// <copyright file="ObjectiveFunction.cs" company="Math.NET">
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// Math.NET Numerics, part of the Math.NET Project
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// http://numerics.mathdotnet.com
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// http://github.com/mathnet/mathnet-numerics
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//
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// Copyright (c) 2009-2017 Math.NET
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//
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// Permission is hereby granted, free of charge, to any person
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// obtaining a copy of this software and associated documentation
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// files (the "Software"), to deal in the Software without
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// restriction, including without limitation the rights to use,
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// copy, modify, merge, publish, distribute, sublicense, and/or sell
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// copies of the Software, and to permit persons to whom the
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// Software is furnished to do so, subject to the following
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// conditions:
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//
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// The above copyright notice and this permission notice shall be
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// included in all copies or substantial portions of the Software.
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//
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// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
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// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
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// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
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// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
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// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
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// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
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// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
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// OTHER DEALINGS IN THE SOFTWARE.
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// </copyright>
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using System;
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using IStation.Numerics.LinearAlgebra;
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using IStation.Numerics.Optimization.ObjectiveFunctions;
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namespace IStation.Numerics.Optimization
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{
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public static class ObjectiveFunction
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{
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/// <summary>
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/// Objective function where neither Gradient nor Hessian is available.
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/// </summary>
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public static IObjectiveFunction Value(Func<Vector<double>, double> function)
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{
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return new ValueObjectiveFunction(function);
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}
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/// <summary>
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/// Objective function where the Gradient is available. Greedy evaluation.
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/// </summary>
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public static IObjectiveFunction Gradient(Func<Vector<double>, Tuple<double, Vector<double>>> function)
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{
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return new GradientObjectiveFunction(function);
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}
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/// <summary>
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/// Objective function where the Gradient is available. Lazy evaluation.
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/// </summary>
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public static IObjectiveFunction Gradient(Func<Vector<double>, double> function, Func<Vector<double>, Vector<double>> gradient)
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{
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return new LazyObjectiveFunction(function, gradient: gradient);
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}
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/// <summary>
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/// Objective function where the Hessian is available. Greedy evaluation.
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/// </summary>
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public static IObjectiveFunction Hessian(Func<Vector<double>, Tuple<double, Matrix<double>>> function)
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{
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return new HessianObjectiveFunction(function);
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}
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/// <summary>
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/// Objective function where the Hessian is available. Lazy evaluation.
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/// </summary>
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public static IObjectiveFunction Hessian(Func<Vector<double>, double> function, Func<Vector<double>, Matrix<double>> hessian)
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{
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return new LazyObjectiveFunction(function, hessian: hessian);
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}
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/// <summary>
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/// Objective function where both Gradient and Hessian are available. Greedy evaluation.
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/// </summary>
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public static IObjectiveFunction GradientHessian(Func<Vector<double>, Tuple<double, Vector<double>, Matrix<double>>> function)
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{
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return new GradientHessianObjectiveFunction(function);
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}
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/// <summary>
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/// Objective function where both Gradient and Hessian are available. Lazy evaluation.
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/// </summary>
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public static IObjectiveFunction GradientHessian(Func<Vector<double>, double> function, Func<Vector<double>, Vector<double>> gradient, Func<Vector<double>, Matrix<double>> hessian)
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{
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return new LazyObjectiveFunction(function, gradient: gradient, hessian: hessian);
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}
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/// <summary>
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/// Objective function where neither first nor second derivative is available.
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/// </summary>
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public static IScalarObjectiveFunction ScalarValue(Func<double, double> function)
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{
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return new ScalarValueObjectiveFunction(function);
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}
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/// <summary>
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/// Objective function where the first derivative is available.
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/// </summary>
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public static IScalarObjectiveFunction ScalarDerivative(Func<double, double> function, Func<double, double> derivative)
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{
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return new ScalarObjectiveFunction(function, derivative);
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}
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/// <summary>
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/// Objective function where the first and second derivatives are available.
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/// </summary>
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public static IScalarObjectiveFunction ScalarSecondDerivative(Func<double, double> function, Func<double, double> derivative, Func<double,double> secondDerivative)
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{
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return new ScalarObjectiveFunction(function, derivative, secondDerivative);
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}
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/// <summary>
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/// objective model with a user supplied jacobian for non-linear least squares regression.
