def _oneEvaluation(self, evaluable):
if self.numEvaluations == 0:
return BlackBoxOptimizer._oneEvaluation(self, evaluable)
else:
self.switchMutations()
if isinstance(evaluable, MaskedParameters):
evaluable.returnZeros = False
x0 = evaluable.params
evaluable.returnZeros = True
def f(x):
evaluable._setParameters(x)
return BlackBoxOptimizer._oneEvaluation(self, evaluable)
else:
f = lambda x: BlackBoxOptimizer._oneEvaluation(self, x)
x0 = evaluable
outsourced = self.localSearch(f, x0,
maxEvaluations = self.localSteps,
desiredEvaluation = self.desiredEvaluation,
minimize = self.minimize,
**self.localSearchArgs)
assert self.localSteps > outsourced.batchSize, 'localSteps too small ('+str(self.localSteps)+\
'), because local search has a batch size of '+str(outsourced.batchSize)
_, fitness = outsourced.learn()
self.switchMutations()
return fitness
def _oneEvaluation(self, evaluable):
if self.numEvaluations == 0:
return BlackBoxOptimizer._oneEvaluation(self, evaluable)
else:
self.switchMutations()
if isinstance(evaluable, MaskedParameters):
evaluable.returnZeros = False
x0 = evaluable.params
evaluable.returnZeros = True
def f(x):
evaluable._setParameters(x)
return BlackBoxOptimizer._oneEvaluation(self, evaluable)
else:
f = lambda x: BlackBoxOptimizer._oneEvaluation(self, x)
x0 = evaluable
outsourced = self.localSearch(
f,
x0,
maxEvaluations=self.localSteps,
desiredEvaluation=self.desiredEvaluation,
minimize=self.minimize,
**self.localSearchArgs)
assert self.localSteps > outsourced.batchSize, 'localSteps too small ('+str(self.localSteps)+\
'), because local search has a batch size of '+str(outsourced.batchSize)
_, fitness = outsourced.learn()
self.switchMutations()
return fitness
def f(x):
evaluable._setParameters(x)
return BlackBoxOptimizer._oneEvaluation(self, evaluable)
def f(x):
evaluable._setParameters(x)
return BlackBoxOptimizer._oneEvaluation(self, evaluable)