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POfill missing values of sequence with neural networks
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  1. POfill missing values of sequence with neural networks
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    copied!<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>
 

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