public void TestPersistEG()
{
IMLDataSet trainingSet = XOR.CreateXORDataSet();
RBFNetwork network = new RBFNetwork(2, 4, 1, RBFEnum.Gaussian);
SVDTraining training = new SVDTraining(network, trainingSet);
training.Iteration();
XOR.VerifyXOR(network, 0.1);
EncogDirectoryPersistence.SaveObject(EG_FILENAME, network);
RBFNetwork network2 = (RBFNetwork)EncogDirectoryPersistence.LoadObject(EG_FILENAME);
XOR.VerifyXOR(network2, 0.1);
}
public void Execute(IExampleInterface app)
{
//Specify the number of dimensions and the number of neurons per dimension
const int dimensions = 2;
const int numNeuronsPerDimension = 7;
//Set the standard RBF neuron width.
//Literature seems to suggest this is a good default value.
const double volumeNeuronWidth = 2.0/numNeuronsPerDimension;
//RBF can struggle when it comes to flats at the edge of the sample space.
//We have added the ability to include wider neurons on the sample space boundary which greatly
//improves fitting to flats
const bool includeEdgeRBFs = true;
#region Setup
//General setup is the same as before
var pattern = new RadialBasisPattern();
pattern.InputNeurons = dimensions;
pattern.OutputNeurons = 1;
//Total number of neurons required.
//Total number of Edges is calculated possibly for future use but not used any further here
int numNeurons = (int) Math.Pow(numNeuronsPerDimension, dimensions);
// int numEdges = (int) (dimensions*Math.Pow(2, dimensions - 1));
pattern.AddHiddenLayer(numNeurons);
var network = (RBFNetwork) pattern.Generate();
//RadialBasisFunctionLayer rbfLayer = (RadialBasisFunctionLayer)network.GetLayer(RadialBasisPattern.RBF_LAYER);
//Position the multidimensional RBF neurons, with equal spacing, within the provided sample space from 0 to 1.
//rbfLayer.SetRBFCentersAndWidthsEqualSpacing(0, 1, RBFEnum.Gaussian, dimensions, volumeNeuronWidth, includeEdgeRBFs);
network.SetRBFCentersAndWidthsEqualSpacing(0, 1, RBFEnum.Gaussian, volumeNeuronWidth, includeEdgeRBFs);
#endregion
//Create some training data that can not easily be represented by gaussians
//There are other training examples for both 1D and 2D
//Degenerate training data only provides outputs as 1 or 0 (averaging over all outputs for a given set of inputs would produce something approaching the smooth training data).
//Smooth training data provides true values for the provided input dimensions.
Create2DSmoothTainingDataGit();
//Create the training set and train.
var trainingSet = new BasicMLDataSet(INPUT, IDEAL);
var train = new SVDTraining(network, trainingSet);
//SVD is a single step solve
int epoch = 1;
do
{
train.Iteration();
Console.WriteLine(@"Epoch #" + epoch + @" Error:" + train.Error);
epoch++;
} while ((epoch < 1) && (train.Error > 0.001));
// test the neural network
Console.WriteLine(@"Neural Network Results:");
//Create a testing array which may be to a higher resolution than the original training data
Set2DTestingArrays(100);
trainingSet = new BasicNeuralDataSet(INPUT, IDEAL);
//Write out the results data
using (var sw = new StreamWriter("results.csv", false))
{
foreach (IMLDataPair pair in trainingSet)
{
var output = network.Compute(pair.Input);
//1D//sw.WriteLine(InverseScale(pair.Input[0]) + ", " + Chop(InverseScale(output[0])));// + ", " + pair.Ideal[0]);
sw.WriteLine(InverseScale(pair.Input[0]) + ", " + InverseScale(pair.Input[1]) + ", " +
Chop(InverseScale(output[0])));
// + ", " + pair.Ideal[0]);// + ",ideal=" + pair.Ideal[0]);
//3D//sw.WriteLine(InverseScale(pair.Input[0]) + ", " + InverseScale(pair.Input[1]) + ", " + InverseScale(pair.Input[2]) + ", " + Chop(InverseScale(output[0])));// + ", " + pair.Ideal[0]);// + ",ideal=" + pair.Ideal[0]);
//Console.WriteLine(pair.Input[0] + ", actual=" + output[0] + ",ideal=" + pair.Ideal[0]);
}
}
Console.WriteLine(@"\nFit output saved to results.csv");
Console.WriteLine(@"\nComplete - Please press the 'any' key to close.");
Console.ReadKey();
}