//The Least Squares algorithm
//It uses a PartialLeastSquaresAnalysis library object using a non-linear iterative partial least squares algorithm
//and runs on the mean-centered and standardized data
private ConfusionMatrix RunLS(Double[,] trainingSet, Double[,] trainingOutput, Double[,] testSet, int[] expected)
{
//Create an analysis
PartialLeastSquaresAnalysis pls = new PartialLeastSquaresAnalysis(trainingSet, trainingOutput,
AnalysisMethod.Standardize, PartialLeastSquaresAlgorithm.NIPALS);
pls.Compute();
//After computing the analysis
//create a linear model to predict new variables
MultivariateLinearRegression regression = pls.CreateRegression();
//This will hold the result of the classifications
var predictedLifted = new int[testSet.GetLength(0)][];
for (int i = 0; i < predictedLifted.Length; ++i)
{
predictedLifted[i] = regression
.Compute(testSet.GetRow(i)) //Retrieve the row vector of the test set
.Select(x => Convert.ToInt32(x))// Convert the result to int
.ToArray();
}
//Unlift the prediction vector
var predicted = predictedLifted
.SelectMany(x => x)
.ToArray();
//For test, assume 1 as positive and 0 as negative
int positive = 0;
int negative = 1;
//Create a new confusion matrix with the calculated parameters
ConfusionMatrix cmatrix = new ConfusionMatrix(predicted, expected, positive, negative);
return cmatrix;
}
