public SelfOrganizingMap(PointSet data, List <int> dimensions, int totalItt, double learningRate)
{
//Get initial learning rate
this.initialLearningRate = learningRate;
this.learningRate = learningRate;
influence = 0;
this.totalItterations = totalItt;
itterations = 1;
this.dimensions = dimensions;
this.data = data;
rng = new Random((int)DateTime.Now.Ticks);
int numNeurons = 1;
foreach (int d in dimensions)
{
numNeurons *= d;
}
int[] pos = new int[dimensions.Count];
for (int i = 0; i < dimensions.Count; i++)
{
pos[i] = 0;
}
//Scale each attribute from [0,1] and store the conversion matrix
conversionMatrix = data.GetMinMaxWeights().Max.Coordinates;
foreach (KPoint d in data.PointList)
{
d.Normalize(conversionMatrix);
}
//Here We will initialize Our Neurons
neurons = new List <SOMNeuron>();
KPoint zero = KPoint.Zero(data[0].Dimensions);
KPoint one = KPoint.One(data[0].Dimensions);
for (int i = 0; i < numNeurons; i++)
{
//Generate our neurons
double[] posNeuron = new double[dimensions.Count];
for (int d = 0; d < dimensions.Count; d++)
{
posNeuron[d] = (double)pos[d];
}
SOMNeuron neuron = new SOMNeuron(zero, one, rng, new KPoint(posNeuron), i);
neurons.Add(neuron);
addOneCarry(ref pos, dimensions);
}
//Get the max radius
setRadius();
this.timeConstant = (double)totalItterations / Math.Log(radius);
}
public HexagonalSelfOrganizingMap(PointSet data, int dimension, double learningRate)
{
_dimension = dimension;
//Get initial learning rate
this.initialLearningRate = learningRate;
this.learningRate = learningRate;
influence = 0;
itterations = 1;
this.data = data;
rng = new Random();
int numNeurons = dimension * dimension;
//Scale each attribute from [0,1] and store the conversion matrix
conversionMatrix = data.GetMinMaxWeights().Max.Coordinates;
foreach (KPoint d in data.PointList)
{
d.Normalize(conversionMatrix);
}
//Here We will initialize Our Neurons
neurons = new List <SOMNeuron>();
KPoint zero = KPoint.Zero(data[0].Dimensions);
KPoint one = KPoint.One(data[0].Dimensions);
double halfNeuronDelta = 1 / Math.Sqrt(3.0);
//Initialize our Quadtree for reference
double centerX = (dimension - 0.5) * (2 * halfNeuronDelta) / 2.0;
double centerY = (dimension - 1.0) / 2.0;
QuadTreePointStruct center = new QuadTreePointStruct()
{
Index = -1, X = centerX, Y = centerY
};
QuadTreePointStruct halfDistance = new QuadTreePointStruct()
{
Index = -1, X = centerX + halfNeuronDelta, Y = centerY + 0.5
};
neuronQuadTree = new QuadTree(new QuadTreeBoundingBox()
{
Center = center, HalfLength = halfDistance
});
int neuronIndex = 0;
for (int r = 0; r < dimension; r++)
{
double xPos = (r % 2 == 1) ? halfNeuronDelta : 0.0;
for (int c = 0; c < dimension; c++)
{
//Generate our neurons
double[] posNeuron = { xPos, (double)r };
SOMNeuron neuron = new SOMNeuron(zero, one, rng, new KPoint(posNeuron), neuronIndex);
QuadTreePointStruct neuronQTPos = new QuadTreePointStruct()
{
Index = neuronIndex,
X = posNeuron[0],
Y = posNeuron[1]
};
neuronQuadTree.Insert(neuronQTPos);
neurons.Add(neuron);
xPos += 2 * halfNeuronDelta;
neuronIndex++;
}
}
//Get the max radius
SetRadius();
timeConstant = (double)totalItterations / Math.Log(radius);
}
