Encog: How do I compute without ideal data?

Question

Here is an example of my code just to explain my question. (My code is not the XOR example and it has much more data):

public static double XOR_INPUT[][] = { { 0.0, 0.0 }, { 1.0, 0.0 }, { 0.0, 1.0 }, { 1.0, 1.0 } };

public static double XOR_IDEAL[][] = { { 0.0 }, { 1.0 }, { 1.0 }, { 0.0 } };

...

MLDataSet trainingSet = new BasicMLDataSet(XOR_INPUT, XOR_IDEAL);

...

for(MLDataPair pair: trainingSet ) {
      final MLData output = network.compute(pair.getInput());
      System.out.println(pair.getInput().getData(0) + "," + pair.getInput().getData(1)
          + ", actual=" + output.getData(0) + ",ideal=" + pair.getIdeal().getData(0));
    }

In an evaluation situation (where I know the ideal output) this works fine.

But in a real situation, with my neural network trained and when I don't know the ideal output: What is the approach in this case? Do I have to "make up" the ideal data?

Can this computation be made through the workbench?

Answer 1

Notice how when it queries the output the above loop just uses pair.getInput(). This is just the input half of the dataset, you do not need to provide ideal/expected. The following code shows how to do it with no ideal values at all. Just wrap the input in a BasicMLData object and you are fine:

    System.out.println("Neural Network Results:");
    for(int i=0;i<XOR_INPUT.length;i++ ) {
        MLData inputData = new BasicMLData(XOR_INPUT[i]);
        final MLData output = network.compute(inputData);
        System.out.println(inputData
                + ":" + output.toString());
    }