public void Calculate_FileWithDuplicates_ReturnsCorrectKendall()
{
// Create lists to feed into test
IEnumerable <decimal> listA = new List <decimal>();
IEnumerable <decimal> listB = new List <decimal>();
for (int i = 1; i <= 10; i++)
{
listA = listA.Append(i);
listB = listB.Append(11 - i);
}
// Add some duplicated numbers to the list
listA = listA.Append(2);
listB = listB.Append(5);
listA = listA.Append(7);
listB = listB.Append(3);
listA = listA.Append(1);
listB = listB.Append(11);
listA = listA.Append(11);
listB = listB.Append(6);
// Calculate using CorrelationCalculator
Correlations.Calculate("A", listA.ToList(), "B", listB.ToList());
// Use Extreme.Statistics Kendall method to test result against
double kendallActual = Stats.KendallTau(listA.ToList(), listB.ToList());
// Check answer
Assert.AreEqual(Math.Round(kendallActual, numberOfDpAccuracy), Math.Round((double)Correlations.kendall, numberOfDpAccuracy));
}
Brush _SignificanceHighlighter(int i, int j, object o)
{
if (Correlations.IsSignificance((double)o))
{
return(Brushes.LightGreen);
}
return(Brushes.White);
}
public Result AbsoluteResult()
{
var absCorr = Correlations.Select(Math.Abs).ToList();
return(new Result()
{
Correlations = absCorr.ToArray(),
MaxIndex = absCorr.IndexOf(absCorr.Max())
});
}
private async Task DoCalcCorrelations(object o)
{
Correlations.Clear();
foreach (var sample in Samples)
{
Correlations.Add(new CorrelateVm(MainSample, sample));
}
await Task.WhenAll(Correlations.Select(c => c.CalculateCorrelationAsync()));
}
public double[,] GetCoefficientsMatrix()
{
double[,] matrix = new double[Correlations.GetLength(0), Correlations.GetLength(1)];
for (int i = 0; i < Correlations.GetLength(0); i++)
{
for (int j = 0; j < Correlations.GetLength(1); j++)
{
matrix[i, j] = Correlations[i, j].Coefficient;
}
}
return(matrix);
}
void LoadFile(string path)
{
Table = new Table(path);
Table.Normalize();
DescriptiveStatistics[] ds = new DescriptiveStatistics[Table.ColumnsCount];
for (int i = 0; i < Table.ColumnsCount; i++)
{
ds[i] = new DescriptiveStatistics(Table.Headers[i], Table.ColumnsValues[i]);
}
DS = ds;
ChiSquaredIntervalsCount = 5;
Correlations = new Correlations(Table, DS);
}
public void Calculate_RandomFile_ReturnsCorrectKendall()
{
// Create random lists to feed into test
IEnumerable <decimal> listA = CreateRandomList(numberOfDataPoints);
IEnumerable <decimal> listB = CreateRandomList(numberOfDataPoints);
// Use Extreme.Statistics Kendall method to test result against
double kendallActual = Stats.KendallTau(listA.ToList(), listB.ToList());
// Calculate using CorrelationCalculator
Correlations.Calculate("A", listA.ToList(), "B", listB.ToList());
// Check answer
Assert.AreEqual(Math.Round(kendallActual, numberOfDpAccuracy), Math.Round((double)Correlations.kendall, numberOfDpAccuracy));
}
Brush _MultipleCorrelationsHighlighter(int i, int j, object o)
{
double d = (double)o;
double F = Correlations.MultipleCorrSignificance[j];
if (i == 0 && Correlations.IsMultipleCoeffSignificance(F))
{
return(Brushes.LightGreen);
}
else if (i == 1 && d >= 0.8)
{
return(Brushes.LightGreen);
}
return(Brushes.White);
}
public void Calculate_PerfectlyCorrelatedFile_ReturnsCorrectSpearman()
{
// Create lists to feed into test
IEnumerable <decimal> listA = new List <decimal>();
IEnumerable <decimal> listB = new List <decimal>();
for (int i = 1; i <= 10; i++)
{
listA = listA.Append(i);
listB = listB.Append(i);
}
// Calculate using CorrelationCalculator
Correlations.Calculate("A", listA.ToList(), "B", listB.ToList());
// Check answer
Assert.AreEqual(1, Math.Round((double)Correlations.spearman, numberOfDpAccuracy));
}
public void Calculate_RandomFile_ReturnsCorrectSpearman()
{
// Create random lists to feed into test
IEnumerable <decimal> listA = CreateRandomList(numberOfDataPoints);
IEnumerable <decimal> listB = CreateRandomList(numberOfDataPoints);
double[] arrayA = listA.Select(dec => (double)dec).ToArray();
