public KohonenInfoViewModel(models.Task task, models.TaskSolver solver)
{
this.task = task;
taskSolver = solver;
KohonenNNTopology t = solver.Description as KohonenNNTopology;
Inputs = Convert.ToInt32(t.GetInputsCount());
Outputs = Convert.ToInt32(t.GetOutputsCount());
Width = t.GetLayerWidth();
Height = t.GetLayerHeight();
Metric = t.GetMetric();
ClassInitializer = t.GetClassInitializer();
ClassEps = t.GetClassEps();
Name = solver.Name;
TaskName = task.Name;
TileGraphs = new ObservableCollection <TileGraphViewModel>();
SizeOfTile = 10;
paramNames = new string[] { };
data = new List <Tuple <int2d, double> >[] { };
addHandler = new ActionHandler(
() =>
{
TileGraphs.Add(new TileGraphViewModel(Width, Height, SizeOfTile)
{
DeleteCallback = DeleteGraph,
ParameterNames = paramNames,
Data = data
});
},
(e) => true);
}
public ArchiveTaskSolver(models.TaskSolver solv)
{
this.ID = solv.ID;
this.Name = solv.Name;
this.TypeName = solv.TypeName;
this.Description = solv.Description;
}
public PerceptronInfoViewModel(models.Task task, models.TaskSolver solver)
{
TaskName = task.Name;
Name = solver.Name;
PerceptronTopology topology = solver.Description as PerceptronTopology;
Layers = new Layer[topology.GetLayersCount() - 1];
var neurons = topology.GetNeuronsInLayersCount();
var delays = topology.HasLayersDelayWeight();
var afs = topology.GetActivationFunctionsNames();
CountInputNeurons = Convert.ToInt32(topology.GetInputsCount());
for (int i = 0; i < Layers.Length - 1; i++)
{
Layers[i] = new Layer
{
Name = String.Format("{0} слой", i + 1),
ActivateFunction = afs[i],
NeuronsCount = neurons[i + 1],
HasW0 = delays[i]
};
}
Layers[Layers.Length - 1] = new Layer
{
Name = "Выходной слой",
ActivateFunction = afs[Layers.Length - 1],
NeuronsCount = neurons[Layers.Length],
HasW0 = delays[Layers.Length - 1]
};
}
public DecisionTreeC4_5InfoViewModel(models.Task task, models.TaskSolver solver)
{
TaskName = task.Name;
Name = solver.Name;
TreeDescriptionC4_5 td = solver.Description as TreeDescriptionC4_5;
MaxTreeDepth = td.MaxDepth;
}
public SolveViewModel(models.Task task, models.TaskSolver solver)
{
TaskID = task.ID;
SolvingList = new ObservableCollection <SolvingInstance>();
SolverName = solver.Name;
TaskName = task.Name;
List <LearningInfo> learningList = new List <LearningInfo>();
List <models.LearnedSolver> learnedSolvers = models.LearnedSolver.learnedSolversOfTaskSolverID(solver.ID);
foreach (models.LearnedSolver learnedSolver in learnedSolvers)
{
models.Selection selection = (models.Selection)models.Selection.getById(learnedSolver.SelectionID, typeof(models.Selection));
List <models.LearningQuality> qualities = models.LearningQuality.qualitiesOfSolverId(learnedSolver.ID);
models.LearningScenario scenario = (models.LearningScenario)models.LearningScenario.getById(learnedSolver.LearningScenarioID, typeof(models.LearningScenario));
models.TaskTemplate template = (models.TaskTemplate)models.TaskTemplate.getById(selection.TaskTemplateID, typeof(models.TaskTemplate));
models.LearningQuality quality = (qualities != null && qualities.Count > 0) ? qualities[0] : null;
List <models.Parameter> parameters = models.Parameter.parametersOfTaskTemplateId(template.ID);
int outputParam = 0;
foreach (models.Parameter param in parameters)
{
if (param.IsOutput == 1)
{
outputParam = param.ID;
break;
}
}
learningList.Add(new LearningInfo
{
SelectionID = selection.ID,
ParameterID = outputParam,
SelectionName = selection.Name,
LearningScenarioName = scenario.Name,
PreprocessingName = template.Name,
TestMistake = (quality != null) ? quality.MistakeTest : 0,
TrainMistake = (quality != null) ? quality.MistakeTrain : 0,
ClosingError = Convert.ToSingle((quality != null) ? quality.ClosingError : 0),
TaskTemplate = template,
LearnedSolver = learnedSolver
});
}
LearningList = learningList.ToArray();
addHandler = new ActionHandler(() =>
{
