public void GenerateNetwork_ShouldReturnNoTNull()
{
var bots = Builder <Bot> .CreateListOfSize(10)
.TheFirst(1)
.With(x => x.Id = 1)
.All()
.With(x => x.Id > 0 && x.IsDeleted == false)
.Build();
var networks = Builder <NeuralNetworkData> .CreateListOfSize(10)
.All()
.With(x => x.Id > 0)
.Build();
this._unitOfWork = new Mock <IUnitOfWork>();
this._unitOfWork.Setup(x => x.NeuralNetworkDataRepository.All())
.Returns(networks.AsEnumerable());
this._unitOfWork.Setup(x => x.BotRepository.All())
.Returns(bots.AsEnumerable());
this._unitOfWork.Setup(x => x.SaveChanges())
.Returns(1);
this._service = new NeuralNetworkService(this._unitOfWork.Object);
var result = this._service.GenerateNetwork(1, 1, false);
Assert.IsNotNull(result);
}
public void RegisterNewNetwork_ShouldThrowCorrectException_IfNoDataFound()
{
var bots = Builder <Bot> .CreateListOfSize(10)
.TheFirst(1)
.With(x => x.Id = 1)
.All()
.With(x => x.Id > 0 && x.Id < 99 && x.IsDeleted == false)
.Build();
var networks = Builder <NeuralNetworkData> .CreateListOfSize(10)
.All()
.With(x => x.Id > 0)
.Build();
this._unitOfWork = new Mock <IUnitOfWork>();
this._unitOfWork.Setup(x => x.NeuralNetworkDataRepository.All())
.Returns(networks.AsEnumerable());
this._unitOfWork.Setup(x => x.BotRepository.All())
.Returns(bots.AsEnumerable());
this._unitOfWork.Setup(x => x.SaveChanges())
.Returns(1);
this._service = new NeuralNetworkService(this._unitOfWork.Object);
Assert.Throws <NotFoundException>(() => this._service.RegisterNewNetwork(new NeuralNetwork(1, 1, false), 100, "test", NeuralNetworkType.Test));
}
private void OnNetworkTrainingComplete(object sender, ProcessResult result)
{
trainingInProgress = false;
if (result == ProcessResult.Success)
{
neuralNetworkService = (NeuralNetworkService)sender;
UpdatePredictionGrid(inputsGrid);
}
}
private void Window_Loaded(object sender, RoutedEventArgs e)
{
StartupFolder = new DirectoryInfo(Environment.CurrentDirectory).Parent.Parent.FullName;
Capture = new VideoCapture(1);
NeuralNetworks = NeuralNetworkService.GetNeuralNetworks();
cmbNeuralNetwork.ItemsSource = NeuralNetworks;
cmbNeuralNetwork.DisplayMemberPath = "Name";
SetDefaultImage();
DeleteTemporalImages();
}
private void OnClickStart(object sender, EventArgs e)
{
if (!trainingInProgress)
{
double learningRate;
double learningDataRatio;
if (!Double.TryParse(textBoxLearningRate.Text, out learningRate))
{
ShowError("Could not parse learning rate value.");
return;
}
if (!Double.TryParse(textBoxLearningDataRatio.Text, out learningDataRatio))
{
ShowError("Could not parse learning data ratio value.");
return;
}
if (learningDataRatio >= 1d)
{
ShowError("Learning ratio must be less than 1.");
return;
}
var intervals = outputIntervalsBindingSource.Cast <Limits>();
if (!ValidateLimits(intervals))
{
return;
}
var neuralNetworkService = new NeuralNetworkService(
columnSettings: columnsBindingSource.List.Cast <ColumnSetting>(),
trainingLogger: trainingLogger,
predictingLogger: predictionLogger,
outputIntervals: intervals,
learningRate: learningRate,
learningDataRatio: learningDataRatio);
neuralNetworkService.TrainingCompleted += OnNetworkTrainingComplete;
neuralNetworkService.PredictionCompleted += OnPredictionComplete;
var thread = new Thread(neuralNetworkService.Start);
thread.Start();
trainingInProgress = true;
}
}
private void CmbNeuralNetwork_SelectionChanged(object sender, SelectionChangedEventArgs e)
{
cmbPredictedObject.Items.Clear();
var nn = cmbNeuralNetwork.SelectedValue;
if (nn != null)
{
btnCreateFile.IsEnabled = true;
TrainingConfig = TrainingService.GetTrainingConfig(((NeuralNetworkDto)nn).Id);
ImageProcessingConfig = NeuralNetworkService.GetImageProcessingActiveConfig(((NeuralNetworkDto)nn).Id);
cmbPredictedObject.ItemsSource = TrainingConfig.PredictedObjects;
cmbPredictedObject.DisplayMemberPath = "ObjectName";
}
else
{
btnCreateFile.IsEnabled = false;
}
}
public void Start()
{
using (var uw = new UnitOfWork())
{
var nnService = new NeuralNetworkService(uw);
double[][] input = new double[][]
{
new double[] { 1, 1, 1, 0, 0, 0, 0, 0, 0 },
new double[] { 0, 0, 0, 1, 1, 1, 0, 0, 0 },
new double[] { 1, 0, 0, 0, 0, 0, 1, 1, 0 },
new double[] { 0, 1, 1, 0, 0, 0, 0, 1, 1 },
};
double[][] output = new double[][]
{
new double[] { 1, 0 },
new double[] { 1, 0 },
new double[] { 0, 1 },
new double[] { 0, 1 }
};
var network = nnService.GenerateNetwork(input[0].Length, output[0].Length, true);
Console.ReadLine();
nnService.TrainNetwork(network, input, output, 0.001);
//var network = nnService.LoadNeuralNetwork(1);
nnService.TestNetwork(network, input, output);
//using (var fileStream = File.Create("test1.txt"))
//{
// network.Save(fileStream);
//}
//var id = nnService.RegisterNewNetwork(network, 1, "admin", NeuralNetworkType.Test);
//Console.WriteLine(id);
//var bot = uw.BotRepository.FindFirstByFilter(new BotFilter() { Id = 1 });
//Console.WriteLine(bot.NeuralNetworkDatas.Count);
}
}
public void RegisterNewNetwork_ShouldReturnCorrectDataType()
{
var bot = Builder <Bot> .CreateNew()
.With(x => x.Id == 1 && x.IsDeleted == false)
.Build();
this._unitOfWork = new Mock <IUnitOfWork>();
this._unitOfWork.Setup(x => x.BotRepository.FindById(It.IsAny <long>(), false))
.Returns(bot);
this._unitOfWork.Setup(x => x.NeuralNetworkDataRepository.Add(It.IsAny <NeuralNetworkData>()));
this._unitOfWork.Setup(x => x.SaveChanges())
.Returns(1);
this._service = new NeuralNetworkService(this._unitOfWork.Object);
var result = this._service.RegisterNewNetwork(new NeuralNetwork(1, 1, false), 1, "test", NeuralNetworkType.Test);
Assert.IsNotNull(result);
}
public void Setup()
{
neuralNetworkService = new NeuralNetworkService();
}