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/// </summary>
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public static IObjectiveModel NonlinearModel(Func<Vector<double>, Vector<double>, Vector<double>> function,
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Func<Vector<double>, Vector<double>, Matrix<double>> derivatives,
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Vector<double> observedX, Vector<double> observedY, Vector<double> weight = null)
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{
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var objective = new NonlinearObjectiveFunction(function, derivatives);
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objective.SetObserved(observedX, observedY, weight);
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return objective;
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}
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/// <summary>
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/// Objective model for non-linear least squares regression.
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/// </summary>
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public static IObjectiveModel NonlinearModel(Func<Vector<double>, Vector<double>, Vector<double>> function,
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Vector<double> observedX, Vector<double> observedY, Vector<double> weight = null,
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int accuracyOrder = 2)
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{
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var objective = new NonlinearObjectiveFunction(function, accuracyOrder: accuracyOrder);
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objective.SetObserved(observedX, observedY, weight);
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return objective;
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}
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/// <summary>
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/// Objective model with a user supplied jacobian for non-linear least squares regression.
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/// </summary>
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public static IObjectiveModel NonlinearModel(Func<Vector<double>, double, double> function,
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Func<Vector<double>, double, Vector<double>> derivatives,
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Vector<double> observedX, Vector<double> observedY, Vector<double> weight = null)
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{
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Vector<double> func(Vector<double> point, Vector<double> x)
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{
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var functionValues = CreateVector.Dense<double>(x.Count);
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for (int i = 0; i < x.Count; i++)
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{
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functionValues[i] = function(point, x[i]);
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}
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return functionValues;
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}
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Matrix<double> prime(Vector<double> point, Vector<double> x)
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{
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var derivativeValues = CreateMatrix.Dense<double>(x.Count, point.Count);
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for (int i = 0; i < x.Count; i++)
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{
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derivativeValues.SetRow(i, derivatives(point, x[i]));
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}
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return derivativeValues;
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}
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var objective = new NonlinearObjectiveFunction(func, prime);
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objective.SetObserved(observedX, observedY, weight);
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return objective;
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}
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/// <summary>
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/// Objective model for non-linear least squares regression.
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/// </summary>
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public static IObjectiveModel NonlinearModel(Func<Vector<double>, double, double> function,
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Vector<double> observedX, Vector<double> observedY, Vector<double> weight = null,
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int accuracyOrder = 2)
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{
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Vector<double> func(Vector<double> point, Vector<double> x)
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{
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var functionValues = CreateVector.Dense<double>(x.Count);
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for (int i = 0; i < x.Count; i++)
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{
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functionValues[i] = function(point, x[i]);
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}
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return functionValues;
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}
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var objective = new NonlinearObjectiveFunction(func, accuracyOrder: accuracyOrder);
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objective.SetObserved(observedX, observedY, weight);
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return objective;
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}
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/// <summary>
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/// Objective function with a user supplied jacobian for nonlinear least squares regression.
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/// </summary>
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public static IObjectiveFunction NonlinearFunction(Func<Vector<double>, Vector<double>, Vector<double>> function,
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Func<Vector<double>, Vector<double>, Matrix<double>> derivatives,
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Vector<double> observedX, Vector<double> observedY, Vector<double> weight = null)
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{
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var objective = new NonlinearObjectiveFunction(function, derivatives);
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objective.SetObserved(observedX, observedY, weight);
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return objective.ToObjectiveFunction();
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}
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/// <summary>
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/// Objective function for nonlinear least squares regression.
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/// The numerical jacobian with accuracy order is used.
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/// </summary>
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public static IObjectiveFunction NonlinearFunction(Func<Vector<double>, Vector<double>, Vector<double>> function,
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Vector<double> observedX, Vector<double> observedY, Vector<double> weight = null,
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int accuracyOrder = 2)
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{
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var objective = new NonlinearObjectiveFunction(function, null, accuracyOrder: accuracyOrder);
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objective.SetObserved(observedX, observedY, weight);
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return objective.ToObjectiveFunction();
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}
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}
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}
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