public void Learning()
{
var networkConfig = new NetworkConfiguration(3, 5);
var learningConfig = new LearningConfiguration
{
ThetaFactorPerEpoch = 0.95,
DefaultRepeatsNumber = 25
};
var network = new KohonenNetwork <Logistic>(networkConfig);
var learning = new UnsupervisedLearning(network, learningConfig);
var inputs = _getInputs();
learning.Learn(inputs);
network.Input(_control[0]);
var res0 = network.GetOutputIndex();
network.Input(_control[1]);
var res1 = network.GetOutputIndex();
network.Input(_control[2]);
var res2 = network.GetOutputIndex();
Assert.NotEqual(res0, res1);
Assert.Equal(res1, res2);
}
public async Task Organizing()
{
var networkConfig = new NetworkConfiguration(3, 1);
var network = new KohonenNetwork <Logistic>(networkConfig);
var learningConfig = new LearningConfiguration
{
ThetaFactorPerEpoch = 0.95,
OrganizingAlgorithm = new Organizing <Logistic>(network, 0.777)
};
var learning = new UnsupervisedLearning(network, learningConfig);
var inputs = _getInputs();
await learning.LearnAsync(inputs, 25);
network.Input(_control[0]);
var res0 = await network.GetOutputIndexAsync();
network.Input(_control[1]);
var res1 = await network.GetOutputIndexAsync();
network.Input(_control[2]);
var res2 = await network.GetOutputIndexAsync();
Assert.NotEqual(res0, res1);
Assert.Equal(res1, res2);
}
private bool _checkRange(IEnumerable <double> input)
{
_network.Input(input);
var index = _network.GetOutputIndex();
var euclidRange = EuclidRangeSummator.GetEuclidRange(_network.OutputLayer.Nodes[index] as ISlaveNode);
return(euclidRange < _criticalRange);
}