From 5167e6f71bb3ff6a8917875054b59a5a7da6c5fb Mon Sep 17 00:00:00 2001 From: mattrmd <78185679+mattrmd@users.noreply.github.com> Date: Tue, 31 Dec 2024 08:08:09 -0500 Subject: [PATCH 1/5] Remove typo from setup.py --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 5cdb6ef..460f8d6 100644 --- a/setup.py +++ b/setup.py @@ -25,7 +25,7 @@ url='https://github.com/TAU-MLwell/Set-Tree', download_url='https://github.com/TAU-MLwell/Set-Tree/archive/refs/tags/0.2.1.tar.gz', - install_requires=['numpy>=1.19.2', 'scikit-learn>= 0.23.1', 'scipy>=pi1.5.2'], + install_requires=['numpy>=1.19.2', 'scikit-learn>= 0.23.1', 'scipy>=1.5.2'], classifiers=["Programming Language :: Python :: 3", "License :: OSI Approved :: MIT License", "Operating System :: OS Independent"], From efae9dd56779dcd93eace76b3c5e2a06cacf0320 Mon Sep 17 00:00:00 2001 From: Matt Raymond Date: Tue, 31 Dec 2024 09:35:26 -0500 Subject: [PATCH 2/5] remove pycache --- settree/__pycache__/__init__.cpython-310.pyc | Bin 0 -> 330 bytes .../__pycache__/explainability.cpython-310.pyc | Bin 0 -> 5773 bytes settree/__pycache__/gbest.cpython-310.pyc | Bin 0 -> 30620 bytes .../__pycache__/gbest_losses.cpython-310.pyc | Bin 0 -> 27503 bytes settree/__pycache__/operations.cpython-310.pyc | Bin 0 -> 5304 bytes settree/__pycache__/set_data.cpython-310.pyc | Bin 0 -> 10077 bytes settree/__pycache__/set_rf.cpython-310.pyc | Bin 0 -> 25078 bytes settree/__pycache__/set_tree.cpython-310.pyc | Bin 0 -> 14290 bytes settree/__pycache__/splitters.cpython-310.pyc | Bin 0 -> 11941 bytes 9 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 settree/__pycache__/__init__.cpython-310.pyc create mode 100644 settree/__pycache__/explainability.cpython-310.pyc create mode 100644 settree/__pycache__/gbest.cpython-310.pyc create mode 100644 settree/__pycache__/gbest_losses.cpython-310.pyc create mode 100644 settree/__pycache__/operations.cpython-310.pyc create mode 100644 settree/__pycache__/set_data.cpython-310.pyc create mode 100644 settree/__pycache__/set_rf.cpython-310.pyc create mode 100644 settree/__pycache__/set_tree.cpython-310.pyc create mode 100644 settree/__pycache__/splitters.cpython-310.pyc diff --git a/settree/__pycache__/__init__.cpython-310.pyc b/settree/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..320d792c71fdcbcdc0aefe5663d6ad7bbac019fc GIT binary patch literal 330 zcmY+8v2MaJ5Qgo9RuLjq#n5+1CGf%sh$rX*lRH=jA7IIeqxg{Ek@^&U57thd*_gOc zN1|u>|MZ{zpY8L!&!|2xVfRh^Y0YCLWEQl;gv2nz9b5g8M<$VJWH0iGm@HY5{!)?^ z{LJ4!if8#60I>lW6P3}Z|99FWnRj<~YX~S`I@?B?tsHjV*U)IRz6$cK>%p;h+E#jP zoyF4^Yf^gsF4vjZ`w~Q{5yi$E5Mi_T-iZoFlnJF4OTf{JejwsBp_HvGDmAW8=WJSf Tb8s-{G%*ZFGM4fCN(TG}0PtG% literal 0 HcmV?d00001 diff --git a/settree/__pycache__/explainability.cpython-310.pyc b/settree/__pycache__/explainability.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..ffeaad4e527ad26bd7da621370b9665135bd2e9c GIT binary patch literal 5773 zcmb_g-EZ8+5#J?wB=7WPE2?58PQx^9;!AAHaS|j=6*s7zG$~rQjnl77gbQ^`ofzMd z=Os_61RhX84w|6IL(!MMReIF_(C7XUd~ILyET9hsiuO0dJKl%n+5vh3N4v|N+1c5d z-^?;zSf~m7K0Ha^jmBV%9g>x zw-tA{!pKj?uWg+lJ>E+-0TEVy_#F84uZUv z1nF?kxm_GWE>&KMVihF$il3%IMAmN7yl&8q^ro%JZa((Cw1*I&OAq*p$`>gy0nTC4}Z#T%UKR@e#Cy{o;w z{JA#PJ8SjkwvM~34RKmnFnJxssxHfA=^C!2zj14|9HUX=ccU4uTt&|^^kZPo9e+2_ zemlsmPMD-qQA`m`4@GwJPmm0;3(tR68OppbQjyBkKop2W>qs2P1F0-k`O-L$x6u1S z{9bHJWn-p`8I>bJqXRR#2`Lmm+lYQ1SFWnw7vQaZX%4JVd;!UsMN&4`fC`m?jR=}s z6Q2tp!MknzFZvs0YfT*5S>;Gdaqo3CPr7|xt~k_VVO9^|#v-)XD=-bZn4}}nkaSu8 z5}0%Vl;c=^%)jx#Sl#ubu+#AoC4Q2G?PwIO+K=vfy+C`tIE>QEUgU?nf#>&nI!4%( z5i>MC*t>xrdCYYweDRaIx9(l{(%ADmokB$7eHx}a9I1%Ypuh%}6Zg|zKOMt|3R@`z z>LBgws9?U%g1cBgjN0Ymbl~HO6yH38&}O9*FO0CORuCz0d9ha5&JZkE-iOyNdoNw~ zuHv_O*}Fsnx-mo!Q38!nCuspE4QFVn7RC4IN1jD~drFjLCDTYp-0AbCmL(L_8)L0X<+8>*mHknu0zWM^% z$Ffo5ZSWC|x1o{1#T$57|0jfAL3>IAvLo${Y zXdF$N>KJDn0kVCqeIPCc#>jB;ZZ2mx! z6-s_dZUa<+5Ot?9SLDbh#gv|hq}(o`DX+1=vxnz0(T~%LV|Dll*fh^P+ylXyd=td2 zJtr?n*IbpBJSXSzw}K=`Des$xW$HIErA2-{`jI}gB)Mer50IH^)EJmWR?AGzQx?^x zs0^PrdA)|0B-yvm|~x;yKDbrg4h0{rV%=qyXm+^z+0;wwPL<21c-| z>okQIe1TeLAP$3Cn_j*Ia!L|K1!b`rcpMtEh|^)FS}HOXAu?yoV%-urcSwo{QlHbL zU*R<`qLsV2x-fG-*NUSSkOeoHAg}BPI!-9VwoqOq`W51Ol?WBKn(}vjUem2Mud1Y3 zOw)_>ZV9BZ1S|?w6Xke;J^C#gt+B`Pa*gu^eicqw3_$~2tJot8EXtXoS?E_S-L=@= z_h~Ukr^g>~a6iQi#-wF?(WFv))xcv~#w0FTQvVS1rZ9TcGL@pCBz# zY;P{%&TJf_!Up=FI;Fewp?xF>hfwJ{sRM*TwO!WQQW?Miu3QJG&~d95X*I0@sel=T zR6}I4()rOU;K%&Ur;))-+{YJizm^BMi37P7S%>p@>mw=i*5*0WWOl;{sRoHB6_Ps7Nx+q~D!zGJ=;~Jo;sj z{mW;ZM|&$s(ubnUNVhu>lhE06T~Z<4)UXmkGNdr)#YdPY!$KF1w$khONC-(>el(Rz zOyLE-BF_3!J_o%++#6$^G;?RW!W zcThzhHYhhM{LkpC5GZ{WLNrvf>QpqmbMIP)8%w?R;AqhAVw)45#03~zckEc?>3^VU z?CCEjA$^%C&IV1CWPa2Nnh8)dXtuY`a(F(zW66aWUmkLcTnBMRrI4npMd9H;M(cP% z|AYo^6Cu=Pr_+RN`e#JATKhS*-Y3G|MUl;g7Q5JSEjQX*dX5Gch>V@=7w9%@_AaHr zhfXegxzvRT#%}fWLw0!|oF`QKS%xJ?{&m4vMEe|n{D6=B+p_e>nDRg$)4#&o|HtY4 z1#*hhd1a_Ke`31+sK-Nbuc=nz!_mK?R)Yw`4jqdAkjO_wW&+TA*ouE9CZEU_2(XM% z#weDeTTR`?cdmZ7w^tnW_$Vn!ag?$dnSH`lSr5hlccUAtekY(WBO;-U?24LMd%=Ci KeagKszw%$WnHiG+ literal 0 HcmV?d00001 diff --git a/settree/__pycache__/gbest.cpython-310.pyc b/settree/__pycache__/gbest.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..355c8da2c0d1f0f10b3ef3f25bda7a93166c04ec GIT binary patch literal 30620 zcmeHwd5|2}d0%%=&rHwk?CgofE-p4fg1}P10-zbn0ttyEcnGqzWJvJP$jt2Qbnosg z=2*V&CAh;`i593MNl})p99D{P7LIJ0&TYp@oGMpED*ur3PRH}HGyPlM&NUoeb6;6?v0 zgNyrd1dFC&c!p=zjgmf_B~z}gk|pOW=b=1JyPG-*k9T&`O*4;#=+7-$&aDlq0%AAkJoQ$ zxFuKe6ZKmghf9YgKUu%+g5mA)rd}~hw|jfNX`GKdYIrl=zE=!y-({QEa0j-X0BArPeHmeh`*}wR$a_wW9Px z7e4s-!%^m;$DVrd{STwkkZ!r6RAmE~g=(w5-e{Ivs(ji*{Skc|{aLDoq3=bLWnB{J zS6=dK%PS!sJfMray{=ZmS_|*4sAWG4aKAX;3LlXs9#%>Qxv%W`YsyDWzr5B8YJ8wl zFXIW;&pzpg7nIL&zXLC^=v#i%Ih|h-a(rVRDHp3HZ z0ER~8BrX=aVOw7t!k6+D252dqUV)_Jc#5jUtk~S&+ZmP3x+CP2GW_Y8pxR z$2EsA24G(mWy$CEM`%E>9Qx(U+FNLf7aRhfF@eR@}&?^(P%JNdcUE9lhX)p7NSxS3Z z&p}DX%b|xu(a6)jT5JXW6aHFDg}$onMVIwO;J%>mxmb|3_0SDlZV-C7n9IBPJzi0j zhL2|gxjd2lDyP2o}B zb6d@-k5=4@dugRs_mjqsU0U&*ZjHSrF1U@B*X?qdc{I`XRcm(cUbnX7)`?om4Oc2n z_cTs;RF5lZukV#hE#>-^>WaI*<^kXIfU8`B?vHsM0tnz^sn!dAd+eoFYq4CzRkulln&Ry?jn1&Rcl5=F;te98e#7O_@c-bqCDnAh4SL4I5?M}U?*55g-KUu zm>XX4E1s{w>-@SGsCS}lJA9)XwU9Rg7m1U1%)FI1^`Dc=~;f&Q`^wCm{Ue7#d(%8q|*{ansZ1mN0-UXUcwn&@%kJ)_t z*PaqWl%SD&sTQudP5)Al{%Imbuzqu>wW@I=0dYP_lp1I>x%<5%qCme8tT*n%GPRwT zPMw*%YiYBwj4S-+?@x+?SsU;QY$^@)p;6>?aV07aqQn~{xbw3`bqZ$zMfH=&MWfvZ zfNBlD8J5*8tU$n4?_!1YQoU6PqYUUf#0GU2Z~O!kLTFUzdwE%?^=wvyyt%6GWsQv2 zD&rmMRz7n-lLwek9O>iU8O=2=pj0?=K9{$~&2b18dDFT^n0KsE;K8Wrm=^w=@4EK5 zWq;Kvw5PVizitYmvdrXcHUh(Fg722gQNBzzw+=zJ92Lst=hrLs_)fN5_FC0)SrJO^Bdq6Wl_ zwCCW+dHL50-jG*##ky=l>=^cn5I?fsh&PI};N=HGf9Hn9J zq^KKashCT9r@VvSAqX=g-f8a^&qZ$3yVX04=f}L;yxVae_l|gX;5^|^dPluuuNZju z9&gsW6J=B0JG|pK@AXc2b2v|XX>hB%qJ7jV9#s{u2FQ7^)e0bc#6orZ9SIT9su2)bE9l_~*Oc0H&2R;~Bh;C3PplCd`qo`bMHP)hufokP?tx*fX6)G=4MW}=; zQMyuJTd4>rN5w|v#WFg!RBl384x;>}iW1#Oh0$oUO!UJYs4F6>r@~gfN@;^X3a%7b z6M^TKanVVE)l&g5%8-ANS{)l{3UtpX+fW&da=HWMp`7VBNVA11aL>7LGBq*I+~qW3&5(5JbbWE?6Zff*}}U9eg~>RTamMsa8*5Sn5e8#9j3ilc$+H zgCrVjK(Ns4HqZ)UG}>Rpxs9e-qP4v85+R05$U8!-q5>CT$*+X#P-&waub|aorS|YS z$OY@@%m&J;$_J!H;{(M4F6eM|t*jT8s2vsh)m%lLPD`2}WUj>ILri`e$?O54XX>x8 z;5jBjAj{0%aPVq@RVqvtnRrZmCQD3�W7a$xx*5YK6%Sd$Wyh3bhLHRB#WHOdf&( z|E&1jpVN1Cq!1u(yFL~tQgoH_L-#j|%m@yVC+-KtcQUI+Xs$wP2%PsBAEiu@qT;)m zgV=0eF*frMg`L%msK?8jL&#^<5j^1l!iO$e$mKlqRfyoYlLwaM0qtqedez=6ghL*% z2J!*q0UUtyRY3VFV0;y@y$T#y1qQ4F+E@2VJ)z;dEn!!EgA}JMN@M%E5sObTM?$SOKWkuY$ z=kC&2(_e;?3bMQ$GZ;0=hrjoJ6UiX#2b`N|48|g{V)G z4I*O^!+a3fz!;H$rJ0OiU57Laf zjZZnS4(YMkOHlu}5e-N|7_nCN{w}HqBx9qgj0r&|Qv~gpgrH?X#jtG~W!wL7=j-|6 zsFhDmLJ-5S#J+70sfVzhfZ<@l%Uv{sl_u~U@20L(O!_zC4XvB%Qz0qXNH})cQZFGlyhTbjLd~Z+5#O7@ zy^pihD}b_o3M~!GC{T-|&~!0=kel zG5=P$#~Tf&!o8r2#_9}PzHDt;%_^^OF3U4xc;@%7Q*&NwjHAXMBsCyW^tE*sJr^3C z%)un)z))X{-*F=DhAx{+)@AF{W^fN`K{8rBr2FL!ff7%(?>|RFxcdT( zFdofrAj4~k-3}z$b79bcMEi85zV3^qnYJ=eZvDudD?)2~uHPK%Hr8R4S@d0)*i0k574&a9)+$LY#x=41nX;JoAe-BJD&eStpa=C z8$c(J>nOdlz6kNzf${@s8PuVH^7fI$E|R`R;NJ46;s9+5ZAojUHGo;x+$_qxcgtixS#6di;; z@OenxkUy92YCm(nDa*qdU-w0;=ob>tU_R&E2kQ0KCD=ltg^){1)xfPN-#yA!j~;iA zvdN>;M>czOu6?w>nZfQIcbkb6`ds_mcIn>@R$A+I&-Gzs!1`jT=UYvmZDT%Yl!YaA z4*inlUW3EN_hwx!V&{91qktu26CWT)ZMGUPAwCqZdujB+Z6#5@Csk;1Q?V@(Mk$s?`C3qG!m0(R zQM8tpCbjE@%hz)LPE5)=*oGhP#X5=q`7+sa(ar}Vi1yBX~V zMa#ZsS@zYuP5VN@nojB!U|X2dwguC^I-Tx4m$#-(+TEsMiRHuYAK-(KsUb4P686s{L!ZeiXfrqsxgdBP z^hNMF&jcMM4Fi9>f;we8%tER*R-rkrrdRVz7OsaNNt0t0kf-#TTQZR=#-$=ngO@@j z>E7?VD>61Xa7sN&K3krkH&P7PlAF!6A2<)Fh)vcaic)+HeW8>U;2^zkT1Jg;z&%hQ zR<2h?rJHl-RSiQFI&|TY*-4e<6{XF{F01CUs7g^MqFQolmnw| z!5jquIoGDFJdCl-iK*6V-*Ka5m)=Wr*I#t1o?LVf>{u14TzOQJU@elRwB??SdszI1 zv|OcebpTfxxys-QBG5usuCll~jw{D=uzWaXbL~^-$Z15E&?^Q`N5!HmZ|~4BWF{f- z6FPMw>n77FGx_&;;}?+3!(EwOsRXdKC`}T!c z?T@~XlRt%G;x250eFrB0TKnjYCcUqnfmtV4B#mQdf?q@e1fkeR!H=^Jvoug+DuiQD zm$76i7B@qCi-MxAomqv4_X-isjLTu(mTN&sqF8Wu2w~9Ux;>WU&c(Hd;@U%A=1C*8 zX%Yvb+AxEsWzD6W_hz&0dmr#Tx8gxSfeKNni=vot-C$wCg(XbYw6X{!bh!{UNE?38 zl~0aQWq_AStm5$e)*%Ui#q}C-k9h8VNVHK}){4tb8}cnU>vf!F?q`{h@JAWls9>q6 zd40I{OROc$aFzuV0ZT-<(sU5h6~cw4uoNN9T_@ewI9$TEgZRU zNdv=^MW(~IuP?_%w57B}A&x;5v1mI-YdTFnyCUg$%!B({TIi0dV6{)&XjDDHBA$;O zmL`rz26iWg2x69k)Q;JIHAU~$s)axd+Dc(V^^A36GY1!Jx?^gs^5^Hle|OUf1Xhf7JRY-0qvhA0EDBULwI8-`X$4Z4~n} z z+PaIK?3Mhc*sPPcq@dqXFFxqOdM2?UM)@GF5BFUk4#C#EVQO$v_x8Sfw~GZFeFxYV z=N|z=iZLqZHv?FY%XfJf5Z+_Mir>^q+6wg;abXeYMKGx>Y5fdXQc?8{2r8|$HQp6V zG7+XxL%0Dgwc(=(N8UP;=dAtt)q}w&vGs z&_;CsNC8+ud%hCgw*72B{XGln-&I=UI1o?+pB^(BAQK7_l; zJ$pf|`$F|+?@+_&w;Ez1)9|;M8)ZTtSu|7@oet7sc|)7uGrae6Of+*@Z`PhiYr<}5 zxKh8$LYA8)d-d&6QLKpaPYK*aBbtUigV?W zaCeX?LXUxnYZY)7zpDl9C=G+(HS6U<#T?4_%{rXSI5(%(9a-F^C`1^=msnUmcuH6sQcTC zOdf~ui2s4^OZX0iG*X~d@@WHJ1jaE8L7YMa14Zb~;V|vlA#H-N8Ft5-VEdsylJ8{ai0ON-woQGX749iubGp2jJxYS%ki4934Q_N-9x{kj5jZ% z{UUraC<&)7<87OJI(yU?X*2Y)TQi-BE1>t}L7fTD-V55xS&^sy7;njf|BOR(b@1F2 z=4?OxkgEp}Ych5bks<28;d-ny5gzP}F<1&dmGjqc`T6tRcg#BN@16ORoVG{6Eh zP)Hksn1Wtt$0MX$3``TCcVV-lADE)BR7a4J#LBwI5Oj20GXqy*l4g@g4jTR7W1&Mr zi_{AXJ*;0?=)LO+A1ZBAH>`~vB*Gfo*u#6lJP^$4dbOn`h}%o5)z~4rCvWv&+9IhP zU_-rkX$`Ca$k*yKtz_yQNaox79v*lG(tkYomo{e)Y2qvDuaH$6^deBt^Xb3ITS=!lB4Fqi`^ zACyL7_9fnHSY)?T*WW-tew);l>|4jTOww@zx~VBFY*o}|U_&C6&D##>sD0H=gRaWi z5$A3GY9YRpzc!u$eFl{-f(nBIo6dOttDv}edNMr@(*#N^>#I)j#^#8`6%*_T*F<+U zT_r0Hjmz-uN~k8Js0l<{BSfz*QA55bj5OBg)M?b-HvDJ=Ubl;WPruv;XXWKOL?Tvc!H9USc9V{Noi|=U^13#+DnT@g4BMA)tRKroOgh8`bWs(NS#TOs z2hT(Mrq$I>XRc$F_x-otw4Q@8C@mfv$D?=Xm+=m<(E{)IUer2~Gon zFrOO7;BRb%>XRM^#%beuT0N^cf`^d&%#M-5^ap^J2#T@I6trB9UdL}Ds^KzxVu)?X zTrr*oAATOTUD_rp_W(oAX>PY094;(8yRabc6Jd~y=>a=!HG53v0!X5Zko6S=htrsQ z30GPOr3Q4YHs{ZQGvYZo&7m=g$pCTX+z=uvk{|YQQPHe=tho1<#}ofr!u)m#YP~5MO@VaG&DFmB@~Ql z^^N0{5-!zXHv{%nTTQSFM%9O{q7F6ThRmqsUKN63GNK$m_azUEOULg;}4yDT`ZQ8+JQH0<) z!Wo?JRgeippR$&&W1&3LfyqnmXT->sg3z9&DQrK@D%naJL`Bdi4!t|Eh$;8nI7H7y zL>~6V(0AA;gtPt%L|nE#}vHcH0N_ozbrj<_+xS*7=qpvzxDm$bZntjP#v z?ws&?KrM!4gn)Z+HW7wmU{2x}-9^<}r$oNEfqp?w?)JiU&%<2}e{ntNVg1Es>k{Bl zzwc3S@MH+pc*6@p_8FL$K>VXl;_lz@!8>hewi$5ZMH9Ib*1za03HFEJ8O7 z(mmFIoa}0YNbK64opYb`ds}_ZEUh=IXBOhX+Bx`hVH^_TjMcdA*CZX=PJ-fo^)(liL9K=GfKGR*a<}`(dke$~<3dAtUI*UF`f%DA z3C0A z6dwVRZ^pY=NHh9Lex*6kUP3>`huE8y0W1Vk2o^qxv$!UffqBmriFGgliNv`BCd$s2XPt-io_UW1E4i`N*zNh63exXj70hS>KGJd%Xcr3n$UEXK?ph> z&@ilAb*%Qe8_<&77HaN3t&qJNnQq&Pa^=AI@Uq_cNCjnl4#&rFA$-&Pj3r~T9>I8W z2{z(le=G}k11S381UdsF<98dZ`aQmeqogjGF)S;8EptsMGdGA;UaSRT-<@@|{3`^y zC+muRkw(DCZupfZ!WGsVF|96PcLcW6ApoW_Qjdp<>B%N zIDmcVhBHRn7Nq0UIHCkBL=j|c=zrIWR{P}lxMWpATlbekh7)uSN3ewi6d&sW6BYc% zTksq6dddjJwE%x2+#?~j=pa-!h1G+}Yt?}hCADee9s+i;o-ljS$BFBFTrVBM86v{x zK5X9^ig&$EaHlKurg*RGY5}eoXKPr@RtRLDF|MQ_jNbkHoEZL#?_vNhcMfDKE0~l= z5Ryp)Ha%ewBDojK=e|V^EZYRpHzy)4tC|P8b3>7*yJfh$putXJB+W=@Cn!>vNRW8! z(%VE$8z32gt@=8wMP83u=#9$BTd&j2ZMAlxln7ZBGAa9>dmMpVE8;zQyLNP-dr7C= zT{?O*Vvpcg1rwXsAH=%bXh@3te-O(B*~(x*VpfqQridPk;5!#WxI&j{?=t!1ahk8g zUr8HZZ{>OuKi_`wDRInTC-}MwC=&8V0vGAZh9$7?C zkP&L9Nl*U#MO4@B4GN~%tcbYqa`r}(B;@mOSEtTjEcq$v8Uhi{b>3|K;bRa_rk?BhRpv)KqC_89-n1c0-L!wO#W`iY2^Pu-M z98fwrq@CrG-%FP^oFdrvAm1;@cCye2ph-yNkqvFZ#`8uy(Au>J&wYvo&847j2)4c|SUQ?cpKVl;0U77{N z;M$|eJ*iVuWNa<3Z$K{X83yPRq2o55GI&8`()GUBN9ATRfbG=c%eeg-sl_-l&R8K0 zCo4!}(LBU&ZrE-221HT!BsD)j4^Bk00YZ5$%+81pT>TWT)%%%z07+D=#CtY1e^h&T zXO79on268)^UVD+lV4%-t4w4vXn$99a;SgHMC>Lskg6{si5!@}!1ocL`5#&KUzv~y zDvqshyuwJ1uxuU)y;8wXBH=Duh|D0985^9!EPl&5=G^N%=In8Xok_%g*;XZgE&nv4 zaYvl2HIp};Tbrway(w=m&W<KGA?X|Rdv7Z5T zRXV5O+1Xzs3pYOte$}y%raCF42nj<9o`n>;3es%HL5l5%NOPS$QfxUyI@Bp3Ep&#F z4tI)3i=7dqBb{-iqa7P5w$>trdmbsa)*{8$TBLhAqe$V*M!L7N7wL3o8tF`D2I;=e zzR#MUGWoG6Eaidu?GySI=Tc@+7DHxG=2HB&AH<~eZ8t6PGTGFsx0@2zUdM(I#2LYI zPdvx2U!hpsfuIeXBwIRG`Ym?wtt~b}vHuz0+#?6<-oGIYq`Xxa206I4RB@w!UT;t*`3C`D%N% zyI=i!DA(H~OJMB&Y-H#aI)@`5iXd_k@%!AlM6Z=ZFhV$EyZm+{e-fD*UND4L= z!52va;p)(~fq)?hYqdrL*4vmZiQ!zn0n`mQJ5H?eCLV5EfR0 zjdczh#u$dGBfkGBbDu#H4Od(1u(UwUq5&;(z=EYjM{?cGdX%KQc65e9iHpBfq&8A( zba`eUn-C5`{_$T362ca-ZE>6)bJI#8x(Hhr+sALdbk`B8xsp6&^$3GKzaz|e;y{*f zlZ?XWW?f;#3ypbJC5MO3#r`&mE!dQftu#}f_H;&MtksG#KLsqS{vxk4maDIbFD#agYN|B-yCQyF^Ov( z;X{cH=eZtWYtwd0yyOy_z;D{&6K1=)(xb61s+7B^!u^V(9_Poo(+c=}*G=(xMEw)n{* zk?ypVYlnz$e0_T?2*N}m?82H^u2&X)M7;|5N+L2WmDxQ6R|lE=Fh0D2J#6h`HwD>v z2sb6Y^)E)L|oCY6G7T=if(p zWpCW7uR?6RN8Pkm%r8;(`}_{1f#|$gS*#&&hZ}LtVM(&NB4^Lek`gvVUZ7Xn=_X88jz z-W`D?5UO9>8yMR6NvQkw9h{o8N7Qa}BT?$`>jQ9SGkx4|dKc~|wG>S+QE_lZv}tpf zfa%^xy5-?}iQSWEAU|%wnZbPy1?YKQ9S!WjH43j);u2%v z%!vnVHx@QIC|hcbx=4&-P^|T`HB#g*X2dQHaI?R3Q*O3fyVx!MKD65Br~foBCz$k{ z^WsMrRrSw!gDR!E%0y@db_N+LgCrV`cfeQudL6sqdvxOeu(GJ5Zy@(uK(&DGHtlqm z??RF(Yt}5OWsZNtIp7qWy>P&f!vVj4bdTb*Bmx7!gYi|+M&>Ph*R^jwk56A0yz$re zi+`P;zt;YB{a<&o@dvOSxy%2%Q#$h4R*rN^yy@5RpnLl}`}_Rp2mgZn z=sqSyhbV|g{pNG4vFU%~vFm&=`iHdUN^o0HT@ajj{RvX5CNhPg9WHd)vH2F<=v@~& z-RNCcde@y!m-?{twd+~65)ABl(m{ceAM*s#b2wzXLsz!gGU<7R6#whMwZ~)oDn8qb zEob6$)d8+=E8Zf!zghWRf50G7JhyN3`F`435syZ?qrT4Mn@oBfNnn+X> zAH4|$pNj?69pWj$r-~zRkqj6`v)q?MK#C2Z^1ZCed-1~S1Jv81=G&qs{v%u0d$7AL zC&3_wU498*r@?#|iBd$hUn7`Pi=v?uMv&{8KYGM+P+r^t^9gUi6RncBZy_#Df3JZ5 zeNJyDhYi!#Iefqdm;iEWRXz^re>;-&%+WQ~r25A5A!?#6w@vu{T2}Q-&t{+#KY%)o9M-Gyu#={C@}3KH{&EaYI^9+XkR^bi*Hi+ zx2FA!tfyMxf1Dbxupfs}t*csLXG^SFWf0?nZ)Ta}-*jf2vB`18`m^s)|A~#>#iYaJ z=aH1Ay1RP2ar1NKMQmYJ7H&p`?oBDe3k_v6Awf`_1N>tM{oi)x*75!CKt8uyx~*H* zYjQdMzYFE=f0|GvQ1Y*#N_)H2<%_@ghdXt>xme}-g7NNMhB(d1jv~ED^9oDZlH3uTDEo(Vl$N_=x@+f86-U)O-Ig Dwmj-Z literal 0 HcmV?d00001 diff --git a/settree/__pycache__/gbest_losses.cpython-310.pyc b/settree/__pycache__/gbest_losses.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..5cb200e53c1848dd3f29207f5f6f8e139d929d6a GIT binary patch literal 27503 zcmeHwTW}m#dfxP9E*K0z5QIpO)TJd-yBvuGMM*2|a&;j^NqZ56wMFj5h_!>pbORh> zFaw<)kizURE-&R>)!KBjwv$vg8=HtX7sgKFBo(KU*h#9A%0tR|aUPQD%7b05ib_>U zRmyxwY?sA+-+#J$W_o5oU{_33wuaRQr%#_geJ=m~|IcA_VxpMA?=RdAR{m%sllcSQ z^#1a=IfE-WYGg8AW+CGlo>@26%mve6-a_75uq1ES?S(ALvi00rej&eBSSUz&u0FO_ zTqsIDUmss9EtDi*s86g-E=)>(4Ea3^dyLHIGG5Uee<$OO`=(dAVJ%FdWWt+7$)rCe zW%j~el2Z`<9@Fwo^z58*0zueE_{sHen+&?Jy2mNV(pFiW@=h^;2@1gJK77ls# zg-qFLFPv`%fgd<-!*i-lrTk#M+3;$O702_J-Hm$7sWob?np=1LpjBIQTTK->%T491 zDA%j`jh3_2Y~o2{rRe$9T2N~?oR;$aV6ON+*QRWQ`Li#bd);rj$WFRTK})&S)|%hC z)b!@dW|;NgUaz&nv3heQ*jU32s*PQI8 zmJ#lGd1GyD^IY8xg4%M;S16j4qBr~%3?pc&Dm&2ovvG3mVFOTWnAC@@1uWmG|EliC28sPG5Gy~3(8~9F@-Iw{i;;LG6BXH_?!C8*pz$`lW z{g|s<%$TnNxjm8m8VW?*=aSMB_Ch)Hmbh&+2qrwWJo==30_OJhO0JpXRu;bmvhp6XK^tZ z$l{{BzXtHDozPPxY4VmH$*WZp>lpu-=XDNOKtji+TBlb4!n{z(9M>twa*`6q+wrDn5!ve7!Rjx}0yPhRj_CoTfAPX<0_ z5Pw&efD#q17p8W7Gb~gp#Fa|rVKjJ~T`3yv`+5OjF0N4pAS6HLc}d4}$h0!oGv74I zMtkn%2nQDz*)pajK|-0JK3JJ^FvflJ&DIyz*6aQn@Wl6CQK|{VP*Z4C#;KEixD6+z zvq^(TQ5LY%IkSDZuhaeY=Bvb__{V(Q_uIVj48XnVx+px?Tw4d2IW2$?d#V?WAee$@ z79}8kA_1aJfMUaQm2x);#K9$Z-FJ>PDuK%Z2FJ@uO*U{*ZQwKRl3(xnkaDk9)|Kzo z1pkO2+coICmTD3mM+;7G3&(16{+x5gt#1I|mQ{02hDUgC>b`r$56azP>W)U_>n`6e zACC#{^YhJy-#4@iGGIrO!Jg60%WB@TWqCtmGu)FJZ#dD{g){^JV5@GuUa1_z!U@3I znE`;+HL6y;;`kdc`fJFmgGkEe@1IXCc*9=-eqZquKGB_)d$Rgis+Ox({MJVT1_-YJ z`@U5a81Wl{zqVBO9T$uh1YKyL1N3%IIHwjDyOY$nw(2lv`hFx~b`_%x(kr<)p6*J_ zmOsTe9zxQ7un(CBD!%uojW^QIW=9Y1-*eTvL zwldeoJ)<+;$?wTLmuZvcscXt%(YS{bJKn|gJ-3V*|9oyC+nG>`7>r`Y}q`R z>EyN`ZN0S*?_|H7S%zc=)>~-hTKRV~mkZaya9;*P#djup-^*t3W}!22d8|_;lfGfj zWS&Lmp38V9h)&*p8-V%cb?6Gd)9wvNFp$GT0T5?vG?mj5s4EdOwFaseznzwt*_mM7CDW|vhTJwAVm!lL`<*i z2vP1fxJydC4X*k@&D+3|ZBM8Ndxfsbi&*5101ebPW%XicPpD82bxzUByL|G);SE1J zUFaj|cj@!sG-(#XUifbHlA{~zu4B~rnj5U{x@rBcBkyXda3k=&ZreaT)qt8%0nPK# z`f|PLwz^F3iMdk=>JWI(N7ccAQ9Zb%YK_$hBbyB^z)}j0MV;72za3MGL`;IXOXW!8Z_n`Fzk}a=pIk>_imZ~-RxpcXCDX~gS-56^ z$U!(gn7M3x*L(}4OQ<V-NR z=a*(P?N9en`r#xdn0v0?1WylMKqhWPNI$>hzFh;#hgo-hy}lXR)h5JRv#}oLHX60J zHvI6w2*QOoD5iy3yCL6f3uT@)P*r%4C1CM3sS{9eLsGmzu*ZZy_PT$ z$4$?ArvQtX?PbLxmhW%FGiA0P8a}^W0}@$&{9~TuJcsMHgQO*+y>4=}7`J)d3?^I9vN&e3`)1p}@j4i@ z6F|oAu_;}`P>Aaam|uh0#96Mjgc3wFF4A9mOb|OG+X=El>e9+cAI@uO3!m!NBaOY} zS690vAhgh#Fdy}#J)gX^9verKWpZhssd+ukQ5nOEtTz?y)vZhEfz8dsG?>TmVg6I< zbxa}9UW{fhA)i9Ua7MbOiA>VvzlMskiZgi=t7zY`@3bEuv5Ldq@8hTx8+ma>1>rj; zG;eCeR>!&tNcPMv^QPIdF56o)xLNb+QB+muk(9IQ0{(<~2v;NB$K>-&gd%>G zISw_Ph;XFp(=64~i!ZT?U<{2;-@&CZ26V8LE99mo6w9l)`hFJf&fp4OK+;R*V2!Xq z(rkEUXzn*)_Ro6-61lM=4Ep0<31xZh%ULK0&g>2Mp7$}87eMJTwSwpEe^2U{nCrDs zmX(}9*K&@Xzfg_=5ekcj=Zk5}gx7Q$&6d;ffpY+TkeW!r7;I6ukixr^^)#VfTFXh0 zZG=alPirClp3`)}`Td0t#pn%Ha6~7)@aQmr4 ztdW8r3OfG`m&Ww*{l=s@4er=JGzdwtA$x#`5|T+70*X%p6d8gIK*4aom;!|XV^Np+ zq|lA>a|-2H^x>JocICDhH1ni%0_W~diyz%E1us(-bF3a_l3K|lDEkk%MEoul+eZdh zFrNIxfZdh4`)tyrHVIvL3^%Y4Lw3`a<;X&8fLFpwO$&8mA;yx6IA1n}J4#>6#U8bJ zq_TV(A5ouW@*I=Y(9WRjcX7$k9<$oz!J&;XFpRVe2$Q8aDjk*&4k-AcU59aFR~r6* ziF;|A!+JSF(bs(-0Y)E&ad1&*hG{b0iISYKgg4R7E%t-56_uDmp=cHMniIu4rL5vp z!cgCj7<>j-@FbFeVy?n+I2 zXO5je`*N8E7;t^YH+-lZSa|aQ1E!dD4%cmxEzN*E>jq4Px9s__0MSB%Al}16{C@96 z2&*nK>N1mU`SBm1#%+@0lF@#6a9-}pl4-$W#{$Z6!&x#A4~!Tr8IGTucEpnHj@8e& z$&e$aY`JfxC^29gB2=;zP^6mb9uwm~kKW@*TM)g#!fa~-PTs6MCZm}0eo>7xhI_3GT875 zTE+St|1UUC;2~lIeCGtJB@*#Rnz4UWd`@bK(13N&LY(rMdzeX=#tV5rg0laOOE_3z z9BSgxAz%@&V8ZJAIN>Jp3YYU8>tLpWRv{PW+NVJ8BmIl6 z+msU->e(6sO58@Q)gkyt=4M`y}Qj~aZ*n}8@#5Yy!tmUk-L`}qxo#r0Y(-Cx24Nol{4 zQj~AiT%z?a&Hi6M(KUb|IH0-SgtrquL+4dh^Sw2I$CFPyaq2@A6?9{6ef^R<%zyfs zE>TOuBpGIM^->M4UVyFtwuD|f0pdJ;-PjxcGC;CX9Ucq##kpiO$DPl?1?g=%aV(>A zb{(UBn-QT9iB3CbQ6UNleJKtIrB3uZ{LHn6f83$H?ra0$7y)&S3oX2du+eI8-1$lq zQ4~*p=D0K8oO3>Pyl3Lx`%k~a-~V{APp`8867dy~gD_r>Mgf{}5pI&Hm>xW>6)TaU zVeFbmJ+U^yRmm9)vn%e}ntMyT6x3cmew&F{nz|rM1C>Mk$stoRat31h4h=w1*Di~y z$&dM1l2ey(qiRgH70@2VOKJ_(+RortwlkQpRZ9!40ulRKF4 z?0rzYV64ouv=hD2$?ZV^55AK1?8DH(rADEX?-UL|GrwH$vIk+l0d)tOMy*+FZ6-6k zRTz1s#TnK{|F>=5x|ydWs#`DT+;=fSy=I^;_IJgJdma z4~ew7O58{!4KPje1GUM7>{GqPgaU!aQ^LB4sqnSX%pEK9NvNE5bz`4^ZX_%U!dDnA zu#gYllEB@CLcP|I9C>Jvj;j+7-7$cQD4&H=0r<}_fI~*fgtO)jg97%$8jjLC)25xD z{F9=%Z}v-SkDwG$L}kp~(7_DVDhfSfs=A3x3VE{;njLHzu=+#6>SP67?Fe0SK-X+i zPIQ$gK-XMSnghD#TgK%a1s)Fq&oca9Ks)<}d9BdNcJdNkmwyh?d3a}xB^qIYfIw5m z_;qq1s}P*PN3-s)r%)&s>xKyC_XLx`PyS3reBN_3Q{Mn!#i2e~k3Jy?nu2Ac5G>5L zebo$9hi@(*LHvAer6E`u+oVzm_Xw*dSf{S>R>Dt)=n4!P4ya`oCi>9IoQ05j2xtWq zIx@%xV@MmWKrm`GF;m>c%}@m*)2Bd~Llg)wL&UkpN(5>oN(AyyBF3Raj6;b)?CK2* z5wD1KMd?JRgzx2XAyReAz~3=kMdlG5OXg|_yBimkqaX0Nz;4H3ktC#FdsyiOUaoHCXg;nyormtEONYU_=`(u|L#~mm!)EBzl)_o9g7_AF9 zShqo4paaViSXiVFl+AgegS{$&7AQo%XrqZ?D!?