/// <summary>
/// This gets the bounding box of all the input values, then creates random vectors within that box
/// </summary>
private static VectorND[] GetRandomNodeWeights(int count, ISOMInput[] inputs)
{
var aabb = MathND.GetAABB(inputs.Select(o => o.Weights));
aabb = MathND.ResizeAABB(aabb, 1.1); // allow the return vectors to be slightly outside the input box
return(Enumerable.Range(0, count).
Select(o => MathND.GetRandomVector(aabb.Item1, aabb.Item2)).
ToArray());
}
/// <summary>
/// This does a bounding box of inputs, and also makes sure all positions are closer to the desired node than
/// other nodes
/// </summary>
private static VectorND[] GetRandomWeights_InsideCell(int count, ISOMInput[] inputs, SOMNode[] nodes, int nodeIndex)
{
var inputAABB = MathND.GetAABB(inputs.Select(o => o.Weights));
// This actually could happen. Detecting an infinite loop instead
//if (!MathND.IsInside(inputAABB, nodes[nodeIndex].Weights))
//{
// throw new ArgumentException("The node sits outside the input's aabb");
//}
List <VectorND> retVal = new List <VectorND>();
for (int cntr = 0; cntr < count; cntr++)
{
int infiniteLoopDetector = 0;
while (true)
{
VectorND attempt = MathND.GetRandomVector(inputAABB.Item1, inputAABB.Item2);
var closest = nodes.
Select((o, i) => new { Index = i, DistSquared = (attempt - o.Weights).LengthSquared }).
OrderBy(o => o.DistSquared).
First();
if (closest.Index == nodeIndex)
{
retVal.Add(attempt);
break;
}
infiniteLoopDetector++;
if (infiniteLoopDetector > 1000)
{
// Instead of giving up, increase the range that the weights can exist in. When testing this other method, there is almost never an improved
// node. It's just an infinite loop in the caller (keeps trying for an improvement, but it never happens)
return(GetRandomWeights_InsideCell_LARGERBUTFAIL(count, inputs, nodes, nodeIndex));
}
}
}
return(retVal.ToArray());
}
/// <summary>
/// Only call this from the other overload. This uses a much larger bounding box (it still makes sure all returned points
/// are closer to the desired node than other nodes)
/// </summary>
private static VectorND[] GetRandomWeights_InsideCell_LARGERBUTFAIL(int count, ISOMInput[] inputs, SOMNode[] nodes, int nodeIndex)
{
//TODO: May want to get a voronoi, then choose random points within the convex hull (more elegant, but may not be any faster)
// This works, but the caller generally never finds a solution that's better then what it already has. So if you need
// to use this overload, you're just going to spin the processor
#region calculate rectangle to choose points from
// Get some nearby nodes
double?largestDistance = Enumerable.Range(0, nodes.Length).
Where(o => o != nodeIndex).
Select(o => (nodes[o].Weights - nodes[nodeIndex].Weights).LengthSquared).
OrderByDescending(o => o).
FirstOrDefault();
var inputAABB = MathND.GetAABB(inputs.Select(o => o.Weights));
if (largestDistance == null)
{
largestDistance = Math.Max(
(inputAABB.Item1 - nodes[nodeIndex].Weights).LengthSquared,
(inputAABB.Item2 - nodes[nodeIndex].Weights).LengthSquared);
}
largestDistance = Math.Sqrt(largestDistance.Value);
var bounds = MathND.GetAABB(new[]
{
nodes[nodeIndex].Weights.Select(o => o - largestDistance.Value).ToArray().ToVectorND(),
nodes[nodeIndex].Weights.Select(o => o + largestDistance.Value).ToArray().ToVectorND(),
inputAABB.Item1,
inputAABB.Item2,
});
#endregion
List <VectorND> retVal = new List <VectorND>();
//int largestIteration = 0;
for (int cntr = 0; cntr < count; cntr++)
{
int infiniteLoopDetector = 0;
while (true)
{
//if(infiniteLoopDetector > largestIteration)
//{
// largestIteration = infiniteLoopDetector; // I tested several times, and averaged about 5-15 iterations
//}
VectorND attempt = MathND.GetRandomVector(bounds.Item1, bounds.Item2);
var closest = nodes.
Select((o, i) => new { Index = i, DistSquared = (attempt - o.Weights).LengthSquared }).
OrderBy(o => o.DistSquared).
First();
if (closest.Index == nodeIndex)
{
retVal.Add(attempt);
break;
}
infiniteLoopDetector++;
if (infiniteLoopDetector > 1000)
{
throw new ApplicationException("Infinite loop detected");
}
}
}
return(retVal.ToArray());
}
