private MNCDContext InitCtx(string databaseName)
{
var options = new DbContextOptionsBuilder <MNCDContext>()
.UseInMemoryDatabase(databaseName)
.Options;
var ctx = new MNCDContext(options);
ctx.DataSets.RemoveRange(ctx.DataSets);
ctx.SaveChanges();
var edgeList = "0 0 1 0 1";
var dataSet = new NetworkDataSet
{
Content = edgeList,
FileType = FileType.EdgeList,
EdgeList = edgeList,
Name = "Data set 1",
Hash = _hasher.GetHashFor(edgeList),
ActorNames = new List <string> {
"A0", "A1"
},
EdgeCount = 1,
LayerCount = 1,
LayerNames = new List <string> {
"L1"
},
NodeCount = 2,
};
ctx.DataSets.Add(dataSet);
ctx.SaveChanges();
return(new MNCDContext(options));
}
/// <summary>
/// Выбрать файл для тестирования.
/// </summary>
/// <param name="sender"></param>
/// <param name="e"></param>
private void Btn_ChooseTestingFile_Click(object sender, EventArgs e)
{
try
{
using (var openFileDialog = new OpenFileDialog())
{
openFileDialog.Filter = "Excel Files|*.xls;*.xlsx;*.xlsm";
openFileDialog.InitialDirectory = CommonUtils.ResourcesDirectory;
if (openFileDialog.ShowDialog() == DialogResult.OK)
{
using (new CursorHandler())
{
var excelFilePath = Path.Combine(CommonUtils.ResourcesDirectory, openFileDialog.FileName);
tbTestingFile.Text = excelFilePath;
var dataTable = _reader.ReadFromFile(excelFilePath);
_testingDataSet = _converter.Convert(dataTable);
_interfaceMediator.DrawDataIntoGrid(_testingDataSet.Entities, _neuralNetwork.InputAttributes, dgvTesingData);
}
}
}
}
catch (Exception ex)
{
MessageBox.Show(ex.Message, "Ошибка", MessageBoxButtons.OK, MessageBoxIcon.Error);
}
}
public static Network ReadDataSet(NetworkDataSet dataSet)
{
return(dataSet.FileType switch
{
FileType.MPX => new MpxReader().FromString(dataSet.Content),
FileType.EdgeList => new EdgeListReader().FromString(dataSet.Content),
_ => throw new ApplicationException("Unsupported file type."),
});
/// <summary>
/// Загрузить данные из файла для обучения сети.
/// </summary>
/// <param name="sender"></param>
/// <param name="e"></param>
private void Btn_ChooseLearningFile_Click(object sender, EventArgs e)
{
try
{
using (var openFileDialog = new OpenFileDialog())
{
openFileDialog.Filter = "Excel Files|*.xls;*.xlsx;*.xlsm";
openFileDialog.InitialDirectory = CommonUtils.ResourcesDirectory;
if (openFileDialog.ShowDialog() == DialogResult.OK)
{
using (new CursorHandler())
{
var excelFilePath = Path.Combine(CommonUtils.ResourcesDirectory, openFileDialog.FileName);
tbLearningFile.Text = excelFilePath;
var learningDataTable = _reader.ReadFromFile(excelFilePath);
_learningDataSet = _converter.Convert(learningDataTable);
_neuralNetwork.InputAttributes = _learningDataSet.Attributes
.Select(a => new InputAttributeBase
{
InputAttributeNumber = a.OrderNumber,
Name = a.Name
})
.ToList();
_interfaceMediator.DrawDataIntoGrid(_learningDataSet.Entities, _neuralNetwork.InputAttributes, dgvInputLearningData);
RecalcMinMaxValues(_learningDataSet);
RecalcMinMaxValues(_testingDataSet);
var normalizedData = _learningDataSet.Entities
.Select(l => new NetworkDataEntity()
{
Name = l.Name,
OrderNumber = l.OrderNumber,
AttributeValues = l.AttributeValues.Select(a => new NetworkEntityAttributeValue
{
Attribute = a.Attribute,
Value = a.GetNormalizedValue(_neuralNetwork.NormalizationType)
}).ToList()
})
.ToList();
_interfaceMediator.DrawDataIntoGrid(normalizedData, _neuralNetwork.InputAttributes, dgvNormalizedLearningData);
}
}
}
}
catch (Exception ex)
{
MessageBox.Show(ex.Message, "Ошибка", MessageBoxButtons.OK, MessageBoxIcon.Error);
}
}
private async Task <Visualization> GetVisualizationFromDataSet(
NetworkDataSet dataSet,
VisualizationType type)
{
if (type == VisualizationType.MultiLayer_Diagonal)
{
var diagonal = dataSet.Visualizations
.FirstOrDefault(v => v.Type == VisualizationType.MultiLayer_Diagonal);
if (diagonal is null)
{
var request = new MultilayerRequest
{
EdgeList = dataSet.EdgeList,
Type = VisualizationType.MultiLayer_Diagonal
