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