private void InitializeManaged(IList <Target> targets, DistanceFunc distance, Func <Target, Extraction> targetToExtraction, Func <Extraction, Pronounceable> extractionToPronounceable)
{
this.targets = targets;
this.distance = distance;
this.targetToExtraction = targetToExtraction;
this.extractionToPronounceable = extractionToPronounceable;
this.nativeDistanceDelegate = this.Distance;
if (targetToExtraction == null)
{
try
{
this.extractions = (IList <Extraction>)targets;
}
catch
{
throw new InvalidCastException(string.Format("Can't cast Target type [{0}] to Extraction type [{1}]. You must provide a conversion function 'targetToExtraction'.", typeof(Target), typeof(Extraction)));
}
}
else
{
this.extractions = new Extraction[targets.Count];
}
if (extractionToPronounceable == null)
{
this.pronounceables = (IList <Pronounceable>) this.extractions;
}
else
{
this.pronounceables = new Pronounceable[targets.Count];
}
}
public DataSet(IList <IObject> objects, IObject target, DistanceFunc distanceFunc, Func <double, double> kernelFunc)
{
_objects = objects;
Target = target;
_distanceFunc = distanceFunc;
_kernelFunc = kernelFunc;
}
public Light(int intensity, int radius, int capacity, TCODColor color)
{
_color = color;
_intensity = intensity;
_radius = radius;
_capacity = capacity;
_coef = (float)(-intensity) / radius;
_elapsed = 0;
_distanceFunc = Math.Distance_Euclide;
}
public Light(int intensity, int radius, int capacity, TCODColor color)
{
_color = color;
_intensity = intensity;
_radius = radius;
_capacity = capacity;
_coef = (float)(-intensity) / radius;
_elapsed = 0;
_distanceFunc = Math.Distance_Euclide;
}
public VoronoiDiagram(int size, int seed, VoronoiOptions options)
{
mSize = size;
delDistanceFunc = DistanceFunctions.GetDistanceFunction(options.metric);
delCombiningFunc = CombinerFunctions.GetFunction(options.combiner);
mNumFeaturePoints = options.numberOfFeaturePoints;
mNumSubregions = options.numberOfSubregions;
mMultiplier = options.multiplier;
aFeaturePoints = new int[0];
mRNG = new LCGRandom(seed);
}
public InfluenceMap(int width, int height, double minInfluence, double maxInfluence, DistanceFunc computeDistanceFunc)
{
this.width = width;
this.height = height;
this._influenceMap = new double[width][];
for (int i = 0; i < width; i++)
{
this._influenceMap[i] = new double[height];
}
this.computeDistanceFunc = computeDistanceFunc;
this.minInfluence = minInfluence;
this.maxInfluence = maxInfluence;
myHashset = new HashSet <XAndY>();
}
private IDWFunc GetConverter()
{
DistanceFunc distanceFunction = m_distanceFunction;
double[] xCoordinates = m_xCoordinates ?? new double[0];
double[] yCoordinates = m_yCoordinates ?? new double[0];
double[] values = m_values ?? new double[0];
double power = m_power;
if (xCoordinates.Length != yCoordinates.Length)
{
throw new InvalidOperationException($"The number of x-coordinates must match the number of y-coordinates. ({xCoordinates.Length} != {yCoordinates.Length})");
}
if (xCoordinates.Length != values.Length)
{
throw new InvalidOperationException($"The number of coordinates must match the number of values. ({xCoordinates.Length} != {values.Length})");
}
if (xCoordinates.Length == 0)
{
return((x, y) => 0.0D);
}
return(m_converter ?? (m_converter = (x, y) =>
{
double numerator = 0.0D;
double denominator = 0.0D;
for (int i = 0; i < xCoordinates.Length; i++)
{
double distance = distanceFunction(x, y, xCoordinates[i], yCoordinates[i]);
if (distance == 0.0D)
{
return values[i];
}
double inverseDistance = Math.Pow(1.0D / distance, power);
numerator += values[i] * inverseDistance;
denominator += inverseDistance;
}
return numerator / denominator;
