100 lines
26 KiB
Plaintext
100 lines
26 KiB
Plaintext
|
ELF > @ €b @ 8 @ H
|
|||
|
|
H
|
|||
|
|
¡ ¡ 0 0 0 ¡ ¡ ¸M ¸] ¸] ÐM Ð] Ð] à à 8 8 8 $ $ Påtd �= �= �= l l Qåtd Råtd ¸M ¸] ¸] H H GNU ZEÍ.ÏÌFre‘²Ø½Þüh ! € ! ÑÁïŽ œ â Û ² + * Ð _ à y , U F " ƒ q º Í ˜ � ¦ ý ® N à Õ 9 ð ð __gmon_start__ _ITM_deregisterTMCloneTable _ITM_registerTMCloneTable __cxa_finalize PyArg_ParseTupleAndKeywords PyEval_SaveThread PyEval_RestoreThread Py_BuildValue _Py_Dealloc PyExc_ValueError PyErr_Format PyExc_TypeError PyErr_SetString PyInit__lsap PyImport_ImportModule PyObject_GetAttrString PyCapsule_Type PyCapsule_GetPointer PyModule_Create2 PyExc_ModuleNotFoundError PyErr_ExceptionMatches PyErr_Print PyExc_ImportError PyErr_Clear PyExc_RuntimeError memmove _Znwm memset _ZSt20__throw_length_errorPKc memcpy __gxx_personality_v0 _Unwind_Resume _ZdlPv libstdc++.so.6 libgcc_s.so.1 libc.so.6 GCC_3.0 GLIBC_2.14 GLIBC_2.2.5 CXXABI_1.3 GLIBCXX_3.4 A P&y Y O 0 ”‘– a ui l 2 Ó¯k x t)’ ƒ ¸] À] Ð È] È] à` �0 è` ©0 (a ²0 0a 3 @a €a €a ¸0 ˆa ˜a Ð3 °_ ¸_ À_ È_ Ð_ Ø_ à_ è_
|
|||
|
|
ð_ ø_ Àa ` ` (` 0` 8`
|
|||
|
|
@` H` P` X` `` h` p` x` €` ˆ` �` ˜` ` ¨` °` ¸` À` È` HƒìH‹µO H…Àtè; HƒÄà ÿ5âO ÿ%äO @ ÿ%âO h éàÿÿÿÿ%ÚO h éÐÿÿÿÿ%ÒO h éÀÿÿÿÿ%ÊO h é°ÿÿÿÿ%ÂO h é ÿÿÿÿ%ºO h é�ÿÿÿÿ%²O h é€ÿÿÿÿ%ªO h épÿÿÿÿ%¢O h é`ÿÿÿÿ%šO h éPÿÿÿÿ%’O h
|
|||
|
|
é@ÿÿÿÿ%ŠO h é0ÿÿÿÿ%‚O h é ÿÿÿÿ%zO h
|
|||
|
|
