Linear Curve Fitting QuickStart Sample (C#)
Illustrates how to fit linear combinations of curves to data using the LinearCurveFitter class and other classes in the Extreme.Mathematics.Curves namespace in C#.
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using System;
namespace Extreme.Mathematics.QuickStart.CSharp
{
// The curve fitting classes reside in the
// Extreme.Mathematics.Curves namespace.
using Extreme.Mathematics.Curves;
using Extreme.Mathematics.LinearAlgebra;
/// <summary>
/// Illustrates least squares curve fitting of polynomials and
/// other linear functions using the LinearCurveFitter class in the
/// Extreme.Mathematics.Curves namespace of the Extreme
/// Optimization Numerical Libraries for .NET.
/// </summary>
class LinearCurveFitting
{
/// <summary>
/// The main entry point for the application.
/// </summary>
[STAThread]
static void Main(string[] args)
{
// This QuickStart sample illustrates linear least squares
// curve fitting using polynomials and linear combinations
// of arbitrary functions.
// Linear least squares fits are calculated using the
// LinearCurveFitter class:
LinearCurveFitter fitter = new LinearCurveFitter();
// We use data from the National Institute for Standards
// and Technology's Statistical Reference Datasets library
// at http://www.itl.nist.gov/div898/strd/.
// Note that, due to round-off error, the results here will not be exactly
// the same as the NIST results, which were calculated using 500 digits
// of precision!
// We use the 'Pontius' dataset, which contains measurement data
// from the calibration of load cells. The independent variable is the load.
// The dependent variable is the deflection.
Vector deflectionData = Vector.Create(.11019, .21956,
.32949, .43899, .54803, .65694, .76562, .87487, .98292,
1.09146, 1.20001, 1.30822, 1.41599, 1.52399, 1.63194, 1.73947,
1.84646, 1.95392, 2.06128, 2.16844, .11052, .22018, .32939,
.43886, .54798, .65739, .76596, .87474, .98300, 1.09150, 1.20004,
1.30818, 1.41613, 1.52408, 1.63159, 1.73965,
1.84696, 1.95445, 2.06177, 2.16829);
Vector loadData =Vector.Create(
150, 300, 450, 600, 750, 900,
1050, 1200, 1350, 1500, 1650, 1800,
1950, 2100, 2250, 2400, 2550, 2700,
2850, 3000, 150, 300, 450, 600,
750, 900, 1050, 1200, 1350, 1500,
1650, 1800, 1950, 2100, 2250, 2400,
2550, 2700, 2850, 3000);
// You must supply the curve whose parameters will be
// fit to the data. The curve must inherit from LinearCombination.
//
// Here, we use a quadratic polynomial:
fitter.Curve = new Polynomial(2);
// The X values go into the XValues property:
fitter.XValues = loadData;
// ...and Y values go into the YValues property:
fitter.YValues = deflectionData;
// The Fit method performs the actual calculation:
fitter.Fit();
// A Vector containing the parameters of the best fit
// can be obtained through the
// BestFitParameters property.
Vector solution = fitter.BestFitParameters;
// The standard deviations associated with each parameter
// are available through the GetStandardDeviations method.
Vector s = fitter.GetStandardDeviations();
Console.WriteLine("Calibration of load cells");
Console.WriteLine(" deflection = c1 + c2*load + c3*load^2 ");
Console.WriteLine("Solution:");
Console.WriteLine("c1: {0,20:E10} {1,20:E10}", solution[0], s[0]);
Console.WriteLine("c2: {0,20:E10} {1,20:E10}", solution[1], s[1]);
Console.WriteLine("c3: {0,20:E10} {1,20:E10}", solution[2], s[2]);
Console.WriteLine("Residual sum of squares: {0}", fitter.Residuals.Norm());
// Now let's redo the same operation, but with observations weighted
// by 1/Y^2. To do this, we set the WeightFunction property.
// The WeightFunctions class defines a set of ready-to-use weight functions.
fitter.WeightFunction = WeightFunctions.OneOverYSquared;
// Refit the curve:
fitter.Fit();
solution = fitter.BestFitParameters;
s = fitter.GetStandardDeviations();
// The solution is slightly different:
Console.WriteLine("Solution (weighted observations):");
Console.WriteLine("c1: {0,20:E10} {1,20:E10}", solution[0], s[0]);
Console.WriteLine("c2: {0,20:E10} {1,20:E10}", solution[1], s[1]);
Console.WriteLine("c3: {0,20:E10} {1,20:E10}", solution[2], s[2]);
Console.WriteLine();
//
// Fitting combinations of arbitrary functions
//
// The following example estimates the two parameters, c1 and c2
// in the theoretical model for conductance:
// k(T) = 1 / (c1 / T + c2 * T*T)
Vector temperature = Vector.Create(12.2900, 13.7500, 14.8200,
16.1200, 18.0400, 18.6700, 20.5200, 22.6800, 25.1500,
27.7200, 30.2400, 33.2100, 36.4800, 39.8600, 50.4000);
Vector conductance = Vector.Create(25.3500, 27.8800, 29.9300,
30.4200, 31.0000, 31.9600, 32.4700, 30.3300, 31.1400,
27.4600, 23.2900, 20.7200, 17.2400, 14.7100, 9.5000);
// First, we transform the dependent variable:
Vector y = Vector.Reciprocal(conductance);
// y is a linear combination of basis functions 1/T and T*T.
// Create a function basis object:
Func<double, double>[] basisFunctions = new Func<double, double>[]
{new Func<double, double>(f1), new Func<double, double>(f2)};
GeneralFunctionBasis basis = new GeneralFunctionBasis(basisFunctions);
// Create a LinearCombination curve using this function basis:
LinearCombination curve = new LinearCombination(basis);
// Set the curve fitter properties:
fitter.Curve = curve;
fitter.XValues = temperature;
fitter.YValues = y;
// Reset the weights
fitter.WeightFunction = null;
fitter.WeightVector = null;
// Now compute the solution:
fitter.Fit();
solution = fitter.BestFitParameters;
s = fitter.GetStandardDeviations();
// Print the results
Console.WriteLine("Conductance of copper: k(T) = 1 / (c1/T + c2*T^2)");
Console.WriteLine("Solution:");
Console.WriteLine("c1: {0,20:E10} {1,20:E10}", solution[0], s[0]);
Console.WriteLine("c2: {0,20:E10} {1,20:E10}", solution[1], s[1]);
Console.WriteLine("Residual sum of squares: {0}", fitter.Residuals.Norm());
Console.Write("Press Enter key to exit...");
Console.ReadLine();
}
// First basis function for the conductance sample.
static double f1(double x)
{
return 1/x;
}
// Second basis function for the conductance sample.
static double f2(double x)
{
return x*x;
}
}
}