public void inverse_gauss()
{
var rand = new Random(Seed);
var n = 1000;
var samples = new float[n];
for (var i = 0; i < samples.Length; i++)
{
samples[i] = (float)GaussHelper.InvPhi(rand.NextDouble());
}
Assert.True(Math.Abs(0 - samples.Mean()) < 0.1);
Assert.True(Math.Abs(1.0 - samples.Variance()) < 0.5);
}
public int[] Train(InstanceRepresentation set)
{
set.Standardize();
var instances = set.Instances.ToArray();
_weights = new float[_gridDimensions[0] * _gridDimensions[1], set.FeauturesCount];
FeaturesCount = set.FeauturesCount;
// Intialize weights with gaussians
var n = _gridDimensions[0] * _gridDimensions[1];
if (!_usePlusPlusInit || n >= instances.Length)
{
for (ushort i = 0; i < n; i++)
{
for (ushort j = 0; j < FeaturesCount; j++)
{
_weights[i, j] = (float)GaussHelper.InvPhi(_random.NextDouble());
}
}
}
else
{
_weights = PlusPlusInitializer.InitializeCentroids(n, instances, _random);
}
for (var i = 0; i < _iterationsCount; i++)
{
var instance = instances[_random.Next(instances.Length)];
// Best Matching Unit
var bmuIndex = Instances.MinEucDistanceIndex(instance, _weights);
BMUCoordinates = ToCoordinates(bmuIndex);
UpdateHexagonWeights((ushort)NeighbourhoodRadius(i), LearningRate(i), BMUCoordinates, instance.GetValues());
}
var instancesClusters = new int[instances.Length];
for (var i = 0; i < instances.Length; i++)
{
instancesClusters[i] = Instances.MinEucDistanceIndex(instances[i], _weights);
}
return(instancesClusters);
}