public ActionResult CheckData()
{
Models.ProcessedDataModels processedData = new Models.ProcessedDataModels();
string path = TempData["FilePath"] as string;
int trainPercent = ((Models.InputDataModels)TempData["form"]).trainPercent;
Loader loader = new Loader(path, trainPercent, ((Models.InputDataModels)TempData["form"]).normData);
Data trainData = loader.GenerateTrainSet();
Data testData = loader.GenerateTestSet();
Topology top = new Topology();
top.SetInputs(trainData.GetNumFeatures());
int numClasses = trainData.GetNumClasses();
testData.GetNumClasses();
//if(numClasses < 2)
//{
// throw new InvalidDataException();
//} else if (numClasses == 2)
//{
top.SetOutputs(1);
//} else
//{
// top.SetOutputs(numClasses);
//}
int end = ((Models.InputDataModels)TempData["form"]).numHidden;
ViewBag.NumItems = end;
for (int i = 0; i < end; ++i)
{
ViewData["Item " + i] = TempData["Item " + i];
top.AddHidden(Convert.ToInt32(TempData["Item " + i]));
}
ViewBag.gamma = ((Models.InputDataModels)TempData["form"]).gamma;
processedData.gamma = ((Models.InputDataModels)TempData["form"]).gamma;
ViewBag.epsilon = ((Models.InputDataModels)TempData["form"]).epsilon;
processedData.epsilon = ((Models.InputDataModels)TempData["form"]).epsilon;
ViewBag.momentum = ((Models.InputDataModels)TempData["form"]).momentum;
processedData.momentum = ((Models.InputDataModels)TempData["form"]).momentum;
ViewBag.trPerc = ((Models.InputDataModels)TempData["form"]).trainPercent;
ViewBag.tsPerc = 100 - (int)((Models.InputDataModels)TempData["form"]).trainPercent;
ViewBag.NumIter = ((Models.InputDataModels)TempData["form"]).numIter;
processedData.maxIter = ((Models.InputDataModels)TempData["form"]).numIter;
ViewBag.numTrain = trainData.GetNumData();
ViewBag.numTest = testData.GetNumData();
Network net = new Network(top, true);
net.SetTrain(trainData);
net.SetTest(testData);
processedData.NeuralNet = net;
TempData["data"] = processedData;
return(View());
}
