public void SaveWeights(SOMNet network)
{
var totalPath = ConfigurationManager.AppSettings["Results"];
using (var file = new StreamWriter(totalPath))
{
for (var i = 0; i < network.Count; i++)
{
file.WriteLine("<{0} , {1}>", network.GetNuronWeights(i).At(0, 0), network.GetNuronWeights(i).At(0, 1));
}
}
}
public override int Run(string[] remainingArguments)
{
InitializeArgumentsFromConfinIfItIsNecessary();
var inputs = _dataLoader.LoadCoordinates(_fileName);
var normalizedInputs = inputs.Select(_matrixHelpers.Normalize).ToList();
var maxInputOne = normalizedInputs.Select(x => x.At(0, 0)).Max();
var maxInputTwo = normalizedInputs.Select(x => x.At(0, 1)).Max();
var network = new SOMNet(maxInputOne, maxInputTwo, inputs.Count);
var rmax = 4;
//var neighborhoodFunction = new Guassian();
var neighborhoodFunction = new Guassian();
var teacher = new Teacher(network, neighborhoodFunction, rmax);
teacher.Learn(normalizedInputs);
var order = new List <int>();
foreach (var input in inputs)
{
order.Add(network.GetIndexOfTheMostMatchesNeuron(input));
}
var saver = new DataSaver();
saver.SaveWeights(network);
saver.SaveNormalizedInputs(normalizedInputs);
saver.SaveOrder(order);
if (shouldVisual)
{
var orderlinesses = inputs.Select((x, i) => new Orderliness(order[i], x)).ToList();
var bm = _visualizer.Visualize(orderlinesses);
bm.Save(ConfigurationManager.AppSettings["Visualization"]);
}
return(0);
}
public Teacher(SOMNet network, INeighborhoodFunction neighborhoodFunction, int rmax)
{
_network = network;
_neighborhoodFunction = neighborhoodFunction;
_rmax = rmax;
}
