Reduces memory usage a bit by using smaller types.

lib/mealy.hpp

@@ -18,9 +18,9 @@
 struct mealy {
 	struct edge {
 		edge() = default;
-		edge(state t, output o) : to(t), output(o) {}
+		edge(state t, output o) : to(t), out(o) {}
 		state to = state(-1);
-		output output = size_t(-1);
+		output out = output(-1);
 	};
 
 	// state -> input -> (output, state)
@@ -34,7 +34,7 @@ struct mealy {
 inline bool is_complete(const mealy & m){
 	for(state n = 0; n < m.graph_size; ++n){
 		if(m.graph[n].size() != m.input_size) return false;
-		for(auto && e : m.graph[n]) if(e.to == state(-1) || e.output == output(-1)) return false;
+		for(auto && e : m.graph[n]) if(e.to == state(-1) || e.out == output(-1)) return false;
 	}
 	return true;
 }
@@ -42,7 +42,7 @@ inline bool is_complete(const mealy & m){
 inline bool defined(mealy const & m, state s, input i) {
 	if (s >= m.graph.size()) return false;
 	if (i >= m.graph[s].size()) return false;
-	if (m.graph[s][i].to == state(-1) || m.graph[s][i].output == output(-1)) return false;
+	if (m.graph[s][i].to == state(-1) || m.graph[s][i].out == output(-1)) return false;
 	return true;
 }
 

lib/reachability.cpp

@@ -29,7 +29,7 @@ mealy reachable_submachine(const mealy& in, state start) {
 
 		for (input i = 0; i < in.input_size; ++i) {
 			const auto ret = apply(in, s, i);
-			const output o = ret.output;
+			const output o = ret.out;
 			const state t = ret.to;
 
 			if (!new_state.count(t)) new_state[t] = max_state++;

lib/separating_family.cpp

@@ -16,7 +16,7 @@ separating_family create_separating_family(const adaptive_distinguishing_sequenc
                                            const splitting_tree & separating_sequences) {
 	const auto N = sequence.CI.size();
 
-	vector<trie> suffixes(N);
+	vector<trie<input>> suffixes(N);
 	separating_family ret(N);
 
 	// First we accumulate the kind-of-UIOs and the separating words we need. We will do this with a

lib/splitting_tree.cpp

@@ -102,7 +102,7 @@ result create_splitting_tree(const mealy & g, options opt) {
 				    end(boom.states), [symbol, depth, &g, &update_succession](state state) {
 				    	const auto r = apply(g, state, symbol);
 				    	update_succession(state, r.to, depth);
-				    	return r.output;
+				    	return r.out;
 				    }, Q);
 
 				// no split -> continue with other input symbols
@@ -144,7 +144,7 @@ result create_splitting_tree(const mealy & g, options opt) {
 				    end(boom.states), [word, depth, &g, &update_succession](state state) {
 				    	const mealy::edge r = apply(g, state, word.begin(), word.end());
 				    	update_succession(state, r.to, depth);
-				    	return r.output;
+				    	return r.out;
 				    }, Q);
 
 				// not a valid split -> continue

lib/trie.cpp

@@ -1,17 +0,0 @@
-#include "trie.hpp"
-
-std::vector<std::vector<size_t>> flatten(const trie & t) {
-	std::vector<std::vector<size_t>> ret;
-	t.for_each([&ret](auto && w) { ret.push_back(w); });
-	return ret;
-}
-
-std::pair<size_t, size_t> total_size(const trie & t) {
-	size_t count = 0;
-	size_t total_count = 0;
-	t.for_each([&count, &total_count](auto && w) {
-		++count;
-		total_count += w.size();
-	});
-	return {count, total_count};
-}

