public void op_AdditionTest()
{
Vector a = new PerceptronLib.Vector(3);
Vector b = new PerceptronLib.Vector(a.Dimension);
for (int i = 0; i < a.Dimension; i++)
{
a[i] = i;
b[i] = i;
}
Vector expected = new PerceptronLib.Vector(a.Dimension);
for (int i = 0; i < a.Dimension; i++)
{
expected[i] = 2 * i;
}
Vector actual;
actual = (a + b);
Assert.AreEqual(expected, actual);
for (int i = 0; i < a.Dimension; i++)
{
a[i] = i + 1;
b[i] = i + 2;
}
expected = new PerceptronLib.Vector(a.Dimension);
for (int i = 0; i < a.Dimension; i++)
{
expected[i] = 2 * (i + 1) + 1;
}
actual = (a + b);
Assert.AreEqual(expected, actual);
}
/// <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 + " ]");
}
}
private void compareButton_Click(object sender, RoutedEventArgs e)
{
if (File.Exists(dataBasePathText.Text) == false)
{
printLine("Nie istnieje plik bazy danych");
return;
}
try
{
openFileDialog.ShowDialog();
if (openFileDialog.FileName != null)
{
string fn = openFileDialog.FileName;
printLine("Porównywanie pliku: " + fn);
Bitmap bitmap = (Bitmap)Bitmap.FromFile(fn);
if (bitmap.Width == examplesWidth && bitmap.Height == examplesHeight)
{
printLine("Wymiary OK");
PerceptronLib.Vector v = new PerceptronLib.Vector(bitmap.Width * bitmap.Height);
int width = bitmap.Width;
int height = bitmap.Height;
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;
}
}
BinaryFormatter formatter = new BinaryFormatter();
FileStream fs = new System.IO.FileStream(dataBasePathText.Text, FileMode.Open, System.IO.FileAccess.Read);
EigenFacesDB db = (EigenFacesDB)formatter.Deserialize(fs);
printLine("Rozpoznany obraz: " + db.compareFace(v));
}
else
{
printLine("Niezgodne wymiary wejścia");
}
}
}
catch (Exception ex)
{
printLine(ex.Message + " [ " + ex.StackTrace + " ]");
}
}
/// <summary>
/// Zapisuje utworzone obrazy na dysku
/// </summary>
private void saveImages(List<PerceptronLib.Vector> vectors, int width, int height)
{
try
{
//if (File.Exists(getDataBaseFileName()) == false)
//{
// printLine("Plik bazy danych nie istnieje");
// return;
//}
// Otwiera plik bazy danych i nadpisuje go
FileStream stream = new System.IO.FileStream(getDataBaseFileName(), FileMode.Create, System.IO.FileAccess.Write);
EigenFacesDB db = new EigenFacesDB(vectors);
BinaryFormatter formatter = new BinaryFormatter();
printLine("Zapisywanie wyników...");
int dimension = examples[0].Example.Dimension;
for (int l = 0; l < examples.Count; l++)
{
LearningExample ex = examples[l];
// Tworzy nowy element bazy danych
EigenNode node = new EigenNode("Przykład" + (l + 1));
PerceptronLib.Vector v = new PerceptronLib.Vector(dimension);
//printLine("outputDim = " + outputDimension + ", vectors.count = " + vectors.Count);
for (int k = 0; k < outputDimension; k++)
{
PerceptronLib.Vector p = vectors[k];
Bitmap img = new Bitmap(examplesWidth, examplesHeight);
if (l == 0)
{
Bitmap eigenImg = new Bitmap(examplesWidth, examplesHeight);
LearningExample eigenEx = new LearningExample(vectors[k], 0);
normalizeRange(eigenEx, 256.0F, width, height);
for (int i = 0; i < width; i++)
{
for (int j = 0; j < height; j++)
{
int index = i * height + j;
byte color = (byte)(eigenEx.Example[index]);
System.Drawing.Color c = System.Drawing.Color.FromArgb(255, color, color, color);
eigenImg.SetPixel(i, j, c);
}
}
eigenImg.Save("eigenVector-" + (k + 1) + ".jpg");
}
double val = p * p;
double activation = p * ex.Example;
node.Coordinates.Add(activation);
v += p * (activation / val);
LearningExample newEx = new LearningExample(v, 0);
normalizeRange(newEx, 256.0F, width, height);
for (int i = 0; i < width; i++)
{
for (int j = 0; j < height; j++)
{
int index = i * height + j;
byte color = (byte)(newEx.Example[index]);
System.Drawing.Color c = System.Drawing.Color.FromArgb(255, color, color, color);
img.SetPixel(i, j, c);
}
}
img.Save("output" + (l + 1) + "-" + (k + 1) + ".jpg");
}
db.add(node);
}
//foreach (EigenNode n in nodes)
//{
// printLine("n.coordinates: " + n.Coordinates.Count);
// db.add(n);
//}
printLine("Wymiar zapisywanej bazy: " + db.Dimension);
try
{
formatter.Serialize(stream, db);
}
catch (Exception ex)
{
printLine("Wujątek: " + ex.Message + " [ " + ex.StackTrace + " ]");
}
stream.Close();
}
catch (Exception ex)
{
printLine(ex.Message + " [ " + ex.StackTrace + " ] " + ex.GetType());
}
}
public void op_MultiplyTest()
{
Vector a = new PerceptronLib.Vector(3);
Vector b = new PerceptronLib.Vector(3);
for (int i = 0; i < 3; i++)
{
a[i] = i;
b[i] = i;
}
double expected = 5F;
double actual;
actual = (a * b);
Assert.AreEqual(expected, actual);
for (int i = 0; i < 3; i++)
{
a[i] = i + 1;
b[i] = i + 2;
}
expected = 20F;
actual = (a * b);
Assert.AreEqual(expected, actual);
}
/// <summary>
/// Zdarzenie przechwytywane w momencie poruszania myszą na canvasie.
/// Ma na celu pokacywanie, jak klasyfikowane są odpowiednie punkty na płaszczyźnie
/// przez stworzoną sieć.
/// </summary>
private void canvas_MouseMove(object sender, MouseEventArgs e)
{
// Sprawdzamy, czy sieć została już utworzona
if (isNetworkCreated)
{
Point p = e.GetPosition(canvas);
// Tworzy wektor przeznaczony do przeliczenia
PerceptronLib.Vector v = new PerceptronLib.Vector(3);
v[0] = 1;
v[1] = p.X - 100;
v[2] = p.Y - 100;
// Tworzy obiekt przykładu
LearningExample ex = new LearningExample(v, 0);
// Sprawdza, na jaki kolor należy pomalować prostokąt
if (network.classify(ex)[1] == 1)
{
// Koloruje na czerwono
classRect.Fill = new SolidColorBrush(Colors.Red);
}
else
{
// Koloruje na niebiesko
classRect.Fill = new SolidColorBrush(Colors.Blue);
}
}
}
/// <summary>
/// Funkcja przeznaczona do dodawania przykładu uczącego do listy
/// </summary>
private void addLearningExample(Point p, double exClass)
{
// Tworzy nowy wektor
PerceptronLib.Vector v = new PerceptronLib.Vector(3);
v[0] = 1.0F;
v[1] = p.X;
v[2] = p.Y;
// Tworzy przykład uczący
learningExamples.Add(new LearningExample(v, exClass));
}
