From c2fa181bc5e7b6811e5a66a0f7b3e9b294045aa5 Mon Sep 17 00:00:00 2001
From: D-X-Y <280835372@qq.com>
Date: Thu, 13 May 2021 13:42:33 +0000
Subject: [PATCH] Upgrade LFNA

---
 exps/LFNA/lfna.py             | 2 +-
 exps/LFNA/lfna_meta_model.py  | 1 +
 lib/datasets/synthetic_env.py | 4 +++-
 3 files changed, 5 insertions(+), 2 deletions(-)

diff --git a/exps/LFNA/lfna.py b/exps/LFNA/lfna.py
index 4d69a5c..3bb2c36 100644
--- a/exps/LFNA/lfna.py
+++ b/exps/LFNA/lfna.py
@@ -1,7 +1,7 @@
 #####################################################
 # Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
 #####################################################
-# python exps/LFNA/lfna.py --env_version v1
+# python exps/LFNA/lfna.py --env_version v1 --device cuda
 #####################################################
 import sys, time, copy, torch, random, argparse
 from tqdm import tqdm
diff --git a/exps/LFNA/lfna_meta_model.py b/exps/LFNA/lfna_meta_model.py
index 588c375..91649b8 100644
--- a/exps/LFNA/lfna_meta_model.py
+++ b/exps/LFNA/lfna_meta_model.py
@@ -55,6 +55,7 @@ class LFNA_Meta(super_core.SuperModule):
                     order=super_core.LayerOrder.PostNorm,
                 )
             )
+        layers.append(super_core.SuperLinear(time_embedding, time_embedding))
         self.meta_corrector = super_core.SuperSequential(*layers)
 
         model_kwargs = dict(
diff --git a/lib/datasets/synthetic_env.py b/lib/datasets/synthetic_env.py
index 9cef5be..767f817 100644
--- a/lib/datasets/synthetic_env.py
+++ b/lib/datasets/synthetic_env.py
@@ -110,8 +110,10 @@ class SyntheticDEnv(data.Dataset):
         if self._seq_length is None:
             return self.__call__(timestamp)
         else:
+            noise = random.random() * self.timestamp_interval * 0.3
             timestamps = [
-                timestamp + i * self.timestamp_interval for i in range(self._seq_length)
+                timestamp + i * self.timestamp_interval + noise
+                for i in range(self._seq_length)
             ]
             xdata = [self.__call__(timestamp) for timestamp in timestamps]
             return zip_sequence(xdata)