def _setParameters(self, p, owner=None):
ParameterContainer._setParameters(self, p, owner)
size = self.dim
self.ingatePeepWeights = self.params[:size]
self.forgetgatePeepWeights = self.params[size:size *
(1 + self.dimensions)]
self.outgatePeepWeights = self.params[size * (1 + self.dimensions):]
def _setInitEvaluable(self, evaluable):
if evaluable is None:
# if there is no initial point specified, we start at one that's sampled
# normally around the origin.
if self.numParameters is not None:
evaluable = randn(self.numParameters)
else:
raise ValueError('Could not determine the dimensionality of the evaluator. '+\
'Please provide an initial search point.')
if isinstance(evaluable, list):
evaluable = array(evaluable)
# If the evaluable is provided as a list of numbers or as an array,
# we wrap it into a ParameterContainer.
if isinstance(evaluable, ndarray):
pc = ParameterContainer(len(evaluable))
pc._setParameters(evaluable)
self._wasWrapped = True
evaluable = pc
self._initEvaluable = evaluable
if isinstance(self._initEvaluable, ParameterContainer):
if self.numParameters is None:
self.numParameters = len(self._initEvaluable)
elif self.numParameters is not len(self._initEvaluable):
raise ValueError("Parameter dimension mismatch: evaluator expects "+str(self.numParameters)\
+" but the evaluable has "+str(len(self._initEvaluable))+".")
def _setParameters(self, p, owner=None):
""" put slices of this array back into the modules """
ParameterContainer._setParameters(self, p, owner)
index = 0
for x in self._containerIterator():
x._setParameters(self.params[index:index + x.paramdim], self)
index += x.paramdim
def _setParameters(self, p, owner = None):
ParameterContainer._setParameters(self, p, owner)
dim = self.outdim
self.ingatePeepWeights = self.params[:dim]
self.forgetgatePeepWeights = self.params[dim:dim*2]
self.outgatePeepWeights = self.params[dim*2:]
evoEval = lambda e: e.x
# starting points
# ----------------------
xlist1 = [2.]
xlist2 = [0.2, 10]
xlist100 = list(range(12, 112))
xa1 = array(xlist1)
xa2 = array(xlist2)
xa100 = array(xlist100)
pc1 = ParameterContainer(1)
pc2 = ParameterContainer(2)
pc100 = ParameterContainer(100)
pc1._setParameters(xa1)
pc2._setParameters(xa2)
pc100._setParameters(xa100)
# for the task object, we need a module
nnet = buildNetwork(task.outdim, 2, task.indim)
# a mimimalistic Evolvable subclass that is not (like usual) a ParameterContainer
class SimpleEvo(Evolvable):
def __init__(self, x):
self.x = x
def mutate(self):
self.x += random() - 0.3