def trainnoParallel(self,scaledAlpha,**kwargs):
dim=self.dimension
alpha=np.random.randn(dim)
variance=np.random.rand(1)
st=np.concatenate((np.sqrt(np.exp(alpha)),np.exp(variance),[0.0]))
args2={}
args2['start']=st
job=misc.kernOptWrapper(self,**args2)
temp=job.xOpt
self.alpha=np.sqrt(np.exp(np.array(temp[0:self.dimension])))
self.variance=np.exp(np.array(temp[self.dimension]))
self.mu=np.array(temp[self.dimension+1])
def trainnoParallel(self,scaledAlpha,**kwargs):
"""
Train the hyperparameters starting in only one point the algorithm.
Args:
-scaledAlpha: The definition may be found above.
"""
dim=self.dimension
alpha=np.random.randn(dim)
variance=np.random.rand(1)
st=np.concatenate((np.sqrt(np.exp(alpha)),np.exp(variance),[0.0]))
args2={}
args2['start']=st
job=misc.kernOptWrapper(self,**args2)
temp=job.xOpt
self.alpha=np.sqrt(np.exp(np.array(temp[0:self.dimension])))
self.variance=np.exp(np.array(temp[self.dimension]))
self.mu=np.array(temp[self.dimension+1])