C++ (Cpp) KNN_prune_critical Examples

Example #1

File: KNN_prune.cpp Project: READSEARCH/praat

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;
}

Example #2

File: KNN_prune.c Project: spirineta/tala

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);
}