/// <summary>
/// Deep copy constructor
/// </summary>
/// <param name="source">Source instance</param>
public ElasticRegrTrainerSettings(ElasticRegrTrainerSettings source)
{
NumOfAttemptEpochs = source.NumOfAttemptEpochs;
Lambda = source.Lambda;
Alpha = source.Alpha;
return;
}
//Constructor
/// <summary>
/// Creates an initialized instance.
/// </summary>
/// <param name="net">The FF network to be trained.</param>
/// <param name="inputVectorCollection">The input vectors (input).</param>
/// <param name="outputVectorCollection">The output vectors (ideal).</param>
/// <param name="cfg">The configuration of the trainer.</param>
public ElasticRegrTrainer(FeedForwardNetwork net,
List <double[]> inputVectorCollection,
List <double[]> outputVectorCollection,
ElasticRegrTrainerSettings cfg
)
{
//Check network readyness
if (!net.Finalized)
{
throw new InvalidOperationException($"Can´t create trainer. Network structure was not finalized.");
}
//Check network conditions
if (net.LayerCollection.Count != 1 || !(net.LayerCollection[0].Activation is AFAnalogIdentity))
{
throw new InvalidOperationException($"Can´t create trainer. Network structure is not complient (single layer having Identity activation).");
}
//Check samples conditions
if (inputVectorCollection.Count == 0)
{
throw new InvalidOperationException($"Can´t create trainer. Missing training samples.");
}
//Collections
_inputVectorCollection = new List <double[]>(inputVectorCollection);
_outputVectorCollection = new List <double[]>(outputVectorCollection);
var rangePartitioner = Partitioner.Create(0, _inputVectorCollection.Count);
_parallelRanges = new List <Tuple <int, int> >(rangePartitioner.GetDynamicPartitions());
//Parameters
_cfg = cfg;
MaxAttempt = _cfg.NumOfAttempts;
MaxAttemptEpoch = _cfg.NumOfAttemptEpochs;
Attempt = 1;
AttemptEpoch = 0;
_net = net;
_gamma = _cfg.Lambda * _cfg.Alpha;
return;
}
