def test_model_creation(self): from mlpy.mdp import MDPModelFactory # create discrete model with pytest.raises(ValueError): MDPModelFactory.create('discretemodel') from mlpy.mdp.stateaction import Action Action.description = { 'out': {'value': [-0.004]}, 'in': {'value': [0.004]}, 'kick': {'value': [-1.0]} } MDPModelFactory.create('discretemodel') # create decision tree model MDPModelFactory.create('decisiontreemodel') MDPModelFactory.create('decisiontreemodel', explorer_type='unknownbonusexplorer') MDPModelFactory.create('decisiontreemodel', explorer_type='leastvisitedbonusexplorer', explorer_params={'rmax': 1.0}) with pytest.raises(ValueError): MDPModelFactory.create('decisiontreemodel', explorer_type='undefined') # create CASML model case_template = { "state": { "type": "float", "value": "data.state", "is_index": True, "retrieval_method": "radius-n", "retrieval_method_params": 0.01 }, "delta_state": { "type": "float", "value": "data.next_state - data.state", "is_index": False, } } MDPModelFactory.create('casml', case_template)
demo = data["state"] except IOError: sys.exit(sys.exc_info()[1]) except KeyError, e: sys.exit("Key not found: {0}".format(e)) kwargs = {} if args.model == "decisiontreemodel": kwargs = {"use_reward_trees": args.use_reward_trees} if args.explorer_type in ["unvisitedbonusexplorer", "leastvisitedbonusexplorer", "unknownbonusexplorer"]: kwargs.update({"explorer_type": args.explorer_type, "rmax": args.rmax}) if args.explorer_type == "leastvisitedbonusexplorer" and args.thresh: kwargs.update({"thresh": args.thresh}) else: args.ignore_unreachable = False model = MDPModelFactory.create(args.model, **kwargs) explorer = None if args.explorer_type in ["egreedyexplorer", "softmaxexplorer"]: explorer = ExplorerFactory.create(args.explorer_type, args.explorer_params, args.decay) if args.learner == "apprenticeshiplearner": learner = None if args.progress: try: learner = ApprenticeshipLearner.load(args.savefile) except IOError: pass if not learner: try: