C# PLS Example

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using System;
using System.IO;

using CenterSpace.NMath.Core;

namespace CenterSpace.NMath.Core.Examples.CSharp
{
  /// <summary>
  /// A .NET example in C# showing basic use of the PLS1 and PLS2 classes for solving 
  /// partial least squares (PLS) problems.
  /// </summary>
  class PLSExample
  {

    static void Main( string[] args )
    {
      // Read in some chemometric data. The response, or Y, variable
      // is a measure of the concentration of substances in a sample and
      // the predictor, or x, variable is the absorption spectra of the sample
      // sampled at discrete wavelengths. We'll read in two sets of predictor
      // values. One to construct the model with and the other for making 
      // predictions.

      // Construct the absorption matrices A and A1, and the concentration
      // matrix C
      var A = new DoubleMatrix( new StreamReader( "chemometricX1.dat" ));
      var A1 = new DoubleMatrix( new StreamReader( "chemometricX2.dat" ));
      var C = new DoubleMatrix( new StreamReader( "chemometricY.dat" ));

      int numComponents = 3;

      //**********************************************************************
      // PLS1 

      // PLS1 is used when the response variable is univariate, or one dimensional. 
      // Pick out the first column of our multivariate response variables as our
      // univariate response variable y.
      DoubleVector y = C.Col( 0 );

      // Construct a PLS1 object and perform the calculation.
      var plsOne = new PLS1();
      Console.WriteLine();
      Console.Write( "Calculating PLS1...  " );
      plsOne.Calculate( A, y, numComponents );

      // Check that the calculation succeeded. If it did not, print out
      // a diagnostic error message and exit.
      if ( plsOne.IsGood )
      {
        Console.WriteLine( "Success" );
      }
      else
      {
        Console.WriteLine();
        Console.WriteLine();
        Console.WriteLine( "PLS1 calculation failed: " + plsOne.Message );
        return;
      }

      // Pull out a sample from our predictor matrix A1 and make a prediction
      // for the corresponding concentration value.
      DoubleVector x = A1.Row( 0 );
      double pls1Yhat = plsOne.Predict( x );
      Console.WriteLine();
      Console.WriteLine( "Predicted value for x =" );
      Console.WriteLine( pls1Yhat.ToString( "G5" ) );

      // Predict the concentration for all the samples in A1.
      DoubleVector pls1YhatVec = plsOne.Predict( A1 );
      Console.WriteLine();
      Console.WriteLine( "Predicted value for A1 =" );
      Console.WriteLine( pls1YhatVec.ToString( "G5" ) );

      // Construct an Analysis of Variance (ANOVA) object for PLS1 model and
      // print the results.
      var plsOneAnova = new PLS1Anova( plsOne );
      Console.WriteLine();
      Console.WriteLine();
      Console.WriteLine( "PLS1 ANOVA results ------------------------" );
      Console.WriteLine( "  Sum of squares Total: " + plsOneAnova.SumOfSquaresTotal );
      Console.WriteLine( "  Sum of squares residuals: " + plsOneAnova.SumOfSquaresResiduals );
      Console.WriteLine( "  Standard Error: " + plsOneAnova.StandardError );
      Console.WriteLine( "  Root means square error prediction: " + plsOneAnova.RootMeanSqrErrorPrediction );
      Console.WriteLine( "  Coefficient of determination (R^2): " + plsOneAnova.CoefficientOfDetermination );

      // Perform the the PLS2 calculation on the multivariate response variable
      // C and check that the calculation succeeded. If it did not, print out
      // a diagnostic error message and exit.

      var plsTwo = new PLS2();

      Console.WriteLine();
      Console.WriteLine();
      Console.Write( "Calculating PLS2...  " );
      plsTwo.Calculate( A, C, numComponents );

      // Check that the PLS computation succeeded.
      if ( plsTwo.IsGood )
      {
        Console.WriteLine( "Success" );
      }
      else
      {
        Console.WriteLine();
        Console.WriteLine();
        Console.WriteLine( "PLS2 calculation failed: " + plsTwo.Message );
        return;
      }

      // Grab a sample from the other absorption matrix and make a prediction
      // of the concentrations of substances in the sample.
      x = A1.Row( 0 );
      DoubleVector yhat = plsTwo.Predict( x );
      Console.WriteLine();
      Console.WriteLine( "Predicted value for x =" );
      Console.WriteLine( yhat.ToString( "G5" ) );

      // Make predictions for all the samples in the absorption matrix A1
      DoubleMatrix yhatMat = plsTwo.Predict( A1 );
      Console.WriteLine();
      Console.WriteLine( "Predicted value for A1 =" );
      Console.WriteLine( yhatMat.ToTabDelimited( "G5" ) );

      // Construct an Analysis of Variance (ANOVA) object for PLS2 model and
      // print the results.
      var plsTwoAnova = new PLS2Anova( plsTwo );
      Console.WriteLine();
      Console.WriteLine( "PLS2 ANOVA results ------------------------" );
      Console.WriteLine();
      Console.WriteLine( "Sum of squares total\n" + plsTwoAnova.SumOfSquaresTotal );
      Console.WriteLine();
      Console.WriteLine( "Sum of squares residuals\n" + plsTwoAnova.SumOfSquaresResiduals );
      Console.WriteLine();
      Console.WriteLine( "Standard error\n" + plsTwoAnova.StandardError );
      Console.WriteLine();
      Console.WriteLine( "Root means square error prediction\n" + plsTwoAnova.RootMeanSqrErrorPrediction );
      Console.WriteLine();
      Console.WriteLine( "Coefficient of determination (R^2)\n" + plsTwoAnova.CoefficientOfDetermination );

      Console.WriteLine();
      Console.WriteLine( "Press Enter Key" );
      Console.Read();
    }
  }
}

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