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using System;
using CenterSpace.NMath.Core;
namespace CenterSpace.NMath.Examples.CSharp
{
class MultiVariableCurveFittingExample
{
/// <summary>
/// The MultiVariableFunctionFitter<T> Needs a parameterized function
/// and a set of data points. One way to specify the parameterized function,
/// and optionally its gradient with respect to the parameters, is to
/// implement an instance of the abstract class DoubleParameterizedFunctional.
/// You must overwrite the Evaluate() method which computes and returns the
/// parameterized function value at a specified set of parameters and
/// point. It is optional to overwrite the GradientWithRespectToParams() method.
/// If you do not overwrite it a numerical approximation using finite differences
/// will be used to approximate the gradient if it is needed.
///
/// Here the parameterized function we are defining is a real valued function
/// of two variables, x0 and x1, and three parameters, p0, p1, and p2, defined
/// by the formula:
///
/// p0*x1*x0^2 + p1*sin(x0) + p2*x1^3
///
/// </summary>
class ParameterizedFunction : DoubleParameterizedFunctional
{
/// <summary>
/// Creates an instance of our parameterized function. We must
/// initialize the base class with the dimension of our functions
/// domain. Since our function is a function of two variables
/// we initialize the base class with 2.
/// </summary>
public ParameterizedFunction()
: base( 2 )
{
;
}
/// <summary>
/// Override the abstract evaluate function.
/// </summary>
/// <param name="parameters">The parameter values.</param>
/// <param name="x">The point to evaluate at.</param>
/// <returns>The value of the parameterized function at the given
/// point and parameters.</returns>
public override double Evaluate( DoubleVector parameters, DoubleVector x )
{
return parameters[0] * x[1] * Math.Pow( x[0], 2.0 ) + parameters[1] * Math.Sin( x[0] ) + parameters[2] * Math.Pow( x[1], 3.0 );
}
/// <summary>
/// Since the gradient of our function is rather easy to derive, we will
/// override the GradientWithRespectToParams() function. Remember, this is
/// the vector of partials with respect to the parameters, NOT the variables.
/// </summary>
/// <param name="parameters">Evaluate the gradient at these parameter values.</param>
/// <param name="x">Evaluate the gradient at this point.</param>
/// <param name="grad">Place the value of the gradient in this vector.</param>
/// <remarks>Note how this function does not return the gradient as a new
/// vector, but places the gradient value in a vector supplied by the
/// calling routine. This is for optimization purposes. The curve fitter uses
/// a optimization algorithm that will most likely be iterative, and thus may
/// need to evaluate the gradient many times. Having the vector
/// passed in to the routine allows the calling code to allocate space for the
/// gradient once and reuse it on successive calls, thus avoiding the potential
/// of allocating a large number of small objects on the managed heap.</remarks>
public override void GradientWithRespectToParams( DoubleVector parameters, DoubleVector x, ref DoubleVector grad )
{
grad[0] = x[0] * x[0] * x[1];
grad[1] = Math.Sin( x[0] );
grad[2] = Math.Pow( x[1], 3 );
}
}
/// <summary>
/// A .NET example in C# showing how to fit a generalized multivariable function to a set
/// of points.
/// </summary>
/// <remarks>
/// Uses the trust-region algorithm.
/// </remarks>
static void Main( string[] args )
{
// Class MultiVariableFunctionFitter fits a parameterized multivariable function to a
// set of points. In the space of the function parameters, beginning at a specified
// starting point, the Fit() method finds a minimum (possibly local) in the sum of
// the squared residuals with respect to the data. Fit() uses a nonlinear least
// squares minimizer specified as a generic.
// For example, here is dataset from the Matlab docs, which fits a function
// z = f(x, y) to three-dimensional data describing a surface
// http://www.mathworks.com/support/solutions/data/1-17YMU.html?solution=1-17YMU
// Since the domain of the function has two dimensions, we use a two-column matrix to
// hold the x,y data.
var xyValues = new DoubleMatrix( 10, 2 );
xyValues[Slice.All, 0] = new DoubleVector( "3.6 7.7 9.3 4.1 8.6 2.8 1.3 7.9 10.0 5.4" );
xyValues[Slice.All, 1] = new DoubleVector( "16.5 150.6 263.1 24.7 208.5 9.9 2.7 163.9 325.0 54.3" );
var zValues = new DoubleVector( "95.09 23.11 60.63 48.59 89.12 76.97 45.68 1.84 82.17 44.47" );
// Published starting guess in the space of the function parameters.
var start = new DoubleVector( "10 10 10" );
// Construct a curve fitting object for our function, then perform the fit. We will use the
// TrustRegionMinimizer implementation of the non-linear least squares minimizer to find the optimal
// set of parameters.
var f = new ParameterizedFunction();
var fitter = new MultiVariableFunctionFitter<TrustRegionMinimizer>( f );
DoubleVector solution = fitter.Fit( xyValues, zValues, start );
Console.WriteLine();
// Display the results
Console.WriteLine( "Fit #1" );
Console.WriteLine( "Matlab solution: " + new DoubleVector( "0.0074 -19.9749 -0.0000" ) );
Console.WriteLine( "NMath solution: " + solution );
Console.WriteLine( "NMath residual: " + fitter.Minimizer.FinalResidual );
Console.WriteLine();
// The parameterized function used by the fitter may also be specified using a delegate.
// here we define a delegate for the same function
// p0*x1*x0^2 + p1*sin(x0) + p2*x1^3
int xDimension = 2; // The dimension of the domain of f.
Func<DoubleVector, DoubleVector, double> fdelegate = delegate( DoubleVector p, DoubleVector x )
{
return p[0] * x[1] * Math.Pow( x[0], 2.0 ) + p[1] * Math.Sin( x[0] ) + p[2] * Math.Pow( x[1], 3.0 );
};
// The delegate for the parameterized function may be used directly in MultiVariableFunctionFitter
// constructors, or may be wrapped by the DoubleVectorParameterizedDelegate, which implements
// DoubleParameterizedFunctional. Here we do the latter.
// Note that we do not supply the gradient with respect
// to parameters here. The gradient will be computed using a finite difference algorithm if
// needed.
fitter.Function = new DoubleVectorParameterizedDelegate( fdelegate, xDimension );
// Perform the fit and display the results
solution = fitter.Fit( xyValues, zValues, start );
Console.WriteLine( "Fit #1 (Repeated without user specified Partial Derivatives)" );
Console.WriteLine( "NMath solution: " + solution );
Console.WriteLine( "NMath residual: " + fitter.Minimizer.FinalResidual );
Console.WriteLine();
// Now lets perform the fit again using some random data. First we generate
// 50 random x,y points in range (0,10).
xyValues = new DoubleMatrix( 50, 2, new RandGenUniform( 0, 10 ) );
// The target solution.
var target = new DoubleVector( "1 2 3" );
// When calculating the z values, we add some noise, so the points
// dont lie exactly on the target surface.
zValues = new DoubleVector( 50, new RandGenUniform( -1, 1 ));
for ( int i = 0; i < zValues.Length; i++ )
{
zValues[i] += fdelegate( target, xyValues.Row( i ) );
}
// Perform the fit and display the results
solution = fitter.Fit( xyValues, zValues, start );
Console.WriteLine( "Fit #2" );
Console.WriteLine( "Target solution: " + target );
Console.WriteLine( "Actual solution: " + solution );
Console.WriteLine( "Residual: " + fitter.Minimizer.FinalResidual );
Console.WriteLine();
Console.WriteLine();
Console.WriteLine( "Press Enter Key" );
Console.Read();
} // Main
} // class
} // namespace
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