éÿÿÿÿ%rO h é ÿÿÿÿ%jO h éðþÿÿÿ%bO h éàþÿÿÿ%ZO h éÐþÿÿÿ%RO h éÀþÿÿÿ%JO h é°þÿÿÿ%BO h é þÿÿÿ%:O h é�þÿÿÿ%2O h é€þÿÿH‹t$pH‰ïL‰åèà H�¼$0 èà H�¼$ è¶ H‹t$pH‰ßè¹ H‹¼$À H‹´$Ð H)þH…ÿtèœ H‹t$pL‰ÿè� H‹t$pL‰ïè‚ H‹t$pL‰÷èu H‹´$¸ H‹|$èc H‹´$° H‹D$0H)ÆH…ÀtH‰ÇèF H‰ïè ÿÿÿf. „ @ H�=aO H�ZO H9øtH‹fM H…Àt ÿà € à € H�=1O H�5*O H)þH‰ðHÁî?HÁøHÆHÑþtH‹-M H…Àtÿàf D à € €=ñN u/UHƒ=M H‰åt H�=ÚJ èýÿÿèhÿÿÿÆÉN ]à € à € é{ÿÿÿf. „ �AWH‰÷1ÀH‰ÖAVH�
|
|||
|
|
M H�Æ AUATUSHƒìHL�L$,L�D$0HÇD$0 ÇD$, è,ýÿÿ…À„à H‹]N ¿ H‹˜( ÿ�h H‹|$0E1É1ÉH‰ÆA¸ 1ÒÿÓH‰ÅH…À„‡ ‹Pƒú…\ H‹XH…Û„Ç H‹@ L�d$8¹ ¾ L‰âL‹0L‹xL‰ðM9÷INÇHƒìE1ÉE1ÀH‰D$@H‹ÒM H‹xj j j ÿ�è I‰ÅHƒÄ H…À„
|
|||
|
|
H‹«M L‰âE1ÉE1ÀHƒì¹ ¾ H‹xj j j ÿ�è E1ÒI‰ÄHƒÄ H…ÀtM‹EL‹HL‰T$L‰D$L‰L$èIüÿÿL‹L$1ÉH‰ÚH‰$‹D$,L‰þL‰÷L‹D$…À•ÁèÒ L‹ $‰ÃL‰ßèüÿÿƒûÿL‹T$„ ƒûþ„Ý L‰âL‰îH�=i 1Àè üÿÿI‰ÂHƒm „‹ Iƒm tlM…ätAIƒ,$u:L‰çL‰$è üÿÿL‹$ë(f D H‹¡J H�5² H‹81ÀèHûÿÿHƒm tE1ÒHƒÄHL‰Ð[]A\A]A^A_à @ H‰ïèÀûÿÿëÝf D L‰ïL‰$è¬ûÿÿL‹$éÿÿÿ H‰ïL‰$è”ûÿÿL‹$é`ÿÿÿ H‹AJ H�5n H‹8è²ûÿÿëˆH‹J H�5R L‰$H‹8è–ûÿÿL‹$éÿÿÿ D H‹éI H�5I L‰$H‹8ènûÿÿL‹$éïþÿÿ D ATH�=C UHƒìè ûÿÿI‰ÄH…À„Ù H�5a L‰çèÒúÿÿIƒ,$H‰Å„” H…í„Æ H‹ŒI H9E…ú 1öH‰ïè@ûÿÿHƒm H‰tK trH…À„€ ÿ= H‹[K ‡ì ÿ�˜ ƒøŽ9 H‹?K ÿ�� …À„ ƒø…å HƒÄ¾õ H�=HJ ]A\é€úÿÿL‰çèHúÿÿé_ÿÿÿ H‰ïè8úÿÿH‹ñJ ézÿÿÿ @ H‹éH H‹8è‰ùÿÿ…Àu%è úÿÿH‹©H H�5j H‹8è:úÿÿHƒÄ1À]A\Ãè úÿÿH�=. èïùÿÿI‰ÄH…À…Òþÿÿë¼�H‹±H H�5Ú H‹8èúùÿÿHƒm užH‰ïè«ùÿÿë”ÿº H�5Û ‰ÁH‹zH H‹81ÀèèøÿÿénÿÿÿH‹dH H�5… H‹8èùÿÿéSÿÿÿH‹IH H�5: H‹8è’ùÿÿé8ÿÿÿ‰ÁH‹,H º H�5È H‹81ÀèŽøÿÿéÿÿÿH‹
|
|||
|
|
H H�5� H‹8èSùÿÿéùþÿÿf. „ @ AWAVAUATUSH�ìh H‰|$8H‰t$@L‰D$`L‰L$hH…ö„ž H…ÿ„• H‰ûI‰ÔA‰ÍH¯ÞH9÷�A
|
|||
|
|
„É…9
|
|||
|
|
H‹D$8H‰4$HÇD$0 H‰D$ HÇ„$° HÇ„$¨ H…Û~hò
|
|||
|
|