double[] arrayB = listB.Select(dec => (double)dec).ToArray();
// Use Extreme.Statistics RankCorrelation method to test result against
double spearmanActual = Stats.RankCorrelation(arrayA, arrayB);
// Calculate using CorrelationCalculator
Correlations.Calculate("A", listA.ToList(), "B", listB.ToList());
// Check answer
Assert.AreEqual(Math.Round(spearmanActual, numberOfDpAccuracy), Math.Round((double)Correlations.spearman, numberOfDpAccuracy));
}
public void AdjustToPositiveSemiDefinite()
{
int iNum = Correlations.GetLength(0);
double minEigen = eigenvalues.Min();
if (minEigen < 0)
{
for (int i = 0; i < iNum; i++)
{
Correlations[i, i].Coefficient = Correlations[i, i].Coefficient - minEigen * 1.000000001;
}
for (int i = 0; i < iNum; i++)
{
for (int j = 0; j < iNum; j++)
{
Correlations[i, j].Coefficient = Correlations[i, j].Coefficient / (1 - minEigen * 1.000000001);
}
}
}
}
void Start()
{
KSpray = GameObject.FindGameObjectWithTag("Spray").GetComponent <Spray>();
Correlation = GameObject.FindGameObjectWithTag("Correlation").GetComponent <Correlations>();
_particleCenter = KSpray.transform;
_sprayMaterial = KSpray.material;
_color = _sprayMaterial.color;
//_emitter = GameObject.FindGameObjectWithTag("Emitter").transform;
_rightHand = GameObject.FindGameObjectWithTag("RightHand").transform;
_leftHand = GameObject.FindGameObjectWithTag("LeftHand").transform;
_bodyCenter = GameObject.FindGameObjectWithTag("BodyCenter").transform;
_rightFoot = GameObject.FindGameObjectWithTag("RightFoot").transform;
_leftFoot = GameObject.FindGameObjectWithTag("LeftFoot").transform;
_lastHandPos = Vector3.zero;
//_particleCenter.position = _emitter.position;
_tmpRightHandPos = transform.position;
}
public static void Draw(Canvas canvas, Correlations correlations)
{
canvas.Children.Clear();
double size = Math.Min(canvas.ActualWidth, canvas.ActualHeight);
const double margin = 30.0;
double centX, centY;
centX = centY = size / 2;
double radius = size / 2 - margin;
// круг
GeometryGroup ggCircle = new GeometryGroup();
ggCircle.Children.Add(new EllipseGeometry(new Point(centX, centY), radius, radius));
// точки
List <Point> points = new List <Point>();
double angle = 2 * Math.PI / correlations.ParametersCount;
for (int i = 0; i < correlations.ParametersCount; i++)
{
var point = new Point(radius * Math.Cos(angle * i) + centX,
radius * Math.Sin(angle * i) + centY);
points.Add(point);
ggCircle.Children.Add(new EllipseGeometry(point, 2, 2));
}
// линии
/*
* GeometryGroup ggLinesStrong = new GeometryGroup();
* GeometryGroup ggLinesCollinear = new GeometryGroup();
* for (int i = 1; i < correlations.ParametersCount; i++)
* {
* for (int j = 0; j < i; j++)
* {
* if (Math.Abs(correlations.CorrMatrix[i, j]) >= 0.7)
* ggLinesCollinear.Children.Add(new LineGeometry(points[i], points[j]));
* else if (Math.Abs(correlations.CorrMatrix[i, j]) >= 0.6)
* ggLinesStrong.Children.Add(new LineGeometry(points[i], points[j]));
* }
* }
*/
// метки
for (int i = 0; i < correlations.ParametersCount; i++)
{
double x = (radius + margin / 2) * Math.Cos(angle * i) + centX - margin * 0.3;
double y = (radius + margin / 2) * Math.Sin(angle * i) + centY - margin * 0.3;
var tbMark = new TextBlock();
tbMark.Text = "X" + i;
Canvas.SetLeft(tbMark, x);
Canvas.SetTop(tbMark, y);
canvas.Children.Add(tbMark);
}
var pathCircle = new Path()
{
Stroke = Brushes.Black,
Data = ggCircle
};
/*
* var pathLinesStrong = new Path()
* {
* Stroke = Brushes.LightGray,
* StrokeDashArray = new DoubleCollection(new[] { 4.0, 4.0 }),
* Data = ggLinesStrong
* };
* var pathLinesCollinear = new Path()
* {
* Stroke = Brushes.LightGray,
* Data = ggLinesCollinear
* };
*/
canvas.Children.Add(pathCircle);
canvas.Children.Add(GetLines(points, correlations.CorrMatrix, 0.4, 0.6));
canvas.Children.Add(GetLines(points, correlations.CorrMatrix, 0.6, 0.9));
canvas.Children.Add(GetLines(points, correlations.CorrMatrix, 0.9, 1.0));
}
