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)
