public void Start(int?noOfSubsets = null, int?testingSubsetIndex = null)
{
var inputVariables = GetNeuralNetworkInputVariables(_testingModel.NeuralNetworkPath);
_excelService = new ExcelService(_testingModel.DataFilePath,
_testingModel.Sheet,
_testingModel.IdColumn,
inputVariables,
_testingModel.OutputVariables);
_worker = new BackgroundWorker();
_worker.DoWork += worker_DoWork;
if (_progressChangedEventHandler != null)
{
_worker.ProgressChanged += _progressChangedEventHandler;
}
_worker.WorkerReportsProgress = true;
_worker.WorkerSupportsCancellation = true;
_worker.RunWorkerCompleted += worker_RunWorkerCompleted;
var workerInfo = new WorkerInfo
{
NoOfSubsets = noOfSubsets,
TestingSubsetIndex = testingSubsetIndex,
TestingRows = GetTestingRows(noOfSubsets, testingSubsetIndex)
};
_worker.RunWorkerAsync(workerInfo);
}
public void Start(int?noOfSubsets = null, int?testingSubsetIndex = null)
{
_excelService = new ExcelService(_trainingModel.TrainingData.FilePath,
_trainingModel.TrainingData.Sheet,
_trainingModel.TrainingData.IdColumn,
_trainingModel.TrainingData.InputVariables,
_trainingModel.TrainingData.OutputVariables);
_worker = new BackgroundWorker();
_worker.DoWork += worker_DoWork;
if (_progressChangedEventHandler != null)
{
_worker.ProgressChanged += _progressChangedEventHandler;
}
_worker.WorkerReportsProgress = true;
_worker.WorkerSupportsCancellation = true;
_worker.RunWorkerCompleted += worker_RunWorkerCompleted;
var workerInfo = new WorkerInfo
{
LearningRate = _trainingModel.LearningRate,
Epochs = _trainingModel.NoOfEpochs,
Error = _trainingModel.MinError,
TrainingRows = GetTrainingRows(noOfSubsets, testingSubsetIndex),
NoOfSubsets = noOfSubsets,
TestingSubsetIndex = testingSubsetIndex
};
_worker.RunWorkerAsync(workerInfo);
}