public IList <TrainFeature> Train()
{
var path = @"..\..\data\trainlist.txt";
var input = new FileStream(path, FileMode.Open, FileAccess.Read);
var fileReader = new StreamReader(input);
char[] splitter = { ' ' };
var strLine = "";
var trainList = new List <TrainFeature>();
while (!fileReader.EndOfStream)
{
strLine = fileReader.ReadLine();
if (strLine == null)
{
break;
}
var strToken = strLine.Split(splitter, StringSplitOptions.None);
var inkPath = strToken[0];
var classId = Convert.ToInt32(strToken[1]);
var traceGroup = _inkFile.ReadFromInkFile(inkPath);
var prepTraceGroup = _preProc.PreProcess(traceGroup);
var feature = _featureExtractor.ExtractFeature(prepTraceGroup);
trainList.Add(new TrainFeature {
ClassId = classId, ShapeFeatures = feature
});
}
return(trainList);
}
public IList <RecoResult> Recognize(TraceGroup testTraceGroup)
{
var testPreProcTraceGroup = _preProc.PreProcess(testTraceGroup);
var testFeature = _featureExtractor.ExtractFeature(testPreProcTraceGroup);
var distanceList = new List <KeyValuePair <int, float> >();
foreach (var train in _trainList)
{
var d = _featureExtractor.ComputeEuclideanDistance(train.ShapeFeatures, testFeature);
distanceList.Add(new KeyValuePair <int, float>(train.ClassId, d));
}
distanceList.Sort((f1, f2) => f1.Value.CompareTo(f2.Value));
var tempRecoResult = new List <RecoResult>();
var sumSimiliarity = 0f;
for (var i = 0; i < distanceList.Count; i++)
{
var foundDuplicated = tempRecoResult.Find(c => c.ShapeId == distanceList[i].Key);
if (foundDuplicated == null)
{
sumSimiliarity += distanceList[i].Value;
tempRecoResult.Add(new RecoResult {
ShapeId = distanceList[i].Key, ConfidenceLevel = distanceList[i].Value
});
if (tempRecoResult.Count == _numOfChoice)
{
break;
}
}
}
for (var i = 0; i < _numOfChoice; i++)
{
tempRecoResult[i].ConfidenceLevel = (sumSimiliarity - tempRecoResult[i].ConfidenceLevel) / sumSimiliarity;
}
return(tempRecoResult);
}
