title: fill missing values of sequence with neural networks
body: <p>I want to make a little project and I want to use neural networks with python. I found that pybrain is the best solution. But until now, all the examples and questions I have found, cannot help me.</p>
  
  <p>I have a sequence of numbers. Hundreds of rows. Some values are missing and instead of a number, there are a "x".</p>
  
  <p>For example</p>
  
  <pre><code>1425234838636**x**40543485435097**x**43953458345345430843967067045764607457607645067045**x**04376037654067458674506704567408576405
  </code></pre>
  
  <p>and so on. This is just an example. Not my sequence.</p>
  
  <p>I thought to read one by one the values and train my neural net and when I find one 'x' I will predict the number and I will continue training it with the following numbers.</p>
  
  <p>What I have found until now are training like this one</p>
  
  <pre><code>trainSet.addSample([0,0,0,0],[1])
  </code></pre>
  
  <p>with some inputs and some outputs.</p>
  
  <p>Any advice how can I continue with it?</p>
  
  <p><strong>Edit:</strong> I figure something and I would like to receive feedback, because I don't know if it is right.</p>
  
  <p>I still have the string for above. I split it in list so I have a list where each entity is a number.</p>
  
  <pre><code>for ind in range(len(myList)):
     if not myList[ind] == "x" and not myList[ind+1]=="x":
         ds.addSample(myList[ind],myList[ind+1])
     else:
         break
  
  net = FeedForwardNetwork() 
  inp = LinearLayer(1) 
  h1 = SigmoidLayer(1) 
  outp = LinearLayer(1)
  
  net.addOutputModule(outp) 
  net.addInputModule(inp) 
  net.addModule(h1)
  
  net.addConnection(FullConnection(inp, h1))  
  net.addConnection(FullConnection(h1, outp))
  
  net.sortModules()
  
  trainer = BackpropTrainer(net, ds)
  trainer.trainOnDataset(ds,1000)
  trainer.testOnData(verbose=True)
  
  lis[ind+1] = net.activate((ind,))
  
  GO to the beggining and continue from the last "x" which replaced from the net.activate()
  </code></pre>
  
  <p>What do you think? Do you believe that something like this will work?</p>