public Support_Vector_Machine(ReadImages RM)
{
for (int g = 0; g < 5; ++g)
{
mm[g] = new List <Point>();
}
this.Clases = RM.classes;
this.DAtaset = RM;
trainData = new Matrix <float>(RM.TrainingSamples.Count, RM.TrainingSamples[0].Feature.Descriptor.Length);
trainClasses = new Matrix <float>(RM.TrainingSamples.Count, 1);
sample = new Matrix <float>(1, RM.TrainingSamples[0].Feature.Descriptor.Length);
string[] all_classes = RM.classes.ToArray();
/// make function to load training data set
for (int u = 0; u < RM.TrainingSamples.Count; u++)
{
for (int p2 = 0; p2 < RM.TrainingSamples[0].Feature.Descriptor.Length; ++p2)
{
trainData[u, p2] = RM.TrainingSamples[u].Feature.Descriptor[p2];
}
trainClasses[u, 0] = Map(RM.TrainingSamples[u].Lable[0]) + 1;
}
model = new SVM();
p = new SVMParams();
p.KernelType = Emgu.CV.ML.MlEnum.SVM_KERNEL_TYPE.LINEAR;
p.SVMType = Emgu.CV.ML.MlEnum.SVM_TYPE.C_SVC;
p.C = 1;
p.TermCrit = new MCvTermCriteria(100, 0.00001);
//bool trained = model.Train(trainData, trainClasses, null, null, p);
bool trained = model.TrainAuto(trainData, trainClasses, null, null, p.MCvSVMParams, 5);
}
public MultiLayerPerceptron(ReadImages RI, double Eta, int NumberOfEpochs, int NumberOfHiddenLayers, int[] NumberOfNeuronsForEachHiddenLayer)
{
for (int g = 0; g < 5; ++g)
{
mm[g] = new List <Point>();
}
this.NumberOfEpochs = NumberOfEpochs;
this.NumberOfHiddenLayers = NumberOfHiddenLayers;
this.NumberOfNeuronsForEachHiddenLayer = NumberOfNeuronsForEachHiddenLayer;
this.AllLayers = new Layer[NumberOfHiddenLayers + 2];
this.Dataset = RI;
AllLayers[0] = new Layer(Dataset.TrainingSamples[0].Feature.Descriptor.Count(), Eta, Dataset.TrainingSamples[0].Feature.Descriptor.Count());
for (int i = 1; i <= this.NumberOfHiddenLayers; ++i)
{
AllLayers[i] = new Layer(NumberOfNeuronsForEachHiddenLayer[i - 1], Eta, AllLayers[i - 1].NumberOfNeurons);
}
AllLayers[this.NumberOfHiddenLayers + 1] = new Layer(Dataset.classes.Count(), Eta, NumberOfNeuronsForEachHiddenLayer[NumberOfNeuronsForEachHiddenLayer.Length - 1]);
}
public SVM_handler(ReadImages RM2, DataGridView confusion_matrix_control, TextBox overall_accuracy_control, Label trainingtime, Label testingtime, Label l1, Label L2)
{
RI = RM2;
confusion_matrix = new int[1, this.RI.classes.Count];
for (int f = 0; f < 1; f++)
{
for (int x = 0; x < this.RI.classes.Count; x++)
{
confusion_matrix[f, x] = 0;
}
}
this.confusion_matrix_control = confusion_matrix_control;
this.trainingtime = trainingtime;
this.testingtime = testingtime;
this.overall_accuracy_control = overall_accuracy_control;
Label1 = l1;
label2 = L2;
Process currentProcess = System.Diagnostics.Process.GetCurrentProcess();
long totalBytesOfMemoryUsed = currentProcess.WorkingSet64;
Benchmark.Start();
S_V_M = new Support_Vector_Machine(RI);
Benchmark.End();
this.trainingtime.Text = " The Training Time : " + Benchmark.GetSeconds().ToString();
Process currentProcess1 = System.Diagnostics.Process.GetCurrentProcess();
long totalBytesOfMemoryUsed1 = currentProcess1.WorkingSet64;
Label1.Text = "The Training Memmory is " + Math.Abs(totalBytesOfMemoryUsed - totalBytesOfMemoryUsed1) + " bytes";
for (int d = 0; d < this.RI.TestingImages.Length; ++d)
{
LM = new loadimage(RI.TestingImages[d]);
Voting(LM.LM, S_V_M.testing(LM), confusion_matrix, this.LM.classes);
}
display_results(this.confusion_matrix_control, this.overall_accuracy_control);
MessageBox.Show("Done Testing");
}
public Task_3_View_handler(ReadImages RM)
{
this.ReadIm = RM;
}
public SVM_View(ReadImages RM)
{
InitializeComponent();
SH = new SVM_handler(RM, this.dataGridView_confusion_matrix, this.textBox_overall_accuracy, this.Trainingtime, this.Testing_time, label1, label2);
}
public Neual_network_form(ReadImages RM)
{
NN = new Task_3_View_handler(RM);
InitializeComponent();
}
