public Form1()
{
InitializeComponent();
p = new Perceptron();
p.InitialiseInputs();
p.InitialiseOutputsSupervise();
p.NormaliseEntrees();
}
static void Main()
{
bool isFace = false;
int numberofTestorValidationValues = 1000; // 301;// 1000;//301 for validation in face and 100 in test for face
int numberOfTrainingValues = 5000; // 451;// 5000; //451 for face
Application.EnableVisualStyles();
Application.SetCompatibleTextRenderingDefault(false);
Application.Run(new Form1());
//Testing the classes here
Input currentInput = new Input(isFace, numberOfTrainingValues, numberofTestorValidationValues);
currentInput.Initialize();
NaiveBayes nb = new NaiveBayes(3, false, numberOfTrainingValues, numberofTestorValidationValues);
nb.TrainAndTest();
//////Perceptron Testing Beginning
Perceptron myPerceptron = new Perceptron(isFace, numberOfTrainingValues, numberofTestorValidationValues);
myPerceptron.runTraining();
myPerceptron.runTesting();
////Perceptron Ending
//KNN Testing Beginning
////So far maximum is at k=3 and it is 92
KNN KClassifier = new KNN(3, isFace);
Dictionary <int, int> output = KClassifier.Run(currentInput.trainingDataPath, currentInput.validationDataPath, numberOfTrainingValues, numberofTestorValidationValues, true);
List <int> validationLabels = currentInput.LoadValidationLabelValues(numberofTestorValidationValues);
int correctCount = 0;
for (int i = 0; i < numberofTestorValidationValues; i++)
{
//List<int> outputtemp = output[i];
string outputString = output[i].ToString();
//Console.WriteLine("Actual : {0} and the predicted value is {1} : ", validationLabels[i], output[i]);
//outputtemp.ForEach(Console.WriteLine);
if (validationLabels[i] == output[i])
{
correctCount += 1;
}
}
//Console.WriteLine("Press any key to exit");
//Console.ReadKey();
double percent = (((double)correctCount / (double)numberofTestorValidationValues) * 100);
//Console.WriteLine("The accuracy is : ", percent);
MessageBox.Show("The percentage value is " + percent.ToString());
//KNN Ending
}