ç$ 1À D òAÄf.Èwf.À{>A¼þÿÿÿH‹D$0H…ÀtH‹´$¨ H‰Çè¶ ëE1äH�Äh D‰à[]A\A]A^A_à @ HƒÀH9Øu§H¸ÿÿÿÿÿÿÿH9D$ ‡ñ H‹D$ H� Å H‰ßH‰œ$¸ è-øÿÿH‰Ú1öH‰ÇH‰D$èûöÿÿH¸ÿÿÿÿÿÿÿH9$‡¢ L‹<$J� ý H‰ßH‰\$pèî÷ÿÿH‰ÇH‰Ú1öI‰Æè¾öÿÿH‰ßèÖ÷ÿÿH�xIƒïHÇ I‰ÅH‰|$PL‰|$XtH‰ØLëH‰\$PH9ût H�Pø1öè}öÿÿH‹l$pH‰ïè�÷ÿÿH‰ÇH‰ê¾ÿ I‰Çè]öÿÿH‹œ$¸ fïÀHÇ„$Ð )„$À H‰ßèU÷ÿÿfHnÀH‰ÇH‰Ú¾ÿ HÃflÀH‰œ$Ð )„$À è öÿÿH‰ïH‰œ$È è÷ÿÿH‰ê¾ÿ H‰ÇH‰l$pH‰ÃèáõÿÿH‹t$ HÇ„$ HÇ„$ HÇ„$ H�n?HÇ„$ HÁíHÇ„$ HÁåH‰ïè¬öÿÿH‹t$ H‰ÇHèH‰êH‰„$ H‰ðH‰¼$ Ç„$ HÁøH�ÇH‰„$ H‰ð1öƒà?‰„$ è<õÿÿH‹4$HÇ„$0 HÇ„$8 HÇ„$@ H�n?HÇ„$H HÁíHÇ„$P HÁåH‰ïèöÿÿH‹4$H‰ÇHèH‰êH‰„$P H‰ðH‰¼$0 Ç„$8 HÁøH�ÇH‰„$@ H‰ð1öƒà?‰„$H è™ôÿÿH‹|$pè¯õÿÿHƒ|$X HÇ H‰Åt H‹t$pH�xHðH9Çt H�Vø1öèaôÿÿH‹4$H‹T$PHÇD$ H‹¼$ ò-! H‰ðL)êHƒæþfo5M! HÑèH‰´$€ HÁàH‰”$ˆ HèH‰D$xH�BøHÁèH�pH‰ðH‰´$ HƒæþHÑèH‰´$˜ HÁàLèHƒúH‰„$� I�Dõ IDÅH‰D$ € Hƒ|$X†¦ ó~$$H‹T$xH‰èfvÛfo
|
|||
|
|
‹ flä € foÑfoÄfÔÎHƒÀfûÂfÔÃ@ðH9ÂußH‹„$€ H9$tKL‹$H�pH� Å L‰ÂH)ÂHƒêH‰TÅ I9ð~'L‰ÂHƒÀH)òHƒêH‰T
|
|||
|
|
L9À}L‰ÂH)ÂH�BÿH‰D
|
|||
|
|
L‹„$ D‹Œ$ L9Ç„5 L‰Â1öD‰L$(H)úL‰D$HèæòÿÿD‹L$(H‹ fo5ò E…ÉfHnèt ¹@ L‹D$HHÇÀÿÿÿÿD)ÉHÓèH÷ÐI! L‹Œ$@ L‹„$0 D‹”$H M9Á„œ L‰Ê1öL‰ÇD‰T$HL)ÂL‰L$(èpòÿÿD‹T$HH‹¤ L‹L$(fo5w E…ÒfHnèt¹@ HÇÀÿÿÿÿD)ÑHÓèH÷ÐI!L‹„$0 L;l$PtMHƒ¼$ˆ t9f(C H‹”$� L‰è „ HƒÀH9ÐuôH‹¼$ H9¼$˜ t H‹D$ò(H‹t$H‹¼$ º fïÛH‰ð‰ñI‰òHÁèHÓâH ÇH‰òH‹4$f „ H‹D$L‹ $IÇÃÿÿÿÿf(Õò$ÐM¯Ê1Òë € f.Êz uHƒ<ÃÿtGHƒÂH9Ö~NH‹DÕ I� òAÌI�LÅ ò òXÃò\ÄòA\Æf/Èv M‰Çf(Èòf/ÑvªI‰ÓHƒÂf(ÑH9Ö²f.