lib/trie.hpp

@@ -10,10 +10,10 @@
 ///
 /// \brief A Trie datastructure used to remove prefixes in a set of words
 ///
-/// The datastructure only works for words over size_t. In principle the symbols
-/// can be unbounded, however having very large symbols degrades the performance
-/// a lot. Some random testing shows that for symbols <= 50 the performance is
-/// similar to std::set (which is solving a different problem).
+/// The datastructure only works for words over integral unsigned types. In principle the symbols
+/// can be unbounded, however having very large symbols degrades the performance a lot. Some random
+/// testing shows that for symbols <= 50 the performance is similar to std::set (which is solving a
+/// different problem).
 ///
 /// Tests : 1M words, avg words length 4 (geometric dist.), alphabet 50 symbols
 /// trie reduction 58% in 1.15s
@@ -23,7 +23,9 @@
 /// There are, however, "internal iterators" exposed as a for_each() member
 /// function (if only we had coroutines already...)
 ///
-struct trie {
+template <typename T> struct trie {
+	static_assert(std::is_integral<T>::value && std::is_unsigned<T>::value, "");
+
 	/// \brief Inserts a word (given by iterators \p begin and \p end)
 	/// \returns true if the element was inserted, false if already there
 	template <typename Iterator> bool insert(Iterator && begin, Iterator && end) {
@@ -46,19 +48,17 @@ struct trie {
 
 	/// \brief Applies \p function to all word (not to the prefixes)
 	template <typename Fun> void for_each(Fun && function) const {
-		std::vector<size_t> word;
+		std::vector<T> word;
 		return for_each_impl(std::forward<Fun>(function), word);
 	}
 
 	/// \brief Empties the complete set
-	void clear() {
-		branches.clear();
-	}
+	void clear() { branches.clear(); }
 
   private:
-	template <typename Fun> void for_each_impl(Fun && function, std::vector<size_t> & word) const {
+	template <typename Fun> void for_each_impl(Fun && function, std::vector<T> & word) const {
 		size_t count = 0;
-		for (size_t i = 0; i < branches.size(); ++i) {
+		for (T i = 0; i < branches.size(); ++i) {
 			auto const & b = branches[i];
 			if (b) {
 				++count;
@@ -80,7 +80,19 @@ struct trie {
 
 /// \brief Flattens a trie \p t
 /// \returns an array of words (without the prefixes)
-std::vector<std::vector<size_t>> flatten(trie const & t);
+template <typename T> std::vector<std::vector<T>> flatten(trie<T> const & t) {
+	std::vector<std::vector<T>> ret;
+	t.for_each([&ret](auto && w) { ret.push_back(w); });
+	return ret;
+}
 
 /// \brief Returns size and total sum of symbols
-std::pair<size_t, size_t> total_size(trie const & t);
+template <typename T> std::pair<size_t, size_t> total_size(trie<T> const & t) {
+	size_t count = 0;
+	size_t total_count = 0;
+	t.for_each([&count, &total_count](auto && w) {
+		++count;
+		total_count += w.size();
+	});
+	return {count, total_count};
+}

lib/types.hpp

@@ -4,9 +4,9 @@
 #include <vector>
 
 // We use size_ts for fast indexing. Note that there is little type safety here
-using state = size_t;
-using input = size_t;
-using output = size_t;
+using state = uint16_t;
+using input = uint16_t;
+using output = uint16_t;
 
 using word = std::vector<input>;
 

src/distance.cpp

@@ -95,7 +95,7 @@ int main(int argc, char * argv[]) {
 			auto q1 = apply(m1, s1, i);
 			auto q2 = apply(m2, s2, i);
 
-			if (q1.output != q2.output) {
+			if (q1.out != q2.out) {
 				current_counterexamples++;
 			}
 

src/methods.cpp

@@ -79,7 +79,7 @@ int main(int argc, char * argv[]) {
 	const auto prefixes = prefixes_fut.get();
 	const auto middles = middles_fut.get();
 	const auto suffixes = suffixes_fut.get();
-	trie test_suite;
+	trie<input> test_suite;
 
 	clog << "start testing" << endl;
 	const state start = 0;

src/trie_test.cpp

@@ -24,7 +24,7 @@ static void test() {
 	word w5 = {5, 5, 3};
 	word w6 = {5, 5, 3, 1};
 
-	trie t;
+	trie<unsigned> t;
 	check(t.insert(w1));
 	check(!t.insert(w1));
 	check(t.insert(w2));
@@ -77,7 +77,7 @@ static void performance() {
 	using seconds = std::chrono::duration<double>;
 
 	auto t_start = clock::now();
-	trie t;
+	trie<unsigned> t;
 	for (auto&& w : corpus) t.insert(w);
 	auto t_end = clock::now();