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 ```1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 ``` ``````#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; ``````