def optVOIParal(self,i,nStart,numProcesses=None):
"""
Runs in parallel the multi-start gradient ascent method
to optimize the VOI.
It restarts the gradient ascent method nStart times.
Args:
i: Iteration of the algorithm.
nStart: Number of restarts of the gradient ascent method.
"""
try:
n1=self._n1
n2=self._dimW
tempN=self.numberTraining+i
Xst=self.Obj.sampleFromX(nStart)
wSt=self.Obj.simulatorW(nStart)
args3=self.getParametersOptVoi(i)
jobs = []
pool = mp.Pool(processes=numProcesses)
for j in range(nStart):
args2=args3.copy()
x1=Xst[j:j+1,:]
w1=wSt[j:j+1,:]
st=np.concatenate((x1,w1),1)
args2['start']=st
job = pool.apply_async(misc.VOIOptWrapper, args=(self,), kwds=args2)
jobs.append(job)
pool.close() # signal that no more data coming in
pool.join() # wait for all the tasks to complete
except KeyboardInterrupt:
print "Ctrl+c received, terminating and joining pool."
pool.terminate()
pool.join()
for j in range(nStart):
try:
self.optRuns.append(jobs[j].get())
except Exception as e:
print "Error optimizing VOI"
if len(self.optRuns):
j = np.argmax([o.fOpt for o in self.optRuns])
fl.writeNewPointSBO(self,self.optRuns[j])
self.optRuns=[]
self.optPointsArray=[]
def optVOInoParal(self,i):
"""
Runs the single-start gradient ascent method to optimize the VOI.
Args:
i: Iteration of the algorithm.
"""
n1=self._n1
n2=self._dimW
Xst=self.Obj.sampleFromX(1)
wSt=self.Obj.simulatorW(1)
x1=Xst[0:0+1,:]
w1=wSt[0:0+1,:]
tempN=self.numberTraining+i
st=np.concatenate((x1,w1),1)
args2=self.getParametersOptVoi(i)
args2['start']=st
self.optRuns.append(misc.VOIOptWrapper(self,**args2))
fl.writeNewPointSBO(self,self.optRuns[0])