Õ‹S J�DÝ H‹H‹ËHƒúÿtVI‰ÉAº HƒîIÁéIÓâO ÈH‹Lõ I‰ÒH‰L‰ÐA» D‰ÑL‰ÒHÁèIÓãL ÇH…ö„ f(Úéÿÿÿ € H‰ÊA¹ HƒîHÁêIÓáM ÐH‹Tõ H‰Hƒùÿt§H‰ÈH…ÉˆÆ H‹t$L‹T$H‰D$(1ÉH‹”$À L‹L$ òAòH‰ðòXÂòAò D H‰ÎA» HÁîIÓãL# ÷t H9ÁtH‹4Êf(ÂòA\Dõ òAXÊòAÊHƒÁL9ÉuÁH‹D$(L‹ $1Éf D I‰Ê¾ IÁêHÓæK#4Ðtf(ÊòA\LÍ òAÎò\ÁòAÎHƒÁL9ÉuÇL‹L$f. „ I‹ ÇI‰ÀH�4ÊH‰ ÃH‹L‰L9ÉuæIƒÁL‰L$L;L$ …²ûÿÿH‹|$@H9|$8�[ H‹|$`L‹D$hH�GI9À•ÀIƒùŸÁ!ÈH�JI9È•Á„È„³ H�GH)ÐHƒø †¢ M…ɸ fo fo* IOÁ1ÉH‰ÆHÑîHÁæfoÈfÔ óo<
|
|||
|
|
A<HƒÁH9ÎuáH‰ÁHƒáþ¨tH‹D$`H‹|$hH‰ ÈH‹ÊH‰ÏE1äë…§ýÿÿ € A¼ÿÿÿÿH‹t$pH‰ïèå
|
|||
|
|
H‹„$0 H…ÀtH‹´$P H‰ÇH)ÆèÅ
|
|||
|
|
H‹„$ H…ÀtH‹´$ H‰ÇH)Æè¥
|
|||
|
|
H‹t$pH‰ßè˜
|
|||
|
|
H‹¼$À H…ÿtH‹´$Ð H)þè{
|
|||
|
|
H‹\$pL‰ÿH‰Þèk
|
|||
|
|
H‰ÞL‰ïè`
|
|||
|
|
H‰ÞL‰÷èU
|
|||
|
|
H‹´$¸ H‹|$èC
|
|||
|
|
énöÿÿE…Ò„¸ûÿÿ¹@ HÇÀÿÿÿÿD)ÑHÓèH÷ÐI! é›ûÿÿE…É„ûÿÿ¹@ HÇÀÿÿÿÿD)ÉHÓèH÷ÐH!é÷úÿÿH¸ÿÿÿÿÿÿÿH9Ç> H�,Ý H‰ïèŸîÿÿH‰ÚH‰D$0HÇ HƒêtHƒÀHÁâ1öH‰ÇèXíÿÿH‹D$0H‹|$@HèH‰„$° H9|$8ŽŽ L‹T$8M…Ò~ZH‰øH…ÿ~RL� Å H÷ØH‹|$01öI‰ÀMÌJ� Õ IÁàK� H‰ú ò HƒÀòHÊI9ÄuìHƒÆHƒÇMÌL9ÖuÒH‹D$8H‰$H‹D$@H‰D$ E„íu5L‹d$0H‰¬$¨ éõÿÿH‹|$0H‰êL‰æèÚíÿÿH‹D$@H‰$H‹D$8H‰D$ H…Û¹ HOËHƒû„ H‰ÊH‹|$0f(
|
|||
|
|
a HÑêHÁâH‰øHúf HƒÀfWÁ@ðH9ÂuëH‰ÈHƒàþƒá„sÿÿÿH‹|$0H�ÇI‰üò fW' ò éWÿÿÿ1Àé¦øÿÿH�¼$à H�´$À èâ H‹¼$à H‹„$è H9øtHH)øH‹´$À H‰Â1ÀH‹ L‹\$`L‹ÎM‰L‹D$hI‰ HƒÀH9ÂuÝH‹´$ð H)þèê éÛüÿÿH…ÿ„Òüÿÿëàf. „ 1ÀH‹|$`H‰ÇH‹|$hH‹ ÂH‰ ÇHƒÀH9D$ ßéžüÿÿ1Àé"ÿÿÿH�=ó è5ëÿÿH�=¶ è)ëÿÿH�=ª è ëÿÿH‰ÅéíÿÿH‰Åé'íÿÿH‰ÅéðìÿÿI‰ÄémìÿÿH‰ÅéuìÿÿH‰ÅémìÿÿH‰ÅérìÿÿH‰Åé„ìÿÿH‰Åé™ìÿÿH‰ÅéžìÿÿH‰Åé£ìÿÿ D ¶Éé˜òÿÿ „ SH‰ûH‹?H…ÿt1H‹s H)þèø HÇC ÇC HÇ ÇC HÇC [Ã�H9þ„Ç AWAVI‰öAUATUH‰ýSH�_HƒìH9Þ„‚ I‰×A¼ ë( H9ÝtH‰ÚJ�|% H‰îH)êèÀêÿÿHƒÃL‰m I9ÞtOL‹+I‹H‹E J�4éH‹>H;<Á|ÄH‹SøH�CøH;<Ñ}>f D H‰PH‰ÇH‹PøHƒèL‹ÑL9|èL‰/HƒÃI9Þu±HƒÄ[]A\A]A^A_à D H‰ßL‰/ëÛ „ Ãf. „ D