/// <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>
/// This is a specific evaluation data generation method used to create
/// - 2 sets of data (one set with only one homogeneous criterion with 4 dimensions, one set with 2 homogeneous and 2 heterogeous matching criteria, each with 4 dimensions)
/// - each set in three variations (data distribution uniformly (all values 0..1 have same possibility, gausian values between 0..1, and only extreme values (0,1))
/// - the 6 combinations of sets (S) and variances (V) are all generated 100 times for 500 participants
/// - automatically named and stored
///
/// Data created with this method has been used to compare the different matchers implemented in namespace GroupAL.Matcher, e.g. to be published in
/// Konert, J.: Interactive Multimedia Learning: Using Social Media for Peer Education in Single-Player Educational Games (accepted for publication). Technische Universität Darmstadt, Germany 2014.
/// </summary>
private void generateDummyParticipants()
{
IParticipantReaderWriter tw = new TextFileParticipantReaderWriter();
for (int i = 0; i < 2; i++)
{// 2 sets
int numhet = 1;
int dimhet = 4;
int numhom = 0;
int dimhom = 4;
if (i == 1)
{
numhet = 2;
numhom = 2;
}
for (int j = 0; j < 3; j++)
{// 3 variations
IRule rule = new EmptyRule();
IValueGenerator valgen = new UniformValueGenerator();
if (j == 1)
{
valgen = new GaussianValueGenerator();
}
if (j == 2)
{
rule = new TeamMakerRule(); // builds 1 and 0 (extreme) values. Only ~one~ 1 for heterogeneous criteria, several 1s for homogeneous criteria
}
for (int k = 1; k <= 100; k++) // number of rounds to do it. (100 evaluation runs)
{
String filename = ".\\data\\GroupALParticipantsSet" + (i + 1) + "V" + (j + 1) + "_" + k + ".csv";
//String filename = "GroupALParticipantsN"+amount+"Het"+numHet+"Dim"+hetDim+"Hom"+numHom+"Dim"+homDim+".csv";
// set here number of participants (500)
ParticipantGenerator gen = new ParticipantGenerator(500, numhet, dimhet, numhom, dimhom, valgen, rule, tw, filename);
}
}
}
}
/// <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);
}
}
public List <Participant> generateNewDummyEntries()
{
participants = ParticipantGenerator.DummyEntries(CountofEntries);
return(participants);
}
