vendor/lib_lightgbm.so in lightgbm-0.2.6 vs vendor/lib_lightgbm.so in lightgbm-0.2.7

- old
+ new

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A C E F I K L M N O Q S V W X Y Z [ \ ] _ a b d e f g i j l n o p q t u v w y }     @@ -188,21 +188,20 @@      -               ! # % & ( + , - / 0 2 3 7 8 9 < > ? @ B E G H J L M N O P Q R S T V X Z [ ^ a b c d e f g h j k m o q r s t u x y z { | }                 ! # $ % ' ) * , . 0 1 2 3 5 6 7 : < = > ? 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J_{rUଓNAKu I]Է.┭9/J_dl swL"OĄ;V++/i ܼnӪ&ĜAm{??qUԯ5S0w.J &~Է}ݶl-QS֣-s^g6T/;Ԗ spJd_6&/ 8? Z=RݟKڴP-! HJӧNҧVKP*5AL)%I0 &%ZdJЬԷś ˾o+rl ;c8oMJYqi ׽JDDk9Qlm&6" .yYsy6 !1}sh$ μΈtш#J1&6zVQ10wN/<w u"+hd/ȼX6M@^1싯,;v!:DE!<\╵㥃2CX*,G^5E @@ -247,11 +246,11 @@ V+{7[ma=gJ ~Y̔[cV4% pVA]p\RΨ%GinFC ĵw0$'FN? `?*G͋#rmL"=^?ٹ7 a WUO}(B+eIW$M ZJGϏFDi b!klhG͌nYABqhc-dz91 dX۰}UDc6 w%b42:r? l` 24wdM: d뒐+ q`|qY̟ǥ@?7js3Zc ( Y&~ L%HH6HJt`-{n,rM͗%yuc R'@߽ؠ85j jIM9=Ka8]fo+_( GlQ'΍(qn]t^y#Xс7G^/YPp] q 7N3F+zd[-2,jM20*Б^O$Woץ3Ʃt)tt{}͎g]A[ kgl*)bv~Yy1N 9`h\sߍԐ)ݎ=ZyAjQ5ݍqC!vZƖ!ڶ^_ _ 3QvwƟG֪(9'VZ P\8gBKt^}@L7 VY5#527ef݈VPڤ_J2$rj}b+#S8cP7FRuĽM0z6)_(`)7x- 'S0#sE<լgsDFG;^bY|uC R :'>`3gt ' X+`(ast;t%YJ_&H7n)y5wC|k;FHS0#xlK[Dxz|M$4֚A`Ty;|e 7*7Ro.;aџ5m}"nd -%]]^pLSuckB!UoyUq뾬ڋ8bAhS0#s uڮ6 ȍ0"[hHUb\SwEJt0K7xr1`{=`'azE;+9Za{)'9C)YM_>:8[&[t Ǜ"mcmMd^$GXhiUB{8R[k 8gyq?hbw>>6O=z{"/Y20/9!S#j8cnNuOHUc Q1[|E7TWy}s26b؉zx"g_>6l612S<H`;4!NٝpZ;pʯoNu/ C{ɭVO*täÜF}a-G=”R1窇ۜtOKBzXW*d>6(_?R -l/O-dq63-pm>iqj!]kZ>oNo>vm!9L>ŵWW0*#*?5kß#Q1 fYG3&Eo__%d'p<'"`w0Ic3rе ‰^isA^ +l/O-dq63-pm>iqj!]kZ>oNo>vm!9L>ŵWW0*#*?5kß#Q1 fYG3&Eo__%d'p<'"`w0Ic3rе ‰^isA^ ѭI;=+.sQթ4>[Jdsq >1U` + # n8Va6}meD(DNi"ʢQͭvśhue[XὌSDi5D_owЊp$.|g,mW=f҉kt ݋xXٷC8&E<dP'C˸ԟˍ/,k}C)+ݿZɽ%?GobE7=Ѷ dI+j]k+`PfzS niD9swҚǠp9sR 1b,Q OG8 HeگV ,Pn =fې{Oۥ,z=4 r*FR}YMtu WwSw߆Տp3뿃} b+<hqdVQ|>.?j{;C/7`ڌ! @@ -260,11 +259,11 @@ H8v_RUm/J#isD]4]VF;'xeYnk.E*þ~)ԍIʠ Ī,dP'򭩴-!TsӒ՜u!̽~_wc ?8\IM#q\L7h$)tL4A?%p#'sݔMeSa<UJvg?"% w]$7< Y@Oկk&UK( \H/0F碏́0q ^$LfX{ kR*bN_)gI5s> g+Ka" nN_IS5gt 6`i=cp>I,Qfj&@NPxXLp%;'G@:Tw^/Ct7[ c085+ɤgԢͮBY&{cNKލhu2%64ہM䛯HIwI h=?DA㈘C?msO"=ȅ̎j/~(b3,˦'{u2abZjP\cF+53nnd "XFcTNЬb=x&Ye9Ki] ;Bmu 4ams̲]7 dH]L.sz`e}#*/\}-mX\ - ҈tnOsoqV(OpU1-]DtobУ1+m1I(Q/\0wC=ƒs;F(柘ܺ X'mZʳ,;gݖVÖX/2͸gYXggaǦ!-Sg7N*-}uI + ҈tnOsoqV(OpU1-]DtobУ1+m1I(Q/\0wC=ƒs;F(柘ܺ X'mZʳ,;gݖVÖX/2͸gYXggaǦ!-Sg7/DN*-}uI ^ʵE:M6]=+x+~}-!TP߃ѻ!UNQ (a85fGp~RiNot+\7@l<Png(9ҩx+7@w5nԟ}-%SX^ߞ^f[xOҔM((jE8a1ȸZ 69gh:5 Bt'F=0w\.w [ p>Nlfx+伶ZZQWfBB6) .bw<vE\prǣK(ߕIf| rJ5K ;!Ks;5rv!g[Jtl/ K9?ژ 7@>Y{Ms갾YHaIUdAPk$ģ+J6 /a"SQI1ΆP֪∟W@4{&mWxso/bK"{Q$aloBtq¿DsAcX Ԅ 98B'vW!+θ8ۧ3X3dh BY xJg@+FD,*dkqUu&@ΤEWuKsMzݩ K}fug#¿:xFb. 9Osڢ;plcPb)`L耩|"rg˛ԊO :F'lM| ^d\![ mINno-1a4] Cܩ,x (u܃.@mʳS¿wLi&TWAOuhS=Sgg\mW|I/3+E[;WUU˻^^́]A5\{:SUp7E$N+8t!-TL;z/e̖nղo;]}VM]biJ/>bK(E\7*ma$o&pc4摌@u/Ɗ2[7q3_{+⼏$\¯P+&ltyi>ZA(Q뱍Ϧ y\ѬxB~z`D0^TyRw_{GYB.(Qnqf !C"Ց&G4*+_{~Q9`Ӗ+[&tN$naڵ`} /(>EU+3?iC#(]z˸\ѭc{?lfAgؒJ0 :/Z<:u}V,us4;-)B^v O8d{)_TCm5 "|* ߄) }{ikkJD>G|yS=jJu4 1: !uuh"!ؕ;ySJz?eeL)fWXqÓc$?Ki _b̌q} e;]G7WhH/'$fONc>˃FIA~$5_^[TTHD @@ -273,320 +272,312 @@ rj!18#upz  M'U؆m'#3}U쫰-"Ƚ"=Z 2p,;rEf)KP< .blJKXau}IYBa+ P+-IgpS轨"Srh!Ԙ-7{9o [=묂0dzלo;idEM2Ky#'"-Ygu[Z~hxEsHuK0Cg,TUz)4}]y?]%8g,͌, -WX+>΄{?ǝKTzb,q57Foɧ344ňfNBh2w{ChK>u9Yswi Y]z _ `*X! 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straintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistog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E__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb0ELb0ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb1ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb1ELb0ELb0EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE0__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE1__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3ILb0ELb1ELb0ELb1ELb1EEEvvEUlddiPKNS_17FeatureConstraintEdPNS_9SplitInfoEE2__ZTSZN8LightGBM16FeatureHistogram17FuncForNumricalL3IL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-9kmP:km;km;km<km=kmp>km0?km@kmOkm0PkmPkmQkm9kX:k;k;k<k=kx>k8?k@kOk8PkPkQk9k`:k=k>k@?k @k Ok9kh:k(;k;k<k=k>kH?k(@k(OkHPkQkQk9k5p:k50;k5;k5<k5=k5>k5P?k50Ok5PPk5Qk5Qk59kcx:kc8;kc;kc<kc=kc>kcX?kc8OkcXPkcQkcQkc9k +9knP:kn;kn;kn<kn=knp>kn0?kn@knOkn0PknPknQkn9kX:k;k;k<k=kx>k8?k@kOk8PkPkQk9k`:k=k>k@?k @k Ok9kh:k(;k;k<k=k>kH?k(@k(OkHPkQkQk9k6p:k60;k6;k6<k6=k6>k6P?k60Ok6PPk6Qk6Qk69kcx:kc8;kc;kc<kc=kc>kcX?kc8OkcXPkcQkcQkc9k :k @;k <k <k =k @@ -607,11 +598,11 @@ `?k @Ok `Pk Qk Qk -9k:kH;k<k<k=k>kh?kH@kHOkhPk(QkQk9k:kP;k<k<k=k>kp?kPOkpPk0QkQk9k :k X;k <k <k =k >k x?k X@k XOk xPk 8Qk Qk 9k:k`;k <k<k>k>k?k`@k`OkPk@QkRk9k :k h@k hOk 9kP +9k:kH;k<k<k=k>kh?kH@kHOkhPk(QkQk9k:kP;k<k<k=k>kp?kPOkpPk0QkQk9k :k X;k <k <k =k >k x?k X@k XOk xPk 8Qk Qk 9k:k`;k <k<k>k>k?k`@k`OkPk@QkRk9k :k h@k hOk 9kP :kP p;kP 0<kP <kP >kP @@ -624,378 +615,384 @@ >k >k ?k x@k xOk -:kg :kg ;kg @<kg =kg >kg >kg ?kg @kg Okg Pkg `Qkg Rkg :k:k@kOk:k :k 0>k >k ?k @k Ok :k{:k{8>k{>k{?k{@k{Ok{0:k8:k:kc:k :k;k ;kh;k$x;k_ ;kh;k;k;k;k;kV 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LL$R LD$ LL$IHk(LC0LK8DH5ʺTH|$@HD$HHHt$@11H|$0HCH|$HD$H1&E19HH9LHH9LAWAVAUATAUHSHHHxHH>TH5T181HSHK(EHSHK02LTuH|$@HD$HIE1ILD$@/LKLT$01LL$LT$fA.T$PHL$H%fD$TLQHcD$TE2D;A@T$P}e9toAIE9uHt$Ll$D$0LL[0L+[(LID[@$H{("FH\Hx[]A\A]A^A_QT$PLc\$TLi(GlEzD)BbfHKHHKHs0H;s8HtHHs0~"HSH;S D$0wHu1fDH{H;{ @l$0Hy@/HSHL[0L;[8HSMtE+IDL[0H+s(AHH H|$L¾ LD$LD$sLL$0IqLS0H{(MI)IMtAL9eLH)HHMtA HOIAL9)HHHtrHtYHtBHt+HtHtw0HHHtDDHHHtHHHt10HHHtDDHHHtHHHt10HHL9twHtDDHHqHtIHHtFBHtD^DZHtN J HtFBHtD^DZHtNJHNHBL9uLWL)HItHt#Ht$(LD$ LL$Ht$(LD$ LL$MLK(Hs0LC8l@Ht$H|$3H6H9I1Ht$H|$L[0L;[8L+[(IMtK I9;HHHl$$LS0H{(HL@LL$LH)HHtD.L9sMI)IIAHtDDEHOHUL94MIItuIt\ItCIt,ItHtD_D]HHHtHHHt12HHHtD)D*HHHtDDHHHtDDHHHtHHL9t}Ht12ILiItIJItAUAPItE]EXItAE A@ ItAuApItAMAHItAUAPIMIPL9uLWL)HLDHtLD$ LL$>LD$ LL$IHk(LC0LK8DFH5ƼTH|$@HD$HHHt$@1l)H|$0HCH|$HD$H1rE1H?H9J,H?H9CLf.H9AUII)ATIUSHH(LGHGL)HI9 +AU fAP IMIPL9oL_L)HLDMHtLD$ LL$B LD$ LL$IHk(LC0LK8DH5TH|$@HD$HHHt$@10H|$0HCH|$HD$H1&E19HH9LHH9LAWAVAUATAUHSHHHxHHnTH5GT1(1HSHK(EHSHK02L(TuH|$@HD$HIE1ILD$@/LKLT$01LL$LT$fA.T$PHL$H%fD$TLQHcD$TE2D;A@T$P}e9toAIE9uHt$Ll$D$0LL[0L+[(LID[@{$H{(FHz\Hx[]A\A]A^A_QT$PLc\$TLi(GlEzD)BbfHKHHKHs0H;s8HtHHs0~"HSH;S D$0wHu1fDH{H;{ @l$0Hy@/HSHL[0L;[8HSMtE+IDL[0H+s(AHH H|$L¾ LD$LD$sLL$0IqLS0H{(MI)IMtAL9eLH)HHMtA HOIAL9)HHHtrHtYHtBHt+HtHtw0HHHtDDHHHtHHHt10HHHtDDHHHtHHHt10HHL9twHtDDHHqHtIHHtFBHtD^DZHtN J HtFBHtD^DZHtNJHNHBL9uLWL)HItHt#Ht$(LD$ LL$Ht$(LD$ LL$MLK(Hs0LC8l@Ht$H|$3H6H9I1Ht$H|$L[0L;[8L+[(IMtK I9;HHHl$$LS0H{(HL@LL$LH)HHtD.L9sMI)IIAHtDDEHOHUL94MIItuIt\ItCIt,ItHtD_D]HHHtHHHt12HHHtD)D*HHHtDDHHHtDDHHHtHHL9t}Ht12ILiItIJItAUAPItE]EXItAE A@ ItAuApItAMAHItAUAPIMIPL9uLWL)HLDHtLD$ LL$.LD$ LL$IHk(LC0LK8DFH5TH|$@HD$HHHt$@1\)H|$0HCH|$HD$H1rE1H?H9J,H?H9CLf.H9AUII)ATIUSHH(LGHGL)HI9 MI)IM9uN,MM)M9xLL)HHMtM MIKI@I9KHHHtwHt^HtEHt,HtHtH)H(HHHtL!L HHHtH9H8HHHtL LHHHtH)H(HHHtL!L HHHtH9H8HHI9HtL LIHiItHIHHIt HEID$It H}I|$It LMML$It HM IL$ It HE(ID$(It H}0I|$0HM8ID$8I9tMLkMI)IMMKA)IHIE)EaHÃHfDaHHHHHtbHtAHtA{MKIHIEAxE[fEXEAHIHDEifDiAYHIHAifiEaHIHD!AyfyEYHIHDEAfDAEiHIHD)AYfYIA)HH)EafDaAyI@H@y8EY<fDY<EA0DA0Ei4fDi4AY(Y(Ai,fi,Ea Da Ay$fy$EYDYEAfDAEiDiAYfYAiiEa fDa A9H9EYfDYgMIZB HjLfIAHۉfFMIIIIthItCItDmDEIZHjE$fEl$LfUuHHIAt$fAT$}DUHHIET$fA|$DMD]HHIE\$fEL$EMHHIAL$fAD$DmDEHHIED$fEl$UuIHAt$fAT$H}DUI@H@ET$fA|$DMD]fEL$E\$EԋMAL$fAD$DmDEfEl$ED$UuAt$fAT$}DUfA|$ET$DMD]E\$fEL$EMfAD$AL$HVfH([]A\A]@N$J,"H9rHH)HHMtHEILMI@L9DHHHtwHt^HtEHt,HtHtM)L(IHHtI9H8IHHtM)L(IHHtI9H8IHHtM)L(IHHtI9H8IHHtM)L(IHL9HtI9H8HMiHtMILHHtIEHGHtMMLOHtIEHGHtMM LO HtIE(HG(HtMM0LO0MM8HG8L9{M)I9OLSrMI)IIAMtHI -HNIBI9DMIItwIt^ItEIt,ItHtL)L(HHHtH9H8HHHtL LHHHtLLHHHtL)L(HHHtH9H8HHHtL LHHI9HtLLHLiHtHIHHHtIEHGHtMMLOHtM]L_HtIM HO HtIE(HG(HtMM0LO0IM8HG8I9{H)MHLcHH}DbDLZLNHHDfDfHHHHHtbHt?HtEkEH}LZEfEiLNASA3HIIAqfAQAKA+HIIAifAIA[AHIIAAfAYEcEHIIEAfEaEkEHIIEQfEiASA3IIAqfAQHAKA+I@I@AifAIA[ACfAYAAEcECEAfEaEkESfEiEQASAsAqfAQAKAkfAIAiA[ACAAfAYEcECfEaEAH_fH?MIMI)IM)M92M9HMCMHHL$HT$Ht$|DLCH;IHt$HT$HL$H9HH)HHMtLM$LWML$I9DHHHtwHt^HtEHt,HtMtMMIIMtM*M)IIMtIIIIMtMMIIMtM*M)IIMtIIIIMtMMIII9MtM*M)LMZHtMRMQHtMKLHHtMkLhHtMSLPHtMK LH HtMk(Lh(HtMS0LP0MS8LH8I9{HGII)MIOlHH)HHMtLMULRMML9DHHHtwHt^HtEHt,HtMtMMIIMtIIIIMtMMIIMtIIIIMtMMIIMtIIIIMtMMIIL9MtIIMIBItMRMQItLHMKItLPMSItLHMKItLP MS ItLH(MK(ItLP0MS0LP8MK8L9{HH)HL9MlLH)HHMtHIUHNIUL9DHHHtwHt^HtEHt,HtHtLLHHHtLLHHHtL L +HNIBI9DMIItwIt^ItEIt,ItHtL)L(HHHtH9H8HHHtL LHHHtLLHHHtL)L(HHHtH9H8HHHtL LHHI9HtLLHLiHtHIHHHtIEHGHtMMLOHtM]L_HtIM HO HtIE(HG(HtMM0LO0IM8HG8I9{H)MHLcHH}DbDLZLNHHDfDfHHHHHtbHt?HtEkEH}LZEfEiLNASA3HIIAqfAQAKA+HIIAifAIA[AHIIAAfAYEcEHIIEAfEaEkEHIIEQfEiASA3IIAqfAQHAKA+I@I@AifAIA[ACfAYAAEcECEAfEaEkESfEiEQASAsAqfAQAKAkfAIAiA[ACAAfAYEcECfEaEAH_fH?MIMI)IM)M92M9HMCMHHL$HT$Ht$\DLCH;IHt$HT$HL$H9HH)HHMtLM$LWML$I9DHHHtwHt^HtEHt,HtMtMMIIMtM*M)IIMtIIIIMtMMIIMtM*M)IIMtIIIIMtMMIII9MtM*M)LMZHtMRMQHtMKLHHtMkLhHtMSLPHtMK LH HtMk(Lh(HtMS0LP0MS8LH8I9{HGII)MIOlHH)HHMtLMULRMML9DHHHtwHt^HtEHt,HtMtMMIIMtIIIIMtMMIIMtIIIIMtMMIIMtIIIIMtMMIIL9MtIIMIBItMRMQItLHMKItLPMSItLHMKItLP MS ItLH(MK(ItLP0MS0LP8MK8L9{HH)HL9MlLH)HHMtHIUHNIUL9DHHHtwHt^HtEHt,HtHtLLHHHtLLHHHtL L HHHtHHHHHtLLHHHtLLHHHtL L -HHL9HtHHILYItHIHJItISIRItMKMJItICIBItIK IJ ItIS(IR(ItMK0MJ0IK8IR8L9{HH)HMlHt -LL#LkHkH([]A\A]DM9Mu1E1MH=L-J,f.DH9USH_H9L_IڐE +HHL9HtHHILYItHIHJItISIRItMKMJItICIBItIK IJ ItIS(IR(ItMK0MJ0IK8IR8L9{HH)HMlHtLL#LkHkH([]A\A]DM9Mu1E1MH=nP-J,f.DH9USH_H9L_IڐE D;LLEBH)HHiIChHHуHAkhfAkBHHHHtfHtKHt0HthHHHfhhHHHfhhHHHfhhHHHfhhHHHfhhHHHfhHhHHfhtxhHH@H8H(fh<h,H0H fh4h$H(Hfh,hH Hfh$hHHfhh HfhhHfh hHHfhuDfDGLIIH9T[]LAjIBH)TA9|0fDHLD@fDAPL@A9|DfhLLLDfhH9IAUII)ATIUSHH(LGHGL)HI9 MI)IM9 J,MI)M9yMM)IIAMtI;I8IKI@I9KMIItwIt^ItEIt,ItHtL LHHHtL!L HHHtL)L(HHHtH9H8HHHtL LHHHtL!L HHHtL)L(HHI9HtH9H8ILIItHIHHIt IAID$It MiMl$It IyI|$It II IL$ It IA(ID$(It Mi0Ml$0II8ID$8I9tMLI)HkIMMkAmIHIA)EeHÃHDa~HHHHt|Ht\Ht=HtA{MkIHIAAxE[EXEEHIHDEMDIA]HIHAmiEeHIHD!A}yE]HIHDEEDAEMHIHD A]YIAmHH)EeDaA}I@H@y8E]<DY<EE0DA0EM4DI4A](Y(Am,i,Ee Da A}$y$E]DYEEDAEMDIA]YAmiEe Da A}H9E]DYvM MjAIBFMIItIteItKIt1ItDDDLDL,ډ,DdDdH<ډ<D\D\HD,D,ދLLHډDDDDHD D ދllHD$D$ދ||HI9DHKLKDDlL[DlʉDDDDB,HKB,FdFdB<LKB<FlFlL[DDDDB,HKHI9B,FdFdB<B<FlFlʉDDDDMfDH([]A\A]@N$J,"H9rHH)HHMtHEILMI@L9DHHHtwHt^HtEHt,HtHtM)L(IHHtI9H8IHHtM)L(IHHtI9H8IHHtM)L(IHHtI9H8IHHtM)L(IHL9HtI9H8HMiHtMILHHtIEHGHtMMLOHtIEHGHtMM LO HtIE(HG(HtMM0LO0MM8HG8L9{M)I9OLSrMI)IIAMtHI -HNIBI9DMIItwIt^ItEIt,ItHtL)L(HHHtH9H8HHHtL LHHHtLLHHHtL)L(HHHtH9H8HHHtL LHHI9HtLLHLiHtHIHHHtIEHGHtMMLOHtM]L_HtIM HO HtIE(HG(HtMM0LO0IM8HG8I9{H)MHLcHDH]AI9DDbDfHHHHtlHtPHt4HtFFFlFlAB<B<FLFLIB B BDBDIBBFDFDIF$F$FTFTIF,F,B|B|IF F BLBLII9BMCMkIKBB\B\F$F$FTFTB<MCB<FLFLMk΋\\F$IKF$FTFTB<MCII9B<FLFLʉ΋\\F$F$FTFTOH?MIMI)IM)M92M9HMCMHHL$HT$Ht$2LCH;IHt$HT$HL$H9HH)HHMtLM$LWML$I9DHHHtwHt^HtEHt,HtMtMMIIMtM*M)IIMtIIIIMtMMIIMtM*M)IIMtIIIIMtMMIII9MtM*M)LMZHtMRMQHtMKLHHtMkLhHtMSLPHtMK LH HtMk(Lh(HtMS0LP0MS8LH8I9{HGII)MIOlHH)HHMtLMULRMML9DHHHtwHt^HtEHt,HtMtMMIIMtIIIIMtMMIIMtIIIIMtMMIIMtIIIIMtMMIIL9MtIIMIBItMRMQItLHMKItLPMSItLHMKItLP MS ItLH(MK(ItLP0MS0LP8MK8L9{HH)HL9MlLH)HHMtHIUHNIUL9DHHHtwHt^HtEHt,HtHtLLHHHtLLHHHtL L +HNIBI9DMIItwIt^ItEIt,ItHtL)L(HHHtH9H8HHHtL LHHHtLLHHHtL)L(HHHtH9H8HHHtL LHHI9HtLLHLiHtHIHHHtIEHGHtMMLOHtM]L_HtIM HO HtIE(HG(HtMM0LO0IM8HG8I9{H)MHLcHDH]AI9DDbDfHHHHtlHtPHt4HtFFFlFlAB<B<FLFLIB B BDBDIBBFDFDIF$F$FTFTIF,F,B|B|IF F BLBLII9BMCMkIKBB\B\F$F$FTFTB<MCB<FLFLMk΋\\F$IKF$FTFTB<MCII9B<FLFLʉ΋\\F$F$FTFTOH?MIMI)IM)M92M9HMCMHHL$HT$Ht$|2LCH;IHt$HT$HL$H9HH)HHMtLM$LWML$I9DHHHtwHt^HtEHt,HtMtMMIIMtM*M)IIMtIIIIMtMMIIMtM*M)IIMtIIIIMtMMIII9MtM*M)LMZHtMRMQHtMKLHHtMkLhHtMSLPHtMK LH HtMk(Lh(HtMS0LP0MS8LH8I9{HGII)MIOlHH)HHMtLMULRMML9DHHHtwHt^HtEHt,HtMtMMIIMtIIIIMtMMIIMtIIIIMtMMIIMtIIIIMtMMIIL9MtIIMIBItMRMQItLHMKItLPMSItLHMKItLP MS ItLH(MK(ItLP0MS0LP8MK8L9{HH)HL9MlLH)HHMtHIUHNIUL9DHHHtwHt^HtEHt,HtHtLLHHHtLLHHHtL L HHHtHHHHHtLLHHHtLLHHHtL L -HHL9HtHHILYItHIHJItISIRItMKMJItICIBItIK IJ ItIS(IR(ItMK0MJ0IK8IR8L9{HH)HMlHt*LL#LkHkH([]A\A]DM9Mu1E1MH=:-J,f.DH9USH_H9L_IڐE +HHL9HtHHILYItHIHJItISIRItMKMJItICIBItIK IJ ItIS(IR(ItMK0MJ0IK8IR8L9{HH)HMlHtLL#LkHkH([]A\A]DM9Mu1E1MH=>-J,f.DH9USH_H9L_IڐE D;LLEBH)HHEIChHHуHAkhAk HHHtwHt^HtEHt,HtHhHHhHhHHhHhHHhHhHHhHhHHhHhHHhHHhHhthHhH@H8h<H(h,H0h4H h$H(h,HhH h$HhHhHh HhhHh HHhhuDDGLIIH9z[]fDLAjIBH)TA9|.HLD@DAPL@A9|DhLLLDhf.AWAVAUATUHSH(HLoHGIHL)HH9D1LH)HH9H)TMHAIIAI9LGH 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HHLd$p|AGAD_E_EKAQEASfAWAVAUIATIUSHHHHDŽ$HDŽ$HPIEHxH$H$D$H$HHGHGH$Ht$-H|$@Hl$pL|$,Lt$0H $Ht$H|$H4$HLCL;CMt+LL$pI@MLT$xHD$xMPHD$pILCL\$xMt&H=j]KICk0$u^H$H$ HH$D A AA @AuAy@vЃ$t,I<$IEH9GHĸH[]A\A]A^A_EtE1A/t:H$HH$D A tA tAq@vL$LHL$M9JDTA/A*uxHIL9H$v#f.I9H$oHЀ<*HPu|/uHH$$AH$H5x$LLZ$H$uLL\$0Ƅ$I{H;=ZK2E1HpI9H$t| +?fDHD$@H5$LHHD$$H$u +Ht$8L\$@Ƅ$I{H;=bKH=bcKLG"AEETLܼGHT$H5ԇ$Ll$H$uLHL$pHt$PƄ$HyD$E1HUHRMH} ADEAfLmHAUVL$D $  L1SHL$PHT$H5=$HHL$$H$u +Ht$H|$PLƄ$HD$E1&Ht$PMLAPMMO t#AЃ:I7LV,AG HAO H|$xHHtH|$PLH蕥HT$ LHz脥H|$hHtH Ld$PHt$HI|$VHL$0Ht$HHy>H=4aKLot`AML賺DEEHDMH=`KLWt2AE:ETLsGEHAADA@DuE^ED]D] AKM Hl$pHt$HH}膤Hl$@Ht$HH}kHHLd$pAGAD_E_EKAQEASfAWAVAUIATIUSHHHHDŽ$HDŽ$HPIEHxH$H$D$H$HHGHGH$Ht$-H|$@Hl$pL|$,Lt$0H $Ht$H|$H4$HLCL;CMt+LL$pI@MLT$xHD$xMPHD$pILCL\$xMt&H= +_KICk0$u^H$H$ HH$D A AA @AuAy@vЃ$t,I<$IEH9GHĸH[]A\A]A^A_EtE1A/t:H$HH$D A tA tAq@vL$LHL$M9JDTA/A*uxHIL9H$v#f.I9H$oHЀ<*HPu|/uHH$$AH$H5$LL$H$uLL\$0Ƅ$I{H;=X\K2E1HpI9H$t| iLHLL)σL$LHB| >HHHtrHtTHt6HtL$IB| L$IB| L$IB| L$IB| @@ -2829,49 +2811,51 @@ 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Check failed: tree_idx >= 0 && static_cast<size_t>(tree_idx) < models_.size() at %s, line %d . Check failed: leaf_idx >= 0 && leaf_idx < models_[tree_idx]->num_leaves() at %s, line %d . /__w/1/s/src/boosting/goss.hppCheck failed: (config_->top_rate + config_->other_rate) <= (1.0f) at %s, line %d . Check failed: (num_tree_per_iteration_) == (num_class_) at %s, line %d . @@ -4777,11 +4755,11 @@ "average_output":",""feature_names":[""],"monotone_constraints":["feature_infos":},basic_string::erase{"min_value":"max_value":"values":[]}vector::reserve"values":["tree_info":["tree_index":"feature_importances":%lu#define USE_HARD_CODE 0#ifndef USE_HARD_CODE#else#endifmonotone_constraintsWrong line at model file: %snum_classnum_tree_per_iterationlabel_indexmax_feature_idxaverage_outputWrong size of feature_namesvector::_M_emplace_back_auxWrong size of feature_infosobjectivetree_sizes[linear_tree: end of parametersversion=num_class=num_tree_per_iteration=label_index=max_feature_idx=objective=feature_names=monotone_constraints=feature_infos=tree_sizes=end of treesfeature_importances: parameters:v3Model format error, expect a tree here. met %s#include <LightGBM/utils/common.h>#include <LightGBM/objective_function.h>#include <LightGBM/prediction_early_stop.h>double (*PredictTreePtr[])(const double*) = { int early_stop_round_counter = 0;std::memset(output, 0, sizeof(double) * num_tree_per_iteration_);for (int i = 0; i < num_iteration_for_pred_; ++i) {for (int k = 0; k < num_tree_per_iteration_; ++k) {output[k] += (*PredictTreePtr[i * num_tree_per_iteration_ + k])(features);if (early_stop->round_period == early_stop_round_counter) {if (early_stop->callback_function(output, num_tree_per_iteration_))void GBDT::PredictRaw(const double* features, double *output, const PredictionEarlyStopInstance* early_stop) const {double (*PredictTreeByMapPtr[])(const std::unordered_map<int, double>&) = { output[k] += (*PredictTreeByMapPtr[i * num_tree_per_iteration_ + k])(features);void GBDT::PredictRawByMap(const std::unordered_map<int, double>& features, double* output, const PredictionEarlyStopInstance* early_stop) const {void GBDT::Predict(const double* features, double *output, const PredictionEarlyStopInstance* early_stop) const {PredictRaw(features, output, early_stop);output[k] /= num_iteration_for_pred_;if (objective_function_ != nullptr) {objective_function_->ConvertOutput(output, output);void GBDT::PredictByMap(const std::unordered_map<int, double>& features, double* output, const PredictionEarlyStopInstance* early_stop) const {PredictRawByMap(features, output, early_stop);double (*PredictTreeLeafPtr[])(const double*) = { void GBDT::PredictLeafIndex(const double* features, double *output) const {int total_tree = num_iteration_for_pred_ * num_tree_per_iteration_;for (int i = 0; i < total_tree; ++i) {output[i] = (*PredictTreeLeafPtr[i])(features);double (*PredictTreeLeafByMapPtr[])(const std::unordered_map<int, double>&) = { void GBDT::PredictLeafIndexByMap(const std::unordered_map<int, double>& features, double* output) const {output[i] = (*PredictTreeLeafByMapPtr[i])(features);Model file %s is not available for writesUnknown importance type: only support split=0 and gain=1mean_absolute_percentage_errorModel file doesn't specify the number of classesModel file doesn't specify the label indexModel file doesn't specify max_feature_idxModel file doesn't contain feature_namesWrong size of monotone_constraintsModel file doesn't contain feature_infosZN8LightGBM9ArrayArgsIiE8ArgMaxMTERKSt6vectorIiSaIiEEEUlimmE_$@d~QJחAu<7~u<7C _Map_base::atBinary early stopping needs predictions to be of length oneUnknown early stopping type: %sMulticlass early stopping needs predictions to be of length two or larger*ZN8LightGBM10CreateNoneERKNS_25PredictionEarlyStopConfigEEUlPKdiE_*ZN8LightGBM16CreateMulticlassERKNS_25PredictionEarlyStopConfigEEUlPKdiE_*ZN8LightGBM12CreateBinaryERKNS_25PredictionEarlyStopConfigEEUlPKdiE_/__w/1/s/src/io/bin.cppMultiValSparseBin::MergeData/__w/1/s/src/io/multi_val_dense_bin.hppCheck failed: (num_data_) == (num_used_indices) at %s, line %d . Check failed: (max_bin) > (0) at %s, line %d . Check failed: (bin_upper_bound.size()) <= (static_cast<size_t>(max_bin)) at %s, line %d . Check failed: (num_bin_) <= (max_bin) at %s, line %d . -Met negative value in categorical features, will convert it to NaNMet categorical feature which contains sparse values. Consider renumbering to consecutive integers started from zero/__w/1/s/src/io/multi_val_sparse_bin.hpp8N8LightGBM11BinIteratorEN8LightGBM3BinEN8LightGBM11MultiValBinEN8LightGBM8DenseBinIhLb1EEEN8LightGBM8DenseBinIhLb0EEEN8LightGBM8DenseBinItLb0EEEN8LightGBM8DenseBinIjLb0EEEN8LightGBM9SparseBinIhEEN8LightGBM9SparseBinItEEN8LightGBM9SparseBinIjEEN8LightGBM16MultiValDenseBinIhEEN8LightGBM16MultiValDenseBinItEEN8LightGBM16MultiValDenseBinIjEEN8LightGBM17MultiValSparseBinIthEEN8LightGBM17MultiValSparseBinIttEEN8LightGBM17MultiValSparseBinItjEEN8LightGBM17MultiValSparseBinIjhEEN8LightGBM17MultiValSparseBinIjtEEN8LightGBM17MultiValSparseBinIjjEEN8LightGBM17MultiValSparseBinImhEEN8LightGBM17MultiValSparseBinImtEEN8LightGBM17MultiValSparseBinImjEEN8LightGBM16DenseBinIteratorIhLb1EEEN8LightGBM16DenseBinIteratorIhLb0EEEN8LightGBM16DenseBinIteratorItLb0EEEN8LightGBM16DenseBinIteratorIjLb0EEEN8LightGBM17SparseBinIteratorIhEEN8LightGBM17SparseBinIteratorItEEN8LightGBM17SparseBinIteratorIjEE??@ffffff?p}?multi_loglossserialcpuregresson_l1intermediatebasicmulti_errorauc_muvotingadvanced[boosting: [objective: [metric: [tree_learner: [device_type: binary_loglossmapkldivkullback_leiblerlambdarankmean_average_precisiondevice_typeUnknown device type %stasktrainconvert_modelrefitUnknown task type %strainingpredictiontestrefit_treetree_learnerUnknown tree learner type %sfeature_paralleldata_parallelvoting_parallelboostinggbrtrandom_forest +Met negative value in categorical features, will convert it to NaNMet categorical feature which contains sparse values. Consider renumbering to consecutive integers started from zero/__w/1/s/src/io/multi_val_sparse_bin.hpp8N8LightGBM11BinIteratorEN8LightGBM3BinEN8LightGBM11MultiValBinEN8LightGBM8DenseBinIhLb1EEEN8LightGBM8DenseBinIhLb0EEEN8LightGBM8DenseBinItLb0EEEN8LightGBM8DenseBinIjLb0EEEN8LightGBM9SparseBinIhEEN8LightGBM9SparseBinItEEN8LightGBM9SparseBinIjEEN8LightGBM16MultiValDenseBinIhEEN8LightGBM16MultiValDenseBinItEEN8LightGBM16MultiValDenseBinIjEEN8LightGBM17MultiValSparseBinIthEEN8LightGBM17MultiValSparseBinIttEEN8LightGBM17MultiValSparseBinItjEEN8LightGBM17MultiValSparseBinIjhEEN8LightGBM17MultiValSparseBinIjtEEN8LightGBM17MultiValSparseBinIjjEEN8LightGBM17MultiValSparseBinImhEEN8LightGBM17MultiValSparseBinImtEEN8LightGBM17MultiValSparseBinImjEEN8LightGBM16DenseBinIteratorIhLb1EEEN8LightGBM16DenseBinIteratorIhLb0EEEN8LightGBM16DenseBinIteratorItLb0EEEN8LightGBM16DenseBinIteratorIjLb0EEEN8LightGBM17SparseBinIteratorIhEEN8LightGBM17SparseBinIteratorItEEN8LightGBM17SparseBinIteratorIjEE??@ffffff?p}?multi_loglossserialcpuregresson_l1intermediatebasicmulti_errorauc_muvotingadvanced[boosting: [objective: [metric: [tree_learner: [device_type: binary_loglossmapkldivkullback_leiblerlambdarankmean_average_precisionboostinggbrtrandom_forestdevice_typeUnknown device type %stasktrainconvert_modelrefitUnknown task type %strainingpredictiontestrefit_treetree_learnerUnknown tree learner type %sfeature_paralleldata_parallelvoting_parallel '"Unknown parameter %sUnknown parameter: %s AUC-mu matrix must have zeros on diagonal. Overwriting value in position %d of auc_mu_weights with 0.auc_mu_weights must have %d elements, but found %dAUC-mu matrix must have non-zero values for non-diagonal entries. Found zero value in position %d of auc_mu_weights.Number of classes should be specified and greater than 1 for multiclass trainingNumber of classes must be 1 for non-multiclass trainingMulticlass objective and metrics don't matchHistogram LRU queue was enabled (histogram_pool_size=%f). Will disable this to reduce communication costsDon't support forcedsplits in %s tree learnerAccuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=%d).Although "deterministic" is set, the results ran by GPU may be non-deterministic.CUDA currently requires double precision calculations.Linear tree learner only works with CPU.Linear tree learner must be serial.zero_as_missing must be false when fitting linear trees.Cannot use regression_l1 objective when fitting linear trees.min_data_in_leaf has been increased to 2 because this is required when path smoothing is active.Cannot use "intermediate" or "advanced" monotone constraints in distributed learning, auto set to "basic" method.Cannot use "intermediate" or "advanced" monotone constraints with feature fraction different from 1, auto set monotone constraints to "basic" method.Monotone penalty greater than tree depth. Monotone features won't be used.Cannot set both min_data_in_leaf and min_sum_hessian_in_leaf to 0. Will set min_data_in_leaf to 1.%s is set=%s, %s=%s will be ignored. Current value: %s=%s%s is set with %s=%s, %s=%s will be ignored. Current value: %s=%s%s is set with %s=%s, will be overridden by %s=%s. Current value: %s=%sParameter %s should be of type int, got "%s"save_binary parameter set to true because task is save_binary??[data: [valid: [num_iterations: [learning_rate: [num_leaves: [num_threads: [deterministic: [force_col_wise: [force_row_wise: [histogram_pool_size: [max_depth: [min_data_in_leaf: [min_sum_hessian_in_leaf: [bagging_fraction: [pos_bagging_fraction: [neg_bagging_fraction: [bagging_freq: [bagging_seed: [feature_fraction: [feature_fraction_bynode: [feature_fraction_seed: [extra_trees: [extra_seed: [early_stopping_round: [first_metric_only: [max_delta_step: [lambda_l1: [lambda_l2: [linear_lambda: [min_gain_to_split: [drop_rate: [max_drop: [skip_drop: [xgboost_dart_mode: [uniform_drop: [drop_seed: [top_rate: [other_rate: [min_data_per_group: [max_cat_threshold: [cat_l2: [cat_smooth: [max_cat_to_onehot: [top_k: [monotone_constraints: [monotone_penalty: [feature_contri: [forcedsplits_filename: [refit_decay_rate: [cegb_tradeoff: [cegb_penalty_split: [cegb_penalty_feature_lazy: [path_smooth: [interaction_constraints: [verbosity: [max_bin: [max_bin_by_feature: [min_data_in_bin: [bin_construct_sample_cnt: [data_random_seed: [is_enable_sparse: [enable_bundle: [use_missing: [zero_as_missing: [feature_pre_filter: [pre_partition: [two_round: [header: [label_column: [weight_column: [group_column: [ignore_column: [categorical_feature: [forcedbins_filename: [precise_float_parser: [objective_seed: [num_class: [is_unbalance: [scale_pos_weight: [sigmoid: [boost_from_average: [reg_sqrt: [alpha: [fair_c: [poisson_max_delta_step: [tweedie_variance_power: [lambdarank_norm: [label_gain: [eval_at: [multi_error_top_k: [auc_mu_weights: [num_machines: [local_listen_port: [time_out: [machine_list_filename: [machines: [gpu_platform_id: [gpu_device_id: [gpu_use_dp: [num_gpu: -+config_fileconfigtask_typeobjective_typeappapplicationboosting_typeboosttrain_datatrain_data_filedata_filenamevalidvalid_datavalid_data_filetest_datatest_data_filevalid_filenamesnum_iterationnum_iterationsn_iternum_treenum_treesnum_roundnum_roundsnum_boost_roundn_estimatorsmax_itershrinkage_ratelearning_rateetanum_leafnum_leavesmax_leavesmax_leafmax_leaf_nodestree_typetree_learner_typenum_threadnum_threadsnthreadnthreadsn_jobsdevicerandom_statehist_pool_sizehistogram_pool_sizemin_data_per_leafmin_data_in_leafmin_datamin_child_samplesmin_samples_leafmin_sum_hessian_per_leafmin_sum_hessian_in_leafmin_sum_hessianmin_hessianmin_child_weightpos_sub_rowpos_bagging_fractionpos_subsamplepos_baggingneg_sub_rowneg_bagging_fractionneg_subsampleneg_baggingsubsample_freqbagging_freqbagging_fraction_seedbagging_seedsub_featurefeature_fractioncolsample_bytreesub_feature_bynodefeature_fraction_bynodecolsample_bynodeextra_treeextra_treesearly_stopping_roundsearly_stopping_roundearly_stoppingn_iter_no_changemax_tree_outputmax_leaf_outputreg_alphalambda_l1l1_regularizationreg_lambdalambda_l2l2_regularizationmin_split_gainmin_gain_to_splitrate_dropdrop_ratetopkmcmonotone_constraintmonotonic_cstmonotone_constraining_methodmonotone_constraints_methodmc_methodmonotone_splits_penaltymonotone_penaltyms_penaltymc_penaltyfeature_contribfeature_contrifcfpfeature_penaltyfsforcedsplits_filenameforced_splits_filenameforced_splits_fileforced_splitsverboseverbositymodel_inputinput_modelmodel_inmodel_outputoutput_modelmodel_outsave_periodsnapshot_freqlinear_treeslinear_treemax_binsmax_binsubsample_for_binbin_construct_sample_cntdata_seeddata_random_seedis_sparseis_enable_sparseis_enable_bundleis_pre_partitiontwo_rounduse_two_round_loadinghas_headerlabel_columnweight_columngroup_columngroup_idquery_columnquery_idignore_featureignore_columnblacklistcat_featurecategorical_featurecategorical_columncat_columncategorical_featuresis_save_binaryis_save_binary_fileis_predict_raw_scorepredict_rawscoreis_predict_leaf_indexis_predict_contribpredict_resultoutput_resultprediction_resultpredict_nameprediction_namepred_namename_predconvert_model_filenum_classesis_unbalanceunbalanced_setsmetricsmetric_typesoutput_freqmetric_freqis_provide_training_metricis_training_metrictrain_metricndcg_eval_atndcg_atmap_eval_atmap_atnum_machinenum_machineslocal_portlocal_listen_portmachine_list_filemachine_list_filenamemachine_listmlistworkersstoddeterministicforce_col_wiseforce_row_wisemax_depthfeature_fraction_seedextra_seedfirst_metric_onlylinear_lambdamax_dropskip_dropxgboost_dart_modeuniform_dropdrop_seedtop_rateother_ratemin_data_per_groupmax_cat_thresholdcat_l2cat_smoothmax_cat_to_onehotrefit_decay_ratecegb_tradeoffcegb_penalty_splitcegb_penalty_feature_lazycegb_penalty_feature_coupledpath_smoothinteraction_constraintssaved_feature_importance_typemax_bin_by_featuremin_data_in_binuse_missingzero_as_missingfeature_pre_filterforcedbins_filenameprecise_float_parserstart_iteration_predictnum_iteration_predictpredict_disable_shape_checkpred_early_stoppred_early_stop_freqpred_early_stop_marginconvert_model_languageobjective_seedscale_pos_weightsigmoidboost_from_averagereg_sqrtfair_cpoisson_max_delta_steptweedie_variance_powerlambdarank_truncation_levellambdarank_normlabel_gainmulti_error_top_kauc_mu_weightstime_outgpu_platform_idgpu_device_idgpu_use_dpnum_gpu[monotone_constraints_method: [cegb_penalty_feature_coupled: [saved_feature_importance_type: [lambdarank_truncation_level: Parameter %s should be of type double, got "%s"Parameter %s should be "true"/"+" or "false"/"-", got "%s"/__w/1/s/src/io/config_auto.cppCheck failed: (num_iterations) >= (0) at %s, line %d . Check failed: (learning_rate) > (0.0) at %s, line %d . Check failed: (num_leaves) > (1) at %s, line %d . @@ -4861,11 +4839,11 @@ Check failed: (*(std::min_element(io_config.max_bin_by_feature.begin(), io_config.max_bin_by_feature.end()))) > (1) at %s, line %d . Check failed: (static_cast<int>(bin_mappers->size())) == (1) at %s, line %d . Cannot add features from other Dataset with a different number of rowsCan only add features from other Dataset if both or neither have raw data.Check failed: num_groups_ == static_cast<int>(features_in_group.size()) at %s, line %d . ______LightGBM_Binary_File_Token______ *ZN8LightGBM21PushDataToMultiValBinEiSt6vectorIjSaIjEES2_PS0_IS0_ISt10unique_ptrINS_11BinIteratorESt14default_deleteIS4_EESaIS7_EESaIS9_EEPNS_11MultiValBinEEUliiiE_*ZN8LightGBM21PushDataToMultiValBinEiSt6vectorIjSaIjEES2_PS0_IS0_ISt10unique_ptrINS_11BinIteratorESt14default_deleteIS4_EESaIS7_EESaIS9_EEPNS_11MultiValBinEEUliiiE0_?Read %.1f GBs from %s.Data file %s is emptyCannot open data file %sbin_upper_boundColumn_name: ,Using column %s as weightUsing column %s as labelMaking second pass...Load from binary file %sCurrent query exceeds the range of the query file, -please ensure the query file is correctQuery id exceeds the range of the query file, please ensure the query file is correctThe output cannot be monotone with respect to categorical featuresUsing too small ``bin_construct_sample_cnt`` may encounter unexpected errors and poor accuracy.Size of feature name error, should be %d, got %dFeatures in dataset should be ordered by groupDataset max_bin %d != config %dDataset min_data_in_bin %d != config %dDataset use_missing %d != config %dDataset zero_as_missing %d != config %dDataset bin_construct_sample_cnt %d != config %dDataset label_idx %d != config %dDataset max_bin_by_feature does not match with configRecommend use integer for label index when loading data from binary for sanity check.Warning: last line of %s has no end of line, still using this lineCould not read binary data from %sBinary file error: token has the wrong sizeBinary file error: header has the wrong sizeBinary file error: header is incorrect/__w/1/s/src/io/dataset_loader.cppCheck failed: (static_cast<size_t>(dataset->num_total_features_)) == (config_.max_bin_by_feature.size()) at %s, line %d . +please ensure the query file is correctQuery id exceeds the range of the query file, please ensure the query file is correctThe output cannot be monotone with respect to categorical featuresUsing too small ``bin_construct_sample_cnt`` may encounter unexpected errors and poor accuracy.Size of feature name error, should be %d, got %dFeatures in dataset should be ordered by groupDataset max_bin %d != config %dDataset min_data_in_bin %d != config %dDataset use_missing %d != config %dDataset zero_as_missing %d != config %dDataset bin_construct_sample_cnt %d != config %dDataset label_idx %d != config %dConfig label_column works only in case of loading data directly from text file. It will be ignored when loading from binary file.Config weight_column works only in case of loading data directly from text file. It will be ignored when loading from binary file.Config group_column works only in case of loading data directly from text file. It will be ignored when loading from binary file.Config ignore_column works only in case of loading data directly from text file. It will be ignored when loading from binary file.Dataset max_bin_by_feature does not match with configRecommend use integer for label index when loading data from binary for sanity check.Warning: last line of %s has no end of line, still using this lineCould not read binary data from %sBinary file error: token has the wrong sizeBinary file error: header has the wrong sizeBinary file error: header is incorrect/__w/1/s/src/io/dataset_loader.cppCheck failed: (static_cast<size_t>(dataset->num_total_features_)) == (config_.max_bin_by_feature.size()) at %s, line %d . Check failed: (*(std::min_element(config_.max_bin_by_feature.begin(), config_.max_bin_by_feature.end()))) > (1) at %s, line %d . Binary file error: meta data has the wrong sizeBinary file error: meta data is incorrectBinary file error: feature %d has the wrong sizeBinary file error: feature %d is incorrect, read count: %dBinary file error: row %d of raw data is incorrect, read count: %dInput file is not LightGBM binary fileCould not open %s. Will ignore.Check failed: forced_bins_json.is_array() at %s, line %d . Check failed: (feature_num) < (num_total_features) at %s, line %d . Feature %d is categorical. Will ignore forced bins for this feature.Size of feature_names error, should equal with total number of featuresFeature (%s) appears more than one time.Found whitespace in feature_names, replace with underlinesDo not support special JSON characters in feature name.Check failed: (static_cast<size_t>(num_col)) == (config_.max_bin_by_feature.size()) at %s, line %d . Could not find label column %s in data file @@ -4934,12 +4912,12 @@ YJ^M:0ܵ$~sީq]V ukP/<R{ ܿ<Z !x\u\S٨|4EM +`]hk䤹Bx郮Ҁ0B$ZR0I<D|.ANyĢXˊ׵e󦑙?n̰ܔٜ =DLLvU_S,Uktwjۂ#-;#[Dz,4x@ УrČV<qe~1Vx5k\(3VFs,WЛgjB_wqBv/? Sc]ɞH|Y{ځo(1r}c<JAnJI+QEE;R bڸ E}ajAQdһS8~cU4_^jIukS\ܺ)c᳹);b (3ʺ)2־ԩYpI0/j\&ҷ]ُ]X%2t.o?0:ͅϧz^KDfQ6^U@4õjP4cع^O 5bbLB},ݬ@!xP] RzRϖ|$ P~tґGQ.G嘡cD^/g?$@Y@@@@j@.AcAחAeA _BvH7BmB@0BļB4&k C7yAC؅W4vCNgmC=`XC@xDPKDMDN8LightGBM6ParserEN8LightGBM9CSVParserEN8LightGBM9TSVParserEN8LightGBM12LibSVMParserE/__w/1/s/src/io/train_share_states.cppCheck failed: cur_num_bin == feature_group->bin_offsets_.back() at %s, line %d . 333333?>%s:%d: assertion failed: %stoo large exponentnegative valueinvalid digit countempty buffersign == '+' || sign == '-'is_digit(*p)shift >= 0exp >= 0unaligned bigintsk is out of rangeshifting error detected"leaf_const":"leaf_features":["leaf_coeff":["leaf_features":[], "leaf_coeff":[] -if (fval <= || Tree::IsZero(fval) || std::isnan(fval)) { && !Tree::IsZero(fval) && !std::isnan(fval)) {) && (((cat_threshold[fval = arr[]; } else { }return fval = arr.count() > 0 ? arr.at() : 0.0f;double PredictTree(const double* arr) { double fval = 0.0f; int int_fval = 0; "split_index":"split_feature":"split_gain":||"threshold":""decision_type":"==","threshold":"decision_type":"<=","default_left":true,"default_left":false,"missing_type":"None","internal_value":"missing_type":"Zero","missing_type":"NaN","internal_weight":"internal_count":"left_child":"right_child":"leaf_index":"leaf_value":"leaf_weight":"leaf_count":"num_leaves":"num_cat":"shrinkage":"tree_structure":{"tree_structure":string pointer is nullFailed to parse double: %sn is too largenum_catleaf_valueshrinkageis_linearleft_childright_childsplit_featureinternal_countinternal_valueinternal_weightleaf_weightleaf_countdecision_typeleaf_constnum_featuresleaf_featuresleaf_coeffcat_boundariesexponent out of rangeNANINFinvalid type specifiernumber is too bigvalue is negativeinvalid number of digitsnegative widthwidth is not integerargument not foundinvalid format stringnegative precisionprecision is not integer0123456789ABCDEFinvalid fill character '{'invalid fillmissing precision specifiermissing '}' in format stringinvalid argument typeunknown format specifier{}{:g}{:.17g}num_leaves=num_cat=split_feature=split_gain=decision_type=left_child=right_child=leaf_value=leaf_weight=leaf_count=internal_value=internal_weight=internal_count=cat_boundaries=cat_threshold=is_linear=leaf_const=num_features=leaf_features=leaf_coeff=shrinkage=/__w/1/s/include/LightGBM/utils/../../../external_libs/fmt/include/fmt/format-inl.h/__w/1/s/include/LightGBM/utils/../../../external_libs/fmt/include/fmt/core.h/__w/1/s/include/LightGBM/utils/../../../external_libs/fmt/include/fmt/format.hbigits_.capacity() <= bigits_capacityif (std::isnan(fval)) { int_fval = -1; } else { int_fval = static_cast<int>(fval); }if (int_fval >= 0 && int_fval < 32 * (if (std::isnan(fval)) { int_fval = 0; } else { int_fval = static_cast<int>(fval); } + int_fval / 32] >> (int_fval & 31)) & 1))) {const std::vector<uint32_t> cat_threshold = {(const std::unordered_map<int, double>& arr) { unmatched '}' in format string/__w/1/s/include/LightGBM/utils/common.hCheck failed: (strs.size()) == (static_cast<size_t>(n)) at %s, line %d . -Tree model should contain num_leaves fieldTree model should contain num_cat fieldTree model string format error, should contain leaf_value fieldTree model string format error, should contain left_child fieldTree model string format error, should contain right_child fieldTree model string format error, should contain split_feature fieldTree model string format error, should contain threshold fieldTree model should contain cat_boundaries field.Tree model should contain cat_threshold fieldcannot switch from manual to automatic argument indexingcannot switch from automatic to manual argument indexingformat specifier requires numeric argumentinvalid format specifier for charformat specifier requires signed argumentprecision not allowed for this argument typeNumerical conversion failed. Buffer is too small. P`0`0int fmt::v7::detail::snprintf_float(T, int, fmt::v7::detail::float_specs, fmt::v7::detail::buffer<char>&) [with T = double]fmt::v7::detail::bigint::~bigint()fmt::v7::detail::bigint& fmt::v7::detail::bigint::operator<<=(int)void fmt::v7::detail::bigint::assign_pow10(int)int fmt::v7::detail::snprintf_float(T, int, fmt::v7::detail::float_specs, fmt::v7::detail::buffer<char>&) [with T = long double]-^85AuLR Cs Nww&Դ"U 5U]_nU4!