}hF|i6TX|x}RC%<j10vhg?F#Fh*~!j|Tuu=|m*L*HY*Da)oSp9=P6(2}jqn+!JvJZ&ucD2eRi%@wYNa1#+BM0plLUW5fo z6tEUa3{6Tx5aEE;5ljGKT>V94({N5|{vEx4OlDlKV zm}P%pTO)CD$POlAvV}|J?c>M*A2U#qP5KP4AtFqGpCF$FxEdWBAer3*z!#&msT{nb z9onYGRZc-UmmaRjLNYP$@`V&GA3@mMzT21Lq2NA~I3eqv@kS8E3 zV4D*Rp{Wg#60i$X;F7ykt3#NGw;9v;my#J+l#&}*l=jtZZ(;a<{2MHy4(ESrH0aEYdi$<0 z^7Ffc5r9v8IKZxBNBal(!_0FUVn2jvn4LohjO*AYh@%UBq+Q5eP>I?b*yXBWMg8m$ zAUQl_nd9a7odiyv*&Uq34Ge$^$()drA4jH~ZQmLdXq<{I)^Pj(Gs0NCL6n+^=34is zeUW+yiH3y~ z&yR4|{T#o`by7E&@RL2=kPb;-zJmw9#lBF!5OQEKnsO~B235gAM|$P{zjrBXeBiWCXCD7WX3MEf5mp?yNi zg&=2MhUbJ!aMFQNJ|G^cGi^UR6mW)E{BQ+1(^5B?C#DZ$6w z5jY26s`Z@Yg7 zz3kP7C0309f9!cI&|`qf=uT}@Udu@0j(eVwg!*r zc>e?J3+_{tsTu14Xr6iGde~*WzJ0va2Lwu@1{&=3Yv91N z20ZiKchd1y_yXSRQcqM1D`S!mPsS%tPo1k%8%DjKI)oh%2!(G1=VAnqw>v~rNZ*C~;p7D~6 zY>?N2=xC3n&C^L#3<(;x8XUU{0(p!vf>e~sW#?p4$^t$-l{`#d2Dj% zp;_H+ot9WbdF^?yNPPrF@=n(xGB14by^UXb@$!2o|D*FBR!Dx^SxP+ANV#%u{ScNC z9Cd|L)v#5kslsv3r`XisO&SDgH*pApbjMBSCt)>t+MG0V@Z6Yp=+!RSQ^t(hJ~jZF zBWx&r(g+zcZEj@B0mZ`>?B7^)nC1~KVlJDw2SWsCnh4=DZf3V&1%csSBKtji*@W*o zzB86hiSv)|@$_Kk(*R%A0r14$G>R|krur7mrx(Bgwv&D)S{)ZG{?TTaEZ!n+DA~lD z5u+u-q98KUri6p;TU*9u8<9d-NSaV#Km|S6vgCQLV{3!zVbX?NCx^Hp3>MoN3wU!Z z-ph!@O)_eCo;2^4AgfSgqIAappvGMN#fjKf#1Rb}O$f zi)UHz5|S_{3mYu#k-rrL?L|AYe~nd!SXg9R;#n?w2{nqaBu*OB#$ z9^*m;k0B8S4OZt~1#JsQVPTs()H7gG-!^r=);^L_&ejmvNydcW(^yTLQ@@CsYLue& zRf0B6<#f^GANQW$-?J*a)V0)K<;S+wu-?QM^^VX-hJ9x{6^leDAw-vP)5}Gm(OSYs z5cvc?V#8s95}qXXW(gt=NYJVY(gQAoy<%B%6r4W>cLszN_7BvVsKBk3ZTAn zz8E4kEH5u!+JgHFK(bz?tEcw~>~@8c6y#9&NI?&aK31^$4s`q>HkWE4i)_AU3%qti z;pEv!AB*LzpC)t)^QY5Bc!c^Ca$%lFxB=_69!9&#pfm3QRy2NM=q^CID;J4Mq|ouygen3&CDR=TcRVSeo3T1{8iNA+4pW&9B?N!WtXFO6dE23f+M8zEQdR5GxGN_nG z?yh1wX%$ZW4a|o6J`#i*i$0-P@GKM2ES_ObR5zh84c>l&$tRioO(tC-ZXa)-Lh>!H z8I^!(OB65cbf3Z1qjo($Y!!E+d7?KXMx;rB8;UNaNeQ+e{{Yny z-XY`sP2|--XF{+H^9}zh4~rY-a1s^edmIvJm<_RFmi~)7?c+lSv0H6P{VIFGzNx>> zWQ$2^QYkn83VNXaE;8+tJN)iY?Wa%ZID_W8+E1UhL)`~q+Oc*6ZO1|h_D5_P2nR)= zHMAYC$PY`t1>-l%yiz`0HA#PS;uR_e7jA_t^H`1?A~ zq7AqWYh|MbQy4t%WbrFYYw%lES0UQSzN%+M%Nfu^h|-)$^<5^vz~mR1jM5r@6Dy_u z3Tm`xd#0Aty+fbO4)lcI<~Q!+Hy%uXV>=yzVifg{bGQO3&1pA`Mq0RE;EI8%(wfbT zqYe#~$vHB%Y+4sL3~IwGCN*Kgh-)UUn4Ei~N7Uks`(5x8z)7ruA1ee@cwmB+!_4pD zq3G{n(fKw=ba4db|Cs|IUQ8AdU-)nuo3@t)C-7b#m*CTXMP`UB7;mno z-krzBYpd%v5a$By9O7JfM8XUoAK_e}#vX0nhHI2@mx#I)xlcB0Lodg^icS$YnAi9x zu7wO(Y+=Zg614bIBoQu&L^viAVUas7xK~4*4n1Y%d`}2tckX*6zVWogCvmL-=0tDG z5Gay0>KFU4kwaGNan`*1Zf+gm_}i@f%S?WSNlLyUZ~Z+kD;Y0^#wp`zcm^K?Y?dss z(|>yqW5`l(&XeqIr~UXL6A*I5_#QW;UlDntE#LQ&Y<`uS}hoTAErk-{i@a`d{&n|1b9hbu9n@ literal 0 HcmV?d00001 diff --git a/settree/__pycache__/operations.cpython-310.pyc b/settree/__pycache__/operations.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..2c3749ec7349312974394e48085b908dd0af5b82 GIT binary patch literal 5304 zcmb_gNpl;=6`r027=Qo>Q4~eRS(LTdCSHAzBd?P01BZ~a5mFPEG1vsX8M;`%4*F8C1$s;9Ht5U2Ht21kF9d4O=v;XU&5YfS%B;8Fxz!_4 zE*6NlP?I;%gra=c3|gY7>RO?ltN2)Tr^AGq!!yPvTJ-M2^q(4fBQOK&iGeXQF!o^RGSzW*HBaubDGK{M zcMmmj(Tt7MxD0ZlMCWjnOjM>GObksV{P)eBgE$IzB0o)cdT|i$B)x+;9_$R$Yomdm zME=fRm|pu>hnNY|l)rJIS8{#yFmrDV{OFUwzu6#1vUZK57iYR(_q_hFpL*W)8-uv# z50abcLyj#};c6QlD|7t8=)lkHp&x~rlWKpM zOsvdK!oj|7atoI)9h13*h*h4&<14D7>Sjx|%%hF7nmki(@gv2h@1~M>l<=DPO$(DwzEV0X{&%lOeBD+(XDK-A7#7yvNYd22ci$;Sm3WsSJOst%54a>4xdZ5GP zARYwE>R0ZCsplt~Jdsk^>ZmA8VjE_wYrs5TMM^LO1Jh zq&@Fjm=t?A)S?9W^lr!0w5)c}bX-ltWOdJr;^1%qcV!LF`}?6k$i1UCIPH!!qauB% zdAR-#oA0ri%PRH|pBuK(t-wUpta&H&hckDc!4eha@uyTHH69zs3W>-(F%`1^6vh)XbCN@- zC%S6F6=18uok$P~tU6bXr%-PJ(uO+PTHrp*1q*N@dz$(d_gzJkQV8SN9K+3Uwsm4Y zGBIY4%|M-)$Yf@o_c~5y4o8_Yia&#F>)0tVp4kypUI4#JOOBA#K@a@^RbpsU0SRYB zo#L@DJ{F6mm?S7_@+umNii)ItY)!?rPt39TDQ?JP=fpU!oEVQ_(IO#K^)>9uTwmY& z>>$*kz7Db^dUbz*L>vyYN*WLPNvgLwKL?S>PKdpXn$USKE5z&OqM7rUom2(7`YKlG z@3WzIa%}R5tU2{yFJx4fxV*x3!sWnTKa_@Ib6J{mal*|3ny4eM^9o-+5QxJky6 zME}LKVolSE%548ZKfyJ?VAa27^KCYBOG$7LAU9AY*|pTsl=BK?&|bos_au?QJS+Y; z;eYF7mTm2l@6Y%0n}q;d>ZnfDr@>!a4t_~IR)4s^lm_%Ka6(D7IS8m5{V&f**Vtt6 zTbiS*!9YZn!LWua`~QGLe+W4AhtCa%-$0B44*lWzaL_lofwnBX{w@brdHpsg-$nzU z%kk& zZ2_JScv|qwuGY0AT5UvYC9{x1@|V#YJj_B<6!r;l{(jw%z00+)qB-JKG4oOJ`XY~s zvy5W7!d4=dmfA&?TirFbl;+4vwe@?Lg<|V_VJ{vAckm%JOd-otW{PYr343_-MP70k zJ-4zSa%w&si*gIEip9V2e2K;4{Bwd)zr!7TfdB8X z^SDC7ZjC0H#$5qVk!j`q`2qI+8FdeYggj&!>LX=9t#SC^TNcyQfSzC*Vj=z?DweM(=So7+5v{VZ$z~| zISJk_mHCv_l3@G;*R52GA$GtO5@w{%bzJjyhy;@ATrc_0Oy=GT<8MZ-A%zjOwifu5 z_2Ngl{u7&(di@cz5m$&_q&gd!Gdzq&59QgBS^g(IE&0#c$73PRoc%$J&uV`5$k(a7 z{^dz1PXR4=;rpCrGSbAjCJeM>DRL;~u{y~$H2Aks{ylw@LMIGveqJDl)KV)v%%^p+`0B zAw3nL#%MT^7tLUWYV1a{(cE2QTQzFPABql3-a~#qIwJX*Xdzle-`U6n4UgsCl{kGf zOv5Bj2TyxGp7*Y=$IaAV>1ls0>_+WYchyI0B8|(Jn>NTQALjX9#GCvINoMI&sgbEj z9XC?7ZR+`gpP70SRBfqIo4IW!r?4OJx@^l-W&~C-b{(8QZlGMr%*fd=^!cdDzNwQ| zGh^M_R@*Wodbp8u#mLMuS9il@(=dcuZs+|_hn+Z$b+G|fof$pesGE+PJsln23{N`RlPJA+0p-B;{J12&dvlek+oG z()yeqwK{P(Y4y5ir)I@`op95?9;3s`3RdVACmY|2^`?&!(#c(%=1KdvTj|=wfM%$p zRyS<7Tj^%~?3ia^nqmg58E{mvM#hc#M?NOlX1o$^w9{Ao#=IXl)^rDXg>FZuIPKssA zGf#W6L~}z6e^*Zr62vvHn z*^AoKil8Tz4JVRX0Ow~@C@dBNRm$) zgXb`4sPSh5;b9P*J>Tv%!M6*@lw%&f-J|7pt6SLW4ivrIsL)+EwZlDBnb4Atx1?uS zoJI0oyh71Y;|ub2dEav6gK&2q8R4$IrB;oCqvI(bn7W1zxeCU0<7HkzQ^JkU8}(Z5 zwUSmhNyBb4&TVc~Xw0iaLC7n~T8Lwr=>{utm~KFQgS?`{?rNM{?YNuIjRchvk2AHC zxV@s6xc=f)TcHhkbtt8KNXy-l@rOwt$J+~sRPpDS^U5_(DbE}%9&l7m?xuW7DI~9w z(@0X|p0NV!8<}_A!mdi$8kZycb6Wn&eumSKFXDYTdA|3Jbr|U5lWc#WWXB`Mw@`5c zp z7pHOD*n)@JF*B6FeQWuun3TGuIVi7)_1DiLkE(*kPMF-#RNTG7bA*JlHI9a?`~i9; zoM=ILYH)H7wNqWE)QpNpzFg#ZA~LvVV;!y)CQB`YTaTlz;NqCm+^MiTuLc3MA_!jQ zaL$9p7bX@THa~hv9uv4??8>q$kV{P=#=>>{Myrm^oyF(aHFa&DyC^->rP$^f+n115(KoqU0_1w z96$_Wwy~U3rb;uRhBjg(aU4kq5d@dfEun9lRSW8bveh12UG%D(%a`kOnwBv4f}qoj zHriZvHVAHRgzX^_P@|~V#LQ2kpV$EX9CM-rE&xkE*6F55yQVPeGfb%S6K6l7Q4Ae* zIiVSG4O@A(>$|pVo0g}_ch-`AQzonwLQW-v$F3xWkI!Hi3ef#%`Ca5tGE?N&<>B5p zZo&9wlQLY8?W5ZRl~)P7f^f*{k@r~UOr+>>WAGhfn05?+n}&MxAi>3n9YfzzHGYSA z@}OAhx0w?gKQ}egyQr5nPAE+~GQ|yAh>^@v;#b9luN!X~*N$#kDA{zdfP9tA&MFzM zBs*%?fc)AwXEI21X74Ix+^nS5jy*K{YVcS7r91te28Q>${jCN`Y{kv`5SY^xE&o-AFu_CX1k5l@E!C^26g%f%H`$ z>KD)>S7~nc`V;ah$FYPlK~L_RLE3{)t0GquS@kk!obS$!^?!Ip$Ld>EQiNIDZI%3fittN> zYfe_p3a(m_eZ$gy)K~8*0MUv7PnTW~FbMYiQ!}$x0EfyE=;=yNXR>G6TCaY2MdbMP ze!ty{A+%k8Bx0rHp1IvhAo@_7LN}-oabYgXyRBU5D3HbNc%O5kSkZQkVP@e9ebfQn zzRD&Z$rxt-iX}?v;2Eexdovi3b&!1$PxLj2dkGVjF8@Accgp3<$`6JP-$_E+LJ;*j zDVQee4_Wmhl1Fclj%z|%-(vCYQ605X5X2ub!4^hQMrT-Ken55UBL=|x#FK;=b$_|vX zDyqz!3k9(7TZ7s^36-uv-At5RQFOOZKYb>v?AZ4eRZ-N}VHpbLq?PsRu3C3j;eFR@ zjKwVhK*1R~UGJU&bu%R@H@qQkRWQcODsnTiHNm5RrhP)hU5SS$vJi2tZK?ZQL#~L^ zLMu?x=UhK!GA*cohN{Bz0-)|;eSq**&<~@7^r$}bP13m1>aO%4YVBT_{@|tD>a@BA za(V|#*1h7~e?p4n@Ch^Ydq{GdHbQ^E@)afmAFeVdq{}8mRE|A@-rt~a!fUEsDaFNw z;Ly;!xJEv0n*X_U*faG<=+fkzA?i><}E2 zh!|Fw{!YQS5q~(5^SSe}wN>46w`yX!tC4%x1#YWScxZvM8t^s*0Cu^yw+)^Fh`X2# z76|igSK%AHY=&O4?hXD@?C@KyTXEM95&qOf5x(AN`w2{QDiJg>vxR{|gbh)(+dY3( z_cr=TVRtXXMl-hA?BN(<-GBaxZX4<26WMJD2bADdeWH-G5J z4Ff3H+!wvFihGFK5bF1l>14nBPV7iIi#QG>bm zfWD-(b!=G^1xA}WvqqZRk6&x+t3FcgA!=1wK6;Zv6}LQM9qXfbA( zJ-8JJa7g2hQ((KBW`vl+-e6xTtF&_o;~p&-l55pcRx|TYBx+r-nVoUS5^XS(uKo_n z4St}LQqFTrUcc*_)?D^5_C+$qNy-sL4=Pk5AeB*x5$_1 zwHMTB^|-xY z+W(^2cgnNSVe|a>CeI>aw4Qg-Eo%bX7gA-zivXE_{oP&@7fqbL&*G&~-H7Y|%qrpf zDFK0ZXsjmAe#~o?+i7?s#vM~IquSIz!PthgDG|=x`A~j~C1oMecyB!GKcTzCYCT*O zU8Q>{PhI4>c}mnpcI0u2lBjh9&d`K=gtLuIiHRdBi$DT}PM<_l<|&eO3twH6Ae8ia z(T5X{r7d5CaID6~2NgO(UiYr5r3y~>IWU+%%rV_T7V0$!e;e#@R{pE;nN;|T+2*_Ksg zjKnd(6es1!E45?6GTDTd$XAfBFuw}v0l-uQn6QaXRu!2!yeyDW-`u+~B455@Yu-SH zadDVK;rlUTo5E>V|_n6iIvV?DG0Fk{;?%;!23s^;hLllC%y3uVh=vQ|dhv-0iE5Q)3=%2IQ z>V$W43tv-n3+mqR09As1uZ6F;6Jjqi_*ARQ59Q@>9DK0S(g+G5w5Q?Y{vFdvh;T_A zRZH+cgV$&_ruvBg9_z(7%(%}`Qon_7ADhokmOdf(a9_61}>aXh5_qeV6sQ@pD8$4@G0o_ zuu0wPOz-r+FfO5QHF1TShbMNG`9Fh;-^7t`G4qtNQ@hBOMJ`7$PH`K`x_lHP$}Mzc z_#tK=Qz<4ge9HR(!A;+9ga`BJTXNCU>n8$${uB*z}uLU%p~T?^#@7bJK%hr8OisQ-Xtgv;EK@Y4i8AM%Kw zHokc)U-Koq+=~9yXpL>=NSwYqsvE6q@f`1={*g?Xfc(4?L+@a+?i-Q%+%6zL4op5o zSYl@MCKr+8k{$XemU5J_X?ou_*B_#7&N8;OID4Ahx;tUwW)R|S0;Y8F=9NLLdx->X zavT4Fk;u3CyaJ2%TR*J3jTwGMDJ|fyIrpbbNLs^6g6}hVDOY!Nht(tt*CHWY=#=U-jnTY@%Jy_EI4ZV^DB%bLxv#xLXFpVxEC-B^#bB5DM@Okv|<+_R2lHF;|7TM=X zk*B-@s{5No0BQj;&i{QlJBq+14R4&!tzJLLt#=X9wcZWyG^{JIY4*E_rZ@1t5Uh&c z#XnheQ>mH#-B5RW-Bwddb8p3HLQJ|Sdhfk|>4Vp=zWv_vmB#!yAH4DUmA5bI4@o0k zxO`YCLS=8Tc#+A^nDFK=7Y;#cf>Z<(6g!tAA1A^^_k8iq=mG@5NT_(O;Q~sWHBTx> X9kSeuuIzCDz~7UubExuvi3Y(T literal 0 HcmV?d00001 diff --git a/settree/__pycache__/set_rf.cpython-310.pyc b/settree/__pycache__/set_rf.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..abf0432c1b1f871e18aa7d9b214b04379697f7da GIT binary patch literal 25078 zcmeHvYm6M%m0s7QAJfzG;Jo+{MU^N~V%y|U5@lJYDcKT5+1^+qElF$TwzS2bu9}%1 z_G5Ufn&gao){ZHgLnqAI!LES>O2R=BWEpHW35+0F=e3(Gg53bgkNgOd3f4$AKNboQ z(;G%Z{7QybG~z`+9M;y41UW$3KlzG%Vhp5 z5Bh(39DEX6@b8RF#>;p{BU9mjvtcyNirKU(R@1K7hCY|AWO2@FfcMbb8Sf!~+AsTg&$*GUJnud19Yonr z;Orybqd5DhKj}XopZz3q4|$Iv_c4De$$bI2k9$+do$^0<)vCORaD2)$ z&SuIVdY4aou53l4wQ5*faxYg^tKM1+qEb+At~C6GdL#5zWHY~PM)?#_?hkK2kYE*-u zzEJnoEA=b&Ry2CXUsQe&wBzIPS2~Tb{%R*&>4Z{(^{c1cpw?Ebf8^Dc|LJykx>aj? zzAD?%%u9Z)9@N{dbISMA+N0_I{NyIls;kj-t=&YseK)9b1l`MieQ_y7tEXS8hSgK; z)+JxDslTL$8uuu@SPlHst-x>2H~eVY#W05T5Jg-+2>gdiw6%6j77q# zTJ*yJ)r{%$^}t=IhoSF9Q?Ab0+K_#&=dUOq75MH-JCIvd8!js1ZSrURkVAvr;;XNn zKJ&u4&%8Q!Hrk8qGt%jo@eqQrZt<+m&6{=?WjD>M##f9k=5Tyv7FVA3 z?D)!zI_{YlGQo3lWj49e*fe-um<#i)Wh!$O$7eHM4kZe#I~?jFN0%z+t1IY0(ZTOi ze&~ctzSHV7=Y8e07aTpPfm3feRcF544!Isy;_O+uh;tw71CLt{k0w7kY{e5Ka896- zQ1W=Ixt(sodwZBZfgkF+o7F3W*E?lPIHHLhi%7G)yvL;YB!~8 zVLPlgoa&`&y-~$HJdrerjXLVo7j*SUP`J)+dv#8r0KH!z5MVBHSlH0Grk3XfOYKg> zbLRbl${Z<$KKtqtZh8Lj6GxmU%jZ8nP)P2GGTZLQJNQ>{$L*ckp@zA3%ipmI-NI{!jCL9ZD zOYL^!SSvib0=R5ej{%~N64j1j1%>f{S1ru0tVVlW0H~YXC4ICHqv~KklEQ>-l*}2! zHpa2(y;1I)6NY*k1!@Gh-j5)c0Np+u0HOdi>sHsg3CQajU31e~x4Sk3u(|e}Osv;b z{SsD_v(RbjrPK+0k58DwKm@2e->Ek|mu(M{VzctJ%ch#aZK6Dd5(cxJjY|FUNgdyDcGYJ7j*)=1EW$vhK)_& z|5@yCR5%t_t#ymg-?bq+rg8?n36e+JXTt{pq@c}$ATFpeZ=nTCC5 z)GQc9qrhS2Xx2oB#}0oil}x_h#6dUj85e9d8bWLDW<6tfrkh(ZXD}fR4F9sBUg?_4 zre|Szix_wt68X{4TDH5z1q1)HUKWpJxP^yuGq;&pAExA7E_93CAw0MeWadz}ghw=j z$z7Da(YTgRcs8yYj&sMlnVZAik?v@BY}3M>A@e1NkKOL&lyhq*-+wtkvw^??&@489 zvseoR#NHB_Rga>>YcsM^P(&9v1 zBV#q0Fj^ms3M}T|&*1&dbEm@!7jYuKo=V9d@us~Q zIX8L@uu~ZWZtRVURJ67Fy7pz6Xv}bAv>iM!Zg^;cC^7_EEssLKp>$@I?<>fheZqElAjXuYdMVMt`gQ!rwjtun#k_2^!aKL9kIIyq<#ON?{HM1-- zsA~ajxfG#LyMP9y7H~G>+FdMhH`^^B&2@*6=CRx}ZlODjbjZWfEt_I9&SUga8zn0B z8!XDj-Jlj^gAP_yRB+vTs~)=#y68mj_YVpdQ^)Dx15 zz#B&#C0eP|@T&`2|L#ptUcANFU{+#p9F@7wtT$k6d{OMwqmB9nXvW%Rhn*0r`jvGt zYzTqQ;Y3?F;P3O-`!RJ~lvx2wBpE`dYl`S~HdZ0U&lWw4c^|zwhm+GGH@dmw`2P#YN z0(5VuRh&Ox3lyX?dy49lYx$l0DeOCS7^q!Dab{QP+upzmK1n8JZfh%1iY@&c!`f^%u# zqC8X_Ku%D$^l}s2$iezw@KuebLhpx(C@4WS?|TQ3{8`NBw~Ffq4eVtY#T%HxuV;+R zMUx0{%b0`ONzEh5Ryl8PnUOjBgnAV@w+uy%C0%vV&`GL|S60;X-@%FR@iOzvcE&VI zcZzq`9_d?aQrGOsM!QyR1kcYV*Ol{%c-Gg6;dHcJEwQtjfHBUYvr8<@RzNHE1JFsa zDyx{!KB_j7Ti8UgDvpa(gGy!Jw(pd(YvWtnG_d%4D!wfKUq&VOxA?v6qBa9;FX!do z&U8(}!lq$l!tBjl*V;60=AmQR&I=}hqEP*1+$xz2dd7+k>BYnsl%Q~l zb1I!Y=`5I?bz;(~>S5)aIN>}o`{c7pbssT&jQkz2V-U#5AngFOEf}v` zz$1RxRfp%e@X5QN#uyPVFZl|*6xf$FU$q^-;WzyjkT)(EYaY6LBwnhT$kK{o0O|n1 z28?KT7MEo1NDF2K+f&R%Yfqg$lS~-a2FZl;=^Sgha;3Wxva>q@Wx8IS4?9mD*)B_J z?MA2Bf^|zuD(UDK`fC0{{jC!NhaZ0Ot+xi6wFsq)9qJz`O^u+zI{zpZO463J1JRI% zN*E5e`oS_k4=NkjdRMemO~OeFNe>Z6daDwu94cMmc>x~k2^xI@?EMEGg-F&u+=n&; z*Y1FXqA+f23{C5^*n$}(i-hj{I@G*ncxLuyzG+@0iWp)a;oqlG=9Z~0;$LLcx0(aR zlZ|>1lEr@h4_PB6!%SxF$bGaixv*T^0@td;o8|l~A%;@mT3wQyTBjLwny`*U&b!)k z5M{$QgmpRFhvUU$M!+hICqXpaHyd<6Qh@KG{@=kSLsHz`5AZpdG$3%ywLN$1RE-1L z`;ph6D@@@2Hl${l*~ncrR3QXoK|*=M@-iJm?F!8e3v$xDj&#}b%)OZn+qDVq8@Wy6 zI@0C5XI(JWG3?2CG9BtzSnxnS!v@MyBbQRlKY=TY8#ynxY09;|8izORn9k+$U7mgP~eaKpTA;QFym$a3V22Q8$#R119>5^{V3 z$CFo$>n8qBsV|1p8@5-xY6icu$llB#cXzl4>C9DQPi8#}QXzjKqrQdf(XRBICK*7| z^Sr748v0lw>CpOw)cfsT9XM;NZ*Q2(d&7N6-pCEp$Xqk8LHv26-q_oP@PTgjszGsi zJsa*v4G&^F;Ej8`sA*p_u36XYYuRhLg`79xO``lm8*zDFfwD6hjYX7M7Vd*lQDA-V*inN|B(%{cGGif$9r&2PZbDrzi~8~pQ6#N zQdgyU2>WNHt)TR3dg5I!r?smuK-LiLj$VSz2xMAyE?1%3!Y>Bx7?@74!jqn()lHs) z_JoqC#d!_-9yVLi0_Rrm4{tzBLbR}{=tvLXt~Y~yfwmJS~A(=~i# zl+AbV;3p8FNiobzG*xWMwP)arqq;d?UF>0}5dJL~EQ!m2K7xcxdG4I*__FHP&h^`c z&w-EWk=YIUuHa39c6_k|PXHPZR_<^D&VgHIaT$)()O}8>Ui$@@<;|n$fC?Qtyx6tN zb$BDuQc!r%dZoAh_jWu=J3bWhNG<=eHnuntc2HmQx1lN*{v)x z;# z^bAk1_kegZEb14Tk&XoA;>S1=6WF1R0L%Qx6X2cz-|t~W9=_{{^el|15K}I0`B6pG zR)?9;q!GbKnbEUnW5c0X4Y{U5_en3ZpOFBIW}XwW!!~F|}ar1};>hto(uDGo26<1?}d^Tqv?(tEb$RJ`N|7#xSyFj z2QLZN{1(v~4f$}l2q~|=%;KYi%r{%!8xr*m6u<+K`6ou2j6a`BG!2M>J?eV{J&Ja< z+$LzTl;_cw6bv%CUY;tayh4#^*33#bScKw(o97qUg8WEIUl@Tk~U z?!98RWR*-%+c}ckwrN@*xQ*QHLas!*+Zs1><~Z0#+Ze^_DA+|~%9_FzIUE&>tPw0_ z*8UN!x1pR_!r78hLOpAvgEJE~W>E>iy!t9((<9^$-Z*r$I{2kop70z%90z0#|x37JJ4{pHZWNB`vumuGq z@Rnp~9E#?tZ*A)5nl7pIuHV8HXL@%nm;*L)fXY2*< zg%-h)aD%F*z4qy32;umXr2@KxNDg5G$ZwIpuTxcTgVB-UJmQG)f24Qpg6WyBY8Tfz z0BRg%A&e77`9ZWnYn6Q~?G0Qcl>ul4WsEm8xTGVoiR)hHNU%2rYsy?Z(EkLIg`HG2 zfUA52iGSry9Q1}EOsPK%0%$)DOpo@1CLSCJ?PZXx%Q^hd)2o8m1sk3e!_ztW8x}Qo z5QK<9*fbs>&045V0LYV(JV&zz!6m95t{$C-5&-f@Gjq&_l0oNdOj^T46C-X3(I>v5 z3mhaGrfE`A+kx@*sTmaCBd&}y9uEHAO~8KZ;1*~m+R8fIq8-?P@bprHPCLNyAUXqa z0X4K6tT;^7d9gP^okL-vMcQkH<9r6f8;hzDqy*uGR{Jv4A$`A3fkjTbQ1jDvCnWv7 z2gs>a(F6=fJpk;(W4cH!DH&ys7>X#IK*z$>Uh4wqaI@N4bvmv3MY!6jhbxA<6Py&GavOaUTa+Fw@hTRe z`XZ8TYm|$dxIyXjY3k%gz+!^tr`;QooUwF`e8m%YU< z5c&%fIq?Fb3&NM$*$H?E;Xi?EdhMZmbP4S6TWp9BCgXGrwGbucVoY5q8eRt>b8b{} zU0MY3nY`|xAWTan)kL+}X_5<0oMYTs90?{zm5W z*jNU#`uAkfiFcC-;B;Wp3h?!!QOKRa_t>bq44vSrdoGj6p8eoFMeo?-#~ z{uQo+Db!ua<)C9ufJmA#%=boboA$vUtKY_n8n%I7;tQuzvdtGh`BVDB=?TwNhO{3% z^@5AXyM!k@6-}P?!>x~E?VB=QT&CMx-y#MG^Rap%vsfZ^f3Ci8L4?>BuINumv~EIHohMJhU zj`glJ>+8m1W*yw$x=p4FjF@*hFv ziC|sW$lnD#lrr66mO##gZkgN#Oes9>`5Wd(;X1qcg``Kf7~z1{XWYpaV(<8$u@1}D z1I(N9=z$+vJ1C2R%F8LO+S1TF+Frl{Sd0xjp~L>T_pr998sr!zu?3_Ugd`fK*YqZc zq75MYURY_Hh9-&xV1Q01QBwT|66{~ZzsR_NY4=x{E1X+8*~kq2BTlv)cEAi7Gk4aW z*>Pea%qWd^>(d)jrsz>rfdDA=xA6amm(C($5=c!#1H729VoP8KomXFdQTIEb7YOn| z%&>XDI+X?*Y;9=7F?Hr#ruV5AtQZ>y(Bf9%tZvj9N9-VgrAHaUHYrc*ZhjhVP{e2T z>r9?z!qunwk5k-}1^QX+MI$jzYl~3*ZRT0HiKb~dnD7Yrmqi!)bG-Bxvk( zh9NYGSEUzW34J38t#QjETNsgOddG{@c~(4}ij6>`v%tXgf_K!5C{Y>Ku~@>oqIk-k zsDH`|3cc27f%DIhr`3;lu=iVh+SJl=U`U`}LBbTES(z5Jtvf~AWZ>VOBI2=%(6~m; zgNAu$)Y`)`_I^1tg~(L%4*A0(I1sWXYX|Q=V+M?UqHbd4?ni^5d_eVpl49J1nGfKA z7QHS2B#w{iB;Vo!aBWEuz`2}*4=RuU1s&Rn`1;IpQIwd+7~r`)Od7+>d==^pm$oJl zz*6QAMVREDBp|}&(ai}Y0Oq(4E85s}VnH^Sp^6tq4R#AU0S zzhtNj;h0Bwv>5*g*F=H zIqIuNtURh+2x)6K;ZX)HC=E+#c(VO8>&U(1B;QVOy!ry-N@5SLob14E?7g^v=pft= zfpIh~srMy!J_sD)TQULcO==|?i3fN{=Wts3NH41Hh_ea<9My_CNabbKS#5WyGfG8O zM_ddDjS>I0c6w`57Y%3I@X`ht1z4&WdPl!YqbVsG4#2#4VS}wgS7k8ao`$!=Z`D@O zUA**BM+82ULZIbq@ZLx{9dW{1<8JlXi@~SSdp%@U{aYfyzd=%-Qv10t0^-kbPxO*+ za8JYog@(}B>ciYi5H!kZT0#l_9l|-7cc7CGvy><}w1~A1vyXA{ z4s#*;twE*(Cm|$i^scJz)G(AU9*q;drcFY zy-A3L`$o!sox|+JQy?piwW#MQsk@ez@Ns~iP;1VmHrQ-o-gF2xV?`1rNEWTwZb#zD zi~4lox{6f+Z5;MihNubP@ajPtt;SlmHV2vnv&sC@P3H(PiI-ZaRrFV!G^KX~r&`uS9cV`x8o2l~aAqDEZ?{j1;S zp}2`% zM<5(@7637zNW6&`8`tp;jKm8AaS*$}7-1mIrXleTMwq`@*vwovfhk0)BJIC8>cL!q z_2%Uy#(0>esmN^Ly0?ZH7ZR3G9&z*!(lYu;SlYmOiN^R0T)c(7Fp@Xu-W{rLqKV1E z8$=qd{&UUKT|?#M9=J1j!44=y200e-9wcLOU&2F-27^eptwxsATr;R99-vu(ixe0* z?KBFe*RE3VS9{zwFDD+#)qO88_5$PD3j^&!cCSX<5_$!Ku+!UpfJ0pC{QazPXyqO_ zgy8g89YL%M<|dvDlrlc)#9f~}N478M!0RAx1&F=ALeBHflj-wh$(^PUR{*N!oY@li z&eB@9)#nFp8n+OGqcBZZ+6>f&3M{U#cmmURKuywjPQVk?JaIn0#_bDK-^M&pn-S*W zG3*HQFh=HK8N36u6Y|H7@*Gw12 z2X9}Pn#%Gmt=t{FvnA3C?`2x{dx*TqjpH3wzCO72@I8AsAkS>nOL=w*TksN+9W>~% z(8ODtV7%zwhV;=o@G#!-l>9Dq=uH^TT?=~dRF~$nS^cbaSLxGeFCI-H{5~bn_KS%8 z+jZf)2_anHTjoEVJW5voR=M0$>xmNuqngE{l@8F;MEhc}L$=_o_d7}P4 zlLaR4Frm6ETDk7Uf9B!0nF#1fB7PY`zVG0~4U!C`JMQ7O-CKB~aJ+D6{HWqpHEaXF z;rJ+c9Xs1Rbz?ht>T>TC?(xoQ?57jIToH&j9!(@;mbgRLz9Fb_Uv4?o`(DnUnpX~Q z!QY1yjSPO`x+~y-qe=H20A7owySwh7qZCue-H?`+_jynCk5PYlSkG!c(x2?=9UOeu zIn_y&A^Z^1bL(I62Ypsx@(5G!9Uz`6Z?Rv;v%EiddJ471BQ=y7?zMZjEW za#_&mh_AbDwubx9rupH}%_1yCBODe>vBWp4`z%GibjG()M-ofX3`coP3lZ`Dl*WbX z*UIeoqNZtvb2w7O}gnxU!aSH@m&@YDfvg-6Di65ZL;A9uu~YPeQ>~-F$;*aCb`bZbbx~8lWC3U1KV*Tg zM}4A8D$M$-Nk1aJhx~#Gf)ymM7p$N|qYy4IknbT_fPqyP4}f|{$dv>sEFnk%Z$Ur^ z@pA~056>gwOGuwFbigH8!X1Encd<8mbF6DEz=6Os9zUQk9`0fZ@o(-yFa$!emdC>h zx(?V*9lih;LEq6;w8X@f7vI*UdHsWuBf(j@wMPbqKd%+(*T$cs!q&*Wb-*+B^vUGNV$XW5HlcHD$-8xEnZp704clJ<#JTe1o` zUo=ameJ5v)A~I_fp71HF&(`ge*Fk^K#K6R+ol8`aiA`OQyb52G5x)&WwaKf%xs8X3 zh!nV_kq=fDUwHy^3;wmpDYFdbmTqFa+Kq_3i@Up)_=WK9Af^;zQXp@$)fdpcm+|%q zzhe{D@fsAq&PRtU{KYX%DpA8JEZ(13TU2%L-sG8fc_uH!Y$xxf8u?}cWNt5Pmju`j ztxY}`mP}d7AD^%e-(39N6J~9>>s^|;rPTJe;pvH;E}uY5!?dZH=q|H0E z^Ij3yh?h~-MZ7VSu8w!l;LmVitUr=OFTmaLd>aa%y^UuWe5VyJ7GJ@Us=S9ok4i+Wj(ECqM_tv4um`7 zTa=g>;Zn2<-&N~wDeEnsC zXqwmc-tJ&P7hm!8qltlReba*c1Cfp(FL?a~KLdP(4WBCl(T?vUP>caRqRD|nw^47_ z!}|ynMfYnfZnd$pRF$Z;ge9H9n9*t+!De9#{yY+G^M>YC@bMvd(5so7_9lFF-LdX? zcUO0!JK3G;PIq_f&{bM)`1%s$2)@2Vr6UhzgkN9cx0w*RiL@x+W*U}nGnM4qOe0-* z`k|{>0`!pC#qd4mRlsX-^Zhso9a-+9cY2wc&Nd%Xi9D`BU0!d9{kIFKQ{tIy2~Fv zEX7c~Gk9sR-F$gVhHei%{K=L!`MQ_Y9i-*me|pO-8QGm8>tF57+}xg> zR#@%_xzgQM7u0@3)cpSS)u-(DzCcGNTH(77JBw~k#5p$NY3%&u%V*zz!#iZCJ2N*| zen~xsov47IN%^X}f<};0s9PIWV$&m#dA_D-ZNzUk;azTgXi~JaQc$a}tj_9BP3U*o zX=|&mAc=DNg&a)>kcz-J+vC>`=OGsrsaM6Rswj*qW4)7jIZ4&=aTQu^_&{WQ2^3hk z*%0H$SNqf$&ZX6nV^ia-X%~|TCX*~N(Yu-@X5}lETA}1h!6#kfy7@X$AfKpJTt13m zl3wdysX=#O1f{%iUzzD$=Eq+|pLL}_`*`UCNGf}JC8Xk6yq^=+#a%A3lhKybacCfJ zpa~a}ABtESjY9*FmgB!}yt6Hvv8w{Q5ne*02ic9X^?GZejeo81_*3`{arKJ&ORSUg zMSmdl6!*TsALR3=8*n^xYIc6Sm7^@_2<&9ckbPg4Yc$wZP-k$kT=1YYuGh>-A&0NZSX?%L> G+5Zn__w})C3j@U);PMvjuQtXIkqJ$IJh>Br6gu-GuxW(-JQ`~ z)~9=9?RXXwuR?5sLP7;dNPu1Lq6ob3OcifDQ1A%VRPjO;lzF1cFH|ap`Mz_yd%Aaa z6{nKf>c7ve&-riXKmYm9*+I2h&f)jU`~K=bUdiSDgPHWtMCNHc{$d(ZhnyXOQ0Q7?q$t6p%~2~Zk@<u= zt*~;{3$7BorGe|U3ivqwF|FgdgvWmwV62UE&D>ZA)W!y&J~jc3ao*LA=8!Ux(q$@- zlp#|Eq)eGAB9)gZ3#o!kmE0V#C@zIoySF|R?O-FEANsqQaA>Ni*&6iLLw&Fw7FRqc z7z!^e1#80d*9Kj;UJlK!w-SVT(Oz8(!ozQPe0y%Q-|U0RrtkFDyPhBBU2h#tlhBP~nWykD5x}E%v z@kEX_kcz%EI&Po7qS$M{xoMw%bF*$gWxx64xlgWa*1?!kv)KYEezQq*`%ce8s<1K; zAX;%y@{(-4tBXT;g_@ef>?C*o%~f85(&-S?RE?ho?jXrIejHV@Z&3+_A760zE&TZ_A8fO z3v;6b(=F=tqEe%iz$8Wg31-Ozsb6BP9) zhfKz}(5&dv#9#rqn!Bql)y*(})9DU9!Kt9;o$mMRdBw~}Sa_J=V{!Fv8p)749^V3p zW|B6l621NB4bha!Wg|f)nw~d1`mPxx(R>mU=8T;f3TEp-mXu1#38^CzZAlPCzZ+vp z?>wGpQY>;(#Q3VoumoiyYtge*vX8PCPVtHOQ=Ll5OL=pR;G=rZm9dc2DEI=+@mw@IL5%%sqqmA@{6%4)4R>5%;|Nak&m2bzu(bkB3Ff zF&g91XUjHzQO>@OwcM6QP8L7kUxT5?Qev-&LC^MYbUjD(ANK8xCm@?%3yWCCG4(dW zV$^QwJ*xbBax!qeiGNHdli>wq_LvMXVOVLo>YC#MV18T#EQ~F{;fEXoCr0hjUz))ko@{^*o$uB9AB)3=Ep0J;^uVdXxYS_n@*Sr9XumNl? zST+`Duw)<56y}fX ztvJJOa51S$dLS0pF#5`{-=ec*2Lrq3IX*lAi4`p&ya{`-Vmr3Idv~F!vlKbhzVF2-)^B7;!u)Bvka}>2^6J|e zrd?#shrvSW&mJ92C|FL-qFy^$-kOp>N>!IswNWM_pA=?uYbGITeclvs&jVmtVs6Izv+H{oDwd#ymt5WG=Hdu z5uMONCo)wLIvF0>dvJ!cF+FvX50X73jYYSOxr*5h&aQ7H=5b={skW3r+|F5rd0vgI zwcLyj1lTm7Nmth3`mo(iOqSf9Of2ct#tay6wV0TpYhvgnm~!xB?NCEN-7xQ>E$>+v zW0>*Qn$vIly_Bv^jUzXJKZC)zj|B18u^qHAcxuQ5DYB+xs}p^8 z`Yl+^h?jl~O8mxJyX)CZ_Gw&|a>rXo`;B(6Cbcsem(1|9+6mtEC)}DKcJa|OvSZ&K z^t&6f8yAzP6<`&L&`426(M{F$yO3@^YGVj=a1KVSrOorBqY`=297oNLY-8KoPP_r3{VO*AdY|Xv;rG zqX4tkb2jWXNJk3UTUntBk^J6Z5Uj;+3wE4d_e=&m&tLaiZOCtfgHK$(EK5F=t(HXn z^rl*M^OIX@ekc8fpMjxPN|7)s9X*Vv-b*r~c$EP~a1Mb*%-3mOZfatFk^VM}^m@B> z!