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>
/// 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);
istream.Close();
var codec = new RBFNetworkGenomeCODEC(4, RbfCount, 3);
IList <BasicData> trainingData = ds.ExtractSupervised(0,
codec.InputCount, 4, codec.OutputCount);
IPopulation pop = InitPopulation(rnd, codec);
IScoreFunction score = new ScoreRegressionData(trainingData);
var genetic = new BasicEA(pop, score)
{
CODEC = codec
};
genetic.AddOperation(0.7, new Splice(codec.Size / 3));
genetic.AddOperation(0.3, new MutatePerturb(0.1));
PerformIterations(genetic, 100000, 0.05, true);
var winner = (RBFNetwork)codec.Decode(genetic.BestGenome);
QueryOneOfN(winner, trainingData, species);
}
/// <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);
}
/// <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);
}
/// <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();
// 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);
istream.Close();
var network = new RBFNetwork(4, 4, 3);
IList <BasicData> trainingData = ds.ExtractSupervised(0, 4, 4, 3);
IScoreFunction score = new ScoreRegressionData(trainingData);
var train = new ContinuousACO(network, score, 30);
PerformIterations(train, 100000, 0.05, true);
train.FinishTraining();
QueryOneOfN(network, trainingData, species);
}