#un6%!3G +if (fval <= || Tree::IsZero(fval) || std::isnan(fval)) { && !Tree::IsZero(fval) && !std::isnan(fval)) {) && (((cat_threshold[fval = arr[]; } else { }return fval = arr.count() > 0 ? arr.at() : 0.0f;double PredictTree(const double* arr) { double fval = 0.0f; int int_fval = 0; "split_index":"split_feature":"split_gain":||"threshold":""decision_type":"==","threshold":"decision_type":"<=","default_left":true,"default_left":false,"missing_type":"None","internal_value":"missing_type":"Zero","missing_type":"NaN","internal_weight":"internal_count":"left_child":"right_child":"leaf_index":"leaf_value":"leaf_weight":"leaf_count":"num_leaves":"num_cat":"shrinkage":"tree_structure":{"tree_structure":string pointer is nullFailed to parse double: %sn is too largenum_catleaf_valueshrinkageis_linearleft_childright_childsplit_featureinternal_countinternal_valueinternal_weightleaf_weightleaf_countdecision_typeleaf_constnum_featuresleaf_featuresleaf_coeffcat_boundariesexponent out of rangeNANINFinvalid type specifiernumber is too bigvalue is negativeinvalid number of digitsnegative widthwidth is not integerargument not foundinvalid format stringnegative precisionprecision is not integer0123456789ABCDEFinvalid fill character '{'invalid fillmissing precision specifiermissing '}' in format stringinvalid argument typeunknown format specifier{}{:.17g}{:g}num_leaves=num_cat=split_feature=split_gain=decision_type=left_child=right_child=leaf_value=leaf_weight=leaf_count=internal_value=internal_weight=internal_count=cat_boundaries=cat_threshold=is_linear=leaf_const=num_features=leaf_features=leaf_coeff=shrinkage=/__w/1/s/include/LightGBM/utils/../../../external_libs/fmt/include/fmt/format-inl.h/__w/1/s/include/LightGBM/utils/../../../external_libs/fmt/include/fmt/core.h/__w/1/s/include/LightGBM/utils/../../../external_libs/fmt/include/fmt/format.hbigits_.capacity() <= bigits_capacityif (std::isnan(fval)) { int_fval = -1; } else { int_fval = static_cast<int>(fval); }if (int_fval >= 0 && int_fval < 32 * (if (std::isnan(fval)) { int_fval = 0; } else { int_fval = static_cast<int>(fval); } + int_fval / 32] >> (int_fval & 31)) & 1))) {const std::vector<uint32_t> cat_threshold = {(const std::unordered_map<int, double>& arr) { unmatched '}' in format string/__w/1/s/include/LightGBM/utils/common.hCheck failed: (strs.size()) == (static_cast<size_t>(n)) at %s, line %d . +Tree model should contain num_leaves fieldTree model should contain num_cat fieldTree model string format error, should contain leaf_value fieldTree model string format error, should contain left_child fieldTree model string format error, should contain right_child fieldTree model string format error, should contain split_feature fieldTree model string format error, should contain threshold fieldTree model should contain cat_boundaries field.Tree model should contain cat_threshold fieldcannot switch from manual to automatic argument indexingcannot switch from automatic to manual argument indexingformat specifier requires numeric argumentinvalid format specifier for charformat specifier requires signed argumentprecision not allowed for this argument typeNumerical conversion failed. Buffer is too small.@P ؑpP int fmt::v7::detail::snprintf_float(T, int, fmt::v7::detail::float_specs, fmt::v7::detail::buffer<char>&) [with T = double]fmt::v7::detail::bigint::~bigint()fmt::v7::detail::bigint& fmt::v7::detail::bigint::operator<<=(int)void fmt::v7::detail::bigint::assign_pow10(int)int fmt::v7::detail::snprintf_float(T, int, fmt::v7::detail::float_specs, fmt::v7::detail::buffer<char>&) [with T = long double]-^85AuLR Cs Nww&Դ"U 5U]_nU4!#un6%!3G ni %Cp(W^j87.h3DJ!jzd)غ`Y3)$oVyԶӥlwHޔ3 M^Pa,\":1H cm̻, `%N *᮴ fMȟP(rbISz%Taw$jDk(sK2 CgN?͓A"xuRCkxk SVk#췆,2{S̰?h@ѡ¹ #2( @@ -4981,11 +4959,11 @@ ףp= ף@P$ (k@C*焑 1_.@v:k #NJbxz&n2xW ?hӠ@aQYȥo: '΄ x9? 6Ngɖ"E@|op+ŝL67V߄\l: {Η H½ Pv1P?%O7и'ƫCưo\{2~]n0b/5E=!֕C)L{QF$+v؈Sﶓh髤8AqfcQlN@< ePKПDǕh"!9oj%p E"&'O֪"Nj+w 3;LզIxŠ'V:qCN'1c$_E^69uD]ȩdLJ:݈MXd슠p`~Ȍ8eްݫ--{9.ڇz贙#"׬+֫* eǃR?Vʸ&ϫ ^xa Z^9wuܠLqm_:JIxX{-KqB]e wet _jhvjE¿;JЕ(aʩ]D&r<uX,iM.wzU?7ߎ~S^=lX![qgpdڲX4A4>QԞ2#Ek0SF|n_OIwmU c*O~MQ5Fe B‹_FiYW7/-k}{x 2cPME fzBΨ?]ߌG#٨l*CwyT^Tj4'RE]dB!ܺ~IriE֒Pś[[26hd#Dvja5ӨŹ7hdZk"!"k*=]S5Ȍ:Bxri %ь[e8y/JXg].\*߈=9tauGѹ"+_vI}Azdұ_F;#J ,(ن"y(+EWAu-/ӷ .|]|%94›.²}DKa2(x5˲az®k[rc? <<Ϗ( CYxķapeFYy "WˇuξTSܷ.$*(VyuUD`גjU'9pbCFTfAXjiQ.A"ZҪ/<Uj; VtubeDžl8Džinќ&E[`BrMx\ӛ Ζ3B)A> iyMh, XEa75 .|BǼ]R5VS!{Z֨Y dp>ԅ.7JŇԱ YJ^M:0ܵ$~sީq]V ukP/<R{ ܿ<Z !x\u\S٨|4EM +`]hk䤹Bx郮Ҁ0B$ZR0I<D|.ANyĢXˊ׵e󦑙?n̰ܔٜ =DLLvU_S,Uktwjۂ#-;#[Dz,4x@ УrČV<qe~1Vx5k\(3VFs,WЛgjB_wqBv/? Sc]ɞH|Y{ځo(1r}c<JAnJI+QEE;R -bڸ E}ajAQdһS8~cU4_^jIukS\ܺ)c᳹);b (3ʺ)2־ԩYpI0/j\&ҷ]ُ]X%2t.o?0:ͅϧz^KDfQ6^U@4õjP4cع^O 5bbLB},ݬ@!xP] RzRϖ|$ P~tґGQ.G嘡cD^/g?$@Y@@@@j@.AcAחAeA _BvH7BmB@0BļB4&k C7yAC؅W4vCNgmC=`XC@xDPKDMD*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEiPdEUliiiE_*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEiPdEUliiiE0_*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEiPdEUliiiE1_*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEiPdEUliiiE2_*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEiPdEUliiiE3_*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEiPdEUliiiE4_*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEiPdEUliiiE5_*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEiPdEUliiiE6_*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEPKiiPdEUliiiE_*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEPKiiPdEUliiiE0_*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEPKiiPdEUliiiE1_*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEPKiiPdEUliiiE2_*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEPKiiPdEUliiiE3_*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEPKiiPdEUliiiE4_*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEPKiiPdEUliiiE5_*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEPKiiPdEUliiiE6_0X0@@000000000HaXa`__]\X\[[x[8[ZZxay/x~ygyPy4yyxxxxwwwxEx\yO{6{{{zzzzozWz>z'zzyh{&fRՁ|h9000 +bڸ E}ajAQdһS8~cU4_^jIukS\ܺ)c᳹);b (3ʺ)2־ԩYpI0/j\&ҷ]ُ]X%2t.o?0:ͅϧz^KDfQ6^U@4õjP4cع^O 5bbLB},ݬ@!xP] RzRϖ|$ P~tґGQ.G嘡cD^/g?$@Y@@@@j@.AcAחAeA _BvH7BmB@0BļB4&k C7yAC؅W4vCNgmC=`XC@xDPKDMD*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEiPdEUliiiE_*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEiPdEUliiiE0_*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEiPdEUliiiE1_*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEiPdEUliiiE2_*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEiPdEUliiiE3_*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEiPdEUliiiE4_*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEiPdEUliiiE5_*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEiPdEUliiiE6_*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEPKiiPdEUliiiE_*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEPKiiPdEUliiiE0_*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEPKiiPdEUliiiE1_*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEPKiiPdEUliiiE2_*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEPKiiPdEUliiiE3_*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEPKiiPdEUliiiE4_*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEPKiiPdEUliiiE5_*ZNK8LightGBM4Tree20AddPredictionToScoreEPKNS_7DatasetEPKiiPdEUliiiE6_ H 00 вг8вввввввввYHYXWWUTHTSShS(SRRhYqpnqWq@q$qqpppoooo p5pLq?s&s srrrrzr_rGr.rrqqXsyyyxyoyVyByyyyxxlxXx)y000 N3fmt2v76detail19fixed_buffer_traitsEN3fmt2v76detail6bufferIcEEN3fmt2v719basic_memory_bufferIcLm500ESaIcEEEN3fmt2v76detail6bufferIjEEN3fmt2v719basic_memory_bufferIjLm32ESaIjEEEN3fmt2v76detail15iterator_bufferIPccNS1_19fixed_buffer_traitsEEE G Number of rows %i exceeds upper limit of %i for a querylabel should be int type (met %f) for ranking task, for the gain of label, please set the label_gain parameterLabel should be non-negative (met %f) for ranking taskLabel %zu is not less than the number of label mappings (%zu)/__w/1/s/src/metric/dcg_calculator.cppCheck failed: (ref_eval_at[i]) > (0) at %s, line %d . @@ -4996,11 +4974,11 @@ Sigmoid param %f should be greater than zeroCheck failed: (threshold) >= (weighted_cdf[pos - 1]) at %s, line %d . Check failed: (threshold) < (weighted_cdf[pos]) at %s, line %d . BoostFromScoreInitBoostFromScoreInitBoostFromScoreN8LightGBM17ObjectiveFunctionEN8LightGBM13BinaryLoglossEN8LightGBM17MulticlassSoftmaxEN8LightGBM13MulticlassOVAEN8LightGBM16RankingObjectiveEN8LightGBM14LambdarankNDCGEN8LightGBM10RankXENDCGEN8LightGBM16RegressionL2lossEN8LightGBM16RegressionL1lossEN8LightGBM19RegressionHuberLossEN8LightGBM18RegressionFairLossEN8LightGBM21RegressionPoissonLossEN8LightGBM22RegressionQuantilelossEN8LightGBM18RegressionMAPELOSSEN8LightGBM19RegressionGammaLossEN8LightGBM21RegressionTweedieLossEN8LightGBM12CrossEntropyEN8LightGBM18CrossEntropyLambdaEZN8LightGBM13MulticlassOVAC1ERKNS_6ConfigEEUlfE_ZN8LightGBM13BinaryLoglossC1ERKNS_6ConfigESt8functionIFbfEEEUlfE_ZN8LightGBM9ArrayArgsIfE8ArgMaxMTERKSt6vectorIfSaIfEEEUlimmE_ZN8LightGBM9ArrayArgsIdE8ArgMaxMTERKSt6vectorIdSaIdEEEUlimmE_?qq C<ư>0y$k CII@@z?????vector<bool>::_M_fill_insert/__w/1/s/src/treelearner/serial_tree_learner.hCheck failed: (tree->num_leaves()) <= (data_partition_->num_leaves()) at %s, line %d . CUDA Tree Learner was not enabled in this build. Please recompile with CMake option -DUSE_CUDA=1GPU Tree Learner was not enabled in this build. -Please recompile with CMake option -DUSE_GPU=1N8LightGBM15CUDATreeLearnerEN8LightGBM14GPUTreeLearnerEN8LightGBM23DataParallelTreeLearnerINS_15CUDATreeLearnerEEEN8LightGBM23DataParallelTreeLearnerINS_14GPUTreeLearnerEEEN8LightGBM23DataParallelTreeLearnerINS_17SerialTreeLearnerEEEZN8LightGBM9ArrayArgsINS_9SplitInfoEE8ArgMaxMTERKSt6vectorIS1_SaIS1_EEEUlimmE_N8LightGBM26FeatureParallelTreeLearnerINS_15CUDATreeLearnerEEEN8LightGBM26FeatureParallelTreeLearnerINS_14GPUTreeLearnerEEEN8LightGBM26FeatureParallelTreeLearnerINS_17SerialTreeLearnerEEE/__w/1/s/external_libs/eigen/Eigen/src/Core/util/Memory.h(size<16 || (std::size_t(result)%16)==0) && "System's malloc returned an unaligned pointer. Compile with EIGEN_MALLOC_ALREADY_ALIGNED=0 to fallback to handmade aligned memory allocator."/__w/1/s/external_libs/eigen/Eigen/src/Core/PlainObjectBase.h((SizeAtCompileTime == Dynamic && (MaxSizeAtCompileTime==Dynamic || size<=MaxSizeAtCompileTime)) || SizeAtCompileTime == size) && size>=0/__w/1/s/external_libs/eigen/Eigen/src/LU/FullPivLU.hm_lu.rows() == m_lu.cols() && "You can't take the inverse of a non-square matrix!"/__w/1/s/external_libs/eigen/Eigen/src/Core/DenseCoeffsBase.h/__w/1/s/external_libs/eigen/Eigen/src/Core/PermutationMatrix.hi>=0 && j>=0 && i<size() && j<size()/__w/1/s/external_libs/eigen/Eigen/src/Core/DenseBase.hrows == this->rows() && cols == this->cols() && "DenseBase::resize() does not actually allow to resize."/__w/1/s/src/treelearner/linear_tree_learner.h(!(RowsAtCompileTime!=Dynamic) || (rows==RowsAtCompileTime)) && (!(ColsAtCompileTime!=Dynamic) || (cols==ColsAtCompileTime)) && (!(RowsAtCompileTime==Dynamic && MaxRowsAtCompileTime!=Dynamic) || (rows<=MaxRowsAtCompileTime)) && (!(ColsAtCompileTime==Dynamic && MaxColsAtCompileTime!=Dynamic) || (cols<=MaxColsAtCompileTime)) && rows>=0 && cols>=0 && "Invalid sizes when resizing a matrix or array."