public SelectionParameters(SelectionSeverity severity, int nRequired = 0, bool getMaxData = false, bool useJourney = true, PackListHistory packListHistory = PackListHistory.NoFilter, List <string> filteringIds = null)
{
this.nRequired = nRequired;
this.getMaxData = getMaxData;
this.severity = severity;
this.useJourney = useJourney;
this.packListHistory = packListHistory;
this.filteringIds = filteringIds;
}
public SelectionParameters(SelectionSeverity severity, int nRequired = 0, bool getMaxData = false, bool useJourney = true, JourneyFilter journeyFilter = JourneyFilter.CurrentJourney, PackListHistory packListHistory = PackListHistory.NoFilter, List <string> filteringIds = null, bool sortDataByDifficulty = false)
{
this.nRequired = nRequired;
this.getMaxData = getMaxData;
this.severity = severity;
this.useJourney = useJourney;
this.journeyFilter = journeyFilter;
this.packListHistory = packListHistory;
this.filteringIds = filteringIds;
this.sortDataByDifficulty = sortDataByDifficulty;
}
public QuestionBuilderParameters()
{
this.correctChoicesHistory = PackListHistory.RepeatWhenFull;
this.wrongChoicesHistory = PackListHistory.RepeatWhenFull;
this.useJourneyForCorrect = true;
this.useJourneyForWrong = false;
this.correctSeverity = SelectionSeverity.MayRepeatIfNotEnough;
this.wrongSeverity = SelectionSeverity.MayRepeatIfNotEnough;
this.letterFilters = new LetterFilters();
this.wordFilters = new WordFilters();
this.phraseFilters = new PhraseFilters();
}
public QuestionBuilderParameters()
{
this.correctChoicesHistory = PackListHistory.RepeatWhenFull;
this.wrongChoicesHistory = PackListHistory.RepeatWhenFull;
this.useJourneyForCorrect = true;
this.useJourneyForWrong = true;
this.correctSeverity = SelectionSeverity.MayRepeatIfNotEnough;
this.wrongSeverity = SelectionSeverity.MayRepeatIfNotEnough;
this.letterEqualityStrictness = LetterEqualityStrictness.LetterOnly;
this.letterFilters = new LetterFilters();
this.wordFilters = new WordFilters();
this.phraseFilters = new PhraseFilters();
this.sortPacksByDifficulty = true;
}
private List <T> WeightedDataSelect <T>(List <T> source_data_list, HashSet <T> currentPSData, int nToSelect, DbTables table, SelectionSeverity severity) where T : IData
{
// Given a (filtered) list of data, select some using weights
List <ScoreData> score_data_list = dbManager.FindScoreDataByQuery("SELECT * FROM ScoreData WHERE TableName = '" + table.ToString() + "'");
string debugString = "-- Teacher Selection Weights";
List <float> weights_list = new List <float>();
foreach (var sourceData in source_data_list)
{
float cumulativeWeight = 0;
debugString += "\n" + sourceData.GetId() + " ---";
// Get score data
var score_data = score_data_list.Find(x => x.ElementId == sourceData.GetId());
float currentScore = 0;
int daysSinceLastScore = 0;
if (score_data != null)
{
var timespanFromLastScoreToNow = GenericUtilities.GetTimeSpanBetween(score_data.LastAccessTimestamp, GenericUtilities.GetTimestampForNow());
daysSinceLastScore = timespanFromLastScoreToNow.Days;
currentScore = score_data.Score;
}
//UnityEngine.Debug.Log("Data " + id + " score: " + currentScore + " days " + daysSinceLastScore);
// Score Weight [0,1]: higher the lower the score [-1,1] is
var scoreWeight = 0.5f * (1 - currentScore);
cumulativeWeight += scoreWeight * ConfigAI.data_scoreWeight;
debugString += " \tScore: " + scoreWeight * ConfigAI.data_scoreWeight + "(" + scoreWeight + ")";
