title: Encog mean value with one input and one output
body: <p>I want to build a neural network with Encog that have 1 input (0/1 or true/false) and 1 ouput (double value) that calculates a mean value if criteria was specified (1 as an input) and 0 if criteria wasn't specified (0 as an input).</p>
  
  <p>For example, if I have the following training dataset</p>
  
  <pre><code>input | ideal
    1   |  0.6
    0   |  0
    1   |  0.2
    1   |  0.4
  </code></pre>
  
  <p>Then I expect something around ~<strong>0.0</strong> if input was 0 and ~<strong>0.4</strong> if input was 1. </p>
  
  <p>I have simplified my problem a lot. But the 2 principal questions are:</p>
  
  <ol>
  <li>Can I go with such dataset or should I calculate average for all
  duplicating input values and start a network training with unique
  inputs? </li>
  <li>What is the best network structure (network type,
  activation function, propagation) for the problem described above?</li>
  </ol>