//reset the ANN
public void resetNetwork() {
if (_bat.OpMode == NeuralNetworkDataAdapter.OperationMode.Learn) {
_netI = new BatchNetworkTrainer(_bat.Conditioner, (TrainingExample[])_bat.Instances);
_netI.finishedProcessingEvent += finishedProcessing;
}
}
//new a network interface, classify or train
public void initNetworkAdapter(string fileName) {
//set current working directory to the directory where the descriptor file is
Directory.SetCurrentDirectory(fileName.Substring(0, fileName.LastIndexOf("\\")));
//these options will become further differentiated once more data source
//adaptors have bben implemented
BatchNetworkAdapter bna = new TabNetworkAdapter(fileName);
if (bna.OpMode == NeuralNetworkDataAdapter.OperationMode.Validate) {
_batchValidator = bna;
if(_bat != null)
_netI = new BatchNetworkTrainer(_bat.Conditioner, (TrainingExample[])_bat.Instances
, (TrainingExample[])_batchValidator.Instances);
} else {
_bat = bna;
if (_bat.OpMode == NeuralNetworkDataAdapter.OperationMode.Learn)
_netI = new BatchNetworkTrainer(_bat.Conditioner, (TrainingExample[])_bat.Instances);
else {
_netI = new BatchClassifier(_bat.Conditioner, _bat.Instances);
}
}
//subscribe to the finished processing event in the network interface
if (_netI != null && _netI.finishedProcessingEvent == null)
_netI.finishedProcessingEvent += finishedProcessing;
}