&d!9^FFimFDu7p0ul1q5}zXJ#u6wtP`O-7k? zfvs0Z@9TlVs1vqm444*Vz_cj0ZkEdu1U&)EtAyC10b;Y(sRp&1IdLhdG0os;XBN*K zo_RbA47GL+;910T5YHi3zr$_c;l33dagFVxD68%Om$$Xx7}Cds`?hrPT5w|fBy)JI z#oP`@JI<)l)DdFi)?Cc>9%p-Cq$jxT+NW6Jo_(@gE5U!e&b z9Q-C~{f`5bwZ&MqqIX$GL{I)9su%s0|97rjF^(Y0H)0%KDjvJZgvv_jj$u)Qeg6}{ z*K#X}+`9U0YZEHAjfngXqPYR0wot@EpanWpTbgU!(8c5LAigWk1m<=gsRCLST@$eb zjj{L(xiRAig|WGXz$enOG|8LzqIe_kUmh2bvTo#%GPX-RP1(^!Q}+B)V0Ox5jeWb; z9erFlp4+tgMU>94H@9?0-$cA0af)b^pyERLm>U~gjJ=eXS|lz0alVmUhT0`7ImhGx z+E{nBJLabGPR_)va}jvcaTI}}^=*Vkb~G(FX8hJzsvEn5fsdlMx+#NP;u)j`r*K%n zA&l2|5nME&(DIu`nLB#rAB9?1Jix|DU?DGl_d0-m5h!_NG$e8fH{CYJs1LuYp z8YmA9_=QUUFQA7oKk~%D7g9r?LV;3c0~mzX^>)8CfD;Cl_ME;9=hjP6Abx}97-vg} zNBDv^u~7o$itxrlGa@C0Hq}2g5lp@wYBv>2By(t8aTE}f`~zftMw%;nxnOB?Mgh#> z-<)1Hd2*qDP%sW@HB5&&eKbGCvR2yQGPs|_V1F4Im@$N_rH<=T$F;+`K28l`fVGbC zs=k@uM09N1gm&e}g&hMbf$%@_%<0T+7hskVT;5?U4KXy-T01$`Ky~ybDIGA029bs< z2n`snFz>H9*d2WirQ$NH3Qf5$63@zZRHC9{Z2o1gc!CFJVO2R1O?3dJWHhlN*##;z zR@%Wo<12Xra14y988v+lZ)ny`%qFzSFo?$r$qC56BC;6dCI%o{mkf|kl1*3JF<~-t z+j-`22*-ROAVZMbBKwM)R!}5EHcQdF9F)d|pp3QT;<&Uu12%G=m60kYDNCkG+m)T1 z|KDg;#rHF4!#7zRV^AE=bZV}CIEQhu=1Dw?2vtr>tK;f;ra6l-t8&b5NbJxi!A>6+ zw={7JM$X#O{I`NR7%&U;RJP20@tPT9Wt6qV2qDu1BP%sFR$hsbHLj&fXUDU^XdW2N zN{s$RVuUukF#7ulqkl*+s@)T#Sz;6{jAuKwakW$1(l_VETq1I}pp9gTliH$x^IGz5gCtiXUNVRN~X?0XMM!d>Wn$j@;`p_GCVg3tzi7PCp z8xtDqL-gVG)^RxWMV9hBQ=W8&^Xmf24|8yRxZ3pOqDhM^I0@_X$~(Kx0xG&_5&RNB zqbwI(c@i3$0^5dgCNVS3ZaO2vJdx!wEYHM4lexw$cPokj*-4`$3uN7#+_a=C%c_L6 z%KgjEDX|Qc{tb`61Td#9Xmj|pbW0we(ikyqVFf;f!_~5^twnW7DdylDEWA2)t>M_U zpjrQJJ~*nyb5dD?%pyjc#dVvjjQ<+|Sj6-|+tU5ZT%BEQ6%!KYt~wk=y@ONpJNZ|Q zO=HvCG^t*x`XcI~-cdW_4(>ic6%DBVIh2++_5S?Wkmv7urwF$Zt#9PSg{Y1r%xvoM z=mHZTsua6@c_Af8xye)SDX-nT>2*ezT{W}1)=tGs5N(a_E>wc17`f)n}q@N&!A!1h+QmG ztNh3;PI9JJ_@Py@;GS7pK`*~&9!af1aX(iwHkNoN!u(rgRa*=Fy(xP zrHzCKR(Ih0cW^K&g^Ps=w${qjmr!3$+9~1Az!Gczr%+mv!c~_m^iD|%w+!K4j_N4f zYD&1+V~8)pl54QBxR9{|QOD`4IpG8G`fN%ZzlZ{!zPs%ft{ME~(g~{>#fa8Fr5I9J zB{{vDgJB@nF_ySRutAWWrQyLufK7(Mnmw;K5F2MGOzuWZ0|%6vsmDURr$GP9JkGf~ zI+4uISg7f~GxDXw!ADM3Y7f={9>M85`X=uXXyO1kTLZVrQNV3u2hM`Z3!W*%vmD{2 zys}4-f@djK;WykhQ|*D^3_F{Mu+YhUCCk=|mQs;4TjTaE9bLyiuA(#pWV8Q$ zfXN>0UD$SYjbuf`?n-|LtnV1BxMG5x54I(jbFbYG?-UMK$IRz2iA`KbF*}7triwCM zWbO#_mdsl!{{)7+kbC_QzQtMlQFy8+a!^Q3ynqp5IxrgVS>@p{ynr^U7EG%bMo(OV zl^zC&vNBqWyHx`bg_c%SXfQervrjuW9Q&q&%S#A>*Ux2$CId*Y+=+b&3D`VHEzsc$ z&dao+(%6I|Iw9@fq{Ruz2yRN@cBfj`6@yecFB-)%mTrmkG3cA9`&P73bzSuEf*IJ^Ur-980Yy5M`UM(|1 zT@$z2J$jCJZNRB4xcL&-2%aW*mOw6Ki%j9fJtBb=Vb3<~)eCBa0JDe-Qr-5*Ys{pE zmo_qXpJ+J|6^R=PrQ%;@r*gY>{2p{%Cl5}dX~n3OY8sY0crW?~@Lc|{W}5I{;PdFF zK6>Co@MKCK=-5yv-X|JV{K-VXTBBda)xsTK9y69Q7qzHLoIhm7`=>>^^2Ez648saPXO3?gBR^Qb_{oKbx?bfahdRV?d z$C&JS>TNLMeU5x8Hz9T%XNy?yCc>Vy#~^A)?Fiv)2*z@vYir6xN;C5SF_1DSMqPa+ z9_E-fnvHoH)$Pka@pbfbFD?msjpB}MCEjK_p|!<<~IBKJ@!@2^p&BAnhNl-rT#+tI2+jkJb)de<(K_b=1?NHWH2XMjD4P*5EGt!4gJb>eo;HEjgIciyNo7qnkAY*Zy!;)oL>y! z4av&*zFXnMy`G6U1TfyURs>IUB8n*{q!tZ$GdB=Mo#sJ@#aMB3SFiedXg zuSYybAeRdYN6BihiC0at!TCDJq~C$M}q@Lm&j5dsCF*4w4N;ezz3a6=q1b6icwnfcl`V?CK;gz8w#mg$BW z9Eo3o<9*0vaYP6TyI>raNcRs0@|yS~G!lOdFtr_2+loJ7y__1!{0NUnSl|q~i+kT0 zyuTgey$Hrm7KVF#F{SAShU6nH!nDo1_xhKyWq(TKf4nXGE^tDo{)b-0{_Mli`u&8~$6}pwy}bnalM#Jr?@qqQ(e47X z(aC#{mN7c`NGbr8z~|(dD8aihebni#ecT`ln{JT*ECBsthJ{3tS2bN;%I7&E&-n~M zdB(xDMs|sSxAL905cG~O9SaZLUA36=>)1hi<%S$vzRErMSs_%6Zs2(}4! z2;}sWdifX3{w2Y~1bYAW)-~&7`Bb@LJzhRh ze$Xo7z@up0ZyESAtvnvnGIRqM>)FfHUYUFTOYEA9LjY~q=20FLi6KMy)sR1SQnJK< z2=)3lj`(=u%l(y$|78ep@;ufV?Pn=Han|(@Ms4#++d`@>_HC24E@DVHh5j7Au&}8$ zT!a+WSIL=iX=(HTt~%k~4zK#)za--8B}}VZCD?8Iql57wF{8VzlYwEmts9-rbTI6< z5udg~h_WV?Jrm|KY+>yf4bElw0W2Oi|S&YAJFObq=iy>i=d7kI)>;nscgh0@WoDpQjT!{oN$&wT65m9`&=tvMnK(@THG92s-fC2Ua z_RLZOyr@(eOvygDQn}<3AAln-Ir)@(QpqW|9OjZkPRdm$JEc;-@1NNhu%w-2P}As{ z>Hhog?!UkO|F5?@GgH=Zef(v-b?yyK`%gZMUltzT#vT7Nl0Xaeme$baztJ${zuC4L zmd}Jx;^`?o%?0y#nh&2kG8>C{J{O$F z^Z9TQ&liG)@Is_FmcsMlxo|#s>epuDVxX^S^$TC1FWPF|NEdH}%_xq#oplw4*IRxZ zN1IWo(x=AX-wn4^7{^^z*V9>lZ?CoQb@xK$C#csnC@R00=Xja8<8L7OT-($GJun_R ziI(Vn{Rn?Iv?J;b5MGU&+ud&KYA3m}hYq&=t7~C$ zh5fx6he;y;_gYbsgeqRy+fU1Hw4yj^cH4Vz)zHwF)NDyRc$RNkVLR+3zS>`Tqt$Kt zt@tfu2Bk5aw(>h$VQRL*PTf#6thAuQW>*EGn=0Xrs_^y9gt!^JirLUNbTyBEXGXPv zm)BXyMUrScdOuJ57~fwROR&B3T9)V79^5LsxZVr>jvL?a`6>+DP^qqRH*p8~oV)D2 zdnpQ6F1u|Mt-E_D7&SSsu?WC~7e5W7t?eZKg6g4*MrSWA`VTPuTOl6xeRU2WvhHzW zvhJDfIAlkpQ?8{hAV1M5ng~rrI<<&Qq8+1Cef`8ZG!o<3>>DRE6aC3* zzCVaMksG!5dMZlx-AiIkZmw$mvAz6)OXd$>H%|kU&THAasq2bsE9*4P2@6MG%^qoy zs*21I}uIhC<{NI)eoCGZK zX0PQZSU$8S7){iSS476`*zLu5&jsZ_=tco+hMzS7lrGnHkL9tBT)z{zut^m{7dPD8 zjG7Uwc0ZHS0?EB1Ws)S7KA+P3#r*Rky?EVsf}-z0;~tdmeIk$ zZLwS-V2NU2(&GGijB#0%CMc|Lho^g*4Mh~YyWw8q`q0@Ws&NE5dn>+D{(kI!8YSCq zg34Gqy=Kx=q5FOJ<4uf?w}Gx*b|ve_-bdT+>KTk17VY~ zMuuuob*~k*BX%I_Boq2`xH`S|2D(7320q70K1gvBv;FCI)PfBmD{C$0*6TKbf}x81 zk&z7>W1sUS$Km0HA%fM64`imzo3K0zj@@pjwJ$alMA&{|(gQWJRYxJW)>rFJS{|8$ zn#GDstDTW=6Q}cMrYv1N^Ge1#JwHXEbYb!(F(iyr{iC{_T5;IgRL`MJ^*ob5Ve)Mz z0;Q4&lscXVEKNM`C47wOk`$Jzdci2@1$|L38wXFH#krM1ohFw4@S;oyH{Zqof~2qS zV6VCePs=05@HQ6s1oRkt0?zE%>Q$r#RYz)Je=8n4$dyijI}6$cO=?-4>glh8FI(Cn zcJv$CCl?Mav|8?47qwq%ee<{iNmbb^&A`H5sqM^gw>-h0uHFALLAqK}+?h@0`UWv@ z#|-RW8OQUGT z>v#2orN@og8KdX8U#G+kB-m7qi`t={Aa|szZ;ENc++mz07^ev*4kk(ygaOVZ<}u!# z=rCVwrir=34Yz>1xPaIOUmd)|GT15856?=~ja9XRmg*H5o@~rs!;8#w`W|zC%7iOA znTGt?;%2MsC$GGmn(b&Xcw#!~%mk9871>1d(Bd-1h;0oGES>YV0McWnA{2`0 zsyn))TNuST+?KigwWS|CH*F+O)t?wtx`@M&WCKnq1_aZI6yeB%)`6F@!y)5}KamxW zLB*ikSo@~r3Vl1^G!*+92Ds2K!tZB&j7(zpi~YipG4ZXG6po91r(fEFhb<)?%0Z3N zuB|rlUohpc+%E@wtN%5nSNdh<`({Ox=c-7lKu&cFlbY3fL!=-NiEm)avz*A*cP;gI zSt&@HDm(ank|EXBAkF+9fH#+)=~@|R!#<7aa;8SW$iZ9{Nc!SyT(B(JFJ9)%zr*BJ zCiEF4ejGEj*ZJ-hCR}p$8Cj4DOoyi4MADcYvk?IaEaCubKPFIQwpT=0_<->>*HB|l zD$q^vGW%=HqU5-#H5|Sm>VOMDE|1Jd@Rwfo=vhl0;EI<+vj<<=V=NC3Y0^3#C`1^ZxDp= zO%Q$4v*PM~q=RkCHa#v#I0W1mPj}MBo*gE?!IzlB!tNBH_|%Cb3b989M!crtA!xlFXmv5CCuAtVQ4pjQd!p_#VSmIg z5Rd7f@FX77qIU4wlxYh)63Kc9xoIxbe5ZOry+xUKm^{zqHWTrI$QU(uQ9|Zs2$AYN zmRMyX+KEo0oj5GBr&|3-6wEhTqEi7#Rnx1+5?q&q<=?OLYF)ooucYw$I|yezFD-js zyBqXc%vU`RY+ft>QuMr_+w?s3B6X8)$qpbrk`B_1QXeuArAG-hSIbCPHPaU6=Ge}% zv*=XH-*zrm3-Y~*?-LiT>uuaI_nry9rk3;AR?g7a!eNuK5e6HKjRh;2O{?!(`+j)xE|TrCWVNU#{N?;+(P?787TfMY+57M{#IFx2EF z-4Sm6iBWk@8jPr=K|MI&LOLiHoCwxFIr|Z*k;l(qCBEvF=l{a#USR$C;_rT@MJGT z8^4!ysX}~TCH&v&ZoRyG=z=y|?RG#L4Rw-&XA~N73Bwkk8bXpd3ScHey0p68UD9vl z;;8Is{=YvVkDa{Y-rgi>ahWAMBSH7ZJ8{n)HU?HI^qZiYXs{#tfTDPbxV*0-@RS@< zT<#`Ko6`qiI-`*5-h(=q#+!Fva7RI0eOwtXfV}Hi1i5ZgL_SH|!Hk?$_f5{xrSYa8 z)8WBn{(nS>9ptkxF=%h{%O9deW-ZxRpjA-FzP6(An@eGfSl*=JPkO#Fr)p zbR7nTXe?^?+GE64E(ojfeh(ZIaHk8i4(*?%qA6_0t`4V+*#x5IdaZ~)_N`}^)TnwNTDugRuCO5E;tlWjIZdDx%A*5!#&3*$YU zG6AdivDn5iYjQ{)zZg$OR0g=oohUM8zcDyz)$e`N;^ExcRd0rpbpWkw`!kH|wGBT>1p34NnT&uhg zdP*H}LNg&QH-Y?Ak8_#9n=A~@6;WZEx{_;i<-M`CO0Q(CszNxZ0~yv?5=`2Ee&_Ov^>}zab!JrdeYMBxb|sv9AO7e z-cPaRtB;v@Og>@qmrPE%N_3O{4R>5dQn*C!RXp>j#(kOTtScQRO1sYL|Aizw)e%Pn z=Y$CA%)~k})M8>ElUNe&bwLw$llfxeJbU`3*ahjnx<5NiE$;Xr!m;Tfs08RAEDs6`r78RQ_^iR5 zID`XP8UkJxke2ZahTINi22)!=AhqxtfbG=cik3M}%h&JTUcY(w_WP@AY57NFO>gp0 zJS|{>qHJaZ3z#Cc!K8_#Zf4Oyz!$>TOuY5A%F?!f>TVN?zB5K9Q* zcvtb}V1B}2Sa%{?VnE4TjD=wnr%ruQq7=+rQj32mOZ9ZL|e7=IpI zn9yWYqHd2nKcd_pGx>5*1^NJZTjR!C#t7Hc?332&@oDzZ>aX3oEe0KahVZ5XFu#NiR0ZxH>(+;XPZslxk57}BGq{-N zv4V*^-bBJ*ASB=qi5s3Zd}7d2NQy@q&S?^zhrEY;vE&m$8EgIsIY~2CSANcm%sRqkmM+mf&NtdWsm>a>?^# zcR+kRvK89Yv#?M3iBZk0=?g}7k_J3C$hTN}^{a!Y&ra%eY^N>pzK%Qo4HAr-ri!Od z$_a>T&eS5U?VMPn{#ft8=1*&U)k-wxj-GFM3j zJIBo&!#Q(yj`_^jl3DB=cavG>W#^E&Oy&anc%JWuD1B=^`*Db{e)rIi`nik-B)6vm zCOjR@3_wuuc|HnMewVqBiR_-VNwvjfn~ChL5pz3Cc9Fo4USM9tQdncLGfDSmTwwNl zuCd7@7QDjA<1cjsHF1`iCjmsm^Gog>Cxm@<8Ww6uf4#XMxm)Tu5q8OrW4r*$)Z?o%^RMQU@ zr?d~Gua|P*SY2IRUl+Oq%_YP&R|3&zO_GO0T8EFXG+%>@b<4 zITMFxUmrYuR<98}ui->?zHtmz7E4_Y$Z*mKF1Bx-m_IcRZJZLo`8(#} zx()VK*nah5QVeivz~4aOW(lWX42r*Uj!WRB$l^Mk`J{}&dt#Zsvr{3U;}EMP`5DP$ z1Hk*5mtV@eg7VE=nv=VJ)UGrGt{rPJ<7gnPSo-JR8A^%0bA9$xpe6hNSt-;N46*T0e47^}VgY~!J2rIolD?d`8HK#kQ!zNzLi zRybcuW>V&F4o55UgDm`)6Gug4Qg1Wqv7#Nx8N3a1c;#jBTGZ$KIM0MemZ>9(E%T9% zr(j;Dd}-~@ySLX7U#uzF@J78K!b$49k#YHNVFa}JTSy!n$Rjd9)KGvs;+SQtTr8Js SCGD?je>}G~_mcDSoc=$1R$;aP literal 0 HcmV?d00001 From c13aedd8963238492975bf7a0920a2aa85c2fae8 Mon Sep 17 00:00:00 2001 From: Matt Raymond Date: Tue, 31 Dec 2024 10:23:08 -0500 Subject: [PATCH 3/5] update code to new sklearn interface --- .gitignore | 4 + README.md | 13 +- example.ipynb | 332 +++++++++++------- settree/__pycache__/__init__.cpython-310.pyc | Bin 330 -> 0 bytes .../explainability.cpython-310.pyc | Bin 5773 -> 0 bytes settree/__pycache__/gbest.cpython-310.pyc | Bin 30620 -> 0 bytes .../__pycache__/gbest_losses.cpython-310.pyc | Bin 27503 -> 0 bytes .../__pycache__/operations.cpython-310.pyc | Bin 5304 -> 0 bytes settree/__pycache__/set_data.cpython-310.pyc | Bin 10077 -> 0 bytes settree/__pycache__/set_rf.cpython-310.pyc | Bin 25078 -> 0 bytes settree/__pycache__/set_tree.cpython-310.pyc | Bin 14290 -> 0 bytes settree/__pycache__/splitters.cpython-310.pyc | Bin 11941 -> 0 bytes settree/gbest.py | 47 +-- settree/set_rf.py | 13 +- settree/settree.egg-info/PKG-INFO | 117 ------ settree/settree.egg-info/SOURCES.txt | 10 - settree/settree.egg-info/dependency_links.txt | 1 - settree/settree.egg-info/requires.txt | 3 - settree/settree.egg-info/top_level.txt | 1 - settree/splitters.py | 4 +- 20 files changed, 237 insertions(+), 308 deletions(-) create mode 100644 .gitignore delete mode 100644 settree/__pycache__/__init__.cpython-310.pyc delete mode 100644 settree/__pycache__/explainability.cpython-310.pyc delete mode 100644 settree/__pycache__/gbest.cpython-310.pyc delete mode 100644 settree/__pycache__/gbest_losses.cpython-310.pyc delete mode 100644 settree/__pycache__/operations.cpython-310.pyc delete mode 100644 settree/__pycache__/set_data.cpython-310.pyc delete mode 100644 settree/__pycache__/set_rf.cpython-310.pyc delete mode 100644 settree/__pycache__/set_tree.cpython-310.pyc delete mode 100644 settree/__pycache__/splitters.cpython-310.pyc delete mode 100644 settree/settree.egg-info/PKG-INFO delete mode 100644 settree/settree.egg-info/SOURCES.txt delete mode 100644 settree/settree.egg-info/dependency_links.txt delete mode 100644 settree/settree.egg-info/requires.txt delete mode 100644 settree/settree.egg-info/top_level.txt diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..fe0cefb --- /dev/null +++ b/.gitignore @@ -0,0 +1,4 @@ +/build/ +/my_env/ +/settree.egg-info/ +*.jpg diff --git a/README.md b/README.md index b351b7e..15995c8 100644 --- a/README.md +++ b/README.md @@ -31,7 +31,7 @@ It contains two main components: `SetDataset` object, that receives `records` as When configuring Set-Tree one should also configure: - `operations` : list of the operations to be used -- `use_attention_set` : binary flag for activating the attention-sets mechanism +- `use_attention_set` : binary flag for activating the attention-sets mechanism - `attention_set_limit` : the number of ancestors levels to derive attention-sets from - `use_attention_set_comp` : binary flag for activating the attention-sets compatibility option @@ -59,9 +59,9 @@ import numpy as np set_data = settree.SetDataset(records=[np.random.randn(2,5) for _ in range(10)]) labels = np.random.randn(10) >= 0.5 -gbest_model = settree.GradientBoostedSetTreeClassifier(learning_rate=0.1, +gbest_model = settree.GradientBoostedSetTreeClassifier(learning_rate=0.1, n_estimators=10, - criterion='mse', + criterion='squared_error', operations=settree.OPERATIONS, use_attention_set=True, use_attention_set_comp=True, @@ -91,10 +91,3 @@ If you use Set-Tree in your work, please cite: ## License Set-Tree is MIT licensed, as found in the [LICENSE](https://github.com/TAU-MLwell/Set-Tree/blob/main/LICENSE) file. - - - - - - - diff --git a/example.ipynb b/example.ipynb index 9e46661..5283986 100644 --- a/example.ipynb +++ b/example.ipynb @@ -2,17 +2,17 @@ "cells": [ { "cell_type": "markdown", + "metadata": { + "collapsed": false + }, "source": [ "## Initialization\n", "Import relevant packages" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 1, "metadata": { "collapsed": true }, @@ -27,22 +27,28 @@ }, { "cell_type": "markdown", + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "## Create dataset\n", "Create synthetic dataset of 2D point, following exp.1 in the paper (first quadrant). This dataset is comprised from sets of 2D points,\n", "a positive set contains a single point from the first quadrant. A negative set is not containing points from the first quadrant.\n", "We also configure a SetDataset object to be used in conjunction with SetTree. This object stores the sets in a convenient way." - ], + ] + }, + { + "cell_type": "code", + "execution_count": 2, "metadata": { "collapsed": false, "pycharm": { - "name": "#%% md\n" + "name": "#%%\n" } - } - }, - { - "cell_type": "code", - "execution_count": 30, + }, "outputs": [ { "name": "stdout", @@ -60,36 +66,42 @@ "N_TRAIN = 1000\n", "N_TEST = 1000\n", "\n", - "x_train, y_train = settree.get_first_quarter_data(N_TRAIN, min_items_set=SET_SIZE, max_items_set=SET_SIZE+1, dim=ITEM_DIM)\n", - "x_test, y_test = settree.get_first_quarter_data(N_TEST, min_items_set=SET_SIZE, max_items_set=SET_SIZE+1, dim=ITEM_DIM)\n", + "np.random.seed(0)\n", + "\n", + "x_train, y_train = settree.get_first_quarter_data(\n", + " N_TRAIN, min_items_set=SET_SIZE, max_items_set=SET_SIZE + 1, dim=ITEM_DIM\n", + ")\n", + "x_test, y_test = settree.get_first_quarter_data(\n", + " N_TEST, min_items_set=SET_SIZE, max_items_set=SET_SIZE + 1, dim=ITEM_DIM\n", + ")\n", "ds_train = settree.SetDataset(records=x_train, is_init=True)\n", "ds_test = settree.SetDataset(records=x_test, is_init=True)\n", "\n", - "print('Train dataset object: ' + str(ds_train))\n", - "print('Test dataset object: ' + str(ds_test))" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + "print(\"Train dataset object: \" + str(ds_train))\n", + "print(\"Test dataset object: \" + str(ds_test))" + ] }, { "cell_type": "markdown", - "source": [ - "Configure the desired set-compatible split criteria for SetTree." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "Configure the desired set-compatible split criteria for SetTree." + ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 3, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "name": "stdout", @@ -102,32 +114,32 @@ "source": [ "list_of_operations = settree.OPERATIONS\n", "print(settree.OPERATIONS)" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + ] }, { "cell_type": "markdown", - "source": [ - "## Configure and train Set-Tree model" - ], "metadata": { "collapsed": false - } + }, + "source": [ + "## Configure and train Set-Tree model" + ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 4, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Set-Tree: Test accuracy: 0.9980\n" + "Set-Tree: Test accuracy: 0.9970\n" ] } ], @@ -139,104 +151,163 @@ "MAX_DEPTH = 6\n", "SEED = 0\n", "\n", - "set_tree_model = settree.SetTree(classifier=True,\n", - " criterion='entropy',\n", - " splitter='sklearn',\n", - " max_features=None,\n", - " min_samples_split=2,\n", - " operations=list_of_operations,\n", - " use_attention_set=USE_ATTN_SET,\n", - " use_attention_set_comp=USE_ATTN_SET_COMP,\n", - " attention_set_limit=ATTN_SET_LIMIT,\n", - " max_depth=MAX_DEPTH,\n", - " min_samples_leaf=None,\n", - " random_state=SEED)\n", + "set_tree_model = settree.SetTree(\n", + " classifier=True,\n", + " criterion=\"entropy\",\n", + " splitter=\"sklearn\",\n", + " max_features=None,\n", + " min_samples_split=2,\n", + " operations=list_of_operations,\n", + " use_attention_set=USE_ATTN_SET,\n", + " use_attention_set_comp=USE_ATTN_SET_COMP,\n", + " attention_set_limit=ATTN_SET_LIMIT,\n", + " max_depth=MAX_DEPTH,\n", + " min_samples_leaf=None,\n", + " random_state=SEED,\n", + ")\n", "set_tree_model.fit(ds_train, y_train)\n", "set_tree_test_acc = (set_tree_model.predict(ds_test) == y_test).mean()\n", - "print('Set-Tree: Test accuracy: {:.4f}'.format(set_tree_test_acc))" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + "print(\"Set-Tree: Test accuracy: {:.4f}\".format(set_tree_test_acc))" + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ - "## Configure and train vanilla decision tree model" - ], - "metadata": { - "collapsed": false - } + "## Configure and train a Set-Tree Gradient Boosted Model" + ] }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 6, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Vanilla decision tree: Test accuracy: 0.7040\n" + "Set-Boost: Test accuracy: 0.9980\n" ] } ], "source": [ - "x_train_flat, x_test_flat = settree.flatten_datasets(ds_train, ds_test, list_of_operations)\n", - "tree_model = DecisionTreeClassifier(criterion=\"gini\",\n", - " splitter=\"best\",\n", - " max_depth=MAX_DEPTH,\n", - " min_samples_split=2,\n", - " min_samples_leaf=1,\n", - " min_weight_fraction_leaf=0.,\n", - " max_features=None,\n", - " random_state=SEED)\n", + "from sklearn.model_selection import train_test_split\n", "\n", - "tree_model.fit(x_train_flat, y_train)\n", - "tree_test_acc = (tree_model.predict(x_test_flat) == y_test).mean()\n", - "print('Vanilla decision tree: Test accuracy: {:.4f}'.format(tree_test_acc))\n" - ], + "train_idx, val_idx = train_test_split(\n", + " np.arange(len(ds_train)), test_size=0.2, random_state=SEED\n", + ")\n", + "\n", + "train_x = ds_train.get_subset(train_idx)\n", + "train_y = y_train[train_idx]\n", + "val_x = ds_train.get_subset(val_idx)\n", + "val_y = y_train[val_idx]\n", + "\n", + "set_boost_model = settree.GradientBoostedSetTreeClassifier(\n", + " n_estimators=50,\n", + " learning_rate=0.1,\n", + " max_depth=2,\n", + " use_attention_set=USE_ATTN_SET,\n", + " use_attention_set_comp=USE_ATTN_SET_COMP,\n", + " attention_set_limit=ATTN_SET_LIMIT,\n", + " random_state=SEED,\n", + " n_iter_no_change=1,\n", + ")\n", + "\n", + "set_boost_model.fit(train_x, train_y)\n", + "set_boost_test_acc = (set_boost_model.predict(ds_test) == y_test).mean()\n", + "print(\"Set-Boost: Test accuracy: {:.4f}\".format(set_boost_test_acc))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "## Configure and train vanilla decision tree model" + ] + }, + { + "cell_type": "code", + "execution_count": 6, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Vanilla decision tree: Test accuracy: 0.6940\n" + ] + } + ], + "source": [ + "x_train_flat, x_test_flat = settree.flatten_datasets(\n", + " ds_train, ds_test, list_of_operations\n", + ")\n", + "tree_model = DecisionTreeClassifier(\n", + " criterion=\"gini\",\n", + " splitter=\"best\",\n", + " max_depth=MAX_DEPTH,\n", + " min_samples_split=2,\n", + " min_samples_leaf=1,\n", + " min_weight_fraction_leaf=0.0,\n", + " max_features=None,\n", + " random_state=SEED,\n", + ")\n", + "\n", + "tree_model.fit(x_train_flat, y_train)\n", + "tree_test_acc = (tree_model.predict(x_test_flat) == y_test).mean()\n", + "print(\"Vanilla decision tree: Test accuracy: {:.4f}\".format(tree_test_acc))" + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false + }, "source": [ "## Visualize Set-Tree\n", "In order to plot the tree's structure please install pydotplus:\n", "conda install -c anaconda pydotplus" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 23, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "Requirement already satisfied: pydotplus in ./my_env/lib/python3.10/site-packages (2.0.2)\n", + "Requirement already satisfied: pyparsing>=2.0.1 in /home/matt/.local/lib/python3.10/site-packages (from pydotplus) (3.0.9)\n", "The trained model has 4 nodes and 5 leafs\n" ] }, { "data": { - "text/plain": "