H�BÿAWI‰ÉAVAUI‰ÕATI‰ÄAƒåUIÁì?H‰ÕSIÄIÑüL‰l$øL9æ�Ê M‹I‰òëf�N‰<×I9Ô~EI‰ÒI�BH� HÁàH�JÿHøH� ÏL‹8L‹3O‹,ûO9,ó~ÍN‰4×I9Ì~ H‰ÊëÈ € H‰ØH‰ÊHƒ|$ø tzL�RÿL‰ÑHÁé?LÑHÑùH9ò~?M‹K� Ëë" H�QÿL‰ H‰ÐHÁè?HÐH‰ÊHÑøH9Î}tH‰ÁL�ÏH�×H‹M‹K9Ã|ÍL‰[]A\A]A^A_à € Hƒ|$ø H�÷H‰òuÜ�H�MþH‰ÍHÁí?HÍHÑýH9Õ…lÿÿÿH�TH� ×L‹L‰H‰ÈéUÿÿÿ € L‰ÐL‰[]A\A]A^A_à € H‰ðH)øH=€ Ž AWI‰×AVI‰ÎAUI‰ýATI‰ôUSHƒìH…Ò„] I‰ðH�_L‰ÀI‹6I‹MIƒïL)èM‹`øH‰ÂHÁè?HÁúN‹æHÐI‹U HÑøM�\Å H�ÎI‹+L‹H‹<îI9ù�Ê L9׌ë M9Ñ�p M‰e I‰PøI‹E H�ÆH‹8H‰ÝL‰Á € L‹M I‰ìJ9<Î|;H�AøH;<Ö}AH�Að D H‹H‰ÁHƒèH9<ÖðH9év3H‰U H‹QøL‰ I‹E H‹<ÆHƒÅë²f. „ H‰ÁH9éwÕ „ L‰ñL‰úL‰ÆH‰ïèÏþÿÿH‰èL)èH=€ Ž¶ M…ÿtJI‰èéìþÿÿM9Ñ} fHnÊfHnÁflÁAE I‹Pøé7ÿÿÿL9׌ ÿÿÿI‰m I‰I‹E I‹PøH�ÆéÿÿÿHÁøH�XþH‰ÅHÑûëHƒëI‹LÝ M‰ðH‰êH‰ÞL‰ïèäüÿÿH…ÛuáIƒì I‹E L‰ãI‹ $M‰ðL)ë1öL‰ïIƒìI‰D$H‰ÚHÁúèüÿÿHƒûÏHƒÄ[]A\A]A^A_ÃfHnÒfHnÁflÂAE I‹Pøé„þÿÿÃf. „ �H¸øÿÿÿÿÿÿAWAVAUATUSHƒìH‹^H+ I‰ÞIÁþH9Ç# fïÀHÇG I‰üM…ö„Ö H‰ßI‰÷èÛæÿÿfHnÀH�,H�xI‰ÅflÀI‰l$HÇ A$Iƒþ„Œ H9ït H�Sø1öè|åÿÿI‰l$L9í„R H‰êL‰èL)êHƒêH‰ÑHÁéHƒÁHƒú†„ H‰Êfo
|
|||
|
|
fo- fïäHÁêHÁâLêf. „ foÁfoÔfþÍHƒÀ ffÐfoØfbÚfjÂXà@ðH9ÐuÓH‰ÎHƒæüI�Tõ ‰ðH9ñt+HcÈH�rH‰
|
|||
|
|
�HH9õtHcɃÀH‰JH�JH9ÍtH˜H‰BM½öº? L‰ùH‰îIƒö?L‰ïD)òHcÒHÒèQüÿÿH�û€ Ž¬ I��€ L‰úL‰ïH‰ÞèïùÿÿH9ÝtNI‹I‰Ø @ M‹I‹PøL‰ÆI�@øJ�<ÉH‹ H9 Ñ~ D H‰PH‰ÆH‹PøHƒèH‹ ÑH9 |èIƒÀL‰I9èu¼HƒÄL‰à[]A\A]A^A_Ãf. „ H‰ý» éwþÿÿ HÇ H‰_HÇG ë¿ L‰úH‰îL‰ïèJùÿÿë¬L‰ê1ÀéÞþÿÿH�=/ è¢ãÿÿf�éËãÿÿ HƒìHƒÄà O|p invalid cost matrix object cost matrix is infeasible OO numpy._core._multiarray_umath numpy.core._multiarray_umath _ARRAY_API _ARRAY_API is NULL pointer cost_matrix maximize _lsap