def learn(self, maxSteps = None):
""" Some BlackBoxOptimizers can only be called one time, and currently
do not support iteratively adding more steps. """
if not self.online:
if self.maxEvaluations != None:
if maxSteps != None:
maxSteps = min(maxSteps, self.maxEvaluations - self.steps)
else:
maxSteps = self.maxEvaluations-self.steps
self._batchLearn(maxSteps)
else:
Learner.learn(self, maxSteps)
if self.wrappingEvaluable != None and isinstance(self.bestEvaluable, ndarray):
xopt = self.bestEvaluable
self.wrappingEvaluable._setParameters(xopt)
self.bestEvaluable = self.wrappingEvaluable
if self.minimize:
self.bestEvaluation *= -1
return self.bestEvaluable, self.bestEvaluation
def learn(self, maxSteps = None):
""" Some BlackBoxOptimizers can only be called one time, and currently
do not support iteratively adding more steps. """
if not self.online:
if self.maxEvaluations != None:
if maxSteps != None:
maxSteps = min(maxSteps, self.maxEvaluations - self.steps)
else:
maxSteps = self.maxEvaluations-self.steps
self._batchLearn(maxSteps)
else:
Learner.learn(self, maxSteps)
if self.wrappingEvaluable != None and isinstance(self.bestEvaluable, ndarray):
xopt = self.bestEvaluable
self.wrappingEvaluable._setParameters(xopt)
self.bestEvaluable = self.wrappingEvaluable
if self.minimize:
self.bestEvaluation *= -1
return self.bestEvaluable, self.bestEvaluation