", - "image/png": "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\n" + "image/png": "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", + "text/plain": [ + "
" + ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ + "! pip install pydotplus\n", "from exps.eval_utils.plotting import save_dt_plot\n", "print('The trained model has {} nodes and {} leafs'.format(set_tree_model.n_nodes,\n", " set_tree_model.n_leafs))\n", @@ -245,31 +316,33 @@ "plt.imshow(plt.imread(os.path.join(os.getcwd(), 'dt_graph.jpg')))\n", "plt.xticks([]), plt.yticks([])\n", "plt.show()" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + ] }, { "cell_type": "markdown", - "source": [ - "## Visualize the items importance" - ], "metadata": { "collapsed": false - } + }, + "source": [ + "## Visualize the items importance" + ] }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 24, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "data": { - "text/plain": "
", - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAFZCAYAAACfc0RyAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAA9hAAAPYQGoP6dpAAAyg0lEQVR4nO3de1xUZeI/8M8Aw6AgxwtyMRG1VVAxl0tycS1dDbXVtP1+FbOdtc2ltTSXzFeJba3ad8P6bnfFsp/lWqZuIdV+UxLL6wImCpppaJsGGiNqOAOUgPD8/jg5OQy3gTlzO5/363Veu/PMc57znOP04cyZ5zxHI4QQICIi1fBydgeIiMixGPxERCrD4CciUhkGPxGRyjD4iYhUhsFPRKQyDH4iIpXxcXYHnKGpqQnfffcdevToAY1G4+zuEBF1mRAC1dXV6NevH7y82j6nV2Xwf/fddwgPD3d2N4iI7K68vBz9+/dvs44qg79Hjx4A5AMUGBjo5N4QEXWdyWRCeHi4Od/aosrgv355JzAwkMFPRB6lI5ev+eMuEZHKMPiJiFSGwU9EpDIMfiIilWHwExGpDIOfiEhlGPxERCqjaPDv27cP06ZNQ79+/aDRaPDBBx+0u87evXsRFxcHPz8/DB48GK+99ppVnezsbAwfPhw6nQ7Dhw9HTk6OAr0nIvJMigZ/bW0tRo0ahdWrV3eo/pkzZ3DnnXdi7NixKC4uxrJly7Bo0SJkZ2eb6xQUFCA1NRV6vR5Hjx6FXq/HrFmzcPDgQaV2g4jIo2gc9bB1jUaDnJwczJgxo9U6jz/+OD766COcPHnSXDZ//nwcPXoUBQUFAIDU1FSYTCbs2LHDXGfy5Mno1asXNm/e3KG+mEwmSJIEo9HIO3eJyCPYkmsudY2/oKAAKSkpFmWTJk1CUVERGhoa2qyTn5/fart1dXUwmUwWCxGRWrlU8BsMBoSEhFiUhYSE4Nq1a7h06VKbdQwGQ6vtZmZmQpIk88KZOckRamtrodFooNFoUFtb6+zuEJm5VPAD1hMMXb8SdWN5S3XampgoIyMDRqPRvJSXl9uxx0RE7sWlZucMDQ21OnOvrKyEj48P+vTp02ad5t8CbqTT6aDT6ezfYSIiN+RSZ/xJSUnIy8uzKNu5cyfi4+Oh1WrbrJOcnOywfhIRuTNFz/hramrw9ddfm1+fOXMGJSUl6N27NwYMGICMjAycP38eGzduBCCP4Fm9ejUWL16MtLQ0FBQUYP369Rajdf785z/jtttuw7PPPovp06fjww8/xK5du3DgwAEld4WIyHMIBe3evVsAsFrmzp0rhBBi7ty54vbbb7dYZ8+ePSImJkb4+vqKgQMHirVr11q1+95774nIyEih1WpFVFSUyM7OtqlfRqNRABBGo7Gzu0bUrpqaGvNnvqamxtndIQ9nS645bBy/K+E4fnKE2tpaBAQEAJC//fr7+zu5R+TJ3HYcPxERKY/BT0SkMgx+IiKVYfATEakMg5+ISGUY/EREKsPgJyJSGQY/EZHKMPiJiFSGwU9EpDIMfiIilWHwExGpDIOfiEhlGPxERCrD4CciUhkGPxGRyjD4iYhUhsFPRKQyDH4iIpVh8BMRqQyDn4hIZRj8REQqw+AnIlIZH2d3gIjIZkIAdReBaz8ATfWAly/g0x3Q9QU0Gmf3zuUpfsaflZWFQYMGwc/PD3Fxcdi/f3+rde+77z5oNBqrZcSIEeY6GzZsaLHO1atXld4VInKWq5eAb/8JFD8O7BoPvCcB20KAjwYB/xcp/++2ELl813i53rf/lNcjK4qe8W/duhXp6enIysrCmDFj8Prrr2PKlCk4ceIEBgwYYFX/5ZdfxqpVq8yvr127hlGjRmHmzJkW9QIDA1FaWmpR5ufnp8xOEJFzCAFcKgROrwG+3QqIa4DGR/7f1lyrBir3ABcP/Fw/YjYwdAHQJ4HfBn6iaPC/8MILmDdvHv74xz8CAF566SV88sknWLt2LTIzM63qS5IESZLMrz/44ANUVVXhD3/4g0U9jUaD0NDQDvejrq4OdXV15tcmk8nWXSEiRzr3IXD0L4DxuGXYtxX6N7qx/rdbgLPvANJIYNT/AP3vUqbPbkSx4K+vr8fhw4exdOlSi/KUlBTk5+d3qI3169dj4sSJiIiIsCivqalBREQEGhsb8ctf/hJPP/00YmJiWm0nMzMTK1assH0nPEFTA3DlGPD9YeD7I8CPFUBTHeClA7qFAb1jgd5xQM9bAC+ts3tLald3GSh6GPh2M8xXojsa9q25vr7xS2DfdCDiHiD+VUDXp2vtujHFgv/SpUtobGxESEiIRXlISAgMBkO761dUVGDHjh149913LcqjoqKwYcMGjBw5EiaTCS+//DLGjBmDo0ePYsiQIS22lZGRgcWLF5tfm0wmhIeHd2Kv3Ejtt8Dp14GvXwPqq+QyjRYQDT/X0WiBr1+X/79vL+AX84EhfwL8I6zbI1Ja+QfAwT8CDVd+Kmiy8wZ+aq/sn4BhJzD6/wHhM+y8Dfeg+KgeTbNrakIIq7KWbNiwAT179sSMGTMsyhMTE5GYmGh+PWbMGMTGxuLVV1/FK6+80mJbOp0OOp3O9s67o3ojULwE+M96QOMFiMaf37sx9Ju/rq8CTj4HnFgF3DwPiH0e0AY6ps+kbkIAJzKBo09APsu3d+A3314jUFcF7L8bGPUMMHyp6q79KzaqJygoCN7e3lZn95WVlVbfApoTQuDNN9+EXq+Hr69vm3W9vLxw66234vTp013us9ur2An8XxTwzVsAhGXod4RolNf75i3gX1Fye0RKEgI4uuyn0AcUD32zn7ZzfdtCOGi7rkGx4Pf19UVcXBzy8vIsyvPy8pCcnNzmunv37sXXX3+NefPmtbsdIQRKSkoQFhbWpf66vdLVwO5JwNVK2wO/OdEIXL0gt3dqjX36R9SSE5nyt0y198HBFL3Us3jxYuj1esTHxyMpKQnr1q1DWVkZ5s+fD0C+9n7+/Hls3LjRYr3169cjISEB0dHRVm2uWLECiYmJGDJkCEwmE1555RWUlJRgzRoVB9SpNcDhh396Ya8zpp/aKVoo/+/QBXZql+gn5R/ccKbvZEeXAdJwoP90Z/fEIRQN/tTUVFy+fBkrV65ERUUFoqOjsX37dvMonYqKCpSVlVmsYzQakZ2djZdffrnFNq9cuYIHHngABoMBkiQhJiYG+/btw+jRo5XcFddVsfPncFZK0UKgxxAgLEXZ7ZB61F2Wf8iFBoArXGbxAgrnAdN+pYrRPhohVHZxC/KoHkmSYDQaERjoxj9g1hvla/pXK6HstVEvwC8EmPYVf/C1QW1tLQICAgDIQ5D9/f2d3CMX8u858uiarl6WtCeNNzAgFRizydk96RRbco2TtLmz4iXyfCWK/yDWBNRVAkceVXg7pArnPpTH6btS6ANyf759Fzj3kbN7ojgGv7uqOSsP2XTUfzyiUd5e7beO2R55JiHkO3JdNnq85P55+IUQVz361J6v18nj9B1J4yVvl6izLhXK0zA4bNimrZoA4xfA5YPO7oiiGPzuqKlBviPX0V+VRSNweq28faLOOL1GnnvHlWl8PH4YM4PfHV059vM0DI5WXwVc+cI52yb3dvXSz7NsurLrE7t58JTODH539P1hdW+f3NOFz1w/9K8T14DK3c7uhWIY/O7o+yPyBGvOoNEy+Klzvj/s+pd5rtP4ePTnnMHvjn6ssJ5wzVFEA3C1/dlViaxcPuheZ/yXPnd2LxTD4HdHTXXt11FSIx9zSTYSQv6m6k6+L/LYYZ0Mfnfk5eQppr35mEuyUd1F+bGI7uRa9U83SHoeBr876hbm3Gv8fh1/7CURAODaD87uQec0/ujsHiiCwe+Oesc69xp/7zjnbJvcV1O9s3vQOY1OvqyqEAa/O3J28Dp7++R+vNp+oJLL8vbMJ/cx+N1Rz1vkZ+Q6g28voOdI52yb3JdPd2f3oHO8uzm7B4pg8LsjL638YHSNt2O3q/EGhjwob5/IFrq+gE8PZ/fCNj495H57IAa/uxryJ0A4eKIr0QT84gHHbpM8g0Yj/zblTnrHe+xD2Bn87so/Arh5nuPO+jXe8vb8IxyzPfI8fRLc687dIM99qh+D353FPg/ogqH8P6OXvJ3Y5xXeDnm03nHudeeuBw9iYPC7M20gkLQBDnkCV9IGPnaRuibk1+51xh883tm9UAyD392FpQDxq5XdRvwaPmidus4vCIhIdf3w1/gAEbPl/nooBr8nGLrghvC31z/pT+3ErwGGPmSnNkn1hixw/cs94pr835QHY/B7iqELgPGfAH4hXf/BV+MttzP+E4Y+2VdQIiCNhOtGj5d8n0yfBGd3RFGuevSpM8JSgKkngcF/AKCx/Q+Axlteb/AfgGlf8fIO2Z9GA4z6H7j0M3dvedpjh3Fep3jwZ2VlYdCgQfDz80NcXBz279/fat09e/ZAo9FYLV999ZVFvezsbAwfPhw6nQ7Dhw9HTk6O0rvhPnwlIOENYPoZYPjjlnf4Np/Y7cbXvr3k+tPPyOvzh1xSSv+7gIh7HH8DYns03kDEHLl/Hk7RX1m2bt2K9PR0ZGVlYcyYMXj99dcxZcoUnDhxAgMGDGh1vdLSUgQG/hw8ffv+fPdcQUEBUlNT8fTTT+Puu+9GTk4OZs2ahQMHDiAhwbO/ntnEPwIY9Tdg5HL5GbnfH5aXqwZ5Pn1vP3mWzd5x8tJzJO/IJceJfxWo2PnTs6Nd4ezfC/DtCcS/4uyOOIRGCOWeNJCQkIDY2FisXbvWXDZs2DDMmDEDmZmZVvX37NmD8ePHo6qqCj179myxzdTUVJhMJuzYscNcNnnyZPTq1QubN2/uUL9MJhMkSYLRaLT4A0NkT7W1tQgICAAA1NTUwN/f38k9cjHlHwD773Z2L3522wdA/+nO7kWn2ZJril3qqa+vx+HDh5GSYnmdOCUlBfn5+W2uGxMTg7CwMEyYMAG7d1s+8LigoMCqzUmTJrXZZl1dHUwmk8VCRE4WPkP+VuoKRj3j1qFvK8WC/9KlS2hsbERISIhFeUhICAyGlp/ZGhYWhnXr1iE7Oxvbtm1DZGQkJkyYgH379pnrGAwGm9oEgMzMTEiSZF7Cw8O7sGdEZDfDM+TF6X1Y6tw+OJjid1Jomv06LoSwKrsuMjISkZGR5tdJSUkoLy/H3//+d9x2222dahMAMjIysHjxYvNrk8nE8CdyBRqNfNav7QEcXQb5XNQR1/x/2s6oTGCEukIfUPCMPygoCN7e3lZn4pWVlVZn7G1JTEzE6dOnza9DQ0NtblOn0yEwMNBiISIXodEAIzLka+y+vZQf7aPxBnS95O2pMPQBBYPf19cXcXFxyMvLsyjPy8tDcnJyh9spLi5GWFiY+XVSUpJVmzt37rSpTSJyQf2nA9NKgQGzfiqwdzz91N6AVGDqKVVd029O0Us9ixcvhl6vR3x8PJKSkrBu3TqUlZVh/vz5AORLMOfPn8fGjRsBAC+99BIGDhyIESNGoL6+Hu+88w6ys7ORnZ1tbvPPf/4zbrvtNjz77LOYPn06PvzwQ+zatQsHDhxQcleIyBF0fYAx78pz+hx9EjB+Ic+d05VpHq6vL42Qbx5TwTj99iga/Kmpqbh8+TJWrlyJiooKREdHY/v27YiIkOd0r6ioQFlZmbl+fX09lixZgvPnz6Nbt24YMWIEPv74Y9x5553mOsnJydiyZQv+8pe/4Mknn8TNN9+MrVu3cgw/kSfpPx246S7g8kHg1Brg2y1yeGu0gGhof/3rYa/RyhOuDV0A9Bnt8XfkdpSi4/hdFcfxkyNwHL8dXb0EVO4GLhcBlw8B3xcB16qt6/n0kJ+cFTRavjExeLxHz7J5I1tyzcXnRyUighzeA2bKCwAIAdRdBBp/BBrrAG+d/GB0XV+e1XcAg5+I3I9GA/gFO7sXbouzcxIRqQyDn4hIZRj8REQqw+AnIlIZBj8Rkcow+ImIVIbBT0SkMgx+IiKVYfATEakMg5+ISGUY/EREKsPgJyJSGQY/EZHKMPiJiFSGwU9EpDIMfiIilWHwExGpDIOfiEhlGPxERCrD4CciUhkGPxGRyige/FlZWRg0aBD8/PwQFxeH/fv3t1p327ZtuOOOO9C3b18EBgYiKSkJn3zyiUWdDRs2QKPRWC1Xr15VeleIiDyCosG/detWpKen44knnkBxcTHGjh2LKVOmoKysrMX6+/btwx133IHt27fj8OHDGD9+PKZNm4bi4mKLeoGBgaioqLBY/Pz8lNwVIiKPoRFCCKUaT0hIQGxsLNauXWsuGzZsGGbMmIHMzMwOtTFixAikpqbiqaeeAiCf8aenp+PKlSud7pfJZIIkSTAajQgMDOx0O0Rtqa2tRUBAAACgpqYG/v7+Tu4ReTJbck2xM/76+nocPnwYKSkpFuUpKSnIz8/vUBtNTU2orq5G7969LcpramoQERGB/v37Y+rUqVbfCJqrq6uDyWSyWIiI1Eqx4L906RIaGxsREhJiUR4SEgKDwdChNp5//nnU1tZi1qxZ5rKoqChs2LABH330ETZv3gw/Pz+MGTMGp0+fbrWdzMxMSJJkXsLDwzu3U0REHkDxH3c1Go3FayGEVVlLNm/ejOXLl2Pr1q0IDg42lycmJuJ3v/sdRo0ahbFjx+Kf//wnhg4dildffbXVtjIyMmA0Gs1LeXl553eIiMjN+SjVcFBQELy9va3O7isrK62+BTS3detWzJs3D++99x4mTpzYZl0vLy/ceuutbZ7x63Q66HS6jneeiMiDKXbG7+vri7i4OOTl5VmU5+XlITk5udX1Nm/ejPvuuw/vvvsufvOb37S7HSEESkpKEBYW1uU+ExGpgWJn/ACwePFi6PV6xMfHIykpCevWrUNZWRnmz58PQL4Ec/78eWzcuBGAHPq///3v8fLLLyMxMdH8baFbt26QJAkAsGLFCiQmJmLIkCEwmUx45ZVXUFJSgjVr1ii5K0REHkPR4E9NTcXly5excuVKVFRUIDo6Gtu3b0dERAQAoKKiwmJM/+uvv45r165hwYIFWLBggbl87ty52LBhAwDgypUreOCBB2AwGCBJEmJiYrBv3z6MHj1ayV0hIvIYio7jd1Ucx0+OwHH85EguMY6fiIhcE4OfiEhlGPxERCrD4CciUhkGPxGRyjD4iYhUhsFPRKQyDH4iIpVh8BMRqQyDn4hIZRj8REQqw+AnIlIZBj8Rkcow+ImIVIbBT0SkMgx+IiKVYfATEakMg5+ISGUY/EREKsPgJyJSGQY/EZHKMPiJiFSGwU9EpDKKB39WVhYGDRoEPz8/xMXFYf/+/W3W37t3L+Li4uDn54fBgwfjtddes6qTnZ2N4cOHQ6fTYfjw4cjJyVGq+0REHkfR4N+6dSvS09PxxBNPoLi4GGPHjsWUKVNQVlbWYv0zZ87gzjvvxNixY1FcXIxly5Zh0aJFyM7ONtcpKChAamoq9Ho9jh49Cr1ej1mzZuHgwYNK7goRkcfQCCGEUo0nJCQgNjYWa9euNZcNGzYMM2bMQGZmplX9xx9/HB999BFOnjxpLps/fz6OHj2KgoICAEBqaipMJhN27NhhrjN58mT06tULmzdv7lC/TCYTJEmC0WhEYGCgTftUW1trU31Sr9raWoSEhAAALly4AH9/fyf3iNxFZz4rtuSaT2c71p76+nocPnwYS5cutShPSUlBfn5+i+sUFBQgJSXFomzSpElYv349GhoaoNVqUVBQgEceecSqzksvvdRqX+rq6lBXV2d+bTKZbNybnwUEBHR6XVKv638AiDpCwfNxAApe6rl06RIaGxutPvAhISEwGAwtrmMwGFqsf+3aNVy6dKnNOq21CQCZmZmQJMm8hIeHd2aXiIg8gmJn/NdpNBqL10IIq7L26jcvt7XNjIwMLF682PzaZDJ1Ovxramo6tR6pDy/1kKtSLPiDgoLg7e1tdSZeWVnZ6tfe0NDQFuv7+PigT58+bdZp66u0TqeDTqfrzG5Y4X+81Bn+/v787JDLUOxSj6+vL+Li4pCXl2dRnpeXh+Tk5BbXSUpKsqq/c+dOxMfHQ6vVtlmntTaJiKgZoaAtW7YIrVYr1q9fL06cOCHS09OFv7+/OHv2rBBCiKVLlwq9Xm+u/80334ju3buLRx55RJw4cUKsX79eaLVa8f7775vr/Pvf/xbe3t5i1apV4uTJk2LVqlXCx8dHFBYWdrhfRqNRABBGo9F+O0vUTE1NjQAgAIiamhpnd4c8nC25pmjwCyHEmjVrREREhPD19RWxsbFi79695vfmzp0rbr/9dov6e/bsETExMcLX11cMHDhQrF271qrN9957T0RGRgqtViuioqJEdna2TX1i8JMjMPjJkWzJNUXH8buqrozjJ+qo2tpa8/DfmpoaXuMnRdmSa5yrh4hIZRj8REQqw+AnIlIZBj8Rkcow+ImIVIbBT0SkMgx+IiKVYfATEakMg5+ISGUY/EREKsPgJyJSGQY/EZHKMPiJiFSGwU9EpDIMfiIilWHwExGpDIOfiEhlGPxERCrj4+wOUDNNjcDF/UBtGXCtBvAJAPwHAH3HAl7ezu4dEXkABr+ruHoR+OZN4NRq4IdzPxVqID+rG0D3/sDQhcDg+wG/vs7qJRF5AF7qcQWnsoCcm4CSZTeEPmAOfUAuL1kGfHCTXJ+IqJMY/M527CmgaAEgGgA0tVO5CWhqkOsfe8oRvSMiD8Tgd6ZTWcDxpzu37vGneeZPRJ3C4HeWqxeBw+lda+NIutwOEZENFA3+qqoq6PV6SJIESZKg1+tx5cqVVus3NDTg8ccfx8iRI+Hv749+/frh97//Pb777juLeuPGjYNGo7FYZs+ereSu2N83bwKisWttNDUC37xln/4QkWooGvxz5sxBSUkJcnNzkZubi5KSEuj1+lbr//DDDzhy5AiefPJJHDlyBNu2bcOpU6dw1113WdVNS0tDRUWFeXn99deV3BX7amqUR++0e02/3Ybkdpq6+AeEiFRFseGcJ0+eRG5uLgoLC5GQkAAAeOONN5CUlITS0lJERkZarSNJEvLy8izKXn31VYwePRplZWUYMGCAubx79+4IDQ1VqvvKuri/2eidLvihXG4vZJx92iMij6fYGX9BQQEkSTKHPgAkJiZCkiTk5+d3uB2j0QiNRoOePXtalG/atAlBQUEYMWIElixZgurq6lbbqKurg8lkslicqrbMvu39UG7f9ojIoyl2xm8wGBAcHGxVHhwcDIPB0KE2rl69iqVLl2LOnDkIDAw0l997770YNGgQQkNDcfz4cWRkZODo0aNW3xauy8zMxIoVKzq3I0q4VgOLm7O6RAM0tP5Hj4ioOZvP+JcvX271w2rzpaioCACg0Wis1hdCtFjeXENDA2bPno2mpiZkZVkOW0xLS8PEiRMRHR2N2bNn4/3338euXbtw5MiRFtvKyMiA0Wg0L+XlTj5D9gmAfUIfcjvaHnZqi4jUwOYz/oULF7Y7gmbgwIE4duwYLly4YPXexYsXERIS0ub6DQ0NmDVrFs6cOYPPPvvM4my/JbGxsdBqtTh9+jRiY2Ot3tfpdNDpdG224VD+A9qvY4vu4fZtj4g8ms3BHxQUhKCgoHbrJSUlwWg04vPPP8fo0aMBAAcPHoTRaERycnKr610P/dOnT2P37t3o06dPu9v68ssv0dDQgLCwsI7viDP1HSvPvWOPH3i7h8vtERF1kGI/7g4bNgyTJ09GWloaCgsLUVhYiLS0NEydOtViRE9UVBRycnIAANeuXcN///d/o6ioCJs2bUJjYyMMBgMMBgPq6+sBAP/5z3+wcuVKFBUV4ezZs9i+fTtmzpyJmJgYjBkzRqndsS8vb3nCtS4ffi+5Hc7aSUQ2UHQc/6ZNmzBy5EikpKQgJSUFt9xyC95++22LOqWlpTAajQCAc+fO4aOPPsK5c+fwy1/+EmFhYebl+kggX19ffPrpp5g0aRIiIyOxaNEipKSkYNeuXfD2dqMAHHw/oOlif7285XaIiGygEULY61dGt2EymSBJEoxGY7u/HyjqVJY84Vpn3ZoFDHnQfv0hu6qtrUVAQAAAoKamBv7+/k7uEXkyW3KNc/U409CHgOgnO7du9JMMfSLqFAa/s92yEohfA3hp0f4/h5dc79YseT0iok5g8LuCoQ8BM84Dv8yUR/uY3XC/Q/dw+f0Z3/FMn4i6hI9edBV+fYHhjwFRj/40l0+5fEeutsfPQzY5eoeI7IDB72q8vDnhGhEpipd6iIhUhsFPRKQyDH4iIpVh8BMRqQyDn4hIZRj8REQqw+AnIlIZBj8RkcrwBi5yT02N8h3OtWXyM4x9AuQnm/EOZ6J2MfjJvVy9CHzzJnBq9Q1PMLvhwfXd+8sPpxl8vzwNBhFZ4aUech+nsoCcm4CSZc0eW3nDIyV+OCe//8FNcn0issLgJ/dw7Cn5oTWiAUBTO5WbgKYGuf6xpxzROyK3wuAn13cqCzj+dOfWPf40z/yJmmHwk2u7ehE4nN61No6ky+0QEQAGP7m6b94ERGPX2mhqBL55yz79IfIADH5yXU2N8uiddq/pt9uQ3E5TF/+AEHkIBj+5rov7m43e6YIfyuX2iIjBTy6stsy+7f1Qbt/2iNyUosFfVVUFvV4PSZIgSRL0ej2uXLnS5jr33XcfNBqNxZKYmGhRp66uDg8//DCCgoLg7++Pu+66C+fO2enMkFzHtRpYPHC+SzTyM4yJSNngnzNnDkpKSpCbm4vc3FyUlJRAr9e3u97kyZNRUVFhXrZv327xfnp6OnJycrBlyxYcOHAANTU1mDp1KhobeQ3Xo/gEwOLmrC4R8oPriUi5KRtOnjyJ3NxcFBYWIiEhAQDwxhtvICkpCaWlpYiMjGx1XZ1Oh9DQ0BbfMxqNWL9+Pd5++21MnDgRAPDOO+8gPDwcu3btwqRJk+y/M+Qc/gPs2173cPu2R+SmFDvjLygogCRJ5tAHgMTEREiShPz8/DbX3bNnD4KDgzF06FCkpaWhsrLS/N7hw4fR0NCAlJQUc1m/fv0QHR3dart1dXUwmUwWC7mBvmPluXfsoXu43B4RKRf8BoMBwcHBVuXBwcEwGAytrjdlyhRs2rQJn332GZ5//nkcOnQIv/71r1FXV2du19fXF7169bJYLyQkpNV2MzMzzb8zSJKE8HCe+bkFL295wrUuf0y95HY4aycRgE78F7V8+XKrH1+bL0VFRQAAjcb6hzkhRIvl16WmpuI3v/kNoqOjMW3aNOzYsQOnTp3Cxx9/3Ga/2mo3IyMDRqPRvJSXc3SH2xh8P6DpYmB7ecvtEBGATlzjX7hwIWbPnt1mnYEDB+LYsWO4cOGC1XsXL15ESEhIh7cXFhaGiIgInD59GgAQGhqK+vp6VFVVWZz1V1ZWIjk5ucU2dDoddDpdh7dJLsSvLxD3kjzhWmfFvQz4BdmtS0TuzubgDwoKQlBQ+/8RJSUlwWg04vPPP8fo0aMBAAcPHoTRaGw1oFty+fJllJeXIywsDAAQFxcHrVaLvLw8zJo1CwBQUVGB48eP47nnnrN1d8gdDH0IuGro3ERt0U8CQx60f5+I3Jhi1/iHDRuGyZMnIy0tDYWFhSgsLERaWhqmTp1qMaInKioKOTk5AICamhosWbIEBQUFOHv2LPbs2YNp06YhKCgId999NwBAkiTMmzcPjz76KD799FMUFxfjd7/7HUaOHGke5UMe6JaVQPwawEuL9j+2XnK9W7Pk9YjIgqJP4Nq0aRMWLVpkHoFz1113YfXq1RZ1SktLYTQaAQDe3t744osvsHHjRly5cgVhYWEYP348tm7dih49fh6D/eKLL8LHxwezZs3Cjz/+iAkTJmDDhg3w9uaPdx5t6EPAgJnyhGunXm3lCVzhNzyBi5d3iFqiEULY6w4Zt2EymSBJEoxGIwIDA53dHeqM68/c/aFcviNX2+PnIZsuMnqntrYWAQEBAORvs/7+/k7uEXkyW3KNz9wl9+TlDYSMc3YviNwSJ2kjIlIZBj8Rkcow+ImIVIbBT0SkMgx+IiKVYfATEakMg5+ISGUY/EREKsPgJyJSGQY/EZHKMPiJiFSGwU9EpDIMfiIilWHwExGpDIOfiEhlGPxERCrD4CciUhkGPxGRyjD4iYhUhsFPRKQyDH4iIpVh8BMRqYyiwV9VVQW9Xg9JkiBJEvR6Pa5cudLmOhqNpsXlf//3f811xo0bZ/X+7NmzldwVIiKP4aNk43PmzMG5c+eQm5sLAHjggQeg1+vxr3/9q9V1KioqLF7v2LED8+bNw3/9139ZlKelpWHlypXm1926dbNjz4mIPJdiwX/y5Enk5uaisLAQCQkJAIA33ngDSUlJKC0tRWRkZIvrhYaGWrz+8MMPMX78eAwePNiivHv37lZ1iYiofYpd6ikoKIAkSebQB4DExERIkoT8/PwOtXHhwgV8/PHHmDdvntV7mzZtQlBQEEaMGIElS5agurq61Xbq6upgMpksFiIitVLsjN9gMCA4ONiqPDg4GAaDoUNt/OMf/0CPHj3w29/+1qL83nvvxaBBgxAaGorjx48jIyMDR48eRV5eXovtZGZmYsWKFbbvBBGRB7L5jH/58uWt/gB7fSkqKgIg/1DbnBCixfKWvPnmm7j33nvh5+dnUZ6WloaJEyciOjoas2fPxvvvv49du3bhyJEjLbaTkZEBo9FoXsrLy23cayIiz2HzGf/ChQvbHUEzcOBAHDt2DBcuXLB67+LFiwgJCWl3O/v370dpaSm2bt3abt3Y2FhotVqcPn0asbGxVu/rdDrodLp22yEiUgObgz8oKAhBQUHt1ktKSoLRaMTnn3+O0aNHAwAOHjwIo9GI5OTkdtdfv3494uLiMGrUqHbrfvnll2hoaEBYWFj7O0BEpHKK/bg7bNgwTJ48GWlpaSgsLERhYSHS0tIwdepUixE9UVFRyMnJsVjXZDLhvffewx//+Eerdv/zn/9g5cqVKCoqwtmzZ7F9+3bMnDkTMTExGDNmjFK7Q0TkMRS9gWvTpk0YOXIkUlJSkJKSgltuuQVvv/22RZ3S0lIYjUaLsi1btkAIgXvuuceqTV9fX3z66aeYNGkSIiMjsWjRIqSkpGDXrl3w9vZWcneIiDyCRgghnN0JRzOZTJAkCUajEYGBgc7uDnmo2tpaBAQEAABqamrg7+/v5B6RJ7Ml1zhXDxGRyjD4iYhUhsFPRKQyDH4iIpVRdHZOIiJP0tgI7N8PlJUBNTVAQAAwYAAwdizgToMKGfxERO24eBF4801g9Wrg3Dm5TKMBro+J7N8fWLgQuP9+oG9f5/Wzo3iph4ioDVlZwE03AcuW/Rz6wM+hD8jly5bJ9bKyHN9HWzH4iYha8dRTwIIFQEMD0NTUdt2mJrneggXyeq6MwU9E1IKsLODppzu37tNPu/aZP4OfiKiZixeB9PSutZGeLrfjihj8RETNvPmmPIKnKxobgbfesk9/7I3BT0R0g8ZGefROe9f029PUJLfT1T8gSmDwExHdYP9+y9E7XVFeLrfnahj8REQ3KCuzb3uu+KRXBj8R0Q1qauSbs+xBowGqq+3Tlj0x+ImIbhAQYHlzVlcIAfToYZ+27InBT0R0gwED7NteeLh927MHBj8R0Q3GjpXn3rGH8HC5PVfD4CciuoG3tzzhmlcX09HLS27HFWftZPATETVz//1dD2xvb7kdV8TgJyJqpm9f4KWXutbGyy8DQUF26Y7dMfiJiFrw0EPAk092bt0nnwQefNC+/bEnBj8RUStWrgTWrAG02vav+Xt5yfWysuT1XBmDn4ioDQ89BJw/D2RmWo72ufEmr/Bw+f3vvnPtM/3rFA3+v/3tb0hOTkb37t3Rs2fPDq0jhMDy5cvRr18/dOvWDePGjcOXX35pUaeurg4PP/wwgoKC4O/vj7vuugvn7DW5BhFRM337Ao89Bpw9C+zeDWzcKE/AtnGj/PrMGfl9V72m35yiwV9fX4+ZM2fiQRv+BD733HN44YUXsHr1ahw6dAihoaG44447UH3Dfc/p6enIycnBli1bcODAAdTU1GDq1KlodMVp8IjIY3h7A+PGAXq9/E1Ar5dfu+KQzTYJB3jrrbeEJEnt1mtqahKhoaFi1apV5rKrV68KSZLEa6+9JoQQ4sqVK0Kr1YotW7aY65w/f154eXmJ3NzcDvXHaDQKAMJoNNq2I0Q2qKmpEQAEAFFTU+Ps7pCHsyXXXOoa/5kzZ2AwGJCSkmIu0+l0uP3225Gfnw8AOHz4MBoaGizq9OvXD9HR0eY6zdXV1cFkMlksRERq5VLBbzAYAAAhISEW5SEhIeb3DAYDfH190atXr1brNJeZmQlJksxLuCtOnkEex9/fH0IICCHg7+/v7O4Qmdkc/MuXL4dGo2lzKSoq6lKnNM3mRBVCWJU111adjIwMGI1G81LuihNkExE5iI+tKyxcuBCzZ89us87AgQM71ZnQ0FAA8ll9WFiYubyystL8LSA0NBT19fWoqqqyOOuvrKxEcnJyi+3qdDrodLpO9YmIyNPYHPxBQUEIUmjM0qBBgxAaGoq8vDzExMQAkEcG7d27F88++ywAIC4uDlqtFnl5eZg1axYAoKKiAsePH8dzzz2nSL+IiDyJzcFvi7KyMnz//fcoKytDY2MjSkpKAAC/+MUvEBAQAACIiopCZmYm7r77bmg0GqSnp+OZZ57BkCFDMGTIEDzzzDPo3r075syZAwCQJAnz5s3Do48+ij59+qB3795YsmQJRo4ciYkTJyq5O0REHkHR4H/qqafwj3/8w/z6+ln87t27MW7cOABAaWkpjEajuc5jjz2GH3/8EQ899BCqqqqQkJCAnTt3oscNj7F58cUX4ePjg1mzZuHHH3/EhAkTsGHDBni73WBaIiLH0whhr4eMuQ+TyQRJkmA0GhEYGOjs7hARdZktueZSwzmJiEh5DH4iIpVh8BMRqQyDn4hIZRj8REQqo+hwTld1fSATJ2sjIk9xPc86MlBTlcF/fW5/TtZGRJ6muroakiS1WUeV4/ibmprw3XffoUePHu1O/nYjk8mE8PBwlJeXc/x/Mzw2LeNxaR2PTcs6e1yEEKiurka/fv3g1c4DglV5xu/l5YX+Nz4800aBgYH8oLaCx6ZlPC6t47FpWWeOS3tn+tfxx10iIpVh8BMRqQyD3wY6nQ5//etfObd/C3hsWsbj0joem5Y54rio8sddIiI14xk/EZHKMPiJiFSGwU9EpDIMfiIilWHwExGpDIO/HX/729+QnJyM7t27o2fPnh1aRwiB5cuXo1+/fujWrRvGjRuHL7/8UtmOOkFVVRX0ej0kSYIkSdDr9bhy5Uqb69x3333QaDQWS2JiomM6rJCsrCwMGjQIfn5+iIuLw/79+9usv3fvXsTFxcHPzw+DBw/Ga6+95qCeOp4tx2bPnj1Wnw2NRoOvvvrKgT1W3r59+zBt2jT069cPGo0GH3zwQbvr2Pszw+BvR319PWbOnIkHH3yww+s899xzeOGFF7B69WocOnQIoaGhuOOOO8yTw3mKOXPmoKSkBLm5ucjNzUVJSQn0en27602ePBkVFRXmZfv27Q7orTK2bt2K9PR0PPHEEyguLsbYsWMxZcoUlJWVtVj/zJkzuPPOOzF27FgUFxdj2bJlWLRoEbKzsx3cc+XZemyuKy0ttfh8DBkyxEE9doza2lqMGjUKq1ev7lB9RT4zgjrkrbfeEpIktVuvqalJhIaGilWrVpnLrl69KiRJEq+99pqCPXSsEydOCACisLDQXFZQUCAAiK+++qrV9ebOnSumT5/ugB46xujRo8X8+fMtyqKiosTSpUtbrP/YY4+JqKgoi7I//elPIjExUbE+Ooutx2b37t0CgKiqqnJA71wDAJGTk9NmHSU+Mzzjt7MzZ87AYDAgJSXFXKbT6XD77bcjPz/fiT2zr4KCAkiShISEBHNZYmIiJElqdz/37NmD4OBgDB06FGlpaaisrFS6u4qor6/H4cOHLf6tASAlJaXVY1BQUGBVf9KkSSgqKkJDQ4NifXW0zhyb62JiYhAWFoYJEyZg9+7dSnbTLSjxmWHw25nBYAAAhISEWJSHhISY3/MEBoMBwcHBVuXBwcFt7ueUKVOwadMmfPbZZ3j++edx6NAh/PrXv0ZdXZ2S3VXEpUuX0NjYaNO/tcFgaLH+tWvXcOnSJcX66midOTZhYWFYt24dsrOzsW3bNkRGRmLChAnYt2+fI7rsspT4zKhyWubly5djxYoVbdY5dOgQ4uPjO72N5vP8CyFsmvvfWTp6bADrfQTa38/U1FTz/4+OjkZ8fDwiIiLw8ccf47e//W0ne+1ctv5bt1S/pXJPYMuxiYyMRGRkpPl1UlISysvL8fe//x233Xabov10dfb+zKgy+BcuXIjZs2e3WWfgwIGdajs0NBSA/Fc6LCzMXF5ZWWn1V9sVdfTYHDt2DBcuXLB67+LFizbtZ1hYGCIiInD69Gmb++psQUFB8Pb2tjqDbevfOjQ0tMX6Pj4+6NOnj2J9dbTOHJuWJCYm4p133rF399yKEp8ZVQZ/UFAQgoKCFGl70KBBCA0NRV5eHmJiYgDI1zv37t2LZ599VpFt2lNHj01SUhKMRiM+//xzjB49GgBw8OBBGI1GJCcnd3h7ly9fRnl5ucUfSXfh6+uLuLg45OXl4e677zaX5+XlYfr06S2uk5SUhH/9618WZTt37kR8fDy0Wq2i/XWkzhyblhQXF7vlZ8OeFPnMdPpnYZX49ttvRXFxsVixYoUICAgQxcXFori4WFRXV5vrREZGim3btplfr1q1SkiSJLZt2ya++OILcc8994iwsDBhMpmcsQuKmTx5srjllltEQUGBKCgoECNHjhRTp061qHPjsamurhaPPvqoyM/PF2fOnBG7d+8WSUlJ4qabbnLbY7Nlyxah1WrF+vXrxYkTJ0R6errw9/cXZ8+eFUIIsXTpUqHX6831v/nmG9G9e3fxyCOPiBMnToj169cLrVYr3n//fWftgmJsPTYvvviiyMnJEadOnRLHjx8XS5cuFQBEdna2s3ZBEdXV1eYcASBeeOEFUVxcLL799lshhGM+Mwz+dsydO1cAsFp2795trgNAvPXWW+bXTU1N4q9//asIDQ0VOp1O3HbbbeKLL75wfOcVdvnyZXHvvfeKHj16iB49eoh7773Xaijejcfmhx9+ECkpKaJv375Cq9WKAQMGiLlz54qysjLHd96O1qxZIyIiIoSvr6+IjY0Ve/fuNb83d+5ccfvtt1vU37Nnj4iJiRG+vr5i4MCBYu3atQ7usePYcmyeffZZcfPNNws/Pz/Rq1cv8atf/Up8/PHHTui1sq4PW22+zJ07VwjhmM8M5+MnIlIZDuckIlIZBj8Rkcow+ImIVIbBT0SkMgx+IiKVYfATEakMg5+ISGUY/EREKsPgJyJSGQY/EZHKMPiJiFTm/wPjEsmJSNfOvQAAAABJRU5ErkJggg==\n" + "image/png": "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", + "text/plain": [ + "
