} // MeasureOverride
protected override void OnRender(DrawingContext dc)
{
base.OnRender(dc);
if (Source != null)
{
Size clientArea = GetSourceSizeDIPed();
Rect r = new Rect(0, 0, clientArea.Width, clientArea.Height);
dc.DrawRectangle(BackgroundBrush, null, r);
int y = 0;
int x = 0;
r.X = Space;
r.Y = Space;
double szDip = Zoom / DPI.Scale;
r.Width = szDip;
r.Height = szDip;
foreach (Raster.Pixel pixel in Source)
{
if (x == Source.Width)
{
x = 0;
y++;
r.X = Space;
r.Y = r.Y + szDip + Space;
}
dc.DrawRectangle(new SolidColorBrush(Color.FromRgb(pixel.Red, pixel.Green, pixel.Blue)), null, r);
if (Correlation != null)
{
// Обводим отображаемые пиксели цветом, зависящим от степени корреляции.
Correlation correlation = Correlation.GetCorrelation(new RasterAffinityDetector.PixelCoordsKey(x, y));
if (correlation.Importance < 0.5)
{
// Малозначимые блоки никак не выделяем.
}
else
{
// Значащие блоки с высокой степень корреляции отмечаем красной точкой, а с низкой степенью корреляции - темно-синей.
//Rect rBounds = new Rect(r.X-Space, r.Y-Space, r.Width+2*Space, r.Height+2*Space);
Rect rBounds = new Rect(r.X + r.Width / 2, r.Y + r.Height / 2, 0.5, 0.5);
Pen pen;
if (correlation.Value > 0.5)
{
pen = new Pen(new SolidColorBrush(Colors.Red), Space);
}
else
{
pen = new Pen(new SolidColorBrush(Colors.Blue), Space);
}
dc.DrawRectangle(null, pen, rBounds);
}
}
r.X = r.X + szDip + Space;
x++;
}
if (Selection != null)
{
// Выделяем желтой рамкой текущий выбранный пиксель, над которым находится указатель мыши.
dc.DrawRectangle(null, SelectionPen, Selection);
}
}
} // OnRender
} // OnRender
protected override void OnMouseMove(MouseEventArgs e)
{
if (Source != null)
{
Point p = e.GetPosition(this);
double szDip = Zoom / DPI.Scale; // размер отображаемой точки растра в DIP-ах
int x = (int)Math.Truncate((p.X - Space) / (szDip + Space)); // координата выбранной мышью точки отображаемого растра по горизонтали
int y = (int)Math.Truncate((p.Y - Space) / (szDip + Space)); // координата выбранной мышью точки отображаемого растра по вертикали
double xDip = x * (szDip + Space) + Space; // начало области отображаемой точки растра в DIP-ах по горизонтали
double yDip = y * (szDip + Space) + Space; // начало области отображаемой точки растра в DIP-ах по вертикали
Selection = new Rect(xDip, yDip, szDip, szDip);
SelectedPixel = Source.Pixels[x, y];
SelectedCoords = new System.Drawing.Point(x, y);
if (Correlation != null)
{
SelectedCorrelation = Correlation.GetCorrelation(new RasterAffinityDetector.PixelCoordsKey(x, y));
}
else
{
SelectedCorrelation = null;
}
InvalidateVisual();
}
} // OnMouseMove
public void Test2([Values(500)] int iterations, [Values(100)] int columns)
{
var testDir = Path.GetDirectoryName(Path.GetDirectoryName(TestContext.CurrentContext.TestDirectory));
var dataPath = Path.Combine(testDir ?? throw new NullReferenceException(), "..", "Data", "data.csv");
var mapper = new CsvMapper();
var data = mapper.ReadDataFromFile(dataPath, 1);
var correlation = Correlation.GetCorrelation(data);
data = data.RemoveLeastRelevantColumn(correlation, columns);
var dataSet = data.SplitData(1, 2, 0);
var trainingData = dataSet[DataType.Training];
var testingData = dataSet[DataType.Testing];
var neuralNetwork = new NeuralNetwork.NeuralNetwork {
MinWeight = -5, MaxWeight = 5
};
