def _batchLearn(self):
""" Batch learning. """
xdim = self.numParameters
# produce samples and evaluate them
try:
self._produceSamples()
# shape their fitnesses
shapedFits = self.shapingFunction(self.allFitnesses[-self.batchSize :])
# update parameters (unbiased: divide by batchsize)
update = self._calcBatchUpdate(shapedFits)
if self.elitism:
self.x = self.bestEvaluable
else:
self.x += self.learningRate * update[:xdim]
self.factorSigma += self.learningRateSigma * flat2triu(update[xdim:], xdim)
self.sigma = dot(self.factorSigma.T, self.factorSigma)
except ValueError:
print("Numerical Instability. Stopping.")
self.maxLearningSteps = self.numLearningSteps
if self._hasConverged():
print("Premature convergence. Stopping.")
self.maxLearningSteps = self.numLearningSteps
if self.verbose:
print("Evals:", self.numEvaluations)
self.allCenters.append(self.x.copy())
self.allFactorSigmas.append(self.factorSigma.copy())
if self.storeAllDistributions:
self._allDistributions.append((self.x.copy(), self.sigma.copy()))
def _batchLearn(self, maxSteps):
""" Batch learning. """
while (self.evalsDone < maxSteps
and not self.bestEvaluation >= self.desiredEvaluation):
# produce samples and evaluate them
try:
self._produceSamples()
# shape their fitnesses
shapedFits = self.shapingFunction(self.allFitnesses[-self.batchSize:])
# update parameters (unbiased: divide by batchsize)
update = self._calcBatchUpdate(shapedFits)
if self.elitism:
self.x = self.bestEvaluable
else:
self.x += self.learningRate * update[:self.xdim]
self.factorSigma += self.learningRateSigma * flat2triu(update[self.xdim:], self.xdim)
self.sigma = dot(self.factorSigma.T, self.factorSigma)
except ValueError:
print 'Numerical Instability. Stopping.'
break
if self._hasConverged():
print 'Premature convergence. Stopping.'
break
if self.verbose:
print 'G:', self.generation, 'Evals:', self.evalsDone, 'MaxG:', max(self.allFitnesses[-self.batchSize:])
self.allCenters.append(self.x.copy())
self.allFactorSigmas.append(self.factorSigma.copy())
self.generation += 1
def _batchLearn(self):
""" Batch learning. """
xdim = self.numParameters
# produce samples and evaluate them
try:
self._produceSamples()
# shape their fitnesses
shapedFits = self.shapingFunction(
self.allFitnesses[-self.batchSize:])
# update parameters (unbiased: divide by batchsize)
update = self._calcBatchUpdate(shapedFits)
if self.elitism:
self.x = self.bestEvaluable
else:
self.x += self.learningRate * update[:xdim]
self.factorSigma += self.learningRateSigma * flat2triu(
update[xdim:], xdim)
self.sigma = dot(self.factorSigma.T, self.factorSigma)
except ValueError:
print 'Numerical Instability. Stopping.'
self.maxLearningSteps = self.numLearningSteps
if self._hasConverged():
print 'Premature convergence. Stopping.'
self.maxLearningSteps = self.numLearningSteps
if self.verbose:
print 'Evals:', self.numEvaluations,
self.allCenters.append(self.x.copy())
self.allFactorSigmas.append(self.factorSigma.copy())
def _onlineLearn(self):
""" Online learning. """
# produce one sample and evaluate
xdim = self.numParameters
self._produceNewSample()
if len(self.allSamples) <= self.batchSize:
return
# shape the fitnesses of the last samples
shapedFits = self.shapingFunction(self.allFitnesses[-self.batchSize :])
# update parameters
update = self._calcOnlineUpdate(shapedFits)
self.x += self.learningRate * update[:xdim]
self.factorSigma += self.learningRateSigma * flat2triu(update[xdim:], xdim)
self.sigma = dot(self.factorSigma.T, self.factorSigma)
def _onlineLearn(self):
""" Online learning. """
# produce one sample and evaluate
xdim = self.numParameters
self._produceNewSample()
if len(self.allSamples) <= self.batchSize:
return
# shape the fitnesses of the last samples
shapedFits = self.shapingFunction(self.allFitnesses[-self.batchSize:])
# update parameters
update = self._calcOnlineUpdate(shapedFits)
self.x += self.learningRate * update[:xdim]
self.factorSigma += self.learningRateSigma * flat2triu(
update[xdim:], xdim)
self.sigma = dot(self.factorSigma.T, self.factorSigma)
def _batchLearn(self, maxSteps):
""" Batch learning. """
while (self.evalsDone < maxSteps
and not self.bestEvaluation >= self.desiredEvaluation):
# produce samples and evaluate them
try:
self._produceSamples()
# shape their fitnesses
shapedFits = self.shapingFunction(
self.allFitnesses[-self.batchSize:])
# update parameters (unbiased: divide by batchsize)
update = self._calcBatchUpdate(shapedFits)
if self.elitism:
self.x = self.bestEvaluable
else:
self.x += self.learningRate * update[:self.xdim]
self.factorSigma += self.learningRateSigma * flat2triu(
update[self.xdim:], self.xdim)
self.sigma = dot(self.factorSigma.T, self.factorSigma)
except ValueError:
print 'Numerical Instability. Stopping.'
break
if self._hasConverged():
print 'Premature convergence. Stopping.'
break
if self.verbose:
print 'G:', self.generation, 'Evals:', self.evalsDone, 'MaxG:', max(
self.allFitnesses[-self.batchSize:])
self.allCenters.append(self.x.copy())
self.allFactorSigmas.append(self.factorSigma.copy())
self.generation += 1
