Fixed tpe test

Signed-off-by: Shintaro Sakoda <shintaro.sakoda@tier4.jp>

Author: SakodaShintaro

localization/tree_structured_parzen_estimator/test/test_tpe.cpp

@@ -28,38 +28,44 @@ TEST(TreeStructuredParzenEstimatorTest, TPE_is_better_than_random_search_on_sphe
     return value;
   };
 
-  constexpr int64_t kOuterTrialsNum = 10;
-  constexpr int64_t kInnerTrialsNum = 100;
+  constexpr int64_t kOuterTrialsNum = 20;
+  constexpr int64_t kInnerTrialsNum = 200;
   std::cout << std::fixed;
   std::vector<double> mean_scores;
   const int64_t n_startup_trials = kInnerTrialsNum / 10;
   const std::string method = ((n_startup_trials == kInnerTrialsNum) ? "Random" : "TPE");
 
-  std::vector<double> scores;
-  for (int64_t i = 0; i < kOuterTrialsNum; i++) {
-    double best_score = std::numeric_limits<double>::lowest();
-    const std::vector<double> sample_mean(5, 0.0);
-    const std::vector<double> sample_stddev(5, 2.0);
-    TreeStructuredParzenEstimator estimator(
-      TreeStructuredParzenEstimator::Direction::MAXIMIZE, n_startup_trials, sample_mean,
-      sample_stddev);
-    for (int64_t trial = 0; trial < kInnerTrialsNum; trial++) {
-      const TreeStructuredParzenEstimator::Input input = estimator.get_next_input();
-      const double score = -sphere_function(input);
-      estimator.add_trial({input, score});
-      best_score = std::max(best_score, score);
+  const std::vector<double> sample_mean(5, 0.0);
+  const std::vector<double> sample_stddev {1.0, 1.0, 0.1, 0.1, 0.1};
+
+  for (const int64_t n_startup_trials : {kInnerTrialsNum, kInnerTrialsNum / 2}) {
+    const std::string method = ((n_startup_trials == kInnerTrialsNum) ? "Random" : "TPE");
+
+    std::vector<double> scores;
+    for (int64_t i = 0; i < kOuterTrialsNum; i++) {
+      double best_score = std::numeric_limits<double>::lowest();
+      TreeStructuredParzenEstimator estimator(
+        TreeStructuredParzenEstimator::Direction::MAXIMIZE, n_startup_trials, sample_mean,
+        sample_stddev);
+      for (int64_t trial = 0; trial < kInnerTrialsNum; trial++) {
+        const TreeStructuredParzenEstimator::Input input = estimator.get_next_input();
+        const double score = -sphere_function(input);
+        estimator.add_trial({input, score});
+        best_score = std::max(best_score, score);
+      }
+      scores.push_back(best_score);
     }
-    scores.push_back(best_score);
-  }
 
-  const double sum = std::accumulate(scores.begin(), scores.end(), 0.0);
-  const double mean = sum / scores.size();
-  mean_scores.push_back(mean);
-  double sq_sum = 0.0;
-  for (const double score : scores) {
-    sq_sum += (score - mean) * (score - mean);
-  }
-  const double stddev = std::sqrt(sq_sum / scores.size());
+    const double sum = std::accumulate(scores.begin(), scores.end(), 0.0);
+    const double mean = sum / scores.size();
+    mean_scores.push_back(mean);
+    double sq_sum = 0.0;
+    for (const double score : scores) {
+      sq_sum += (score - mean) * (score - mean);
+    }
+    const double stddev = std::sqrt(sq_sum / scores.size());
 
-  std::cout << method << ", mean = " << mean << ", stddev = " << stddev << std::endl;
+    std::cout << method << ", mean = " << mean << ", stddev = " << stddev << std::endl;
+  }
+  ASSERT_LT(mean_scores[0], mean_scores[1]);
 }