/// <summary>
/// Perform the example.
/// </summary>
public void Process()
{
var trainingData = BasicData.ConvertArrays(XorInput, XorIdeal);
var network = new RBFNetwork(2, 5, 1);
var score = new ScoreRegressionData(trainingData);
var train = new TrainGreedyRandom(true, network, score);
PerformIterations(train, 1000000, 0.01, true);
Query(network, trainingData);
}
/// <summary>
/// Run the example.
/// </summary>
public void Process()
{
IList <BasicData> trainingData = GenerateTrainingData();
var poly = new PolynomialFn(3);
IScoreFunction score = new ScoreRegressionData(trainingData);
var train = new TrainGreedyRandom(true, poly, score);
PerformIterations(train, 1000000, 0.01, true);
Console.WriteLine(poly.ToString());
}
public void TestGreedyRandom()
{
var train = new TrainGreedyRandom(true, new TrialAlgo(), new TrialScore())
{
LowRange = 0, HighRange = 10
};
Assert.AreEqual(0, train.LowRange, AIFH.DefaultPrecision);
Assert.AreEqual(10, train.HighRange, AIFH.DefaultPrecision);
PerformTest(train);
}
/// <summary>
/// Run the example.
/// </summary>
public void Process()
{
// read the iris data from the resources
Assembly assembly = Assembly.GetExecutingAssembly();
var res = assembly.GetManifestResourceStream("AIFH_Vol1.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.EncodeEquilateral(4);
IList <BasicData> trainingData = ds.ExtractSupervised(0, 4, 4, 2);
var network = new RBFNetwork(4, 4, 2);
IScoreFunction score = new ScoreRegressionData(trainingData);
var train = new TrainGreedyRandom(true, network, score);
PerformIterations(train, 100000, 0.01, true);
QueryEquilateral(network, trainingData, species, 0, 1);
}
