jube-fork/Accord.Statistics/Models/Regression/Nonlinear/GeneralizedLinearRegression.cs

// Accord Statistics 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.Statistics.Models.Regression
{
    using System;
    using System.Linq;
    using Accord.Statistics.Links;
    using Accord.Statistics.Testing;
    using Accord.MachineLearning;
    using Accord.Math;
    using Accord.Statistics.Models.Regression.Linear;
    using System.Runtime.Serialization;
    using Accord.Compat;

    /// <summary>
    ///   Generalized Linear Model Regression.
    /// </summary>
    /// 
    /// <remarks>
    /// 
    /// <para>
    ///   References:
    ///   <list type="bullet">
    ///     <item><description>
    ///       Bishop, Christopher M.; Pattern Recognition and Machine Learning. 
    ///       Springer; 1st ed. 2006.</description></item>
    ///     <item><description>
    ///       Amos Storkey. (2005). Learning from Data: Learning Logistic Regressors. School of Informatics.
    ///       Available on: http://www.inf.ed.ac.uk/teaching/courses/lfd/lectures/logisticlearn-print.pdf </description></item>
    ///     <item><description>
    ///       Cosma Shalizi. (2009). Logistic Regression and Newton's Method. Available on:
    ///       http://www.stat.cmu.edu/~cshalizi/350/lectures/26/lecture-26.pdf </description></item>
    ///     <item><description>
    ///       Edward F. Conor. Logistic Regression. Website. Available on: 
    ///       http://userwww.sfsu.edu/~efc/classes/biol710/logistic/logisticreg.htm </description></item>
    ///   </list></para>  
    /// </remarks>
    /// 
    /// <example>
    ///     <code source="Unit Tests\Accord.Tests.Statistics\Models\Regression\LogisticRegressionTest.cs" region="doc_log_reg_1" />
    /// </example>
    /// 
    [Serializable]
    public class GeneralizedLinearRegression : BinaryLikelihoodClassifierBase<double[]>, ICloneable
    {
        private MultipleLinearRegression linear;
        private ILinkFunction linkFunction;

#pragma warning disable 0649
        [Obsolete]
        private double[] coefficients;
#pragma warning restore 0649

        private double[] standardErrors;


        /// <summary>
        ///   Creates a new Generalized Linear Regression Model.
        /// </summary>
        /// 
        /// <param name="function">The link function to use.</param>
        /// 
        public GeneralizedLinearRegression(ILinkFunction function)
        {
            this.linear = new MultipleLinearRegression();
            this.linkFunction = function;
            this.NumberOfInputs = 1;
        }

        /// <summary>
        ///   Creates a new Generalized Linear Regression Model.
        /// </summary>
        /// 
        /// <param name="function">The link function to use.</param>
        /// <param name="inputs">The number of input variables for the model.</param>
        /// 
        [Obsolete("Please use the default constructor and set the NumberOfInputs property instead.")]
        public GeneralizedLinearRegression(ILinkFunction function, int inputs)
            : this(function)
        {
            this.NumberOfInputs = inputs;
        }

        /// <summary>
        ///   Creates a new Generalized Linear Regression Model.
        /// </summary>
        /// 
        /// <param name="function">The link function to use.</param>
        /// <param name="inputs">The number of input variables for the model.</param>
        /// <param name="intercept">The starting intercept value. Default is 0.</param>
        /// 
        [Obsolete("Please use the default constructor and set the NumberofInputs and Intercept properties instead.")]
        public GeneralizedLinearRegression(ILinkFunction function, int inputs, double intercept)
            : this(function)
        {
            this.NumberOfInputs = inputs;
            this.Intercept = intercept;
        }

        /// <summary>
        ///   Creates a new Generalized Linear Regression Model.
        /// </summary>
        /// 
        public GeneralizedLinearRegression()
            : this(new LogitLinkFunction())
        {
        }

        /// <summary>
        ///   Creates a new Generalized Linear Regression Model.
        /// </summary>
        /// 
        /// <param name="function">The link function to use.</param>
        /// <param name="coefficients">The coefficient vector.</param>
        /// <param name="standardErrors">The standard error vector.</param>
        /// 
        [Obsolete("Please use the default constructor and set the Weights and StandardErrors properties instead.")]
        public GeneralizedLinearRegression(ILinkFunction function,
            double[] coefficients, double[] standardErrors)
            : this()
        {
            this.linkFunction = function;
            this.Weights = coefficients.Get(1, 0);
            this.StandardErrors = standardErrors;
        }