SolvingList.Add(new SolvingInstance(this, this.SelectedLearning.TaskTemplate));
solveHandler.RaiseCanExecuteChanged();
saveHandler.RaiseCanExecuteChanged();
SelectedSolution = Solutions[0];
NotifyPropertyChanged("SelectedSolution");
NotifyPropertyChanged("Solutions");
}, e => SelectedLearning != null);
solveHandler = new ActionHandler(Solve, e => SolvingList.Count > 0);
saveHandler = new ActionHandler(saveSolutions, e => SolvingList.Count > 0);
SelectedSolution = Solutions[0];
}
public SolverLeaf(models.Task task, models.TaskSolver solver, string solverType,
TaskTreeViewModel vm) : base(solver.Name)
{
parentTask = task;
this.solverType = solverType;
this.solver = solver;
showSolverInfoHandler = new ActionHandler(() => vm.SendRequestCreateView(CreateSolverInfoViewModel()), e => true);
showSolveDialogHandler = new ActionHandler(() => vm.SendRequestCreateView(new SolveViewModel(parentTask, solver)), e => true);
showLearnDialogHandler = new ActionHandler(() => vm.SendRequestCreateView(new LearnSolverViewModel(parentTask, solver)), e => true);
}
public ConvNNInfoViewModel(models.Task task, models.TaskSolver solver)
{
TaskName = task.Name;
Name = solver.Name;
var t = solver.Description as ConvNNTopology;
InputDimention = String.Format("{0}x{1}x{2}", t.GetInputWidth(), t.GetInputHeigth(), t.GetInputDepth());
ilayers = t.GetLayers().ToArray();
var volumes = t.GetVolumeDimentions();
Layers = new ConvNNLayer[ilayers.Length];
for (int i = 0; i < ilayers.Length; i++)
{
if (ilayers[i] is FullyConnectedLayer)
{
Layers[i] = new ConvNNLayer
{
Number = i + 1,
Type = "FC",
Width = volumes[i].Width,
Height = volumes[i].Heigth,
Depth = volumes[i].Depth
};
}
else if (ilayers[i] is ConvolutionLayer)
{
Layers[i] = new ConvNNLayer
{
Number = i + 1,
Type = "Conv",
Width = volumes[i].Width,
Height = volumes[i].Heigth,
Depth = volumes[i].Depth
};
}
else if (ilayers[i] is PoolingLayer)
{
Layers[i] = new ConvNNLayer
{
Number = i + 1,
Type = "Pool",
Width = volumes[i].Width,
Height = volumes[i].Heigth,
Depth = volumes[i].Depth
};
}
}
}
public WardNetInfoViewModel(models.Task task, models.TaskSolver solver)
{
TaskName = task.Name;
Name = solver.Name;
WardNNTopology t = solver.Description as WardNNTopology;
var input_layer = t.GetInputLayer();
var layers = t.GetLayers();
int gsize = 0;
int adsize = (input_layer.ForwardConnection > 0) ? 1 : 0;
for (int i = 0; i < layers.Count; i++)
{
gsize += layers[i].Groups.Count;
if (layers[i].ForwardConnection > 0)
{
adsize++;
}
}
CountInputNeurons = input_layer.NeuronsCount;
Groups = new WardNetGroup[gsize];
AdditionalConns = new AdditionalConnection[adsize];
int gindex = 0;
int last_layer_index = layers.Count - 1;
for (int i = 0; i < last_layer_index; i++)
{
for (int j = 0; j < layers[i].Groups.Count; j++)
{
Groups[gindex++] = new WardNetGroup
{
LayerName = String.Format("{0} слой", i + 1),
GroupName = String.Format("{0} группа", j + 1),
NeuronsCount = layers[i].Groups[j].NeuronsCount,
ActivateFunction = layers[i].Groups[j].ActivationFunction,
HasW0 = layers[i].Groups[j].HasDelay
};
}
}
for (int j = 0; j < layers[last_layer_index].Groups.Count; j++)
{
Groups[gindex++] = new WardNetGroup
{
LayerName = "Выходной слой",
GroupName = String.Format("{0} группа", j + 1),
NeuronsCount = layers[last_layer_index].Groups[j].NeuronsCount,
ActivateFunction = layers[last_layer_index].Groups[j].ActivationFunction,
HasW0 = layers[last_layer_index].Groups[j].HasDelay
};
}
int adindex = 0;
if (input_layer.ForwardConnection > 0)
{
AdditionalConns[adindex++] = new AdditionalConnection
{
Start = String.Format("Входной слой"),
End = String.Format("{0} слой", input_layer.ForwardConnection + 1)
};
}
for (int i = 0; i < layers.Count; i++)
{
if (layers[i].ForwardConnection > 0)
{
AdditionalConns[adindex++] = new AdditionalConnection
{
Start = String.Format("{0} слой", i + 1),
End = String.Format("{0} слой", i + 1 + layers[i].ForwardConnection + 1)
};
}
}
}