
}
/// <summary>
/// Called on awake.
/// </summary>
protected sealed override void OnAwake()
{
//Determine the cost and path smoothing providers to use
ISmoothPaths pathSmoother;
var pathSmoothProvider = this.As <IPathSmootherFactory>();
if (pathSmoothProvider == null)
{
pathSmoother = new PathSmoother();
}
else
{
pathSmoother = pathSmoothProvider.CreateSmoother();
}
IMoveCost moveCostProvider;
if (this.moveCost == CostProviderType.Custom)
{
var moveCostProviderFactory = this.As <IMoveCostFactory>();
if (moveCostProviderFactory == null)
{
moveCostProvider = new DiagonalDistance(10);
Debug.LogWarning("Path Service Component: Cost Provider set to Custom but no Cost Provider Factory found!.");
}
else
{
moveCostProvider = moveCostProviderFactory.CreateMoveCostProvider();
}
}
else
{
switch (this.moveCost)
{
case CostProviderType.Euclidean:
{
moveCostProvider = new EuclideanDistance(10);
break;
}
case CostProviderType.Cardinal:
{
moveCostProvider = new CardinalDistance(10);
break;
}
case CostProviderType.Manhattan:
{
moveCostProvider = new ManhattanDistance(10);
break;
}
default:
{
moveCostProvider = new DiagonalDistance(10);
break;
}
}
}
var preProcessors = this.GetComponents(typeof(IRequestPreProcessor)).Cast <IRequestPreProcessor>().OrderByDescending(p => p.priority).ToArray();
//Setup the pathing engine to use
IPathingEngine engine;
if (this.engineType == PathingEngineType.Astar)
{
engine = new PathingAStar(this.initialHeapSize, moveCostProvider, GameServices.cellCostStrategy, pathSmoother, preProcessors);
}
else
{
engine = new PathingJumpPointSearch(this.initialHeapSize, moveCostProvider, GameServices.cellCostStrategy, pathSmoother, preProcessors);
}
_pathService = new PathService(engine, new ThreadFactory(), this.useThreadPoolForAsyncOperations);
_pathService.runAsync = this.runAsync;
GameServices.pathService = _pathService;
}
public void Test1(string wire1, string wire2, int expectedDistance)
{
var actualDistance = ManhattanDistance.Calculate(wire1, wire2);
actualDistance.Should().Be(expectedDistance, because: "It should");
}
public RandomColorBehaviour()
{
_manhattanDistance = new ManhattanDistance();
}
public void SetupBase()
{
Subject = new ManhattanDistance(MemoryWriter);
}
