public double GetLikelihoodRatio(GeneralizedLinearRegression model)
        {
#pragma warning disable 612, 618
            return(regression.GetLogLikelihoodRatio(inputData, outputData, model));

#pragma warning restore 612, 618
        }
 private void constructor(GeneralizedLinearRegression regression)
 {
     this.regression     = regression;
     this.parameterCount = regression.Coefficients.Length;
     this.hessian        = new double[parameterCount, parameterCount];
     this.gradient       = new double[parameterCount];
 }
Exemplo n.º 3
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        private void constructor(GeneralizedLinearRegression regression)
        {
            if (regression == null)
            {
                throw new ArgumentNullException("regression");
            }

            this.regression     = regression;
            this.parameterCount = regression.Coefficients.Length;
            this.hessian        = new double[parameterCount, parameterCount];
            this.gradient       = new double[parameterCount];
        }
Exemplo n.º 4
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        public void ComputeTest2()
        {
            double[][] input =
            {
                new double[] { 55, 0 }, // 0 - no cancer
                new double[] { 28, 0 }, // 0
                new double[] { 65, 1 }, // 0
                new double[] { 46, 0 }, // 1 - have cancer
                new double[] { 86, 1 }, // 1
                new double[] { 56, 1 }, // 1
                new double[] { 85, 0 }, // 0
                new double[] { 33, 0 }, // 0
                new double[] { 21, 1 }, // 0
                new double[] { 42, 1 }, // 1
            };

            double[] output =
            {
                0, 0, 0, 1, 1, 1, 0, 0, 0, 1
            };


            var regression = new GeneralizedLinearRegression(new ProbitLinkFunction(), inputs: 2);

            var teacher = new IterativeReweightedLeastSquares(regression);

            double delta = 0;

            do
            {
                // Perform an iteration
                delta = teacher.Run(input, output);
            } while (delta > 0.001);


            Assert.AreEqual(3, regression.Coefficients.Length);
            Assert.AreEqual(-1.4807594445304693, regression.Coefficients[0], 1e-10);
            Assert.AreEqual(0.012417175632016827, regression.Coefficients[1], 1e-10);
            Assert.AreEqual(1.072665379969842, regression.Coefficients[2], 1e-10);
            Assert.IsFalse(regression.Coefficients.HasNaN());

            Assert.AreEqual(3, regression.StandardErrors.Length);
            Assert.AreEqual(1.6402037052797314, regression.StandardErrors[0], 1e-10);
            Assert.AreEqual(0.026119425452145524, regression.StandardErrors[1], 1e-10);
            Assert.AreEqual(1.1297252500874606, regression.StandardErrors[2], 1e-10);
            Assert.IsFalse(regression.StandardErrors.HasNaN());
        }
        protected override void EndProcessing()
        {
            double[][] inputs;
            double[]   outputs;

            if (ParameterSetName == "XY")
            {
                inputs  = Converter.ToDoubleJaggedArray(X);
                outputs = Converter.ToDoubleArray(Y);
            }
            else
            {
                outputs = _data.GetColumn(OutputName).ToDoubleArray();

                _data.RemoveColumn(OutputName);
                inputs = _data.ToDoubleJaggedArray();
            }

            double[] w = null;
            if (Weights != null)
            {
                w = Converter.ToDoubleArray(Weights);
            }

            var model = new GeneralizedLinearRegression(LinkFunctionConvert.Get(LinkFunction))
            {
                NumberOfInputs = inputs[0].Length
            };

            var learner = new IterativeReweightedLeastSquares(model)
            {
                MaxIterations  = 200,
                Regularization = 0
            };

            learner.Learn(inputs, outputs, w);

            WriteObject(model);
        }
Exemplo n.º 6
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        public void ComputeTest()
        {
            // Example from http://bayes.bgsu.edu/bcwr/vignettes/probit_regression.pdf

            double[][] input =
            {
                new double[] { 525 },
                new double[] { 533 },
                new double[] { 545 },
                new double[] { 582 },
                new double[] { 581 },
                new double[] { 576 },
                new double[] { 572 },
                new double[] { 609 },
                new double[] { 559 },
                new double[] { 543 },
                new double[] { 576 },
                new double[] { 525 },
                new double[] { 574 },
                new double[] { 582 },
                new double[] { 574 },
                new double[] { 471 },
                new double[] { 595 },
                new double[] { 557 },
                new double[] { 557 },
                new double[] { 584 },
                new double[] { 599 },
                new double[] { 517 },
                new double[] { 649 },
                new double[] { 584 },
                new double[] { 463 },
                new double[] { 591 },
                new double[] { 488 },
                new double[] { 563 },
                new double[] { 553 },
                new double[] { 549 }
            };

            double[] output =
            {
                0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1
            };


            var regression = new GeneralizedLinearRegression(new ProbitLinkFunction(), inputs: 1);

            var teacher = new IterativeReweightedLeastSquares(regression);


            double delta = 0;

            do
            {
                // Perform an iteration
                delta = teacher.Run(input, output);
            } while (delta > 1e-6);



            Assert.AreEqual(2, regression.Coefficients.Length);
            Assert.AreEqual(-17.6984, regression.Coefficients[0], 1e-4);
            Assert.AreEqual(0.03293, regression.Coefficients[1], 1e-4);

            Assert.AreEqual(2, regression.StandardErrors.Length);
            Assert.AreEqual(9.2731983954911374, regression.StandardErrors[0], 1e-6);
            Assert.AreEqual(0.016768779446085, regression.StandardErrors[1], 1e-6);
        }
Exemplo n.º 7
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        //---------------------------------------------


        #region Public Methods
        /// <summary>
        ///   Gets the Log-Likelihood Ratio between this model and another model.
        /// </summary>
        ///
        /// <param name="model">Another logistic regression model.</param>
        /// <returns>The Likelihood-Ratio between the two models.</returns>
        ///
        public double GetLikelihoodRatio(GeneralizedLinearRegression model)
        {
            return(regression.GetLogLikelihoodRatio(inputData, outputData, model));
        }
 /// <summary>
 ///   Constructs a new Iterative Reweighted Least Squares.
 /// </summary>
 ///
 /// <param name="regression">The regression to estimate.</param>
 ///
 public IterativeReweightedLeastSquares(GeneralizedLinearRegression regression)
 {
     constructor(regression);
 }
        /// <summary>
        ///   Constructs a new Iterative Reweighted Least Squares.
        /// </summary>
        ///
        /// <param name="regression">The regression to estimate.</param>
        ///
        public IterativeReweightedLeastSquares(LogisticRegression regression)
        {
            var glm = GeneralizedLinearRegression.FromLogisticRegression(regression, makeCopy: false);

            constructor(glm);
        }
 private void constructor(GeneralizedLinearRegression regression)
 {
     this.regression = regression;
     this.parameterCount = regression.Coefficients.Length;
     this.hessian = new double[parameterCount, parameterCount];
     this.gradient = new double[parameterCount];
 }
 /// <summary>
 ///   Constructs a new Iterative Reweighted Least Squares.
 /// </summary>
 /// 
 /// <param name="regression">The regression to estimate.</param>
 /// 
 public IterativeReweightedLeastSquares(GeneralizedLinearRegression regression)
 {
     constructor(regression);
 }
        private void constructor(GeneralizedLinearRegression regression)
        {
            if (regression == null)
                throw new ArgumentNullException("regression");

            this.regression = regression;
            this.parameterCount = regression.Coefficients.Length;
            this.hessian = new double[parameterCount, parameterCount];
            this.gradient = new double[parameterCount];
        }