long KNN_prune_prune
(
KNN me, // the classifier to be pruned
double n, // pruning degree: noise, 0 <= n <= 1
double r, // pruning redundancy: noise, 0 <= n <= 1
long k // k(!)
)
{
autoCategories uniqueCategories = Categories_selectUniqueItems (my output.get());
if (Categories_getSize (uniqueCategories.get()) == my nInstances)
return 0;
long removals = 0;
long ncandidates = 0;
autoNUMvector <long> candidates (0L, my nInstances - 1);
if (my nInstances <= 1)
return 0;
for (long y = 1; y <= my nInstances; y ++) {
if (KNN_prune_noisy (my input.get(), my output.get(), y, k)) {
if (n == 1 || NUMrandomUniform (0, 1) <= n) {
KNN_removeInstance (me, y);
++ removals;
}
}
}
for (long y = 1; y <= my nInstances; ++ y) {
if (KNN_prune_superfluous (my input.get(), my output.get(), y, k, 0) && ! KNN_prune_critical (my input.get(), my output.get(), y, k))
candidates [ncandidates ++] = y;
}
KNN_prune_sort (my input.get(), my output.get(), k, candidates.peek(), ncandidates);
for (long y = 0; y < ncandidates; ++ y) {
if (KNN_prune_superfluous (my input.get(), my output.get(), candidates [y], k, 0) && ! KNN_prune_critical (my input.get(), my output.get(), candidates [y], k)) {
if (r == 1.0 || NUMrandomUniform (0.0, 1.0) <= r) {
KNN_removeInstance (me, candidates[y]);
for (long i = y + 1; i < ncandidates; ++ i) {
if(candidates[i] > candidates[y])
-- candidates[i];
}
++ removals;
}
}
}
return removals;
}
long KNN_prune_prune
(
///////////////////////////////
// Parameters //
///////////////////////////////
KNN me, // the classifier to be pruned
//
double n, // pruning degree: noise, 0 <= n <= 1
//
double r, // pruning redundancy: noise, 0 <= n <= 1
//
long k // k(!)
//
)
{
Categories uniqueCategories = Categories_selectUniqueItems(my output, 1);
if(Categories_getSize(uniqueCategories) == my nInstances)
return(0);
long removals = 0;
long ncandidates = 0;
long *candidates = NUMlvector (0, my nInstances - 1);
double progress = 1 / (double) my nInstances;
if(my nInstances <= 1)
return(0);
for (long y = 1; y <= my nInstances; y++)
{
if (!Melder_progress1(1 - (double) y * progress, L"Pruning noisy instances")) return(removals);
if (KNN_prune_noisy(my input, my output, y, k))
{
if (n == 1 || NUMrandomUniform(0, 1) <= n)
{
KNN_removeInstance(me, y);
++removals;
}
}
}
Melder_progress1(1.0, NULL);
for (long y = 1; y <= my nInstances; ++y)
{
if (!Melder_progress1(1 - (double) y * progress, L"Identifying superfluous and critical instances")) return(removals);
if (KNN_prune_superfluous(my input, my output, y, k, 0) && !KNN_prune_critical(my input, my output, y, k))
candidates[ncandidates++] = y;
}
Melder_progress1(1.0, NULL);
KNN_prune_sort(my input, my output, k, candidates, ncandidates);
progress = 1 / ncandidates;
for (long y = 0; y < ncandidates; ++y)
{
if (!Melder_progress1(1 - (double) y * progress, L"Pruning superfluous non-critical instances")) return(removals);
if (KNN_prune_superfluous(my input, my output, candidates[y], k, 0) && !KNN_prune_critical(my input, my output, candidates[y], k))
{
if (r == 1 || NUMrandomUniform(0, 1) <= r)
{
KNN_removeInstance(me, candidates[y]);
for(long i = y + 1; i < ncandidates; ++i)
if(candidates[i] > candidates[y])
--candidates[i];
++removals;
}
}
}
Melder_progress1(1.0, NULL);
NUMlvector_free (candidates, 0);
return(removals);
}