// // 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) 2009-2010 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. // using System; using System.Globalization; using MathNet.Numerics.LinearAlgebra.Double; namespace Examples.LinearAlgebra.FactorizationExamples { /// /// SVD factorization example. Suppose M is an m-by-n matrix whose entries are real numbers. /// Then there exists a factorization of the form M = UΣVT where: /// - U is an m-by-m unitary matrix; /// - Σ is m-by-n diagonal matrix with nonnegative real numbers on the diagonal; /// - VT denotes transpose of V, an n-by-n unitary matrix; /// Such a factorization is called a singular-value decomposition of M. A common convention is to order the diagonal /// entries Σ(i,i) in descending order. In this case, the diagonal matrix Σ is uniquely determined /// by M (though the matrices U and V are not). The diagonal entries of Σ are known as the singular values of M. /// /// public class Svd : IExample { /// /// Gets the name of this example /// public string Name { get { return "Svd factorization"; } } /// /// Gets the description of this example /// public string Description { get { return "Perform the Svd factorization"; } } /// /// Run example /// /// SVD decomposition public void Run() { // Format matrix output to console var formatProvider = (CultureInfo) CultureInfo.InvariantCulture.Clone(); formatProvider.TextInfo.ListSeparator = " "; // Create square matrix var matrix = DenseMatrix.OfArray(new[,] {{4.0, 1.0}, {3.0, 2.0}}); Console.WriteLine(@"Initial square matrix"); Console.WriteLine(matrix.ToString("#0.00\t", formatProvider)); Console.WriteLine(); // Perform full SVD decomposition var svd = matrix.Svd(); Console.WriteLine(@"Perform full SVD decomposition"); // 1. Left singular vectors Console.WriteLine(@"1. Left singular vectors"); Console.WriteLine(svd.U.ToString("#0.00\t", formatProvider)); Console.WriteLine(); // 2. Singular values as vector Console.WriteLine(@"2. Singular values as vector"); Console.WriteLine(svd.S.ToString("#0.00\t", formatProvider)); Console.WriteLine(); // 3. Singular values as diagonal matrix Console.WriteLine(@"3. Singular values as diagonal matrix"); Console.WriteLine(svd.W.ToString("#0.00\t", formatProvider)); Console.WriteLine(); // 4. Right singular vectors Console.WriteLine(@"4. Right singular vectors"); Console.WriteLine(svd.VT.ToString("#0.00\t", formatProvider)); Console.WriteLine(); // 5. Multiply U matrix by its transpose var identinty = svd.U*svd.U.Transpose(); Console.WriteLine(@"5. Multiply U matrix by its transpose"); Console.WriteLine(identinty.ToString("#0.00\t", formatProvider)); Console.WriteLine(); // 6. Multiply V matrix by its transpose identinty = svd.VT.TransposeAndMultiply(svd.VT); Console.WriteLine(@"6. Multiply V matrix by its transpose"); Console.WriteLine(identinty.ToString("#0.00\t", formatProvider)); Console.WriteLine(); // 7. Reconstruct initial matrix: A = U*Σ*VT var reconstruct = svd.U*svd.W*svd.VT; Console.WriteLine(@"7. Reconstruct initial matrix: A = U*S*VT"); Console.WriteLine(reconstruct.ToString("#0.00\t", formatProvider)); Console.WriteLine(); // 8. Condition Number of the matrix Console.WriteLine(@"8. Condition Number of the matrix"); Console.WriteLine(svd.ConditionNumber); Console.WriteLine(); // 9. Determinant of the matrix Console.WriteLine(@"9. Determinant of the matrix"); Console.WriteLine(svd.Determinant); Console.WriteLine(); // 10. 2-norm of the matrix Console.WriteLine(@"10. 2-norm of the matrix"); Console.WriteLine(svd.L2Norm); Console.WriteLine(); // 11. Rank of the matrix Console.WriteLine(@"11. Rank of the matrix"); Console.WriteLine(svd.Rank); Console.WriteLine(); // Perform partial SVD decomposition, without computing the singular U and VT vectors svd = matrix.Svd(false); Console.WriteLine(@"Perform partial SVD decomposition, without computing the singular U and VT vectors"); // 12. Singular values as vector Console.WriteLine(@"12. Singular values as vector"); Console.WriteLine(svd.S.ToString("#0.00\t", formatProvider)); Console.WriteLine(); // 13. Singular values as diagonal matrix Console.WriteLine(@"13. Singular values as diagonal matrix"); Console.WriteLine(svd.W.ToString("#0.00\t", formatProvider)); Console.WriteLine(); // 14. Access to left singular vectors when partial SVD decomposition was performed try { Console.WriteLine(@"14. Access to left singular vectors when partial SVD decomposition was performed"); Console.WriteLine(svd.U.ToString("#0.00\t", formatProvider)); } catch (Exception ex) { Console.WriteLine(ex.Message); Console.WriteLine(); } // 15. Access to right singular vectors when partial SVD decomposition was performed try { Console.WriteLine(@"15. Access to right singular vectors when partial SVD decomposition was performed"); Console.WriteLine(svd.VT.ToString("#0.00\t", formatProvider)); } catch (Exception ex) { Console.WriteLine(ex.Message); Console.WriteLine(); } } } }