" + ] }, "metadata": {}, "output_type": "display_data" @@ -294,31 +367,38 @@ "\n", "test_indx = np.where(y_test == SAMPLE_LABEL)[0][N]\n", "sample_record = x_test[test_indx]\n", - "point2rank = settree.get_item2rank_from_tree(set_tree_model, settree.SetDataset(records=[sample_record], is_init=True))\n", + "point2rank = settree.get_item2rank_from_tree(\n", + " set_tree_model, settree.SetDataset(records=[sample_record], is_init=True)\n", + ")\n", "\n", - "min_val = SCALE * 2**(-max(list(point2rank.values())))\n", - "fig=plt.figure(figsize=(4,4), dpi= 100, facecolor='w', edgecolor='k')\n", + "min_val = SCALE * 2 ** (-max(list(point2rank.values())))\n", + "fig = plt.figure(figsize=(4, 4), dpi=100, facecolor=\"w\", edgecolor=\"k\")\n", "for i, point in enumerate(sample_record):\n", " if i in point2rank:\n", - " plt.scatter(point[0], point[1], s=SCALE * 2**(-point2rank[i]), color='orange')\n", + " plt.scatter(point[0], point[1], s=SCALE * 2 ** (-point2rank[i]), color=\"orange\")\n", " else:\n", - " plt.scatter(point[0], point[1], s=min_val, color='blue')\n", - "plt.hlines(0, -1, 1, colors='black')\n", - "plt.vlines(0, -1, 1, colors='black')\n", + " plt.scatter(point[0], point[1], s=min_val, color=\"blue\")\n", + "plt.hlines(0, -1, 1, colors=\"black\")\n", + "plt.vlines(0, -1, 1, colors=\"black\")\n", "plt.show()\n", "\n", - "print('This is a visualization of a sample test set of points in 2D.\\nEach circle represents a point from the set of points and '\n", - " 'it\\'s scale is proportional to its importance rank.')\n", - "print('Legend:\\nOrange points: appear in the model\\'s attention-sets\\nBlue points: don\\'t appear in the model\\'s attention-sets\\n'\n", - " 'The scale of the points is proportional to their relative importance, where larger circle means the point'\n", - " ' is more important in the decision process of the model.')" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + "print(\n", + " \"This is a visualization of a sample test set of points in 2D.\\nEach circle represents a point from the set of points and \"\n", + " \"it's scale is proportional to its importance rank.\"\n", + ")\n", + "print(\n", + " \"Legend:\\nOrange points: appear in the model's attention-sets\\nBlue points: don't appear in the model's attention-sets\\n\"\n", + " \"The scale of the points is proportional to their relative importance, where larger circle means the point\"\n", + " \" is more important in the decision process of the model.\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -330,16 +410,16 @@ "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" + "pygments_lexer": "ipython3", + "version": "3.10.16" } }, "nbformat": 4, "nbformat_minor": 0 -} \ No newline at end of file +} diff --git a/settree/__pycache__/__init__.cpython-310.pyc b/settree/__pycache__/__init__.cpython-310.pyc deleted file mode 100644 index 320d792c71fdcbcdc0aefe5663d6ad7bbac019fc..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 330 zcmY+8v2MaJ5Qgo9RuLjq#n5+1CGf%sh$rX*lRH=jA7IIeqxg{Ek@^&U57thd*_gOc zN1|u>|MZ{zpY8L!&!|2xVfRh^Y0YCLWEQl;gv2nz9b5g8M<$VJWH0iGm@HY5{!)?^ z{LJ4!if8#60I>lW6P3}Z|99FWnRj<~YX~S`I@?B?tsHjV*U)IRz6$cK>%p;h+E#jP zoyF4^Yf^gsF4vjZ`w~Q{5yi$E5Mi_T-iZoFlnJF4OTf{JejwsBp_HvGDmAW8=WJSf Tb8s-{G%*ZFGM4fCN(TG}0PtG% diff --git a/settree/__pycache__/explainability.cpython-310.pyc b/settree/__pycache__/explainability.cpython-310.pyc deleted file mode 100644 index ffeaad4e527ad26bd7da621370b9665135bd2e9c..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 5773 zcmb_g-EZ8+5#J?wB=7WPE2?58PQx^9;!AAHaS|j=6*s7zG$~rQjnl77gbQ^`ofzMd z=Os_61RhX84w|6IL(!MMReIF_(C7XUd~ILyET9hsiuO0dJKl%n+5vh3N4v|N+1c5d z-^?;zSf~m7K0Ha^jmBV%9g>x zw-tA{!pKj?uWg+lJ>E+-0TEVy_#F84uZUv z1nF?kxm_GWE>&KMVihF$il3%IMAmN7yl&8q^ro%JZa((Cw1*I&OAq*p$`>gy0nTC4}Z#T%UKR@e#Cy{o;w z{JA#PJ8SjkwvM~34RKmnFnJxssxHfA=^C!2zj14|9HUX=ccU4uTt&|^^kZPo9e+2_ zemlsmPMD-qQA`m`4@GwJPmm0;3(tR68OppbQjyBkKop2W>qs2P1F0-k`O-L$x6u1S z{9bHJWn-p`8I>bJqXRR#2`Lmm+lYQ1SFWnw7vQaZX%4JVd;!UsMN&4`fC`m?jR=}s z6Q2tp!MknzFZvs0YfT*5S>;Gdaqo3CPr7|xt~k_VVO9^|#v-)XD=-bZn4}}nkaSu8 z5}0%Vl;c=^%)jx#Sl#ubu+#AoC4Q2G?PwIO+K=vfy+C`tIE>QEUgU?nf#>&nI!4%( z5i>MC*t>xrdCYYweDRaIx9(l{(%ADmokB$7eHx}a9I1%Ypuh%}6Zg|zKOMt|3R@`z z>LBgws9?U%g1cBgjN0Ymbl~HO6yH38&}O9*FO0CORuCz0d9ha5&JZkE-iOyNdoNw~ zuHv_O*}Fsnx-mo!Q38!nCuspE4QFVn7RC4IN1jD~drFjLCDTYp-0AbCmL(L_8)L0X<+8>*mHknu0zWM^% z$Ffo5ZSWC|x1o{1#T$57|0jfAL3>IAvLo${Y zXdF$N>KJDn0kVCqeIPCc#>jB;ZZ2mx! z6-s_dZUa<+5Ot?9SLDbh#gv|hq}(o`DX+1=vxnz0(T~%LV|Dll*fh^P+ylXyd=td2 zJtr?n*IbpBJSXSzw}K=`Des$xW$HIErA2-{`jI}gB)Mer50IH^)EJmWR?AGzQx?^x zs0^PrdA)|0B-yvm|~x;yKDbrg4h0{rV%=qyXm+^z+0;wwPL<21c-| z>okQIe1TeLAP$3Cn_j*Ia!L|K1!b`rcpMtEh|^)FS}HOXAu?yoV%-urcSwo{QlHbL zU*R<`qLsV2x-fG-*NUSSkOeoHAg}BPI!-9VwoqOq`W51Ol?WBKn(}vjUem2Mud1Y3 zOw)_>ZV9BZ1S|?w6Xke;J^C#gt+B`Pa*gu^eicqw3_$~2tJot8EXtXoS?E_S-L=@= z_h~Ukr^g>~a6iQi#-wF?(WFv))xcv~#w0FTQvVS1rZ9TcGL@pCBz# zY;P{%&TJf_!Up=FI;Fewp?xF>hfwJ{sRM*TwO!WQQW?Miu3QJG&~d95X*I0@sel=T zR6}I4()rOU;K%&Ur;))-+{YJizm^BMi37P7S%>p@>mw=i*5*0WWOl;{sRoHB6_Ps7Nx+q~D!zGJ=;~Jo;sj z{mW;ZM|&$s(ubnUNVhu>lhE06T~Z<4)UXmkGNdr)#YdPY!$KF1w$khONC-(>el(Rz zOyLE-BF_3!J_o%++#6$^G;?RW!W zcThzhHYhhM{LkpC5GZ{WLNrvf>QpqmbMIP)8%w?R;AqhAVw)45#03~zckEc?>3^VU z?CCEjA$^%C&IV1CWPa2Nnh8)dXtuY`a(F(zW66aWUmkLcTnBMRrI4npMd9H;M(cP% z|AYo^6Cu=Pr_+RN`e#JATKhS*-Y3G|MUl;g7Q5JSEjQX*dX5Gch>V@=7w9%@_AaHr zhfXegxzvRT#%}fWLw0!|oF`QKS%xJ?{&m4vMEe|n{D6=B+p_e>nDRg$)4#&o|HtY4 z1#*hhd1a_Ke`31+sK-Nbuc=nz!_mK?R)Yw`4jqdAkjO_wW&+TA*ouE9CZEU_2(XM% z#weDeTTR`?cdmZ7w^tnW_$Vn!ag?$dnSH`lSr5hlccUAtekY(WBO;-U?24LMd%=Ci KeagKszw%$WnHiG+ diff --git a/settree/__pycache__/gbest.cpython-310.pyc b/settree/__pycache__/gbest.cpython-310.pyc deleted file mode 100644 index 355c8da2c0d1f0f10b3ef3f25bda7a93166c04ec..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 30620 zcmeHwd5|2}d0%%=&rHwk?CgofE-p4fg1}P10-zbn0ttyEcnGqzWJvJP$jt2Qbnosg z=2*V&CAh;`i593MNl})p99D{P7LIJ0&TYp@oGMpED*ur3PRH}HGyPlM&NUoeb6;6?v0 zgNyrd1dFC&c!p=zjgmf_B~z}gk|pOW=b=1JyPG-*k9T&`O*4;#=+7-$&aDlq0%AAkJoQ$ zxFuKe6ZKmghf9YgKUu%+g5mA)rd}~hw|jfNX`GKdYIrl=zE=!y-({QEa0j-X0BArPeHmeh`*}wR$a_wW9Px z7e4s-!%^m;$DVrd{STwkkZ!r6RAmE~g=(w5-e{Ivs(ji*{Skc|{aLDoq3=bLWnB{J zS6=dK%PS!sJfMray{=ZmS_|*4sAWG4aKAX;3LlXs9#%>Qxv%W`YsyDWzr5B8YJ8wl zFXIW;&pzpg7nIL&zXLC^=v#i%Ih|h-a(rVRDHp3HZ z0ER~8BrX=aVOw7t!k6+D252dqUV)_Jc#5jUtk~S&+ZmP3x+CP2GW_Y8pxR z$2EsA24G(mWy$CEM`%E>9Qx(U+FNLf7aRhfF@eR@}&?^(P%JNdcUE9lhX)p7NSxS3Z z&p}DX%b|xu(a6)jT5JXW6aHFDg}$onMVIwO;J%>mxmb|3_0SDlZV-C7n9IBPJzi0j zhL2|gxjd2lDyP2o}B zb6d@-k5=4@dugRs_mjqsU0U&*ZjHSrF1U@B*X?qdc{I`XRcm(cUbnX7)`?om4Oc2n z_cTs;RF5lZukV#hE#>-^>WaI*<^kXIfU8`B?vHsM0tnz^sn!dAd+eoFYq4CzRkulln&Ry?jn1&Rcl5=F;te98e#7O_@c-bqCDnAh4SL4I5?M}U?*55g-KUu zm>XX4E1s{w>-@SGsCS}lJA9)XwU9Rg7m1U1%)FI1^`Dc=~;f&Q`^wCm{Ue7#d(%8q|*{ansZ1mN0-UXUcwn&@%kJ)_t z*PaqWl%SD&sTQudP5)Al{%Imbuzqu>wW@I=0dYP_lp1I>x%<5%qCme8tT*n%GPRwT zPMw*%YiYBwj4S-+?@x+?SsU;QY$^@)p;6>?aV07aqQn~{xbw3`bqZ$zMfH=&MWfvZ zfNBlD8J5*8tU$n4?_!1YQoU6PqYUUf#0GU2Z~O!kLTFUzdwE%?^=wvyyt%6GWsQv2 zD&rmMRz7n-lLwek9O>iU8O=2=pj0?=K9{$~&2b18dDFT^n0KsE;K8Wrm=^w=@4EK5 zWq;Kvw5PVizitYmvdrXcHUh(Fg722gQNBzzw+=zJ92Lst=hrLs_)fN5_FC0)SrJO^Bdq6Wl_ zwCCW+dHL50-jG*##ky=l>=^cn5I?fsh&PI};N=HGf9Hn9J zq^KKashCT9r@VvSAqX=g-f8a^&qZ$3yVX04=f}L;yxVae_l|gX;5^|^dPluuuNZju z9&gsW6J=B0JG|pK@AXc2b2v|XX>hB%qJ7jV9#s{u2FQ7^)e0bc#6orZ9SIT9su2)bE9l_~*Oc0H&2R;~Bh;C3PplCd`qo`bMHP)hufokP?tx*fX6)G=4MW}=; zQMyuJTd4>rN5w|v#WFg!RBl384x;>}iW1#Oh0$oUO!UJYs4F6>r@~gfN@;^X3a%7b z6M^TKanVVE)l&g5%8-ANS{)l{3UtpX+fW&da=HWMp`7VBNVA11aL>7LGBq*I+~qW3&5(5JbbWE?6Zff*}}U9eg~>RTamMsa8*5Sn5e8#9j3ilc$+H zgCrVjK(Ns4HqZ)UG}>Rpxs9e-qP4v85+R05$U8!-q5>CT$*+X#P-&waub|aorS|YS z$OY@@%m&J;$_J!H;{(M4F6eM|t*jT8s2vsh)m%lLPD`2}WUj>ILri`e$?O54XX>x8 z;5jBjAj{0%aPVq@RVqvtnRrZmCQD3�W7a$xx*5YK6%Sd$Wyh3bhLHRB#WHOdf&( z|E&1jpVN1Cq!1u(yFL~tQgoH_L-#j|%m@yVC+-KtcQUI+Xs$wP2%PsBAEiu@qT;)m zgV=0eF*frMg`L%msK?8jL&#^<5j^1l!iO$e$mKlqRfyoYlLwaM0qtqedez=6ghL*% z2J!*q0UUtyRY3VFV0;y@y$T#y1qQ4F+E@2VJ)z;dEn!!EgA}JMN@M%E5sObTM?$SOKWkuY$ z=kC&2(_e;?3bMQ$GZ;0=hrjoJ6UiX#2b`N|48|g{V)G z4I*O^!+a3fz!;H$rJ0OiU57Laf zjZZnS4(YMkOHlu}5e-N|7_nCN{w}HqBx9qgj0r&|Qv~gpgrH?X#jtG~W!wL7=j-|6 zsFhDmLJ-5S#J+70sfVzhfZ<@l%Uv{sl_u~U@20L(O!_zC4XvB%Qz0qXNH})cQZFGlyhTbjLd~Z+5#O7@ zy^pihD}b_o3M~!GC{T-|&~!0=kel zG5=P$#~Tf&!o8r2#_9}PzHDt;%_^^OF3U4xc;@%7Q*&NwjHAXMBsCyW^tE*sJr^3C z%)un)z))X{-*F=DhAx{+)@AF{W^fN`K{8rBr2FL!ff7%(?>|RFxcdT( zFdofrAj4~k-3}z$b79bcMEi85zV3^qnYJ=eZvDudD?)2~uHPK%Hr8R4S@d0)*i0k574&a9)+$LY#x=41nX;JoAe-BJD&eStpa=C z8$c(J>nOdlz6kNzf${@s8PuVH^7fI$E|R`R;NJ46;s9+5ZAojUHGo;x+$_qxcgtixS#6di;; z@OenxkUy92YCm(nDa*qdU-w0;=ob>tU_R&E2kQ0KCD=ltg^){1)xfPN-#yA!j~;iA zvdN>;M>czOu6?w>nZfQIcbkb6`ds_mcIn>@R$A+I&-Gzs!1`jT=UYvmZDT%Yl!YaA z4*inlUW3EN_hwx!V&{91qktu26CWT)ZMGUPAwCqZdujB+Z6#5@Csk;1Q?V@(Mk$s?`C3qG!m0(R zQM8tpCbjE@%hz)LPE5)=*oGhP#X5=q`7+sa(ar}Vi1yBX~V zMa#ZsS@zYuP5VN@nojB!U|X2dwguC^I-Tx4m$#-(+TEsMiRHuYAK-(KsUb4P686s{L!ZeiXfrqsxgdBP z^hNMF&jcMM4Fi9>f;we8%tER*R-rkrrdRVz7OsaNNt0t0kf-#TTQZR=#-$=ngO@@j z>E7?VD>61Xa7sN&K3krkH&P7PlAF!6A2<)Fh)vcaic)+HeW8>U;2^zkT1Jg;z&%hQ zR<2h?rJHl-RSiQFI&|TY*-4e<6{XF{F01CUs7g^MqFQolmnw| z!5jquIoGDFJdCl-iK*6V-*Ka5m)=Wr*I#t1o?LVf>{u14TzOQJU@elRwB??SdszI1 zv|OcebpTfxxys-QBG5usuCll~jw{D=uzWaXbL~^-$Z15E&?^Q`N5!HmZ|~4BWF{f- z6FPMw>n77FGx_&;;}?+3!(EwOsRXdKC`}T!c z?T@~XlRt%G;x250eFrB0TKnjYCcUqnfmtV4B#mQdf?q@e1fkeR!H=^Jvoug+DuiQD zm$76i7B@qCi-MxAomqv4_X-isjLTu(mTN&sqF8Wu2w~9Ux;>WU&c(Hd;@U%A=1C*8 zX%Yvb+AxEsWzD6W_hz&0dmr#Tx8gxSfeKNni=vot-C$wCg(XbYw6X{!bh!{UNE?38 zl~0aQWq_AStm5$e)*%Ui#q}C-k9h8VNVHK}){4tb8}cnU>vf!F?q`{h@JAWls9>q6 zd40I{OROc$aFzuV0ZT-<(sU5h6~cw4uoNN9T_@ewI9$TEgZRU zNdv=^MW(~IuP?_%w57B}A&x;5v1mI-YdTFnyCUg$%!B({TIi0dV6{)&XjDDHBA$;O zmL`rz26iWg2x69k)Q;JIHAU~$s)axd+Dc(V^^A36GY1!Jx?^gs^5^Hle|OUf1Xhf7JRY-0qvhA0EDBULwI8-`X$4Z4~n} z z+PaIK?3Mhc*sPPcq@dqXFFxqOdM2?UM)@GF5BFUk4#C#EVQO$v_x8Sfw~GZFeFxYV z=N|z=iZLqZHv?FY%XfJf5Z+_Mir>^q+6wg;abXeYMKGx>Y5fdXQc?8{2r8|$HQp6V zG7+XxL%0Dgwc(=(N8UP;=dAtt)q}w&vGs z&_;CsNC8+ud%hCgw*72B{XGln-&I=UI1o?+pB^(BAQK7_l; zJ$pf|`$F|+?@+_&w;Ez1)9|;M8)ZTtSu|7@oet7sc|)7uGrae6Of+*@Z`PhiYr<}5 zxKh8$LYA8)d-d&6QLKpaPYK*aBbtUigV?W zaCeX?LXUxnYZY)7zpDl9C=G+(HS6U<#T?4_%{rXSI5(%(9a-F^C`1^=msnUmcuH6sQcTC zOdf~ui2s4^OZX0iG*X~d@@WHJ1jaE8L7YMa14Zb~;V|vlA#H-N8Ft5-VEdsylJ8{ai0ON-woQGX749iubGp2jJxYS%ki4934Q_N-9x{kj5jZ% z{UUraC<&)7<87OJI(yU?X*2Y)TQi-BE1>t}L7fTD-V55xS&^sy7;njf|BOR(b@1F2 z=4?OxkgEp}Ych5bks<28;d-ny5gzP}F<1&dmGjqc`T6tRcg#BN@16ORoVG{6Eh zP)Hksn1Wtt$0MX$3``TCcVV-lADE)BR7a4J#LBwI5Oj20GXqy*l4g@g4jTR7W1&Mr zi_{AXJ*;0?=)LO+A1ZBAH>`~vB*Gfo*u#6lJP^$4dbOn`h}%o5)z~4rCvWv&+9IhP zU_-rkX$`Ca$k*yKtz_yQNaox79v*lG(tkYomo{e)Y2qvDuaH$6^deBt^Xb3ITS=!lB4Fqi`^ zACyL7_9fnHSY)?T*WW-tew);l>|4jTOww@zx~VBFY*o}|U_&C6&D##>sD0H=gRaWi z5$A3GY9YRpzc!u$eFl{-f(nBIo6dOttDv}edNMr@(*#N^>#I)j#^#8`6%*_T*F<+U zT_r0Hjmz-uN~k8Js0l<{BSfz*QA55bj5OBg)M?b-HvDJ=Ubl;WPruv;XXWKOL?Tvc!H9USc9V{Noi|=U^13#+DnT@g4BMA)tRKroOgh8`bWs(NS#TOs z2hT(Mrq$I>XRc$F_x-otw4Q@8C@mfv$D?=Xm+=m<(E{)IUer2~Gon zFrOO7;BRb%>XRM^#%beuT0N^cf`^d&%#M-5^ap^J2#T@I6trB9UdL}Ds^KzxVu)?X zTrr*oAATOTUD_rp_W(oAX>PY094;(8yRabc6Jd~y=>a=!HG53v0!X5Zko6S=htrsQ z30GPOr3Q4YHs{ZQGvYZo&7m=g$pCTX+z=uvk{|YQQPHe=tho1<#}ofr!u)m#YP~5MO@VaG&DFmB@~Ql z^^N0{5-!zXHv{%nTTQSFM%9O{q7F6ThRmqsUKN63GNK$m_azUEOULg;}4yDT`ZQ8+JQH0<) z!Wo?JRgeippR$&&W1&3LfyqnmXT->sg3z9&DQrK@D%naJL`Bdi4!t|Eh$;8nI7H7y zL>~6V(0AA;gtPt%L|nE#}vHcH0N_ozbrj<_+xS*7=qpvzxDm$bZntjP#v z?ws&?KrM!4gn)Z+HW7wmU{2x}-9^<}r$oNEfqp?w?)JiU&%<2}e{ntNVg1Es>k{Bl zzwc3S@MH+pc*6@p_8FL$K>VXl;_lz@!8>hewi$5ZMH9Ib*1za03HFEJ8O7 z(mmFIoa}0YNbK64opYb`ds}_ZEUh=IXBOhX+Bx`hVH^_TjMcdA*CZX=PJ-fo^)(liL9K=GfKGR*a<}`(dke$~<3dAtUI*UF`f%DA z3C0A z6dwVRZ^pY=NHh9Lex*6kUP3>`huE8y0W1Vk2o^qxv$!UffqBmriFGgliNv`BCd$s2XPt-io_UW1E4i`N*zNh63exXj70hS>KGJd%Xcr3n$UEXK?ph> z&@ilAb*%Qe8_<&77HaN3t&qJNnQq&Pa^=AI@Uq_cNCjnl4#&rFA$-&Pj3r~T9>I8W z2{z(le=G}k11S381UdsF<98dZ`aQmeqogjGF)S;8EptsMGdGA;UaSRT-<@@|{3`^y zC+muRkw(DCZupfZ!WGsVF|96PcLcW6ApoW_Qjdp<>B%N zIDmcVhBHRn7Nq0UIHCkBL=j|c=zrIWR{P}lxMWpATlbekh7)uSN3ewi6d&sW6BYc% zTksq6dddjJwE%x2+#?~j=pa-!h1G+}Yt?}hCADee9s+i;o-ljS$BFBFTrVBM86v{x zK5X9^ig&$EaHlKurg*RGY5}eoXKPr@RtRLDF|MQ_jNbkHoEZL#?_vNhcMfDKE0~l= z5Ryp)Ha%ewBDojK=e|V^EZYRpHzy)4tC|P8b3>7*yJfh$putXJB+W=@Cn!>vNRW8! z(%VE$8z32gt@=8wMP83u=#9$BTd&j2ZMAlxln7ZBGAa9>dmMpVE8;zQyLNP-dr7C= zT{?O*Vvpcg1rwXsAH=%bXh@3te-O(B*~(x*VpfqQridPk;5!#WxI&j{?=t!1ahk8g zUr8HZZ{>OuKi_`wDRInTC-}MwC=&8V0vGAZh9$7?C zkP&L9Nl*U#MO4@B4GN~%tcbYqa`r}(B;@mOSEtTjEcq$v8Uhi{b>3|K;bRa_rk?BhRpv)KqC_89-n1c0-L!wO#W`iY2^Pu-M z98fwrq@CrG-%FP^oFdrvAm1;@cCye2ph-yNkqvFZ#`8uy(Au>J&wYvo&847j2)4c|SUQ?cpKVl;0U77{N z;M$|eJ*iVuWNa<3Z$K{X83yPRq2o55GI&8`()GUBN9ATRfbG=c%eeg-sl_-l&R8K0 zCo4!}(LBU&ZrE-221HT!BsD)j4^Bk00YZ5$%+81pT>TWT)%%%z07+D=#CtY1e^h&T zXO79on268)^UVD+lV4%-t4w4vXn$99a;SgHMC>Lskg6{si5!@}!1ocL`5#&KUzv~y zDvqshyuwJ1uxuU)y;8wXBH=Duh|D0985^9!EPl&5=G^N%=In8Xok_%g*;XZgE&nv4 zaYvl2HIp};Tbrway(w=m&W<KGA?X|Rdv7Z5T zRXV5O+1Xzs3pYOte$}y%raCF42nj<9o`n>;3es%HL5l5%NOPS$QfxUyI@Bp3Ep&#F z4tI)3i=7dqBb{-iqa7P5w$>trdmbsa)*{8$TBLhAqe$V*M!L7N7wL3o8tF`D2I;=e zzR#MUGWoG6Eaidu?GySI=Tc@+7DHxG=2HB&AH<~eZ8t6PGTGFsx0@2zUdM(I#2LYI zPdvx2U!hpsfuIeXBwIRG`Ym?wtt~b}vHuz0+#?6<-oGIYq`Xxa206I4RB@w!UT;t*`3C`D%N% zyI=i!DA(H~OJMB&Y-H#aI)@`5iXd_k@%!AlM6Z=ZFhV$EyZm+{e-fD*UND4L= z!52va;p)(~fq)?hYqdrL*4vmZiQ!zn0n`mQJ5H?eCLV5EfR0 zjdczh#u$dGBfkGBbDu#H4Od(1u(UwUq5&;(z=EYjM{?cGdX%KQc65e9iHpBfq&8A( zba`eUn-C5`{_$T362ca-ZE>6)bJI#8x(Hhr+sALdbk`B8xsp6&^$3GKzaz|e;y{*f zlZ?XWW?f;#3ypbJC5MO3#r`&mE!dQftu#}f_H;&MtksG#KLsqS{vxk4maDIbFD#agYN|B-yCQyF^Ov( z;X{cH=eZtWYtwd0yyOy_z;D{&6K1=)(xb61s+7B^!u^V(9_Poo(+c=}*G=(xMEw)n{* zk?ypVYlnz$e0_T?2*N}m?82H^u2&X)M7;|5N+L2WmDxQ6R|lE=Fh0D2J#6h`HwD>v z2sb6Y^)E)L|oCY6G7T=if(p zWpCW7uR?6RN8Pkm%r8;(`}_{1f#|$gS*#&&hZ}LtVM(&NB4^Lek`gvVUZ7Xn=_X88jz z-W`D?5UO9>8yMR6NvQkw9h{o8N7Qa}BT?$`>jQ9SGkx4|dKc~|wG>S+QE_lZv}tpf zfa%^xy5-?}iQSWEAU|%wnZbPy1?YKQ9S!WjH43j);u2%v z%!vnVHx@QIC|hcbx=4&-P^|T`HB#g*X2dQHaI?R3Q*O3fyVx!MKD65Br~foBCz$k{ z^WsMrRrSw!gDR!E%0y@db_N+LgCrV`cfeQudL6sqdvxOeu(GJ5Zy@(uK(&DGHtlqm z??RF(Yt}5OWsZNtIp7qWy>P&f!vVj4bdTb*Bmx7!gYi|+M&>Ph*R^jwk56A0yz$re zi+`P;zt;YB{a<&o@dvOSxy%2%Q#$h4R*rN^yy@5RpnLl}`}_Rp2mgZn z=sqSyhbV|g{pNG4vFU%~vFm&=`iHdUN^o0HT@ajj{RvX5CNhPg9WHd)vH2F<=v@~& z-RNCcde@y!m-?{twd+~65)ABl(m{ceAM*s#b2wzXLsz!gGU<7R6#whMwZ~)oDn8qb zEob6$)d8+=E8Zf!zghWRf50G7JhyN3`F`435syZ?qrT4Mn@oBfNnn+X> zAH4|$pNj?69pWj$r-~zRkqj6`v)q?MK#C2Z^1ZCed-1~S1Jv81=G&qs{v%u0d$7AL zC&3_wU498*r@?#|iBd$hUn7`Pi=v?uMv&{8KYGM+P+r^t^9gUi6RncBZy_#Df3JZ5 zeNJyDhYi!#Iefqdm;iEWRXz^re>;-&%+WQ~r25A5A!?#6w@vu{T2}Q-&t{+#KY%)o9M-Gyu#={C@}3KH{&EaYI^9+XkR^bi*Hi+ zx2FA!tfyMxf1Dbxupfs}t*csLXG^SFWf0?nZ)Ta}-*jf2vB`18`m^s)|A~#>#iYaJ z=aH1Ay1RP2ar1NKMQmYJ7H&p`?oBDe3k_v6Awf`_1N>tM{oi)x*75!CKt8uyx~*H* zYjQdMzYFE=f0|GvQ1Y*#N_)H2<%_@ghdXt>xme}-g7NNMhB(d1jv~ED^9oDZlH3uTDEo(Vl$N_=x@+f86-U)O-Ig Dwmj-Z diff --git a/settree/__pycache__/gbest_losses.cpython-310.pyc b/settree/__pycache__/gbest_losses.cpython-310.pyc deleted file mode 100644 index 5cb200e53c1848dd3f29207f5f6f8e139d929d6a..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 27503 zcmeHwTW}m#dfxP9E*K0z5QIpO)TJd-yBvuGMM*2|a&;j^NqZ56wMFj5h_!>pbORh> zFaw<)kizURE-&R>)!KBjwv$vg8=HtX7sgKFBo(KU*h#9A%0tR|aUPQD%7b05ib_>U zRmyxwY?sA+-+#J$W_o5oU{_33wuaRQr%#_geJ=m~|IcA_VxpMA?=RdAR{m%sllcSQ z^#1a=IfE-WYGg8AW+CGlo>@26%mve6-a_75uq1ES?S(ALvi00rej&eBSSUz&u0FO_ zTqsIDUmss9EtDi*s86g-E=)>(4Ea3^dyLHIGG5Uee<$OO`=(dAVJ%FdWWt+7$)rCe zW%j~el2Z`<9@Fwo^z58*0zueE_{sHen+&?Jy2mNV(pFiW@=h^;2@1gJK77ls# zg-qFLFPv`%fgd<-!*i-lrTk#M+3;$O702_J-Hm$7sWob?np=1LpjBIQTTK->%T491 zDA%j`jh3_2Y~o2{rRe$9T2N~?oR;$aV6ON+*QRWQ`Li#bd);rj$WFRTK})&S)|%hC z)b!@dW|;NgUaz&nv3heQ*jU32s*PQI8 zmJ#lGd1GyD^IY8xg4%M;S16j4qBr~%3?pc&Dm&2ovvG3mVFOTWnAC@@1uWmG|EliC28sPG5Gy~3(8~9F@-Iw{i;;LG6BXH_?!C8*pz$`lW z{g|s<%$TnNxjm8m8VW?*=aSMB_Ch)Hmbh&+2qrwWJo==30_OJhO0JpXRu;bmvhp6XK^tZ z$l{{BzXtHDozPPxY4VmH$*WZp>lpu-=XDNOKtji+TBlb4!n{z(9M>twa*`6q+wrDn5!ve7!Rjx}0yPhRj_CoTfAPX<0_ z5Pw&efD#q17p8W7Gb~gp#Fa|rVKjJ~T`3yv`+5OjF0N4pAS6HLc}d4}$h0!oGv74I zMtkn%2nQDz*)pajK|-0JK3JJ^FvflJ&DIyz*6aQn@Wl6CQK|{VP*Z4C#;KEixD6+z zvq^(TQ5LY%IkSDZuhaeY=Bvb__{V(Q_uIVj48XnVx+px?Tw4d2IW2$?d#V?WAee$@ z79}8kA_1aJfMUaQm2x);#K9$Z-FJ>PDuK%Z2FJ@uO*U{*ZQwKRl3(xnkaDk9)|Kzo z1pkO2+coICmTD3mM+;7G3&(16{+x5gt#1I|mQ{02hDUgC>b`r$56azP>W)U_>n`6e zACC#{^YhJy-#4@iGGIrO!Jg60%WB@TWqCtmGu)FJZ#dD{g){^JV5@GuUa1_z!U@3I znE`;+HL6y;;`kdc`fJFmgGkEe@1IXCc*9=-eqZquKGB_)d$Rgis+Ox({MJVT1_-YJ z`@U5a81Wl{zqVBO9T$uh1YKyL1N3%IIHwjDyOY$nw(2lv`hFx~b`_%x(kr<)p6*J_ zmOsTe9zxQ7un(CBD!%uojW^QIW=9Y1-*eTvL zwldeoJ)<+;$?wTLmuZvcscXt%(YS{bJKn|gJ-3V*|9oyC+nG>`7>r`Y}q`R z>EyN`ZN0S*?_|H7S%zc=)>~-hTKRV~mkZaya9;*P#djup-^*t3W}!22d8|_;lfGfj zWS&Lmp38V9h)&*p8-V%cb?6Gd)9wvNFp$GT0T5?vG?mj5s4EdOwFaseznzwt*_mM7CDW|vhTJwAVm!lL`<*i z2vP1fxJydC4X*k@&D+3|ZBM8Ndxfsbi&*5101ebPW%XicPpD82bxzUByL|G);SE1J zUFaj|cj@!sG-(#XUifbHlA{~zu4B~rnj5U{x@rBcBkyXda3k=&ZreaT)qt8%0nPK# z`f|PLwz^F3iMdk=>JWI(N7ccAQ9Zb%YK_$hBbyB^z)}j0MV;72za3MGL`;IXOXW!8Z_n`Fzk}a=pIk>_imZ~-RxpcXCDX~gS-56^ z$U!(gn7M3x*L(}4OQ<V-NR z=a*(P?N9en`r#xdn0v0?1WylMKqhWPNI$>hzFh;#hgo-hy}lXR)h5JRv#}oLHX60J zHvI6w2*QOoD5iy3yCL6f3uT@)P*r%4C1CM3sS{9eLsGmzu*ZZy_PT$ z$4$?ArvQtX?PbLxmhW%FGiA0P8a}^W0}@$&{9~TuJcsMHgQO*+y>4=}7`J)d3?^I9vN&e3`)1p}@j4i@ z6F|oAu_;}`P>Aaam|uh0#96Mjgc3wFF4A9mOb|OG+X=El>e9+cAI@uO3!m!NBaOY} zS690vAhgh#Fdy}#J)gX^9verKWpZhssd+ukQ5nOEtTz?y)vZhEfz8dsG?>TmVg6I< zbxa}9UW{fhA)i9Ua7MbOiA>VvzlMskiZgi=t7zY`@3bEuv5Ldq@8hTx8+ma>1>rj; zG;eCeR>!&tNcPMv^QPIdF56o)xLNb+QB+muk(9IQ0{(<~2v;NB$K>-&gd%>G zISw_Ph;XFp(=64~i!ZT?U<{2;-@&CZ26V8LE99mo6w9l)`hFJf&fp4OK+;R*V2!Xq z(rkEUXzn*)_Ro6-61lM=4Ep0<31xZh%ULK0&g>2Mp7$}87eMJTwSwpEe^2U{nCrDs zmX(}9*K&@Xzfg_=5ekcj=Zk5}gx7Q$&6d;ffpY+TkeW!r7;I6ukixr^^)#VfTFXh0 zZG=alPirClp3`)}`Td0t#pn%Ha6~7)@aQmr4 ztdW8r3OfG`m&Ww*{l=s@4er=JGzdwtA$x#`5|T+70*X%p6d8gIK*4aom;!|XV^Np+ zq|lA>a|-2H^x>JocICDhH1ni%0_W~diyz%E1us(-bF3a_l3K|lDEkk%MEoul+eZdh zFrNIxfZdh4`)tyrHVIvL3^%Y4Lw3`a<;X&8fLFpwO$&8mA;yx6IA1n}J4#>6#U8bJ zq_TV(A5ouW@*I=Y(9WRjcX7$k9<$oz!J&;XFpRVe2$Q8aDjk*&4k-AcU59aFR~r6* ziF;|A!+JSF(bs(-0Y)E&ad1&*hG{b0iISYKgg4R7E%t-56_uDmp=cHMniIu4rL5vp z!cgCj7<>j-@FbFeVy?n+I2 zXO5je`*N8E7;t^YH+-lZSa|aQ1E!dD4%cmxEzN*E>jq4Px9s__0MSB%Al}16{C@96 z2&*nK>N1mU`SBm1#%+@0lF@#6a9-}pl4-$W#{$Z6!