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    <p>In general, if you are training your ANN using back propagation, you are basically training an input-output map. This means that your training set has to comprise known input-output relations (none of your unknown values included in the training set). The ANN then becomes an approximation of the actual relationship between your inputs and outputs.</p> <p>You can then call <code>x = net.activate([seq])</code> where <code>seq</code> is the input sequence associated with the unknown value <code>x</code>.</p> <p>If <code>x</code> is an unknown input sequence for a known result, then you have to call the inverse of the ANN. I do not think there is a simple way of inverting an ANN in pybrain, but you could just train an ANN with the inverse of your original training data. In other words, use your known results as the training inputs, and their associated sequences as the training results.</p> <p>The main thing to consider is the appropriateness of the tool and the training data for what you are trying to do. If you just want to predict <code>x</code> as a function of the previous number, then I think you are training correctly. I am guessing <code>x</code> is going to be a function of the previous <code>n</code> numbers though, in which case you want to update your data set as:</p> <pre><code>n = 10 for ind in range(len(myList)): # Don't overrun our bounds if ind == len(myList)-1: break # Check that our sequence is valid for i in range(ind-n, ind+1): if i &gt;= 0 and myList[i] == "x": # we have an invalid sequence ind += i # start next seq after invalid entry break # Add valid training sequence to data set ds.addSample(myList[ind-n:ind],myList[ind+1]) </code></pre>
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    1. COHello @Engineero. The problem with your answer is that each time the input will have different lenght. And I think that when I set my NN like this ds = SupervisedDataSet(1, 1), I have to fix the number of input sequence. I don't know if I can add a sample with lenght 10 and on the other iteration another sample with lenght 20 for example.
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    2. CO@Tasos, you could begin the above loop with `for ind in range(n, len(myList)):` and that way you should only end up with length = 10 input training sequences.
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    3. COSo your opinion is to add my sample with a fix number which I choose? A number bigger than 1 is better to have better predictions. Right?
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