def _setInitEvaluable(self, evaluable):
evaluable = array(evaluable)
ContinuousOptimizer._setInitEvaluable(self, evaluable)
self.gradient.init(evaluable)
self.prevFitness = sys.float_info.max
self.currentFitness = sys.float_info.min
def _setInitEvaluable(self, evaluable):
ContinuousOptimizer._setInitEvaluable(self, evaluable)
self.current = self._initEvaluable
self.gd = GradientDescent()
self.gd.alpha = self.learningRate
if self.learningRateDecay is not None:
self.gd.alphadecay = self.learningRateDecay
self.gd.momentum = self.momentum
self.gd.rprop = self.rprop
self.gd.init(self._initEvaluable)
def _setInitEvaluable(self, evaluable):
ContinuousOptimizer._setInitEvaluable(self, evaluable)
self.current = self._initEvaluable
self.gd = GradientDescent()
self.gd.alpha = self.learningRate
if self.learningRateDecay is not None:
self.gd.alphadecay = self.learningRateDecay
self.gd.momentum = self.momentum
self.gd.rprop = self.rprop
self.gd.init(self._initEvaluable)
def _setInitEvaluable(self, evaluable):
if evaluable is not None:
logging.warning("Initial point provided was ignored.")
ContinuousOptimizer._setInitEvaluable(self, evaluable)
def _setInitEvaluable(self, evaluable):
if evaluable is not None:
logging.warning("Initial point provided was ignored.")
ContinuousOptimizer._setInitEvaluable(self, evaluable)
