public BestLineThruOriginInterpolation(IEnumerable<DPoint> standards)
{
bestLineFit = new BestLineFit(standards);
//I don't have a formula yet for rsquared, so I'll work it out
// r^2 = 1 - SSE/SST
// SSE = Σ(y-mean(y))^2
//SST = Σ(y-f(x))^2
//first find the mean of y
double mean = 0;
double SST = 0;
double SSE = 0;
double count = 0;
foreach (DPoint point in standards)
{
mean += point.Y;
count++;
}
mean /= count;
foreach (DPoint point in standards)
{
double temp = (point.Y - mean);
SST += temp*temp;
temp = point.Y-bestLineFit.SlopeThroughZero*point.X;
SSE += temp * temp;
}
rsquared = 1 - SSE / SST;
}
/// <summary>
/// Uses a moving sample and best line slope to calculate the differentials for each data point
/// </summary>
/// <returns></returns>
DPoint[] SampleAveragedDifferentiation()
{
//ensure points are sorted, in order of x value
points.Sort((a, b) => a.X.CompareTo(b.X));
List <DPoint> diffPoints = new List <DPoint>();
int start = sampleSize / 2;
int end = points.Count - start;
//Start points: calculate best line fit of the first batch of points
//and set the differential as the lines slope.
List <DPoint> selection = points.GetRange(0, sampleSize);
BestLineFit bl = new BestLineFit(selection);
for (int i = 0; i < start; i++)
{
diffPoints.Add(new DPoint(points[i].X, bl.Slope));
}
//mid points
for (int i = start; i < end; i++)
{
selection = points.GetRange(i - start, sampleSize);
bl = new BestLineFit(selection);
diffPoints.Add(new DPoint(points[i].X, bl.Slope));
}
//end points, use the last calculated best line fit
for (int i = end; i < points.Count; i++)
{
diffPoints.Add(new DPoint(points[i].X, bl.Slope));
}
return(diffPoints.ToArray());
}
public BestLineThruOriginInterpolation(IEnumerable <DPoint> standards)
{
bestLineFit = new BestLineFit(standards);
//I don't have a formula yet for rsquared, so I'll work it out
// r^2 = 1 - SSE/SST
// SSE = Σ(y-mean(y))^2
//SST = Σ(y-f(x))^2
//first find the mean of y
double mean = 0;
double SST = 0;
double SSE = 0;
double count = 0;
foreach (DPoint point in standards)
{
mean += point.Y;
count++;
}
mean /= count;
foreach (DPoint point in standards)
{
double temp = (point.Y - mean);
SST += temp * temp;
temp = point.Y - bestLineFit.SlopeThroughZero * point.X;
SSE += temp * temp;
}
rsquared = 1 - SSE / SST;
}
/// <summary>
/// Simply fits a best line to calculate the differential
/// </summary>
/// <returns></returns>
DPoint[] SmallDataSet()
{
List <DPoint> diffPoints = new List <DPoint>();
BestLineFit bl = new BestLineFit(points);
for (int i = 0; i < points.Count; i++)
{
diffPoints.Add(new DPoint(points[i].X, bl.Slope));
}
return(diffPoints.ToArray());
}
public void Test1()
{
//Excel calculates for the points:
// y = 2.7x -1.7
//R^2 = 0.9406
//Zero line fit, y = 2.2593x
BestLineFit target = new BestLineFit();
target.Add(2, 3);
target.Add(3, 7);
target.Add(4, 10);
target.Add(5, 11);
Assert.AreEqual(2.7, target.Slope);
Assert.AreEqual("-1.7000", target.Intercept.ToString("0.0000"));
Assert.AreEqual("0.9406", target.RSquared.ToString("0.0000"));
Assert.AreEqual("2.2593", target.SlopeThroughZero.ToString("0.0000"));
}
public BestLineInterpolation(IEnumerable<DPoint> standards)
{
bestLineFit = new BestLineFit(standards);
}
public BestLineInterpolation(IEnumerable <DPoint> standards)
{
bestLineFit = new BestLineFit(standards);
}