// RecentPlay Weight [1,0]: higher the more in the past we saw that data
const float dayLinerWeightDecrease = 1f / ConfigAI.daysForMaximumRecentPlayMalus;
float weightMalus = daysSinceLastScore * dayLinerWeightDecrease;
float recentPlayWeight = 1f - UnityEngine.Mathf.Min(1, weightMalus);
cumulativeWeight += recentPlayWeight * ConfigAI.data_recentPlayWeight;
debugString += " \tRecent: " + recentPlayWeight * ConfigAI.data_recentPlayWeight + "(" + recentPlayWeight + ")";
// Current focus weight [1,0]: higher if the data is part of the current play session
float currentPlaySessionWeight = currentPSData.Contains(sourceData) ? 1 : 0f;
cumulativeWeight += currentPlaySessionWeight * ConfigAI.data_currentPlaySessionWeight;
debugString += " \tCurrentPS: " + currentPlaySessionWeight * ConfigAI.data_currentPlaySessionWeight + "(" + currentPlaySessionWeight + ")";
// If the cumulative weight goes to the negatives, we give it a fixed weight
if (cumulativeWeight <= 0)
{
cumulativeWeight = ConfigAI.data_minimumTotalWeight;
continue;
}
// Save cumulative weight
weights_list.Add(cumulativeWeight);
debugString += " TOTw: " + cumulativeWeight;
}
if (ConfigAI.verboseDataSelection)
{
UnityEngine.Debug.Log(debugString);
}
// Select data from the list
List <T> selected_data_list = new List <T>();
if (source_data_list.Count > 0)
{
int nToSelectFromCurrentList = 0;
List <T> chosenData = null;
switch (severity)
{
case SelectionSeverity.AsManyAsPossible:
case SelectionSeverity.AllRequired:
nToSelectFromCurrentList = UnityEngine.Mathf.Min(source_data_list.Count, nToSelect);
chosenData = RandomHelper.RouletteSelectNonRepeating(source_data_list, weights_list, nToSelectFromCurrentList);
selected_data_list.AddRange(chosenData);
break;
case SelectionSeverity.MayRepeatIfNotEnough:
int nRemainingToSelect = nToSelect;
while (nRemainingToSelect > 0)
{
var listCopy = new List <T>(source_data_list);
nToSelectFromCurrentList = UnityEngine.Mathf.Min(source_data_list.Count, nRemainingToSelect);
chosenData = RandomHelper.RouletteSelectNonRepeating(listCopy, weights_list, nToSelectFromCurrentList);
selected_data_list.AddRange(chosenData);
nRemainingToSelect -= nToSelectFromCurrentList;
}
break;
}
}
return(selected_data_list);
}
private List <T> WeightedDataSelect <T>(List <T> source_data_list, int nToSelect, SelectionSeverity severity) where T : IData
{
VocabularyDataType dataType = VocabularyDataType.Letter;
if (typeof(T) == typeof(LetterData))
{
dataType = VocabularyDataType.Letter;
}
else if (typeof(T) == typeof(WordData))
{
dataType = VocabularyDataType.Word;
}
else if (typeof(T) == typeof(PhraseData))
{
dataType = VocabularyDataType.Phrase;
}
// Given a (filtered) list of data, select some using weights
var score_data_list = dbManager.Query <VocabularyScoreData>("SELECT * FROM " + typeof(VocabularyScoreData).Name + " WHERE VocabularyDataType = '" + (int)dataType + "'");
if (ConfigAI.VerboseDataSelection)
{
weightedData_debugString += ConfigAI.FormatTeacherReportHeader("Selection Weights");
weightedData_debugString = "";
}
var weights_list = new List <float>();
foreach (var sourceData in source_data_list)
{
float cumulativeWeight = 0;
if (ConfigAI.VerboseDataSelection)
{
weightedData_debugString += "\n" + sourceData.GetId() + " ---";
}
// Get score data
var score_data = score_data_list.Find(x => x.ElementId == sourceData.GetId());
float currentScore = 0;
int daysSinceLastScore = 0;
if (score_data != null)
{
