/// <summary>
/// It traverses all connected synapses of the column and calculates the span, which synapses
/// spans between all input bits. Then it calculates average of spans accross all dimensions.
/// </summary>
/// <param name="column"></param>
/// <param name="htmConfig">Topology</param>
/// <returns></returns>
public static double CalcAvgSpanOfConnectedSynapses(Column column, HtmConfig htmConfig)
{
// Gets synapses connected to input bits.(from pool of the column)
int[] connected = column.ProximalDendrite.GetConnectedSynapsesSparse();
if (connected == null || connected.Length == 0)
{
return(0);
}
int[] maxCoord = new int[htmConfig.InputModuleTopology.Dimensions.Length];
int[] minCoord = new int[maxCoord.Length];
ArrayUtils.FillArray(maxCoord, -1);
ArrayUtils.FillArray(minCoord, ArrayUtils.Max(htmConfig.InputModuleTopology.Dimensions));
//
// It takes all connected synapses
for (int i = 0; i < connected.Length; i++)
{
maxCoord = ArrayUtils.MaxBetween(maxCoord, AbstractFlatMatrix.ComputeCoordinates(htmConfig.InputModuleTopology.Dimensions.Length,
htmConfig.InputModuleTopology.DimensionMultiplies, htmConfig.InputModuleTopology.IsMajorOrdering, connected[i]));
minCoord = ArrayUtils.MinBetween(minCoord, AbstractFlatMatrix.ComputeCoordinates(htmConfig.InputModuleTopology.Dimensions.Length,
htmConfig.InputModuleTopology.DimensionMultiplies, htmConfig.InputModuleTopology.IsMajorOrdering, connected[i]));
}
return(ArrayUtils.Average(ArrayUtils.Add(ArrayUtils.Subtract(maxCoord, minCoord), 1)));
}
/// <summary>
/// Uniform Column Mapping <br></br>
/// Maps a column to its respective input index, keeping to the topology of the region. It takes the index of the column as an argument and determines
/// what is the index of the flattened input vector that is to be the center of the column's potential pool. It distributes the columns over the inputs
/// uniformly. The return value is an integer representing the index of the input bit. Examples of the expected output of this method:
/// <list type="bullet">
/// <item>
/// If the topology is one dimensional, and the column index is 0, this method will return the input index 0. If the column index is 1, and there are
/// 3 columns over 7 inputs, this method will return the input index 3.
/// </item>
/// <item>If the topology is two dimensional, with column dimensions [3, 5] and input dimensions [7, 11], and the column index is 3, the method returns
/// input index 8.
/// </item>
/// </list>
/// </summary>
/// <param name="columnIndex">The index identifying a column in the permanence, potential and connectivity matrices.</param>
/// <param name="colTop"></param>
/// <param name="inpTop"></param>
/// <returns>Flat index of mapped column.</returns>
public static int MapColumn(int columnIndex, HtmModuleTopology colTop, HtmModuleTopology inpTop)
{
int[] columnCoords = AbstractFlatMatrix.ComputeCoordinates(colTop.NumDimensions,
colTop.DimensionMultiplies, colTop.IsMajorOrdering, columnIndex);
double[] colCoords = ArrayUtils.ToDoubleArray(columnCoords);
double[] columnRatios = ArrayUtils.Divide(
colCoords, ArrayUtils.ToDoubleArray(colTop.Dimensions), 0, 0);
double[] inputCoords = ArrayUtils.Multiply(
ArrayUtils.ToDoubleArray(inpTop.Dimensions), columnRatios, 0, 0);
var colSpanOverInputs = ArrayUtils.Divide(
ArrayUtils.ToDoubleArray(inpTop.Dimensions),
ArrayUtils.ToDoubleArray(colTop.Dimensions), 0, 0);
inputCoords = ArrayUtils.AddOffset(inputCoords, ArrayUtils.Multiply(colSpanOverInputs, 0.5));
// Makes sure that inputCoords are in range [0, inpDims]
int[] inputCoordInts = ArrayUtils.Clip(ArrayUtils.ToIntArray(inputCoords), inpTop.Dimensions, -1);
return(AbstractFlatMatrix.ComputeIndex(inputCoordInts, inpTop.Dimensions, inpTop.NumDimensions,
inpTop.DimensionMultiplies, inpTop.IsMajorOrdering, true));
}
