public SeparationOfDataSet(ISolver solver, LearningScenario learnignScenario, float [][] x, float [] y)
{
ISolver = solver;
LS = learnignScenario;
InputData = x;
OutputData = y;
}
private void importScenario(ArchiveScenario archScenario)
{
LearningScenario scen = new LearningScenario(archScenario);
scen.save();
archScenario.ID = scen.ID;
}
public LearningScenarioManagerViewModel()
{
learningScenario = new LearningScenario();
listLearningScenarion = Entity.all(typeof(LearningScenario));
ScenarioList = new ObservableCollection <LearningScenarioViewModel>();
updateLearningScenariosInfo();
propertiesHandler = new ActionHandler(() => requestShowLearningScenario?.Invoke(SelectedScenario), e => SelectedScenario != null);
deleteHandler = new ActionHandler(() => removeLearningScenario(SelectedScenario), e => SelectedScenario != null);
createHandler = new ActionHandler(() => requestCreateLearningScenario?.Invoke(), e => true);
}
public void createScenario()
{
LearningScenario ls = new LearningScenario()
{
Name = this.Name,
LearningAlgorithmName = TeacherType,
LAParameters = AlgoParam,
SelectionParameters = SelectionType + "," + MixSeed + "," + SeparationParamValue
};
ls.save();
ID = ls.ID;
OnClose?.Invoke(this, null);
}
public PreprocessingLearnViewModel(List <LearnedSolver> learnedSolvers)
{
List <LearningStatistic> learningStatistic = new List <LearningStatistic>();
foreach (LearnedSolver learnedSolver in learnedSolvers)
{
LearningScenario learningScenario = (LearningScenario)LearningScenario.getById(learnedSolver.LearningScenarioID, typeof(LearningScenario));
learningStatistic.Add(new LearningStatistic(learnedSolver)
{
LearningScenarioName = learningScenario.Name
});
}
LearningStatistics = learningStatistic.ToArray();
}
private static void Predict()
{
var matches = JsonDb.GetMatches();
ILearningScenario <Match> scenario = new LearningScenario <Match>();
scenario.MaxIterations = 1000;
scenario.MaxError = 0.1;
scenario.InputParams = new List <IParamDefinition <Match> >();
scenario.InputParams.Add(MatchParams.GetLastFiveHomeMatches(matches));
scenario.InputParams.Add(MatchParams.GetLastFiveAwayMatches(matches));
scenario.OutputParams = new List <IParamDefinition <Match> >();
scenario.OutputParams.Add(MatchParams.GetResultValue(matches));
var network = Training.Train(scenario, matches);
Prediction.Compute(network, scenario, matches);
}
private void learnSolver()
{
foreach (LearningModel learningModel in LearningList)
{
TaskTemplate taskTemplate = (TaskTemplate)TaskTemplate.where (new Query("TaskTemplate").addTypeQuery(TypeQuery.select)
.addCondition("Name", "=", learningModel.SelectedPreprocessing), typeof(TaskTemplate))[0];
Selection selection = (Selection)Selection.where (new Query("Selection").addTypeQuery(TypeQuery.select)
.addCondition("TaskTemplateID", "=", taskTemplate.ID.ToString())
.addCondition("Name", "=", learningModel.SelectedSelection), typeof(Selection))[0];
int countRows = selection.RowCount;
LearningScenario learningScenario = (LearningScenario)LearningScenario.where (new Query("LearningScenario").addTypeQuery(TypeQuery.select)
.addCondition("Name", "=", learningModel.SelectedScenario), typeof(LearningScenario))[0];
List <Entity> selectionRows = SelectionRow.where (new Query("SelectionRow").addTypeQuery(TypeQuery.select)
.addCondition("SelectionID", "=", selection.ID.ToString()), typeof(SelectionRow));
List <Entity> parameters = dms.models.Parameter.where (new Query("Parameter").addTypeQuery(TypeQuery.select)
.addCondition("TaskTemplateID", "=", selection.TaskTemplateID.ToString()), typeof(dms.models.Parameter));
int stepRow = 0;
float[][] inputData = new float[countRows][];
float[] outputData = new float[countRows];
for (int i = 0; i < countRows; i++)
{
inputData[i] = new float[parameters.Count - 1];
}
int outputParam = 0;
for (int i = 0; i < parameters.Count; i++)
