diff --git a/src/gomoku.cpp b/src/gomoku.cpp
index 4668285..a413f91 100644
--- a/src/gomoku.cpp
+++ b/src/gomoku.cpp
@@ -57,7 +57,7 @@ bool Gomoku::set_rule(unsigned int rule_flag)
 
 bool Gomoku::is_illegal(unsigned int x, unsigned int y)
 {
-  return x > this->n - 1 || y > this->n - 1 || this->board[x][y] != 0;
+  return (x > this->n - 1 || y > this->n - 1 || this->board[x][y] != 0);
 }
 
 std::vector<int> Gomoku::get_legal_moves()
diff --git a/src/onnx.cpp b/src/onnx.cpp
index 552dddd..989aeff 100644
--- a/src/onnx.cpp
+++ b/src/onnx.cpp
@@ -200,7 +200,7 @@ NeuralNetwork::~NeuralNetwork()
     this->shared_session.reset();
 }
 
-std::future<NeuralNetwork::return_type> NeuralNetwork::commit(Gomoku *gomoku)
+std::future<NeuralNetwork::return_type> NeuralNetwork::commit(const Gomoku *gomoku)
 {
   std::vector<float> state = transorm_gomoku_to_Tensor(gomoku);
 
@@ -282,7 +282,7 @@ std::vector<float> NeuralNetwork::transorm_board_to_Tensor(const board_type &boa
   //  return cat({ state0, state1, state2 }, 1);
 }
 
-std::vector<float> NeuralNetwork::transorm_gomoku_to_Tensor(Gomoku *gomoku)
+std::vector<float> NeuralNetwork::transorm_gomoku_to_Tensor(const Gomoku *gomoku)
 {
   return NeuralNetwork::transorm_board_to_Tensor(gomoku->get_board(), gomoku->get_last_move(), gomoku->get_current_color());
 }
diff --git a/src/onnx.h b/src/onnx.h
index 1c5ebb2..7a1cbc4 100644
--- a/src/onnx.h
+++ b/src/onnx.h
@@ -52,9 +52,9 @@ class NeuralNetwork
   // void save_weights(std::string model_path);
   ~NeuralNetwork();
 
-  std::future<return_type> commit(Gomoku *gomoku); // commit task to queue
+  std::future<return_type> commit(const Gomoku *gomoku); // commit task to queue
   // std::shared_ptr<torch::jit::script::Module> module;  // torch module    origin:private
-  static std::vector<float> transorm_gomoku_to_Tensor(Gomoku *gomoku);
+  static std::vector<float> transorm_gomoku_to_Tensor(const Gomoku *gomoku);
   static std::vector<float> transorm_board_to_Tensor(const board_type &board, int last_move, int cur_player);
 
   bool set_batch_size(unsigned int u_batch_size);
diff --git a/src/play.cpp b/src/play.cpp
index 6d0186e..a752878 100644
--- a/src/play.cpp
+++ b/src/play.cpp
@@ -62,7 +62,7 @@ void SelfPlay::play(unsigned int saved_id)
     while (game_state.first == 0)
     {
         // std::cout << "game id: " << saved_id << std::endl;
-        double temp = step < EXPLORE_STEP ? 1.0 : 1e-3;
+        double temp = (step < EXPLORE_STEP) ? 1.0 : 1e-3;
         auto action_probs = mcts->get_action_probs(g.get(), temp);
         // auto action_probs = m->get_action_probs(g.get(), 1);
         // int best_action = m->get_best_action_from_prob(action_probs);
diff --git a/src/train_and_eval/train_eval_net.cpp b/src/train_and_eval/train_eval_net.cpp
index ca664c9..970c0e3 100644
--- a/src/train_and_eval/train_eval_net.cpp
+++ b/src/train_and_eval/train_eval_net.cpp
@@ -57,7 +57,7 @@ void play_for_eval(NeuralNetwork *a, NeuralNetwork *b, bool a_first, int *win_ta
     // std::cout << episode << " th game!!" << std::endl;
     while (game_state.first == 0)
     {
-        int res = (step + a_first) % 2 ? ma.get_best_action(g.get()) : mb.get_best_action(g.get());
+        int res = ((step + a_first) % 2) ? ma.get_best_action(g.get()) : mb.get_best_action(g.get());
         ma.update_with_move(res);
         mb.update_with_move(res);
         g->execute_move(res);