/__w/1/s/external_libs/eigen/Eigen/src/Core/MapBase.h(dataPtr == 0) || ( rows >= 0 && (RowsAtCompileTime == Dynamic || RowsAtCompileTime == rows) && cols >= 0 && (ColsAtCompileTime == Dynamic || ColsAtCompileTime == cols))/__w/1/s/external_libs/eigen/Eigen/src/Core/Block.hstartRow >= 0 && blockRows >= 0 && startRow <= xpr.rows() - blockRows && startCol >= 0 && blockCols >= 0 && startCol <= xpr.cols() - blockCols(i>=0) && ( ((BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) && i<xpr.rows()) ||((BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) && i<xpr.cols()))/__w/1/s/external_libs/eigen/Eigen/src/Core/util/XprHelper.h/__w/1/s/external_libs/eigen/Eigen/src/Core/CwiseNullaryOp.hrows >= 0 && (RowsAtCompileTime == Dynamic || RowsAtCompileTime == rows) && cols >= 0 && (ColsAtCompileTime == Dynamic || ColsAtCompileTime == cols)/__w/1/s/external_libs/eigen/Eigen/src/Core/AssignEvaluator.hdst.rows() == src.rows() && dst.cols() == src.cols()/__w/1/s/external_libs/eigen/Eigen/src/Core/util/BlasUtil.hm_lu.rows()<=NumTraits<int>::highest() && m_lu.cols()<=NumTraits<int>::highest()/__w/1/s/external_libs/eigen/Eigen/src/Core/Redux.hthis->rows()>0 && this->cols()>0 && "you are using an empty matrix"/__w/1/s/external_libs/eigen/Eigen/src/Core/Visitor.hrow >= 0 && row < rows() && col >= 0 && col < cols()rows()==other.rows() && cols()==other.cols()/__w/1/s/external_libs/eigen/Eigen/src/Core/ProductEvaluators.hdst.rows() == dstRows && dst.cols() == dstCols/__w/1/s/external_libs/eigen/Eigen/src/Core/CwiseBinaryOp.haLhs.rows() == aRhs.rows() && aLhs.cols() == aRhs.cols()/__w/1/s/external_libs/eigen/Eigen/src/Core/products/GeneralBlockPanelKernel.h((!PanelMode) && stride==0 && offset==0) || (PanelMode && stride>=depth && offset<=stride)/__w/1/s/external_libs/eigen/Eigen/src/Core/SolveTriangular.hderived().cols() == derived().rows() && ((Side==OnTheLeft && derived().cols() == other.rows()) || (Side==OnTheRight && derived().cols() == other.cols()))m_isInitialized && "LU is not initialized."/__w/1/s/external_libs/eigen/Eigen/src/Core/Product.hlhs.cols() == rhs.rows() && "invalid matrix product" && "if you wanted a coeff-wise or a dot product use the respective explicit functions"/__w/1/s/external_libs/eigen/Eigen/src/Core/products/GeneralMatrixMatrix.hdst.rows()==a_lhs.rows() && dst.cols()==a_rhs.cols()/__w/1/s/external_libs/eigen/Eigen/src/Core/SolverBase.hderived().m_isInitialized && "Solver is not initialized."(Transpose_?derived().cols():derived().rows())==b.rows() && "SolverBase::solve(): invalid number of rows of the right hand side matrix b"No further splits with positive gain, best gain: %fTrained a tree with leaves = %d and depth = %dDetected that num_threads changed during training (from %d to %d), it may cause unexpected errors.index >= 0 && index < size()v == T(Value)incr==1SerialTreeLearner::Trainp.---.-----------.-------..-.---0.--P.---P.--------.....--x/..- .`.0.@.//8/@.H/X/h/-----------------------p.-P.--------------- ...- .`..--..- .`.- ..0.@.--.Eigen::DenseCoeffsBase<Derived, 1>::Scalar& Eigen::DenseCoeffsBase<Derived, 1>::operator()(Eigen::Index) [with Derived = Eigen::Matrix<double, -1, -1>; Eigen::DenseCoeffsBase<Derived, 1>::Scalar = double; Eigen::Index = long int]void Eigen::PlainObjectBase<Derived>::resize(Eigen::Index, Eigen::Index) [with Derived = Eigen::Matrix<double, 1, -1>; Eigen::Index = long int]Eigen::Block<XprType, BlockRows, BlockCols, InnerPanel>::Block(XprType&, Eigen::Index) [with XprType = const Eigen::CwiseUnaryOp<Eigen::internal::scalar_opposite_op<double>, const Eigen::Inverse<Eigen::FullPivLU<Eigen::Matrix<double, -1, -1> > > >; int BlockRows = 1; int BlockCols = -1; bool InnerPanel = false; Eigen::Index = long int]void Eigen::internal::resize_if_allowed(DstXprType&, const SrcXprType&, const Eigen::internal::assign_op<T1, T2>&) [with DstXprType = Eigen::Matrix<double, -1, -1>; SrcXprType = Eigen::CwiseUnaryOp<Eigen::internal::scalar_opposite_op<double>, const Eigen::Inverse<Eigen::FullPivLU<Eigen::Matrix<double, -1, -1> > > >; T1 = double; T2 = double]static void Eigen::internal::generic_product_impl<Lhs, Rhs, Eigen::DenseShape, Eigen::DenseShape, 8>::scaleAndAddTo(Dest&, const Lhs&, const Rhs&, const Scalar&) [with Dest = Eigen::Matrix<double, -1, -1>; Lhs = Eigen::CwiseUnaryOp<Eigen::internal::scalar_opposite_op<double>, const Eigen::Inverse<Eigen::FullPivLU<Eigen::Matrix<double, -1, -1> > > >; Rhs = Eigen::Matrix<double, -1, -1>; Eigen::internal::generic_product_impl<Lhs, Rhs, Eigen::DenseShape, Eigen::DenseShape, 8>::Scalar = double]Eigen::CwiseNullaryOp<NullaryOp, MatrixType>::CwiseNullaryOp(Eigen::Index, Eigen::Index, const NullaryOp&) [with NullaryOp = Eigen::internal::scalar_constant_op<double>; PlainObjectType = Eigen::Matrix<double, -1, -1>; Eigen::Index = long int]typename Eigen::internal::traits<T>::Scalar Eigen::DenseBase<Derived>::redux(const Func&) const [with BinaryOp = Eigen::internal::scalar_sum_op<double, double>; Derived = Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double, double>, const Eigen::Transpose<const Eigen::Block<const Eigen::Matrix<double, -1, -1>, 1, -1, false> >, const Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, 1, true> >; typename Eigen::internal::traits<T>::Scalar = double]Eigen::CwiseBinaryOp<BinaryOp, Lhs, Rhs>::CwiseBinaryOp(const Lhs&, const Rhs&, const BinaryOp&) [with BinaryOp = Eigen::internal::scalar_product_op<double, double>; LhsType = const Eigen::Transpose<const Eigen::Block<const Eigen::Matrix<double, -1, -1>, 1, -1, false> >; RhsType = const Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, 1, true>; Eigen::CwiseBinaryOp<BinaryOp, Lhs, Rhs>::Lhs = Eigen::Transpose<const Eigen::Block<const Eigen::Matrix<double, -1, -1>, 1, -1, false> >; Eigen::CwiseBinaryOp<BinaryOp, Lhs, Rhs>::Rhs = Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, 1, true>]Eigen::MapBase<Derived, 0>::MapBase(Eigen::MapBase<Derived, 0>::PointerType, Eigen::Index, Eigen::Index) [with Derived = Eigen::Block<const Eigen::Matrix<double, -1, -1>, 1, -1, false>; Eigen::MapBase<Derived, 0>::PointerType = const double*; Eigen::Index = long int]Eigen::Block<XprType, BlockRows, BlockCols, InnerPanel>::Block(XprType&, Eigen::Index) [with XprType = const Eigen::Matrix<double, -1, -1>; int BlockRows = 1; int BlockCols = -1; bool InnerPanel = false; Eigen::Index = long int]Eigen::MapBase<Derived, 0>::MapBase(Eigen::MapBase<Derived, 0>::PointerType, Eigen::Index, Eigen::Index) [with Derived = Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, 1, true>; Eigen::MapBase<Derived, 0>::PointerType = const double*; Eigen::Index = long int]Eigen::Block<XprType, BlockRows, BlockCols, InnerPanel>::Block(XprType&, Eigen::Index) [with XprType = const Eigen::Matrix<double, -1, -1>; int BlockRows = -1; int BlockCols = 1; bool InnerPanel = true; Eigen::Index = long int]void Eigen::internal::resize_if_allowed(DstXprType&, const SrcXprType&, const Eigen::internal::assign_op<T1, T2>&) [with DstXprType = Eigen::Matrix<double, -1, -1>; SrcXprType = Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double, double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1> >, const Eigen::Product<Eigen::Inverse<Eigen::FullPivLU<Eigen::Matrix<double, -1, -1> > >, Eigen::Matrix<double, -1, -1>, 1> >; T1 = double; T2 = double]Eigen::CwiseNullaryOp<NullaryOp, MatrixType>::CwiseNullaryOp(Eigen::Index, Eigen::Index, const NullaryOp&) [with NullaryOp = Eigen::internal::scalar_constant_op<double>; PlainObjectType = Eigen::Matrix<double, 1, -1>; Eigen::Index = long int]const PermutationQType& Eigen::FullPivLU<MatrixType>::permutationQ() const [with _MatrixType = Eigen::Matrix<double, -1, -1>; Eigen::FullPivLU<MatrixType>::PermutationQType = Eigen::PermutationMatrix<-1, -1, int>]void Eigen::TriangularViewImpl<_MatrixType, _Mode, Eigen::Dense>::solveInPlace(const Eigen::MatrixBase<OtherDerived>&) const [with int Side = 1; OtherDerived = Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>; _MatrixType = const Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, -1, false>; unsigned int _Mode = 2u]void Eigen::internal::resize_if_allowed(DstXprType&, const SrcXprType&, const Functor&) [with DstXprType = Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>; SrcXprType = Eigen::Matrix<double, -1, -1>; Functor = Eigen::internal::sub_assign_op<double, double>]Eigen::MapBase<Derived, 0>::MapBase(Eigen::MapBase<Derived, 0>::PointerType, Eigen::Index, Eigen::Index) [with Derived = Eigen::Block<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1>, 1, -1, false> >, -1, 1, true>; Eigen::MapBase<Derived, 0>::PointerType = double*; Eigen::Index = long int]static void Eigen::internal::generic_product_impl<Lhs, Rhs, Eigen::DenseShape, Eigen::DenseShape, 8>::scaleAndAddTo(Dest&, const Lhs&, const Rhs&, const Scalar&) [with Dest = Eigen::Matrix<double, -1, -1>; Lhs = Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, -1, false>; Rhs = Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>; Eigen::internal::generic_product_impl<Lhs, Rhs, Eigen::DenseShape, Eigen::DenseShape, 8>::Scalar = double]typename Eigen::internal::traits<T>::Scalar Eigen::DenseBase<Derived>::redux(const Func&) const [with BinaryOp = Eigen::internal::scalar_sum_op<double, double>; Derived = Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double, double>, const Eigen::Transpose<const Eigen::Block<const Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, -1, false>, 1, -1, false> >, const Eigen::Block<const Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>, -1, 1, true> >; typename Eigen::internal::traits<T>::Scalar = double]Eigen::CwiseBinaryOp<BinaryOp, Lhs, Rhs>::CwiseBinaryOp(const Lhs&, const Rhs&, const BinaryOp&) [with BinaryOp = Eigen::internal::scalar_product_op<double, double>; LhsType = const Eigen::Transpose<const Eigen::Block<const Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, -1, false>, 1, -1, false> >; RhsType = const Eigen::Block<const Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>, -1, 1, true>; Eigen::CwiseBinaryOp<BinaryOp, Lhs, Rhs>::Lhs = Eigen::Transpose<const Eigen::Block<const Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, -1, false>, 1, -1, false> >; Eigen::CwiseBinaryOp<BinaryOp, Lhs, Rhs>::Rhs = Eigen::Block<const Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>, -1, 1, true>]Eigen::MapBase<Derived, 0>::MapBase(Eigen::MapBase<Derived, 0>::PointerType, Eigen::Index, Eigen::Index) [with Derived = Eigen::Block<const Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, -1, false>, 1, -1, false>; Eigen::MapBase<Derived, 0>::PointerType = const double*; Eigen::Index = long int]Eigen::Block<XprType, BlockRows, BlockCols, InnerPanel>::Block(XprType&, Eigen::Index) [with XprType = const Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, -1, false>; int BlockRows = 1; int BlockCols = -1; bool InnerPanel = false; Eigen::Index = long int]Eigen::MapBase<Derived, 0>::MapBase(Eigen::MapBase<Derived, 0>::PointerType, Eigen::Index, Eigen::Index) [with Derived = Eigen::Block<const Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>, -1, 1, true>; Eigen::MapBase<Derived, 0>::PointerType = const double*; Eigen::Index = long int]Eigen::Block<XprType, BlockRows, BlockCols, InnerPanel>::Block(XprType&, Eigen::Index) [with XprType = const Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>; int BlockRows = -1; int BlockCols = 1; bool InnerPanel = true; Eigen::Index = long int]void Eigen::internal::resize_if_allowed(DstXprType&, const SrcXprType&, const Eigen::internal::assign_op<T1, T2>&) [with DstXprType = Eigen::Matrix<double, -1, -1>; SrcXprType = Eigen::Product<Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, -1, false>, Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>, 1>; T1 = double; T2 = double]Eigen::Product<Lhs, Rhs, Option>::Product(const Lhs&, const Rhs&) [with _Lhs = Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, -1, false>; _Rhs = Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>; int Option = 1; Eigen::Product<Lhs, Rhs, Option>::Lhs = Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, -1, false>; Eigen::Product<Lhs, Rhs, Option>::Rhs = Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>]Eigen::Product<Lhs, Rhs, Option>::Product(const Lhs&, const Rhs&) [with _Lhs = Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, -1, false>; _Rhs = Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>; int Option = 0; Eigen::Product<Lhs, Rhs, Option>::Lhs = Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, -1, false>; Eigen::Product<Lhs, Rhs, Option>::Rhs = Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>]Eigen::MapBase<Derived, 