&x#A4~!Tr8IGTucEpnHj@8e& z$&e$aY`JfxC^29gB2=;zP^6mb9uwm~kKW@*TM)g#!fa~-PTs6MCZm}0eo>7xhI_3GT875 zTE+St|1UUC;2~lIeCGtJB@*#Rnz4UWd`@bK(13N&LY(rMdzeX=#tV5rg0laOOE_3z z9BSgxAz%@&V8ZJAIN>Jp3YYU8>tLpWRv{PW+NVJ8BmIl6 z+msU->e(6sO58@Q)gkyt=4M`y}Qj~aZ*n}8@#5Yy!tmUk-L`}qxo#r0Y(-Cx24Nol{4 zQj~AiT%z?a&Hi6M(KUb|IH0-SgtrquL+4dh^Sw2I$CFPyaq2@A6?9{6ef^R<%zyfs zE>TOuBpGIM^->M4UVyFtwuD|f0pdJ;-PjxcGC;CX9Ucq##kpiO$DPl?1?g=%aV(>A zb{(UBn-QT9iB3CbQ6UNleJKtIrB3uZ{LHn6f83$H?ra0$7y)&S3oX2du+eI8-1$lq zQ4~*p=D0K8oO3>Pyl3Lx`%k~a-~V{APp`8867dy~gD_r>Mgf{}5pI&Hm>xW>6)TaU zVeFbmJ+U^yRmm9)vn%e}ntMyT6x3cmew&F{nz|rM1C>Mk$stoRat31h4h=w1*Di~y z$&dM1l2ey(qiRgH70@2VOKJ_(+RortwlkQpRZ9!40ulRKF4 z?0rzYV64ouv=hD2$?ZV^55AK1?8DH(rADEX?-UL|GrwH$vIk+l0d)tOMy*+FZ6-6k zRTz1s#TnK{|F>=5x|ydWs#`DT+;=fSy=I^;_IJgJdma z4~ew7O58{!4KPje1GUM7>{GqPgaU!aQ^LB4sqnSX%pEK9NvNE5bz`4^ZX_%U!dDnA zu#gYllEB@CLcP|I9C>Jvj;j+7-7$cQD4&H=0r<}_fI~*fgtO)jg97%$8jjLC)25xD z{F9=%Z}v-SkDwG$L}kp~(7_DVDhfSfs=A3x3VE{;njLHzu=+#6>SP67?Fe0SK-X+i zPIQ$gK-XMSnghD#TgK%a1s)Fq&oca9Ks)<}d9BdNcJdNkmwyh?d3a}xB^qIYfIw5m z_;qq1s}P*PN3-s)r%)&s>xKyC_XLx`PyS3reBN_3Q{Mn!#i2e~k3Jy?nu2Ac5G>5L zebo$9hi@(*LHvAer6E`u+oVzm_Xw*dSf{S>R>Dt)=n4!P4ya`oCi>9IoQ05j2xtWq zIx@%xV@MmWKrm`GF;m>c%}@m*)2Bd~Llg)wL&UkpN(5>oN(AyyBF3Raj6;b)?CK2* z5wD1KMd?JRgzx2XAyReAz~3=kMdlG5OXg|_yBimkqaX0Nz;4H3ktC#FdsyiOUaoHCXg;nyormtEONYU_=`(u|L#~mm!)EBzl)_o9g7_AF9 zShqo4paaViSXiVFl+AgegS{$&7AQo%XrqZ?D!?}hF|i6TX|x}RC%<j10vhg?F#Fh*~!j|Tuu=|m*L*HY*Da)oSp9=P6(2}jqn+!JvJZ&ucD2eRi%@wYNa1#+BM0plLUW5fo z6tEUa3{6Tx5aEE;5ljGKT>V94({N5|{vEx4OlDlKV zm}P%pTO)CD$POlAvV}|J?c>M*A2U#qP5KP4AtFqGpCF$FxEdWBAer3*z!#&msT{nb z9onYGRZc-UmmaRjLNYP$@`V&GA3@mMzT21Lq2NA~I3eqv@kS8E3 zV4D*Rp{Wg#60i$X;F7ykt3#NGw;9v;my#J+l#&}*l=jtZZ(;a<{2MHy4(ESrH0aEYdi$<0 z^7Ffc5r9v8IKZxBNBal(!_0FUVn2jvn4LohjO*AYh@%UBq+Q5eP>I?b*yXBWMg8m$ zAUQl_nd9a7odiyv*&Uq34Ge$^$()drA4jH~ZQmLdXq<{I)^Pj(Gs0NCL6n+^=34is zeUW+yiH3y~ z&yR4|{T#o`by7E&@RL2=kPb;-zJmw9#lBF!5OQEKnsO~B235gAM|$P{zjrBXeBiWCXCD7WX3MEf5mp?yNi zg&=2MhUbJ!aMFQNJ|G^cGi^UR6mW)E{BQ+1(^5B?C#DZ$6w z5jY26s`Z@Yg7 zz3kP7C0309f9!cI&|`qf=uT}@Udu@0j(eVwg!*r zc>e?J3+_{tsTu14Xr6iGde~*WzJ0va2Lwu@1{&=3Yv91N z20ZiKchd1y_yXSRQcqM1D`S!mPsS%tPo1k%8%DjKI)oh%2!(G1=VAnqw>v~rNZ*C~;p7D~6 zY>?N2=xC3n&C^L#3<(;x8XUU{0(p!vf>e~sW#?p4$^t$-l{`#d2Dj% zp;_H+ot9WbdF^?yNPPrF@=n(xGB14by^UXb@$!2o|D*FBR!Dx^SxP+ANV#%u{ScNC z9Cd|L)v#5kslsv3r`XisO&SDgH*pApbjMBSCt)>t+MG0V@Z6Yp=+!RSQ^t(hJ~jZF zBWx&r(g+zcZEj@B0mZ`>?B7^)nC1~KVlJDw2SWsCnh4=DZf3V&1%csSBKtji*@W*o zzB86hiSv)|@$_Kk(*R%A0r14$G>R|krur7mrx(Bgwv&D)S{)ZG{?TTaEZ!n+DA~lD z5u+u-q98KUri6p;TU*9u8<9d-NSaV#Km|S6vgCQLV{3!zVbX?NCx^Hp3>MoN3wU!Z z-ph!@O)_eCo;2^4AgfSgqIAappvGMN#fjKf#1Rb}O$f zi)UHz5|S_{3mYu#k-rrL?L|AYe~nd!SXg9R;#n?w2{nqaBu*OB#$ z9^*m;k0B8S4OZt~1#JsQVPTs()H7gG-!^r=);^L_&ejmvNydcW(^yTLQ@@CsYLue& zRf0B6<#f^GANQW$-?J*a)V0)K<;S+wu-?QM^^VX-hJ9x{6^leDAw-vP)5}Gm(OSYs z5cvc?V#8s95}qXXW(gt=NYJVY(gQAoy<%B%6r4W>cLszN_7BvVsKBk3ZTAn zz8E4kEH5u!+JgHFK(bz?tEcw~>~@8c6y#9&NI?&aK31^$4s`q>HkWE4i)_AU3%qti z;pEv!AB*LzpC)t)^QY5Bc!c^Ca$%lFxB=_69!9&#pfm3QRy2NM=q^CID;J4Mq|ouygen3&CDR=TcRVSeo3T1{8iNA+4pW&9B?N!WtXFO6dE23f+M8zEQdR5GxGN_nG z?yh1wX%$ZW4a|o6J`#i*i$0-P@GKM2ES_ObR5zh84c>l&$tRioO(tC-ZXa)-Lh>!H z8I^!(OB65cbf3Z1qjo($Y!!E+d7?KXMx;rB8;UNaNeQ+e{{Yny z-XY`sP2|--XF{+H^9}zh4~rY-a1s^edmIvJm<_RFmi~)7?c+lSv0H6P{VIFGzNx>> zWQ$2^QYkn83VNXaE;8+tJN)iY?Wa%ZID_W8+E1UhL)`~q+Oc*6ZO1|h_D5_P2nR)= zHMAYC$PY`t1>-l%yiz`0HA#PS;uR_e7jA_t^H`1?A~ zq7AqWYh|MbQy4t%WbrFYYw%lES0UQSzN%+M%Nfu^h|-)$^<5^vz~mR1jM5r@6Dy_u z3Tm`xd#0Aty+fbO4)lcI<~Q!+Hy%uXV>=yzVifg{bGQO3&1pA`Mq0RE;EI8%(wfbT zqYe#~$vHB%Y+4sL3~IwGCN*Kgh-)UUn4Ei~N7Uks`(5x8z)7ruA1ee@cwmB+!_4pD zq3G{n(fKw=ba4db|Cs|IUQ8AdU-)nuo3@t)C-7b#m*CTXMP`UB7;mno z-krzBYpd%v5a$By9O7JfM8XUoAK_e}#vX0nhHI2@mx#I)xlcB0Lodg^icS$YnAi9x zu7wO(Y+=Zg614bIBoQu&L^viAVUas7xK~4*4n1Y%d`}2tckX*6zVWogCvmL-=0tDG z5Gay0>KFU4kwaGNan`*1Zf+gm_}i@f%S?WSNlLyUZ~Z+kD;Y0^#wp`zcm^K?Y?dss z(|>yqW5`l(&XeqIr~UXL6A*I5_#QW;UlDntE#LQ&Y<`uS}hoTAErk-{i@a`d{&n|1b9hbu9n@ diff --git a/settree/__pycache__/operations.cpython-310.pyc b/settree/__pycache__/operations.cpython-310.pyc deleted file mode 100644 index 2c3749ec7349312974394e48085b908dd0af5b82..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 5304 zcmb_gNpl;=6`r027=Qo>Q4~eRS(LTdCSHAzBd?P01BZ~a5mFPEG1vsX8M;`%4*F8C1$s;9Ht5U2Ht21kF9d4O=v;XU&5YfS%B;8Fxz!_4 zE*6NlP?I;%gra=c3|gY7>RO?ltN2)Tr^AGq!!yPvTJ-M2^q(4fBQOK&iGeXQF!o^RGSzW*HBaubDGK{M zcMmmj(Tt7MxD0ZlMCWjnOjM>GObksV{P)eBgE$IzB0o)cdT|i$B)x+;9_$R$Yomdm zME=fRm|pu>hnNY|l)rJIS8{#yFmrDV{OFUwzu6#1vUZK57iYR(_q_hFpL*W)8-uv# z50abcLyj#};c6QlD|7t8=)lkHp&x~rlWKpM zOsvdK!oj|7atoI)9h13*h*h4&<14D7>Sjx|%%hF7nmki(@gv2h@1~M>l<=DPO$(DwzEV0X{&%lOeBD+(XDK-A7#7yvNYd22ci$;Sm3WsSJOst%54a>4xdZ5GP zARYwE>R0ZCsplt~Jdsk^>ZmA8VjE_wYrs5TMM^LO1Jh zq&@Fjm=t?A)S?9W^lr!0w5)c}bX-ltWOdJr;^1%qcV!LF`}?6k$i1UCIPH!!qauB% zdAR-#oA0ri%PRH|pBuK(t-wUpta&H&hckDc!4eha@uyTHH69zs3W>-(F%`1^6vh)XbCN@- zC%S6F6=18uok$P~tU6bXr%-PJ(uO+PTHrp*1q*N@dz$(d_gzJkQV8SN9K+3Uwsm4Y zGBIY4%|M-)$Yf@o_c~5y4o8_Yia&#F>)0tVp4kypUI4#JOOBA#K@a@^RbpsU0SRYB zo#L@DJ{F6mm?S7_@+umNii)ItY)!?rPt39TDQ?JP=fpU!oEVQ_(IO#K^)>9uTwmY& z>>$*kz7Db^dUbz*L>vyYN*WLPNvgLwKL?S>PKdpXn$USKE5z&OqM7rUom2(7`YKlG z@3WzIa%}R5tU2{yFJx4fxV*x3!sWnTKa_@Ib6J{mal*|3ny4eM^9o-+5QxJky6 zME}LKVolSE%548ZKfyJ?VAa27^KCYBOG$7LAU9AY*|pTsl=BK?&|bos_au?QJS+Y; z;eYF7mTm2l@6Y%0n}q;d>ZnfDr@>!a4t_~IR)4s^lm_%Ka6(D7IS8m5{V&f**Vtt6 zTbiS*!9YZn!LWua`~QGLe+W4AhtCa%-$0B44*lWzaL_lofwnBX{w@brdHpsg-$nzU z%kk& zZ2_JScv|qwuGY0AT5UvYC9{x1@|V#YJj_B<6!r;l{(jw%z00+)qB-JKG4oOJ`XY~s zvy5W7!d4=dmfA&?TirFbl;+4vwe@?Lg<|V_VJ{vAckm%JOd-otW{PYr343_-MP70k zJ-4zSa%w&si*gIEip9V2e2K;4{Bwd)zr!7TfdB8X z^SDC7ZjC0H#$5qVk!j`q`2qI+8FdeYggj&!>LX=9t#SC^TNcyQfSzC*Vj=z?DweM(=So7+5v{VZ$z~| zISJk_mHCv_l3@G;*R52GA$GtO5@w{%bzJjyhy;@ATrc_0Oy=GT<8MZ-A%zjOwifu5 z_2Ngl{u7&(di@cz5m$&_q&gd!Gdzq&59QgBS^g(IE&0#c$73PRoc%$J&uV`5$k(a7 z{^dz1PXR4=;rpCrGSbAjCJeM>DRL;~u{y~$H2Aks{ylw@LMIGveqJDl)KV)v%%^p+`0B zAw3nL#%MT^7tLUWYV1a{(cE2QTQzFPABql3-a~#qIwJX*Xdzle-`U6n4UgsCl{kGf zOv5Bj2TyxGp7*Y=$IaAV>1ls0>_+WYchyI0B8|(Jn>NTQALjX9#GCvINoMI&sgbEj z9XC?7ZR+`gpP70SRBfqIo4IW!r?4OJx@^l-W&~C-b{(8QZlGMr%*fd=^!cdDzNwQ| zGh^M_R@*Wodbp8u#mLMuS9il@(=dcuZs+|_hn+Z$b+G|fof$pesGE+PJsln23{N`RlPJA+0p-B;{J12&dvlek+oG z()yeqwK{P(Y4y5ir)I@`op95?9;3s`3RdVACmY|2^`?&!(#c(%=1KdvTj|=wfM%$p zRyS<7Tj^%~?3ia^nqmg58E{mvM#hc#M?NOlX1o$^w9{Ao#=IXl)^rDXg>FZuIPKssA zGf#W6L~}z6e^*Zr62vvHn z*^AoKil8Tz4JVRX0Ow~@C@dBNRm$) zgXb`4sPSh5;b9P*J>Tv%!M6*@lw%&f-J|7pt6SLW4ivrIsL)+EwZlDBnb4Atx1?uS zoJI0oyh71Y;|ub2dEav6gK&2q8R4$IrB;oCqvI(bn7W1zxeCU0<7HkzQ^JkU8}(Z5 zwUSmhNyBb4&TVc~Xw0iaLC7n~T8Lwr=>{utm~KFQgS?`{?rNM{?YNuIjRchvk2AHC zxV@s6xc=f)TcHhkbtt8KNXy-l@rOwt$J+~sRPpDS^U5_(DbE}%9&l7m?xuW7DI~9w z(@0X|p0NV!8<}_A!mdi$8kZycb6Wn&eumSKFXDYTdA|3Jbr|U5lWc#WWXB`Mw@`5c zp z7pHOD*n)@JF*B6FeQWuun3TGuIVi7)_1DiLkE(*kPMF-#RNTG7bA*JlHI9a?`~i9; zoM=ILYH)H7wNqWE)QpNpzFg#ZA~LvVV;!y)CQB`YTaTlz;NqCm+^MiTuLc3MA_!jQ zaL$9p7bX@THa~hv9uv4??8>q$kV{P=#=>>{Myrm^oyF(aHFa&DyC^->rP$^f+n115(KoqU0_1w z96$_Wwy~U3rb;uRhBjg(aU4kq5d@dfEun9lRSW8bveh12UG%D(%a`kOnwBv4f}qoj zHriZvHVAHRgzX^_P@|~V#LQ2kpV$EX9CM-rE&xkE*6F55yQVPeGfb%S6K6l7Q4Ae* zIiVSG4O@A(>$|pVo0g}_ch-`AQzonwLQW-v$F3xWkI!Hi3ef#%`Ca5tGE?N&<>B5p zZo&9wlQLY8?W5ZRl~)P7f^f*{k@r~UOr+>>WAGhfn05?+n}&MxAi>3n9YfzzHGYSA z@}OAhx0w?gKQ}egyQr5nPAE+~GQ|yAh>^@v;#b9luN!X~*N$#kDA{zdfP9tA&MFzM zBs*%?fc)AwXEI21X74Ix+^nS5jy*K{YVcS7r91te28Q>${jCN`Y{kv`5SY^xE&o-AFu_CX1k5l@E!C^26g%f%H`$ z>KD)>S7~nc`V;ah$FYPlK~L_RLE3{)t0GquS@kk!obS$!^?!Ip$Ld>EQiNIDZI%3fittN> zYfe_p3a(m_eZ$gy)K~8*0MUv7PnTW~FbMYiQ!}$x0EfyE=;=yNXR>G6TCaY2MdbMP ze!ty{A+%k8Bx0rHp1IvhAo@_7LN}-oabYgXyRBU5D3HbNc%O5kSkZQkVP@e9ebfQn zzRD&Z$rxt-iX}?v;2Eexdovi3b&!1$PxLj2dkGVjF8@Accgp3<$`6JP-$_E+LJ;*j zDVQee4_Wmhl1Fclj%z|%-(vCYQ605X5X2ub!4^hQMrT-Ken55UBL=|x#FK;=b$_|vX zDyqz!3k9(7TZ7s^36-uv-At5RQFOOZKYb>v?AZ4eRZ-N}VHpbLq?PsRu3C3j;eFR@ zjKwVhK*1R~UGJU&bu%R@H@qQkRWQcODsnTiHNm5RrhP)hU5SS$vJi2tZK?ZQL#~L^ zLMu?x=UhK!GA*cohN{Bz0-)|;eSq**&<~@7^r$}bP13m1>aO%4YVBT_{@|tD>a@BA za(V|#*1h7~e?p4n@Ch^Ydq{GdHbQ^E@)afmAFeVdq{}8mRE|A@-rt~a!fUEsDaFNw z;Ly;!xJEv0n*X_U*faG<=+fkzA?i><}E2 zh!|Fw{!YQS5q~(5^SSe}wN>46w`yX!tC4%x1#YWScxZvM8t^s*0Cu^yw+)^Fh`X2# z76|igSK%AHY=&O4?hXD@?C@KyTXEM95&qOf5x(AN`w2{QDiJg>vxR{|gbh)(+dY3( z_cr=TVRtXXMl-hA?BN(<-GBaxZX4<26WMJD2bADdeWH-G5J z4Ff3H+!wvFihGFK5bF1l>14nBPV7iIi#QG>bm zfWD-(b!=G^1xA}WvqqZRk6&x+t3FcgA!=1wK6;Zv6}LQM9qXfbA( zJ-8JJa7g2hQ((KBW`vl+-e6xTtF&_o;~p&-l55pcRx|TYBx+r-nVoUS5^XS(uKo_n z4St}LQqFTrUcc*_)?D^5_C+$qNy-sL4=Pk5AeB*x5$_1 zwHMTB^|-xY z+W(^2cgnNSVe|a>CeI>aw4Qg-Eo%bX7gA-zivXE_{oP&@7fqbL&*G&~-H7Y|%qrpf zDFK0ZXsjmAe#~o?+i7?s#vM~IquSIz!PthgDG|=x`A~j~C1oMecyB!GKcTzCYCT*O zU8Q>{PhI4>c}mnpcI0u2lBjh9&d`K=gtLuIiHRdBi$DT}PM<_l<|&eO3twH6Ae8ia z(T5X{r7d5CaID6~2NgO(UiYr5r3y~>IWU+%%rV_T7V0$!e;e#@R{pE;nN;|T+2*_Ksg zjKnd(6es1!E45?6GTDTd$XAfBFuw}v0l-uQn6QaXRu!2!yeyDW-`u+~B455@Yu-SH zadDVK;rlUTo5E>V|_n6iIvV?DG0Fk{;?%;!23s^;hLllC%y3uVh=vQ|dhv-0iE5Q)3=%2IQ z>V$W43tv-n3+mqR09As1uZ6F;6Jjqi_*ARQ59Q@>9DK0S(g+G5w5Q?Y{vFdvh;T_A zRZH+cgV$&_ruvBg9_z(7%(%}`Qon_7ADhokmOdf(a9_61}>aXh5_qeV6sQ@pD8$4@G0o_ zuu0wPOz-r+FfO5QHF1TShbMNG`9Fh;-^7t`G4qtNQ@hBOMJ`7$PH`K`x_lHP$}Mzc z_#tK=Qz<4ge9HR(!A;+9ga`BJTXNCU>n8$${uB*z}uLU%p~T?^#@7bJK%hr8OisQ-Xtgv;EK@Y4i8AM%Kw zHokc)U-Koq+=~9yXpL>=NSwYqsvE6q@f`1={*g?Xfc(4?L+@a+?i-Q%+%6zL4op5o zSYl@MCKr+8k{$XemU5J_X?ou_*B_#7&N8;OID4Ahx;tUwW)R|S0;Y8F=9NLLdx->X zavT4Fk;u3CyaJ2%TR*J3jTwGMDJ|fyIrpbbNLs^6g6}hVDOY!Nht(tt*CHWY=#=U-jnTY@%Jy_EI4ZV^DB%bLxv#xLXFpVxEC-B^#bB5DM@Okv|<+_R2lHF;|7TM=X zk*B-@s{5No0BQj;&i{QlJBq+14R4&!tzJLLt#=X9wcZWyG^{JIY4*E_rZ@1t5Uh&c z#XnheQ>mH#-B5RW-Bwddb8p3HLQJ|Sdhfk|>4Vp=zWv_vmB#!yAH4DUmA5bI4@o0k zxO`YCLS=8Tc#+A^nDFK=7Y;#cf>Z<(6g!tAA1A^^_k8iq=mG@5NT_(O;Q~sWHBTx> X9kSeuuIzCDz~7UubExuvi3Y(T diff --git a/settree/__pycache__/set_rf.cpython-310.pyc b/settree/__pycache__/set_rf.cpython-310.pyc deleted file mode 100644 index abf0432c1b1f871e18aa7d9b214b04379697f7da..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 25078 zcmeHvYm6M%m0s7QAJfzG;Jo+{MU^N~V%y|U5@lJYDcKT5+1^+qElF$TwzS2bu9}%1 z_G5Ufn&gao){ZHgLnqAI!LES>O2R=BWEpHW35+0F=e3(Gg53bgkNgOd3f4$AKNboQ z(;G%Z{7QybG~z`+9M;y41UW$3KlzG%Vhp5 z5Bh(39DEX6@b8RF#>;p{BU9mjvtcyNirKU(R@1K7hCY|AWO2@FfcMbb8Sf!~+AsTg&$*GUJnud19Yonr z;Orybqd5DhKj}XopZz3q4|$Iv_c4De$$bI2k9$+do$^0<)vCORaD2)$ z&SuIVdY4aou53l4wQ5*faxYg^tKM1+qEb+At~C6GdL#5zWHY~PM)?#_?hkK2kYE*-u zzEJnoEA=b&Ry2CXUsQe&wBzIPS2~Tb{%R*&>4Z{(^{c1cpw?Ebf8^Dc|LJykx>aj? zzAD?%%u9Z)9@N{dbISMA+N0_I{NyIls;kj-t=&YseK)9b1l`MieQ_y7tEXS8hSgK; z)+JxDslTL$8uuu@SPlHst-x>2H~eVY#W05T5Jg-+2>gdiw6%6j77q# zTJ*yJ)r{%$^}t=IhoSF9Q?Ab0+K_#&=dUOq75MH-JCIvd8!js1ZSrURkVAvr;;XNn zKJ&u4&%8Q!Hrk8qGt%jo@eqQrZt<+m&6{=?WjD>M##f9k=5Tyv7FVA3 z?D)!zI_{YlGQo3lWj49e*fe-um<#i)Wh!$O$7eHM4kZe#I~?jFN0%z+t1IY0(ZTOi ze&~ctzSHV7=Y8e07aTpPfm3feRcF544!Isy;_O+uh;tw71CLt{k0w7kY{e5Ka896- zQ1W=Ixt(sodwZBZfgkF+o7F3W*E?lPIHHLhi%7G)yvL;YB!~8 zVLPlgoa&`&y-~$HJdrerjXLVo7j*SUP`J)+dv#8r0KH!z5MVBHSlH0Grk3XfOYKg> zbLRbl${Z<$KKtqtZh8Lj6GxmU%jZ8nP)P2GGTZLQJNQ>{$L*ckp@zA3%ipmI-NI{!jCL9ZD zOYL^!SSvib0=R5ej{%~N64j1j1%>f{S1ru0tVVlW0H~YXC4ICHqv~KklEQ>-l*}2! zHpa2(y;1I)6NY*k1!@Gh-j5)c0Np+u0HOdi>sHsg3CQajU31e~x4Sk3u(|e}Osv;b z{SsD_v(RbjrPK+0k58DwKm@2e->Ek|mu(M{VzctJ%ch#aZK6Dd5(cxJjY|FUNgdyDcGYJ7j*)=1EW$vhK)_& z|5@yCR5%t_t#ymg-?bq+rg8?n36e+JXTt{pq@c}$ATFpeZ=nTCC5 z)GQc9qrhS2Xx2oB#}0oil}x_h#6dUj85e9d8bWLDW<6tfrkh(ZXD}fR4F9sBUg?_4 zre|Szix_wt68X{4TDH5z1q1)HUKWpJxP^yuGq;&pAExA7E_93CAw0MeWadz}ghw=j z$z7Da(YTgRcs8yYj&sMlnVZAik?v@BY}3M>A@e1NkKOL&lyhq*-+wtkvw^??&@489 zvseoR#NHB_Rga>>YcsM^P(&9v1 zBV#q0Fj^ms3M}T|&*1&dbEm@!7jYuKo=V9d@us~Q zIX8L@uu~ZWZtRVURJ67Fy7pz6Xv}bAv>iM!Zg^;cC^7_EEssLKp>$@I?<>fheZqElAjXuYdMVMt`gQ!rwjtun#k_2^!aKL9kIIyq<#ON?{HM1-- zsA~ajxfG#LyMP9y7H~G>+FdMhH`^^B&2@*6=CRx}ZlODjbjZWfEt_I9&SUga8zn0B z8!XDj-Jlj^gAP_yRB+vTs~)=#y68mj_YVpdQ^)Dx15 zz#B&#C0eP|@T&`2|L#ptUcANFU{+#p9F@7wtT$k6d{OMwqmB9nXvW%Rhn*0r`jvGt zYzTqQ;Y3?F;P3O-`!RJ~lvx2wBpE`dYl`S~HdZ0U&lWw4c^|zwhm+GGH@dmw`2P#YN z0(5VuRh&Ox3lyX?dy49lYx$l0DeOCS7^q!Dab{QP+upzmK1n8JZfh%1iY@&c!`f^%u# zqC8X_Ku%D$^l}s2$iezw@KuebLhpx(C@4WS?|TQ3{8`NBw~Ffq4eVtY#T%HxuV;+R zMUx0{%b0`ONzEh5Ryl8PnUOjBgnAV@w+uy%C0%vV&`GL|S60;X-@%FR@iOzvcE&VI zcZzq`9_d?aQrGOsM!QyR1kcYV*Ol{%c-Gg6;dHcJEwQtjfHBUYvr8<@RzNHE1JFsa zDyx{!KB_j7Ti8UgDvpa(gGy!Jw(pd(YvWtnG_d%4D!wfKUq&VOxA?v6qBa9;FX!do z&U8(}!lq$l!tBjl*V;60=AmQR&I=}hqEP*1+$xz2dd7+k>BYnsl%Q~l zb1I!Y=`5I?bz;(~>S5)aIN>}o`{c7pbssT&jQkz2V-U#5AngFOEf}v` zz$1RxRfp%e@X5QN#uyPVFZl|*6xf$FU$q^-;WzyjkT)(EYaY6LBwnhT$kK{o0O|n1 z28?KT7MEo1NDF2K+f&R%Yfqg$lS~-a2FZl;=^Sgha;3Wxva>q@Wx8IS4?9mD*)B_J z?MA2Bf^|zuD(UDK`fC0{{jC!NhaZ0Ot+xi6wFsq)9qJz`O^u+zI{zpZO463J1JRI% zN*E5e`oS_k4=NkjdRMemO~OeFNe>Z6daDwu94cMmc>x~k2^xI@?EMEGg-F&u+=n&; z*Y1FXqA+f23{C5^*n$}(i-hj{I@G*ncxLuyzG+@0iWp)a;oqlG=9Z~0;$LLcx0(aR zlZ|>1lEr@h4_PB6!%SxF$bGaixv*T^0@td;o8|l~A%;@mT3wQyTBjLwny`*U&b!)k z5M{$QgmpRFhvUU$M!+hICqXpaHyd<6Qh@KG{@=kSLsHz`5AZpdG$3%ywLN$1RE-1L z`;ph6D@@@2Hl${l*~ncrR3QXoK|*=M@-iJm?F!8e3v$xDj&#}b%)OZn+qDVq8@Wy6 zI@0C5XI(JWG3?2CG9BtzSnxnS!v@MyBbQRlKY=TY8#ynxY09;|8izORn9k+$U7mgP~eaKpTA;QFym$a3V22Q8$#R119>5^{V3 z$CFo$>n8qBsV|1p8@5-xY6icu$llB#cXzl4>C9DQPi8#}QXzjKqrQdf(XRBICK*7| z^Sr748v0lw>CpOw)cfsT9XM;NZ*Q2(d&7N6-pCEp$Xqk8LHv26-q_oP@PTgjszGsi zJsa*v4G&^F;Ej8`sA*p_u36XYYuRhLg`79xO``lm8*zDFfwD6hjYX7M7Vd*lQDA-V*inN|B(%{cGGif$9r&2PZbDrzi~8~pQ6#N zQdgyU2>WNHt)TR3dg5I!r?smuK-LiLj$VSz2xMAyE?1%3!Y>Bx7?@74!jqn()lHs) z_JoqC#d!_-9yVLi0_Rrm4{tzBLbR}{=tvLXt~Y~yfwmJS~A(=~i# zl+AbV;3p8FNiobzG*xWMwP)arqq;d?UF>0}5dJL~EQ!m2K7xcxdG4I*__FHP&h^`c z&w-EWk=YIUuHa39c6_k|PXHPZR_<^D&VgHIaT$)()O}8>Ui$@@<;|n$fC?Qtyx6tN zb$BDuQc!r%dZoAh_jWu=J3bWhNG<=eHnuntc2HmQx1lN*{v)x z;# z^bAk1_kegZEb14Tk&XoA;>S1=6WF1R0L%Qx6X2cz-|t~W9=_{{^el|15K}I0`B6pG zR)?9;q!GbKnbEUnW5c0X4Y{U5_en3ZpOFBIW}XwW!!~F|}ar1};>hto(uDGo26<1?}d^Tqv?(tEb$RJ`N|7#xSyFj z2QLZN{1(v~4f$}l2q~|=%;KYi%r{%!8xr*m6u<+K`6ou2j6a`BG!2M>J?eV{J&Ja< z+$LzTl;_cw6bv%CUY;tayh4#^*33#bScKw(o97qUg8WEIUl@Tk~U z?!98RWR*-%+c}ckwrN@*xQ*QHLas!*+Zs1><~Z0#+Ze^_DA+|~%9_FzIUE&>tPw0_ z*8UN!x1pR_!r78hLOpAvgEJE~W>E>iy!t9((<9^$-Z*r$I{2kop70z%90z0#|x37JJ4{pHZWNB`vumuGq z@Rnp~9E#?tZ*A)5nl7pIuHV8HXL@%nm;*L)fXY2*< zg%-h)aD%F*z4qy32;umXr2@KxNDg5G$ZwIpuTxcTgVB-UJmQG)f24Qpg6WyBY8Tfz z0BRg%A&e77`9ZWnYn6Q~?G0Qcl>ul4WsEm8xTGVoiR)hHNU%2rYsy?Z(EkLIg`HG2 zfUA52iGSry9Q1}EOsPK%0%$)DOpo@1CLSCJ?PZXx%Q^hd)2o8m1sk3e!_ztW8x}Qo z5QK<9*fbs>&045V0LYV(JV&zz!6m95t{$C-5&-f@Gjq&_l0oNdOj^T46C-X3(I>v5 z3mhaGrfE`A+kx@*sTmaCBd&}y9uEHAO~8KZ;1*~m+R8fIq8-?P@bprHPCLNyAUXqa z0X4K6tT;^7d9gP^okL-vMcQkH<9r6f8;hzDqy*uGR{Jv4A$`A3fkjTbQ1jDvCnWv7 z2gs>a(F6=fJpk;(W4cH!DH&ys7>X#IK*z$>Uh4wqaI@N4bvmv3MY!6jhbxA<6Py&GavOaUTa+Fw@hTRe z`XZ8TYm|$dxIyXjY3k%gz+!^tr`;QooUwF`e8m%YU< z5c&%fIq?Fb3&NM$*$H?E;Xi?EdhMZmbP4S6TWp9BCgXGrwGbucVoY5q8eRt>b8b{} zU0MY3nY`|xAWTan)kL+}X_5<0oMYTs90?{zm5W z*jNU#`uAkfiFcC-;B;Wp3h?!!QOKRa_t>bq44vSrdoGj6p8eoFMeo?-#~ z{uQo+Db!ua<)C9ufJmA#%=boboA$vUtKY_n8n%I7;tQuzvdtGh`BVDB=?TwNhO{3% z^@5AXyM!k@6-}P?!>x~E?VB=QT&CMx-y#MG^Rap%vsfZ^f3Ci8L4?>BuINumv~EIHohMJhU zj`glJ>+8m1W*yw$x=p4FjF@*hFv ziC|sW$lnD#lrr66mO##gZkgN#Oes9>`5Wd(;X1qcg``Kf7~z1{XWYpaV(<8$u@1}D z1I(N9=z$+vJ1C2R%F8LO+S1TF+Frl{Sd0xjp~L>T_pr998sr!zu?3_Ugd`fK*YqZc zq75MYURY_Hh9-&xV1Q01QBwT|66{~ZzsR_NY4=x{E1X+8*~kq2BTlv)cEAi7Gk4aW z*>Pea%qWd^>(d)jrsz>rfdDA=xA6amm(C($5=c!#1H729VoP8KomXFdQTIEb7YOn| z%&>XDI+X?*Y;9=7F?Hr#ruV5AtQZ>y(Bf9%tZvj9N9-VgrAHaUHYrc*ZhjhVP{e2T z>r9?z!qunwk5k-}1^QX+MI$jzYl~3*ZRT0HiKb~dnD7Yrmqi!)bG-Bxvk( zh9NYGSEUzW34J38t#QjETNsgOddG{@c~(4}ij6>`v%tXgf_K!5C{Y>Ku~@>oqIk-k zsDH`|3cc27f%DIhr`3;lu=iVh+SJl=U`U`}LBbTES(z5Jtvf~AWZ>VOBI2=%(6~m; zgNAu$)Y`)`_I^1tg~(L%4*A0(I1sWXYX|Q=V+M?UqHbd4?ni^5d_eVpl49J1nGfKA z7QHS2B#w{iB;Vo!aBWEuz`2}*4=RuU1s&Rn`1;IpQIwd+7~r`)Od7+>d==^pm$oJl zz*6QAMVREDBp|}&(ai}Y0Oq(4E85s}VnH^Sp^6tq4R#AU0S zzhtNj;h0Bwv>5*g*F=H zIqIuNtURh+2x)6K;ZX)HC=E+#c(VO8>&U(1B;QVOy!ry-N@5SLob14E?7g^v=pft= zfpIh~srMy!J_sD)TQULcO==|?i3fN{=Wts3NH41Hh_ea<9My_CNabbKS#5WyGfG8O zM_ddDjS>I0c6w`57Y%3I@X`ht1z4&WdPl!YqbVsG4#2#4VS}wgS7k8ao`$!=Z`D@O zUA**BM+82ULZIbq@ZLx{9dW{1<8JlXi@~SSdp%@U{aYfyzd=%-Qv10t0^-kbPxO*+ za8JYog@(}B>ciYi5H!kZT0#l_9l|-7cc7CGvy><}w1~A1vyXA{ z4s#*;twE*(Cm|$i^scJz)G(AU9*q;drcFY zy-A3L`$o!sox|+JQy?piwW#MQsk@ez@Ns~iP;1VmHrQ-o-gF2xV?`1rNEWTwZb#zD zi~4lox{6f+Z5;MihNubP@ajPtt;SlmHV2vnv&sC@P3H(PiI-ZaRrFV!G^KX~r&`uS9cV`x8o2l~aAqDEZ?{j1;S zp}2`% zM<5(@7637zNW6&`8`tp;jKm8AaS*$}7-1mIrXleTMwq`@*vwovfhk0)BJIC8>cL!q z_2%Uy#(0>esmN^Ly0?ZH7ZR3G9&z*!(lYu;SlYmOiN^R0T)c(7Fp@Xu-W{rLqKV1E z8$=qd{&UUKT|?#M9=J1j!44=y200e-9wcLOU&2F-27^eptwxsATr;R99-vu(ixe0* z?KBFe*RE3VS9{zwFDD+#)qO88_5$PD3j^&!cCSX<5_$!Ku+!UpfJ0pC{QazPXyqO_ zgy8g89YL%M<|dvDlrlc)#9f~}N478M!0RAx1&F=ALeBHflj-wh$(^PUR{*N!oY@li z&eB@9)#nFp8n+OGqcBZZ+6>f&3M{U#cmmURKuywjPQVk?JaIn0#_bDK-^M&pn-S*W zG3*HQFh=HK8N36u6Y|H7@*Gw12 z2X9}Pn#%Gmt=t{FvnA3C?`2x{dx*TqjpH3wzCO72@I8AsAkS>nOL=w*TksN+9W>~% z(8ODtV7%zwhV;=o@G#!-l>9Dq=uH^TT?=~dRF~$nS^cbaSLxGeFCI-H{5~bn_KS%8 z+jZf)2_anHTjoEVJW5voR=M0$>xmNuqngE{l@8F;MEhc}L$=_o_d7}P4 zlLaR4Frm6ETDk7Uf9B!0nF#1fB7PY`zVG0~4U!C`JMQ7O-CKB~aJ+D6{HWqpHEaXF z;rJ+c9Xs1Rbz?ht>T>TC?(xoQ?57jIToH&j9!(@;mbgRLz9Fb_Uv4?o`(DnUnpX~Q z!QY1yjSPO`x+~y-qe=H20A7owySwh7qZCue-H?`+_jynCk5PYlSkG!c(x2?=9UOeu zIn_y&A^Z^1bL(I62Ypsx@(5G!9Uz`6Z?Rv;v%EiddJ471BQ=y7?zMZjEW za#_&mh_AbDwubx9rupH}%_1yCBODe>vBWp4`z%GibjG()M-ofX3`coP3lZ`Dl*WbX z*UIeoqNZtvb2w7O}gnxU!aSH@m&@YDfvg-6Di65ZL;A9uu~YPeQ>~-F$;*aCb`bZbbx~8lWC3U1KV*Tg zM}4A8D$M$-Nk1aJhx~#Gf)ymM7p$N|qYy4IknbT_fPqyP4}f|{$dv>sEFnk%Z$Ur^ z@pA~056>gwOGuwFbigH8!X1Encd<8mbF6DEz=6Os9zUQk9`0fZ@o(-yFa$!emdC>h zx(?V*9lih;LEq6;w8X@f7vI*UdHsWuBf(j@wMPbqKd%+(*T$cs!q&*Wb-*+B^vUGNV$XW5HlcHD$-8xEnZp704clJ<#JTe1o` zUo=ameJ5v)A~I_fp71HF&(`ge*Fk^K#K6R+ol8`aiA`OQyb52G5x)&WwaKf%xs8X3 zh!nV_kq=fDUwHy^3;wmpDYFdbmTqFa+Kq_3i@Up)_=WK9Af^;zQXp@$)fdpcm+|%q zzhe{D@fsAq&PRtU{KYX%DpA8JEZ(13TU2%L-sG8fc_uH!Y$xxf8u?}cWNt5Pmju`j ztxY}`mP}d7AD^%e-(39N6J~9>>s^|;rPTJe;pvH;E}uY5!?dZH=q|H0E z^Ij3yh?h~-MZ7VSu8w!l;LmVitUr=OFTmaLd>aa%y^UuWe5VyJ7GJ@Us=S9ok4i+Wj(ECqM_tv4um`7 zTa=g>;Zn2<-&N~wDeEnsC zXqwmc-tJ&P7hm!8qltlReba*c1Cfp(FL?a~KLdP(4WBCl(T?vUP>caRqRD|nw^47_ z!}|ynMfYnfZnd$pRF$Z;ge9H9n9*t+!De9#{yY+G^M>YC@bMvd(5so7_9lFF-LdX? zcUO0!JK3G;PIq_f&{bM)`1%s$2)@2Vr6UhzgkN9cx0w*RiL@x+W*U}nGnM4qOe0-* z`k|{>0`!pC#qd4mRlsX-^Zhso9a-+9cY2wc&Nd%Xi9D`BU0!d9{kIFKQ{tIy2~Fv zEX7c~Gk9sR-F$gVhHei%{K=L!`MQ_Y9i-*me|pO-8QGm8>tF57+}xg> zR#@%_xzgQM7u0@3)cpSS)u-(DzCcGNTH(77JBw~k#5p$NY3%&u%V*zz!#iZCJ2N*| zen~xsov47IN%^X}f<};0s9PIWV$&m#dA_D-ZNzUk;azTgXi~JaQc$a}tj_9BP3U*o zX=|&mAc=DNg&a)>kcz-J+vC>`=OGsrsaM6Rswj*qW4)7jIZ4&=aTQu^_&{WQ2^3hk z*%0H$SNqf$&ZX6nV^ia-X%~|TCX*~N(Yu-@X5}lETA}1h!6#kfy7@X$AfKpJTt13m zl3wdysX=#O1f{%iUzzD$=Eq+|pLL}_`*`UCNGf}JC8Xk6yq^=+#a%A3lhKybacCfJ zpa~a}ABtESjY9*FmgB!}yt6Hvv8w{Q5ne*02ic9X^?GZejeo81_*3`{arKJ&ORSUg zMSmdl6!*TsALR3=8*n^xYIc6Sm7^@_2<&9ckbPg4Yc$wZP-k$kT=1YYuGh>-A&0NZSX?%L> G+5Zn__w})C3j@U);PMvjuQtXIkqJ$IJh>Br6gu-GuxW(-JQ`~ z)~9=9?RXXwuR?5sLP7;dNPu1Lq6ob3OcifDQ1A%VRPjO;lzF1cFH|ap`Mz_yd%Aaa z6{nKf>c7ve&-riXKmYm9*+I2h&f)jU`~K=bUdiSDgPHWtMCNHc{$d(ZhnyXOQ0Q7?q$t6p%~2~Zk@<u= zt*~;{3$7BorGe|U3ivqwF|FgdgvWmwV62UE&D>ZA)W!y&J~jc3ao*LA=8!Ux(q$@- zlp#|Eq)eGAB9)gZ3#o!kmE0V#C@zIoySF|R?O-FEANsqQaA>Ni*&6iLLw&Fw7FRqc z7z!^e1#80d*9Kj;UJlK!w-SVT(Oz8(!ozQPe0y%Q-|U0RrtkFDyPhBBU2h#tlhBP~nWykD5x}E%v z@kEX_kcz%EI&Po7qS$M{xoMw%bF*$gWxx64xlgWa*1?!kv)KYEezQq*`%ce8s<1K; zAX;%y@{(-4tBXT;g_@ef>?C*o%~f85(&-S?RE?ho?jXrIejHV@Z&3+_A760zE&TZ_A8fO z3v;6b(=F=tqEe%iz$8Wg31-Ozsb6BP9) zhfKz}(5&dv#9#rqn!Bql)y*(})9DU9!Kt9;o$mMRdBw~}Sa_J=V{!Fv8p)749^V3p zW|B6l621NB4bha!Wg|f)nw~d1`mPxx(R>mU=8T;f3TEp-mXu1#38^CzZAlPCzZ+vp z?