private static void CreateTrainingData(OCR.ShapeNet ShapeNet, List <Example> Examples)
{
NeuralNetwork.LearningData oLearningData;
OCR.PageComponent PageComponent;
sNeuralInput newInput;
foreach (Example Example in Examples)
{
PageComponent = new OCR.PageComponent();
PageComponent.LoadBitmap(Example.Filename);
ExtractFeatures.ExecuteExtractFeatures(PageComponent, true);
PageComponent.Position.Ascent = ShapeNet.ShapeList[Example.ShapeId].Position.Ascent;
PageComponent.Position.Height = ShapeNet.ShapeList[Example.ShapeId].Position.Height;
PageComponent.Position.Center = ShapeNet.ShapeList[Example.ShapeId].Position.Center;
PageComponent.Position.Base = ShapeNet.ShapeList[Example.ShapeId].Position.Base;
PageComponent.Position.Descent = ShapeNet.ShapeList[Example.ShapeId].Position.Descent;
//Fill the
oLearningData = new NeuralNetwork.LearningData();
oLearningData.oInput.fInputs.Clear();
oLearningData.oOutput.fOutputs.Clear();
newInput = RecogniseComponent.CalculateNetworkInput(PageComponent);
oLearningData.oInput = newInput;
for (long lIndex2 = 0; lIndex2 < ShapeNet.ShapeList.Count; lIndex2++)
{
if (Example.ShapeId == lIndex2)
{
oLearningData.oOutput.fOutputs.Add(1);
}
else
{
oLearningData.oOutput.fOutputs.Add(0);
}
}
ShapeNet.NeuralNetwork.AddSituation(oLearningData);
}
ShapeNet.NeuralNetwork.ComputeInputRatios();
}
public void AddSituation(LearningData poLearningData)
{
oLearningData.Add(poLearningData);
}
public NetworkData Teach(LearningData data)
{
return(Teach(data.Input.RawValues, data.ExpectedOutput.RawValues));
}