        /// <summary>
        /// Gets the number of inputs accepted by the model.
        /// </summary>
        /// 
        public override int NumberOfInputs
        {
            get { return Linear.NumberOfInputs; }
            set
            {
                Linear.NumberOfInputs = value;
                this.standardErrors = Vector.Create(value + 1, this.standardErrors);
            }
        }

        /// <summary>
        ///   Obsolete. For quick compatibility fixes in the short term, use
        ///   <see cref="GetCoefficient(int)"/> and <see cref="SetCoefficient(int, double)"/>.
        /// </summary>
        /// 
        [Obsolete("Please use Weights and Intercept instead.")]
        public double[] Coefficients
        {
            get { return Intercept.Concatenate(Weights); }
        }

        /// <summary>
        ///   Gets the number of parameters in this model (equals the NumberOfInputs + 1).
        /// </summary>
        /// 
        public int NumberOfParameters
        {
            get { return Linear.NumberOfParameters; }
        }

        /// <summary>
        ///   Gets or sets the linear weights of the regression model. The
        ///   intercept term is not stored in this vector, but is instead
        ///   available through the <see cref="Intercept"/> property.
        /// </summary>
        /// 
        public double[] Weights
        {
            get { return Linear.Weights; }
            set { linear.Weights = value; }
        }

        /// <summary>
        ///   Gets the standard errors associated with each
        ///   coefficient during the model estimation phase.
        /// </summary>
        /// 
        public double[] StandardErrors
        {
            get { return standardErrors; }
            set { standardErrors = value; }
        }

        /// <summary>
        ///   Gets the number of inputs handled by this model.
        /// </summary>
        /// 
        [Obsolete("Please use NumberOfInputs instead.")]
        public int Inputs
        {
            get { return NumberOfInputs; }
        }

        /// <summary>
        ///   Gets the link function used by
        ///   this generalized linear model.
        /// </summary>
        /// 
        public ILinkFunction Link
        {
            get { return linkFunction; }
            protected set { linkFunction = value; }
        }

        /// <summary>
        ///   Gets the underlying linear regression.
        /// </summary>
        /// 
        public MultipleLinearRegression Linear
        {
            get { return linear; }
            protected set { linear = value; }
        }

        /// <summary>
        ///   Gets or sets the intercept term. This is always the 
        ///   first value of the <see cref="Coefficients"/> array.
        /// </summary>
        /// 
        public double Intercept
        {
            get { return linear.Intercept; }
            set { linear.Intercept = value; }
        }

        /// <summary>
        ///   Gets a coefficient value, where 0 is the intercept term
        ///   and the other coefficients are indexed starting at 1.
        /// </summary>
        /// 
        public double GetCoefficient(int index)
        {
            if (index == 0)
                return Intercept;
            return Weights[index - 1];
        }

        /// <summary>
        ///   Sets a coefficient value, where 0 is the intercept term
        ///   and the other coefficients are indexed starting at 1.
        /// </summary>
        /// 
        public void SetCoefficient(int index, double value)
        {
            if (index == 0)
            {
                Intercept = value;
            }
            else
            {
                Weights[index - 1] = value;
            }
        }


        /// <summary>
        ///   Computes the model output for the given input vector.
        /// </summary>
        /// 
        /// <param name="input">The input vector.</param>
        /// 
        /// <returns>The output value.</returns>
        /// 
        [Obsolete("Please use the Probability method instead.")]
        public double Compute(double[] input)
        {
            return Probability(input);
        }

        /// <summary>
        ///   Computes the model output for each of the given input vectors.
        /// </summary>
        /// 
        /// <param name="input">The array of input vectors.</param>
        /// 
        /// <returns>The array of output values.</returns>
        /// 
        [Obsolete("Please use the Probability method instead.")]
        public double[] Compute(double[][] input)
        {
            return Probability(input);
        }


