private void OnLeaderboardDataResponse(GameSparks.Api.Responses.LeaderboardDataResponse response)
{
if (response.HasErrors == true)
{
Debug.LogError(response.Errors.JSON);
}
else
{
List <DistanceEntry> entries = new List <DistanceEntry> ();
foreach (var item in response.Data)
{
DistanceEntry entry = JsonUtility.FromJson <DistanceEntry>(item.JSONString);
entries.Add(entry);
}
ReturnLeaderboardEntries(entries);
}
}
/// <typeparam name="T"></typeparam>
/// <param name="list">unsorted list of elements</param>
/// <param name="count">how many closest elements to get</param>
/// <param name="DistanceFunction">heuristic to determine how close an element is</param>
/// <param name="sorted">if true, minor additional process cost (another O(c log c) function) un-heaping, to sort</param>
/// <returns>a list of count elements if list length is greater count. these are the closest to 0 according to the given distance heuristic. if sorted is false, they are not in gauranteed order.</returns>
public static T[] GetClosest <T>(this IEnumerable <T> list, int count, Func <T, float> DistanceFunction, bool sorted = true)
{
List <T> result = new List <T>();
Comparer <DistanceEntry <T> > comparer = Comparer <DistanceEntry <T> > .Create((a, b) => a.distance.CompareTo(b.distance));
MaxHeap <DistanceEntry <T> > maxheap = new MaxHeap <DistanceEntry <T> >(comparer);
foreach (T e in list)
{
DistanceEntry <T> de = new DistanceEntry <T>(e, DistanceFunction(e));
maxheap.Add(de);
if (maxheap.Count > count)
{
maxheap.Pop();
}
}
return(sorted ? maxheap.ToSortedArray(sortedElement => sortedElement.value)
: maxheap.ToArray(partiallySortedElement => partiallySortedElement.value));
}
/// <summary>
/// This method calculates the edit distances for all selected subjects
/// scanpath with the current settings and populates the data grid views
/// with the Loci and Sequence similarity matrices.
/// </summary>
private void RefillDistancesTable()
{
// Clear Existing Rows and Columns
this.dgvLociSimilarity.Rows.Clear();
this.dgvLociSimilarity.Columns.Clear();
this.dgvSequenceSimilarity.Rows.Clear();
this.dgvSequenceSimilarity.Columns.Clear();
if (!this.calcDistances)
{
return;
}
// Skip if no data available
if (this.scanpathsPicture.GazeFixations == null)
{
return;
}
if (this.aggregateDistancesByCategory)
{
var categories = new List<string>();
// Create loci similarity matrix
int rows = this.selectedSubjects.Count;
Array lociSimilarities = new DistanceEntry[rows, rows];
Array globalSimilarities = new DistanceEntry[rows, rows];
DataTable fixations = this.scanpathsPicture.GazeFixations;
var fixationsView = new DataView(
fixations,
string.Empty,
"CountInTrial",
DataViewRowState.CurrentRows);
DataTable mainTable = Document.ActiveDocument.DocDataSet.SubjectsAdapter.GetData();
foreach (DataRow row in mainTable.Rows)
{
string entry = row["Category"].ToString();
if (entry == string.Empty || entry == " ")
{
entry = "no category";
}
if (!categories.Contains(entry))
{
categories.Add(entry);
}
}
int rowCounter = 0;
foreach (KeyValuePair<string, ScanpathProperties> subjectRowKVP in this.selectedSubjects)
{
int columnCounter = 0;
////DataView fixationsOfS = new DataView(
//// fixations,
//// "(SubjectName='" + subjectRowKVP.Value.SubjectName + "')",
//// "CountInTrial",
//// DataViewRowState.CurrentRows);
foreach (KeyValuePair<string, ScanpathProperties> subjectColumnKVP in this.selectedSubjects)
{
////DataView fixationsOfT = new DataView(
//// fixations,
//// "(SubjectName='" + subjectColumnKVP.Value.SubjectName + "')",