var timespanFromLastScoreToNow = GenericHelper.GetTimeSpanBetween(score_data.UpdateTimestamp, GenericHelper.GetTimestampForNow());
daysSinceLastScore = timespanFromLastScoreToNow.Days;
currentScore = score_data.Score;
}
//UnityEngine.Debug.Log("Data " + id + " score: " + currentScore + " days " + daysSinceLastScore);
// Score Weight [0,1]: higher the lower the score [-1,1] is
var scoreWeight = 0.5f * (1 - currentScore);
cumulativeWeight += scoreWeight * ConfigAI.Vocabulary_Score_Weight;
if (ConfigAI.VerboseDataSelection)
{
weightedData_debugString += " \tScore: " + scoreWeight * ConfigAI.Vocabulary_Score_Weight + "(" + scoreWeight + ")";
}
// RecentPlay Weight [1,0]: higher the more in the past we saw that data
const float dayLinerWeightDecrease = 1f / ConfigAI.DaysForMaximumRecentPlayMalus;
float weightMalus = daysSinceLastScore * dayLinerWeightDecrease;
float recentPlayWeight = 1f - UnityEngine.Mathf.Min(1, weightMalus);
cumulativeWeight += recentPlayWeight * ConfigAI.Vocabulary_RecentPlay_Weight;
if (ConfigAI.VerboseDataSelection)
{
weightedData_debugString += " \tRecent: " + recentPlayWeight * ConfigAI.Vocabulary_RecentPlay_Weight + "(" + recentPlayWeight + ")";
}
// Current focus weight [1,0]: higher if the data is part of the current play session / learning block / stage
float currentPlaySessionWeight = 0;
if (currentPlaySessionContents.Contains(sourceData))
{
currentPlaySessionWeight = 1;
}
else if (currentLearningBlockContents.Contains(sourceData))
{
currentPlaySessionWeight = 0.5f;
}
else if (currentStageContents.Contains(sourceData))
{
currentPlaySessionWeight = 0.2f;
}
cumulativeWeight += currentPlaySessionWeight * ConfigAI.Vocabulary_CurrentPlaySession_Weight;
if (ConfigAI.VerboseDataSelection)
{
weightedData_debugString += " \tFocus: " + currentPlaySessionWeight * ConfigAI.Vocabulary_CurrentPlaySession_Weight + "(" + currentPlaySessionWeight + ")";
}
// If the cumulative weight goes to the negatives, we give it a fixed weight
// TODO check if we shound use if (cumulativeWeight <= ConfigAI.Vocabulary_MinTotal_Weight)
// TODO check the "continue" because it wont' save the data
if (cumulativeWeight <= 0)
{
cumulativeWeight = ConfigAI.Vocabulary_MinTotal_Weight;
continue;
}
// Save cumulative weight
weights_list.Add(cumulativeWeight);
if (ConfigAI.VerboseDataSelection)
{
weightedData_debugString += " TOTw: " + cumulativeWeight;
}
}
//return source_data_list;
if (ConfigAI.VerboseDataSelection)
{
ConfigAI.AppendToTeacherReport(weightedData_debugString);
}
// Select data from the list
var selected_data_list = new List <T>();
if (source_data_list.Count > 0)
{
int nToSelectFromCurrentList = 0;
List <T> chosenData = null;
switch (severity)
{
case SelectionSeverity.AsManyAsPossible:
case SelectionSeverity.AllRequired:
nToSelectFromCurrentList = UnityEngine.Mathf.Min(source_data_list.Count, nToSelect);
chosenData = RandomHelper.RouletteSelectNonRepeating(source_data_list, weights_list, nToSelectFromCurrentList);
selected_data_list.AddRange(chosenData);
break;
case SelectionSeverity.MayRepeatIfNotEnough:
int nRemainingToSelect = nToSelect;
while (nRemainingToSelect > 0)
{
var listCopy = new List <T>(source_data_list);
nToSelectFromCurrentList = UnityEngine.Mathf.Min(source_data_list.Count, nRemainingToSelect);
chosenData = RandomHelper.RouletteSelectNonRepeating(listCopy, weights_list, nToSelectFromCurrentList);
selected_data_list.AddRange(chosenData);
nRemainingToSelect -= nToSelectFromCurrentList;
}
break;
}
}
return(selected_data_list);
}
