int KNN_prune_superfluous
(
///////////////////////////////
// Parameters //
///////////////////////////////
Pattern p, // source
//
Categories c, // source
//
long y, // source instance index
//
long k, // k(!)
//
long skipper // Skipping instance skipper
//
)
{
if (y > p->ny) y = p->ny; // safety belt
if (k > p->ny) k = p->ny;
FeatureWeights fws = FeatureWeights_create(p->nx);
if (fws)
{
long *indices = NUMlvector (0, k - 1);
long *freqindices = NUMlvector (0, k - 1);
double *distances = NUMdvector (0, k - 1);
double *freqs = NUMdvector (0, k - 1);
// KNN_kNeighboursSkip(p, p, fws, y, k, indices, skipper); .OS.081011 ->
if(!KNN_kNeighboursSkip(p, p, fws, y, k, indices, skipper))
return(0);
// .OS.081011 <-
long ncategories = KNN_kIndicesToFrequenciesAndDistances(c, k, indices, distances, freqs, freqindices);
forget(fws);
int result = FRIENDS(c->item[y], c->item[freqindices[KNN_max(freqs, ncategories)]]);
NUMlvector_free (indices, 0);
NUMlvector_free (freqindices, 0);
NUMdvector_free (distances, 0);
NUMdvector_free (freqs, 0);
if (result)
return 1;
}
return 0;
}
int KNN_prune_superfluous
(
PatternList p, // source
Categories c, // source
long y, // source instance index
long k, // k(!)
long skipper // Skipping instance skipper
)
{
if (y > p -> ny) y = p -> ny; // safety belt
if (k > p -> ny) k = p -> ny;
autoFeatureWeights fws = FeatureWeights_create (p -> nx);
autoNUMvector <long> indices (0L, k - 1);
autoNUMvector <long> freqindices (0L, k - 1);
autoNUMvector <double> distances (0L, k - 1);
autoNUMvector <double> freqs (0L, k - 1);
if (! KNN_kNeighboursSkip (p, p, fws.get(), y, k, indices.peek(), skipper)) return 0;
long ncategories = KNN_kIndicesToFrequenciesAndDistances (c, k, indices.peek(), distances.peek(), freqs.peek(), freqindices.peek());
int result = FeatureWeights_areFriends (c->at [y], c->at [freqindices [KNN_max (freqs.peek(), ncategories)]]);
if (result)
return 1;
return 0;
}
