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)