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#N canvas 411 75 586 409 10;
#X obj 61 120 mtx_distance2;
#X obj 61 96 t a a;
#X obj 61 164 t a a;
#X obj 61 199 mtx_*;
#X obj 61 245 outlet matched;
#X obj 171 245 outlet matcher;
#X obj 147 97 inlet dest;
#X obj 61 69 inlet source;
#X text 25 25 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 61 141 pd mtx_rowpermutate;
#X text 34 303 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 158 114 greater distances have an extra penalty (^2) \; is
this ok?;
#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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