/// <summary>
/// The deep copy constructor.
/// </summary>
/// <param name="source">The source instance.</param>
public QRDRegrTrainerSettings(QRDRegrTrainerSettings source)
{
NumOfAttempts = source.NumOfAttempts;
NumOfAttemptEpochs = source.NumOfAttemptEpochs;
NoiseZeroMargin = source.NoiseZeroMargin;
NoiseFinderCfg = (ParamValFinderSettings)source.NoiseFinderCfg.DeepClone();
return;
}
//Constructor
/// <summary>
/// Constructs an initialized instance
/// </summary>
/// <param name="net">FF network to be trained</param>
/// <param name="inputVectorCollection">Predictors (input)</param>
/// <param name="outputVectorCollection">Ideal outputs (the same number of rows as number of inputs)</param>
/// <param name="rand">Random object to be used for adding a white-noise to predictors</param>
/// <param name="settings">Startup parameters of the trainer</param>
public QRDRegrTrainer(FeedForwardNetwork net,
List <double[]> inputVectorCollection,
List <double[]> outputVectorCollection,
QRDRegrTrainerSettings settings,
Random rand
)
{
//Check network readyness
if (!net.Finalized)
{
throw new InvalidOperationException($"Can´t create trainer. Network structure was not finalized.");
}
//Check network conditions
if (net.LayerCollection.Count != 1 || !(net.LayerCollection[0].Activation is Identity))
{
throw new InvalidOperationException($"Can´t create trainer. Network structure is not complient (single layer having Identity activation).");
}
//Check samples conditions
if (inputVectorCollection.Count < inputVectorCollection[0].Length + 1)
{
throw new InvalidOperationException($"Can´t create trainer. Insufficient number of training samples {inputVectorCollection.Count}. Minimum is {(inputVectorCollection[0].Length + 1)}.");
}
//Parameters
_settings = settings;
MaxAttempt = _settings.NumOfAttempts;
MaxAttemptEpoch = _settings.NumOfAttemptEpochs;
_net = net;
_rand = rand;
_inputVectorCollection = inputVectorCollection;
_outputVectorCollection = outputVectorCollection;
_outputSingleColMatrixCollection = new List <Matrix>(_net.NumOfOutputValues);
for (int outputIdx = 0; outputIdx < _net.NumOfOutputValues; outputIdx++)
{
Matrix outputSingleColMatrix = new Matrix(_outputVectorCollection.Count, 1);
for (int row = 0; row < _outputVectorCollection.Count; row++)
{
//Output
outputSingleColMatrix.Data[row][0] = _outputVectorCollection[row][outputIdx];
}
_outputSingleColMatrixCollection.Add(outputSingleColMatrix);
}
//Start training attempt
Attempt = 0;
NextAttempt();
return;
}