def __init__(self, conf, acquisition, exploration_weight, initial_points, environment, optimisation_iterations, exact_feval):
        ''' :param string acquisition: choose the model of acquisition function between "MPI","EI" and "LCB"
            :param scalar exploration_weight: module the exploration in the acquistion (base 2 for LCB and 0.01 for others)
            :param scalar initial_points: the number of initial points to give for the bayesian optimisation
            :pram Environment environment: environment on which is used the optimisation
            :param scalar optimisation_iterations: number of iterations of the optimisation
            :param boolean exact_feval: must be False if the environment is noisy
        '''
        for attr in ['m_dims', 's_dims']:
            setattr(self, attr, getattr(conf, attr))
        self.dim_x = len(self.m_dims)
        self.dim_y = len(self.s_dims)
        self.acquisition = acquisition
        self.exploration_weight = exploration_weight
        self.initial_points = initial_points
        self.dataset  = Dataset(len(self.m_dims), len(self.s_dims))
        self.conf = conf
        self.mode = 'explore'
        self.environment = environment
        self.optimisation_iterations = optimisation_iterations
        self.exact_feval = exact_feval
def __init__ ( self , conf , acquisition , exploration_weight , initial_points , environment , optimisation_iterations , exact_feval ) : for attr in [ 'm_dims' , 's_dims' ] : setattr ( self , attr , getattr ( conf , attr ) ) self . dim_x = len ( self . m_dims ) self . dim_y = len ( self . s_dims ) self . acquisition = acquisition self . exploration_weight = exploration_weight self . initial_points = initial_points self . dataset = Dataset ( len ( self . m_dims ) , len ( self . s_dims ) ) self . conf = conf self . mode = 'explore' self . environment = environment self . optimisation_iterations = optimisation_iterations self . exact_feval = exact_feval
:param string acquisition: choose the model of acquisition function between "MPI","EI" and "LCB"
            :param scalar exploration_weight: module the exploration in the acquistion (base 2 for LCB and 0.01 for others)
            :param scalar initial_points: the number of initial points to give for the bayesian optimisation
            :pram Environment environment: environment on which is used the optimisation
            :param scalar optimisation_iterations: number of iterations of the optimisation
            :param boolean exact_feval: must be False if the environment is noisy