input: Write a python function to predict the class of a given string using Gaussian Naive Bayes classifier from sklearn.
  assert predict_class("abcda") == 0
  assert predict_class("abbd") == 1
  assert predict_class("aabbc") == 0
output: def predict_class(input_string):
      from sklearn.naive_bayes import GaussianNB
      import numpy as np
      
      # Sample dataset for training the classifier
      X_train = np.array([
          [1, 1, 1, 1, 0],  # "abcda"
          [1, 1, 1, 0, 0],  # "abbd"
          [1, 1, 1, 0, 1]   # "aabbc"
      ])
      y_train = np.array([0, 1, 0])
      
      # Fit the model
      model = GaussianNB()
      model.fit(X_train, y_train)
      
      # Convert input string to features
      features = [0] * 5
      for i, char in enumerate('abcde'):
          features[i] = input_string.count(char)
      
      # Predict class
      return model.predict([features])[0]