/// <summary>
/// Tworzy listę przykładów uczących na podstawie obrazów wczytanych z dysku
/// </summary>
private List<LearningExample> createLearningExamples(List<FileInfo> files)
{
List<LearningExample> examples = new List<LearningExample>();
foreach (FileInfo f in files)
{
double sum = 0.0F;
Bitmap bitmap = (Bitmap)Bitmap.FromFile(f.FullName);
examplesWidth = bitmap.Width;
examplesHeight = bitmap.Height;
//printLine(f.Name + ": " + bitmap.Width + "x" + bitmap.Height);
printLine(f.Name + ": " + bitmap.Width + "x" + bitmap.Height);
PerceptronLib.Vector v = new PerceptronLib.Vector(bitmap.Width * bitmap.Height);
int width = bitmap.Width;
int height = bitmap.Height;
double max = 0;
double min = 255;
for (int i = 0; i < width; i++)
{
for (int j = 0; j < height; j++)
{
int index = i * height + j;
//v[index] = (double)bitmap.GetPixel(i, j).R / (256.0F * width * height) * 3000;
v[index] = (double)bitmap.GetPixel(i, j).R;
sum += v[index];
if (min > v[index]) min = v[index];
if (max < v[index]) max = v[index];
}
}
double medium = sum / (double)(width * height);
for (int i = 0; i < width; i++)
{
for (int j = 0; j < height; j++)
{
int index = i * height + j;
v[index] -= medium;
}
}
v.normalizeWeights();
//printLine("Max = " + max + ", Min = " + min + ", Sum = " + sum
// + ", medium = " + medium);
examples.Add(new LearningExample(v, 0));
}
return examples;
}
/// <summary>
/// Algorytm redukcji składowych głównych
/// </summary>
internal void reduction(List<LearningExample> exampleList)
{
try
{
Dispatcher.Invoke(OnReductionStarted, this, new EventArgs());
List<LearningExample> list = new List<LearningExample>(exampleList);
int dimension = exampleList[0].Example.Dimension;
List<PerceptronLib.Vector> principalComponents = new List<PerceptronLib.Vector>(outputDimension);
for (int i = 0; i < outputDimension; i++)
{
#if DEBUG
printLine("i = " + i);
#endif
principalComponents.Add(ojLearn(list).Weights);
PerceptronLib.Vector w = principalComponents[i];
//printLine("Składowa główna: " + w[0] + ", " + w[1] + ", " + w[2] + ", " + w[3]);
//printLine("Składowa główna: długość = " + w.Length);
List<LearningExample> nextList = new List<LearningExample>();
foreach (LearningExample ex in list)
{
PerceptronLib.Vector x = ex.Example;
double val = w * w;
double activation = w * x;
PerceptronLib.Vector nextExVector = new PerceptronLib.Vector(dimension);
nextExVector = x - w * (activation / val);
nextExVector.normalizeWeights();
LearningExample nextEx = new LearningExample(nextExVector, 0);
nextList.Add(nextEx);
}
list = nextList;
}
saveImages(principalComponents, examplesWidth, examplesHeight);
Dispatcher.Invoke(OnReductionFinished, this, new EventArgs());
}
catch (Exception ex)
{
printLine(ex.Message + " [ " + ex.StackTrace + " ]");
}
}
