/*metricType:
* 0: CosineDissimilarity
* 1: L1Distance
* 2: EuclideanDistance
*
* energyPercentage:
* PCA: components = samples * energyPercentage
* LDA: components = (c-1) *energyPercentage
* LLP: components = (c-1) *energyPercentage
*
* featureExtractionMode
* 0: PCA
* 1: LDA
* 2: LLP
*
* trainNums: how many numbers in 1..10 are assigned to be training faces
* for each class, randomly generate the set
*
* knn_k: number of K for KNN algorithm
*
* */
static double test(int metricType, int noOfComponents, int faceRecognizerFlag, int trainNums, int knn_k)
{
//determine which metric is used
//metric
Metric metric = null;
if (metricType == 0)
{
metric = new CosineDissimilarity();
}
else if (metricType == 1)
{
metric = new L1Distance();
}
else if (metricType == 2)
{
metric = new EuclideanDistance();
}
//////////assert metric != null : "metricType is wrong!";////////
//set expectedComponents according to energyPercentage
//componentsRetained
// int trainingSize = trainNums * 10;
// int componentsRetained = 0;
// if(featureExtractionMode == 0)
// componentsRetained = (int) (trainingSize * energyPercentage);
// else if(featureExtractionMode == 1)
// componentsRetained = (int) ((10 -1) * energyPercentage);
// else if(featureExtractionMode == 2)
// componentsRetained = (int) ((10 -1) * energyPercentage);
//set trainSet and testSet
Dictionary <String, List <int> > trainMap = new Dictionary <string, List <int> >();
Dictionary <String, List <int> > testMap = new Dictionary <string, List <int> >();
for (int i = 1; i <= 40; i++)
{
String label = "s" + i;
List <int> train = generateTrainNums(trainNums);
List <int> test = generateTestNums(train);
trainMap[label] = train;
testMap[label] = test;
}
//trainingSet & respective labels
List <Matrix> trainingSet = new List <Matrix>();
List <String> labels = new List <String>();
HashSet <String> labelSet = new HashSet <string>(trainMap.Keys);
foreach (String label in labelSet)
{
List <int> cases = trainMap[label];
for (int i = 0; i < cases.Count; i++)
{
String filePath = "E:/faces/" + label + "/" + cases[i] + ".bmp";
Matrix temp;
try {
temp = FileManager.GetBitMapColorMatrix(filePath);
if (faceRecognizerFlag == 3)
{
trainingSet.Add(temp);
}
else
{
trainingSet.Add(convertTo1D(temp));
}
labels.Add(label);
} catch (Exception e) {
}
}
}
//testingSet & respective true labels
List <Matrix> testingSet = new List <Matrix>();
List <String> trueLabels = new List <String>();
labelSet = new HashSet <string>(trainMap.Keys);
foreach (string label in labelSet)
{
List <int> cases = testMap[label];
for (int i = 0; i < cases.Count(); i++)
{
String filePath = "E:/faces/" + label + "/" + cases[i] + ".bmp";
Matrix temp;
try
{
temp = FileManager.GetBitMapColorMatrix(filePath);
testingSet.Add(convertTo1D(temp));
trueLabels.Add(label);
}
catch (Exception e) { }
}
}
//set featureExtraction
FaceRecognizer fe = null;
if (faceRecognizerFlag == 0)
{
fe = new EigenFaceRecognizer(trainingSet, labels, noOfComponents);
}
else if (faceRecognizerFlag == 1)
{
fe = new LDA(trainingSet, labels, noOfComponents);
}
else if (faceRecognizerFlag == 2)
{
fe = new FisherFaceRecognizer(trainingSet, labels, noOfComponents);
}
else if (faceRecognizerFlag == 3)
{
fe = new ModularFaceRecognitionAlgorithms(trainingSet, labels, 200, 16, trainNums);
}
FileManager.convertMatricetoImage(fe.getWeightMatrix(), faceRecognizerFlag);
//use test cases to validate
//testingSet trueLables
List <ProjectMatrix> projectSet = fe.getProjectSet();
int accurateNum = 0;
for (int i = 0; i < testingSet.Count; i++)
{
Matrix testCase = fe.getWeightMatrix().Transpose().Multiply(testingSet[i].Subtract(fe.getMeanMatrix()));
String result = KNN.assignLabel(projectSet.ToArray(), testCase, knn_k, metric);
if (result == trueLabels[i])
{
accurateNum++;
}
}
double accuracy = accurateNum / (double)testingSet.Count;
Console.WriteLine("The accuracy is " + accuracy);
return(accuracy);
}