/// <summary>
/// Objective model with a user supplied jacobian for non-linear least squares regression.
/// </summary>
public static IObjectiveModel NonlinearModel(Func <Vector <double>, double, double> function,
Func <Vector <double>, double, Vector <double> > derivatives,
Vector <double> observedX, Vector <double> observedY, Vector <double> weight = null)
{
Vector <double> func(Vector <double> point, Vector <double> x)
{
var functionValues = CreateVector.Dense <double>(x.Count);
for (int i = 0; i < x.Count; i++)
{
functionValues[i] = function(point, x[i]);
}
return(functionValues);
}
Matrix <double> prime(Vector <double> point, Vector <double> x)
{
var derivativeValues = CreateMatrix.Dense <double>(x.Count, point.Count);
for (int i = 0; i < x.Count; i++)
{
derivativeValues.SetRow(i, derivatives(point, x[i]));
}
return(derivativeValues);
}
var objective = new NonlinearObjectiveFunction(func, prime);
objective.SetObserved(observedX, observedY, weight);
return(objective);
}
/// <summary>
/// Objective function for nonlinear least squares regression.
/// The numerical jacobian with accuracy order is used.
/// </summary>
public static IObjectiveFunction NonlinearFunction(Func <Vector <double>, Vector <double>, Vector <double> > function,
Vector <double> observedX, Vector <double> observedY, Vector <double> weight = null,
int accuracyOrder = 2)
{
var objective = new NonlinearObjectiveFunction(function, null, accuracyOrder: accuracyOrder);
objective.SetObserved(observedX, observedY, weight);
return(objective.ToObjectiveFunction());
}
/// <summary>
/// Objective function with a user supplied jacobian for nonlinear least squares regression.
/// </summary>
public static IObjectiveFunction NonlinearFunction(Func <Vector <double>, Vector <double>, Vector <double> > function,
Func <Vector <double>, Vector <double>, Matrix <double> > derivatives,
Vector <double> observedX, Vector <double> observedY, Vector <double> weight = null)
{
var objective = new NonlinearObjectiveFunction(function, derivatives);
objective.SetObserved(observedX, observedY, weight);
return(objective.ToObjectiveFunction());
}
/// <summary>
/// Objective model for non-linear least squares regression.
/// </summary>
public static IObjectiveModel NonlinearModel(Func <Vector <double>, double, double> function,
Vector <double> observedX, Vector <double> observedY, Vector <double> weight = null,
int accuracyOrder = 2)
{
Func <Vector <double>, Vector <double>, Vector <double> > func = (point, x) =>
{
var functionValues = CreateVector.Dense <double>(x.Count);
for (int i = 0; i < x.Count; i++)
{
functionValues[i] = function(point, x[i]);
}
return(functionValues);
};
var objective = new NonlinearObjectiveFunction(func, accuracyOrder: accuracyOrder);
objective.SetObserved(observedX, observedY, weight);
return(objective);
}