public NeuralNetwork CreateNetwork(NNAlgorithm supportedAlgorithms = NNAlgorithm.None)
{
Validate();
var layers = CreateLayers();
if (layers == null || layers.Count == 0) throw new InvalidOperationException("Created layer collection is null or empty.");
var nnType = InitParameters.NeuralNetworkType;
var constructor = (from ci in nnType.GetConstructors()
let pars = ci.GetParameters()
where pars.Length == 3 &&
pars[0].ParameterType.IsAssignableFrom(layers.GetType()) &&
pars[1].ParameterType.IsAssignableFrom(InitParameters.GetType()) &&
pars[2].ParameterType.IsAssignableFrom(typeof(NNAlgorithm))
select ci).FirstOrDefault();
if (constructor == null) throw new InvalidOperationException("Architecture compatible constructor not found on Neural Network type '" + nnType + "'.");
try
{
return (NeuralNetwork)constructor.Invoke(new object[] { layers, InitParameters, supportedAlgorithms });
}
catch (Exception ex)
{
throw new InvalidOperationException("Cannot invoke Architecture compatible constructor on Neural Network type '" + nnType + "'.", ex);
}
}
public ManagedNeuralNetwork(ICollection<ConnectableLayer> layers, ManagedNNInitParameters parameters, NNAlgorithm supportedAlgorithms = NNAlgorithm.None)
: base(layers, parameters, supportedAlgorithms)
{
Contract.Requires(layers != null);
Contract.Requires(layers.Count != 0);
Contract.Requires(parameters != null);
}
/// <summary>
/// Sorts the <see cref="Color"/> values.
/// </summary>
/// <remarks>This method try to sort the colors using the Nearest Neighbour algorithm, trying to ensure that the black color (0, 0, 0), if exists in palette, be the first color.
/// This implementation is based on this article: https://www.alanzucconi.com/2015/09/30/colour-sorting/ </remarks>
public void Sort()
{
#if SORT_BY_HSV
var vectors = this._colors.Select(e => e.ToHSV(ColorFormat.DAC)).ToList();
#elif SORT_BY_HSL
var vectors = this._colors.Select(e => e.ToHSL(ColorFormat.DAC)).ToList();
#elif SORT_BY_HLV_STEP
var vectors = this._colors.Select(e => e.Step()).ToList();
#else
var vectors = this._colors.Select(e => e.Normalize(ColorFormat.DAC)).ToList();
#endif
int start = vectors.FindIndex(e => e == Vector3.Zero); // Try to localize the black color.
List <int> path = NNAlgorithm.CalculatePath(vectors, start == -1 ? 0 : start, out _);
this._colors = path.Select(e => this._colors[e]).ToArray();
}
static void Main(string[] args)
{
String dataFilePath = args[0];
String weightsFilePath = args[1];
String outputFilePath = args[2];
int metric = Int32.Parse(args[3]);
int numThreads = Int32.Parse(args[4]);
Stopwatch w = new Stopwatch();
w.Start();
List <double[]> samples = CSVIO.Load <double>(dataFilePath);
List <double[]> weights = CSVIO.Load <double>(weightsFilePath);
w.Stop();
long loadingMS = w.ElapsedMilliseconds;
w.Reset();
w.Start();
DistanceFunctions distFunc = distFunc = new SquareEuclideanDistanceFunction();
if (metric == 2)
{
distFunc = new Cosine();
}
if (metric == 3)
{
distFunc = new Pearson();
}
Console.WriteLine("Using distance function with brute force: {0} and numthreads: {1}", metric, numThreads);
NNAlgorithm nnMethod = null;
// if euclidean then can use fast kdtree
if (metric == 1)
{
nnMethod = new KDTreeNN(weights, distFunc);
}
else
{
nnMethod = new BruteForceNN(weights, distFunc);
}
List <int[]> nearestNeighbours = NearestNeighbour.GetNearestNeighbours(samples, nnMethod, numThreads);
w.Stop();
long vqMS = w.ElapsedMilliseconds;
w.Reset();
w.Start();
CSVIO.Save <int>(outputFilePath, nearestNeighbours);
w.Stop();
long savingMS = w.ElapsedMilliseconds;
Console.WriteLine("Loading Time: {0} NN Time: {1} Saving Time: {2}", loadingMS, vqMS, savingMS);
}
