Nonlinear Curve Fitting in Visual Basic QuickStart Sample
Illustrates nonlinear least squares curve fitting of predefined and user-defined curves using the NonlinearCurveFitter class in Visual Basic.
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Option Infer On
' The curve fitting classes reside in the
' Extreme.Mathematics.Curves namespace.
Imports Extreme.Mathematics.Curves
' The predefined non-linear curves reside in the
' Extreme.Mathematics.Curves namespace.
Imports Extreme.Mathematics.Curves.Nonlinear
' Vectors reside in the Extreme.Mathemaics.LinearAlgebra
' namespace
Imports Extreme.Mathematics
Imports Extreme.Mathematics.Algorithms
Imports Extreme.Mathematics.LinearAlgebra
' Illustrates nonlinear least squares curve fitting using the
' NonlinearCurveFitter class in the
' Extreme.Mathematics.Curves namespace of Extreme Numerics.NET.
Module NonlinearCurveFitting
Sub Main()
' The license is verified at runtime. We're using
' a demo license here. For more information, see
' https://numerics.net/trial-key
Extreme.License.Verify("Demo license")
' Nonlinear least squares fits are calculated using the
' NonlinearCurveFitter class:
Dim fitter As New NonlinearCurveFitter
' In the first example, we fit a dose response curve
' to a data set that includes error information.
' The data points must be supplied as Vector objects:
Dim dose = Vector.Create(1.46247, 2.3352,
4, 7, 12, 18, 23, 30, 40, 60, 90, 160, 290, 490, 860)
Dim response = Vector.Create(95.49073, 95.14551, 94.86448,
92.66762, 85.36377, 74.72183, 62.76747, 51.04137, 38.20257,
28.01712, 19.40086, 13.18117, 9.87161, 7.64622, 7.21826)
Dim errors = Vector.Create(4.74322, 4.74322, 4.74322,
4.63338, 4.26819, 3.73609, 3.13837, 3.55207, 3.91013,
2.40086, 2.6, 3.65906, 2.49358, 2.38231, 2.36091)
' You must supply the curve whose parameters will be
' fit to the data. The curve must inherit from NonlinearCurve.
' The FourParameterLogistic curve is one of several
' predefined nonlinear curves:
Dim doseResponseCurve As FourParameterLogisticCurve _
= New FourParameterLogisticCurve
' Now we set the curve fitter's Curve property:
fitter.Curve = doseResponseCurve
' The GetInitialFitParameters method computes
' initial values appropriate for the data:
fitter.InitialGuess = doseResponseCurve.GetInitialFitParameters(dose, response)
' and the data values:
fitter.XValues = dose
fitter.YValues = response
' The GetWeightVectorFromErrors method of the WeightFunctions
' class lets us convert the error values to weights:
fitter.WeightVector = WeightFunctions.GetWeightVectorFromErrors(errors)
' The Fit method performs the actual calculation.
fitter.Fit()
' The standard deviations associated with each parameter
' are available through the GetStandardDeviations method.
Dim s = fitter.GetStandardDeviations()
' We can now print the results:
Console.WriteLine("Dose response curve")
Console.WriteLine("Initial value: {0,10:F6} +/- {1:F4}", doseResponseCurve.InitialValue, s(0))
Console.WriteLine("Final value: {0,10:F6} +/- {1:F4}", doseResponseCurve.FinalValue, s(1))
Console.WriteLine("Center: {0,10:F6} +/- {1:F4}", doseResponseCurve.Center, s(2))
Console.WriteLine("Hill slope: {0,10:F6} +/- {1:F4}", doseResponseCurve.HillSlope, s(3))
' We can also show some statistics about the calculation:
Console.WriteLine("Residual sum of squares: {0}", fitter.Residuals.Norm())
' The Optimizer property returns the MultidimensionalOptimization object
' used to perform the calculation:
Console.WriteLine("# iterations: {0}", fitter.Optimizer.IterationsNeeded)
Console.WriteLine("# function evaluations: {0}", fitter.Optimizer.EvaluationsNeeded)
Console.WriteLine()
'
' Defining your own nonlinear curve
'
' In this example, we use one of the datasets (MGH10)
' from the National Institute for Statistics and Technology
' (NIST) Statistical Reference Datasets.
