input: Write a function to create a machine learning classifier in Python for categorizing user input using the Multinomial Naive Bayes algorithm. The function should take an input string, perform feature extraction and transformation, train the classifier, and return the prediction.
  assert classify_string("Hello") == prediction_for("Hello")
  assert classify_string("Goodbye") == prediction_for("Goodbye")
  assert classify_string("How are you?") == prediction_for("How are you?")
  assert classify_string("") == prediction_for("")
  assert classify_string("Python programming") == prediction_for("Python programming")
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
      train_data = ["Hello", "Goodbye", "How are you?", "Python programming"]
      train_labels = [0, 1, 2, 3]  # Corresponding labels for the training data
  
      # Feature extraction
      vectorizer = CountVectorizer()
      X_train = vectorizer.fit_transform(train_data)
  
      # Train the classifier
      classifier = MultinomialNB()
      classifier.fit(X_train, train_labels)
  
      # Transform the input string
      X_input = vectorizer.transform([input_string])
  
      # Predict the category
      prediction = classifier.predict(X_input)
  
      return int(prediction[0])