LFNA ok on the valid data

xautodl/datasets/synthetic_env.py

@@ -66,11 +66,6 @@ class SyntheticDEnv(data.Dataset):
         self._cov_functors = cov_functors
 
         self._oracle_map = None
-        self._seq_length = None
-
-    @property
-    def seq_length(self):
-        return self._seq_length
 
     @property
     def min_timestamp(self):
@@ -84,14 +79,12 @@ class SyntheticDEnv(data.Dataset):
     def timestamp_interval(self):
         return self._timestamp_generator.interval
 
-    def random_timestamp(self):
-        return (
-            random.random() * (self.max_timestamp - self.min_timestamp)
-            + self.min_timestamp
-        )
-
-    def reset_max_seq_length(self, seq_length):
-        self._seq_length = seq_length
+    def random_timestamp(self, min_timestamp=None, max_timestamp=None):
+        if min_timestamp is None:
+            min_timestamp = self.min_timestamp
+        if max_timestamp is None:
+            max_timestamp = self.max_timestamp
+        return random.random() * (max_timestamp - min_timestamp) + min_timestamp
 
     def get_timestamp(self, index):
         if index is None:
@@ -119,19 +112,7 @@ class SyntheticDEnv(data.Dataset):
     def __getitem__(self, index):
         assert 0 <= index < len(self), "{:} is not in [0, {:})".format(index, len(self))
         index, timestamp = self._timestamp_generator[index]
-        if self._seq_length is None:
-            return self.__call__(timestamp)
-        else:
-            noise = (
-                random.random() * self.timestamp_interval * self._timestamp_noise_scale
-            )
-            timestamps = [
-                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)
-            return self.seq_call(timestamps)
+        return self.__call__(timestamp)
 
     def seq_call(self, timestamps):
         with torch.no_grad():