0>::MapBase(Eigen::MapBase<Derived, 0>::PointerType, Eigen::Index, Eigen::Index) [with Derived = Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, -1, false>; Eigen::MapBase<Derived, 0>::PointerType = const double*; Eigen::Index = long int]Eigen::Block<XprType, BlockRows, BlockCols, InnerPanel>::Block(XprType&, Eigen::Index, Eigen::Index, Eigen::Index, Eigen::Index) [with XprType = const Eigen::Matrix<double, -1, -1>; int BlockRows = -1; int BlockCols = -1; bool InnerPanel = false; Eigen::Index = long int]void Eigen::internal::gemm_pack_rhs<Scalar, Index, DataMapper, nr, 0, Conjugate, PanelMode>::operator()(Scalar*, const DataMapper&, Index, Index, Index, Index) [with Scalar = double; Index = long int; DataMapper = Eigen::internal::blas_data_mapper<double, long int, 0, 0, 1>; int nr = 4; bool Conjugate = false; bool PanelMode = true]Eigen::internal::blas_data_mapper<Scalar, Index, StorageOrder, AlignmentType, 1>::blas_data_mapper(Scalar*, Index, Index) [with Scalar = double; Index = long int; int StorageOrder = 0; int AlignmentType = 0]void Eigen::TriangularViewImpl<_MatrixType, _Mode, Eigen::Dense>::solveInPlace(const Eigen::MatrixBase<OtherDerived>&) const [with int Side = 1; OtherDerived = Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>; _MatrixType = const Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, -1, false>; unsigned int _Mode = 5u]void Eigen::DenseBase<Derived>::resize(Eigen::Index, Eigen::Index) [with Derived = Eigen::Block<Eigen::Matrix<double, -1, -1>, 1, -1, false>; Eigen::Index = long int]Eigen::Product<Lhs, Rhs, Option>::Product(const Lhs&, const Rhs&) [with _Lhs = Eigen::PermutationMatrix<-1, -1, int>; _Rhs = Eigen::CwiseNullaryOp<Eigen::internal::scalar_identity_op<double>, Eigen::Matrix<double, -1, -1> >; int Option = 2; Eigen::Product<Lhs, Rhs, Option>::Lhs = Eigen::PermutationMatrix<-1, -1, int>; Eigen::Product<Lhs, Rhs, Option>::Rhs = Eigen::CwiseNullaryOp<Eigen::internal::scalar_identity_op<double>, Eigen::Matrix<double, -1, -1> >]const PermutationPType& Eigen::FullPivLU<MatrixType>::permutationP() const [with _MatrixType = Eigen::Matrix<double, -1, -1>; Eigen::FullPivLU<MatrixType>::PermutationPType = Eigen::PermutationMatrix<-1, -1, int>]Eigen::Index Eigen::FullPivLU<MatrixType>::rank() const [with _MatrixType = Eigen::Matrix<double, -1, -1>; Eigen::Index = long int]void Eigen::SolverBase<Derived>::_check_solve_assertion(const Rhs&) const [with bool Transpose_ = false; Rhs = Eigen::MatrixBase<Eigen::CwiseNullaryOp<Eigen::internal::scalar_identity_op<double>, Eigen::Matrix<double, -1, -1> > >; Derived = Eigen::FullPivLU<Eigen::Matrix<double, -1, -1> >]Eigen::CwiseNullaryOp<NullaryOp, MatrixType>::CwiseNullaryOp(Eigen::Index, Eigen::Index, const NullaryOp&) [with NullaryOp = Eigen::internal::scalar_identity_op<double>; PlainObjectType = Eigen::Matrix<double, -1, -1>; Eigen::Index = long int]Eigen::CwiseBinaryOp<BinaryOp, Lhs, Rhs>::CwiseBinaryOp(const Lhs&, const Rhs&, const BinaryOp&) [with BinaryOp = Eigen::internal::scalar_product_op<double, double>; LhsType = const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1> >; RhsType = const Eigen::Product<Eigen::Inverse<Eigen::FullPivLU<Eigen::Matrix<double, -1, -1> > >, Eigen::Matrix<double, -1, -1>, 1>; Eigen::CwiseBinaryOp<BinaryOp, Lhs, Rhs>::Lhs = Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1> >; Eigen::CwiseBinaryOp<BinaryOp, Lhs, Rhs>::Rhs = Eigen::Product<Eigen::Inverse<Eigen::FullPivLU<Eigen::Matrix<double, -1, -1> > >, Eigen::Matrix<double, -1, -1>, 1>]Eigen::CwiseNullaryOp<NullaryOp, MatrixType>::CwiseNullaryOp(Eigen::Index, Eigen::Index, const NullaryOp&) [with NullaryOp = Eigen::internal::scalar_constant_op<double>; PlainObjectType = const Eigen::Matrix<double, -1, -1>; Eigen::Index = long int]Eigen::Product<Lhs, Rhs, Option>::Product(const Lhs&, const Rhs&) [with _Lhs = Eigen::Inverse<Eigen::FullPivLU<Eigen::Matrix<double, -1, -1> > >; _Rhs = Eigen::Matrix<double, -1, -1>; int Option = 1; Eigen::Product<Lhs, Rhs, Option>::Lhs = Eigen::Inverse<Eigen::FullPivLU<Eigen::Matrix<double, -1, -1> > >; Eigen::Product<Lhs, Rhs, Option>::Rhs = Eigen::Matrix<double, -1, -1>]Eigen::Product<Lhs, Rhs, Option>::Product(const Lhs&, const Rhs&) [with _Lhs = Eigen::CwiseUnaryOp<Eigen::internal::scalar_opposite_op<double>, const Eigen::Inverse<Eigen::FullPivLU<Eigen::Matrix<double, -1, -1> > > >; _Rhs = Eigen::Matrix<double, -1, -1>; int Option = 0; Eigen::Product<Lhs, Rhs, Option>::Lhs = Eigen::CwiseUnaryOp<Eigen::internal::scalar_opposite_op<double>, const Eigen::Inverse<Eigen::FullPivLU<Eigen::Matrix<double, -1, -1> > > >; Eigen::Product<Lhs, Rhs, Option>::Rhs = Eigen::Matrix<double, -1, -1>]const Eigen::Inverse<Eigen::FullPivLU<MatrixType> > Eigen::FullPivLU<MatrixType>::inverse() const [with _MatrixType = Eigen::Matrix<double, -1, -1>]Derived& Eigen::PermutationBase<Derived>::applyTranspositionOnTheRight(Eigen::Index, Eigen::Index) [with Derived = Eigen::PermutationMatrix<-1, -1, int>; Eigen::Index = long int]void Eigen::internal::resize_if_allowed(DstXprType&, const SrcXprType&, const Functor&) [with DstXprType = Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>, -1, 1, true>; SrcXprType = Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double, double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1> >, const Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, 1, true>, -1, 1, false> >; Functor = Eigen::internal::sub_assign_op<double, double>]Eigen::MapBase<Derived, 0>::MapBase(Eigen::MapBase<Derived, 0>::PointerType, Eigen::Index, Eigen::Index) [with Derived = Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>, -1, 1, true>; Eigen::MapBase<Derived, 0>::PointerType = double*; Eigen::Index = long int]Eigen::Block<XprType, BlockRows, BlockCols, InnerPanel>::Block(XprType&, Eigen::Index) [with XprType = Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>; int BlockRows = -1; int BlockCols = 1; bool InnerPanel = true; Eigen::Index = long int]Eigen::CwiseNullaryOp<NullaryOp, MatrixType>::CwiseNullaryOp(Eigen::Index, Eigen::Index, const NullaryOp&) [with NullaryOp = Eigen::internal::scalar_constant_op<double>; PlainObjectType = const Eigen::Matrix<double, -1, 1>; Eigen::Index = long int]static void Eigen::internal::Assignment<DstXprType, Eigen::Product<Lhs, Rhs, Options>, Eigen::internal::sub_assign_op<Scalar, Scalar>, Eigen::internal::Dense2Dense, typename Eigen::internal::enable_if<((Options == DefaultProduct) || (Options == AliasFreeProduct))>::type>::run(DstXprType&, const SrcXprType&, const Eigen::internal::sub_assign_op<Scalar, Scalar>&) [with DstXprType = Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>; Lhs = Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, 1, true>, -1, 1, false>; Rhs = Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1>, 1, -1, false>, 1, -1, false>; int Options = 0; Scalar = double; Eigen::internal::Assignment<DstXprType, Eigen::Product<Lhs, Rhs, Options>, Eigen::internal::sub_assign_op<Scalar, Scalar>, Eigen::internal::Dense2Dense, typename Eigen::internal::enable_if<((Options == DefaultProduct) || (Options == AliasFreeProduct))>::type>::SrcXprType = Eigen::Product<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, 1, true>, -1, 1, false>, Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1>, 1, -1, false>, 1, -1, false>, 0>]Eigen::MapBase<Derived, 0>::MapBase(Eigen::MapBase<Derived, 0>::PointerType, Eigen::Index, Eigen::Index) [with Derived = Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, 1, true>, -1, 1, false>; Eigen::MapBase<Derived, 0>::PointerType = double*; Eigen::Index = long int]Eigen::Block<XprType, BlockRows, BlockCols, InnerPanel>::Block(XprType&, Eigen::Index, Eigen::Index, Eigen::Index, Eigen::Index) [with XprType = Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, 1, true>; int BlockRows = -1; int BlockCols = 1; bool InnerPanel = false; Eigen::Index = long int]Eigen::MapBase<Derived, 0>::MapBase(Eigen::MapBase<Derived, 0>::PointerType, Eigen::Index, Eigen::Index) [with Derived = Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, 1, true>; Eigen::MapBase<Derived, 0>::PointerType = double*; Eigen::Index = long int]Eigen::Block<XprType, BlockRows, BlockCols, InnerPanel>::Block(XprType&, Eigen::Index) [with XprType = Eigen::Matrix<double, -1, -1>; int BlockRows = -1; int BlockCols = 1; bool InnerPanel = true; Eigen::Index = long int]Eigen::MapBase<Derived, 0>::MapBase(Eigen::MapBase<Derived, 0>::PointerType, Eigen::Index, Eigen::Index) [with Derived = Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1>, 1, -1, false>, 1, -1, false>; Eigen::MapBase<Derived, 0>::PointerType = double*; Eigen::Index = long int]Eigen::Block<XprType, BlockRows, BlockCols, InnerPanel>::Block(XprType&, Eigen::Index, Eigen::Index, Eigen::Index, Eigen::Index) [with XprType = Eigen::Block<Eigen::Matrix<double, -1, -1>, 1, -1, false>; int BlockRows = 1; int BlockCols = -1; bool InnerPanel = false; Eigen::Index = long int]Eigen::MapBase<Derived, 0>::MapBase(Eigen::MapBase<Derived, 0>::PointerType, Eigen::Index, Eigen::Index) [with Derived = Eigen::Block<Eigen::Matrix<double, -1, -1>, 1, -1, false>; Eigen::MapBase<Derived, 0>::PointerType = double*; Eigen::Index = long int]Eigen::Block<XprType, BlockRows, BlockCols, InnerPanel>::Block(XprType&, Eigen::Index) [with XprType = Eigen::Matrix<double, -1, -1>; int BlockRows = 1; int BlockCols = -1; bool InnerPanel = false; Eigen::Index = long int]void Eigen::DenseBase<Derived>::swap(const Eigen::DenseBase<OtherDerived>&) [with OtherDerived = Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, 1, true>; Derived = Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, 1, true>]void Eigen::DenseBase<Derived>::swap(const Eigen::DenseBase<OtherDerived>&) [with OtherDerived = Eigen::Block<Eigen::Matrix<double, -1, -1>, 1, -1, false>; Derived = Eigen::Block<Eigen::Matrix<double, -1, -1>, 1, -1, false>]typename Eigen::internal::traits<T>::Scalar Eigen::DenseBase<Derived>::maxCoeff(IndexType*, IndexType*) const [with IndexType = long int; Derived = Eigen::CwiseUnaryOp<Eigen::internal::scalar_score_coeff_op<double>, const Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false> >; typename Eigen::internal::traits<T>::Scalar = double]Eigen::MapBase<Derived, 0>::MapBase(Eigen::MapBase<Derived, 0>::PointerType, Eigen::Index, Eigen::Index) [with Derived = Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>; Eigen::MapBase<Derived, 0>::PointerType = double*; Eigen::Index = long int]Eigen::Block<XprType, BlockRows, BlockCols, InnerPanel>::Block(XprType&, Eigen::Index, Eigen::Index, Eigen::Index, Eigen::Index) [with XprType = Eigen::Matrix<double, -1, -1>; int BlockRows = -1; int BlockCols = -1; bool InnerPanel = false; Eigen::Index = long int]typename Eigen::internal::traits<T>::Scalar Eigen::DenseBase<Derived>::redux(const Func&) const [with BinaryOp = Eigen::internal::scalar_sum_op<double, double>; Derived = Eigen::Block<const Eigen::CwiseUnaryOp<Eigen::internal::scalar_abs_op<double>, const Eigen::Matrix<double, -1, -1> >, -1, 1, true>; typename Eigen::internal::traits<T>::Scalar = double]Eigen::internal::variable_if_dynamic<T, Value>::variable_if_dynamic(T) [with T = long int; int Value = 1]typename Eigen::internal::traits<T>::Scalar Eigen::DenseBase<Derived>::redux(const Func&) const [with BinaryOp = Eigen::internal::scalar_max_op<double, double>; Derived = Eigen::PartialReduxExpr<const Eigen::CwiseUnaryOp<Eigen::internal::scalar_abs_op<double>, const Eigen::Matrix<double, -1, -1> >, Eigen::internal::member_sum<double, double>, 0>; typename Eigen::internal::traits<T>::Scalar = double]void Eigen::FullPivLU<MatrixType>::computeInPlace() [with _MatrixType = Eigen::Matrix<double, -1, -1>]void Eigen::internal::resize_if_allowed(DstXprType&, const SrcXprType&, const Eigen::internal::assign_op<T1, T2>&) [with DstXprType = Eigen::Matrix<double, -1, -1>; SrcXprType = Eigen::Matrix<double, -1, -1>; T1 = double; T2 = double]void Eigen::PlainObjectBase<Derived>::resize(Eigen::Index) [with Derived = Eigen::Matrix<int, 1, -1>; Eigen::Index = long int]void Eigen::PlainObjectBase<Derived>::resize(Eigen::Index) [with Derived = Eigen::Matrix<int, -1, 1>; Eigen::Index = long int]Eigen::DenseCoeffsBase<Derived, 1>::Scalar& Eigen::DenseCoeffsBase<Derived, 1>::operator()(Eigen::Index, Eigen::Index) [with Derived = Eigen::Matrix<double, -1, -1>; Eigen::DenseCoeffsBase<Derived, 1>::Scalar = double; Eigen::Index = long int]void* Eigen::internal::aligned_malloc(std::size_t)void Eigen::PlainObjectBase<Derived>::resize(Eigen::Index, Eigen::Index) [with Derived = Eigen::Matrix<double, -1, -1>; Eigen::Index = long int]N8LightGBM17LinearTreeLearnerEj@<SerialTreeLearner::ConstructHistogramsSerialTreeLearner::BeforeFindBestSplit/__w/1/s/src/treelearner/serial_tree_learner.cppCheck failed: (num_machines) > (1) at %s, line %d . +Please recompile with CMake option -DUSE_GPU=1N8LightGBM15CUDATreeLearnerEN8LightGBM14GPUTreeLearnerEN8LightGBM23DataParallelTreeLearnerINS_15CUDATreeLearnerEEEN8LightGBM23DataParallelTreeLearnerINS_14GPUTreeLearnerEEEN8LightGBM23DataParallelTreeLearnerINS_17SerialTreeLearnerEEEZN8LightGBM9ArrayArgsINS_9SplitInfoEE8ArgMaxMTERKSt6vectorIS1_SaIS1_EEEUlimmE_N8LightGBM26FeatureParallelTreeLearnerINS_15CUDATreeLearnerEEEN8LightGBM26FeatureParallelTreeLearnerINS_14GPUTreeLearnerEEEN8LightGBM26FeatureParallelTreeLearnerINS_17SerialTreeLearnerEEE/__w/1/s/external_libs/eigen/Eigen/src/Core/util/Memory.h(size<16 || (std::size_t(result)%16)==0) && "System's malloc returned an unaligned pointer. Compile with EIGEN_MALLOC_ALREADY_ALIGNED=0 to fallback to handmade aligned memory allocator."