text: import numpy as np
  from sequentia.classifiers import HMM, HMMClassifier
  
  # Set of possible labels
  labels = ['class{}'.format(i) for i in range(5)]
  
  # Create and fit some sample HMMs
  hmms = []
  for i, label in enumerate(labels):
      hmm = HMM(label=label, n_states=(i + 3), topology='left-right')
      hmm.set_random_initial()
      hmm.set_random_transitions()
      hmm.fit([np.arange((i + j * 20) * 30).reshape(-1, 3) for j in range(1, 4)])
      hmms.append(hmm)
  
  # Create some sample test data and labels
  X = [np.random.random((10 * i, 3)) for i in range(1, 4)]
  y = ['class0', 'class1', 'class1']
  
  # Create a classifier and calculate predictions and evaluations
  clf = HMMClassifier()
  clf.fit(hmms)
  predictions = clf.predict(X)
  accuracy, confusion = clf.evaluate(X, y, labels=labels)