/// <summary>
/// Initialization Prior to receiving any inputs, the region is initialized by computing a list of initial potential
/// synapses for each column. This consists of a random set of inputs selected from the input space. Each input is
/// represented by a synapse and assigned a random permanence value. The random permanence values are chosen with two
/// criteria. First, the values are chosen to be in a small range around connectedPerm (the minimum permanence value
/// at which a synapse is considered "connected"). This enables potential synapses to become connected (or
/// disconnected) after a small number of training iterations. Second, each column has a natural center over the
/// input region, and the permanence values have a bias towards this center (they have higher values near the
/// center).
/// </summary>
/// <param name="input"></param>
public void Init(HtmInput input)
{
_input = input;
_columnList = new List <HtmColumn>();
_activeColumns = new List <HtmColumn>();
var inputIndexList = new List <int>();
for (int i = 0; i < input.Matrix.GetLength(0) * input.Matrix.GetLength(1); i++)
{
inputIndexList.Add(i);
}
IEnumerable <KMeansCluster> clusters = KMeansAlgorithm.FindMatrixClusters(input.Matrix.GetLength(0), input.Matrix.GetLength(1), HtmParameters.ColumnsCount);
foreach (KMeansCluster cluster in clusters)
{
List <int> htmSynapses = inputIndexList.Shuffle(Ran).ToList();
var synapses = new List <HtmForwardSynapse>();
for (int j = 0; j < HtmParameters.AmountOfPotentialSynapses; j++)
{
var newSynapse = new HtmForwardSynapse(HtmParameters.ConnectedPermanence)
{
Input = input,
Y = htmSynapses[j] / input.Matrix.GetLength(0),
X = htmSynapses[j] % input.Matrix.GetLength(0),
Permanance = (Ran.Next(5)) / (double)10,
};
synapses.Add(newSynapse);
}
_columnList.Add(new HtmColumn
{
Y = (int)Math.Round(cluster.Location.Y),
X = (int)Math.Round(cluster.Location.X),
PotentialSynapses = synapses
});
}
_activeColumns = new List <HtmColumn>();
}
