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#N canvas 411 75 676 452 10;
#X obj 61 190 mtx_distance2;
#X obj 61 166 t a a;
#X obj 61 234 t a a;
#X obj 61 269 mtx_*;
#X obj 61 315 outlet matched;
#X obj 171 315 outlet matcher;
#X obj 147 167 inlet dest;
#X obj 61 139 inlet source;
#X text 235 131 both "source" and "dest" must have the same number
of columns (dimension in feature-space);
#N canvas 0 0 618 479 mtx_rowpermutate 0;
#X obj 150 214 t b a;
#X obj 150 235 mtx;
#X obj 150 256 mtx_*;
#X obj 111 192 mtx_pivot -1;
#X text 17 19 this unfortunately does not guarantee to give as a minimum
trace of the result!;
#X obj 150 309 outlet rowpermutator;
#X obj 111 141 inlet permutans;
#X connect 0 0 1 0;
#X connect 0 1 2 1;
#X connect 1 0 2 0;
#X connect 2 0 5 0;
#X connect 3 1 0 0;
#X connect 3 2 1 1;
#X connect 6 0 3 0;
#X restore 61 211 pd mtx_rowpermutate;
#X text 220 224 this algorithm is _far_ from being perfect! the [mtx_rowpermutate]
does not really guarantee that the trace of the result is a global
mimimum \; if it is not \, than the match will be sub-optimal! this
will be even worse \, if there are several _equal_ (sub)minima in the
distance-matrix.;
#X text 158 184 greater distances have an extra penalty (^2) \; is
this ok?;
#X text 120 346 (c) 2005 by IOhannes m zmoelnig \, IEM KUG \, graz
austria;
#X text 120 363 for license-details see LICENSE.txt that must come
with iemmatrix.;
#X text 57 12 mtx_matcher:: match 2 sets of vectors;
#X text 46 42 2 vectors are matching if their euclidean distance is
a minimum. each "source" vector is assigned to a "dest" vector in order
to get a maximum matching. the output is a permutation "matched" of
the "source" vector-field \, and a permutation matrix "matcher".;
#X connect 0 0 9 0;
#X connect 1 0 0 0;
#X connect 1 1 3 1;
#X connect 2 0 3 0;
#X connect 2 1 5 0;
#X connect 3 0 4 0;
#X connect 6 0 0 1;
#X connect 7 0 1 0;
#X connect 9 0 2 0;
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