/// <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 TestComputeRegression()
{
var network = new RBFNetwork(2, 1, 1);
double[] ltm = {
2.0, // input 1 to RBF 1
2.0, // input 2 to RBF 1
5.0, // RBF width
2.0, // RBF, center-0
4.0, // RBF, center-1
3.0, // RBF1 to Output 1
4.0}; // Bias to Output 1
Array.Copy(ltm, 0, network.LongTermMemory, 0, ltm.Length);
double[] x = { 1, 2 };
double y = network.ComputeRegression(x)[0];
// Inputs: (2*1) + (2*2) = 6
// RBF: Gaussian(6) = 1
// Outputs: (1*3) + (1*4) = 7
Assert.AreEqual(7, y, AIFH.DefaultPrecision);
}
/// <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);
}
public void TestBasics()
{
var network = new RBFNetwork(2, 1, 1);
// should be 7, (2*1) + (1+(1 bias))*1 + 3 RBF params
// 2 + 2 + 3 = 7
Assert.AreEqual(7, network.LongTermMemory.Length);
Assert.AreEqual("[RBFNetwork:inputCount=2,outputCount=1,RBFs=[GaussianFunction:width=0.00,center=0.00,0.00],]", network.ToString());
}
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);
}
public void TestComputeClassification()
{
var network = new RBFNetwork(2, 1, 2);
double[] ltm = {
2.0, // input 1 to RBF 1
2.0, // input 2 to RBF 1
5.0, // RBF width
2.0, // RBF, center-0
4.0, // RBF, center-1
3.0, // RBF1 to Output 1
4.0, // Bias to Output 1
5.0, // RBF1 to Output 2
6.0}; // Bias to Output 2
Array.Copy(ltm, 0, network.LongTermMemory, 0, ltm.Length);
double[] x = { 1, 2 };
double[] y = network.ComputeRegression(x);
// Inputs: (2*1) + (2*2) = 6
// RBF: Gaussian(6) = 1
// Outputs: (1*3) + (1*4) = 7
Assert.AreEqual(7, y[0], AIFH.DefaultPrecision);
// Inputs: (2*1) + (2*2) = 6
// RBF: Gaussian(6) = 1
// Outputs: (1*5) + (1*6) = 11
Assert.AreEqual(11, y[1], AIFH.DefaultPrecision);
int cls = network.ComputeClassification(x);
// class 1 is higher than class 0
Assert.AreEqual(1, cls);
}