{
if (((models.Parameter)parameters[i]).IsOutput == 1)
{
outputParam = parameters[i].ID;
}
}
string[][] vals = Selection.valuesOfSelectionId(selection.ID);
float[][] fvals = new float[selection.RowCount][];
for (int i = 0; i < selection.RowCount; i++)
{
fvals[i] = new float[parameters.Count];
for (int j = 0; j < parameters.Count - 1; j++)
{
inputData[i][j] = float.Parse(vals[i][j].Replace(".", ","));
}
outputData[i] = float.Parse(vals[i][parameters.Count - 1].Replace(".", ","));
}
ISolver isolver = null;
if (Solver.Description is PerceptronTopology)
{
PerceptronTopology topology = Solver.Description as PerceptronTopology;
isolver = new PerceptronManaged(topology);
}
else if (Solver.Description is ConvNNTopology)
{
ConvNNTopology topology = Solver.Description as ConvNNTopology;
isolver = new ConvNNManaged(topology);
}
else if (Solver.Description is WardNNTopology)
{
WardNNTopology topology = Solver.Description as WardNNTopology;
isolver = new WardNNManaged(topology);
}
else if (Solver.Description is KohonenNNTopology)
{
KohonenNNTopology topology = Solver.Description as KohonenNNTopology;
isolver = new KohonenManaged(topology);
}
else if (Solver.Description is TreeDescription)
{
TreeDescription topology = Solver.Description as TreeDescription;
isolver = new DecisionTree(topology);
}
else if (Solver.Description is TreeDescriptionC4_5)
{
TreeDescriptionC4_5 topology = Solver.Description as TreeDescriptionC4_5;
isolver = new DecisionTreeC4_5(topology);
}
else
{
throw new EntryPointNotFoundException();
}
SeparationOfDataSet s = new SeparationOfDataSet(isolver, learningScenario, inputData, outputData);
LearnedSolver ls = new LearnedSolver()
{
SelectionID = selection.ID,
LearningScenarioID = learningScenario.ID,
TaskSolverID = Solver.ID,
Soul = s.separationAndLearn(selection.ID, outputParam)
};
ls.save();
LearningQuality lq = new LearningQuality()
{
LearnedSolverID = ls.ID,
MistakeTrain = Convert.ToInt32(s.MistakeTrain),
MistakeTest = Convert.ToInt32(s.MistakeTest),
ClosingError = s.ClosingError
};
lq.save();
}
OnClose?.Invoke(this, null);
}
private void learnSolver()
{
foreach (LearningModel learningModel in LearningList)
{
TaskTemplate taskTemplate = (TaskTemplate)TaskTemplate.where (new Query("TaskTemplate").addTypeQuery(TypeQuery.select)
.addCondition("Name", "=", learningModel.SelectedPreprocessing), typeof(TaskTemplate))[0];
Selection selection = (Selection)Selection.where (new Query("Selection").addTypeQuery(TypeQuery.select)
.addCondition("TaskTemplateID", "=", taskTemplate.ID.ToString())
.addCondition("Name", "=", learningModel.SelectedSelection), typeof(Selection))[0];
int countRows = selection.RowCount;
LearningScenario learningScenario = (LearningScenario)LearningScenario.where (new Query("LearningScenario").addTypeQuery(TypeQuery.select)
.addCondition("Name", "=", learningModel.SelectedScenario), typeof(LearningScenario))[0];
List <Entity> selectionRows = SelectionRow.where (new Query("SelectionRow").addTypeQuery(TypeQuery.select)
.addCondition("SelectionID", "=", selection.ID.ToString()), typeof(SelectionRow));
List <Entity> parameters = dms.models.Parameter.where (new Query("Parameter").addTypeQuery(TypeQuery.select)
.addCondition("TaskTemplateID", "=", selection.TaskTemplateID.ToString()), typeof(dms.models.Parameter));
int stepRow = 0;
float[][] inputData = new float[countRows][];
float[] outputData = new float[countRows];
for (int i = 0; i < countRows; i++)
{
inputData[i] = new float[parameters.Count - 1];
}
int outputParam = 0;
foreach (Entity selRow in selectionRows)
{
int selectionRowId = selRow.ID;
int stepParam = 0;
foreach (Entity param in parameters)
{
int paramId = param.ID;
List <Entity> value = ValueParameter.where (new Query("ValueParameter").addTypeQuery(TypeQuery.select)
.addCondition("ParameterID", "=", paramId.ToString()).
