Major bug in my code when setting input array values:

    for (Int32 j = 0; j < box.InputCount; j++)
    {
    box.InputSignalArray[0] = Convert.ToDouble(dr[j]);
    }

should be

    for (Int32 j = 0; j < box.InputCount; j++)
    {
    box.InputSignalArray[j] = Convert.ToDouble(dr[j]);
    }

So if you are following my example, fix the bug.  The bug leads to a complete lack of convergence.

--- In neat@yahoogroups.com, "gotnobluemilk" <gotnobluemilk@...> wrote:
>
> I probably should have elaborated on MakeDecision().
>
> public void MakeDecision(DataRow dr)
> {
> // Clear the network
> box.ResetState();
>
> // Reset all inputs.
> inputArr.Reset();
> // set all inputs.
> for (Int32 j = 0; j < box.InputCount; j++)
> {
> box.InputSignalArray[0] = Convert.ToDouble(dr[j]);
> }
>
> // Activate the network
> box.Activate();
>
> }
>
>
> --- In neat@yahoogroups.com, "gotnobluemilk" gotnobluemilk@ wrote:
> >
> > Well, I didn't expect much reponse since the other posts about this subject received no reponses. Anyway, he is the approach I took. I just put the needed changes in the Evaluate function:
> >
> > public FitnessInfo Evaluate(IBlackBox box)
> > {
> > double fitness = 0;
> >
> > foreach (DataRow dr in dtTrain.Rows)
> > {
> > // Make decision
> > MakeDecision(dr);
> > if (!brainTrain.Brain.IsStateValid)
> > return FitnessInfo.Zero;
> >
> > // Update the fitness score of the network
> > fitness += getScore();
> > }
> >
> > // Update the evaluation counter.
> > _evalCount++;
> >
> > // Return the fitness score
> > return new FitnessInfo(Math.Max(0, fitness), Math.Max(0, fitness));
> > }
> >
> > --- In neat@yahoogroups.com, "gotnobluemilk" <gotnobluemilk@> wrote:
> > >
> > > I'm using SharpNeat V2 and doing my first experiment. Lot's to learn trying to understand both the concept and the software.
> > >
> > > All of the experiments I have seen deal with robots of some sort. I'm looking for an experiment based on training off of a data set. For example, I have 5,000 cases in an XML DataSet, I want to use 6 inputs, predict two outputs. I would run through all 5,000 cases as one generation of the training and base my fitness off of how accurately it predicted the correct output for the 5,000 cases.
> > >
> > > If anyone is aware of an experiment or posting that would point me in the right direction I would appreciate it.
> > >
> > > Thanks.
> > >
> >
>