fix: add history mem cap and mid point pruning

src/easyvae/autoencoder.py

@@ -245,20 +245,28 @@ class VariationalAutoencoder(AAutoencoder):
 
 
 class Label:
-    def __init__(self, name: str, embedding_size: int):
+    def __init__(self,
+                 name: str,
+                 embedding_size: int,
+                 N=100):
         self.name = name
         self.embedding_size = embedding_size
-        self.history = []
+        self.N = N
+        self.idx = 0
+        self.history = np.zeros((self.N, embedding_size))
 
     def observe(self, code: np.ndarray):
-        self.history.append(code)
+        if self.idx < self.N:
-
+            self.history[self.idx] = code
-    def cache(self):
+            self.idx += 1
-        self.history_np = np.array(self.history)
+        else:
+            diffs = np.linalg.norm(self.history - code, axis=0)
+            idx = np.argmin(diffs)
+            self.history[idx] = (self.history[idx] + code) / 2
 
     def p(self, x: np.ndarray):
         return np.mean(
-            np.exp(-np.abs(self.history_np - x))
+            np.exp(-np.abs(self.history - x))
         )
 
 
@@ -282,8 +290,6 @@ class LabelingVAE(VariationalAutoencoder):
                 else:
                     label = self.labels[idx]
                 label.observe(y_i)
-        for label in self.labels:
-            label.cache()
 
     def label(self, x: np.ndarray):
         probs = {}