def __init__(self, lower, upper, explore_priority=0.0001): """ Initialise the Delaunay class. .. note :: Currently only supports rectangular type restrictions on the parameter space Parameters ---------- lower : array_like Lower or minimum bounds for the parameter space upper : array_like Upper or maximum bounds for the parameter space explore_priority : float, optional The priority of exploration against exploitation """ Sampler.__init__(self, lower, upper) self.triangulation = None # Delaunay model self.simplex_cache = {} # Pre-computed values of simplices self.explore_priority = explore_priority
def __init__(self, lower, upper, explore_priority=0.0001): """ Initialise the Delaunay class. .. note :: Currently only supports rectangular type restrictions on the parameter space Parameters ---------- lower : array_like Lower or minimum bounds for the parameter space upper : array_like Upper or maximum bounds for the parameter space explore_priority : float, optional The priority of exploration against exploitation """ Sampler.__init__(self, lower, upper) self.triangulation = None # Delaunay model self.simplex_cache = {} # Pre-computed values of simplices self.explore_priority = explore_priority
def __init__(self, lower, upper, kerneldef=None, n_train=50, acq_name='var_sum', explore_priority=1., seed=None): """ Initialise the GaussianProcess class. .. note:: Currently only supports rectangular type restrictions on the parameter space Parameters ---------- lower : array_like Lower or minimum bounds for the parameter space upper : array_like Upper or maximum bounds for the parameter space kerneldef : function Kernel function definition. See the 'gp' module. n_train : int Number of training samples required before sampler can be trained acq_name : str A string specifying the type of acquisition function used explore_priority : float, optional The priority of exploration against exploitation """ Sampler.__init__(self, lower, upper) self.kerneldef = kerneldef self.n_min = n_train self.acq_name = acq_name self.explore_priority = explore_priority self.hyperparams = None self.regressors = None self.y_mean = None self.n_tasks = None if seed: np.random.seed(seed)