}));
}
public static float Get3D(float x, float y, float z, CombinerFuncEnum combinerFunc, DistanceFuncEnum distanceFunc)
{
DistanceFunc _dFunc = null;
switch (distanceFunc)
{
case DistanceFuncEnum.Euclidean:
_dFunc = EuclidianDistanceFunc;
break;
case DistanceFuncEnum.Manhattan:
_dFunc = ManhattanDistanceFunc;
break;
case DistanceFuncEnum.Chebyshev:
_dFunc = ChebyshevDistanceFunc;
break;
}
CombinerFunc _cFunc = null;
switch (combinerFunc)
{
case CombinerFuncEnum.D1:
_cFunc = CombinerFunc1; // i => i[0];
break;
case CombinerFuncEnum.D2MinusD1:
_cFunc = CombinerFunc2; //i => i[1] - i[0];
break;
case CombinerFuncEnum.D3MinusD1:
_cFunc = CombinerFunc3; //i => i[2] - i[0];
break;
}
return(noise3d(new Vector3(x, y, z), _cFunc, _dFunc));
}
public static DistanceFunc GetDistanceFunction(DistanceMetric distanceMetric)
{
DistanceFunc ret;
switch(distanceMetric)
{
case DistanceMetric.EuclidianSq:
ret = new DistanceFunc(EuclidianSq);
break;
case DistanceMetric.Euclidian:
ret = new DistanceFunc(Euclidian);
break;
case DistanceMetric.Manhattan:
ret = new DistanceFunc(Manhattan);
break;
case DistanceMetric.Chebyshev:
ret = new DistanceFunc(Chebyshev);
break;
default:
ret = new DistanceFunc(EuclidianSq);
break;
}
return ret;
}
/// <summary>
/// Sets the function to be used to calculate the distance between two points.
/// </summary>
/// <param name="distanceFunction">The function used to calculate distance between two points.</param>
/// <returns>A reference to the inverse distance weighting function.</returns>
public InverseDistanceWeightingFunction SetDistanceFunction(DistanceFunc distanceFunction)
{
m_converter = null;
m_distanceFunction = distanceFunction ?? DefaultDistanceFunction;
return(this);
}
/// <summary>
/// Initializes a new instance of the <see cref="FuzzyMatcher{Target, Extraction}"/> class.
/// </summary>
/// <param name="targets">The set of objects that will be matched against. The order of equal targets is not guaranteed to be preserved.</param>
/// <param name="distance">The distance delegate.</param>
/// <param name="targetToExtraction">A mapping of the input types to the query(extraction) type. Note that Extraction == Pronounceable for the usual case.</param>
/// <param name="isAccelerated">Whether the fuzzy matcher uses accelerated implementation or not.</param>
public FuzzyMatcher(IList <Target> targets, DistanceFunc distance, Func <Target, Extraction> targetToExtraction = null, bool isAccelerated = false)
: base(isAccelerated, targets, distance, null, targetToExtraction)
{
}
public WorleyNoise(int Seed, DistanceFunc DFunc, CombinerFunc CFunc)
: this(Seed)
{
DistanceFunction = DFunc;
CombinerFunction = CFunc;
}
public AcceleratedFuzzyMatcher(IList <Target> targets, DistanceFunc distance, Func <Target, Extraction> targetToExtraction = null)
: base(targets, distance, targetToExtraction, true)
{
}
private ValidationResult LeaveOneOut(IList <IObject> objects, int classCount, DistanceFunc distFunc, KernelFunc kernelFunc, int neighborCount)
{
var matrix = new int[classCount][];
for (int i = 0; i < classCount; i++)
{
matrix[i] = new int[classCount];
}
objects
.AsParallel()
.ForAll(obj => Validate(new DataSet(objects.Without(obj).ToList(), obj, distFunc, _kernelDict[kernelFunc]), neighborCount, matrix));
var confMatrix = new ConfusionMatrix(matrix);
return(new ValidationResult
{
DistanceFunc = distFunc,
KernelFunc = kernelFunc,
NeighborCount = neighborCount + 1,
F1Score = (confMatrix.MicroF1Score() + confMatrix.MacroF1Score()) / 2
});
}
/// <summary>
/// Initializes a new instance of the <see cref="AbstractFuzzyMatcher{Target,Extraction,Pronounceable}"/> class.