/__w/1/s/external_libs/eigen/Eigen/src/Core/PlainObjectBase.h((SizeAtCompileTime == Dynamic && (MaxSizeAtCompileTime==Dynamic || size<=MaxSizeAtCompileTime)) || SizeAtCompileTime == size) && size>=0/__w/1/s/external_libs/eigen/Eigen/src/LU/FullPivLU.hm_lu.rows() == m_lu.cols() && "You can't take the inverse of a non-square matrix!"/__w/1/s/external_libs/eigen/Eigen/src/Core/DenseCoeffsBase.h/__w/1/s/external_libs/eigen/Eigen/src/Core/PermutationMatrix.hi>=0 && j>=0 && i<size() && j<size()m_isInitialized && "LU is not initialized."/__w/1/s/external_libs/eigen/Eigen/src/Core/DenseBase.hrows == this->rows() && cols == this->cols() && "DenseBase::resize() does not actually allow to resize."/__w/1/s/src/treelearner/linear_tree_learner.h(!(RowsAtCompileTime!=Dynamic) || (rows==RowsAtCompileTime)) && (!(ColsAtCompileTime!=Dynamic) || (cols==ColsAtCompileTime)) && (!(RowsAtCompileTime==Dynamic && MaxRowsAtCompileTime!=Dynamic) || (rows<=MaxRowsAtCompileTime)) && (!(ColsAtCompileTime==Dynamic && MaxColsAtCompileTime!=Dynamic) || (cols<=MaxColsAtCompileTime)) && rows>=0 && cols>=0 && "Invalid sizes when resizing a matrix or array."/__w/1/s/external_libs/eigen/Eigen/src/Core/MapBase.h(dataPtr == 0) || ( rows >= 0 && (RowsAtCompileTime == Dynamic || RowsAtCompileTime == rows) && cols >= 0 && (ColsAtCompileTime == Dynamic || ColsAtCompileTime == cols))/__w/1/s/external_libs/eigen/Eigen/src/Core/Block.hstartRow >= 0 && blockRows >= 0 && startRow <= xpr.rows() - blockRows && startCol >= 0 && blockCols >= 0 && startCol <= xpr.cols() - blockCols(i>=0) && ( ((BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) && i<xpr.rows()) ||((BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) && i<xpr.cols()))/__w/1/s/external_libs/eigen/Eigen/src/Core/util/XprHelper.h/__w/1/s/external_libs/eigen/Eigen/src/Core/CwiseNullaryOp.hrows >= 0 && (RowsAtCompileTime == Dynamic || RowsAtCompileTime == rows) && cols >= 0 && (ColsAtCompileTime == Dynamic || ColsAtCompileTime == cols)/__w/1/s/external_libs/eigen/Eigen/src/Core/AssignEvaluator.hdst.rows() == src.rows() && dst.cols() == src.cols()/__w/1/s/external_libs/eigen/Eigen/src/Core/util/BlasUtil.hm_lu.rows()<=NumTraits<int>::highest() && m_lu.cols()<=NumTraits<int>::highest()/__w/1/s/external_libs/eigen/Eigen/src/Core/Redux.hthis->rows()>0 && this->cols()>0 && "you are using an empty matrix"/__w/1/s/external_libs/eigen/Eigen/src/Core/Visitor.hrow >= 0 && row < rows() && col >= 0 && col < cols()rows()==other.rows() && cols()==other.cols()/__w/1/s/external_libs/eigen/Eigen/src/Core/ProductEvaluators.hdst.rows() == dstRows && dst.cols() == dstCols/__w/1/s/external_libs/eigen/Eigen/src/Core/CwiseBinaryOp.haLhs.rows() == aRhs.rows() && aLhs.cols() == aRhs.cols()/__w/1/s/external_libs/eigen/Eigen/src/Core/products/GeneralBlockPanelKernel.h((!PanelMode) && stride==0 && offset==0) || (PanelMode && stride>=depth && offset<=stride)/__w/1/s/external_libs/eigen/Eigen/src/Core/SolveTriangular.hderived().cols() == derived().rows() && ((Side==OnTheLeft && derived().cols() == other.rows()) || (Side==OnTheRight && derived().cols() == other.cols()))/__w/1/s/external_libs/eigen/Eigen/src/Core/Product.hlhs.cols() == rhs.rows() && "invalid matrix product" && "if you wanted a coeff-wise or a dot product use the respective explicit functions"/__w/1/s/external_libs/eigen/Eigen/src/Core/products/GeneralMatrixMatrix.hdst.rows()==a_lhs.rows() && dst.cols()==a_rhs.cols()/__w/1/s/external_libs/eigen/Eigen/src/Core/SolverBase.hderived().m_isInitialized && "Solver is not initialized."(Transpose_?derived().cols():derived().rows())==b.rows() && "SolverBase::solve(): invalid number of rows of the right hand side matrix b"No further splits with positive gain, best gain: %fTrained a tree with leaves = %d and depth = %dDetected that num_threads changed during training (from %d to %d), it may cause unexpected errors.index >= 0 && index < size()v == T(Value)incr==1SerialTreeLearner::Train+0+`+0+,0+`+0+0+0+0+`+0+0+0+0+0,0+0+0+0+0+0+0+P,@,0++0+0+0++0+0++0+0+0++0+0+0+0+0+0+0+0+p+`,p++ , +0+,p++ +++++p,,,+,,,0+0+0+0+0+0+0+0+0+0+0+0+0+0+0+0+0+`+0+0+0+0+0++`++0+0+0+0+0+0+0+0+0+0+0+0+0+0+0++p++ ++++ + +p++ +++ +++++0+0+,Eigen::DenseCoeffsBase<Derived, 1>::Scalar& Eigen::DenseCoeffsBase<Derived, 1>::operator()(Eigen::Index) [with Derived = Eigen::Matrix<double, -1, -1>; Eigen::DenseCoeffsBase<Derived, 1>::Scalar = double; Eigen::Index = long int]void Eigen::PlainObjectBase<Derived>::resize(Eigen::Index, Eigen::Index) [with Derived = Eigen::Matrix<double, 1, -1>; Eigen::Index = long int]Eigen::Block<XprType, BlockRows, BlockCols, InnerPanel>::Block(XprType&, Eigen::Index) [with XprType = const Eigen::CwiseUnaryOp<Eigen::internal::scalar_opposite_op<double>, const Eigen::Inverse<Eigen::FullPivLU<Eigen::Matrix<double, -1, -1> > > >; int BlockRows = 1; int BlockCols = -1; bool InnerPanel = false; Eigen::Index = long int]void Eigen::internal::resize_if_allowed(DstXprType&, const SrcXprType&, const Eigen::internal::assign_op<T1, T2>&) [with DstXprType = Eigen::Matrix<double, -1, -1>; SrcXprType = Eigen::CwiseUnaryOp<Eigen::internal::scalar_opposite_op<double>, const Eigen::Inverse<Eigen::FullPivLU<Eigen::Matrix<double, -1, -1> > > >; T1 = double; T2 = double]static void Eigen::internal::generic_product_impl<Lhs, Rhs, Eigen::DenseShape, Eigen::DenseShape, 8>::scaleAndAddTo(Dest&, const Lhs&, const Rhs&, const Scalar&) [with Dest = Eigen::Matrix<double, -1, -1>; Lhs = Eigen::CwiseUnaryOp<Eigen::internal::scalar_opposite_op<double>, const Eigen::Inverse<Eigen::FullPivLU<Eigen::Matrix<double, -1, -1> > > >; Rhs = Eigen::Matrix<double, -1, -1>; Eigen::internal::generic_product_impl<Lhs, Rhs, Eigen::DenseShape, Eigen::DenseShape, 8>::Scalar = double]Eigen::CwiseNullaryOp<NullaryOp, MatrixType>::CwiseNullaryOp(Eigen::Index, Eigen::Index, const NullaryOp&) [with NullaryOp = Eigen::internal::scalar_constant_op<double>; PlainObjectType = Eigen::Matrix<double, -1, -1>; Eigen::Index = long int]typename Eigen::internal::traits<T>::Scalar Eigen::DenseBase<Derived>::redux(const Func&) const [with BinaryOp = Eigen::internal::scalar_sum_op<double, double>; Derived = Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double, double>, const Eigen::Transpose<const Eigen::Block<const Eigen::Matrix<double, -1, -1>, 1, -1, false> >, const Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, 1, true> >; typename Eigen::internal::traits<T>::Scalar = double]Eigen::CwiseBinaryOp<BinaryOp, Lhs, Rhs>::CwiseBinaryOp(const Lhs&, const Rhs&, const BinaryOp&) [with BinaryOp = Eigen::internal::scalar_product_op<double, double>; LhsType = const Eigen::Transpose<const Eigen::Block<const Eigen::Matrix<double, -1, -1>, 1, -1, false> >; RhsType = const Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, 1, true>; Eigen::CwiseBinaryOp<BinaryOp, Lhs, Rhs>::Lhs = Eigen::Transpose<const Eigen::Block<const Eigen::Matrix<double, -1, -1>, 1, -1, false> >; Eigen::CwiseBinaryOp<BinaryOp, Lhs, Rhs>::Rhs = Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, 1, true>]Eigen::MapBase<Derived, 0>::MapBase(Eigen::MapBase<Derived, 0>::PointerType, Eigen::Index, Eigen::Index) [with Derived = Eigen::Block<const Eigen::Matrix<double, -1, -1>, 1, -1, false>; Eigen::MapBase<Derived, 0>::PointerType = const double*; Eigen::Index = long int]Eigen::Block<XprType, BlockRows, BlockCols, InnerPanel>::Block(XprType&, Eigen::Index) [with XprType = const Eigen::Matrix<double, -1, -1>; int BlockRows = 1; int BlockCols = -1; bool InnerPanel = false; Eigen::Index = long int]Eigen::MapBase<Derived, 0>::MapBase(Eigen::MapBase<Derived, 0>::PointerType, Eigen::Index, Eigen::Index) [with Derived = Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, 1, true>; Eigen::MapBase<Derived, 0>::PointerType = const double*; Eigen::Index = long int]Eigen::Block<XprType, BlockRows, BlockCols, InnerPanel>::Block(XprType&, Eigen::Index) [with XprType = const Eigen::Matrix<double, -1, -1>; int BlockRows = -1; int BlockCols = 1; bool InnerPanel = true; Eigen::Index = long int]void Eigen::internal::resize_if_allowed(DstXprType&, const SrcXprType&, const Eigen::internal::assign_op<T1, T2>&) [with DstXprType = Eigen::Matrix<double, -1, -1>; SrcXprType = Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double, double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1> >, const Eigen::Product<Eigen::Inverse<Eigen::FullPivLU<Eigen::Matrix<double, -1, -1> > >, Eigen::Matrix<double, -1, -1>, 1> >; T1 = double; T2 = double]Eigen::CwiseNullaryOp<NullaryOp, MatrixType>::CwiseNullaryOp(Eigen::Index, Eigen::Index, const NullaryOp&) [with NullaryOp = Eigen::internal::scalar_constant_op<double>; PlainObjectType = Eigen::Matrix<double, 1, -1>; Eigen::Index = long int]const PermutationQType& Eigen::FullPivLU<MatrixType>::permutationQ() const [with _MatrixType = Eigen::Matrix<double, -1, -1>; Eigen::FullPivLU<MatrixType>::PermutationQType = Eigen::PermutationMatrix<-1, -1, int>]void Eigen::TriangularViewImpl<_MatrixType, _Mode, Eigen::Dense>::solveInPlace(const Eigen::MatrixBase<OtherDerived>&) const [with int Side = 1; OtherDerived = Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>; _MatrixType = const Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, -1, false>; unsigned int _Mode = 2u]void Eigen::internal::resize_if_allowed(DstXprType&, const SrcXprType&, const Functor&) [with DstXprType = Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>; SrcXprType = Eigen::Matrix<double, -1, -1>; Functor = Eigen::internal::sub_assign_op<double, double>]Eigen::MapBase<Derived, 0>::MapBase(Eigen::MapBase<Derived, 0>::PointerType, Eigen::Index, Eigen::Index) [with Derived = Eigen::Block<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1>, 1, -1, false> >, -1, 1, true>; Eigen::MapBase<Derived, 0>::PointerType = double*; Eigen::Index = long int]static void Eigen::internal::generic_product_impl<Lhs, Rhs, Eigen::DenseShape, Eigen::DenseShape, 8>::scaleAndAddTo(Dest&, const Lhs&, const Rhs&, const Scalar&) [with Dest = Eigen::Matrix<double, -1, -1>; Lhs = Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, -1, false>; Rhs = Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>; Eigen::internal::generic_product_impl<Lhs, Rhs, Eigen::DenseShape, Eigen::DenseShape, 8>::Scalar = double]typename Eigen::internal::traits<T>::Scalar Eigen::DenseBase<Derived>::redux(const Func&) const [with BinaryOp = Eigen::internal::scalar_sum_op<double, double>; Derived = Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double, double>, const Eigen::Transpose<const Eigen::Block<const Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, -1, false>, 1, -1, false> >, const Eigen::Block<const Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>, -1, 1, true> >; typename Eigen::internal::traits<T>::Scalar = double]Eigen::CwiseBinaryOp<BinaryOp, Lhs, Rhs>::CwiseBinaryOp(const