linear_sum_assignment expected a matrix (2-D array), got a %d array matrix contains invalid numeric entries _ARRAY_API is not PyCapsule object module compiled against ABI version 0x%x but this version of numpy is 0x%x module was compiled against NumPy C-API version 0x%x (NumPy 1.23) but the running NumPy has C-API version 0x%x. Check the section C-API incompatibility at the Troubleshooting ImportError section at https://numpy.org/devdocs/user/troubleshooting-importerror.html#c-api-incompatibility for indications on how to solve this problem. FATAL: module compiled as unknown endian FATAL: module compiled as little endian, but detected different endianness at runtime numpy._core.multiarray failed to import Solves the rectangular linear sum assignment. Solve the linear sum assignment problem.
|
|||
|
|
|
|||
|
|
Parameters
|
|||
|
|
----------
|
|||
|
|
cost_matrix : array
|
|||
|
|
The cost matrix of the bipartite graph.
|
|||
|
|
|
|||
|
|
maximize : bool (default: False)
|
|||
|
|
Calculates a maximum weight matching if true.
|
|||
|
|
|
|||
|
|
Returns
|
|||
|
|
-------
|
|||
|
|
row_ind, col_ind : array
|
|||
|
|
An array of row indices and one of corresponding column indices giving
|
|||
|
|
the optimal assignment. The cost of the assignment can be computed
|
|||
|
|
as ``cost_matrix[row_ind, col_ind].sum()``. The row indices will be
|
|||
|
|
sorted; in the case of a square cost matrix they will be equal to
|
|||
|
|
``numpy.arange(cost_matrix.shape[0])``.
