/// <summary>
/// Starts next training attempt
/// </summary>
public bool NextAttempt()
{
if (Attempt < MaxAttempt)
{
//Next attempt is allowed
++Attempt;
//Reset
_maxNoiseSeeker = new ParamSeeker(_settings.MaxNoiseSeekerCfg);
_currMaxNoise = 1e6;
MSE = 0;
AttemptEpoch = 0;
return(true);
}
else
{
//Max attempt reached -> do nothhing and return false
return(false);
}
}
//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="settings">Optional startup parameters of the trainer</param>
public RidgeRegrTrainer(FeedForwardNetwork net,
List <double[]> inputVectorCollection,
List <double[]> outputVectorCollection,
RidgeRegrTrainerSettings settings
)
{
//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 == 0)
{
throw new InvalidOperationException($"Can´t create trainer. Missing training samples.");
}
//Collections
_inputVectorCollection = new List <double[]>(inputVectorCollection);
_outputVectorCollection = new List <double[]>(outputVectorCollection);
//Parameters
_settings = settings;
MaxAttempt = _settings.NumOfAttempts;
MaxAttemptEpoch = _settings.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
_lambdaSeeker = new ParamSeeker(_settings.LambdaSeekerCfg);
_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;
}
//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="settings">Optional startup parameters of the trainer</param>
/// <param name="rand">Random object to be used</param>
public RidgeRegrTrainer(FeedForwardNetwork net,
List <double[]> inputVectorCollection,
List <double[]> outputVectorCollection,
RidgeRegrTrainerSettings settings,
Random rand
)
{
//Check network readyness
if (!net.Finalized)
{
throw new Exception("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 Exception("Can´t create trainer. Network structure is not complient (single layer having Identity activation).");
}
//Check samples conditions
if (inputVectorCollection.Count == 0)
{
throw new Exception("Can´t create trainer. Missing training samples.");
}
//Collections
_inputVectorCollection = new List <double[]>(inputVectorCollection);
_outputVectorCollection = new List <double[]>(outputVectorCollection);
//Parameters
_settings = settings;
MaxAttempt = _settings.NumOfAttempts;
MaxAttemptEpoch = _settings.NumOfAttemptEpochs;
Attempt = 1;
AttemptEpoch = 0;
_net = net;
_rand = rand;
_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
_lambdaSeeker = new ParamSeeker(_settings.LambdaSeekerCfg);
_currLambda = 1e6;
//Matrix setup
Matrix predictorsMatrix = new Matrix(inputVectorCollection.Count, _net.NumOfInputValues + 1);
for (int row = 0; row < inputVectorCollection.Count; row++)
{
//Predictors
for (int col = 0; col < _net.NumOfInputValues; col++)
{
predictorsMatrix.Data[row][col] = inputVectorCollection[row][col];
}
//Add constant bias to predictors
predictorsMatrix.Data[row][_net.NumOfInputValues] = 1;
}
_transposedPredictorsMatrix = predictorsMatrix.Transpose();
_baseSquareMatrix = _transposedPredictorsMatrix * predictorsMatrix;
return;
}
