/// <summary>
/// Classify the input into one of the output clusters.
/// </summary>
/// <param name="input">The input.</param>
/// <returns>The cluster it was clasified into.</returns>
public int Classify(IMLData input)
{
if (input.Count > InputCount)
{
throw new NeuralNetworkError(
"Can't classify SOM with input size of " + InputCount
+ " with input data of count " + input.Count);
}
double[][] m = _weights.Data;
double minDist = Double.PositiveInfinity;
int result = -1;
for (int i = 0; i < OutputCount; i++)
{
double dist = EngineArray.EuclideanDistance(input, m[i]);
if (dist < minDist)
{
minDist = dist;
result = i;
}
}
return(result);
}
public void TestPersistMediumEG()
{
BasicNetwork network = new BasicNetwork();
network.AddLayer(new BasicLayer(null, true, 10));
network.AddLayer(new BasicLayer(new ActivationSigmoid(), true, 10));
network.AddLayer(new BasicLayer(new ActivationSigmoid(), false, 10));
network.Structure.FinalizeStructure();
network.Reset();
EncogDirectoryPersistence.SaveObject(EG_FILENAME, network);
BasicNetwork network2 = (BasicNetwork)EncogDirectoryPersistence.LoadObject(EG_FILENAME);
double d = EngineArray.EuclideanDistance(network.Structure.Flat.Weights,
network2.Structure.Flat.Weights);
Assert.IsTrue(d < 0.01);
}