/// <summary>
/// Least-Squares fitting the points (x, y) to a logarithm
/// y : x -> a + b * ln(x).
/// </summary>
public static IModelledFunction LogarithmFunc(double[] xArray, double[] yArray)
{
Tuple <double, double> parameters = Fit.Logarithm(xArray, yArray);
double a = parameters.Item1;
double b = parameters.Item2;
Func <double, double> function = t => a + b * System.Math.Log(t);
return(new LogarithmFunction(function, parameters));
}
public void FitsToLogarithmSameAsExcelTrendLine()
{
// X Y
// 1 0.2
// 2 0.3
// 4 1.3
// 6 4.2
// -> y = -0.4338 + 1.9981*ln(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.Logarithm(x, y);
Assert.AreEqual(-0.4338, resp.Item1, 1e-3);
Assert.AreEqual(1.9981, resp.Item2, 1e-3);
var resf = Fit.LogarithmFunc(x, y);
foreach (var z in Enumerable.Range(-3, 10))
{
Assert.AreEqual(-0.4338 + 1.9981 * Math.Log(z), resf(z), 1e-2);
}
}
