/// <summary>
/// Open any datasets that were used by the input layer.
/// </summary>
private void OpenDataSet()
{
// clear out any data sets already there
this.readDataSet.Clear();
this.dataSetFieldMap.Clear();
this.dataSetIteratorMap.Clear();
// only add each iterator once
IDictionary <INeuralDataSet, NeuralDataFieldHolder> uniqueSets = new Dictionary <INeuralDataSet, NeuralDataFieldHolder>();
// find the unique files
foreach (IInputField field in this.inputFields)
{
if (field is InputFieldNeuralDataSet)
{
InputFieldNeuralDataSet dataSetField = (InputFieldNeuralDataSet)field;
INeuralDataSet dataSet = dataSetField.NeuralDataSet;
if (!uniqueSets.ContainsKey(dataSet))
{
IEnumerator <INeuralDataPair> iterator = dataSet
.GetEnumerator();
NeuralDataFieldHolder holder = new NeuralDataFieldHolder(
iterator, dataSetField);
uniqueSets[dataSet] = holder;
this.readDataSet.Add(iterator);
}
NeuralDataFieldHolder holder2 = uniqueSets[dataSet];
this.dataSetFieldMap[dataSetField] = holder2;
this.dataSetIteratorMap[holder2.GetEnumerator()] = holder2;
}
}
}
/// <summary>
/// Obtain the length of the training data.
/// </summary>
/// <param name="training">The training date to check.</param>
/// <returns>The length of the training data.</returns>
private static long ObtainTrainingLength(INeuralDataSet training)
{
if (training is IIndexable)
{
return ((IIndexable)training).Count;
}
long result = 0;
IEnumerator<INeuralDataPair> itr = training.GetEnumerator();
while (itr.MoveNext())
result++;
return result;
}
/// <summary>
/// Obtain the length of the training data.
/// </summary>
/// <param name="training">The training date to check.</param>
/// <returns>The length of the training data.</returns>
private static long ObtainTrainingLength(INeuralDataSet training)
{
if (training is IIndexable)
{
return(((IIndexable)training).Count);
}
long result = 0;
IEnumerator <INeuralDataPair> itr = training.GetEnumerator();
while (itr.MoveNext())
{
result++;
}
return(result);
}