public static long BenchmarkEncogFlat(double[][] input, double[][] output)
{
var network = new FlatNetwork(input[0].Length, HIDDEN_COUNT, 0,
output[0].Length, false);
network.Randomize();
var trainingSet = new BasicMLDataSet(input, output);
var train = new TrainFlatNetworkBackPropagation(
network, trainingSet, 0.7, 0.7);
var a = new double[2];
var b = new double[1];
var sw = new Stopwatch();
sw.Start();
// run epoch of learning procedure
for (int i = 0; i < ITERATIONS; i++)
{
train.Iteration();
}
sw.Stop();
return sw.ElapsedMilliseconds;
}