input: Write a function to implement a simple machine learning classifier in Python that categorizes user input using the Multinomial Naive Bayes algorithm. The function should take a string as input, extract and transform features from the string to numeric values, train the classifier on provided data, and then return the prediction for the input string.
  assert classify_string("I love programming")=="Positive"
  assert classify_string("I hate bugs")=="Negative"
  assert classify_string("The weather is nice today")=="Neutral"
  assert classify_string("")=="Neutral"
output: def classify_string(input_string):
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
      import numpy as np
      
      data = [
          ("I love programming", "Positive"),
          ("I hate bugs", "Negative"),
          ("The weather is nice today", "Neutral"),
          ("", "Neutral")
      ]
      
      texts, labels = zip(*data)
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(texts)
      y = np.array(labels)
      
      classifier = MultinomialNB()
      classifier.fit(X, y)
      
      input_vector = vectorizer.transform([input_string])
      prediction = classifier.predict(input_vector)
      
      return prediction[0]