|
|||
|
|
|
|||
|
|
See Also
|
|||
|
|
--------
|
|||
|
|
scipy.sparse.csgraph.min_weight_full_bipartite_matching : for sparse inputs
|
|||
|
|
|
|||
|
|
Notes
|
|||
|
|
-----
|
|||
|
|
|
|||
|
|
The linear sum assignment problem [1]_ is also known as minimum weight
|
|||
|
|
matching in bipartite graphs. A problem instance is described by a matrix
|
|||
|
|
C, where each C[i,j] is the cost of matching vertex i of the first partite
|
|||
|
|
set (a 'worker') and vertex j of the second set (a 'job'). The goal is to
|
|||
|
|
find a complete assignment of workers to jobs of minimal cost.
|
|||
|
|
|
|||
|
|
Formally, let X be a boolean matrix where :math:`X[i,j] = 1` iff row i is
|
|||
|
|
assigned to column j. Then the optimal assignment has cost
|
|||
|
|
|
|||
|
|
.. math::
|
|||
|
|
\min \sum_i \sum_j C_{i,j} X_{i,j}
|
|||
|
|
|
|||
|
|
where, in the case where the matrix X is square, each row is assigned to
|
|||
|
|
exactly one column, and each column to exactly one row.
|
|||
|
|
|
|||
|
|
This function can also solve a generalization of the classic assignment
|
|||
|
|
problem where the cost matrix is rectangular. If it has more rows than
|
|||
|
|
columns, then not every row needs to be assigned to a column, and vice
|
|||
|
|
versa.
|
|||
|
|
|
|||
|
|
This implementation is a modified Jonker-Volgenant algorithm with no
|
|||
|
|
initialization, described in ref. [2]_.
|
|||
|
|
|
|||
|
|
.. versionadded:: 0.17.0
|
|||
|
|
|
|||
|
|
References
|
|||
|
|
----------
|
|||
|
|
|
|||
|
|
.. [1] https://en.wikipedia.org/wiki/Assignment_problem
|
|||
|
|
|
|||
|
|
.. [2] DF Crouse. On implementing 2D rectangular assignment algorithms.
|
|||
|
|
*IEEE Transactions on Aerospace and Electronic Systems*,
|
|||
|
|
52(4):1679-1696, August 2016, :doi:`10.1109/TAES.2016.140952`
|
|||
|
|
|
|||
|
|
Examples
|
|||
|
|
--------
|
|||
|
|
>>> import numpy as np
|
|||
|
|
>>> cost = np.array([[4, 1, 3], [2, 0, 5], [3, 2, 2]])
|
|||
|
|
>>> from scipy.optimize import linear_sum_assignment
|
|||
|
|
>>> row_ind, col_ind = linear_sum_assignment(cost)
|
|||
|
|
>>> col_ind
|
|||
|
|
array([1, 0, 2])
|
|||
|
|
>>> cost[row_ind, col_ind].sum()
|
|||
|
|
5
|
|||
|
|
cannot create std::vector larger than max_size() vector::_M_default_append € € € ð ð ðÿÿÿÿÿÿïÿ;l �Òÿÿˆ Ôÿÿl �Õÿÿ° `Øÿÿ( €Úÿÿì àçÿÿ„ ðçÿÿ` 0èÿÿ| éÿÿÐ pêÿÿ( ìÿÿ€ ïÿÿ˜ zR x � $ Òÿÿ€ FJ w€ ?;*3$" t D ØÔÿÿË B�JŽP �B(ŒA0†A8ƒD€¨ˆX�B˜B M€]ˆP�B˜B P€à
|
|||
|
|
8D0A(B BBBE 4 ¼ 0×ÿÿ BŒH†D ®
|