/// <summary>
/// Example: compare Group-centric Matcher with matcher implementations of TeamMaker and Omado Genesis with one heterogeneous criterion as e.g. in a similar way used for publication
/// Konert, J.; Burlak, D.; Göbel, S.; Steinmetz, R.: GroupAL: ein Algorithmus zur Formation und Qualitätsbewertung von Lerngruppen in E-Learning-Szenarien mittels n-dimensionaler Gütekriterien. In: Proceedings of the DeLFI 2013: Die 11. e-Learning Fachtagung Informatik der Gesellschaft für Informatik e.V. (Hrsg. Breitner, A.; C. Rensing) (pp. 71–82). Bremen, Germany: Köllen, 2013.
/// or more detailed in
/// Konert, J.; Burlak, D.; Göbel, S.; Steinmetz, R.: GroupAL: ein Algorithmus zur Formation und Qualitätsbewertung von Lerngruppen in E-Learning-Szenarien. i-com , 13(1) (2014), 70–81 doi:10.1515/icom-2014-0010
/// </summary>
/// <param name="runs">number of runs to repeat (each with 500 new participants generated)</param>
internal void TaskRunCompareMatchersGcmOmadoTeammaker1HetCrit(int runs)
{
TextFileCohortReaderWriter CohortWriter = new TextFileCohortReaderWriter();
IRule rule = new TeamMakerRule();
int CountOfPraticipants = 500;
int GroupSize = 3;
string fileName = "TaskRunCompareMatchers_" + (rule == null ? "nR" : "R") + "_" + GroupSize + "G_1K.csv";
string output = "";
CohortWriter.WriteTemporaryResultsToFile("CohortAveragePerformance" + separator + "CohortPerformanceIndex" + separator + "Matcher" + newLine, fileName, true);
for (int i = 0; i < runs; i++)
{
participants = ParticipantGenerator.DummyEntries(CountOfPraticipants, rule);
//participants = ParticipantGenerator.PerfectDummyEntries();
GroupFormationAlgorithm gfTM = new GroupFormationAlgorithm(participants, new TeamMakerMatcher(), new TeamMakerEvaluator(), new GroupALOptimizer(new GroupALGroupCentricMatcher()), GroupSize);
gfTM.DoOneFormation();
GroupFormationAlgorithm gfGcM = new GroupFormationAlgorithm(participants, new GroupALGroupCentricMatcher(), new GroupALEvaluator(), new GroupALOptimizer(new GroupALGroupCentricMatcher()), GroupSize);
gfGcM.DoOneFormation();
//omadogenesis is Random
GroupFormationAlgorithm gfRandom = new GroupFormationAlgorithm(participants, new RandomMatcher(), new GroupALEvaluator(), new GroupALOptimizer(new GroupALGroupCentricMatcher()), GroupSize);
gfRandom.DoOneFormation();
output = (gfTM.Cohort.results.avg + separator + gfTM.Cohort.results.performanceIndex + separator + gfTM.Cohort.whichMatcherUsed).Replace(",", ".") + newLine;
output += (gfGcM.Cohort.results.avg + separator + gfGcM.Cohort.results.performanceIndex + separator + gfGcM.Cohort.whichMatcherUsed).Replace(",", ".") + newLine;
output += (gfRandom.Cohort.results.avg + separator + gfRandom.Cohort.results.performanceIndex + separator + gfRandom.Cohort.whichMatcherUsed).Replace(",", ".") + newLine;
CohortWriter.WriteTemporaryResultsToFile(output, fileName, false);
}
}
/// <summary>
/// Example method evaluating optimization cycles of different matchers with 100 generated dummy participants
/// </summary>
public void TaskRunCompareMatchersOptimization()
{
GroupFormationAlgorithm gf;
TextFileCohortReaderWriter CohortWriter = new TextFileCohortReaderWriter();
string filename = "ExampleOptimizationProcessForeachMatcher.csv";
// columns header of CSV file
string output = "MatcherName" + separator + "Cycle" + separator + "CohortPerformanceIndex" + separator + "CohortAverage" + separator + "OptimizationCount" + newLine;
CohortWriter.WriteTemporaryResultsToFile(output, filename, true);
List <IMatcher> matchers = new List <IMatcher> {
new GroupALGroupCentricMatcher(), new GroupALParticipantCentricMatcher(), new RandomMatcher()
};
int GroupSize = 6;
int cycles = 10;
int CountOfOptimizations = 5;
for (int i = 0; i < cycles; i++)
{
participants = ParticipantGenerator.DummyEntries(100);
foreach (IMatcher matcher in matchers)
{
gf = new GroupFormationAlgorithm(participants, matcher, new GroupALEvaluator(), new GroupALOptimizer(matcher), GroupSize);
gf.DoOneFormation();
for (int j = 0; j < CountOfOptimizations; j++)
{
if (j == 0)
{
