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

examples/mnist_test.py

@@ -37,7 +37,7 @@ def mnist_train(
         autoencoder = cls(
             [in_len, 256, 2],
             [2, 256, in_len],
-            0.0001,
+            0.001,
             LeakyReLU()
         )
     print("CTRL+C to interrupt training.")
@@ -91,8 +91,8 @@ def plot_random_reconstruction(
     print(f'{code.tolist()}')
 
 
-def mnist_test(model: str | AAutoencoder):
-    x_train, _, x_test, y_test = load_mnist()
+def mnist_test(model: str | AAutoencoder | LabelingVAE):
+    x_train, y_train, x_test, y_test = load_mnist()
     in_len = x_train[0].shape[0] * x_train[0].shape[0]
     img_shape = x_train[0].shape
     x_train.resize(x_train.shape[0], in_len)
@@ -107,6 +107,11 @@ def mnist_test(model: str | AAutoencoder):
     print(autoencoder)
     idx = np.random.randint(0, len(x_test))
     example: np.ndarray = x_test[idx]
+    y_train = [str(int(i)) for i in y_train]
+    autoencoder.learn_labels(x_train, y_train, 5)
+    res = autoencoder.label(x_train[idx])
+    for k, v in res.items():
+        print(f"{k} => {v}")
     plot_random_reconstruction(autoencoder, example, img_shape, y_test[idx])
     if autoencoder.space_dim == 2:
         plot_mnist_latent_space(autoencoder, x_test, y_test)
@@ -150,6 +155,6 @@ if __name__ == "__main__":
             args.m,
             args.e,
             args.p,
-            VariationalAutoencoder
+            LabelingVAE
         )
         mnist_test(autoencoder)

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
+            )
+        )