jube-fork/Accord.Math/Optimization/Base/ILeastSquaresMethod.cs

// Accord Math Library
// The Accord.NET Framework
// http://accord-framework.net
//
// Copyright © César Souza, 2009-2017
// cesarsouza at gmail.com
//
//    This library is free software; you can redistribute it and/or
//    modify it under the terms of the GNU Lesser General Public
//    License as published by the Free Software Foundation; either
//    version 2.1 of the License, or (at your option) any later version.
//
//    This library is distributed in the hope that it will be useful,
//    but WITHOUT ANY WARRANTY; without even the implied warranty of
//    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
//    Lesser General Public License for more details.
//
//    You should have received a copy of the GNU Lesser General Public
//    License along with this library; if not, write to the Free Software
//    Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA  02110-1301  USA
//

namespace Accord.Math.Optimization
{
    using Accord.Compat;
    using System.Threading;

    /// <summary>
    ///   Least Squares function delegate.
    /// </summary>
    /// 
    /// <remarks>
    ///   This delegate represents a parameterized function that, given a set of
    ///   <paramref name="parameters"/> and an <paramref name="input"/> vector,
    ///   produces an associated output value.
    /// </remarks>
    /// 
    /// <param name="parameters">The function parameters, also known as weights or coefficients.</param>
    /// <param name="input">An input vector.</param>
    /// 
    /// <returns>The output value produced given the <paramref name="input"/> vector
    ///   using the given <paramref name="parameters"/>.</returns>
    /// 
    public delegate double LeastSquaresFunction(double[] parameters, double[] input);

    /// <summary>
    ///   Gradient function delegate.
    /// </summary>
    /// 
    /// <remarks>
    ///   This delegate represents the gradient of a <see cref="LeastSquaresFunction">Least
    ///   Squares objective function</see>. This function should compute the gradient vector
    ///   in respect to the function <paramref name="parameters"/>.
    /// </remarks>
    /// 
    /// <param name="parameters">The function parameters, also known as weights or coefficients.</param>
    /// <param name="input">An input vector.</param>
    /// <param name="result">The resulting gradient vector (w.r.t to the parameters).</param>
    /// 
    public delegate void LeastSquaresGradientFunction(double[] parameters, double[] input, double[] result);


    /// <summary>
    ///   Common interface for Least Squares algorithms, i.e. algorithms
    ///   that can be used to solve Least Squares optimization problems.
    /// </summary>
    /// 
    /// <example>
    ///   <code source="Unit Tests\Accord.Tests.Statistics\Models\Regression\GaussNewtonTest.cs" region="doc_minimize"/>
    ///   <code source="Unit Tests\Accord.Tests.Statistics\Models\Regression\LevenbergMarquardtTest.cs" region="doc_minimize"/>
    /// </example>
    /// 
    /// <seealso cref="GaussNewton"/>
    /// <seealso cref="LevenbergMarquardt"/>
    /// 
    public interface ILeastSquaresMethod
    {

        /// <summary>
        ///   Gets or sets a cancellation token that can be used to
        ///   stop the learning algorithm while it is running.
        /// </summary>
        /// 
        CancellationToken Token { get; set; }

        /// <summary>
        ///   Gets or sets a parameterized model function mapping input vectors
        ///   into output values, whose optimum parameters must be found.
        /// </summary>
        /// 
        /// <value>The function to be optimized.</value>
        /// 
        LeastSquaresFunction Function { get; set; }

        /// <summary>
        ///   Gets or sets a function that computes the gradient vector in respect
        ///   to the function parameters, given a set of input and output values.
        /// </summary>
        /// 
        /// <value>The gradient function.</value>
        /// 
        LeastSquaresGradientFunction Gradient { get; set; }

        /// <summary>
        ///   Gets the number of variables (free parameters) in the optimization problem.
        /// </summary>
        /// 
        /// <value>The number of parameters.</value>
        /// 
        int NumberOfVariables { get; set; }

        /// <summary>
        ///   Attempts to find the best values for the parameter vector
        ///   minimizing the discrepancy between the generated outputs
        ///   and the expected outputs for a given set of input data.
        /// </summary>
        /// 
        /// <param name="inputs">A set of input data.</param>
        /// <param name="outputs">The values associated with each 
        ///   vector in the <paramref name="inputs"/> data.</param>
        ///   
        /// <example>
        ///   <code source="Unit Tests\Accord.Tests.Statistics\Models\Regression\GaussNewtonTest.cs" region="doc_minimize"/>
        ///   <code source="Unit Tests\Accord.Tests.Statistics\Models\Regression\LevenbergMarquardtTest.cs" region="doc_minimize"/>
        /// </example>
        /// 
        double Minimize(double[][] inputs, double[] outputs);

        /// <summary>
        ///   Gets the solution found, the values of the parameters which
        ///   optimizes the function, in a least squares sense.
        /// </summary>
        /// 
        double[] Solution { get; set; }

        /// <summary>
        ///   Gets standard error for each parameter in the solution.
        /// </summary>
        /// 
        double[] StandardErrors { get; }

        /// <summary>
        ///   Gets the value at the solution found. This should be
        ///   the minimum value found for the objective function.
        /// </summary>
        /// 
        double Value { get; set; }
    }
}