output = gf.Cohort.whichMatcherUsed + separator + i + separator + gf.Cohort.results.performanceIndex + separator + gf.Cohort.results.avg + separator + j + newLine;
CohortWriter.WriteTemporaryResultsToFile(output.Replace(",", replacementForNumberComma), filename, false);
continue;
}
gf.OptimizeCohort();
output = gf.Cohort.whichMatcherUsed + separator + i + separator + gf.Cohort.results.performanceIndex + separator + gf.Cohort.results.avg + separator + j + newLine;
CohortWriter.WriteTemporaryResultsToFile(output.Replace(",", replacementForNumberComma), filename, false);
}
}
}
}
/// <summary>
/// Example method comparing comutation time for different matchers
/// </summary>
public void TaskRunCompareMatchersRuntime()
{
TextFileCohortReaderWriter CohortWriter = new TextFileCohortReaderWriter();
string filename = "ExampleTimeComparisonBetweenMatcher.csv";
// columns header of CSV file
string output = "MatcherName" + separator + "countOfParticipants" + separator + "TimeNeeded" + newLine;
CohortWriter.WriteTemporaryResultsToFile(output, filename, true);
for (int i = 0; i < 3; i++)
{
participants = ParticipantGenerator.DummyEntries((i == 0 ? 100 : i * 500));
GroupFormationAlgorithm gf = new GroupFormationAlgorithm(participants, new GroupALParticipantCentricMatcher(), new GroupALEvaluator(), new GroupALOptimizer(new GroupALGroupCentricMatcher()), GroupSize);
sw.Start();
gf.DoOneFormation();
sw.Stop();
output = gf.Cohort.whichMatcherUsed + separator + participants.Count + separator + sw.ElapsedMilliseconds + newLine;
GroupFormationAlgorithm gfGbG = new GroupFormationAlgorithm(participants, new GroupALGroupCentricMatcher(), new GroupALEvaluator(), new GroupALOptimizer(new GroupALGroupCentricMatcher()), GroupSize);
sw.Restart();
gfGbG.DoOneFormation();
sw.Stop();
output += gfGbG.Cohort.whichMatcherUsed + separator + participants.Count + separator + sw.ElapsedMilliseconds + newLine;
GroupFormationAlgorithm gfRandom = new GroupFormationAlgorithm(participants, new RandomMatcher(), new GroupALEvaluator(), new GroupALOptimizer(new GroupALGroupCentricMatcher()), GroupSize);
sw.Restart();
gfRandom.DoOneFormation();
sw.Stop();
output += gfRandom.Cohort.whichMatcherUsed + separator + participants.Count + separator + sw.ElapsedMilliseconds + newLine;
CohortWriter.WriteTemporaryResultsToFile(output.Replace(",", replacementForNumberComma), filename, false);
}
}
/// <summary>
/// Helper method writing all given group formations ("runs") to one CSV statistical string (for file output most likely)
/// Items will occur in following order: "CohortAveragePerformance (av. GPI)" "CohortVarianz" "CohortPerformanceIndex (CPI)" "AverageOfAverages" "AverageVarianz" "VarianzeOfVarianzes" "Matcher"
/// </summary>
/// <param name="gfOutputList"></param>
/// <returns></returns>
private string printAllGfsToSTring(List <GroupFormationAlgorithm> gfOutputList)
{
string output = "";
var groups = gfOutputList.GroupBy(x => x.Cohort.whichMatcherUsed).Select(g => new Statistics {
Matcher = g.Key, avg = g.Average(x => x.Cohort.results.avg), stDev = g.Average(x => x.Cohort.results.stDev), averageVarianz = g.Average(x => x.Cohort.results.varianz)
});
if (gfOutputList.Count < 1)
{
return("");
}
for (int i = 0; i < gfOutputList.Count; i++)
{
GroupFormationAlgorithm gf = gfOutputList[i];
float averageOfAverage = groups.Single(x => x.Matcher == gf.Cohort.whichMatcherUsed).avg;
float varianzeOfVarianz = groups.Single(x => x.Matcher == gf.Cohort.whichMatcherUsed).varianz;
float averageVarianze = groups.Single(x => x.Matcher == gf.Cohort.whichMatcherUsed).averageVarianz;
// Items will occur in following order: "CohortAveragePerformance (av. GPI)" "CohortVarianz" "CohortPerformanceIndex (CPI)" "AverageOfAverages" "AverageVarianz" "VarianzeOfVarianzes" "Matcher"
//special treatment for the last gfs
output += (gf.Cohort.results.avg + separator + gf.Cohort.results.varianz + separator + gf.Cohort.results.performanceIndex + separator + averageOfAverage + separator + averageVarianze + separator + varianzeOfVarianz + separator + gf.Cohort.whichMatcherUsed).Replace(",", replacementForNumberComma) + newLine;
}
return(output);
}
