view util/ksvd utils/dictdist.m @ 77:62f20b91d870

add routines from sparco problems privite folder to {root}\util
some changes to ksvd vs rlsdla image denoising example

function [dist,ratio] = dictdist(approxD,D,epsilon)
%DICTDIST Distance between dictionaries.
%  [DIST,RATIO] = DICTDIST(APPROXD,D) computes the distance between the
%  approximate dictionary APPROXD and the true dictionary D, where APPROXD
%  is NxK and D is NxM.
%
%  The distance between the dictionary APPROXD and a single atom A of D is
%  defined as:
%
%      DIST(APPROXD,A) = min  { 1-abs(APPROXD(:,i)' * A) }
%                         i
%
%  The distance between the dictionaries APPROXD and D is defined as:
%
%      DIST(APPROXD,D) = sum { dist(APPROXD, D(:,k)) } / M
%                         k
%
%  Note that 0 <= DIST(APPROXD,D) <= 1, where 0 implies that all atoms in D
%  appear in APPROXD, and 1 implies that the atoms of D are orthogonal to
%  APPROXD.
%
%  The similarity ratio between APPROXD and D is defined as:
%
%      RATIO(APPROXD,D) = #(atoms in D that appear in APPROXD) / M
%
%  where two atoms are considered identical when DIST(A1,A2) < EPSILON with
%  EPSILON=0.01 by default. Note that 0 <= RATIO(APPROXD,D) <= 1, where 0
%  means APPROXD and D have no identical atoms, and 1 means that all atoms
%  of D appear in APPROXD.
%
%  [DIST,RATIO] = DICTDIST(DICT1,DICT2,EPSILON) specifies a different value
%  for EPSILON.

%  Ron Rubinstein
%  Computer Science Department
%  Technion, Haifa 32000 Israel
%  ronrubin@cs
%
%  October 2007


if (nargin < 3), epsilon = 0.01; end

[n,m] = size(D);

approxD = normcols(approxD*spdiag(sign(approxD(1,:))));
D = normcols(D*spdiag(sign(D(1,:))));

identical_atoms = 0;
dist = 0;

for i = 1:m
  atom = D(:,i);
  distances = 1-abs(atom'*approxD);
  mindist = min(distances);
  dist = dist + mindist;
  identical_atoms = identical_atoms + (mindist < epsilon);
end

dist = dist / m;
ratio = identical_atoms / m;