From 8c28c1a7eeb316510e9688e813cf4ec5b1031826 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E9=83=91=E4=BC=9F=E9=91=AB?= <3482525266@qq.com>
Date: Wed, 7 Jun 2023 16:58:45 +0800
Subject: [PATCH] fix the bug running in Python3

---
 main.py          | 8 ++++----
 models/LSTNet.py | 2 +-
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/main.py b/main.py
index 05bfa0a..996a068 100755
--- a/main.py
+++ b/main.py
@@ -29,8 +29,8 @@ def evaluate(data, X, Y, model, evaluateL2, evaluateL1, batch_size):
             test = torch.cat((test, Y));
         
         scale = data.scale.expand(output.size(0), data.m)
-        total_loss += evaluateL2(output * scale, Y * scale).data[0]
-        total_loss_l1 += evaluateL1(output * scale, Y * scale).data[0]
+        total_loss += evaluateL2(output * scale, Y * scale).item()
+        total_loss_l1 += evaluateL1(output * scale, Y * scale).item()
         n_samples += (output.size(0) * data.m);
     rse = math.sqrt(total_loss / n_samples)/data.rse
     rae = (total_loss_l1/n_samples)/data.rae
@@ -42,7 +42,7 @@ def evaluate(data, X, Y, model, evaluateL2, evaluateL1, batch_size):
     mean_p = predict.mean(axis = 0)
     mean_g = Ytest.mean(axis = 0)
     index = (sigma_g!=0);
-    correlation = ((predict - mean_p) * (Ytest - mean_g)).mean(axis = 0)/(sigma_p * sigma_g);
+    correlation = ((predict - mean_p) * (Ytest - mean_g)).mean(axis = 0)/(sigma_p * sigma_g + 1e-15);
     correlation = (correlation[index]).mean();
     return rse, rae, correlation;
 
@@ -57,7 +57,7 @@ def train(data, X, Y, model, criterion, optim, batch_size):
         loss = criterion(output * scale, Y * scale);
         loss.backward();
         grad_norm = optim.step();
-        total_loss += loss.data[0];
+        total_loss += loss.item();
         n_samples += (output.size(0) * data.m);
     return total_loss / n_samples
     
diff --git a/models/LSTNet.py b/models/LSTNet.py
index 61d667e..5588f92 100644
--- a/models/LSTNet.py
+++ b/models/LSTNet.py
@@ -13,7 +13,7 @@ def __init__(self, args, data):
         self.hidS = args.hidSkip;
         self.Ck = args.CNN_kernel;
         self.skip = args.skip;
-        self.pt = (self.P - self.Ck)/self.skip
+        self.pt = (self.P - self.Ck)//self.skip
         self.hw = args.highway_window
         self.conv1 = nn.Conv2d(1, self.hidC, kernel_size = (self.Ck, self.m));
         self.GRU1 = nn.GRU(self.hidC, self.hidR);