public LaplacianFaceRecognizer(List <Matrix> trainingSet, List <String> labels, int numOfComponents)
{
this.numOfComponents = numOfComponents;
this.imgRows = trainingSet[0].RowDimension;
int n = trainingSet.Count; // sample size
HashSet <String> tempSet = new HashSet <String>(labels);
int c = tempSet.Count; // class size
// process in PCA
EigenFaceRecognizer pca = new EigenFaceRecognizer(trainingSet, labels, numOfComponents);
//construct the nearest graph
Matrix S = constructGraph(pca.getWeights());
Matrix D = constructD(S);
Matrix L = D.Subtract(S);
//reconstruct the trainingSet into required X;
Matrix X = constructTrainingMatrix(pca.getWeights());
Matrix XLXT = X.Multiply(L).Multiply(X.Transpose());
Matrix XDXT = X.Multiply(D).Multiply(X.Transpose());
//calculate the eignevalues and eigenvectors of (XDXT)^-1 * (XLXT)
XDXT.Inverse();
Matrix targetForEigen = XDXT.Inverse().Multiply(XLXT);
EigenvalueDecomposition feature = targetForEigen.Eigen();
double[] eigenValues = feature.RealEigenvalues;
int[] indexOfChosenEigenValues = this.getIndOfHigherEV(eigenValues, eigenValues.Length);//higher eigen values
Matrix eigenVectors = feature.GetV();
Matrix selectedEigenVectors = eigenVectors.GetMatrix(0, eigenVectors.RowDimension - 1, indexOfChosenEigenValues);
this.weightMatrix = pca.getWeightMatrix().Multiply(selectedEigenVectors);
//Construct projectedTrainingMatrix
this.weights = new List <ProjectMatrix>();
for (int i = 0; i < trainingSet.Count(); i++)
{
ProjectMatrix pm = new ProjectMatrix(this.weightMatrix.Transpose().Multiply(trainingSet[i].Subtract(pca.getMeanMatrix())),
labels[i]);
this.weights.Add(pm);
}
this.meanMatrix = pca.getMeanMatrix();
}
public LinearDescriminantAnalysis(List <Matrix> trainingSet, List <String> labels)
{
this.trainingSet = trainingSet;
this.imgRows = trainingSet[0].RowDimension;
int n = trainingSet.Count(); // sample size
HashSet <string> uniqueLabels = new HashSet <string>(labels);
int c = uniqueLabels.Count(); // class size
this.numOfComponents = c - 1;
EigenFaceRecognizer pca = new EigenFaceRecognizer(trainingSet, labels, n - c);
Matrix meanTotal = new Matrix(n - c, 1);
Dictionary <string, List <Matrix> > dic = new Dictionary <string, List <Matrix> >();
List <ProjectMatrix> pcaWeights = pca.getWeights();
for (int i = 0; i < pcaWeights.Count(); i++)
{
string key = pcaWeights[i].getLabel();
meanTotal.AddEquals(pcaWeights[i].getImgMat());
if (!dic.ContainsKey(key))
{
List <Matrix> temp = new List <Matrix>();
temp.Add(pcaWeights[i].getImgMat());
dic.Add(key, temp);
}
else
{
List <Matrix> temp = dic[key];
temp.Add(pcaWeights[i].getImgMat());
dic[key] = temp;
}
}
meanTotal.Multiply((double)1 / n);
// calculate Sw, Sb
Matrix Sw = new Matrix(n - c, n - c);
Matrix Sb = new Matrix(n - c, n - c);
uniqueLabels = new HashSet <string>(dic.Keys);
foreach (string s in uniqueLabels)
{
List <Matrix> matrixWithinThatClass = dic[s];
Matrix meanOfCurrentClass = getMean(matrixWithinThatClass);
for (int i = 0; i < matrixWithinThatClass.Count(); i++)
{
Matrix mat = matrixWithinThatClass[i].Subtract(meanOfCurrentClass);
mat = mat.Multiply(mat.Transpose());
Sw.AddEquals(mat);
}
Matrix temp = meanOfCurrentClass.Subtract(meanTotal);
temp = temp.Multiply(temp.Transpose()).Multiply(matrixWithinThatClass.Count());
Sb.AddEquals(temp);
}
// calculate the eigenvalues and vectors of Sw^-1 * Sb
Matrix targetForEigen = Sw.Inverse().Multiply(Sb);
EigenvalueDecomposition feature = targetForEigen.Eigen();
double[] eigenValues = feature.RealEigenvalues;
int[] indexOfChosenEigenValues = getIndOfHigherEV(eigenValues, c - 1);
Matrix eigenVectors = feature.GetV();
Matrix selectedEigenVectors = eigenVectors.GetMatrix(0, eigenVectors.RowDimension - 1, indexOfChosenEigenValues);
this.weightMatrix = pca.getWeightMatrix().Multiply(selectedEigenVectors);
// Construct weights
this.weights = new List <ProjectMatrix>();
for (int i = 0; i < trainingSet.Count(); i++)
{
ProjectMatrix ptm = new ProjectMatrix(this.weightMatrix.Transpose().Multiply(trainingSet[i].Subtract(pca.getMeanMatrix()))
, labels[i]);
this.weights.Add(ptm);
}
this.meanMatrix = pca.getMeanMatrix();
}
