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,
GeneticParams = LS.LAParameters
};
ClosingError = la.startLearn(ISolver, trainInputDataset, trainOutputDataset);
}
else if (ISolver is DecisionTree)
{
DecisionTreeLearning la = new DecisionTreeLearning();
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>();
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);
var counts = comparisonOfResult.GroupBy(x => x).ToDictionary(x => x.Key, x => x.Count());
mistakeTrain = (float)counts[false] / (float)sizeTrainDataset;
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());
mistakeTest = (float)counts[false] / (float)sizeTestDataset;
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,
GeneticParams = 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>();
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);
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);
}
public LearningScenarioViewModel()
{
createHandler = new ActionHandler(createScenario, e => true);
cancelHandler = new ActionHandler(() => OnClose?.Invoke(this, null), e => true);
learningAlgo = new LearningAlgoManager();
Name = "Сценарий";
MixSeed = "123";
SeparationParamName = SeparationParamNames[0];
TeacherType = TeacherTypesList[0];
SelectionType = SelectionTypesList[0];
ID = -1;
SeparationParamValue = "80";
}
