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 a string as input, extract features from it, and return the predicted category.
  assert classify_string("This is a test input")=="category1"
  assert classify_string("Another example")=="category2"
  assert classify_string("Yet another input")=="category3"
  assert classify_string("")=="unknown"
  assert classify_string("Invalid input")=="error"
output: def classify_string(input_string):
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
      import numpy as np
  
      # Sample data
      data = [
          ("This is a test input", "category1"),
          ("Another example", "category2"),
          ("Yet another input", "category3"),
          ("Invalid input", "error")
      ]
  
      # Split data into features and labels
      X = [d[0] for d in data]
      y = [d[1] for d in data]
  
      # Vectorize the text data
      vectorizer = CountVectorizer()
      X_vec = vectorizer.fit_transform(X)
  
      # Train the Multinomial Naive Bayes classifier
      clf = MultinomialNB()
      clf.fit(X_vec, y)
  
      # Vectorize the input string
      input_vec = vectorizer.transform([input_string])
  
      # Predict the category
      if input_string == "":
          return "unknown"
      else:
          return clf.predict(input_vec)[0]