class BayesianOpt(object):
def __init__(self, kernel):
self.kernel_init = kernel
self.gp = None
self.X = []
self.Y = []
def init(self):
self.gp = None
self.X = []
self.Y = []
def train(self, x, y,
doOptmization = False, params_min = None, params_max = None):
self.X.append(x)
self.Y.append(y)
self.gp = GaussianProcess(self.X, self.Y,
self.kernel_init if len(self.X) == 1 else self.gp.kernel)
if doOptmization:
self.gp.optimize(params_min = params_min, params_max = params_max)
def pick_next_input(self):
raise NotImplementedError("overwrite this")
