/// <summary>
/// Create an initial population.
/// </summary>
/// <param name="rnd">Random number generator.</param>
/// <param name="codec">The codec, the type of network to use.</param>
/// <returns>The population.</returns>
public static IPopulation InitPopulation(IGenerateRandom rnd, RBFNetworkGenomeCODEC codec)
{
// Create a RBF network to get the length.
var network = new RBFNetwork(codec.InputCount, codec.RbfCount, codec.OutputCount);
int size = network.LongTermMemory.Length;
// Create a new population, use a single species.
IPopulation result = new BasicPopulation(PopulationSize, new DoubleArrayGenomeFactory(size));
var defaultSpecies = new BasicSpecies {
Population = result
};
result.Species.Add(defaultSpecies);
// Create a new population of random networks.
for (int i = 0; i < PopulationSize; i++)
{
var genome = new DoubleArrayGenome(size);
network.Reset(rnd);
Array.Copy(network.LongTermMemory, 0, genome.Data, 0, size);
defaultSpecies.Add(genome);
}
// Set the genome factory to use the double array genome.
result.GenomeFactory = new DoubleArrayGenomeFactory(size);
return(result);
}
public void TestResetCompute()
{
var network = new RBFNetwork(2, 1, 1);
double total = network.LongTermMemory.Sum();
Assert.AreEqual(0, total, AIFH.DefaultPrecision);
network.Reset(new BasicGenerateRandom());
total += network.LongTermMemory.Sum();
Assert.IsTrue(Math.Abs(total) > AIFH.DefaultPrecision);
}
/// <summary>
/// Run the example.
/// </summary>
public void Process()
{
// read the iris data from the resources
Assembly assembly = Assembly.GetExecutingAssembly();
var res = assembly.GetManifestResourceStream("AIFH_Vol3.Resources.iris.csv");
// did we fail to read the resouce
if (res == null)
{
Console.WriteLine("Can't read iris data from embedded resources.");
return;
}
// load the data
var istream = new StreamReader(res);
DataSet ds = DataSet.Load(istream);
istream.Close();
// The following ranges are setup for the Iris data set. If you wish to normalize other files you will
// need to modify the below function calls other files.
ds.NormalizeRange(0, 0, 1);
ds.NormalizeRange(1, 0, 1);
ds.NormalizeRange(2, 0, 1);
ds.NormalizeRange(3, 0, 1);
IDictionary <String, int> species = ds.EncodeOneOfN(4);
IList <BasicData> trainingData = ds.ExtractSupervised(0, 4, 4, 3);
var network = new RBFNetwork(4, 4, 2);
network.Reset(new MersenneTwisterGenerateRandom());
IScoreFunction score = new ScoreRegressionData(trainingData);
var train = new TrainAnneal(network, score);
PerformIterations(train, 100000, 0.01, true);
QueryOneOfN(network, trainingData, species);
}