import pickle
from pybrain.tools.shortcuts import buildNetwork
from pybrain.datasets import SupervisedDataSet
from pybrain.supervised.trainers import BackpropTrainer
class summator():
    def __init__(self):
        self.x = []
        self.net = buildNetwork(2,5,5,1)
        self.ds =  SupervisedDataSet(2,1)
        self.ds.addSample([1,1],1)
        self.ds.addSample([0,0],1)
        self.ds.addSample([1,0],0)
        self.ds.addSample([0,1],0)
        
        self.trainer = BackpropTrainer(self.net, self.ds)
    def add(self, dx):
        self.x.append(dx)
    def GetData(self):
        return self.x
    def Save(self, filename):
        pickle.dump(self, open(filename,'wb'))
    def TrainNet(self):
        res = self.trainer.train()
        return res

>>> a = summator()
>>> a.TrainNet()
0.61932256419312537
>>> a.TrainNet()
0.52224738939510462
>>> a.Save('x.xx')
>>> a = 0
>>> with open('x.xx','rb') as xfile:
	     a = pickle.load(xfile)

	
>>> a.TrainNet()
0.46746910805434289
>>> a.TrainNet()
0.46746910805434289
>>> a.TrainNet()
0.46746910805434289
>>> a.TrainNet()
0.46746910805434289
>>> a.TrainNet()
0.46746910805434289
>>> a.TrainNet()
0.46746910805434289
>>>