//Methods
private BuildingInstr DefaultRegressionController(BuildingState buildingState)
{
BuildingInstr instructions = new BuildingInstr
{
CurrentIsBetter = IsBetter(buildingState.BinaryOutput,
buildingState.CurrNetwork,
buildingState.BestNetwork
),
StopProcess = (BinaryOutput &&
buildingState.BestNetwork.TrainingBinErrorStat.TotalErrStat.Sum == 0 &&
buildingState.BestNetwork.TestingBinErrorStat.TotalErrStat.Sum == 0 &&
buildingState.CurrNetwork.CombinedPrecisionError > buildingState.BestNetwork.CombinedPrecisionError
)
};
return(instructions);
}
/// <summary>
/// Builds trained network
/// </summary>
/// <returns>Trained network</returns>
public TrainedNetwork Build()
{
TrainedNetwork bestNetwork = null;
int lastImprovementEpoch = 0;
double lastImprovementCombinedPrecisionError = 0d;
double lastImprovementCombinedBinaryError = 0d;
//Create network and trainer
NonRecurrentNetUtils.CreateNetworkAndTrainer(_networkSettings,
_trainingBundle.InputVectorCollection,
_trainingBundle.OutputVectorCollection,
_rand,
out INonRecurrentNetwork net,
out INonRecurrentNetworkTrainer trainer
);
//Iterate training cycles
while (trainer.Iteration())
{
//Compute current error statistics after training iteration
//Training data part
TrainedNetwork currNetwork = new TrainedNetwork
{
NetworkName = _networkName,
BinBorder = _binBorder,
Network = net,
TrainerInfoMessage = trainer.InfoMessage,
TrainingErrorStat = net.ComputeBatchErrorStat(_trainingBundle.InputVectorCollection, _trainingBundle.OutputVectorCollection, out List <double[]> trainingComputedOutputsCollection)
};
if (BinaryOutput)
{
currNetwork.TrainingBinErrorStat = new BinErrStat(_binBorder, trainingComputedOutputsCollection, _trainingBundle.OutputVectorCollection);
currNetwork.CombinedBinaryError = currNetwork.TrainingBinErrorStat.TotalErrStat.Sum;
}
currNetwork.CombinedPrecisionError = currNetwork.TrainingErrorStat.ArithAvg;
//Testing data part
currNetwork.TestingErrorStat = net.ComputeBatchErrorStat(_testingBundle.InputVectorCollection, _testingBundle.OutputVectorCollection, out List <double[]> testingComputedOutputsCollection);
currNetwork.CombinedPrecisionError = Math.Max(currNetwork.CombinedPrecisionError, currNetwork.TestingErrorStat.ArithAvg);
if (BinaryOutput)
{
currNetwork.TestingBinErrorStat = new BinErrStat(_binBorder, testingComputedOutputsCollection, _testingBundle.OutputVectorCollection);
currNetwork.CombinedBinaryError = Math.Max(currNetwork.CombinedBinaryError, currNetwork.TestingBinErrorStat.TotalErrStat.Sum);
}
//Expected precision accuracy
currNetwork.ExpectedPrecisionAccuracy = Math.Min((1d - (currNetwork.TrainingErrorStat.ArithAvg / currNetwork.Network.OutputRange.Span)), (1d - (currNetwork.TestingErrorStat.ArithAvg / currNetwork.Network.OutputRange.Span)));
//Expected binary accuracy
if (BinaryOutput)
{
currNetwork.ExpectedBinaryAccuracy = Math.Min((1d - currNetwork.TrainingBinErrorStat.TotalErrStat.ArithAvg), (1d - currNetwork.TestingBinErrorStat.TotalErrStat.ArithAvg));
}
else
{
currNetwork.ExpectedBinaryAccuracy = double.NaN;
}
//Restart lastImprovementEpoch when new trainer's attempt started
if (trainer.AttemptEpoch == 1)
{
lastImprovementEpoch = trainer.AttemptEpoch;
lastImprovementCombinedPrecisionError = currNetwork.CombinedPrecisionError;
lastImprovementCombinedBinaryError = currNetwork.CombinedBinaryError;
}
//First initialization of the best network
bestNetwork = bestNetwork ?? currNetwork.DeepClone();
//RegrState instance
BuildingState regrState = new BuildingState(_networkName, _binBorder, _foldNum, _numOfFolds, _foldNetworkNum, _numOfFoldNetworks, trainer.Attempt, trainer.MaxAttempt, trainer.AttemptEpoch, trainer.MaxAttemptEpoch, currNetwork, bestNetwork, lastImprovementEpoch);
//Call controller
BuildingInstr instructions = _controller(regrState);
//Better?
if (instructions.CurrentIsBetter)
{
//Adopt current regression unit as a best one
bestNetwork = currNetwork.DeepClone();
regrState.BestNetwork = bestNetwork;
lastImprovementEpoch = trainer.AttemptEpoch;
lastImprovementCombinedPrecisionError = currNetwork.CombinedPrecisionError;
lastImprovementCombinedBinaryError = currNetwork.CombinedBinaryError;
}
if (currNetwork.CombinedBinaryError < lastImprovementCombinedBinaryError || currNetwork.CombinedPrecisionError < lastImprovementCombinedPrecisionError)
{
lastImprovementEpoch = trainer.AttemptEpoch;
lastImprovementCombinedPrecisionError = currNetwork.CombinedPrecisionError;
lastImprovementCombinedBinaryError = currNetwork.CombinedBinaryError;
}
//Raise notification event
RegressionEpochDone(regrState, instructions.CurrentIsBetter);
//Process instructions
if (instructions.StopProcess)
{
break;
}
else if (instructions.StopCurrentAttempt)
{
if (!trainer.NextAttempt())
{
break;
}
}
}//while (iteration)
//Create statistics of the best network weights
bestNetwork.OutputWeightsStat = bestNetwork.Network.ComputeWeightsStat();
return(bestNetwork);
}
