/// <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.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);
}
public void TestGeneral()
{
IList<BasicData> training = BasicData.ConvertArrays(TestInput, TestIdeal);
var score = new ScoreRegressionData(training);
IErrorCalculation ec = new ErrorCalculationMSE();
score.ErrorCalc = ec;
Assert.AreEqual(ec, score.ErrorCalc);
}
/// <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 TestRegression()
{
double[] actual = { 0.0, 1.0, 0.0, 0.0 };
IList<BasicData> training = BasicData.ConvertArrays(TestInput, TestIdeal);
var score = new ScoreRegressionData(training);
var simple = new SimpleAlgo(actual);
double s = score.CalculateScore(simple);
Assert.AreEqual(training, score.TrainingData);
Assert.AreEqual(0.25, s, AIFH.DefaultPrecision);
}
