def eval(self,iterationparameters, observedparameters, objectivefunctionparameters):
#search for number of vectors
numberofvectors = 0
numberofvectors = numberofvectors+iterationparameters.__len__()
numberofvectors = numberofvectors+observedparameters.__len__()
numberofvectors = numberofvectors+objectivefunctionparameters.__len__()
if(numberofvectors!=1):
pycalimero.log("Only one vector is allowed in ReverseVector", pycalimero.Warning)
#search for vectors and check their size
vectors = pycalimero.doublevectorvector()
for var in iterationparameters:
vectors.append(var.getValues())
for var in observedparameters:
vectors.append(var.getValues())
for var in objectivefunctionparameters:
vectors.append(var.getValues())
#calculate
result = pycalimero.doublevector()
for value in vectors[0]:
result.append(-value)
return result
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):
self.calibrationvars = calibrationvars
self.objectivevars = objectivevars
self.calibration = calibration
self.env = env
global error
global maxerror
global alg
global minimize
maxerror = float(self.getValueOfParameter("max Error"))
global baseevo
error = False
minimize = False
verbose = False
if self.getValueOfParameter("minimize objetive funtion") == "1":
minimize = True
if self.getValueOfParameter("verbose") == "1":
verbose = True
pycalimero.log("Pybrain_GA START", pycalimero.Standard)
baseevo = SimpleEvo(extractData(self.calibrationvars), calibrationvars, objectivevars, env, calibration)
alg = GA(objf, baseevo.x)
alg.maxEvaluations = int(self.getValueOfParameter("max Iteration"))
alg.minimize = minimize
alg.desiredEvaluation = float(self.getValueOfParameter("max Error"))
alg.verbose = verbose
alg.learn()
pycalimero.log("Pybrain_GA DONE", pycalimero.Standard)
return True
def bruteforcesearch(self, calibrationvars, currentvar, env):
var = calibrationvars[currentvar]
for currentvalue in frange(var.min,var.max,var.step) + [var.max]:
result = pycalimero.doublevector()
result.append(currentvalue)
var.setValues(result)
if (currentvar==(calibrationvars.__len__()-1)):
if (pycalimero.execIteration(calibrationvars) == False):
pycalimero.log("BruteForceSearch_P stoped by user",pycalimero.Standard)
return False
else:
if(self.bruteforcesearch(calibrationvars,currentvar+1,env)==False):
return True
return True
def eval(self,iterationparameters, observedparameters, objectivefunctionparameters):
#search for vectors and check their size
vectors = pycalimero.doublevectorvector()
for var in iterationparameters:
vectors.append(var.getValues())
for var in observedparameters:
vectors.append(var.getValues())
for var in objectivefunctionparameters:
vectors.append(var.getValues())
if(vectors.__len__()!=2):
pycalimero.log("Only two vectors are allowed in VectorErrorSquare", pycalimero.Error)
return pycalimero.doublevector(1,999999999999999)
if(vectors[0].__len__() < vectors[1].__len__()):
tmp = pycalimero.doublevector(vectors[0])
while(tmp.__len__() < vectors[1].__len__()):
tmp.append(999)
vectors[0]=tmp;
if(vectors[0].__len__() > vectors[1].__len__()):
tmp = pycalimero.doublevector(vectors[1])
while(tmp.__len__() < vectors[0].__len__()):
tmp.append(999)
vectors[1]=tmp;
#calculate
result = pycalimero.doublevector(vectors[0].__len__())
for index,value1 in enumerate(vectors[0]):
result[index] = math.pow(value1 - vectors[1][index],2)
return result
def eval(self,iterationparameters, observedparameters, objectivefunctionparameters):
#search for number of vectors
numberofvectors = 0
numberofvectors = numberofvectors+iterationparameters.__len__()
numberofvectors = numberofvectors+observedparameters.__len__()
numberofvectors = numberofvectors+objectivefunctionparameters.__len__()
if(numberofvectors!=2):
pycalimero.log("Only two vectors are allowed in VectorError", pycalimero.Warning)
#search for vectors and check their size
vectors = pycalimero.doublevectorvector()
for var in iterationparameters:
vectors.append(var.getValues())
for var in observedparameters:
vectors.append(var.getValues())
for var in objectivefunctionparameters:
vectors.append(var.getValues())
if(vectors[0].__len__()!=vectors[1].__len__()):
pycalimero.log("Vectors do not have the same size in VectorError", pycalimero.Warning)
#calculate
result = pycalimero.doublevector()
index = 0
for value1 in vectors[0]:
currentresult = value1 - vectors[1][index]
result.append(currentresult)
index = index+1
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
