private void importLearnSolver(ArchiveLearnedSolver archSolver)
{
LearnedSolver solver = new LearnedSolver(archSolver);
solver.save();
archSolver.ID = solver.ID;
importQualities(archSolver.Qualities, archSolver.ID);
}
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);
}