private int LearnNextImage(int CurrentItem, double[] errors)
{
int nbofvalidelements = 0;
TeacherFile tf = new TeacherFile();
tf.SetFeatureNumber(NBOFFEATURES);
double[] CurrentFeatures = new double[NBOFFEATURES];
Boolean[] UsedFeatures = new Boolean[NBOFFEATURES];
UsedFeatures[0] = tf.GetUsedFeatures_RComponent();
UsedFeatures[1] = tf.GetUsedFeatures_GComponent();
UsedFeatures[2] = tf.GetUsedFeatures_BComponent();
UsedFeatures[3] = tf.GetUsedFeatures_Grayscale();
UsedFeatures[4] = tf.GetUsedFeatures_Area();
UsedFeatures[5] = tf.GetUsedFeatures_MinDiameter();
UsedFeatures[6] = tf.GetUsedFeatures_MaxDiameter();
UsedFeatures[7] = tf.GetUsedfeatures_Aspectratio();
UsedFeatures[8] = tf.GetUsedfeatures_Circularity();
double[] Decisions = new double[nbOfUnits_3];
string filename = tf.GetFileName(CurrentItem + 1);
int nbofteachers = tf.GetNumberOfTeachers(filename);
if (nbofteachers == 0)
{
return(nbofvalidelements);
}
int decision = 0;
int errorindex = 0;
for (int ii = 0; ii < nbofteachers; ii++)
{
int ExpectedDecision = tf.RetrieveTeacherFeatures(filename, ii + 1, CurrentFeatures, 3, NBOFFEATURES);
if (ExpectedDecision == -1)
{
continue;
}
for (int jj = 0; jj < NBOFFEATURES; jj++)
{
if (UsedFeatures[jj] == false)
{
CurrentFeatures[jj] = 0.0; // (discard the extracted value)
}
}
decision = CImageProcessingUtils.TeachNeuralNetwork(CurrentFeatures, Weights,
nbOfUnits_1, nbOfUnits_2, nbOfUnits_3, factor, ExpectedDecision, Decisions);
Decisions[ExpectedDecision - 1] -= 1.0; // this element of 'Decisions' vector is the correct answer
for (int jj = 0; jj < nbOfUnits_3; jj++)
{
errors[errorindex] = Decisions[jj];
nbofvalidelements++;
errorindex++;
}
}
return(nbofvalidelements);
}
private void InitializeWeights()
{
// init. the weights
CImageProcessingUtils.InitNeuralNetwork(Weights,
nbOfUnits_1, nbOfUnits_2, nbOfUnits_3);
}
