/// <summary>
/// Ининциализация обученияю
/// </summary>
/// <param name="data">Данные.</param>
private void Initialize(List <double> data, int countOfLayerNeurons)
{
_dataSets = new Dictionary <int, List <double> >();
// TODO: Создание датасетов.
var countOfRepeats = data.Count - TrainingConstants.COUNT_OF_VALUES;
for (var repeat = 0; repeat < countOfRepeats; ++repeat)
{
var currentData = data.GetRange(repeat, TrainingConstants.COUNT_OF_VALUES);
var currentValues = new List <double>();
for (var index = 0; index < currentData.Count; ++index)
{
var nextIndex = index + 1;
if (nextIndex.Equals(currentData.Count))
{
continue;
}
var value = currentData[nextIndex] - currentData[index];
currentValues.Add(MathFunctionsHelper.HTanFunction(value));
}
_dataSets.Add(repeat, currentValues);
var lastDayValue = data[repeat + TrainingConstants.COUNT_OF_VALUES];
var lastCurrentValue = currentData.Last();
var result = MathFunctionsHelper.HTanFunction(lastDayValue - lastCurrentValue);
_idealResults.Add(result);
}
_configuration.IterationsInEpochCount = _idealResults.Count;
_configuration.IdealResult = _idealResults[0];
var hiddenLayer = new HiddenLayer(_dataSets[0], countOfLayerNeurons);
hiddenLayer.Initialize();
var outputLyer = new OutputLayer(hiddenLayer.GetData());
outputLyer.Initialize();
_layers = new List <Layer> {
hiddenLayer, outputLyer
};
}
/// <summary>
/// Нормализация входных данных.
/// </summary>
/// <param name="inputData">Входные данные.</param>
private void NormilizeData(List <double> inputData)
{
_data = new List <double>();
for (var index = 0; index < inputData.Count; ++index)
{
var nextIndex = index + 1;
if (nextIndex.Equals(inputData.Count))
{
continue;
}
var value = inputData[nextIndex] - inputData[index];
_data.Add(MathFunctionsHelper.HTanFunction(value));
}
}
