Refactor LSTM class: Improve test method and add comments

neural_network/lstm.py

@@ -346,18 +346,12 @@ class LongShortTermMemory:
 
             self.backward_pass(errors, inputs)
 
-    def test(self) -> None:
+    def test(self):
         """
-        Test the trained LSTM network on the input data and print the accuracy.
+        Test the LSTM model.
 
-        >>> lstm = LongShortTermMemory("abcde" * 50, hidden_layer_size=10,
-        ... training_epochs=5, learning_rate=0.01)
-        >>> lstm is not None
-        True
-        >>> lstm.train()
-        >>> output = lstm.test()
-        >>> output is not None
-        True
+        Returns:
+            str: The output predictions.
         """
         accuracy = 0
         probabilities = self.forward_pass(
@@ -366,6 +360,7 @@ class LongShortTermMemory:
 
         output = ""
         for t in range(len(self.target_sequence)):
+            # Apply softmax to get probabilities for predictions
             probs = self.softmax(probabilities[t].reshape(-1))
             prediction_index = self.random_generator.choice(
                 self.vocabulary_size, p=probs
@@ -374,17 +369,18 @@ class LongShortTermMemory:
 
             output += prediction
 
+            # Calculate accuracy
             if prediction == self.target_sequence[t]:
                 accuracy += 1
 
-        # print(f"Ground Truth:\n{self.target_sequence}\n")
-        # print(f"Predictions:\n{output}\n")
-
-        # print(f"Accuracy: {round(accuracy * 100 / len(self.input_sequence), 2)}%")
+        print(f"Ground Truth:\n{self.target_sequence}\n")
+        print(f"Predictions:\n{output}\n")
+        print(f"Accuracy: {round(accuracy * 100 / len(self.input_sequence), 2)}%")
 
         return output
 
 
+
 if __name__ == "__main__":
     sample_data = """Long Short-Term Memory (LSTM) networks are a type
          of recurrent neural network (RNN) capable of learning "