dsearch

Search for nearest point

Syntax

• K = dsearch(x,y,TRI,xi,yi)
  K = dsearch(x,y,TRI,xi,yi,S)
  

Description

K = dsearch(x,y,TRI,xi,yi) returns the index into x and y of the nearest point to the point (xi,yi). dsearch requires a triangulation TRI of the points x,y obtained using delaunay. If xi and yi are vectors, K is a vector of the same size.

K = dsearch(x,y,TRI,xi,yi,S) uses the sparse matrix S instead of computing it each time:

• S = sparse(TRI(:,[1 1 2 2 3 3]),TRI(:,[2 3 1 3 1 2]),1,nxy,nxy)
  

where nxy = prod(size(x)).

dsearchn

n-D nearest point search

Syntax

• k = dsearchn(X,T,XI)
  k = dsearchn(X,T,XI,outval)
  k = dsearchn(X,XI)
  [k,d] = dsearchn(X,...)
  

Description

k = dsearchn(X,T,XI) returns the indices k of the closest points in X for each point in XI. X is an m-by-n matrix representing m points in n-D space. XI is a p-by-n matrix, representing p points in n-D space. T is a numt-by-n+1 matrix, a tessellation of the data X generated by delaunayn. The output k is a column vector of length p.

k = dsearchn(X,T,XI,outval) returns the indices k of the closest points in X for each point in XI, unless a point is outside the convex hull. If XI(J,:) is outside the convex hull, then K(J) is assigned outval, a scalar double. Inf is often used for outval. If outval is [], then k is the same as in the case k = dsearchn(X,T,XI).

k = dsearchn(X,XI) performs the search without using a tessellation. With large X and small XI, this approach is faster and uses much less memory.

[k,d] = dsearchn(X,...) also returns the distances d to the closest points. d is a column vector of length p.