        /// <summary>
        ///   Gets the Wald Test for a given coefficient.
        /// </summary>
        /// 
        /// <remarks>
        ///   The Wald statistical test is a test for a model parameter in which
        ///   the estimated parameter θ is compared with another proposed parameter
        ///   under the assumption that the difference between them will be approximately
        ///   normal. There are several problems with the use of the Wald test. Please
        ///   take a look on substitute tests based on the log-likelihood if possible.
        /// </remarks>
        /// 
        /// <param name="index">
        ///   The coefficient's index. The first value
        ///   (at zero index) is the intercept value.
        /// </param>
        /// 
        public WaldTest GetWaldTest(int index)
        {
            // TODO: Eventually change this function so index is 0-based instead of 1-based
            //       (user could select -1 for the intercept term, or no arguments at all)
            return new WaldTest(GetCoefficient(index), 0.0, standardErrors[index]);
        }


        /// <summary>
        ///   Gets the Log-Likelihood for the model.
        /// </summary>
        /// 
        /// <param name="input">A set of input data.</param>
        /// <param name="output">A set of output data.</param>
        /// <returns>
        ///   The Log-Likelihood (a measure of performance) of
        ///   the model calculated over the given data sets.
        /// </returns>
        /// 
        public double GetLogLikelihood(double[][] input, double[] output)
        {
            double sum = 0;

            for (int i = 0; i < input.Length; i++)
            {
                double actualOutput = Probability(input[i]);
                double expectedOutput = output[i];

                if (actualOutput != 0)
                    sum += expectedOutput * Math.Log(actualOutput);

                if (actualOutput != 1)
                    sum += (1 - expectedOutput) * Math.Log(1 - actualOutput);

                Accord.Diagnostics.Debug.Assert(!Double.IsNaN(sum));
            }

            return sum;
        }

        /// <summary>
        ///   Gets the Log-Likelihood for the model.
        /// </summary>
        /// 
        /// <param name="input">A set of input data.</param>
        /// <param name="output">A set of output data.</param>
        /// <param name="weights">The weights associated with each input vector.</param>
        /// 
        /// <returns>
        ///   The Log-Likelihood (a measure of performance) of
        ///   the model calculated over the given data sets.
        /// </returns>
        /// 
        public double GetLogLikelihood(double[][] input, double[] output, double[] weights)
        {
            double sum = 0;

            for (int i = 0; i < input.Length; i++)
            {
                double w = weights[i];
                double actualOutput = Probability(input[i]);
                double expectedOutput = output[i];

                if (actualOutput != 0)
                    sum += expectedOutput * Math.Log(actualOutput) * w;

                if (actualOutput != 1)
                    sum += (1 - expectedOutput) * Math.Log(1 - actualOutput) * w;

                Accord.Diagnostics.Debug.Assert(!Double.IsNaN(sum));
            }

            return sum;
        }

        /// <summary>
        ///   Gets the Deviance for the model.
        /// </summary>
        /// 
        /// <remarks>
        ///   The deviance is defined as -2*Log-Likelihood.
        /// </remarks>
        /// 
        /// <param name="input">A set of input data.</param>
        /// <param name="output">A set of output data.</param>
        /// <returns>
        ///   The deviance (a measure of performance) of the model
        ///   calculated over the given data sets.
        /// </returns>
        /// 
        public double GetDeviance(double[][] input, double[] output)
        {
            return -2.0 * GetLogLikelihood(input, output);
        }

        /// <summary>
        ///   Gets the Deviance for the model.
        /// </summary>
        /// 
        /// <remarks>
        ///   The deviance is defined as -2*Log-Likelihood.
        /// </remarks>
        /// 
        /// <param name="input">A set of input data.</param>
        /// <param name="output">A set of output data.</param>
        /// <param name="weights">The weights associated with each input vector.</param>
        /// 
        /// <returns>
        ///   The deviance (a measure of performance) of the model
        ///   calculated over the given data sets.
        /// </returns>
        /// 
        public double GetDeviance(double[][] input, double[] output, double[] weights)
        {
            return -2.0 * GetLogLikelihood(input, output, weights);
        }

