/// <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(); 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> /// 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); }