def start(self,calibrationvars, objectivevars, env, calibration):
pycalimero.log("Start BruteForceSearch_P",pycalimero.Standard)
result = self.bruteforcesearch(calibrationvars,0,env)
pycalimero.barrier()
pycalimero.log("BruteForceSearch_P DONE",pycalimero.Standard)
return result;
def start(self,calibrationvars, objectivevars, env, calibration):
pycalimero.log("Start TemplateAlgorithm",pycalimero.Standard)
for i in range(0,int(self.getValueOfParameter("max iterations"))):
#pycalimero.log("Run iteration",pycalimero.LogLevel.standard)
if (pycalimero.execIteration(calibrationvars) == False):
pycalimero.log("TemplateAlgorithm stoped by user",pycalimero.Standard)
return True
pycalimero.barrier()
#pycalimero.log("Finish iteration",pycalimero.LogLevel.standard)
pycalimero.log("TemplateAlgorithm DONE",pycalimero.Standard)
return True
def objf(x):
xpars = array([], float32)
calibrationvars = None
calibration = None
objectivevars = None
env = None
global error
global baseevo
global maxerror
global minimize
global alg
if error:
alg.maxEvaluations = 1
return maxerror
if isinstance(x, Evolvable):
xpars = x.x
calibrationvars = x.calibrationvars
calibration = x.calibration
objectivevars = x.objectivevars
env = x.env
else:
xpars = x
calibrationvars = baseevo.calibrationvars
calibration = baseevo.calibration
objectivevars = baseevo.objectivevars
env = baseevo.env
for index, value in enumerate(xpars):
correctvalue = max(
calibrationvars[index].min,
min(calibrationvars[index].max, floor(value / calibrationvars[index].step) * calibrationvars[index].step),
)
currentvalue = pycalimero.doublevector()
currentvalue.append(double(correctvalue))
calibrationvars[index].setValues(currentvalue)
if error:
return maxerror
if not pycalimero.execIteration(calibrationvars):
error = True
return maxerror
else:
pycalimero.barrier()
result = 0.0
resultindex = calibration.getNumOfComplete() - 1
iterationresult = calibration.getIterationResults()[resultindex]
for opar in objectivevars:
result = result + sum(iterationresult.getResults(opar.getName()))
if minimize and (result < maxerror):
pycalimero.log("Algorithm reached maximal error", pycalimero.Standard)
alg.maxEvaluations = 1
return maxerror
if (not minimize) and result > maxerror:
pycalimero.log("Algorithm reached maximal error", pycalimero.Standard)
alg.maxEvaluations = 1
return maxerror
return result