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As some processed distances are already ^2, use template to select whether or not we have to ^2 in KMeanspp

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This commit is contained in:
Pierre-Emmanuel Viel 2013-12-17 13:26:55 +01:00
parent 5aeeaa6fce
commit fa749de0dc
1 changed files with 59 additions and 3 deletions
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62
modules/flann/include/opencv2/flann/kmeans_index.h
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@ -53,6 +53,62 @@
namespace cvflann
{
template <typename Distance, typename ElementType>
struct squareDistance
{
typedef typename Distance::ResultType ResultType;
ResultType operator()( ResultType dist ) { return dist*dist; }
};
template <typename ElementType>
struct squareDistance<L2_Simple<ElementType>, ElementType>
{
typedef typename L2_Simple<ElementType>::ResultType ResultType;
ResultType operator()( ResultType dist ) { return dist; }
};
template <typename ElementType>
struct squareDistance<L2<ElementType>, ElementType>
{
typedef typename L2<ElementType>::ResultType ResultType;
ResultType operator()( ResultType dist ) { return dist; }
};
template <typename ElementType>
struct squareDistance<MinkowskiDistance<ElementType>, ElementType>
{
typedef typename MinkowskiDistance<ElementType>::ResultType ResultType;
ResultType operator()( ResultType dist ) { return dist; }
};
template <typename ElementType>
struct squareDistance<HellingerDistance<ElementType>, ElementType>
{
typedef typename HellingerDistance<ElementType>::ResultType ResultType;
ResultType operator()( ResultType dist ) { return dist; }
};
template <typename ElementType>
struct squareDistance<ChiSquareDistance<ElementType>, ElementType>
{
typedef typename ChiSquareDistance<ElementType>::ResultType ResultType;
ResultType operator()( ResultType dist ) { return dist; }
};
template <typename Distance>
typename Distance::ResultType ensureSquareDistance( typename Distance::ResultType dist )
{
typedef typename Distance::ElementType ElementType;
squareDistance<Distance, ElementType> dummy;
return dummy( dist );
}
struct KMeansIndexParams : public IndexParams
{
KMeansIndexParams(int branching = 32, int iterations = 11,
@ -211,7 +267,7 @@ public:
for (int i = 0; i < n; i++) {
closestDistSq[i] = distance_(dataset_[indices[i]], dataset_[indices[index]], dataset_.cols);
closestDistSq[i] *= closestDistSq[i];
closestDistSq[i] = ensureSquareDistance<Distance>( closestDistSq[i] );
currentPot += closestDistSq[i];
}
@ -239,7 +295,7 @@ public:
double newPot = 0;
for (int i = 0; i < n; i++) {
DistanceType dist = distance_(dataset_[indices[i]], dataset_[indices[index]], dataset_.cols);
newPot += std::min( dist*dist, closestDistSq[i] );
newPot += std::min( ensureSquareDistance<Distance>(dist), closestDistSq[i] );
}
// Store the best result
@ -254,7 +310,7 @@ public:
currentPot = bestNewPot;
for (int i = 0; i < n; i++) {
DistanceType dist = distance_(dataset_[indices[i]], dataset_[indices[bestNewIndex]], dataset_.cols);
closestDistSq[i] = std::min( dist*dist, closestDistSq[i] );
closestDistSq[i] = std::min( ensureSquareDistance<Distance>(dist), closestDistSq[i] );
}
}