};
diagonal = await VisualizeMultilayer(request);
dataSet.Visualizations.Add(diagonal);
await _ctx.SaveChangesAsync();
}
return(diagonal);
}
else if (type == VisualizationType.MultiLayer_Slices)
{
var slices = dataSet.Visualizations
.FirstOrDefault(v => v.Type == VisualizationType.MultiLayer_Slices);
if (slices is null)
{
var request = new MultilayerRequest
{
EdgeList = dataSet.EdgeList,
Type = VisualizationType.MultiLayer_Slices
};
slices = await VisualizeMultilayer(request);
dataSet.Visualizations.Add(slices);
await _ctx.SaveChangesAsync();
}
return(slices);
}
throw new NotSupportedException($"Visualization type '{type}' is not supported for datasets.");
}
/// <summary>
/// Пересчитать минимальное и максимальое значение.
/// </summary>
/// <param name="dataSet">Данные.</param>
private void RecalcMinMaxValues(NetworkDataSet dataSet)
{
if (!(dataSet?.Attributes?.Any() ?? false))
{
return;
}
foreach (var attribute in dataSet.Attributes)
{
attribute.Max = dataSet.Entities
.Max(e => e.AttributeValues.First(a => a.Attribute.OrderNumber == attribute.OrderNumber).Value);
attribute.Min = dataSet.Entities
.Min(e => e.AttributeValues.First(a => a.Attribute.OrderNumber == attribute.OrderNumber).Value);
dataSet.Entities
.ForEach(entity => entity.AttributeValues.First(a => a.Attribute.OrderNumber == attribute.OrderNumber).Attribute = attribute);
}
}
/// <summary>
/// Изменить кластер - повторная кластеризация.
/// </summary>
/// <param name="sender"></param>
/// <param name="e"></param>
private void Btn_EditCluster_Click(object sender, EventArgs e)
{
try
{
var isEdit = _networkBase?.NetworkId > 0;
if (!isEdit)
{
throw new Exception("Невозможно изменить кластер у несохраненной нейронной сети.");
}
if (tvClusters.SelectedNode?.Tag == null || string.IsNullOrEmpty(tvClusters.SelectedNode.Tag.ToString()))
{
throw new Exception("Выберите кластер для изменения.");
}
var cluster = tvClusters.SelectedNode.Tag as NetworkCluster;
var neuron = _neuralNetwork.Neurons.First(n => n.NeuronNumber == cluster.Number);
if (neuron.NeuronId <= 0)
{
throw new Exception("Невозможно изменить кластер у несохраненной нейронной сети.");
}
var dataSet = new NetworkDataSet()
{
Entities = _testingDataSet.Entities
.Where(t => cluster.Entities.Select(c => c.OrderNumber).Contains(t.OrderNumber))
.ToList(),
Attributes = _testingDataSet.Attributes
};
var mainForm = IoC.Instance.Resolve<MainForm>(
new IoC.NinjectArgument("networkId", cluster.NetworkId),
new IoC.NinjectArgument("parentNeuronId", neuron.NeuronId),
new IoC.NinjectArgument("parentNeuronDataSet", dataSet.Clone() as NetworkDataSet));
if (mainForm.ShowDialog() == DialogResult.OK)
{
// Обновить кластеры
InitializeExistingNetwork(_networkBase.NetworkId);
Btn_Test_Click();
}
}
catch (Exception ex)
{
MessageBox.Show(ex.Message, "Ошибка", MessageBoxButtons.OK, MessageBoxIcon.Error);
}
}
public async Task <NetworkDataSet> AddDataSet(string name, string content, FileType fileType)
{
if (string.IsNullOrEmpty(name))
{
throw new ArgumentException("Name must not be empty.", nameof(name));
}
if (string.IsNullOrEmpty(content))
{
throw new ArgumentException("Content must not be empty.", nameof(content));
}
var hash = _hashService.GetHashFor(content);
if (await ExistsDataSet(hash))
{
throw new NetworkDataSetExistsException($"Data set already exists in the system.");
}
var network = ReadNetwork(content, fileType);
var edgeList = GetEdgeList(network);
var dataSet = new NetworkDataSet
{
Name = name,
Content = content,
EdgeList = edgeList,
FileType = fileType,
Hash = hash,
NodeCount = network.ActorCount,
EdgeCount = network.Layers.Sum(l => l.Edges.Count) + network.InterLayerEdges.Count,
LayerCount = network.LayerCount,
ActorNames = network.Actors.Select(a => a.Name).ToList(),
LayerNames = network.Layers.Select(l => l.Name).ToList()
};
await _ctx.DataSets.AddAsync(dataSet);
await _ctx.SaveChangesAsync();
return(dataSet);
}
/// <summary>
/// Обучить нейронную сеть.