/// </summary>
/// <param name="isAccelerated">Whether the fuzzy matcher is accelerated or linear.</param>
/// <param name="targets">The set of objects that will be matched against. The order of equal targets is not guaranteed to be preserved.</param>
/// <param name="distance">The distance delegate.</param>
/// <param name="extractionToPronounceable">A mapping of the extraction type to a type understood by the distance function. Note that this is only required if Extraction != Pronounceable</param>
/// <param name="targetToExtraction">A mapping of the input types to the query(extraction) type. Note that Extraction == Pronounceable for the usual case.</param>
internal AbstractFuzzyMatcher(bool isAccelerated, IList <Target> targets, DistanceFunc distance, Func <Extraction, Pronounceable> extractionToPronounceable, Func <Target, Extraction> targetToExtraction = null)
: base(isAccelerated, targets, distance, extractionToPronounceable, targetToExtraction)
{
}
/// <summary>
/// Initializes a new instance of the <see cref="AbstractFuzzyMatcher{Target,Extraction,Pronounceable}"/> class.
/// </summary>
/// <param name="targets">The set of objects that will be matched against. The order of equal targets is not guaranteed to be preserved.</param>
/// <param name="distance">The distance delegate.</param>
/// <param name="extractionToPronounceable">A mapping of the extraction type to a type understood by the distance function. Note that this is only required if Extraction != Pronounceable</param>
/// <param name="targetToExtraction">A mapping of the input types to the query(extraction) type. Note that Extraction == Pronounceable for the usual case.</param>
internal AbstractFuzzyMatcher(IList <Target> targets, DistanceFunc distance, Func <Extraction, Pronounceable> extractionToPronounceable, Func <Target, Extraction> targetToExtraction = null)
: this(false, targets, distance, extractionToPronounceable, targetToExtraction)
{
}
/// <summary>
/// Sets the function to be used to calculate the distance between two points.
/// </summary>
/// <param name="distanceFunction">The function used to calculate distance between two points.</param>
/// <returns>A reference to the inverse distance weighting function.</returns>
public InverseDistanceWeightingFunction SetDistanceFunction(DistanceFunc distanceFunction)
{
m_converter = null;
m_distanceFunction = distanceFunction ?? DefaultDistanceFunction;
return this;
}
/// <summary>
/// Creates a new instance of the <see cref="InverseDistanceWeightingFunction"/> class.
/// </summary>
public InverseDistanceWeightingFunction()
{
m_distanceFunction = DefaultDistanceFunction;
m_power = 1;
}
/// <summary>
/// Creates a new instance of the <see cref="InverseDistanceWeightingFunction"/> class.
/// </summary>
public InverseDistanceWeightingFunction()
{
m_distanceFunction = DefaultDistanceFunction;
m_power = 1;
}
static float noise3d(Vector3 input, CombinerFunc combinerFunc, DistanceFunc distanceFunc)
{
// var value = 0;
uint lastRandom;
uint numberFeaturePoints;
Vector3 randomDiff = new Vector3(0, 0, 0);
Vector3 featurePoint = new Vector3(0, 0, 0);
int cubeX, cubeY, cubeZ;
for (var i = 0; i < DistanceArray.Length; i++)
{
DistanceArray[i] = 6666;
}
var evalCubeX = (int)(Mathf.Floor(input.x));
var evalCubeY = (int)(Mathf.Floor(input.y));
var evalCubeZ = (int)(Mathf.Floor(input.z));
for (var i = -1; i < 2; ++i)
{
for (var j = -1; j < 2; ++j)
{
for (var k = -1; k < 2; ++k)
{
cubeX = evalCubeX + i;
cubeY = evalCubeY + j;
cubeZ = evalCubeZ + k;
//2. Generate a reproducible random number generator for the cube
lastRandom = lcgRandom(hash((cubeX + Seed) & 0xffffffff, (cubeY) & 0xffffffff, (cubeZ) & 0xffffffff));
//3. Determine how many feature points are in the cube
numberFeaturePoints = (uint)probLookup(lastRandom);
//4. Randomly place the feature points in the cube
for (var l = 0; l < numberFeaturePoints; ++l)
{
lastRandom = lcgRandom(lastRandom);
randomDiff.x = (float)lastRandom / 0x100000000;
lastRandom = lcgRandom(lastRandom);
randomDiff.y = (float)lastRandom / 0x100000000;
lastRandom = lcgRandom(lastRandom);
randomDiff.z = (float)lastRandom / 0x100000000;
featurePoint = new Vector3(randomDiff.x + cubeX, randomDiff.y + cubeY, randomDiff.z + cubeZ);
//5. Find the feature point closest to the evaluation point.
//This is done by inserting the distances to the feature points into a sorted list
float v = distanceFunc(input, featurePoint);
insert(DistanceArray, v);
}
//6. Check the neighboring cubes to ensure their are no closer evaluation points.