//// "CountInTrial",
//// DataViewRowState.CurrentRows);
////float malsburgSimilarity = EditDistance.MalsburgDistance(sFixations, tFixations, 0.5f);
List<string> rowStringList = GetStringListForExtendedString(subjectRowKVP.Value.ScanpathString);
List<string> columnStringList = GetStringListForExtendedString(subjectColumnKVP.Value.ScanpathString);
float localSimilarity = EditDistance.CharacterSimilarityExtended(
rowStringList,
columnStringList) * 100;
float globalSimilarity = EditDistance.SequenceSimilarityExtended(
rowStringList,
columnStringList) * 100;
lociSimilarities.SetValue(
new DistanceEntry(
categories.IndexOf(subjectRowKVP.Value.SubjectCategory),
categories.IndexOf(subjectColumnKVP.Value.SubjectCategory),
localSimilarity),
rowCounter,
columnCounter);
globalSimilarities.SetValue(
new DistanceEntry(
categories.IndexOf(subjectRowKVP.Value.SubjectCategory),
categories.IndexOf(subjectColumnKVP.Value.SubjectCategory),
globalSimilarity),
rowCounter,
columnCounter);
columnCounter++;
}
rowCounter++;
}
int mergedRows = categories.Count;
Array mergedLociSimilarities = new CategoryEntry[mergedRows, mergedRows];
Array mergedGlobalSimilarities = new CategoryEntry[mergedRows, mergedRows];
// Merge categories
for (int i = 0; i <= lociSimilarities.GetUpperBound(0); i++)
{
for (int j = 0; j <= lociSimilarities.GetUpperBound(1); j++)
{
// Only take values above the diagonal
if (i >= j)
{
continue;
}
DistanceEntry currentLociValue = (DistanceEntry)lociSimilarities.GetValue(i, j);
CategoryEntry currentCategoryValue = (CategoryEntry)mergedLociSimilarities.GetValue(currentLociValue.RowCategory, currentLociValue.ColumnCategory);
currentCategoryValue.Divisor++;
CategoryEntry newCategoryValue = new CategoryEntry(currentCategoryValue.Divisor, currentLociValue.DistanceValue + currentCategoryValue.DistanceValue);
mergedLociSimilarities.SetValue(newCategoryValue, currentLociValue.RowCategory, currentLociValue.ColumnCategory);
mergedLociSimilarities.SetValue(newCategoryValue, currentLociValue.ColumnCategory, currentLociValue.RowCategory);
DistanceEntry currentGlobalValue = (DistanceEntry)globalSimilarities.GetValue(i, j);
CategoryEntry currentGlobalCategoryValue = (CategoryEntry)mergedGlobalSimilarities.GetValue(currentGlobalValue.RowCategory, currentGlobalValue.ColumnCategory);
currentGlobalCategoryValue.Divisor++;
CategoryEntry newGlobalCategoryValue = new CategoryEntry(currentGlobalCategoryValue.Divisor, currentGlobalValue.DistanceValue + currentGlobalCategoryValue.DistanceValue);
mergedGlobalSimilarities.SetValue(newGlobalCategoryValue, currentGlobalValue.RowCategory, currentGlobalValue.ColumnCategory);
mergedGlobalSimilarities.SetValue(newGlobalCategoryValue, currentGlobalValue.ColumnCategory, currentGlobalValue.RowCategory);
}
}
this.dgvLociSimilarity.Columns.Add("columnLociSubjects", "Loci Similarity");
this.dgvSequenceSimilarity.Columns.Add("columnSequenceSubjects", "Sequence Similarity");
foreach (string category in categories)
{
this.dgvLociSimilarity.Columns.Add(category, category);
this.dgvSequenceSimilarity.Columns.Add(category, category);
}
// Ouput category distances
for (int i = 0; i <= mergedLociSimilarities.GetUpperBound(0); i++)
{
var lociSimilarity = new List<object>();
var sequenceSimilarity = new List<object>();
for (int j = 0; j <= mergedLociSimilarities.GetUpperBound(1); j++)
{
CategoryEntry currentCategoryValue = (CategoryEntry)mergedLociSimilarities.GetValue(i, j);
lociSimilarity.Add(currentCategoryValue.DistanceValue / currentCategoryValue.Divisor);
