/**
* Calculates local distances array.
*
* @param pattern feature extractor object of the pattern
*/
private void CalculateLocalDistances(Extractor pattern)
{
int patternSize = pattern.GetFramesCount();
for (int i = 0; i < from.GetFramesCount(); ++i)
{
Array.Resize(ref points[i], patternSize);
for (int j = 0; j < patternSize; j++)
{
points[i][j] = new DtwPoint(i, j, distanceFn(from.GetVector(i), pattern.GetVector(j)));
}
}
}
/**
* Returns the lowest-cost path in the DTW array.
*
* @return path
*/
public LinkedList <KeyValuePair <int, int> > GetPath()
{
LinkedList <KeyValuePair <int, int> > path = new LinkedList <KeyValuePair <int, int> >();
int width = points.Length;
int height = points[0].Length;
//C++ TO C# CONVERTER TODO TASK: Pointer arithmetic is detected on this variable, so pointers on this variable are left unchanged.
DtwPoint point = points[width - 1][height - 1];
while (point.previous != null)
{
path.AddLast(new KeyValuePair <int, int>(point.x, point.y));
//C++ TO C# CONVERTER WARNING: The following line was determined to be a copy assignment (rather than a reference assignment) - this should be verified and a 'CopyFrom' method should be created if it does not yet exist:
//ORIGINAL LINE: point = point->previous;
//point.CopyFrom(point.previous);
point = point.previous;
}
return(path);
}
/**
* Calculates local distances array.
*
* @param pattern feature extractor object of the pattern
*/
private void CalculateLocalDistances(Extractor pattern)
{
int patternSize = pattern.GetFramesCount();
for (int i = 0; i < from.GetFramesCount(); ++i)
{
Array.Resize(ref points[i], patternSize);
for (int j = 0; j < patternSize; j++)
points[i][j] = new DtwPoint(i, j, distanceFn(from.GetVector(i), pattern.GetVector(j)));
}
}
public double GetDistance(Extractor pattern, NormalizationType normalization)
{
CalculateLocalDistances(pattern);
int signalSize = from.GetFramesCount();
int patternSize = pattern.GetFramesCount();
DtwPoint top = new DtwPoint();
DtwPoint center = new DtwPoint();
DtwPoint bottom = new DtwPoint();
DtwPoint previous = new DtwPoint();
for (int i = 1; i < signalSize; ++i)
{
for (int j = 1; j < patternSize; ++j)
{
center = points[i - 1][j - 1];
if (PassType.Neighbors == passType)
{
top = points[i - 1][j];
bottom = points[i][j - 1];
}
else // Diagonals
{
if (i > 1 && j > 1)
{
top = points[i - 2][j - 1];
bottom = points[i - 1][j - 2];
}
else
{
top = points[i - 1][j];
bottom = points[i][j - 1];
}
}
if (top.dAccumulated < center.dAccumulated)
previous = top;
else
previous = center;
if (bottom.dAccumulated < previous.dAccumulated)
previous = bottom;
points[i][j].dAccumulated = points[i][j].dLocal + previous.dAccumulated;
points[i][j].previous = previous;
}
}
double distance = points[signalSize - 1][patternSize - 1].dAccumulated;
switch (normalization)
{
case NormalizationType.Diagonal:
distance /= Math.Sqrt(signalSize *signalSize + patternSize *patternSize);
break;
case NormalizationType.SumOfSides:
distance /= signalSize + patternSize;
break;
case NormalizationType.NoNormalization:
default:
break;
}
return distance;
}
public double GetDistance(Extractor pattern, NormalizationType normalization)
{
CalculateLocalDistances(pattern);
int signalSize = from.GetFramesCount();
int patternSize = pattern.GetFramesCount();
DtwPoint top = new DtwPoint();
DtwPoint center = new DtwPoint();
DtwPoint bottom = new DtwPoint();
DtwPoint previous = new DtwPoint();
for (int i = 1; i < signalSize; ++i)
{
for (int j = 1; j < patternSize; ++j)
{
center = points[i - 1][j - 1];
if (PassType.Neighbors == passType)
{
top = points[i - 1][j];
bottom = points[i][j - 1];
}
else // Diagonals
{
if (i > 1 && j > 1)
{
top = points[i - 2][j - 1];
bottom = points[i - 1][j - 2];
}
else
{
top = points[i - 1][j];
bottom = points[i][j - 1];
}
}
if (top.dAccumulated < center.dAccumulated)
{
previous = top;
}
else
{
previous = center;
}
if (bottom.dAccumulated < previous.dAccumulated)
{
previous = bottom;
}
points[i][j].dAccumulated = points[i][j].dLocal + previous.dAccumulated;
points[i][j].previous = previous;
}
}
double distance = points[signalSize - 1][patternSize - 1].dAccumulated;
switch (normalization)
{
case NormalizationType.Diagonal:
distance /= Math.Sqrt(signalSize * signalSize + patternSize * patternSize);
break;
case NormalizationType.SumOfSides:
distance /= signalSize + patternSize;
break;
case NormalizationType.NoNormalization:
default:
break;
}
return(distance);
}
