feat: add monte-carlo method and MSE to labeling method

src/easyvae/autoencoder.py

@@ -290,8 +290,11 @@ class LabelingVAE(VariationalAutoencoder):
             for label in self.labels:
                 label.cache()
 
-    def label(self, x: np.ndarray):
-        y = self.encode(x)
+    def label(self, x: np.ndarray, samples=10):
+        y = np.zeros((samples, self.encoder.out_size))
+        for i in range(samples):
+            y[i] = self.encode(x)
+        y = np.mean(y, axis=0)
         probs = {}
         total = 0
         for label in self.labels:
@@ -300,4 +303,10 @@ class LabelingVAE(VariationalAutoencoder):
             total += p
         for k in probs:
             probs[k] = float(probs[k] / total)
-        return dict(sorted(probs.items()))
+        return dict(
+            sorted(
+                probs.items(),
+                key=lambda item: item[1],
+                reverse=True
+            )
+        )