        /// <summary>
        ///   Gets the Log-Likelihood Ratio between two models.
        /// </summary>
        /// 
        /// <remarks>
        ///   The Log-Likelihood ratio is defined as 2*(LL - LL0).
        /// </remarks>
        /// 
        /// <param name="input">A set of input data.</param>
        /// <param name="output">A set of output data.</param>
        /// <param name="regression">Another Logistic Regression model.</param>
        /// 
        /// <returns>The Log-Likelihood ratio (a measure of performance
        /// between two models) calculated over the given data sets.</returns>
        /// 
        public double GetLogLikelihoodRatio(double[][] input, double[] output, GeneralizedLinearRegression regression)
        {
            return 2.0 * (this.GetLogLikelihood(input, output) - regression.GetLogLikelihood(input, output));
        }

        /// <summary>
        ///   Gets the Log-Likelihood Ratio between two models.
        /// </summary>
        /// 
        /// <remarks>
        ///   The Log-Likelihood ratio is defined as 2*(LL - LL0).
        /// </remarks>
        /// 
        /// <param name="input">A set of input data.</param>
        /// <param name="output">A set of output data.</param>
        /// <param name="weights">The weights associated with each input vector.</param>
        /// <param name="regression">Another Logistic Regression model.</param>
        /// 
        /// <returns>The Log-Likelihood ratio (a measure of performance
        /// between two models) calculated over the given data sets.</returns>
        /// 
        public double GetLogLikelihoodRatio(double[][] input, double[] output, double[] weights, GeneralizedLinearRegression regression)
        {
            return 2.0 * (this.GetLogLikelihood(input, output, weights) - regression.GetLogLikelihood(input, output, weights));
        }


        /// <summary>
        ///   The likelihood ratio test of the overall model, also called the model chi-square test.
        /// </summary>
        /// 
        /// <param name="input">A set of input data.</param>
        /// <param name="output">A set of output data.</param>
        /// 
        /// <remarks>
        ///   <para>
        ///   The Chi-square test, also called the likelihood ratio test or the log-likelihood test
        ///   is based on the deviance of the model (-2*log-likelihood). The log-likelihood ratio test 
        ///   indicates whether there is evidence of the need to move from a simpler model to a more
        ///   complicated one (where the simpler model is nested within the complicated one).</para>
        ///   <para>
        ///   The difference between the log-likelihood ratios for the researcher's model and a
        ///   simpler model is often called the "model chi-square".</para>
        /// </remarks>
        /// 
        public ChiSquareTest ChiSquare(double[][] input, double[] output)
        {
            double y0 = 0;
            double y1 = 0;

            for (int i = 0; i < output.Length; i++)
            {
                y0 += 1.0 - output[i];
                y1 += output[i];
            }

            var regression = new GeneralizedLinearRegression(linkFunction)
            {
                NumberOfInputs = NumberOfInputs,
                Intercept = Math.Log(y1 / y0)
            };

            double ratio = GetLogLikelihoodRatio(input, output, regression);
            return new ChiSquareTest(ratio, NumberOfInputs);
        }

        /// <summary>
        ///   The likelihood ratio test of the overall model, also called the model chi-square test.
        /// </summary>
        /// 
        /// <param name="input">A set of input data.</param>
        /// <param name="output">A set of output data.</param>
        /// <param name="weights">The weights associated with each input vector.</param>
        /// 
        /// <remarks>
        ///   <para>
        ///   The Chi-square test, also called the likelihood ratio test or the log-likelihood test
        ///   is based on the deviance of the model (-2*log-likelihood). The log-likelihood ratio test 
        ///   indicates whether there is evidence of the need to move from a simpler model to a more
        ///   complicated one (where the simpler model is nested within the complicated one).</para>
        ///   <para>
        ///   The difference between the log-likelihood ratios for the researcher's model and a
        ///   simpler model is often called the "model chi-square".</para>
        /// </remarks>
        /// 
        public ChiSquareTest ChiSquare(double[][] input, double[] output, double[] weights)
        {
            double y0 = 0;
            double y1 = 0;

            for (int i = 0; i < output.Length; i++)
            {
                y0 += (1.0 - output[i]) * weights[i];
                y1 += output[i] * weights[i];
            }

            var regression = new GeneralizedLinearRegression(linkFunction)
            { 
                NumberOfInputs = NumberOfInputs,
                Intercept = Math.Log(y1 / y0)
            };

            double ratio = GetLogLikelihoodRatio(input, output, weights, regression);
            return new ChiSquareTest(ratio, NumberOfInputs);
        }

