text: import pytest
  
  from lcs.agents.yacs.yacs import Configuration, Classifier, ClassifierTrace
  
  
  class TestClassifier:
  
      @pytest.fixture
      def cfg(self):
          return Configuration(
              classifier_length=2,
              number_of_possible_actions=2,
              feature_possible_values=[2, 2])
  
      def test_should_add_to_trace(self, cfg):
          cls = Classifier(cfg=cfg)
          assert len(cls.trace) == 0
  
          cls.add_to_trace(ClassifierTrace.GOOD)
          assert len(cls.trace) == 1
  
          cls.add_to_trace(ClassifierTrace.BAD)
          cls.add_to_trace(ClassifierTrace.BAD)
          cls.add_to_trace(ClassifierTrace.BAD)
          cls.add_to_trace(ClassifierTrace.BAD)
          cls.add_to_trace(ClassifierTrace.BAD)
          assert len(cls.trace) == cfg.trace_length
          assert all([True for t in cls.trace if t == ClassifierTrace.BAD])
  
      def test_should_determine_specializable_classifier(self, cfg):
          # Correct case - trace full and oscillating
          cl1 = Classifier(cfg=cfg)
          cl1.add_to_trace(ClassifierTrace.BAD)
          cl1.add_to_trace(ClassifierTrace.BAD)
          cl1.add_to_trace(ClassifierTrace.BAD)
          cl1.add_to_trace(ClassifierTrace.GOOD)
          cl1.add_to_trace(ClassifierTrace.BAD)
          assert cl1.is_specializable() is True
  
          # Wrong case - trace not full and oscillating
          cl2 = Classifier(cfg=cfg)
          cl2.add_to_trace(ClassifierTrace.BAD)
          cl2.add_to_trace(ClassifierTrace.GOOD)
          cl2.add_to_trace(ClassifierTrace.BAD)
          assert cl2.is_specializable() is False
  
          # Wrong case - trace full not oscillating
          cl3 = Classifier(cfg=cfg)
          cl3.add_to_trace(ClassifierTrace.BAD)
          cl3.add_to_trace(ClassifierTrace.BAD)
          cl3.add_to_trace(ClassifierTrace.BAD)
          cl3.add_to_trace(ClassifierTrace.BAD)
          cl3.add_to_trace(ClassifierTrace.BAD)
          assert cl3.is_specializable() is False
  
      def test_should_update_reward(self, cfg):
          # given
          cl = Classifier(cfg=cfg)
          env_reward = 100
  
          assert cl.r == 0
  
          # test two steps
          cl.update_reward(env_reward)
          assert cl.r == 10
  
          cl.update_reward(env_reward)
          assert cl.r == 19.00
  
          # test convergence
          for _ in range(0, 100):
              cl.update_reward(env_reward)
  
          assert abs(cl.r - env_reward) < 0.1
  
  
  