addCondition("SelectionRowID", "=", selectionRowId.ToString()), typeof(ValueParameter));
if (((dms.models.Parameter)param).IsOutput == 1)
{
outputParam = param.ID;
string outputValue = ((ValueParameter)value[0]).Value;
float outputFloat;
if (float.TryParse(outputValue, out outputFloat))
{
outputData[stepRow] = outputFloat;
}
}
else
{
inputData[stepRow][stepParam] = float.Parse(((ValueParameter)value[0]).Value, CultureInfo.InvariantCulture.NumberFormat);
}
stepParam++;
}
stepRow++;
}
ISolver isolver = null;
if (Solver.Description is PerceptronTopology)
{
PerceptronTopology topology = Solver.Description as PerceptronTopology;
isolver = new PerceptronManaged(topology);
}
else if (Solver.Description is ConvNNTopology)
{
ConvNNTopology topology = Solver.Description as ConvNNTopology;
isolver = new ConvNNManaged(topology);
}
else if (Solver.Description is WardNNTopology)
{
WardNNTopology topology = Solver.Description as WardNNTopology;
isolver = new WardNNManaged(topology);
}
else if (Solver.Description is TreeDescription)
{
TreeDescription topology = Solver.Description as TreeDescription;
isolver = new DecisionTree(topology);
}
else
{
throw new EntryPointNotFoundException();
}
SeparationOfDataSet s = new SeparationOfDataSet(isolver, learningScenario, inputData, outputData);
LearnedSolver ls = new LearnedSolver()
{
SelectionID = selection.ID,
LearningScenarioID = learningScenario.ID,
TaskSolverID = Solver.ID,
Soul = s.separationAndLearn(selection.ID, outputParam)
};
ls.save();
LearningQuality lq = new LearningQuality()
{
LearnedSolverID = ls.ID,
MistakeTrain = Convert.ToInt32(s.MistakeTrain),
MistakeTest = Convert.ToInt32(s.MistakeTest),
ClosingError = s.ClosingError
};
lq.save();
}
OnClose?.Invoke(this, null);
}
public Archive generateArchiveSystem()
{
scenarioRelations.Clear();
selectionRelations.Clear();
taskSolverRelations.Clear();
Archive archive = new Archive();
List <Entity> tasks = models.Task.all(typeof(models.Task));
foreach (models.Task task in tasks)
{
ArchiveTask archTask = new ArchiveTask(task);
List <TaskTemplate> templates = TaskTemplate.templatesOfTaskId(task.ID);
foreach (TaskTemplate template in templates)
{
ArchiveTemplate archTemp = new ArchiveTemplate(template);
List <Selection> selections = Selection.selectionsOfTaskTemplateId(template.ID);
foreach (Selection sel in selections)
{
ArchiveSelection archSel = new ArchiveSelection(sel);
selectionRelations.Add(new RelationExport(sel.ID, archSel));
archTemp.Selections.Add(archSel);
}
List <Parameter> parameters = Parameter.parametersOfTaskTemplateId(template.ID);
foreach (Parameter par in parameters)
{
ArchiveParameter archPar = new ArchiveParameter(par);
archTemp.Parameters.Add(archPar);
}
archTask.Templates.Add(archTemp);
}
List <TaskSolver> solvers = TaskSolver.solversOfTaskId(task.ID);
foreach (TaskSolver solver in solvers)
{
ArchiveTaskSolver archSol = new ArchiveTaskSolver(solver);
taskSolverRelations.Add(new RelationExport(solver.ID, archSol));
archTask.Solvers.Add(archSol);
}
archive.Tasks.Add(archTask);
}
List <Entity> scenarios = LearningScenario.all(typeof(LearningScenario));
foreach (LearningScenario scenario in scenarios)
{
ArchiveScenario archScenario = new ArchiveScenario(scenario);
scenarioRelations.Add(new RelationExport(scenario.ID, archScenario));
archive.Scenarios.Add(archScenario);
}
List <Entity> lSolvers = LearnedSolver.all(typeof(LearnedSolver));
foreach (LearnedSolver solver in lSolvers)
{
ArchiveLearnedSolver archSol = new ArchiveLearnedSolver(solver);
List <LearningQuality> quailties = LearningQuality.qualitiesOfSolverId(solver.ID);
foreach (LearningQuality quality in quailties)
{
ArchiveLearningQuality archQ = new ArchiveLearningQuality(quality);
archSol.Qualities.Add(archQ);
}
var find = taskSolverRelations.Find(x => x.ID == solver.TaskSolverID);
archSol.TaskSolver = (find != null) ? (ArchiveTaskSolver)find.model : null;
find = selectionRelations.Find(x => x.ID == solver.SelectionID);
archSol.Selection = (find != null) ? (ArchiveSelection)find.model : null;
find = scenarioRelations.Find(x => x.ID == solver.LearningScenarioID);
archSol.Scenario = (find != null) ? (ArchiveScenario)find.model : null;
archive.LearnedSolvers.Add(archSol);
}
return(archive);
}