        /// <summary>
        /// Gets the degrees of freedom when fitting the regression.
        /// </summary>
        /// 
        public double GetDegreesOfFreedom(int numberOfSamples)
        {
            return Linear.GetDegreesOfFreedom(numberOfSamples);
        }

        /// <summary>
        /// Gets the standard error for each coefficient.
        /// </summary>
        /// 
        /// <param name="informationMatrix">The information matrix obtained when training the model (see <see cref="OrdinaryLeastSquares.GetInformationMatrix()"/>).</param>
        /// 
        public double[] GetStandardErrors(double[][] informationMatrix)
        {
            double[] se = new double[informationMatrix.Length];
            for (int i = 0; i < se.Length; i++)
                se[i] = Math.Sqrt(informationMatrix[i][i]);
            return se;
        }

        /// <summary>
        /// Gets the standard error of the fit for a particular input vector.
        /// </summary>
        /// 
        /// <param name="input">The input vector where the standard error of the fit should be computed.</param>
        /// <param name="informationMatrix">The information matrix obtained when training the model (see <see cref="OrdinaryLeastSquares.GetInformationMatrix()"/>).</param>
        /// 
        /// <returns>The standard error of the fit at the given input point.</returns>
        /// 
        public double GetStandardError(double[] input, double[][] informationMatrix)
        {
            double rim = predictionVariance(input, informationMatrix);
            return Math.Sqrt(rim);
        }

        /// <summary>
        /// Gets the standard error of the prediction for a particular input vector.
        /// </summary>
        /// 
        /// <param name="input">The input vector where the standard error of the prediction should be computed.</param>
        /// <param name="informationMatrix">The information matrix obtained when training the model (see <see cref="OrdinaryLeastSquares.GetInformationMatrix()"/>).</param>
        /// 
        /// <returns>The standard error of the prediction given for the input point.</returns>
        /// 
        public double GetPredictionStandardError(double[] input, double[][] informationMatrix)
        {
            double rim = predictionVariance(input, informationMatrix);
            return Math.Sqrt(1 + rim);
        }

        /// <summary>
        /// Gets the confidence interval for an input point.
        /// </summary>
        /// 
        /// <param name="input">The input vector.</param>
        /// <param name="numberOfSamples">The number of training samples used to fit the model.</param>
        /// <param name="informationMatrix">The information matrix obtained when training the model (see <see cref="OrdinaryLeastSquares.GetInformationMatrix()"/>).</param>
        /// <param name="percent">The prediction interval confidence (default is 95%).</param>
        /// 
        public DoubleRange GetConfidenceInterval(double[] input, int numberOfSamples, double[][] informationMatrix, double percent = 0.95)
        {
            double se = GetStandardError(input, informationMatrix);
            return computeInterval(input, numberOfSamples, percent, se);
        }

        /// <summary>
        /// Gets the prediction interval for an input point.
        /// </summary>
        /// 
        /// <param name="input">The input vector.</param>
        /// <param name="numberOfSamples">The number of training samples used to fit the model.</param>
        /// <param name="informationMatrix">The information matrix obtained when training the model (see <see cref="OrdinaryLeastSquares.GetInformationMatrix()"/>).</param>
        /// <param name="percent">The prediction interval confidence (default is 95%).</param>
        /// 
        public DoubleRange GetPredictionInterval(double[] input, int numberOfSamples, double[][] informationMatrix, double percent = 0.95)
        {
            double se = GetPredictionStandardError(input, informationMatrix);
            return computeInterval(input, numberOfSamples, percent, se);
        }

        private static double predictionVariance(double[] input, double[][] im)
        {
            if (input.Length < im.Length)
                input = (1.0).Concatenate(input);
            return input.Dot(im).Dot(input);
        }

        private DoubleRange computeInterval(double[] input, int numberOfSamples, double percent, double se)
        {
            double y = linear.Transform(input);
            double df = GetDegreesOfFreedom(numberOfSamples);
            var t = new TTest(estimatedValue: y, standardError: se, degreesOfFreedom: df);
            DoubleRange lci = t.GetConfidenceInterval(percent);
            DoubleRange nci = new DoubleRange(linkFunction.Inverse(lci.Min), linkFunction.Inverse(lci.Max));
            return nci;
        }

