/// <summary>
/// This is similar logic as standard deviation, but this returns the max distance from the average
/// NOTE: Spread is probably the wrong word, since this only returns the max distance (radius instead of diameter)
/// </summary>
public static double GetTotalSpread(IEnumerable <VectorND> values)
{
VectorND mean = MathND.GetCenter(values);
double distancesSquared = values.
Select(o => (o - mean).LengthSquared).
OrderByDescending().
First();
return(Math.Sqrt(distancesSquared));
}
/// <summary>
/// This version starts with a SOM, then potentially splits the largest node and/or gathers the smallest nodes into a single
/// </summary>
/// <returns></returns>
public static SOMResult Train(ISOMInput[] inputs, SOMRules rules, bool isDisplay2D)
{
SOMResult result = SelfOrganizingMaps.TrainSOM(inputs, rules, isDisplay2D);
if (result.Nodes.Length == 0)
{
return(result);
}
else if (result.Nodes.Length == 1)
{
#region kmeans single node
if (inputs.Length < 20)
{
return(result);
}
return(SelfOrganizingMaps.TrainKMeans(inputs, 5, true));
#endregion
}
var categorized = GetSOM_SplitMerge_Categorize(result);
List <SOMNode> nodes = new List <SOMNode>();
List <ISOMInput[]> newInputs = new List <ISOMInput[]>();
foreach (NodeCombo set in UtilityCore.Iterate(categorized.kmeans, categorized.keep)) // UtilityCore.Iterate gracefully skips nulls
{
nodes.Add(set.Node);
newInputs.Add(set.Inputs);
}
if (categorized.remaining != null)
{
nodes.Add(new SOMNode()
{
Position = MathND.GetCenter(categorized.remaining.Select(o => o.Node.Position)),
Weights = MathND.GetCenter(categorized.remaining.Select(o => o.Node.Weights)),
});
newInputs.Add(categorized.remaining.
SelectMany(o => o.Inputs).
ToArray());
}
return(new SOMResult(nodes.ToArray(), newInputs.ToArray(), false));
}
private static void AdjustKMeansCenters(SOMNode[] nodes, ISOMInput[][] inputsByNode)
{
if (nodes.Length != inputsByNode.Length)
{
throw new ArgumentException("Arrays must be the same size");
}
for (int cntr = 0; cntr < nodes.Length; cntr++)
{
if (inputsByNode[cntr].Length == 0)
{
// This happened when there were a bunch of identical images. Otherwise, it should never happen
//throw new ArgumentException("Must have inputs for every node");
continue;
}
nodes[cntr].Weights = MathND.GetCenter(inputsByNode[cntr].Select(o => o.Weights));
}
}
