Regression: DirectRegressionMethod to choose the method via parameter.

src/Numerics/LinearRegression/MultipleRegression.cs

@@ -4,7 +4,7 @@
 // http://github.com/mathnet/mathnet-numerics
 // http://mathnetnumerics.codeplex.com
 // 
-// Copyright (c) 2009-2013 Math.NET
+// Copyright (c) 2009-2014 Math.NET
 // 
 // Permission is hereby granted, free of charge, to any person
 // obtaining a copy of this software and associated documentation
@@ -36,6 +36,96 @@ namespace MathNet.Numerics.LinearRegression
 {
     public static class MultipleRegression
     {
+        /// <summary>
+        /// Find the model parameters β such that X*β with predictor X becomes as close to response Y as possible, with least squares residuals.
+        /// </summary>
+        /// <param name="x">Predictor matrix X</param>
+        /// <param name="y">Response vector Y</param>
+        /// <param name="method">The direct method to be used to compute the regression.</param>
+        /// <returns>Best fitting vector for model parameters β</returns>
+        public static Vector<T> DirectMethod<T>(Matrix<T> x, Vector<T> y, DirectRegressionMethod method = DirectRegressionMethod.NormalEquations) where T : struct, IEquatable<T>, IFormattable
+        {
+            switch (method)
+            {
+                case DirectRegressionMethod.NormalEquations:
+                    return NormalEquations(x, y);
+                case DirectRegressionMethod.QR:
+                    return QR(x, y);
+                case DirectRegressionMethod.Svd:
+                    return Svd(x, y);
+                default:
+                    throw new NotSupportedException(method.ToString());
+            }
+        }
+
+        /// <summary>
+        /// Find the model parameters β such that X*β with predictor X becomes as close to response Y as possible, with least squares residuals.
+        /// </summary>
+        /// <param name="x">Predictor matrix X</param>
+        /// <param name="y">Response matrix Y</param>
+        /// <param name="method">The direct method to be used to compute the regression.</param>
+        /// <returns>Best fitting vector for model parameters β</returns>
+        public static Matrix<T> DirectMethod<T>(Matrix<T> x, Matrix<T> y, DirectRegressionMethod method = DirectRegressionMethod.NormalEquations) where T : struct, IEquatable<T>, IFormattable
+        {
+            switch (method)
+            {
+                case DirectRegressionMethod.NormalEquations:
+                    return NormalEquations(x, y);
+                case DirectRegressionMethod.QR:
+                    return QR(x, y);
+                case DirectRegressionMethod.Svd:
+                    return Svd(x, y);
+                default:
+                    throw new NotSupportedException(method.ToString());
+            }
+        }
+
+        /// <summary>
+        /// Find the model parameters β such that their linear combination with all predictor-arrays in X become as close to their response in Y as possible, with least squares residuals.
+        /// </summary>
+        /// <param name="x">List of predictor-arrays.</param>
+        /// <param name="y">List of responses</param>
+        /// <param name="intercept">True if an intercept should be added as first artificial predictor value. Default = false.</param>
+        /// <param name="method">The direct method to be used to compute the regression.</param>
+        /// <returns>Best fitting list of model parameters β for each element in the predictor-arrays.</returns>
+        public static T[] DirectMethod<T>(T[][] x, T[] y, bool intercept = false, DirectRegressionMethod method = DirectRegressionMethod.NormalEquations) where T : struct, IEquatable<T>, IFormattable
+        {
+            switch (method)
+            {
+                case DirectRegressionMethod.NormalEquations:
+                    return NormalEquations(x, y, intercept);
+                case DirectRegressionMethod.QR:
+                    return QR(x, y, intercept);
+                case DirectRegressionMethod.Svd:
+                    return Svd(x, y, intercept);
+                default:
+                    throw new NotSupportedException(method.ToString());
+            }
+        }
+
+        /// <summary>
+        /// Find the model parameters β such that their linear combination with all predictor-arrays in X become as close to their response in Y as possible, with least squares residuals.
+        /// Uses the cholesky decomposition of the normal equations.
+        /// </summary>
+        /// <param name="samples">Sequence of predictor-arrays and their response.</param>
+        /// <param name="intercept">True if an intercept should be added as first artificial predictor value. Default = false.</param>
+        /// <param name="method">The direct method to be used to compute the regression.</param>
+        /// <returns>Best fitting list of model parameters β for each element in the predictor-arrays.</returns>
+        public static T[] DirectMethod<T>(IEnumerable<Tuple<T[], T>> samples, bool intercept = false, DirectRegressionMethod method = DirectRegressionMethod.NormalEquations) where T : struct, IEquatable<T>, IFormattable
+        {
+            switch (method)
+            {
+                case DirectRegressionMethod.NormalEquations:
+                    return NormalEquations(samples, intercept);
+                case DirectRegressionMethod.QR:
+                    return QR(samples, intercept);
+                case DirectRegressionMethod.Svd:
+                    return Svd(samples, intercept);
+                default:
+                    throw new NotSupportedException(method.ToString());
+            }
+        }
+
         /// <summary>
         /// Find the model parameters β such that X*β with predictor X becomes as close to response Y as possible, with least squares residuals.
         /// Uses the cholesky decomposition of the normal equations.

src/Numerics/LinearRegression/Options.cs

@@ -0,0 +1,39 @@
+// <copyright file="Options.cs" company="Math.NET">
+// Math.NET Numerics, part of the Math.NET Project
+// http://numerics.mathdotnet.com
+// http://github.com/mathnet/mathnet-numerics
+// http://mathnetnumerics.codeplex.com
+// 
+// Copyright (c) 2009-2014 Math.NET
+// 
+// Permission is hereby granted, free of charge, to any person
+// obtaining a copy of this software and associated documentation
+// files (the "Software"), to deal in the Software without
+// restriction, including without limitation the rights to use,
+// copy, modify, merge, publish, distribute, sublicense, and/or sell
+// copies of the Software, and to permit persons to whom the
+// Software is furnished to do so, subject to the following
+// conditions:
+// 
+// The above copyright notice and this permission notice shall be
+// included in all copies or substantial portions of the Software.
+// 
+// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
+// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+// OTHER DEALINGS IN THE SOFTWARE.
+// </copyright>
+
+namespace MathNet.Numerics.LinearRegression
+{
+    public enum DirectRegressionMethod
+    {
+        NormalEquations = 0,
+        QR,
+        Svd
+    }
+}

src/Numerics/Numerics.csproj

@@ -94,6 +94,7 @@
     <Compile Include="Interpolation\QuadraticSpline.cs" />
     <Compile Include="LinearAlgebra\Options.cs" />
     <Compile Include="LinearAlgebra\Solvers\DelegateStopCriterion.cs" />
+    <Compile Include="LinearRegression\Options.cs" />
     <Compile Include="Precision.Comparison.cs" />
     <Compile Include="Precision.Equality.cs" />
     <Compile Include="Distributions\Bernoulli.cs" />