>wGpQY>;(#Q3VoumoiyYtge*vX8PCPVtHOQ=Ll5OL=pR;G=rZm9dc2DEI=+@mw@IL5%%sqqmA@{6%4)4R>5%;|Nak&m2bzu(bkB3Ff zF&g91XUjHzQO>@OwcM6QP8L7kUxT5?Qev-&LC^MYbUjD(ANK8xCm@?%3yWCCG4(dW zV$^QwJ*xbBax!qeiGNHdli>wq_LvMXVOVLo>YC#MV18T#EQ~F{;fEXoCr0hjUz))ko@{^*o$uB9AB)3=Ep0J;^uVdXxYS_n@*Sr9XumNl? zST+`Duw)<56y}fX ztvJJOa51S$dLS0pF#5`{-=ec*2Lrq3IX*lAi4`p&ya{`-Vmr3Idv~F!vlKbhzVF2-)^B7;!u)Bvka}>2^6J|e zrd?#shrvSW&mJ92C|FL-qFy^$-kOp>N>!IswNWM_pA=?uYbGITeclvs&jVmtVs6Izv+H{oDwd#ymt5WG=Hdu z5uMONCo)wLIvF0>dvJ!cF+FvX50X73jYYSOxr*5h&aQ7H=5b={skW3r+|F5rd0vgI zwcLyj1lTm7Nmth3`mo(iOqSf9Of2ct#tay6wV0TpYhvgnm~!xB?NCEN-7xQ>E$>+v zW0>*Qn$vIly_Bv^jUzXJKZC)zj|B18u^qHAcxuQ5DYB+xs}p^8 z`Yl+^h?jl~O8mxJyX)CZ_Gw&|a>rXo`;B(6Cbcsem(1|9+6mtEC)}DKcJa|OvSZ&K z^t&6f8yAzP6<`&L&`426(M{F$yO3@^YGVj=a1KVSrOorBqY`=297oNLY-8KoPP_r3{VO*AdY|Xv;rG zqX4tkb2jWXNJk3UTUntBk^J6Z5Uj;+3wE4d_e=&m&tLaiZOCtfgHK$(EK5F=t(HXn z^rl*M^OIX@ekc8fpMjxPN|7)s9X*Vv-b*r~c$EP~a1Mb*%-3mOZfatFk^VM}^m@B> z!&d!9^FFimFDu7p0ul1q5}zXJ#u6wtP`O-7k? zfvs0Z@9TlVs1vqm444*Vz_cj0ZkEdu1U&)EtAyC10b;Y(sRp&1IdLhdG0os;XBN*K zo_RbA47GL+;910T5YHi3zr$_c;l33dagFVxD68%Om$$Xx7}Cds`?hrPT5w|fBy)JI z#oP`@JI<)l)DdFi)?Cc>9%p-Cq$jxT+NW6Jo_(@gE5U!e&b z9Q-C~{f`5bwZ&MqqIX$GL{I)9su%s0|97rjF^(Y0H)0%KDjvJZgvv_jj$u)Qeg6}{ z*K#X}+`9U0YZEHAjfngXqPYR0wot@EpanWpTbgU!(8c5LAigWk1m<=gsRCLST@$eb zjj{L(xiRAig|WGXz$enOG|8LzqIe_kUmh2bvTo#%GPX-RP1(^!Q}+B)V0Ox5jeWb; z9erFlp4+tgMU>94H@9?0-$cA0af)b^pyERLm>U~gjJ=eXS|lz0alVmUhT0`7ImhGx z+E{nBJLabGPR_)va}jvcaTI}}^=*Vkb~G(FX8hJzsvEn5fsdlMx+#NP;u)j`r*K%n zA&l2|5nME&(DIu`nLB#rAB9?1Jix|DU?DGl_d0-m5h!_NG$e8fH{CYJs1LuYp z8YmA9_=QUUFQA7oKk~%D7g9r?LV;3c0~mzX^>)8CfD;Cl_ME;9=hjP6Abx}97-vg} zNBDv^u~7o$itxrlGa@C0Hq}2g5lp@wYBv>2By(t8aTE}f`~zftMw%;nxnOB?Mgh#> z-<)1Hd2*qDP%sW@HB5&&eKbGCvR2yQGPs|_V1F4Im@$N_rH<=T$F;+`K28l`fVGbC zs=k@uM09N1gm&e}g&hMbf$%@_%<0T+7hskVT;5?U4KXy-T01$`Ky~ybDIGA029bs< z2n`snFz>H9*d2WirQ$NH3Qf5$63@zZRHC9{Z2o1gc!CFJVO2R1O?3dJWHhlN*##;z zR@%Wo<12Xra14y988v+lZ)ny`%qFzSFo?$r$qC56BC;6dCI%o{mkf|kl1*3JF<~-t z+j-`22*-ROAVZMbBKwM)R!}5EHcQdF9F)d|pp3QT;<&Uu12%G=m60kYDNCkG+m)T1 z|KDg;#rHF4!#7zRV^AE=bZV}CIEQhu=1Dw?2vtr>tK;f;ra6l-t8&b5NbJxi!A>6+ zw={7JM$X#O{I`NR7%&U;RJP20@tPT9Wt6qV2qDu1BP%sFR$hsbHLj&fXUDU^XdW2N zN{s$RVuUukF#7ulqkl*+s@)T#Sz;6{jAuKwakW$1(l_VETq1I}pp9gTliH$x^IGz5gCtiXUNVRN~X?0XMM!d>Wn$j@;`p_GCVg3tzi7PCp z8xtDqL-gVG)^RxWMV9hBQ=W8&^Xmf24|8yRxZ3pOqDhM^I0@_X$~(Kx0xG&_5&RNB zqbwI(c@i3$0^5dgCNVS3ZaO2vJdx!wEYHM4lexw$cPokj*-4`$3uN7#+_a=C%c_L6 z%KgjEDX|Qc{tb`61Td#9Xmj|pbW0we(ikyqVFf;f!_~5^twnW7DdylDEWA2)t>M_U zpjrQJJ~*nyb5dD?%pyjc#dVvjjQ<+|Sj6-|+tU5ZT%BEQ6%!KYt~wk=y@ONpJNZ|Q zO=HvCG^t*x`XcI~-cdW_4(>ic6%DBVIh2++_5S?Wkmv7urwF$Zt#9PSg{Y1r%xvoM z=mHZTsua6@c_Af8xye)SDX-nT>2*ezT{W}1)=tGs5N(a_E>wc17`f)n}q@N&!A!1h+QmG ztNh3;PI9JJ_@Py@;GS7pK`*~&9!af1aX(iwHkNoN!u(rgRa*=Fy(xP zrHzCKR(Ih0cW^K&g^Ps=w${qjmr!3$+9~1Az!Gczr%+mv!c~_m^iD|%w+!K4j_N4f zYD&1+V~8)pl54QBxR9{|QOD`4IpG8G`fN%ZzlZ{!zPs%ft{ME~(g~{>#fa8Fr5I9J zB{{vDgJB@nF_ySRutAWWrQyLufK7(Mnmw;K5F2MGOzuWZ0|%6vsmDURr$GP9JkGf~ zI+4uISg7f~GxDXw!ADM3Y7f={9>M85`X=uXXyO1kTLZVrQNV3u2hM`Z3!W*%vmD{2 zys}4-f@djK;WykhQ|*D^3_F{Mu+YhUCCk=|mQs;4TjTaE9bLyiuA(#pWV8Q$ zfXN>0UD$SYjbuf`?n-|LtnV1BxMG5x54I(jbFbYG?-UMK$IRz2iA`KbF*}7triwCM zWbO#_mdsl!{{)7+kbC_QzQtMlQFy8+a!^Q3ynqp5IxrgVS>@p{ynr^U7EG%bMo(OV zl^zC&vNBqWyHx`bg_c%SXfQervrjuW9Q&q&%S#A>*Ux2$CId*Y+=+b&3D`VHEzsc$ z&dao+(%6I|Iw9@fq{Ruz2yRN@cBfj`6@yecFB-)%mTrmkG3cA9`&P73bzSuEf*IJ^Ur-980Yy5M`UM(|1 zT@$z2J$jCJZNRB4xcL&-2%aW*mOw6Ki%j9fJtBb=Vb3<~)eCBa0JDe-Qr-5*Ys{pE zmo_qXpJ+J|6^R=PrQ%;@r*gY>{2p{%Cl5}dX~n3OY8sY0crW?~@Lc|{W}5I{;PdFF zK6>Co@MKCK=-5yv-X|JV{K-VXTBBda)xsTK9y69Q7qzHLoIhm7`=>>^^2Ez648saPXO3?gBR^Qb_{oKbx?bfahdRV?d z$C&JS>TNLMeU5x8Hz9T%XNy?yCc>Vy#~^A)?Fiv)2*z@vYir6xN;C5SF_1DSMqPa+ z9_E-fnvHoH)$Pka@pbfbFD?msjpB}MCEjK_p|!<<~IBKJ@!@2^p&BAnhNl-rT#+tI2+jkJb)de<(K_b=1?NHWH2XMjD4P*5EGt!4gJb>eo;HEjgIciyNo7qnkAY*Zy!;)oL>y! z4av&*zFXnMy`G6U1TfyURs>IUB8n*{q!tZ$GdB=Mo#sJ@#aMB3SFiedXg zuSYybAeRdYN6BihiC0at!TCDJq~C$M}q@Lm&j5dsCF*4w4N;ezz3a6=q1b6icwnfcl`V?CK;gz8w#mg$BW z9Eo3o<9*0vaYP6TyI>raNcRs0@|yS~G!lOdFtr_2+loJ7y__1!{0NUnSl|q~i+kT0 zyuTgey$Hrm7KVF#F{SAShU6nH!nDo1_xhKyWq(TKf4nXGE^tDo{)b-0{_Mli`u&8~$6}pwy}bnalM#Jr?@qqQ(e47X z(aC#{mN7c`NGbr8z~|(dD8aihebni#ecT`ln{JT*ECBsthJ{3tS2bN;%I7&E&-n~M zdB(xDMs|sSxAL905cG~O9SaZLUA36=>)1hi<%S$vzRErMSs_%6Zs2(}4! z2;}sWdifX3{w2Y~1bYAW)-~&7`Bb@LJzhRh ze$Xo7z@up0ZyESAtvnvnGIRqM>)FfHUYUFTOYEA9LjY~q=20FLi6KMy)sR1SQnJK< z2=)3lj`(=u%l(y$|78ep@;ufV?Pn=Han|(@Ms4#++d`@>_HC24E@DVHh5j7Au&}8$ zT!a+WSIL=iX=(HTt~%k~4zK#)za--8B}}VZCD?8Iql57wF{8VzlYwEmts9-rbTI6< z5udg~h_WV?Jrm|KY+>yf4bElw0W2Oi|S&YAJFObq=iy>i=d7kI)>;nscgh0@WoDpQjT!{oN$&wT65m9`&=tvMnK(@THG92s-fC2Ua z_RLZOyr@(eOvygDQn}<3AAln-Ir)@(QpqW|9OjZkPRdm$JEc;-@1NNhu%w-2P}As{ z>Hhog?!UkO|F5?@GgH=Zef(v-b?yyK`%gZMUltzT#vT7Nl0Xaeme$baztJ${zuC4L zmd}Jx;^`?o%?0y#nh&2kG8>C{J{O$F z^Z9TQ&liG)@Is_FmcsMlxo|#s>epuDVxX^S^$TC1FWPF|NEdH}%_xq#oplw4*IRxZ zN1IWo(x=AX-wn4^7{^^z*V9>lZ?CoQb@xK$C#csnC@R00=Xja8<8L7OT-($GJun_R ziI(Vn{Rn?Iv?J;b5MGU&+ud&KYA3m}hYq&=t7~C$ zh5fx6he;y;_gYbsgeqRy+fU1Hw4yj^cH4Vz)zHwF)NDyRc$RNkVLR+3zS>`Tqt$Kt zt@tfu2Bk5aw(>h$VQRL*PTf#6thAuQW>*EGn=0Xrs_^y9gt!^JirLUNbTyBEXGXPv zm)BXyMUrScdOuJ57~fwROR&B3T9)V79^5LsxZVr>jvL?a`6>+DP^qqRH*p8~oV)D2 zdnpQ6F1u|Mt-E_D7&SSsu?WC~7e5W7t?eZKg6g4*MrSWA`VTPuTOl6xeRU2WvhHzW zvhJDfIAlkpQ?8{hAV1M5ng~rrI<<&Qq8+1Cef`8ZG!o<3>>DRE6aC3* zzCVaMksG!5dMZlx-AiIkZmw$mvAz6)OXd$>H%|kU&THAasq2bsE9*4P2@6MG%^qoy zs*21I}uIhC<{NI)eoCGZK zX0PQZSU$8S7){iSS476`*zLu5&jsZ_=tco+hMzS7lrGnHkL9tBT)z{zut^m{7dPD8 zjG7Uwc0ZHS0?EB1Ws)S7KA+P3#r*Rky?EVsf}-z0;~tdmeIk$ zZLwS-V2NU2(&GGijB#0%CMc|Lho^g*4Mh~YyWw8q`q0@Ws&NE5dn>+D{(kI!8YSCq zg34Gqy=Kx=q5FOJ<4uf?w}Gx*b|ve_-bdT+>KTk17VY~ zMuuuob*~k*BX%I_Boq2`xH`S|2D(7320q70K1gvBv;FCI)PfBmD{C$0*6TKbf}x81 zk&z7>W1sUS$Km0HA%fM64`imzo3K0zj@@pjwJ$alMA&{|(gQWJRYxJW)>rFJS{|8$ zn#GDstDTW=6Q}cMrYv1N^Ge1#JwHXEbYb!(F(iyr{iC{_T5;IgRL`MJ^*ob5Ve)Mz z0;Q4&lscXVEKNM`C47wOk`$Jzdci2@1$|L38wXFH#krM1ohFw4@S;oyH{Zqof~2qS zV6VCePs=05@HQ6s1oRkt0?zE%>Q$r#RYz)Je=8n4$dyijI}6$cO=?-4>glh8FI(Cn zcJv$CCl?Mav|8?47qwq%ee<{iNmbb^&A`H5sqM^gw>-h0uHFALLAqK}+?h@0`UWv@ z#|-RW8OQUGT z>v#2orN@og8KdX8U#G+kB-m7qi`t={Aa|szZ;ENc++mz07^ev*4kk(ygaOVZ<}u!# z=rCVwrir=34Yz>1xPaIOUmd)|GT15856?=~ja9XRmg*H5o@~rs!;8#w`W|zC%7iOA znTGt?;%2MsC$GGmn(b&Xcw#!~%mk9871>1d(Bd-1h;0oGES>YV0McWnA{2`0 zsyn))TNuST+?KigwWS|CH*F+O)t?wtx`@M&WCKnq1_aZI6yeB%)`6F@!y)5}KamxW zLB*ikSo@~r3Vl1^G!*+92Ds2K!tZB&j7(zpi~YipG4ZXG6po91r(fEFhb<)?%0Z3N zuB|rlUohpc+%E@wtN%5nSNdh<`({Ox=c-7lKu&cFlbY3fL!=-NiEm)avz*A*cP;gI zSt&@HDm(ank|EXBAkF+9fH#+)=~@|R!#<7aa;8SW$iZ9{Nc!SyT(B(JFJ9)%zr*BJ zCiEF4ejGEj*ZJ-hCR}p$8Cj4DOoyi4MADcYvk?IaEaCubKPFIQwpT=0_<->>*HB|l zD$q^vGW%=HqU5-#H5|Sm>VOMDE|1Jd@Rwfo=vhl0;EI<+vj<<=V=NC3Y0^3#C`1^ZxDp= zO%Q$4v*PM~q=RkCHa#v#I0W1mPj}MBo*gE?!IzlB!tNBH_|%Cb3b989M!crtA!xlFXmv5CCuAtVQ4pjQd!p_#VSmIg z5Rd7f@FX77qIU4wlxYh)63Kc9xoIxbe5ZOry+xUKm^{zqHWTrI$QU(uQ9|Zs2$AYN zmRMyX+KEo0oj5GBr&|3-6wEhTqEi7#Rnx1+5?q&q<=?OLYF)ooucYw$I|yezFD-js zyBqXc%vU`RY+ft>QuMr_+w?s3B6X8)$qpbrk`B_1QXeuArAG-hSIbCPHPaU6=Ge}% zv*=XH-*zrm3-Y~*?-LiT>uuaI_nry9rk3;AR?g7a!eNuK5e6HKjRh;2O{?!(`+j)xE|TrCWVNU#{N?;+(P?787TfMY+57M{#IFx2EF z-4Sm6iBWk@8jPr=K|MI&LOLiHoCwxFIr|Z*k;l(qCBEvF=l{a#USR$C;_rT@MJGT z8^4!ysX}~TCH&v&ZoRyG=z=y|?RG#L4Rw-&XA~N73Bwkk8bXpd3ScHey0p68UD9vl z;;8Is{=YvVkDa{Y-rgi>ahWAMBSH7ZJ8{n)HU?HI^qZiYXs{#tfTDPbxV*0-@RS@< zT<#`Ko6`qiI-`*5-h(=q#+!Fva7RI0eOwtXfV}Hi1i5ZgL_SH|!Hk?$_f5{xrSYa8 z)8WBn{(nS>9ptkxF=%h{%O9deW-ZxRpjA-FzP6(An@eGfSl*=JPkO#Fr)p zbR7nTXe?^?+GE64E(ojfeh(ZIaHk8i4(*?%qA6_0t`4V+*#x5IdaZ~)_N`}^)TnwNTDugRuCO5E;tlWjIZdDx%A*5!#&3*$YU zG6AdivDn5iYjQ{)zZg$OR0g=oohUM8zcDyz)$e`N;^ExcRd0rpbpWkw`!kH|wGBT>1p34NnT&uhg zdP*H}LNg&QH-Y?Ak8_#9n=A~@6;WZEx{_;i<-M`CO0Q(CszNxZ0~yv?5=`2Ee&_Ov^>}zab!JrdeYMBxb|sv9AO7e z-cPaRtB;v@Og>@qmrPE%N_3O{4R>5dQn*C!RXp>j#(kOTtScQRO1sYL|Aizw)e%Pn z=Y$CA%)~k})M8>ElUNe&bwLw$llfxeJbU`3*ahjnx<5NiE$;Xr!m;Tfs08RAEDs6`r78RQ_^iR5 zID`XP8UkJxke2ZahTINi22)!=AhqxtfbG=cik3M}%h&JTUcY(w_WP@AY57NFO>gp0 zJS|{>qHJaZ3z#Cc!K8_#Zf4Oyz!$>TOuY5A%F?!f>TVN?zB5K9Q* zcvtb}V1B}2Sa%{?VnE4TjD=wnr%ruQq7=+rQj32mOZ9ZL|e7=IpI zn9yWYqHd2nKcd_pGx>5*1^NJZTjR!C#t7Hc?332&@oDzZ>aX3oEe0KahVZ5XFu#NiR0ZxH>(+;XPZslxk57}BGq{-N zv4V*^-bBJ*ASB=qi5s3Zd}7d2NQy@q&S?^zhrEY;vE&m$8EgIsIY~2CSANcm%sRqkmM+mf&NtdWsm>a>?^# zcR+kRvK89Yv#?M3iBZk0=?g}7k_J3C$hTN}^{a!Y&ra%eY^N>pzK%Qo4HAr-ri!Od z$_a>T&eS5U?VMPn{#ft8=1*&U)k-wxj-GFM3j zJIBo&!#Q(yj`_^jl3DB=cavG>W#^E&Oy&anc%JWuD1B=^`*Db{e)rIi`nik-B)6vm zCOjR@3_wuuc|HnMewVqBiR_-VNwvjfn~ChL5pz3Cc9Fo4USM9tQdncLGfDSmTwwNl zuCd7@7QDjA<1cjsHF1`iCjmsm^Gog>Cxm@<8Ww6uf4#XMxm)Tu5q8OrW4r*$)Z?o%^RMQU@ zr?d~Gua|P*SY2IRUl+Oq%_YP&R|3&zO_GO0T8EFXG+%>@b<4 zITMFxUmrYuR<98}ui->?zHtmz7E4_Y$Z*mKF1Bx-m_IcRZJZLo`8(#} zx()VK*nah5QVeivz~4aOW(lWX42r*Uj!WRB$l^Mk`J{}&dt#Zsvr{3U;}EMP`5DP$ z1Hk*5mtV@eg7VE=nv=VJ)UGrGt{rPJ<7gnPSo-JR8A^%0bA9$xpe6hNSt-;N46*T0e47^}VgY~!J2rIolD?d`8HK#kQ!zNzLi zRybcuW>V&F4o55UgDm`)6Gug4Qg1Wqv7#Nx8N3a1c;#jBTGZ$KIM0MemZ>9(E%T9% zr(j;Dd}-~@ySLX7U#uzF@J78K!b$49k#YHNVFa}JTSy!n$Rjd9)KGvs;+SQtTr8Js SCGD?je>}G~_mcDSoc=$1R$;aP diff --git a/settree/gbest.py b/settree/gbest.py index b34b63e..8e3679d 100644 --- a/settree/gbest.py +++ b/settree/gbest.py @@ -120,7 +120,7 @@ def __init__(self, *, loss, learning_rate, n_estimators, criterion, splitter='sk max_depth, min_impurity_decrease, min_impurity_split, init, subsample, max_features, ccp_alpha, random_state, alpha=0.9, verbose=0, max_leaf_nodes=None, - warm_start=False, validation_fraction=0.1, + warm_start=False, n_iter_no_change=None, tol=1e-4): self.n_estimators = n_estimators @@ -150,11 +150,9 @@ def __init__(self, *, loss, learning_rate, n_estimators, criterion, splitter='sk self.verbose = verbose self.max_leaf_nodes = max_leaf_nodes self.warm_start = warm_start - self.validation_fraction = validation_fraction self.n_iter_no_change = n_iter_no_change self.tol = tol - def _fit_stage(self, i, X_set, y, raw_predictions, sample_weight, sample_mask, random_state): """Fit another stage of ``n_classes_`` trees to the boosting model. """ @@ -295,7 +293,7 @@ def _check_params(self): def _init_state(self): """Initialize model state and allocate model state data structures. """ - #np.random.seed(self.random_state) + # np.random.seed(self.random_state) self._rng = check_random_state(self.random_state) self.init_ = self.init @@ -303,7 +301,7 @@ def _init_state(self): self.init_ = self.loss_.init_estimator() self.estimators_ = np.empty((self.n_estimators, self.loss_.K), - dtype=np.object) + dtype=object) self.train_score_ = np.zeros((self.n_estimators,), dtype=np.float64) # do oob? if self.subsample < 1.0: @@ -317,7 +315,7 @@ def _init_state(self): def _clear_state(self): """Clear the state of the gradient boosting model. """ if hasattr(self, 'estimators_'): - self.estimators_ = np.empty((0, 0), dtype=np.object) + self.estimators_ = np.empty((0, 0), dtype=object) if hasattr(self, 'train_score_'): del self.train_score_ if hasattr(self, 'oob_improvement_'): @@ -354,7 +352,7 @@ def _check_initialized(self): """Check that the estimator is initialized, raising an error if not.""" check_is_fitted(self) - def fit(self, X_set, y, sample_weight=None, monitor=None): + def fit(self, X_set, y, X_set_val = None, y_val = None, sample_weight=None, sample_weight_val=None, monitor=None): y = check_array(y, dtype=DTYPE, ensure_2d=False) n_samples, self.n_features_ = X_set.shape @@ -366,20 +364,7 @@ def fit(self, X_set, y, sample_weight=None, monitor=None): y = column_or_1d(y, warn=True) y = self._validate_y(y, sample_weight) - if self.n_iter_no_change is not None: - stratify = y if is_classifier(self) else None - inds, inds_val = (train_test_split(range(len(X_set)), - random_state=self.random_state, - test_size=self.validation_fraction, - stratify=stratify)) - X_set_val = X_set.get_subset(inds_val) - X_set = X_set.get_subset(inds) - - y_val = y.take(inds_val) - y = y.take(inds) - sample_weight_val = sample_weight.take(inds_val) - sample_weight = sample_weight.take(inds) - + if self.n_iter_no_change is not None and X_set_val is not None and y_val is not None: if is_classifier(self): if self.n_classes_ != np.unique(y).shape[0]: # We choose to error here. The problem is that the init @@ -393,6 +378,7 @@ def fit(self, X_set, y, sample_weight=None, monitor=None): ) else: X_set_val = y_val = sample_weight_val = None + self.n_iter_no_change = None self._check_params() @@ -442,7 +428,7 @@ def fit(self, X_set, y, sample_weight=None, monitor=None): # The requirements of _decision_function (called in two lines # below) are more constrained than fit. It accepts only CSR # matrices. - #X = check_array(X, dtype=DTYPE, order="C", accept_sparse='csr') + # X = check_array(X, dtype=DTYPE, order="C", accept_sparse='csr') raw_predictions = self._raw_predict(X_set) self._resize_state() @@ -554,7 +540,7 @@ def _make_estimator(self, append=True): def _raw_predict_init(self, X_set): """Check input and compute raw predictions of the init estimator.""" self._check_initialized() - #X = self.estimators_[0, 0]._validate_X_predict(X_set, check_input=True) + # X = self.estimators_[0, 0]._validate_X_predict(X_set, check_input=True) if X_set.shape[1] != self.n_features_: raise ValueError("X.shape[1] should be {0:d}, not {1:d}.".format( self.n_features_, X_set.shape[1])) @@ -591,7 +577,7 @@ def _staged_raw_predict(self, X_set): Regression and binary classification are special cases with ``k == 1``, otherwise ``k==n_classes``. """ - #X = check_array(X, dtype=DTYPE, order="C", accept_sparse='csr') + # X = check_array(X, dtype=DTYPE, order="C", accept_sparse='csr') raw_predictions = self._raw_predict_init(X_set) for i in range(self.estimators_.shape[0]): predict_stage(self.estimators_, i, X_set, self.learning_rate, @@ -695,7 +681,7 @@ def apply(self, X_set): """ self._check_initialized() - #X = self.estimators_[0, 0]._validate_X_predict(X, check_input=True) + # X = self.estimators_[0, 0]._validate_X_predict(X, check_input=True) # n_classes will be equal to 1 in the binary classification or the # regression case. @@ -715,7 +701,7 @@ class GradientBoostedSetTreeClassifier(ClassifierMixin, BaseGradientBoostedSetTr @_deprecate_positional_args def __init__(self, *, loss='deviance', learning_rate=0.1, n_estimators=100, - subsample=1.0, criterion='mse', + subsample=1.0, criterion='squared_error', splitter='sklearn', operations=OPERATIONS, use_attention_set=True, use_attention_set_comp=True, attention_set_limit=1, save_path=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0., @@ -723,7 +709,7 @@ def __init__(self, *, loss='deviance', learning_rate=0.1, n_estimators=100, min_impurity_split=None, init=None, random_state=None, max_features=None, verbose=0, max_leaf_nodes=None, warm_start=False, - validation_fraction=0.1, n_iter_no_change=None, tol=1e-4, + n_iter_no_change=None, tol=1e-4, ccp_alpha=0.0): super().__init__( @@ -739,7 +725,7 @@ def __init__(self, *, loss='deviance', learning_rate=0.1, n_estimators=100, max_leaf_nodes=max_leaf_nodes, min_impurity_decrease=min_impurity_decrease, min_impurity_split=min_impurity_split, - warm_start=warm_start, validation_fraction=validation_fraction, + warm_start=warm_start, n_iter_no_change=n_iter_no_change, tol=tol, ccp_alpha=ccp_alpha) def _validate_y(self, y, sample_weight): @@ -914,14 +900,14 @@ class GradientBoostedSetTreeRegressor(RegressorMixin, BaseGradientBoostedSetTree @_deprecate_positional_args def __init__(self, *, loss='ls', learning_rate=0.1, n_estimators=100, - subsample=1.0, criterion='mse', splitter='sklearn', + subsample=1.0, criterion='squared_error', splitter='sklearn', operations=OPERATIONS, use_attention_set=True, use_attention_set_comp=True, attention_set_limit=1, save_path=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0., max_depth=3, min_impurity_decrease=0., min_impurity_split=None, init=None, random_state=None, max_features=None, alpha=0.9, verbose=0, max_leaf_nodes=None, - warm_start=False, validation_fraction=0.1, + warm_start=False, n_iter_no_change=None, tol=1e-4, ccp_alpha=0.0): super().