using IStation.Numerics.LinearAlgebra;
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using System.Collections.Generic;
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namespace IStation.Numerics.Optimization
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{
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public interface IObjectiveModelEvaluation
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{
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IObjectiveModel CreateNew();
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/// <summary>
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/// Get the y-values of the observations.
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/// </summary>
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Vector<double> ObservedY { get; }
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/// <summary>
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/// Get the values of the weights for the observations.
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/// </summary>
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Matrix<double> Weights { get; }
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/// <summary>
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/// Get the y-values of the fitted model that correspond to the independent values.
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/// </summary>
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Vector<double> ModelValues { get; }
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/// <summary>
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/// Get the values of the parameters.
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/// </summary>
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Vector<double> Point { get; }
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/// <summary>
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/// Get the residual sum of squares.
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/// </summary>
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double Value { get; }
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/// <summary>
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/// Get the Gradient vector. G = J'(y - f(x; p))
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/// </summary>
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Vector<double> Gradient { get; }
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/// <summary>
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/// Get the approximated Hessian matrix. H = J'J
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/// </summary>
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Matrix<double> Hessian { get; }
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/// <summary>
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/// Get the number of calls to function.
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/// </summary>
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int FunctionEvaluations { get; set; }
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/// <summary>
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/// Get the number of calls to jacobian.
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/// </summary>
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int JacobianEvaluations { get; set; }
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/// <summary>
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/// Get the degree of freedom.
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/// </summary>
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int DegreeOfFreedom { get; }
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bool IsGradientSupported { get; }
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bool IsHessianSupported { get; }
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}
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public interface IObjectiveModel : IObjectiveModelEvaluation
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{
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void SetParameters(Vector<double> initialGuess, List<bool> isFixed = null);
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void EvaluateAt(Vector<double> parameters);
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IObjectiveModel Fork();
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IObjectiveFunction ToObjectiveFunction();
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}
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}
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