input: Write a function to create a machine learning classifier in Python for categorizing user input using the Naive Bayes algorithm. Implement this by extracting features from the input string, training the classifier with the data, and then making predictions based on user input. If the input string is empty or the feature extraction process fails to produce numeric data, return an empty list as the prediction.
  assert classify_string("This is a sample input")==[0]
  assert classify_string("Another example")==[1]
  assert classify_string("")==[]
  assert classify_string("Some input without clear category")==[2]
  assert classify_string("Yet another test case")==[1]
output: def classify_string(input_string):
      if not input_string:
          return []
      
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
      import numpy as np
  
      data = [
          "This is a sample input",
          "Another example",
          "Some input without clear category",
          "Yet another test case"
      ]
      labels = [0, 1, 2, 1]
  
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(data)
      classifier = MultinomialNB()
      classifier.fit(X, labels)
  
      try:
          input_vector = vectorizer.transform([input_string])
          if input_vector.sum() == 0:
              return []
          prediction = classifier.predict(input_vector)
          return prediction.tolist()
      except:
          return []