// This is done by repeating steps 1 through 5 above for each neighboring cube
}
}
}
var color = combinerFunc(DistanceArray);
if (color < 0)
{
color = 0;
}
if (color > 1)
{
color = 1;
}
return(color);
}
static float noise2d(Vector2 input, CombinerFunc combinerFunc, DistanceFunc distanceFunc)
{
uint lastRandom;
uint numberFeaturePoints;
Vector2 randomDiff = new Vector2(0, 0);
Vector2 featurePoint = new Vector2(0, 0);
int cubeX, cubeY;
for (var i = 0; i < DistanceArray.Length; i++)
{
DistanceArray[i] = 6666;
}
var evalCubeX = (int)(Mathf.Floor(input.x));
var evalCubeY = (int)(Mathf.Floor(input.y));
for (var i = -1; i < 2; ++i)
{
for (var j = -1; j < 2; ++j)
{
cubeX = evalCubeX + i;
cubeY = evalCubeY + j;
//2. Generate a reproducible random number generator for the cube
lastRandom = lcgRandom(hash((cubeX + Seed) & 0xffffffff, (cubeY) & 0xffffffff));
//3. Determine how many feature points are in the cube
numberFeaturePoints = (uint)probLookup(lastRandom);
//4. Randomly place the feature points in the cube
for (var l = 0; l < numberFeaturePoints; ++l)
{
lastRandom = lcgRandom(lastRandom);
randomDiff.x = (float)lastRandom / 0x100000000;
lastRandom = lcgRandom(lastRandom);
randomDiff.y = (float)lastRandom / 0x100000000;
featurePoint = new Vector2(randomDiff.x + cubeX, randomDiff.y + cubeY);
//5. Find the feature point closest to the evaluation point.
//This is done by inserting the distances to the feature points into a sorted list
float v = distanceFunc(input, featurePoint);
insert(DistanceArray, v);
}
//6. Check the neighboring cubes to ensure their are no closer evaluation points.
// This is done by repeating steps 1 through 5 above for each neighboring cube
}
}
var color = combinerFunc(DistanceArray);
if (color < 0)
{
color = 0;
}
if (color > 1)
{
color = 1;
}
return(color);
/*
* //Declare some values for later use
* uint lastRandom, numberFeaturePoints;
* Vector2 randomDiff, featurePoint;
* int cubeX, cubeY;
*
* float[] distanceArray = new float[3];
*
* //Initialize values in distance array to large values
* for (int i = 0; i < distanceArray.Length; i++)
* distanceArray[i] = 6666;
*
* //1. Determine which cube the evaluation point is in
* int evalCubeX = (int)Mathf.Floor(input.x);
* int evalCubeY = (int)Mathf.Floor(input.y);
*
* for (int i = -1; i < 2; ++i)
* {
* for (int j = -1; j < 2; ++j)
* {
* cubeX = evalCubeX + i;
* cubeY = evalCubeY + j;
*
* //2. Generate a reproducible random number generator for the cube
* lastRandom = lcgRandom(hash((uint)(cubeX + inSeed), (uint)(cubeY)));
*
* //3. Determine how many feature points are in the cube
* numberFeaturePoints = probLookup(lastRandom);
* //4. Randomly place the feature points in the cube
* for (uint l = 0; l < numberFeaturePoints; ++l)
* {
* lastRandom = lcgRandom(lastRandom);
* randomDiff.x = (float)lastRandom / 0x100000000;
*
* lastRandom = lcgRandom(lastRandom);
* randomDiff.y = (float)lastRandom / 0x100000000;
*
* featurePoint = new Vector2(randomDiff.x + (float)cubeX, randomDiff.y + (float)cubeY);
*
* //5. Find the feature point closest to the evaluation point.
* //This is done by inserting the distances to the feature points into a sorted list
* insert(distanceArray, DistanceFunc2(input, featurePoint));
* }
* //6. Check the neighboring cubes to ensure their are no closer evaluation points.
* // This is done by repeating steps 1 through 5 above for each neighboring cube
* }
* }
*
* return Mathf.Clamp01(CombineFunc(distanceArray));
*/
}
public WorleyNoise(int Seed, DistanceFunc DFunc, CombinerFunc CFunc) :
this(Seed)
{
DistanceFunction = DFunc;
CombinerFunction = CFunc;
}
