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);
}
public ISolver simpleSeparation(int percentTrain)
{
int sizeTrainDataset = Convert.ToInt32(InputData.Length * ((double)percentTrain / 100));
int sizeTestDataset = InputData.Length - sizeTrainDataset;
float[][] trainInputDataset = new float[sizeTrainDataset][];
float[][] testInputDataset = new float[InputData.Length - sizeTrainDataset][];
float[] trainOutputDataset = new float[sizeTrainDataset];
float[] testOutputDataset = new float[InputData.Length - sizeTrainDataset];
Array.Copy(InputData, trainInputDataset, sizeTrainDataset);
Array.Copy(InputData, sizeTrainDataset, testInputDataset, 0, sizeTestDataset);
Array.Copy(OutputData, trainOutputDataset, sizeTrainDataset);
Array.Copy(OutputData, sizeTrainDataset, testOutputDataset, 0, sizeTestDataset);
if (ISolver is INeuralNetwork)
{
LearningAlgoManager la = new LearningAlgoManager()
{
usedAlgo = LS.LearningAlgorithmName,
LAParams = LS.LAParameters
};
ClosingError = la.startLearn(ISolver, trainInputDataset, trainOutputDataset);
}
else if (ISolver is DecisionTreeC4_5)
{
DecisionTreeC4_5LearningAlgo la = new DecisionTreeC4_5LearningAlgo();
ClosingError = la.startLearn(ISolver, trainInputDataset, trainOutputDataset);
}
else if (ISolver is DecisionTree)
{
DecisionTreeCARTLearningAlgo la = new DecisionTreeCARTLearningAlgo();
ClosingError = la.startLearn(ISolver, trainInputDataset, trainOutputDataset);
}
PreprocessingManager preprocessing = new PreprocessingManager();
mistakeTrain = 0;
List <string> expectedOutputValues = trainOutputDataset.Select(x => Convert.ToString(x)).ToList();
List <string> obtainedOutputValues = new List <string>();
if (ISolver is KohonenManaged)
{
KohonenManaged koh = ISolver as KohonenManaged;
koh.startLogWinners();
}
for (int i = 0; i < sizeTrainDataset; i++)
{
obtainedOutputValues.Add(Convert.ToString(ISolver.Solve(trainInputDataset[i])[0]));
}
List <bool> comparisonOfResult = preprocessing.compareExAndObValues(expectedOutputValues, obtainedOutputValues, SelectionID, ParameterID);
if (ISolver is KohonenManaged)
{
KohonenManaged koh = ISolver as KohonenManaged;
koh.stopLogWinners();
koh.declareWinnersOutput(comparisonOfResult);
}
var counts = comparisonOfResult.GroupBy(x => x).ToDictionary(x => x.Key, x => x.Count());
if (counts.ContainsKey(false))
{
mistakeTrain = (float)counts[false] / (float)sizeTrainDataset;
}
else
{
mistakeTrain = 0;
}
mistakeTest = 0;
expectedOutputValues = testOutputDataset.Select(x => Convert.ToString(x)).ToList();
obtainedOutputValues.Clear();
for (int i = 0; i < sizeTestDataset; i++)
{
obtainedOutputValues.Add(Convert.ToString(ISolver.Solve(testInputDataset[i])[0]));
}
comparisonOfResult = preprocessing.compareExAndObValues(expectedOutputValues, obtainedOutputValues, SelectionID, ParameterID);
counts = comparisonOfResult.GroupBy(x => x).ToDictionary(x => x.Key, x => x.Count());
if (counts.ContainsKey(false))
{
mistakeTest = (float)counts[false] / (float)sizeTestDataset;
}
else
{
mistakeTest = 0;
}
return(ISolver);
}
private ISolver crossValidation(int folds)
{
ISolver curSolver = ISolver.Copy();
List <float> listOfTestMistakes = new List <float>();
for (int k = 0; k < folds; k++)
{
float kMistakeTrain = 0;
float kMistakeTest = 0;
float[][] trainInputDataset = GetInputTrainData(InputData, folds, k);
float[][] testInputDataset = GetInputTestData(InputData, folds, k);
float[] trainOutputDataset = GetOutputTrainData(OutputData, folds, k);
float[] testOutputDataset = GetTestOutputData(OutputData, folds, k);
LearningAlgoManager la = new LearningAlgoManager()
{
usedAlgo = LS.LearningAlgorithmName,
LAParams = LS.LAParameters
};
PreprocessingManager preprocessing = new PreprocessingManager();
ClosingError = la.startLearn(ISolver, trainInputDataset, trainOutputDataset);
int sizeTrainDataset = trainInputDataset.Length;
List <string> expectedOutputValues = trainOutputDataset.Select(x => Convert.ToString(x)).ToList();
List <string> obtainedOutputValues = new List <string>();
if (ISolver is KohonenManaged)
{
KohonenManaged koh = ISolver as KohonenManaged;
koh.startLogWinners();
}
for (int i = 0; i < sizeTrainDataset; i++)
{
obtainedOutputValues.Add(Convert.ToString(ISolver.Solve(trainInputDataset[i])[0]));
}
List <bool> comparisonOfResult = preprocessing.compareExAndObValues(expectedOutputValues, obtainedOutputValues, SelectionID, ParameterID);
if (ISolver is KohonenManaged)
{
KohonenManaged koh = ISolver as KohonenManaged;
koh.stopLogWinners();
koh.declareWinnersOutput(comparisonOfResult);
}
var counts = comparisonOfResult.GroupBy(x => x).ToDictionary(x => x.Key, x => x.Count());
kMistakeTrain = (float)counts[false] / (float)sizeTrainDataset;
int sizeTestDataset = testOutputDataset.Length;
expectedOutputValues = testOutputDataset.Select(x => Convert.ToString(x)).ToList();
obtainedOutputValues.Clear();
for (int i = 0; i < sizeTestDataset; i++)
{
obtainedOutputValues.Add(Convert.ToString(ISolver.Solve(testInputDataset[i])[0]));
}
comparisonOfResult = preprocessing.compareExAndObValues(expectedOutputValues, obtainedOutputValues, SelectionID, ParameterID);
counts = comparisonOfResult.GroupBy(x => x).ToDictionary(x => x.Key, x => x.Count());
kMistakeTest = (float)counts[false] / (float)sizeTestDataset;
if (k != 0 && kMistakeTest < listOfTestMistakes.Min())
{
curSolver = ISolver.Copy();
mistakeTest = kMistakeTest;
mistakeTrain = kMistakeTrain;
}
listOfTestMistakes.Add(kMistakeTest);
}
ISolver = curSolver.Copy() as INeuralNetwork;
return(ISolver);
}
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 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
{
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);
}
