public List<ResultsGroup>[] evaluate(OpenFileDialog FileLinks, Feature featureData)
{
List<ResultsGroup>[] AllFinalResults = new List<ResultsGroup>[Convert.ToInt32(FileLinks.FileNames.Count())];
Int32 Count = 0;
//Each data file is treated separately, hence the for loop.
foreach (String filename in FileLinks.FileNames)
{
//Get the Parameters.
ParametersForm parameterForm = new ParametersForm();
ParametersForm.ParameterSettings parameters = parameterForm.GetParameters();
//Perform the First and second grouping and getting data for the features by the Grouping function.
List<ResultsGroup> LRLR = new List<ResultsGroup>();
LRLR = Groupings(filename, parameters);
//Generate scores.
SupervisedLearner sl = new SupervisedLearner();
AllFinalResults[Count] = sl.Scorings(LRLR, featureData, parameters);
Count++;
}
return AllFinalResults;
}
public List<ResultsGroup>[] run(OpenFileDialog FileLinks)
{
List<ResultsGroup>[] AllFinalResults = new List<ResultsGroup>[Convert.ToInt32(FileLinks.FileNames.Count())];
Int32 Count = 0;
//Each data file is treated separately, hence the for loop.
foreach (String filename in FileLinks.FileNames)
{
//Get the Parameters.
ParametersForm parameter = new ParametersForm();
ParametersForm.ParameterSettings paradata = parameter.GetParameters();
//Perform the First and second grouping and getting data for the features by the Grouping function.
List<ResultsGroup> LRLR = new List<ResultsGroup>();
LRLR = Groupings(filename, paradata);
//##############Logistic Regression####################
Features fe = new Features();
//current features
Feature featureData = fe.readFeature();
//default features
String defaultpath = Application.StartupPath + "\\FeatureDefault.fea";
Feature defaultData = fe.readFeature(defaultpath);
//Features that will be used
Feature finalfeatureData = new Feature();
//Here are the beta values in logistic regression.
finalfeatureData.Initial = featureData.Initial * 0.5 + defaultData.Initial * 0.5;
finalfeatureData.numChargeStates = featureData.numChargeStates * 0.5 + defaultData.numChargeStates * 0.5;
finalfeatureData.ScanDensity = featureData.ScanDensity * 0.5 + defaultData.ScanDensity * 0.5;
finalfeatureData.numModiStates = featureData.numModiStates * 0.5 + defaultData.numModiStates * 0.5;
finalfeatureData.totalVolume = featureData.totalVolume * 0.5 + defaultData.totalVolume * 0.5;
finalfeatureData.ExpectedA = featureData.ExpectedA * 0.5 + defaultData.ExpectedA * 0.5;
finalfeatureData.CentroidScan = featureData.CentroidScan * 0.5 + defaultData.CentroidScan * 0.5;
finalfeatureData.numOfScan = featureData.numOfScan * 0.5 + defaultData.numOfScan * 0.5;
finalfeatureData.avgSigNoise = featureData.avgSigNoise * 0.5 + defaultData.avgSigNoise * 0.5;
//Generate scores.
SupervisedLearner sl = new SupervisedLearner();
AllFinalResults[Count] = sl.Scorings(LRLR, finalfeatureData, paradata);
Count++;
}
return AllFinalResults;
}