(self, models=[], optimize_restarts=5)
:param models: list of GPflow models representing our beliefs about the problem
        :param optimize_restarts: number of optimization restarts to use when training the models
:param models: list of GPflow models representing our beliefs about the problem
        :param optimize_restarts: number of optimization restarts to use when training the models
: param models : list of GPflow models representing our beliefs about the problem : param optimize_restarts : number of optimization restarts to use when training the models
def __init__(self, models=[], optimize_restarts=5):
        """
        :param models: list of GPflow models representing our beliefs about the problem
        :param optimize_restarts: number of optimization restarts to use when training the models
        """
        super(Acquisition, self).__init__()
        models = np.atleast_1d(models)
        assert all(isinstance(model, (Model, ModelWrapper)) for model in models)
        self._models = ParamList([DataScaler(m) for m in models])

        assert (optimize_restarts >= 0)
        self.optimize_restarts = optimize_restarts
        self._needs_setup = True
def __init__ ( self , models = [ ] , optimize_restarts = 5 ) : super ( Acquisition , self ) . __init__ ( ) models = np . atleast_1d ( models ) assert all ( isinstance ( model , ( Model , ModelWrapper ) ) for model in models ) self . _models = ParamList ( [ DataScaler ( m ) for m in models ] ) assert ( optimize_restarts >= 0 ) self . optimize_restarts = optimize_restarts self . _needs_setup = True