__init__( loss=loss, learning_rate=learning_rate, n_estimators=n_estimators, criterion=criterion, splitter=splitter, @@ -936,7 +922,6 @@ def __init__(self, *, loss='ls', learning_rate=0.1, n_estimators=100, min_impurity_split=min_impurity_split, random_state=random_state, alpha=alpha, verbose=verbose, max_leaf_nodes=max_leaf_nodes, warm_start=warm_start, - validation_fraction=validation_fraction, n_iter_no_change=n_iter_no_change, tol=tol, ccp_alpha=ccp_alpha) def predict(self, X_set): diff --git a/settree/set_rf.py b/settree/set_rf.py index f749588..be570e6 100644 --- a/settree/set_rf.py +++ b/settree/set_rf.py @@ -17,7 +17,6 @@ from sklearn.utils import check_random_state, check_array, compute_sample_weight from sklearn.exceptions import DataConversionWarning from sklearn.ensemble._base import BaseEnsemble, _partition_estimators -from sklearn.utils.fixes import _joblib_parallel_args from sklearn.utils.multiclass import check_classification_targets from sklearn.utils.validation import check_is_fitted, _check_sample_weight from sklearn.utils.validation import _deprecate_positional_args @@ -184,7 +183,7 @@ def apply(self, X_set): """ #X = self._validate_X_predict(X) results = Parallel(n_jobs=self.n_jobs, verbose=self.verbose, - **_joblib_parallel_args(prefer="threads"))( + prefer="threads")( delayed(tree.apply)(X_set) for tree in self.estimators_) @@ -214,7 +213,7 @@ def decision_path(self, X_set): """ #X = self._validate_X_predict(X) indicators = Parallel(n_jobs=self.n_jobs, verbose=self.verbose, - **_joblib_parallel_args(prefer='threads'))( + backend="threading")( delayed(tree.decision_path)(X_set) for tree in self.estimators_) @@ -311,7 +310,7 @@ def fit(self, X_set, y, sample_weight=None): # parallel_backend contexts set at a higher level, # since correctness does not rely on using threads. trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose, - **_joblib_parallel_args(prefer='threads'))( + backend="threading")( delayed(_parallel_build_trees)( t, self, X_set, y, sample_weight, i, len(trees), verbose=self.verbose, class_weight=self.class_weight, @@ -368,7 +367,7 @@ def feature_importances_(self): check_is_fitted(self) all_importances = Parallel(n_jobs=self.n_jobs, - **_joblib_parallel_args(prefer='threads'))( + backend="threading")( delayed(getattr)(tree, 'feature_importances_') for tree in self.estimators_ if tree.tree_.node_count > 1) @@ -596,7 +595,7 @@ def predict_proba(self, X_set): for j in np.atleast_1d(self.n_classes_)] lock = threading.Lock() Parallel(n_jobs=n_jobs, verbose=self.verbose, - **_joblib_parallel_args(require="sharedmem"))( + backend="threading")( delayed(_accumulate_prediction)(e.predict_proba, X_set, all_proba, lock) for e in self.estimators_) @@ -703,7 +702,7 @@ def predict(self, X_set): # Parallel loop lock = threading.Lock() Parallel(n_jobs=n_jobs, verbose=self.verbose, - **_joblib_parallel_args(require="sharedmem"))( + backend="threading")( delayed(_accumulate_prediction)(e.predict, X_set, [y_hat], lock) for e in self.estimators_) diff --git a/settree/settree.egg-info/PKG-INFO b/settree/settree.egg-info/PKG-INFO deleted file mode 100644 index 1cc58fc..0000000 --- a/settree/settree.egg-info/PKG-INFO +++ /dev/null @@ -1,117 +0,0 @@ -Metadata-Version: 2.1 -Name: settree -Version: 0.1.6 -Summary: A framework for learning tree-based models over sets -Home-page: https://github.com/TAU-MLwell/Set-Tree -Author: Roy Hirsch -Author-email: royhirsch@mail.tau.ac.il -License: UNKNOWN -Platform: UNKNOWN -Classifier: Programming Language :: Python :: 3 -Classifier: License :: OSI Approved :: MIT License -Classifier: Operating System :: OS Independent -Requires-Python: >=3.6 -Description-Content-Type: text/markdown -License-File: LICENSE - -# Set-Tree -### Extending decision trees to process sets -This is the official repository for the paper: "Trees with Attention for Set Prediction Tasks" (ICML21). - -This repository contains a prototypical implementaion of Set-Tree and GBeST (Gradient Boosted Set-Tree) algorithms - -## Getting Started -The Set-Tree package can be downloaded from PIP: -`pip install settree` - -We also supply the code and datasets for reproducing our experimetns under `exps` folder. - -## Background and motivation -In many machine learning applications, each record represents a set of items. A set is an unordered group of items, the number of items may differ between different sets. Problems comprised from sets of items are present in diverse fields, from particle physics and cosmology to statistics and computer graphics. In this work, we present a novel tree-based algorithm for processing sets. - -![set_problems](images/set_problems.PNG) - -## The model -Set-Tree model comprised from two components: -1) **Set-compatible split creteria**: we specifically support the familly of split creteria defined by the following equation and parametrized by alpha and beta. -2) **Attention-Sets**: a mechanism for allplying the split creteria to subsets of the input. The attention-sets are derived forn previous split-creteria and allows the model to learn more complex set-functions. - - - - -## Implementation - -The current implementation is based on Sklean's `BaseEstimator` class and is fully compatible with Sklearn. -It contains two main components: `SetDataset` object, that receives `records` as attribute. `records` is a list of numpy arrays, each array with shape `(n_i, d)` represents a single record (set). `d` is the dimention of each item in the record and `n_i` is the number of items in the i's record and may differ between records. The second componnent is `SetTree` model inherited from Sklean's `BaseEstimator` and has simillar attributes. - -When configuring Set-Tree one should also configure: -- `operations` : list of the operations to be used -- `use_attention_set` : binary flag for activating the attention-sets mechanism -- `attention_set_limit` : the number of ancestors levels to derive attention-sets from -- `use_attention_set_comp` : binary flag for activating the attention-sets compatibility option - -A simplified code snippet for training Set-Tree: -``` -import settree -import numpy as np - -set_data = settree.SetDataset(records=[np.random.randn(2,5) for _ in range(10)]) -labels = np.random.randn(10) >= 0.5 -set_tree_model = settree.SetTree(classifier=True, - criterion='entropy', - operations=settree.OPERATIONS, - use_attention_set=True, - use_attention_set_comp=True, - attention_set_limit=5, - max_depth=10) -set_tree_model.fit(set_data, labels) -``` - -A simplified code snippet for training GBeST: -``` -import settree -import numpy as np - -set_data = settree.SetDataset(records=[np.random.randn(2,5) for _ in range(10)]) -labels = np.random.randn(10) >= 0.5 -gbest_model = settree.GradientBoostedSetTreeClassifier(learning_rate=0.1, - n_estimators=10, - criterion='mse', - operations=settree.OPERATIONS, - use_attention_set=True, - use_attention_set_comp=True, - attention_set_limit=5, - max_depth=10) -gbest_model.fit(set_data, labels) -``` - -For further details and examples see: `example.ipynb`. - -## Citation -If you use Set-Tree in your work, please cite: -``` -@InProceedings{pmlr-v139-hirsch21a, - title = {Trees with Attention for Set Prediction Tasks}, - author = {Hirsch, Roy and Gilad-Bachrach, Ran}, - booktitle = {Proceedings of the 38th International Conference on Machine Learning}, - pages = {4250--4261}, - year = {2021}, - editor = {Meila, Marina and Zhang, Tong}, - volume = {139}, - series = {Proceedings of Machine Learning Research}, - month = {18--24 Jul}, - publisher = {PMLR} -} -``` - -## License -Set-Tree is MIT licensed, as found in the [LICENSE](https://github.com/TAU-MLwell/Set-Tree/blob/main/LICENSE) file. - - - - - - - - - diff --git a/settree/settree.egg-info/SOURCES.txt b/settree/settree.egg-info/SOURCES.txt deleted file mode 100644 index 82e6dd9..0000000 --- a/settree/settree.egg-info/SOURCES.txt +++ /dev/null @@ -1,10 +0,0 @@ -LICENSE -README.md -pyproject.toml -setup.cfg -setup.py -settree/settree.egg-info/PKG-INFO -settree/settree.egg-info/SOURCES.txt -settree/settree.egg-info/dependency_links.txt -settree/settree.egg-info/requires.txt -settree/settree.egg-info/top_level.txt \ No newline at end of file diff --git a/settree/settree.egg-info/dependency_links.txt b/settree/settree.egg-info/dependency_links.txt deleted file mode 100644 index 8b13789..0000000 --- a/settree/settree.egg-info/dependency_links.txt +++ /dev/null @@ -1 +0,0 @@ - diff --git a/settree/settree.egg-info/requires.txt b/settree/settree.egg-info/requires.txt deleted file mode 100644 index ac7a7df..0000000 --- a/settree/settree.egg-info/requires.txt +++ /dev/null @@ -1,3 +0,0 @@ -numpy>=1.19.2 -scikit-learn>=0.23.1 -scipy>=pi1.5.2 diff --git a/settree/settree.egg-info/top_level.txt b/settree/settree.egg-info/top_level.txt deleted file mode 100644 index 8b13789..0000000 --- a/settree/settree.egg-info/top_level.txt +++ /dev/null @@ -1 +0,0 @@ - diff --git a/settree/splitters.py b/settree/splitters.py index 7e99e4d..143315d 100644 --- a/settree/splitters.py +++ b/settree/splitters.py @@ -354,7 +354,7 @@ def node_split(self, X_set, y, mask_inds, cur_depth): max_features=self.max_features, random_state=rand_state).fit(X, y) else: - clf = DecisionTreeRegressor(criterion='mse', + clf = DecisionTreeRegressor(criterion='squared_error', splitter='best', max_depth=1, min_samples_split=2, @@ -381,4 +381,4 @@ def node_split(self, X_set, y, mask_inds, cur_depth): SPLITTERS = {'sklearn': SklearnSetSplitter} -CRITERIONS = {'gini': gini, 'entropy': entropy, 'mse': mse} +CRITERIONS = {'gini': gini, 'entropy': entropy, 'squared_error': mse} From aab26412fc34699a4a4cd0bab940923070e49288 Mon Sep 17 00:00:00 2001 From: Matt Raymond Date: Tue, 31 Dec 2024 10:23:33 -0500 Subject: [PATCH 4/5] update gitignore --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index fe0cefb..043c443 100644 --- a/.gitignore +++ b/.gitignore @@ -2,3 +2,4 @@ /my_env/ /settree.egg-info/ *.jpg +**/__pycache__/ From b4811ec4d2357322c97b9bce2d5247eec88d82b8 Mon Sep 17 00:00:00 2001 From: Matt Raymond Date: Sun, 5 Jan 2025 19:33:55 -0500 Subject: [PATCH 5/5] fix issues with new sklearn/numpy versions --- exps/eval_utils/train_utils.py | 11 +++-- exps/first_quadrant/first_quadrant_gbest.py | 2 +- exps/jets/qg_jets_gbest.py | 5 +-- exps/jets/top_quark_gbdt.py | 4 +- exps/mimic/mimic_gbest.py | 2 +- exps/point_cloud/modelnet40_gbest.py | 2 +- exps/redshift/redshift_gbest.py | 2 +- settree/gbest.py | 4 +- setup.py | 50 ++++++++++----------- tmp.py | 22 ++++----- 10 files changed, 52 insertions(+), 52 deletions(-) diff --git a/exps/eval_utils/train_utils.py b/exps/eval_utils/train_utils.py index 5f70850..b80bdd8 100644 --- a/exps/eval_utils/train_utils.py +++ b/exps/eval_utils/train_utils.py @@ -252,7 +252,7 @@ def train_and_predict_set_gbdt(params, ds_train, train_y, ds_test, test_y, else: gbdt = GradientBoostedSetTreeRegressor(**params) eval_met = mse - eval_met_name = 'mse' + eval_met_name = 'squared_error' timer = Timer() @@ -373,7 +373,7 @@ def train_and_predict_set_tree(params, ds_train, train_y, ds_test, test_y, eval_met_name = 'acc' else: eval_met = mse - eval_met_name = 'mse' + eval_met_name = 'squared_error' timer = Timer() tree.fit(ds_train, train_y) @@ -435,7 +435,7 @@ def train_and_predict_xgboost(params, else: gbdt = xgb.XGBRegressor(**params) eval_met = mse - eval_met_name = 'mse' + eval_met_name = 'squared_error' if verbose: logging.info('Params: {}'.format(params)) @@ -499,7 +499,7 @@ def train_and_predict_sklearn_gbtd(params, else: gbdt = GradientBoostingRegressor(**params) eval_met = mse - eval_met_name = 'mse' + eval_met_name = 'squared_error' if verbose: logging.info('Params: {}'.format(params)) @@ -568,7 +568,7 @@ def train_and_predict_sklearn_dt(params, else: dt = DecisionTreeRegressor(**params) eval_met = mse - eval_met_name = 'mse' + eval_met_name = 'squared_error' if verbose: logging.info('Params: {}'.format(params)) @@ -610,4 +610,3 @@ def train_and_predict_sklearn_dt(params, return dt, train_met, test_met else: return dt - diff --git a/exps/first_quadrant/first_quadrant_gbest.py b/exps/first_quadrant/first_quadrant_gbest.py index 6f88899..ab65123 100644 --- a/exps/first_quadrant/first_quadrant_gbest.py +++ b/exps/first_quadrant/first_quadrant_gbest.py @@ -64,7 +64,7 @@ 'verbose': 3} xgboost_params = {'n_estimators': params['n_estimators'], - 'criterion': 'mse', + 'criterion': 'squared_error', 'learning_rate': params['learning_rate'], 'max_depth': params['max_depth'], 'max_features': params['max_features'], diff --git a/exps/jets/qg_jets_gbest.py b/exps/jets/qg_jets_gbest.py index 73ae616..57a4e32 100644 --- a/exps/jets/qg_jets_gbest.py +++ b/exps/jets/qg_jets_gbest.py @@ -62,7 +62,7 @@ 'max_depth': 5, 'max_features': None, 'subsample': 0.5, - 'criterion': 'mse', + 'criterion': 'squared_error', 'early_stopping_rounds': 5, 'random_state': args.seed} @@ -85,7 +85,7 @@ 'verbose': 3} sklearn_params = {'n_estimators': shared_gbdt_params['n_estimators'], - 'criterion': 'mse', + 'criterion': 'squared_error', 'learning_rate': shared_gbdt_params['learning_rate'], 'max_depth': shared_gbdt_params['max_depth'], 'max_features': shared_gbdt_params['max_features'], @@ -129,4 +129,3 @@ pkl_filename = os.path.join(log_dir, '{}_model.pkl'.format(args.exp_name)) with open(pkl_filename, 'wb') as file: pickle.dump(set_gbtd, file) - diff --git a/exps/jets/top_quark_gbdt.py b/exps/jets/top_quark_gbdt.py index 3080c9c..f9837a0 100644 --- a/exps/jets/top_quark_gbdt.py +++ b/exps/jets/top_quark_gbdt.py @@ -83,7 +83,7 @@ def get_top_quark_datset(train=None, val=None, test=None): 'max_depth': 8, 'max_features': None, 'subsample': 0.5, - 'criterion': 'mse', + 'criterion': 'squared_error', 'early_stopping_rounds': 5, 'random_state': 42} @@ -106,7 +106,7 @@ def get_top_quark_datset(train=None, val=None, test=None): 'verbose': 3} sklearn_params = {'n_estimators': shared_gbdt_params['n_estimators'], - 'criterion': 'mse', + 'criterion': 'squared_error', 'learning_rate': shared_gbdt_params['learning_rate'], 'max_depth': shared_gbdt_params['max_depth'], 'max_features': shared_gbdt_params['max_features'], diff --git a/exps/mimic/mimic_gbest.py b/exps/mimic/mimic_gbest.py index 1498322..06bfe7e 100644 --- a/exps/mimic/mimic_gbest.py +++ b/exps/mimic/mimic_gbest.py @@ -46,7 +46,7 @@ 'verbose': 3} sklearn_params = {'n_estimators': shared_gbdt_params['n_estimators'], - 'criterion': 'mse', + 'criterion': 'squared_error', 'learning_rate': shared_gbdt_params['learning_rate'], 'max_depth': shared_gbdt_params['max_depth'], 'max_features': shared_gbdt_params['max_features'], diff --git a/exps/point_cloud/modelnet40_gbest.py b/exps/point_cloud/modelnet40_gbest.py index f831560..2c17f81 100644 --- a/exps/point_cloud/modelnet40_gbest.py +++ b/exps/point_cloud/modelnet40_gbest.py @@ -64,7 +64,7 @@ 'verbose': 3} sklearn_params = {'n_estimators': shared_gbdt_params['n_estimators'], - 'criterion': 'mse', + 'criterion': 'squared_error', 'learning_rate': shared_gbdt_params['learning_rate'], 'max_depth': shared_gbdt_params['max_depth'], 'max_features': shared_gbdt_params['max_features'], diff --git a/exps/redshift/redshift_gbest.py b/exps/redshift/redshift_gbest.py index 4d2d1e9..f70517a 100644 --- a/exps/redshift/redshift_gbest.py +++ b/exps/redshift/redshift_gbest.py @@ -61,7 +61,7 @@ def eval_scatter(model, x, y): 'max_depth': 8, 'max_features': None, 'subsample': 0.5, - 'criterion': 'mse', + 'criterion': 'squared_error', #'early_stopping_rounds': 5, 'random_state': 42} diff --git a/settree/gbest.py b/settree/gbest.py index 8e3679d..e95d6e2 100644 --- a/settree/gbest.py +++ b/settree/gbest.py @@ -156,7 +156,7 @@ def __init__(self, *, loss, learning_rate, n_estimators, criterion, splitter='sk def _fit_stage(self, i, X_set, y, raw_predictions, sample_weight, sample_mask, random_state): """Fit another stage of ``n_classes_`` trees to the boosting model. """ - assert sample_mask.dtype == np.bool + assert sample_mask.dtype == bool loss = self.loss_ original_y = y @@ -459,7 +459,7 @@ def _fit_stages(self, X_set, y, raw_predictions, sample_weight, n_samples = X_set.shape[0] do_oob = self.subsample < 1.0 - sample_mask = np.ones((n_samples,), dtype=np.bool) + sample_mask = np.ones((n_samples,), dtype=bool) n_inbag = max(1, int(self.subsample * n_samples)) loss_ = self.loss_ diff --git a/setup.py b/setup.py index 460f8d6..a4fa026 100644 --- a/setup.py +++ b/setup.py @@ -2,32 +2,32 @@ setup( name="settree", - packages=['settree'], + packages=["settree"], version="0.2.1", author="Roy Hirsch", - license='MIT', + license="MIT", description="A framework for learning tree-based models over sets", - long_description='Set-Tree\nExtending decision trees to process sets\n\n' - 'This is the official repository for the paper: "Trees with Attention for Set Prediction Tasks" (ICML21).\n' - 'This repository contains a prototypical implementaion of Set-Tree and GBeST (Gradient Boosted Set-Tree) algorithms\n' - 'The Set-Tree package can be downloaded from PIP: pip install settree\n' - 'We also supply the code and datasets for reproducing our experimetns under exps folder.\n\n' - 'In many machine learning applications, each record represents a set of items. A set is an unordered group of items,' - ' the number of items may differ between different sets. Problems comprised from sets of items are present in diverse fields,' - ' from particle physics and cosmology to statistics and computer graphics.' - ' In this work, we present a novel tree-based algorithm for processing sets.\n\n' - 'Set-Tree model comprised from two components:\n' - 'Set-compatible split creteria: we specifically support the familly of split creteria defined' - ' by the following equation and parametrized by alpha and beta.\n' - 'Attention-Sets: a mechanism for allplying the split creteria to subsets of the input.' - ' The attention-sets are derived forn previous split-creteria and allows the model to learn more complex set-functions.', - author_email='royhirsch@mail.tau.ac.il', - url='https://github.com/TAU-MLwell/Set-Tree', - download_url='https://github.com/TAU-MLwell/Set-Tree/archive/refs/tags/0.2.1.tar.gz', - - install_requires=['numpy>=1.19.2', 'scikit-learn>= 0.23.1', 'scipy>=1.5.2'], - classifiers=["Programming Language :: Python :: 3", - "License :: OSI Approved :: MIT License", - "Operating System :: OS Independent"], + long_description="Set-Tree\nExtending decision trees to process sets\n\n" + 'This is the official repository for the paper: "Trees with Attention for Set Prediction Tasks" (ICML21).\n' + "This repository contains a prototypical implementaion of Set-Tree and GBeST (Gradient Boosted Set-Tree) algorithms\n" + "The Set-Tree package can be downloaded from PIP: pip install settree\n" + "We also supply the code and datasets for reproducing our experimetns under exps folder.\n\n" + "In many machine learning applications, each record represents a set of items. A set is an unordered group of items," + " the number of items may differ between different sets. Problems comprised from sets of items are present in diverse fields," + " from particle physics and cosmology to statistics and computer graphics." + " In this work, we present a novel tree-based algorithm for processing sets.\n\n" + "Set-Tree model comprised from two components:\n" + "Set-compatible split creteria: we specifically support the familly of split creteria defined" + " by the following equation and parametrized by alpha and beta.\n" + "Attention-Sets: a mechanism for allplying the split creteria to subsets of the input." + " The attention-sets are derived forn previous split-creteria and allows the model to learn more complex set-functions.", + author_email="royhirsch@mail.tau.ac.il", + url="https://github.com/TAU-MLwell/Set-Tree", + download_url="https://github.com/TAU-MLwell/Set-Tree/archive/refs/tags/0.2.1.tar.gz", + install_requires=["numpy>=1.26.4", "scikit-learn>=1.5.1", "scipy>=1.14.0"], + classifiers=[ + "Programming Language :: Python :: 3", + "License :: OSI Approved :: MIT License", + "Operating System :: OS Independent", + ], ) - diff --git a/tmp.py b/tmp.py index 44f5167..4f68dae 100644 --- a/tmp.py +++ b/tmp.py @@ -28,7 +28,7 @@ # labels = np.random.randn(1000) >= 0.5 # gbest_model = settree.GradientBoostedSetTreeClassifier(learning_rate=0.1, # n_estimators=3, -# criterion='mse', +# criterion='squared_error', # operations=settree.OPERATIONS, # use_attention_set=True, # use_attention_set_comp=True, @@ -46,13 +46,15 @@ mod_records.append(i) set_data = settree.SetDataset(records=mod_records) labels = np.array([0] * 50 + [1] * 50) -gbest_model = settree.GradientBoostedSetTreeClassifier(learning_rate=0.1, - n_estimators=30, - criterion='mse', - operations=settree.OPERATIONS, - use_attention_set=True, - use_attention_set_comp=True, - attention_set_limit=5, - max_depth=10) +gbest_model = settree.GradientBoostedSetTreeClassifier( + learning_rate=0.1, + n_estimators=30, + criterion="squared_error", + operations=settree.OPERATIONS, + use_attention_set=True, + use_attention_set_comp=True, + attention_set_limit=5, + max_depth=10, +) gbest_model.fit(set_data, labels) -print(gbest_model.feature_importances_) \ No newline at end of file +print(gbest_model.feature_importances_)