int KNN_prune_noisy
(
///////////////////////////////
// Parameters //
///////////////////////////////
Pattern p, // source
//
Categories c, // source
//
long y, // source instance index
//
long k // k(!)
//
)
{
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, p->ny - 1); // the coverage is not bounded by k but by n
// long reachability = KNN_kNeighboursSkip(p, p, fws, y, k, indices, 0); .OS.081011
long reachability = KNN_kNeighboursSkip(p, p, fws, y, k, indices, y);
long coverage = KNN_prune_kCoverage(p, c, y, k, indices);
NUMlvector_free (indices, 0);
forget(fws);
if (!KNN_prune_superfluous(p, c, y, k, 0) && reachability > coverage)
return(1);
}
return(0);
}
long KNN_prune_kCoverage
(
PatternList p, // source
Categories c, // source
long y, // source instance index
long k, // k(!)
long * indices // Out: kCoverage set
)
{
Melder_assert (y <= p->ny);
Melder_assert (k > 0 && k <= p->ny);
long cc = 0;
autoFeatureWeights fws = FeatureWeights_create (p -> nx);
autoNUMvector <long> tempindices (0L, p -> ny - 1);
for (long yy = 1; yy <= p -> ny; yy ++) {
if (y != yy && FeatureWeights_areFriends (c->at [y], c->at [yy])) {
long n = KNN_kNeighboursSkip (p, p, fws.get(), yy, k, tempindices.peek(), y);
while (n) {
Melder_assert (n <= p -> ny);
if (tempindices [-- n] == y) {
indices [cc ++] = yy;
break;
}
}
}
}
return cc;
}
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;
}
long KNN_prune_kCoverage
(
///////////////////////////////
// Parameters //
///////////////////////////////
Pattern p, // source
//
Categories c, // source
//
long y, // source instance index
//
long k, // k(!)
//
long * indices // Out: kCoverage set
//
)
{
Melder_assert(y <= p->ny);
Melder_assert(k > 0 && k <= p->ny);
long cc = 0;
FeatureWeights fws = FeatureWeights_create(p->nx);
if (fws)
{
long *tempindices = NUMlvector (0, p->ny - 1);
for (long yy = 1; yy <= p->ny; yy++)
{
if (y != yy && FRIENDS(c->item[y], c->item[yy]))
{
// long n = KNN_kNeighboursSkip(p, p, fws, yy, k, tempindices, 0); .OS.081011
long n = KNN_kNeighboursSkip(p, p, fws, yy, k, tempindices, y);
while (n)
{
Melder_assert (n <= p->ny);
if (tempindices[--n] == y)
{
indices[cc++] = yy;
break;
}
}
}
}
NUMlvector_free (tempindices, 0);
forget(fws);
}
return(cc);
}
int KNN_prune_noisy
(
PatternList p, // source
Categories c, // source
long y, // source instance index
long k // k(!)
)
{
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, p->ny - 1); // the coverage is not bounded by k but by n
long reachability = KNN_kNeighboursSkip (p, p, fws.get(), y, k, indices.peek(), y);
long coverage = KNN_prune_kCoverage (p, c, y, k, indices.peek());
if (! KNN_prune_superfluous (p, c, y, k, 0) && reachability > coverage)
return 1;
return 0;
}
int KNN_prune_critical
(
PatternList p, // source
Categories c, // source
long y, // source instance index
long k // k(!)
)
{
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);
long ncollected = KNN_kNeighboursSkip (p, p, fws.get(), y, k, indices.peek(), y);
for (long ic = 0; ic < ncollected; ic ++) {
if (! KNN_prune_superfluous (p, c, indices [ic], k, 0) || ! KNN_prune_superfluous (p, c, indices [ic], k, y)) {
return 1;
}
}
return 0;
}
int KNN_prune_critical
(
///////////////////////////////
// Parameters //
///////////////////////////////
Pattern p, // source
//
Categories c, // source
//
long y, // source instance index
//
long k // k(!)
//
)
{
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 ncollected = KNN_kNeighboursSkip(p, p, fws, y, k, indices, 0); .OS.081011
long ncollected = KNN_kNeighboursSkip(p, p, fws, y, k, indices, y);
for (long ic = 0; ic < ncollected; ic++)
if (!KNN_prune_superfluous(p, c, indices[ic], k, 0) || !KNN_prune_superfluous(p, c, indices[ic], k, y))
{
NUMlvector_free (indices, 0);
forget(fws);
return(1);
}
NUMlvector_free (indices, 0);
}
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;
}
