From 25e048080555c74d15159b10b8c32662bdb12ce5 Mon Sep 17 00:00:00 2001
From: lisyarus <lisyarus@gmail.com>
Date: Fri, 15 Jan 2021 22:37:53 +0300
Subject: [PATCH] Remove obsolete 'optimize' mode from neural animation example

---
 examples/animation_2d.cpp | 78 ++-------------------------------------
 1 file changed, 3 insertions(+), 75 deletions(-)

diff --git a/examples/animation_2d.cpp b/examples/animation_2d.cpp
index f96a5236..811c6dcc 100644
--- a/examples/animation_2d.cpp
+++ b/examples/animation_2d.cpp
@@ -669,13 +669,12 @@ struct animation_2d_app
 	enum class mode
 	{
 		train,
-		optimize,
 		test,
 	} mode = mode::train;
 
 	std::vector<controller> population;
-	std::size_t const population_size = 256;
-	std::size_t const max_train_frames = 60.f / physics.dt;
+	std::size_t const population_size = 16;
+	std::size_t const max_train_frames = 10.f / physics.dt;
 	std::size_t const max_train_variations = 1;
 	float const position_variation_amplitude = 0.f;
 	float const angle_variation_amplitude = 0.f;
@@ -686,10 +685,6 @@ struct animation_2d_app
 	static constexpr auto mutation_amplitude = [](float t){ return 10.f * geom::lerp(1.f, 0.01f, t); };
 //	static constexpr auto mutation_amplitude = [](float){ return 0.00001f; };
 
-	std::size_t optimize_iterations = 0;
-	std::size_t const max_optimize_iterations = 1;
-	float const optimize_amplitude = 0.1f;
-
 	float best_score = 0.f;
 	bool const warm_start = false;
 	bool const enable_testing = true;
@@ -820,18 +815,6 @@ void animation_2d_app::update()
 	{
 		do_train();
 		if (train_iterations >= max_train_iterations)
-		{
-			mode = mode::optimize;
-		}
-		else if (train_iterations >= max_train_iterations / 2 && best_score < -1000.f)
-		{
-//			train_iterations = 0;
-		}
-	}
-	else if (mode == mode::optimize)
-	{
-		do_optimize();
-		if (optimize_iterations >= max_optimize_iterations)
 		{
 			mode = mode::test;
 			frame_clock.restart();
@@ -1243,61 +1226,6 @@ void animation_2d_app::do_train()
 	best_score = scores.front().first;
 }
 
-void animation_2d_app::do_optimize()
-{
-	auto & c = population[0];
-
-	auto rng = this->rng;
-
-	float current_score = eval_score(c, rng);
-
-	float eps = 0.1f;
-
-	std::vector<float> scores(c.param_count, 0.f);
-	std::atomic<std::size_t> dispatched{0};
-	for (std::size_t i = 0; i < c.param_count; ++i)
-	{
-		bg.dispatch([&, i, rng = rng]() mutable {
-			controller cc = c;
-			cc.params()[i] += eps;
-			scores[i] = eval_score(cc, rng);
-			++dispatched;
-		});
-	}
-//	bg.wait();
-	while (dispatched.load() < scores.size());
-
-	for (auto & s : scores)
-		s = (s - current_score) / eps;
-
-	float m = 0.f;
-	for (auto s : scores)
-		m = std::max(m, std::abs(s));
-
-	log::info() << "Max gradient: " << m;
-
-	for (std::size_t k = 0; k < 30; ++k)
-	{
-		auto cc = c;
-		for (std::size_t i = 0; i < c.param_count; ++i)
-			cc.params()[i] += optimize_amplitude * scores[i];
-		float new_score = eval_score(cc, rng);
-
-		if (new_score > current_score)
-		{
-			log::info() << "k = " << k;
-			log::info() << "Score change: " << current_score << " -> " << new_score;
-			c = cc;
-			break;
-		}
-
-		for (auto & s : scores)
-			s /= 2.f;
-	}
-
-	++optimize_iterations;
-}
-
 void animation_2d_app::do_test()
 {
 	std::optional<geom::point<float, 2>> m;
@@ -1466,7 +1394,7 @@ void animation_2d_app::present()
 		opts.x = gfx::painter::x_align::left;
 		opts.y = gfx::painter::y_align::top;
 		opts.scale = 2.f;
-		painter.text({40.f, 40.f}, util::to_string(train_iterations, "/", max_train_iterations, "     ", optimize_iterations, "/", max_optimize_iterations), opts);
+		painter.text({40.f, 40.f}, util::to_string(train_iterations, "/", max_train_iterations), opts);
 		painter.text({40.f, 64.f}, util::to_string("Best score: ", std::setprecision(10), best_score), opts);
 		painter.text({40.f, 88.f}, util::to_string("Model: ", test_id, "/", population.size(), ", gen ", population[test_id].generation), opts);
 		painter.text({40.f, 112.f}, util::to_string(util::pretty(test_clock.duration(), std::chrono::milliseconds{1})), opts);