private void btnEuclidean_Click(object sender, EventArgs e)
{
var featureExtractor = new PointFloatShapeFeatureExtractor();
var preProc = new PreProcessing();
var traceGroup = _drawingInk.Ink.ToTraceGroup();
var sampledTraceGroup = preProc.ResampleTraceGroup(chkIsNormalized.Checked?preProc.NormalizeSize(traceGroup): traceGroup);
var features = featureExtractor.ExtractFeature(sampledTraceGroup);
var testTraceGroup = _resultInk.Ink.ToTraceGroup();
var sampledTestTraceGroup = preProc.ResampleTraceGroup(chkIsNormalized.Checked?preProc.NormalizeSize(testTraceGroup): testTraceGroup);
var testFeatures = featureExtractor.ExtractFeature(sampledTestTraceGroup);
var eucDistance = featureExtractor.ComputeEuclideanDistance(features, testFeatures);
MessageBox.Show(@"Euclidean Distance = " + eucDistance);
}
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);
}
