input: Write a function to create a machine learning classifier in Python for categorizing user input. The classifier should use the Multinomial Naive Bayes algorithm. The function should take an input string, extract features, train the classifier, and return the prediction. Use the following assert test cases to validate the function.
  assert classify_string("example input")=="expected_category"
  assert classify_string("another example")=="another_expected_category"
  assert classify_string("different input")=="different_category"
  assert classify_string("test input")=="test_category"
  assert classify_string("unknown input")=="default_category"
output: def classify_string(input_string):
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
      import numpy as np
  
      # Sample training data
      data = [
          ("example input", "expected_category"),
          ("another example", "another_expected_category"),
          ("different input", "different_category"),
          ("test input", "test_category")
      ]
  
      # Split data into features and labels
      texts, labels = zip(*data)
  
      # Vectorize the text data
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(texts)
  
      # Train the classifier
      clf = MultinomialNB()
      clf.fit(X, labels)
  
      # Vectorize the input string
      input_vector = vectorizer.transform([input_string])
  
      # Predict the category
      prediction = clf.predict(input_vector)
  
      # Return the predicted category
      return prediction[0] if prediction[0] in labels else "default_category"