/*
* Gets (and removes) a random example from the 'presentlyProcessed'
*/
public NNExample getExample(int index)
{
NNExample result = presentlyProcessed[index];
presentlyProcessed.RemoveAt(index);
return(result);
}
/*
* Gets (and removes) a random example from the 'presentlyProcessed'
*/
public NNExample getExampleAtRandom()
{
int i = Util.randomNumberBetween(0, (presentlyProcessed.Count - 1));
NNExample result = presentlyProcessed[i];
presentlyProcessed.RemoveAt(i);
return(result);
}
public void trainOn(NNDataSet innds, int numberofEpochs)
{
for (int i = 0; i < numberofEpochs; i++)
{
innds.refreshDataset();
while (innds.hasMoreExamples())
{
NNExample nne = innds.getExampleAtRandom();
processInput(nne.getInput());
Vector error = layer.errorVectorFrom(nne.getTarget());
processError(error);
}
}
}
public int[] testOnDataSet(NNDataSet nnds)
{
int[] result = new int[] { 0, 0 };
nnds.refreshDataset();
while (nnds.hasMoreExamples())
{
NNExample nne = nnds.getExampleAtRandom();
Vector prediction = predict(nne);
if (nne.isCorrect(prediction))
{
result[0] = result[0] + 1;
}
else
{
result[1] = result[1] + 1;
}
}
return(result);
}
public Vector predict(NNExample nne)
{
return(processInput(nne.getInput()));
}
public Vector predict(NNExample nne) {
return processInput(nne.getInput());
}