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); }