def __init__(self, conf):
Metamodel.__init__(self)
self.conf = conf
self.interpolators = []
for m in self.conf.modules:
self.interpolators.append(LinearRegression())
self.inputs = []
self.outputs =[]
def __init__(self, conf):
Metamodel.__init__(self)
self.conf = conf
self.svrs = []
self.inputs = []
self.outputs = []
for m in conf.modules:
#self.svrs.append(SVR(kernel='rbf', C=1e3, gamma=0.1))
self.svrs.append(SVR(kernel='linear', C=1.0))
self.inputs.append([])
self.outputs.append([])
def __init__(self, conf):
Metamodel.__init__(self)
self.conf = conf
self.ds = []
self.nets = []
self.trainers = []
for m in conf.modules:
input = conf.modNum
output = 1
self.ds.append(SupervisedDataSet(input, output))
self.nets.append(buildNetwork(input, 1, output, hiddenclass=LinearLayer, outclass=LinearLayer))
self.trainers.append(RPropMinusTrainer(self.nets[len(self.nets)-1]))
def __init__(self):
Metamodel.__init__(self)
def add(self, system):
if not Metamodel.add(self, system):
return
def add(self, system):
if not Metamodel.add(self, system):
return
for m in system.modules:
self.ds[m.num].addSample(self.sys2array(system), m.time)
def add(self, system):
Metamodel.add(self, system)
def add(self, system):
if not Metamodel.add(self, system):
return
for m in system.modules:
self.inputs[m.num].append(self.sys2array(system))
self.outputs[m.num].append(m.time)
def __init__(self,k):
Metamodel.__init__(self)
self.k = k
