With the new approach implemented we can now lift our restriction on the input domain size. Effectively there is no need for constraining the grid domain when the algorithm works on the edges instead of grid input combinations.
The original implementation converted vertices and edges into integer representations (using index = y*size + x), but this approach is no longer applicable when the input domain is unbounded. The first step in fixing this is to refactor the model to store vertices and edges as structs instead:
public struct VertexData
{
public int x, y;
public VertexData(int x, int y)
{
this.x = x;
this.y = y;
}
}
public struct EdgeData
{
public VertexData v1, v2;
public EdgeData(VertexData from, VertexData to)
{
this.v1 = from;
this.v2 = to;
}
}
A SetContainer can take a struct, such that our definition of active vertices in the model changes to:
static SetContainer<VertexData> vertices = new SetContainer<VertexData>();
Because structs are simple data structures instead of object the SetContainer will correctly identify permutations of the same sequence as being equivalent, whereas had we used the Vertex class directly, the SetContainer would be storing object references instead of the object data and sequence would matter.