def sample_combinations(samples, sample_labels, per_class, classifier, alg_args): n_features = shape(samples)[1]; features = tuple(range(n_features)); ultimate = (-inf, features),; per_k = 4; subsets = []; for k in range(1, n_features): combs = [comb for comb in combinations(features, k)]; subsets += native_sample(combs, min([per_k, len(combs)])); candidates = tuple((merit(samples, sample_labels, comb), comb) for comb in subsets); return candidates;
def stochastic_selector(samples, frontier, successors, beam_width, classifier): successors = tuple(native_sample(successors, min((beam_width, len(successors))))); return tuple((merit(samples, sample_labels, s), s) for s in successors);