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);
}
public void standardize_dense_instance()
{
var instance1 = new[] { -1f, 5f, 0f, 1f };
var instance2 = new[] { 0f, 6f, 1f, 1f };
var instance3 = new[] { 3f, 7f, 1f, 1f };
var instance4 = new[] { 4f, 0f, 1f, 0f };
var feature1Sigma = Math.Sqrt(new[] { -1f, 0f, 3f, 4f }.Variance());
var feature2Sigma = Math.Sqrt(new[] { 5f, 6f, 7f, 0f }.Variance());
var feature1Mean = new[] { -1f, 0f, 3f, 4f }.Mean();
var feature2Mean = new[] { 5f, 6f, 7f, 0f }.Mean();
var inputFeaturesTypes = new InputFeatureTypes[] {
InputFeatureTypes.Ordinal,
InputFeatureTypes.Ordinal,
InputFeatureTypes.Flags,
InputFeatureTypes.Flags
};
var mixedDenseSet = new InstanceRepresentation(
inputFeaturesTypes,
sparse: false);
mixedDenseSet.AddInstance(instance1);
mixedDenseSet.AddInstance(instance2);
mixedDenseSet.AddInstance(instance3);
mixedDenseSet.AddInstance(instance4);
mixedDenseSet.Standardize();
var value1 = mixedDenseSet.Instances[0].GetValues();
Assert.True(Math.Abs((-1f - feature1Mean) / feature1Sigma - value1[0]) < Epsilon);
Assert.True((Math.Abs((5f - feature2Mean) / feature2Sigma - value1[1]) < Epsilon));
Assert.Equal(-1f, value1[2]);
Assert.Equal(1f, value1[3]);
}
