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