fix goss in multi-classification

src/boosting/goss.hpp

@@ -70,9 +70,12 @@ public:
   }
 
   data_size_t BaggingHelper(Random& cur_rand, data_size_t start, data_size_t cnt, data_size_t* buffer, data_size_t* buffer_right) {
-    std::vector<score_t> tmp_gradients(cnt);
+    std::vector<score_t> tmp_gradients(cnt, 0.0f);
     for (data_size_t i = 0; i < cnt; ++i) {
-      tmp_gradients[i] = std::fabs(gradients_[start + i] * hessians_[start + i]);
+      for (int curr_class = 0; curr_class < num_class_; ++curr_class) {
+        int idx = curr_class * num_data_ + start + i;
+        tmp_gradients[i] += std::fabs(gradients_[idx] * hessians_[idx]);
+      }
     }
     data_size_t top_k = static_cast<data_size_t>(cnt * gbdt_config_->top_rate);
     data_size_t other_k = static_cast<data_size_t>(cnt * gbdt_config_->other_rate);
@@ -85,7 +88,12 @@ public:
     data_size_t cur_right_cnt = 0;
     data_size_t big_weight_cnt = 0;
     for (data_size_t i = 0; i < cnt; ++i) {
-      if (std::fabs(gradients_[start + i] * hessians_[start + i]) >= threshold) {
+      score_t grad = 0.0f;
+      for (int curr_class = 0; curr_class < num_class_; ++curr_class) {
+        int idx = curr_class * num_data_ + start + i;
+        grad += std::fabs(gradients_[idx] * hessians_[idx]);
+      }
+      if (grad >= threshold) {
         buffer[cur_left_cnt++] = start + i;
         ++big_weight_cnt;
       } else {