def optimizeKernel(self,start=None,optimizer=None,**kwargs):
if start is None:
start=np.concatenate((np.log(self.alpha**2),np.log(self.variance),self.mu))
if optimizer is None:
optimizer=self.optimizationMethod
optimizer = getOptimizationMethod(optimizer)
opt=optimizer(start,**kwargs)
opt.run(f=self.minuslogLikelihoodParameters,df=self.minusGradLogLikelihoodParameters)
self.optRuns.append(opt)
self.optPointsArray.append(opt.xOpt)
def optimizeKernel(self,start=None,optimizer=None,**kwargs):
"""
Optimize the minus log-likelihood using the optimizer method and starting in start.
Args:
start: starting point of the algorithm.
optimizer: Name of the optimization algorithm that we want to use;
e.g. 'bfgs'.
"""
if start is None:
start=np.concatenate((np.log(self.alpha**2),np.log(self.variance),self.mu))
if optimizer is None:
optimizer=self.optimizationMethod
optimizer = getOptimizationMethod(optimizer)
opt=optimizer(start,**kwargs)
opt.run(f=self.minuslogLikelihoodParameters,df=self.minusGradLogLikelihoodParameters)
self.optRuns.append(opt)
self.optPointsArray.append(opt.xOpt)
