/// <inheritdoc/>
public bool NextAttempt()
{
if (Attempt < MaxAttempt)
{
//Next attempt is allowed
++Attempt;
//Reset
_noiseSeeker = new ParamValFinder(_settings.NoiseFinderCfg);
_currNoise = 1e6;
MSE = 0;
AttemptEpoch = 0;
return(true);
}
else
{
//Max attempt reached -> do nothhing and return false
return(false);
}
}
//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 RidgeRegrTrainer(FeedForwardNetwork net,
List <double[]> inputVectorCollection,
List <double[]> outputVectorCollection,
RidgeRegrTrainerSettings 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);
//Parameters
_cfg = cfg;
MaxAttempt = _cfg.NumOfAttempts;
MaxAttemptEpoch = _cfg.NumOfAttemptEpochs;
Attempt = 1;
AttemptEpoch = 0;
_net = net;
_outputSingleColVectorCollection = new List <Vector>(_net.NumOfOutputValues);
for (int outputIdx = 0; outputIdx < _net.NumOfOutputValues; outputIdx++)
{
Vector outputSingleColVector = new Vector(outputVectorCollection.Count);
for (int row = 0; row < outputVectorCollection.Count; row++)
{
//Output
outputSingleColVector.Data[row] = outputVectorCollection[row][outputIdx];
}
_outputSingleColVectorCollection.Add(outputSingleColVector);
}
//Lambda seeker
_lambdaFinder = new ParamValFinder(_cfg.LambdaFinderCfg);
_currLambda = 0;
//Matrix setup
Matrix X = new Matrix(inputVectorCollection.Count, _net.NumOfInputValues + 1);
for (int row = 0; row < inputVectorCollection.Count; row++)
{
//Add constant bias
X.Data[row][0] = 1d;
//Add predictors
inputVectorCollection[row].CopyTo(X.Data[row], 1);
}
_XT = X.Transpose();
_XTdotX = _XT * X;
_XTdotY = new Vector[_net.NumOfOutputValues];
for (int outputIdx = 0; outputIdx < _net.NumOfOutputValues; outputIdx++)
{
_XTdotY[outputIdx] = _XT * _outputSingleColVectorCollection[outputIdx];
}
return;
}