/// </summary>
/// <param name="inputDataSet">Набор входных данных для обучения.</param>
/// <param name="neuronsCount">Количество нейронов.</param>
/// <param name="iterationsCount">Количество эпох.</param>
public virtual void Study(NetworkDataSet inputDataSet, int neuronsCount, int iterationsCount)
{
GenerateNeurons(inputDataSet?.Attributes, neuronsCount);
for (int iteration = 0; iteration < iterationsCount; iteration++)
{
var randomNumber = _random.Next(0, inputDataSet.Entities.Count - 1);
var randomEntity = inputDataSet.Entities[randomNumber];
var attributeValues = randomEntity.AttributeValues
.Select(e => new InputAttributeValue
{
InputAttributeNumber = e.Attribute.OrderNumber,
Value = e.GetNormalizedValue(NormalizationType)
})
.ToList();
var totalError = StudyInputEntity(attributeValues, iteration, iterationsCount);
IterationCompleted?.Invoke(this, null);
}
}
public MainForm(
INetworkService networkService,
IReader reader,
AbstractNetwork network,
int? networkId = null,
int? parentNeuronId = null,
NetworkDataSet parentNeuronDataSet = null)
{
InitializeComponent();
_reader = reader;
_networkService = networkService;
_converter = new NetworkDataSetConverter();
_interfaceMediator = new InterfaceHelpers();
_neuralNetwork = network;
_neuralNetwork.IterationCompleted += OnNetworkWeightsChanged;
// При редактировании существующей сети скрыть обучение
var isEdit = networkId > 0;
if (isEdit)
{
InitializeExistingNetwork(networkId ?? 0);
}
else
{
_networkBase = new NetworkBase
{
ParentNeuronId = parentNeuronId
};
}
if (parentNeuronId > 0)
{
if (parentNeuronDataSet == null || !(parentNeuronDataSet?.Entities?.Any() ?? false))
{
throw new Exception("Не задан набор обучающих данных для повторной кластеризации.");
}
_learningDataSet = parentNeuronDataSet;
_testingDataSet = parentNeuronDataSet;
btnChooseLearningFile.Enabled = false;
btnChooseTestingFile.Enabled = false;
RecalcMinMaxValues(_learningDataSet);
RecalcMinMaxValues(_testingDataSet);
_neuralNetwork.InputAttributes = _learningDataSet.Attributes
.Select(a => new InputAttributeBase
{
InputAttributeNumber = a.OrderNumber,
Name = a.Name
})
.ToList();
_interfaceMediator.DrawDataIntoGrid(_learningDataSet.Entities, _neuralNetwork.InputAttributes, dgvInputLearningData);
var normalizedData = _learningDataSet.Entities
.Select(l => new NetworkDataEntity()
{
Name = l.Name,
OrderNumber = l.OrderNumber,
AttributeValues = l.AttributeValues.Select(a => new NetworkEntityAttributeValue
{
Attribute = a.Attribute,
Value = a.GetNormalizedValue(_neuralNetwork.NormalizationType)
}).ToList()
})
.ToList();
_interfaceMediator.DrawDataIntoGrid(normalizedData, _neuralNetwork.InputAttributes, dgvNormalizedLearningData);
_interfaceMediator.DrawDataIntoGrid(_testingDataSet.Entities, _neuralNetwork.InputAttributes, dgvTesingData);
}
btnEditCluster.Enabled = isEdit;
SetSaveButtonEnabled();
}