        /// <summary>
        ///   Creates a new GeneralizedLinearRegression that is a copy of the current instance.
        /// </summary>
        /// 
        public object Clone()
        {
            return new GeneralizedLinearRegression()
             {
                 Link = (ILinkFunction)linkFunction.Clone(),
                 Linear = (MultipleLinearRegression)this.Linear.Clone(),
                 StandardErrors = (double[])this.StandardErrors.Clone()
             };
        }


        /// <summary>
        ///   Creates a GeneralizedLinearRegression from a <see cref="LogisticRegression"/> object. 
        /// </summary>
        /// 
        /// <param name="regression">A <see cref="LogisticRegression"/> object.</param>
        /// <param name="makeCopy">True to make a copy of the logistic regression values, false
        /// to use the actual values. If the actual values are used, changes done on one model
        /// will be reflected on the other model.</param>
        /// 
        /// <returns>A new <see cref="GeneralizedLinearRegression"/> which is a copy of the 
        /// given <see cref="LogisticRegression"/>.</returns>
        /// 
        [Obsolete("Simply cast the logistic regression to a GeneralizedLinearRegression instead, using Clone() if necessary.")]
        public static GeneralizedLinearRegression FromLogisticRegression(LogisticRegression regression, bool makeCopy)
        {
#pragma warning disable 612, 618
            if (makeCopy)
            {
                double[] coefficients = (double[])regression.Coefficients.Clone();
                double[] standardErrors = (double[])regression.StandardErrors.Clone();
                return new GeneralizedLinearRegression(new LogitLinkFunction(),
                    coefficients, standardErrors);
            }
            else
            {
                return new GeneralizedLinearRegression(new LogitLinkFunction(),
                    regression.Coefficients, regression.StandardErrors);
            }
#pragma warning restore 612, 618
        }


        /// <summary>
        /// Computes a numerical score measuring the association between
        /// the given <paramref name="input" /> vector and each class.
        /// </summary>
        /// <param name="input">The input vector.</param>
        /// <param name="result">An array where the result will be stored,
        /// avoiding unnecessary memory allocations.</param>
        /// <returns>System.Double[].</returns>
        public override double[] Score(double[][] input, double[] result)
        {
            linear.Transform(input, result: result);
            //for (int i = 0; i < input.Length; i++)
            //    result[i] = linkFunction.Inverse(result[i]);
            //return result.Subtract(0.5, result: result);
            return result;
        }

        /// <summary>
        /// Predicts a class label vector for the given input vectors, returning the
        /// log-likelihood that the input vector belongs to its predicted class.
        /// </summary>
        /// <param name="input">The input vector.</param>
        /// <param name="result">An array where the log-likelihoods will be stored,
        /// avoiding unnecessary memory allocations.</param>
        /// <returns>System.Double[].</returns>
        public override double[] LogLikelihood(double[][] input, double[] result)
        {
            linear.Transform(input, result: result);
            for (int i = 0; i < input.Length; i++)
                result[i] = linkFunction.Inverse(result[i]);
            return Elementwise.Log(result, result: result);
        }

        /// <summary>
        /// Predicts a class label for the given input vector, returning the
        /// probability that the input vector belongs to its predicted class.
        /// </summary>
        /// <param name="input">The input vector.</param>
        /// <param name="result">An array where the probabilities will be stored,
        /// avoiding unnecessary memory allocations.</param>
        /// <returns>System.Double[].</returns>
        public override double[] Probability(double[][] input, double[] result)
        {
            linear.Transform(input, result: result);
            for (int i = 0; i < input.Length; i++)
                result[i] = linkFunction.Inverse(result[i]);
            return result;
        }


        [OnDeserialized]
        private void SetValuesOnDeserialized(StreamingContext context)
        {
            if (linear == null)
            {
                linear = new MultipleLinearRegression()
                {
#pragma warning disable 612, 618
                    Weights = coefficients.Get(1, 0),
                    Intercept = coefficients[0]
#pragma warning restore 612, 618
                };
            }
        }
    }
}