public void Fit(List <List <double> > xs)
{
PCANetwork n = (PCANetwork)this.network;
double previousTotalWeights = n.CalculateTotalWeights();
int epoch_executed = 0;
while (epoch_executed < this.epoch)
{
for (var index = 0; index < xs.Count; index++)
{
var x_vector = xs[index];
algorithm.Process(x_vector, null);
}
epoch_executed++;
Console.WriteLine(string.Format("SYS: epoch {0} done.", epoch_executed));
var currentTotalWeights = n.CalculateTotalWeights();
var diff = Math.Abs(currentTotalWeights - previousTotalWeights);
previousTotalWeights = currentTotalWeights;
Console.WriteLine("DIFF:" + diff);
if (diff <= this.stopCondition)
{
Console.WriteLine("Condition OK");
break;
}
}
}
public static void Demo()
{
PCANetwork network = PCANetwork.Create(64, 8);
var dataSet = DataGenerator.GenerateDataSet1();
var pca = new PCA(network, 0.7);
PCATrainer trainer = new PCATrainer(network, 50, pca, 0.00000005);
Normalizer3 normalizer = new Normalizer3();
//Normalizer normalizer = new Normalizer();
normalizer.Fit(dataSet.XList);
var normalizedX = normalizer.Normalize(dataSet.XList);
Utils.DisplayListList(dataSet.XList);
Console.WriteLine("--------------------------");
Utils.DisplayListList(normalizedX);
//Utils.DisplayListList(normalizer.DeNormalize(normalizedX));
trainer.Fit(normalizedX);
List <List <double> > convertedX = trainer.GetConvertedDim(normalizedX);
//convertedX=normalizer.DeNormalize(convertedX);
BPNetwork bpNetwork = BPNetwork.Create(8, new int[] { 4 /*, 4, 3*/ }, 3, ActivationFunction.SIGMOID, ActivationFunction.SIGMOID);
var bp = new BP(bpNetwork, 0.6);
BPTrainer bpTrainer = new BPTrainer(bpNetwork, 2000, bp);
bpTrainer.Fit(convertedX, dataSet.YList);
List <List <double> > predicted_ys = bpTrainer.Predict(convertedX);
CorrectCalculator correctCalculator = new CorrectCalculator();
var convertedPredictValue = NeuronOutputConverter.OrderInteger(predicted_ys);
double correct = correctCalculator.Calculate(convertedPredictValue, dataSet.YList);
Console.WriteLine("Score: {0}%", correct * 100);
}
public static void Demo()
{
PCANetwork network = PCANetwork.Create(2, 1);
var dataSet = DataGenerator.GenerateDataSet2();
var pca = new PCA(network, 0.7);
PCATrainer trainer = new PCATrainer(network, 50, pca, 0.0000005);
Normalizer2 normalizer = new Normalizer2();
normalizer.Fit(dataSet.XList);
var normalizedX = normalizer.Normalize(dataSet.XList);
trainer.Fit(normalizedX);
List <List <double> > convertedX = trainer.GetConvertedDim(normalizedX);
List <List <double> > denormalX = normalizer.DeNormalize(convertedX);
network.Display();
Console.WriteLine("OLD VECTORS===>");
Utils.DisplayListList(dataSet.XList);
Console.WriteLine("OLD VECTORS(NORMALIZED)===>");
Utils.DisplayListList(normalizedX);
Console.WriteLine("NEW VECTORS(NORMALIZED)===>");
Utils.DisplayListList(convertedX);
Console.WriteLine("NEW VECTORS===>");
Utils.DisplayListList(denormalX);
}
public PCATrainer(PCANetwork network, int epoch, PCA algorithm, double stopCondition) : base(network, epoch, algorithm)
{
this.stopCondition = stopCondition;
}
public PCA(PCANetwork network, double learningRate)
{
this.network = network;
this.learningRate = learningRate;
}
