From 75eae85b7317b6a27de0100aeaeff068f6e1c6a1 Mon Sep 17 00:00:00 2001
From: Zezhi Shao <864453277@qq.com>
Date: Wed, 11 Dec 2024 13:07:55 +0800
Subject: [PATCH] =?UTF-8?q?docs:=20=E2=9C=8F=EF=B8=8F=20fix=20lint?=
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---
 basicts/metrics/corr.py     | 2 +-
 basicts/metrics/r_square.py | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/basicts/metrics/corr.py b/basicts/metrics/corr.py
index 95cbdcd..4e10ee4 100644
--- a/basicts/metrics/corr.py
+++ b/basicts/metrics/corr.py
@@ -47,4 +47,4 @@ def masked_corr(prediction: torch.Tensor, target: torch.Tensor, null_val: float
     loss = loss * mask  # Apply the mask to the loss
     loss = torch.nan_to_num(loss)  # Replace any NaNs in the loss with zero
 
-    return torch.mean(loss)
\ No newline at end of file
+    return torch.mean(loss)
diff --git a/basicts/metrics/r_square.py b/basicts/metrics/r_square.py
index 5034440..2d3ce51 100644
--- a/basicts/metrics/r_square.py
+++ b/basicts/metrics/r_square.py
@@ -36,9 +36,9 @@ def masked_r2(prediction: torch.Tensor, target: torch.Tensor, null_val: float =
 
     ss_res = torch.sum(torch.pow((target - prediction), 2), dim=1)  # 残差平方和
     ss_tot = torch.sum(torch.pow(target - torch.mean(target, dim=1, keepdim=True), 2), dim=1)  # 总平方和
-    
+
     # 计算 R^2
     loss = 1 - (ss_res / (ss_tot + eps))
-    
+
     loss = torch.nan_to_num(loss)  # Replace any NaNs in the loss with zero
     return torch.mean(loss)