' See http://www.itl.nist.gov/div898/strd for details
' Here, we need to define our own curve.
' The MyCurve class is defined below.
fitter.Curve = New MyCurve
' For simple functions, you can use Automatic Differentiation
' to compute the derivative of the function and the partial
' derivatives with respect to the curve parameters.
' To do so, call the FromExpression method of the NonlinearCurve
' class with a lambda expression for the value of the function.
' The first argument of the lambda is the x value. The remaining
' arguments correspond to the curve parameters:
fitter.Curve = NonlinearCurve.FromExpression(Function(x, a, b, c) a * Math.Exp(b / (x + c)))
' In this case, we have to specify the initial values
' for the parameters:
fitter.InitialGuess = Vector.Create(0.2, 40000, 2500)
' The data is provided as Vector objects.
' X values go into the XValues property...
fitter.XValues = Vector.Create(
50.0, 55.0, 60.0, 65.0,
70.0, 75.0, 80.0, 85.0,
90.0, 95.0, 100.0, 105.0,
110.0, 115.0, 120.0, 125.0)
' ...and Y values go into the YValues property:
fitter.YValues = Vector.Create(
34780.0, 28610.0, 23650.0, 19630.0,
16370.0, 13720.0, 11540.0, 9744.0,
8261.0, 7030.0, 6005.0, 5147.0,
4427.0, 3820.0, 3307.0, 2872.0)
fitter.WeightVector = Nothing
' 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.
Dim solution = fitter.BestFitParameters
s = fitter.GetStandardDeviations()
Console.WriteLine("NIST Reference Data Set")
Console.WriteLine("Solution:")
Console.WriteLine("b1: {0,20} {1,20}", solution(0), s(0))
Console.WriteLine("b2: {0,20} {1,20}", solution(1), s(1))
Console.WriteLine("b3: {0,20} {1,20}", solution(2), s(2))
Console.WriteLine("Certified values:")
Console.WriteLine("b1: {0,20} {1,20}", 0.005609636471, 0.00015687892471)
Console.WriteLine("b2: {0,20} {1,20}", 6181.3463463, 23.309021107)
Console.WriteLine("b3: {0,20} {1,20}", 345.22363462, 0.78486103508)
' 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("b1: {0,20} {1,20}", solution(0), s(0))
Console.WriteLine("b2: {0,20} {1,20}", solution(1), s(1))
Console.WriteLine("b3: {0,20} {1,20}", solution(2), s(2))
Console.Write("Press Enter key to exit...")
Console.ReadLine()
End Sub
' This is our nonlinear curve implementation. For details, see
' http:'www.itl.nist.gov/div898/strd/nls/data/mgh10.shtml
' You must inherit from NonlinearCurve:
Public Class MyCurve : Inherits NonlinearCurve
' Call the base constructor with the number of
' parameters.
Public Sub New()
MyBase.New(3)
' It is convenient to set common starting values
' for the curve parameters in the constructor:
Me.Parameters(0) = 0.2
Me.Parameters(1) = 40000
Me.Parameters(2) = 2500
End Sub
' The ValueAt method evaluates the function:
Public Overrides Function ValueAt(x As Double) As Double
Return Parameters(0) * Math.Exp(Parameters(1) / (x + Parameters(2)))
End Function
' The SlopeAt method evaluates the derivative:
Public Overrides Function SlopeAt(x As Double) As Double
Return Parameters(0) * Parameters(1) * Math.Exp(Parameters(1) / (x + Parameters(2))) _
/ Math.Pow(x + Parameters(2), 2)
End Function
' The FillPartialDerivatives evaluates the partial derivatives
' with respect to the curve parameters, and returns
' the result in a vector. If you don't supply this method,
' a numerical approximation is used.
Public Overrides Sub FillPartialDerivatives(x As Double, f As DenseVector(Of Double))
Dim exp As Double = Math.Exp(Parameters(1) / (x + Parameters(2)))
f(0) = exp
f(1) = Parameters(0) * exp / (x + Parameters(2))
f(2) = -Parameters(0) * Parameters(1) * exp / Math.Pow(x + Parameters(2), 2)
End Sub
End Class
End Module