CategoryEntry currentGlobalCategoryValue = (CategoryEntry)mergedGlobalSimilarities.GetValue(i, j);
sequenceSimilarity.Add(currentGlobalCategoryValue.DistanceValue / currentGlobalCategoryValue.Divisor);
}
var lociColumns = new List<object> { categories[i] };
lociColumns.AddRange(lociSimilarity.ToArray());
this.dgvLociSimilarity.Rows.Add(lociColumns.ToArray());
var sequenceColumns = new List<object> { categories[i] };
sequenceColumns.AddRange(sequenceSimilarity.ToArray());
this.dgvSequenceSimilarity.Rows.Add(sequenceColumns.ToArray());
}
}
else
{
this.dgvLociSimilarity.Columns.Add("columnLociSubjects", "Loci Similarity");
this.dgvSequenceSimilarity.Columns.Add("columnSequenceSubjects", "Sequence Similarity");
foreach (string subject in this.selectedSubjects.Keys)
{
this.dgvLociSimilarity.Columns.Add(subject, subject);
this.dgvSequenceSimilarity.Columns.Add(subject, subject);
}
this.dgvLociSimilarity.Columns.Add("columnLociString", "Scanpath string");
this.dgvSequenceSimilarity.Columns.Add("columnSequenceString", "Scanpath string");
this.dgvLociSimilarity.Columns.Add("columnLociSubjectGroup", "Subject Group");
this.dgvSequenceSimilarity.Columns.Add("columnSequenceSubjectGroup", "Subject Group");
DataTable fixations = this.scanpathsPicture.GazeFixations;
////DataView fixationsView = new DataView(
//// fixations,
//// string.Empty,
//// "CountInTrial",
//// DataViewRowState.CurrentRows);
foreach (KeyValuePair<string, ScanpathProperties> subjectRowKVP in this.selectedSubjects)
{
var lociSimilarity = new List<object>();
var sequenceSimilarity = new List<object>();
////DataView fixationsOfS = new DataView(
//// fixations,
//// "(SubjectName='" + subjectRowKVP.Value.SubjectName + "')",
//// "CountInTrial",
//// DataViewRowState.CurrentRows);
foreach (KeyValuePair<string, ScanpathProperties> subjectColumnKVP in this.selectedSubjects)
{
////DataView fixationsOfT = new DataView(
//// fixations,
//// "(SubjectName='" + subjectColumnKVP.Value.SubjectName + "')",
//// "CountInTrial",
//// DataViewRowState.CurrentRows);
////float malsburgSimilarity = EditDistance.MalsburgDistance(sFixations, tFixations, 0.5f);
List<string> rowStringList = GetStringListForExtendedString(subjectRowKVP.Value.ScanpathString);
List<string> columnStringList = GetStringListForExtendedString(subjectColumnKVP.Value.ScanpathString);
float localSimilarity = EditDistance.CharacterSimilarityExtended(
rowStringList,
columnStringList) * 100;
float globalSimilarity = EditDistance.SequenceSimilarityExtended(
rowStringList,
columnStringList) * 100;
lociSimilarity.Add(localSimilarity.ToString("N0") + " %");
sequenceSimilarity.Add(globalSimilarity.ToString("N0") + " %");
}
var lociColumns = new List<object>();
lociColumns.Add(subjectRowKVP.Value.SubjectName);
lociColumns.AddRange(lociSimilarity.ToArray());
lociColumns.Add(subjectRowKVP.Value.ScanpathString);
lociColumns.Add(subjectRowKVP.Value.SubjectCategory);
this.dgvLociSimilarity.Rows.Add(lociColumns.ToArray());
var sequenceColumns = new List<object>();
sequenceColumns.Add(subjectRowKVP.Value.SubjectName);
sequenceColumns.AddRange(sequenceSimilarity.ToArray());
sequenceColumns.Add(subjectRowKVP.Value.ScanpathString);
sequenceColumns.Add(subjectRowKVP.Value.SubjectCategory);
this.dgvSequenceSimilarity.Rows.Add(sequenceColumns.ToArray());
}
}
this.dgvLociSimilarity.AutoResizeColumns(DataGridViewAutoSizeColumnsMode.DisplayedCells);
this.dgvSequenceSimilarity.AutoResizeColumns(DataGridViewAutoSizeColumnsMode.DisplayedCells);
}
