/// <summary>
/// Train one fold.
/// </summary>
/// <param name="k">The fold id.</param>
/// <param name="fold">The fold.</param>
public void TrainFold(int k, CrossValidateFold fold)
{
int noImprove = 0;
double localBest = 0;
// Get the training and cross validation sets.
IList<BasicData> training = fold.TrainingSet;
IList<BasicData> validation = fold.ValidationSet;
// Create random particles for the RBF.
IGenerateRandom rnd = new MersenneTwisterGenerateRandom();
var particles = new RBFNetwork[TitanicConfig.ParticleCount];
for (int i = 0; i < particles.Length; i++)
{
particles[i] = new RBFNetwork(TitanicConfig.InputFeatureCount, TitanicConfig.RbfCount, 1);
particles[i].Reset(rnd);
}
/**
* Construct a network to hold the best network.
*/
if (_bestNetwork == null)
{
_bestNetwork = new RBFNetwork(TitanicConfig.InputFeatureCount, TitanicConfig.RbfCount, 1);
}
/**
* Setup the scoring function.
*/
IScoreFunction score = new ScoreTitanic(training);
IScoreFunction scoreValidate = new ScoreTitanic(validation);
/**
* Setup particle swarm.
*/
bool done = false;
var train = new TrainPSO(particles, score);
int iterationNumber = 0;
var line = new StringBuilder();
do
{
iterationNumber++;
train.Iteration();
var best = (RBFNetwork) train.BestParticle;
double trainingScore = train.LastError;
double validationScore = scoreValidate.CalculateScore(best);
if (validationScore > _bestScore)
{
Array.Copy(best.LongTermMemory, 0, _bestNetwork.LongTermMemory, 0, best.LongTermMemory.Length);
_bestScore = validationScore;
}
if (validationScore > localBest)
{
noImprove = 0;
localBest = validationScore;
}
else
{
noImprove++;
}
line.Length = 0;
line.Append("Fold #");
line.Append(k + 1);
line.Append(", Iteration #");
line.Append(iterationNumber);
line.Append(": training correct: ");
line.Append(trainingScore);
line.Append(", validation correct: ");
line.Append(validationScore);
line.Append(", no improvement: ");
line.Append(noImprove);
if (noImprove > TitanicConfig.AllowNoImprovement)
{
done = true;
}
Console.WriteLine(line.ToString());
} while (!done);
fold.Score = localBest;
}
/// <summary>
/// Run the example.
/// </summary>
public void Process()
{
// read the iris data from the resources
Assembly assembly = Assembly.GetExecutingAssembly();
Stream res = assembly.GetManifestResourceStream("AIFH_Vol2.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();
IGenerateRandom rnd = new MersenneTwisterGenerateRandom();
// 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, -1, 1);
ds.NormalizeRange(1, -1, 1);
ds.NormalizeRange(2, -1, 1);
ds.NormalizeRange(3, -1, 1);
IDictionary<string, int> species = ds.EncodeOneOfN(4);
var particles = new RBFNetwork[ParticleCount];
for (int i = 0; i < particles.Length; i++)
{
particles[i] = new RBFNetwork(4, 4, 3);
particles[i].Reset(rnd);
}
IList<BasicData> trainingData = ds.ExtractSupervised(0, 4, 4, 3);
IScoreFunction score = new ScoreRegressionData(trainingData);
var train = new TrainPSO(particles, score);
PerformIterations(train, 100000, 0.05, true);
var winner = (RBFNetwork) train.BestParticle;
QueryOneOfN(winner, trainingData, species);
}