public ExponentialViewModel(double[] xdata, double[] ydata, Tuple <double, double> xlimit, Tuple <double, double> ylimit)
{
displayed = true;
DirectRegressionMethod method = DirectRegressionMethod.QR;
Tuple <double, double> p = Fit.Exponential(xdata, ydata, method);
double a = p.Item1;
double r = p.Item2;
if (!Double.IsNaN(a) && !Double.IsNaN(r))
{
this.FunctionModel = new PlotModel {
Title = "Exponential regresion [ f(x) = " + a + " * exp( " + r + " * x ) ]"
};
Func <double, double> exponentialFunction = (x) => a *Math.Exp(r *x);
FunctionModel.Series.Add(new FunctionSeries(exponentialFunction, xlimit.Item1, xlimit.Item2, 0.0001));
// view limits
FunctionModel.Axes.Add(new LinearAxis {
Position = AxisPosition.Bottom, Minimum = xlimit.Item1, Maximum = xlimit.Item2
});
FunctionModel.Axes.Add(new LinearAxis {
Position = AxisPosition.Left, Minimum = ylimit.Item1, Maximum = ylimit.Item2
});
}
else
{
displayed = false;
}
}
/// <summary>
/// Least-Squares fitting the points (x, y) to an exponential
/// y : x -> a * exp(r * x).
/// </summary>
public static IModelledFunction ExponentialFunc(double[] xArray, double[] yArray)
{
Tuple <double, double> parameters = Fit.Exponential(xArray, yArray);
double a = parameters.Item1;
double r = parameters.Item2;
Func <double, double> function = t => a *System.Math.Exp(r *t);
return(new ExponentialFunction(function, parameters));
}
public List <DataPoint> CalculatePoints(List <DataPoint> points)
{
var pointsLine = new List <DataPoint>();
Tuple <double, double> p = Fit.Exponential(points.Select(x => x.X).ToArray(), points.Select(x => x.Y).ToArray());
double a = p.Item1;
double b = p.Item2;
foreach (var item in points)
{
var y = a * Math.Exp(item.X * b);
pointsLine.Add(new DataPoint(item.X, y));
}
return(pointsLine);
}
public FitData CalcFit(List <DataPoint> points)
{
FitData result = new FitData("Exponential");
var xdata = points.Select(x => x.X).ToArray();
var ydata = points.Select(x => x.Y).ToArray();
Tuple <double, double> p = Fit.Exponential(xdata, ydata);
result.A = p.Item1;
result.R = p.Item2;
result.Points = DataPoints(points).ToList();
return(result);
//local function for return list of regresion Points[need to C# 8]
IEnumerable <DataPoint> DataPoints(List <DataPoint> points)
{
foreach (var point in points)
{
yield return(new DataPoint(point.X, result.A * Math.Exp(result.R * point.X)));
}
}
}
public void FitsToExponentialSameAsExcelTrendLine()
{
// X Y
// 1 0.2
// 2 0.3
// 4 1.3
// 6 4.2
// -> y = 0.0981*exp(0.6284*x)
var x = new[] { 1.0, 2.0, 4.0, 6.0 };
var y = new[] { 0.2, 0.3, 1.3, 4.2 };
var resp = Fit.Exponential(x, y);
Assert.AreEqual(0.0981, resp.Item1, 1e-3);
Assert.AreEqual(0.6284, resp.Item2, 1e-3);
var resf = Fit.ExponentialFunc(x, y);
foreach (var z in Enumerable.Range(-3, 10))
{
Assert.AreEqual(0.0981 * Math.Exp(0.6284 * z), resf(z), 1e-2);
}
}
public virtual Tuple <double, double> ExponentialFitting(double[] x, double[] y)
{
return(Fit.Exponential(x, y));
}