var activationFunction = new SigmoidFunction(-1);
neuralNetwork.ActivationFunction = activationFunction;
neuralNetwork.AddLayer(new Layer(10, columns));
neuralNetwork.AddLayer(new Layer(1, 10));
var genetic = new GeneticAlgorithm.GeneticAlgorithm(100, neuralNetwork.GetConnectionCount())
{
Selection = new TournamentSelection(100, 10),
Crossover = new TwoPointCrossover(0.65, 100),
Mutation = new FlipBitMutation(0.05)
};
var keepLooping = true;
for (var it = 0; it < iterations && keepLooping; it++)
{
foreach (var chromosome in genetic.Population)
{
neuralNetwork.SetWeights(chromosome.Genome);
Parallel.For(0, trainingData.Attributes.Count, i =>
{
var res = neuralNetwork.GetResult(trainingData.Attributes[i]);
lock (chromosome)
{
chromosome.Fitness += Math.Abs(res[0] - trainingData.ObjectClass[i]);
}
});
}
keepLooping = !genetic.Population.Any(chromosome => chromosome.Fitness < 5.0);
Console.WriteLine($@"iteration: {it} | " +
$@"best: {genetic.Population.MinBy(chromosome => chromosome.Fitness).First().Fitness}");
if (keepLooping && it < iterations - 1)
{
genetic.NextGeneration();
}
}
var best = genetic.Population.MinBy(chromosome => chromosome.Fitness).First();
neuralNetwork.SetWeights(best.Genome);
var correct = testingData.Attributes
.AsParallel()
.Where((t, i) => Math.Abs(neuralNetwork.GetResult(t)[0] - testingData.ObjectClass[i]) < 0.3)
.Count();
Console.WriteLine(
$@"Population: {100}, iterations: {iterations} - fitness: {best.Fitness:0.0} " +
$@"| correctly recognized: {correct}/{testingData.Attributes.Count}");
}
private void ButtonStartAlgorithm_Click(object sender, RoutedEventArgs e)
{
if (_algorithm != null)
{
var s = ((ToggleButton)sender).IsChecked;
_algorithm.IsPaused = !s ?? !_algorithm.IsPaused;
return;
}
RectangleDataGridTest.Dispatcher?.Invoke(() => RectangleDataGridTest.Visibility = Visibility.Collapsed);
DataGridTest.Dispatcher?.Invoke(() =>
{
DataGridTest.Visibility = Visibility.Collapsed;
});
AlgorithmErrorValues.Clear();
AlgorithmValidationValues.Clear();
var neuralNetwork = SmartGenAlgorithm.CreateNeuralNetwork();
var geneticAlgorithm = SmartGenAlgorithm.CreateGeneticAlgorithm(neuralNetwork.GetConnectionCount());
if (Settings.Default.InputLayerSize < Data.Attributes.First().Count)
{
var correlation = Correlation.GetCorrelation(Data);
Data = Data.RemoveLeastRelevantColumn(correlation, Settings.Default.InputLayerSize);
}
var dataSets = Data.SplitData(Settings.Default.TrainingRatio,
Settings.Default.TestRatio,
Settings.Default.ValidationRatio);
_algorithm = new SmartGenAlgorithm(geneticAlgorithm, neuralNetwork)
{
DataSet = dataSets,
ErrorTolerance = Settings.Default.ErrorTolerance,
MaxIterations = Settings.Default.MaxGenerationCount
};
_algorithm.IterationEvent += (iteration, error, validationError) =>
{
var current = DateTimeOffset.Now.ToUnixTimeMilliseconds();
if (current - _milliseconds < 500)
{
return;
}
_milliseconds = current;
AlgorithmErrorValues.Add(new ObservablePoint(iteration, error));
AlgorithmValidationValues.Add(new ObservablePoint(iteration, validationError));
if (AlgorithmErrorValues.Count <= 11)
{
return;
}
AlgorithmErrorValues.RemoveAt(0);
AlgorithmValidationValues.RemoveAt(0);
};
_algorithmTask = Task.Run(() => _algorithm.Run());
_algorithmTask.ContinueWith(HandleAlgorithmResult);
ButtonStop.IsEnabled = true;
}