Lhs&, const Rhs&, const BinaryOp&) [with BinaryOp = Eigen::internal::scalar_product_op<double, double>; LhsType = const Eigen::Transpose<const Eigen::Block<const Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, -1, false>, 1, -1, false> >; RhsType = const Eigen::Block<const Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>, -1, 1, true>; Eigen::CwiseBinaryOp<BinaryOp, Lhs, Rhs>::Lhs = Eigen::Transpose<const Eigen::Block<const Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, -1, false>, 1, -1, false> >; Eigen::CwiseBinaryOp<BinaryOp, Lhs, Rhs>::Rhs = Eigen::Block<const Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>, -1, 1, true>]Eigen::MapBase<Derived, 0>::MapBase(Eigen::MapBase<Derived, 0>::PointerType, Eigen::Index, Eigen::Index) [with Derived = Eigen::Block<const Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, -1, false>, 1, -1, false>; Eigen::MapBase<Derived, 0>::PointerType = const double*; Eigen::Index = long int]Eigen::Block<XprType, BlockRows, BlockCols, InnerPanel>::Block(XprType&, Eigen::Index) [with XprType = const Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, -1, false>; int BlockRows = 1; int BlockCols = -1; bool InnerPanel = false; Eigen::Index = long int]Eigen::MapBase<Derived, 0>::MapBase(Eigen::MapBase<Derived, 0>::PointerType, Eigen::Index, Eigen::Index) [with Derived = Eigen::Block<const Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>, -1, 1, true>; Eigen::MapBase<Derived, 0>::PointerType = const double*; Eigen::Index = long int]Eigen::Block<XprType, BlockRows, BlockCols, InnerPanel>::Block(XprType&, Eigen::Index) [with XprType = const Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>; int BlockRows = -1; int BlockCols = 1; bool InnerPanel = true; Eigen::Index = long int]void Eigen::internal::resize_if_allowed(DstXprType&, const SrcXprType&, const Eigen::internal::assign_op<T1, T2>&) [with DstXprType = Eigen::Matrix<double, -1, -1>; SrcXprType = Eigen::Product<Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, -1, false>, Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>, 1>; T1 = double; T2 = double]Eigen::Product<Lhs, Rhs, Option>::Product(const Lhs&, const Rhs&) [with _Lhs = Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, -1, false>; _Rhs = Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>; int Option = 1; Eigen::Product<Lhs, Rhs, Option>::Lhs = Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, -1, false>; Eigen::Product<Lhs, Rhs, Option>::Rhs = Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>]Eigen::Product<Lhs, Rhs, Option>::Product(const Lhs&, const Rhs&) [with _Lhs = Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, -1, false>; _Rhs = Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>; int Option = 0; Eigen::Product<Lhs, Rhs, Option>::Lhs = Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, -1, false>; Eigen::Product<Lhs, Rhs, Option>::Rhs = Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>]Eigen::MapBase<Derived, 0>::MapBase(Eigen::MapBase<Derived, 0>::PointerType, Eigen::Index, Eigen::Index) [with Derived = Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, -1, false>; Eigen::MapBase<Derived, 0>::PointerType = const double*; Eigen::Index = long int]Eigen::Block<XprType, BlockRows, BlockCols, InnerPanel>::Block(XprType&, Eigen::Index, Eigen::Index, Eigen::Index, Eigen::Index) [with XprType = const Eigen::Matrix<double, -1, -1>; int BlockRows = -1; int BlockCols = -1; bool InnerPanel = false; Eigen::Index = long int]void Eigen::internal::gemm_pack_rhs<Scalar, Index, DataMapper, nr, 0, Conjugate, PanelMode>::operator()(Scalar*, const DataMapper&, Index, Index, Index, Index) [with Scalar = double; Index = long int; DataMapper = Eigen::internal::blas_data_mapper<double, long int, 0, 0, 1>; int nr = 4; bool Conjugate = false; bool PanelMode = true]Eigen::internal::blas_data_mapper<Scalar, Index, StorageOrder, AlignmentType, 1>::blas_data_mapper(Scalar*, Index, Index) [with Scalar = double; Index = long int; int StorageOrder = 0; int AlignmentType = 0]void Eigen::TriangularViewImpl<_MatrixType, _Mode, Eigen::Dense>::solveInPlace(const Eigen::MatrixBase<OtherDerived>&) const [with int Side = 1; OtherDerived = Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>; _MatrixType = const Eigen::Block<const Eigen::Matrix<double, -1, -1>, -1, -1, false>; unsigned int _Mode = 5u]void Eigen::DenseBase<Derived>::resize(Eigen::Index, Eigen::Index) [with Derived = Eigen::Block<Eigen::Matrix<double, -1, -1>, 1, -1, false>; Eigen::Index = long int]Eigen::Product<Lhs, Rhs, Option>::Product(const Lhs&, const Rhs&) [with _Lhs = Eigen::PermutationMatrix<-1, -1, int>; _Rhs = Eigen::CwiseNullaryOp<Eigen::internal::scalar_identity_op<double>, Eigen::Matrix<double, -1, -1> >; int Option = 2; Eigen::Product<Lhs, Rhs, Option>::Lhs = Eigen::PermutationMatrix<-1, -1, int>; Eigen::Product<Lhs, Rhs, Option>::Rhs = Eigen::CwiseNullaryOp<Eigen::internal::scalar_identity_op<double>, Eigen::Matrix<double, -1, -1> >]const PermutationPType& Eigen::FullPivLU<MatrixType>::permutationP() const [with _MatrixType = Eigen::Matrix<double, -1, -1>; Eigen::FullPivLU<MatrixType>::PermutationPType = Eigen::PermutationMatrix<-1, -1, int>]Eigen::Index Eigen::FullPivLU<MatrixType>::rank() const [with _MatrixType = Eigen::Matrix<double, -1, -1>; Eigen::Index = long int]void Eigen::SolverBase<Derived>::_check_solve_assertion(const Rhs&) const [with bool Transpose_ = false; Rhs = Eigen::MatrixBase<Eigen::CwiseNullaryOp<Eigen::internal::scalar_identity_op<double>, Eigen::Matrix<double, -1, -1> > >; Derived = Eigen::FullPivLU<Eigen::Matrix<double, -1, -1> >]Eigen::CwiseNullaryOp<NullaryOp, MatrixType>::CwiseNullaryOp(Eigen::Index, Eigen::Index, const NullaryOp&) [with NullaryOp = Eigen::internal::scalar_identity_op<double>; PlainObjectType = Eigen::Matrix<double, -1, -1>; Eigen::Index = long int]Eigen::CwiseBinaryOp<BinaryOp, Lhs, Rhs>::CwiseBinaryOp(const Lhs&, const Rhs&, const BinaryOp&) [with BinaryOp = Eigen::internal::scalar_product_op<double, double>; LhsType = const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1> >; RhsType = const Eigen::Product<Eigen::Inverse<Eigen::FullPivLU<Eigen::Matrix<double, -1, -1> > >, Eigen::Matrix<double, -1, -1>, 1>; Eigen::CwiseBinaryOp<BinaryOp, Lhs, Rhs>::Lhs = Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1> >; Eigen::CwiseBinaryOp<BinaryOp, Lhs, Rhs>::Rhs = Eigen::Product<Eigen::Inverse<Eigen::FullPivLU<Eigen::Matrix<double, -1, -1> > >, Eigen::Matrix<double, -1, -1>, 1>]Eigen::CwiseNullaryOp<NullaryOp, MatrixType>::CwiseNullaryOp(Eigen::Index, Eigen::Index, const NullaryOp&) [with NullaryOp = Eigen::internal::scalar_constant_op<double>; PlainObjectType = const Eigen::Matrix<double, -1, -1>; Eigen::Index = long int]Eigen::Product<Lhs, Rhs, Option>::Product(const Lhs&, const Rhs&) [with _Lhs = Eigen::Inverse<Eigen::FullPivLU<Eigen::Matrix<double, -1, -1> > >; _Rhs = Eigen::Matrix<double, -1, -1>; int Option = 1; Eigen::Product<Lhs, Rhs, Option>::Lhs = Eigen::Inverse<Eigen::FullPivLU<Eigen::Matrix<double, -1, -1> > >; Eigen::Product<Lhs, Rhs, Option>::Rhs = Eigen::Matrix<double, -1, -1>]Eigen::Product<Lhs, Rhs, Option>::Product(const Lhs&, const Rhs&) [with _Lhs = Eigen::CwiseUnaryOp<Eigen::internal::scalar_opposite_op<double>, const Eigen::Inverse<Eigen::FullPivLU<Eigen::Matrix<double, -1, -1> > > >; _Rhs = Eigen::Matrix<double, -1, -1>; int Option = 0; Eigen::Product<Lhs, Rhs, Option>::Lhs = Eigen::CwiseUnaryOp<Eigen::internal::scalar_opposite_op<double>, const Eigen::Inverse<Eigen::FullPivLU<Eigen::Matrix<double, -1, -1> > > >; Eigen::Product<Lhs, Rhs, Option>::Rhs = Eigen::Matrix<double, -1, -1>]const Eigen::Inverse<Eigen::FullPivLU<MatrixType> > Eigen::FullPivLU<MatrixType>::inverse() const [with _MatrixType = Eigen::Matrix<double, -1, -1>]Derived& Eigen::PermutationBase<Derived>::applyTranspositionOnTheRight(Eigen::Index, Eigen::Index) [with Derived = Eigen::PermutationMatrix<-1, -1, int>; Eigen::Index = long int]void Eigen::internal::resize_if_allowed(DstXprType&, const SrcXprType&, const Functor&) [with DstXprType = Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>, -1, 1, true>; SrcXprType = Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double, double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1> >, const Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, 1, true>, -1, 1, false> >; Functor = Eigen::internal::sub_assign_op<double, double>]Eigen::MapBase<Derived, 0>::MapBase(Eigen::MapBase<Derived, 0>::PointerType, Eigen::Index, Eigen::Index) [with Derived = Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>, -1, 1, true>; Eigen::MapBase<Derived, 0>::PointerType = double*; Eigen::Index = long int]Eigen::Block<XprType, BlockRows, BlockCols, InnerPanel>::Block(XprType&, Eigen::Index) [with XprType = Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>; int BlockRows = -1; int BlockCols = 1; bool InnerPanel = true; Eigen::Index = long int]Eigen::CwiseNullaryOp<NullaryOp, MatrixType>::CwiseNullaryOp(Eigen::Index, Eigen::Index, const NullaryOp&) [with NullaryOp = Eigen::internal::scalar_constant_op<double>; PlainObjectType = const Eigen::Matrix<double, -1, 1>; Eigen::Index = long int]static void Eigen::internal::Assignment<DstXprType, Eigen::Product<Lhs, Rhs, Options>, Eigen::internal::sub_assign_op<Scalar, Scalar>, Eigen::internal::Dense2Dense, typename Eigen::internal::enable_if<((Options == DefaultProduct) || (Options == AliasFreeProduct))>::type>::run(DstXprType&, const SrcXprType&, const Eigen::internal::sub_assign_op<Scalar, Scalar>&) [with DstXprType = Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>; Lhs = Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, 1, true>, -1, 1, false>; Rhs = Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1>, 1, -1, false>, 1, -1, false>; int Options = 0; Scalar = double; Eigen::internal::Assignment<DstXprType, Eigen::Product<Lhs, Rhs, Options>, Eigen::internal::sub_assign_op<Scalar, Scalar>, Eigen::internal::Dense2Dense, typename Eigen::internal::enable_if<((Options == DefaultProduct) || (Options == AliasFreeProduct))>::type>::SrcXprType = Eigen::Product<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, 1, true>, -1, 1, false>, Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1>, 1, -1, false>, 1, -1, false>, 0>]Eigen::MapBase<Derived, 0>::MapBase(Eigen::MapBase<Derived, 0>::PointerType, Eigen::Index, Eigen::Index) [with Derived = Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, 1, true>, -1, 1, false>; Eigen::MapBase<Derived, 0>::PointerType = double*; Eigen::Index = long int]Eigen::Block<XprType, BlockRows, BlockCols, InnerPanel>::Block(XprType&, Eigen::Index, Eigen::Index, Eigen::Index, Eigen::Index) [with XprType = Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, 1, true>; int BlockRows = -1; int BlockCols = 1; bool InnerPanel = false; Eigen::Index = long int]Eigen::MapBase<Derived, 0>::MapBase(Eigen::MapBase<Derived, 0>::PointerType, Eigen::Index, Eigen::Index) [with Derived = Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, 1, true>; Eigen::MapBase<Derived, 0>::PointerType = double*; Eigen::Index = long int]Eigen::Block<XprType, BlockRows, BlockCols, InnerPanel>::Block(XprType&, Eigen::Index) [with XprType = Eigen::Matrix<double, -1, -1>; int BlockRows = -1; int BlockCols = 1; bool InnerPanel = true; Eigen::Index = long int]Eigen::MapBase<Derived, 0>::MapBase(Eigen::MapBase<Derived, 0>::PointerType, Eigen::Index, Eigen::Index) [with Derived = Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1>, 1, -1, false>, 1, -1, false>; Eigen::MapBase<Derived, 0>::PointerType = double*; Eigen::Index = long int]Eigen::Block<XprType, BlockRows, BlockCols, InnerPanel>::Block(XprType&, Eigen::Index, Eigen::Index, Eigen::Index, Eigen::Index) [with XprType = Eigen::Block<Eigen::Matrix<double, -1, -1>, 1, -1, false>; int BlockRows = 1; int BlockCols = -1; bool InnerPanel = false; Eigen::Index = long int]Eigen::MapBase<Derived, 0>::MapBase(Eigen::MapBase<Derived, 0>::PointerType, Eigen::Index, Eigen::Index) [with Derived = Eigen::Block<Eigen::Matrix<double, -1, -1>, 1, -1, false>; Eigen::MapBase<Derived, 0>::PointerType = double*; Eigen::Index = long int]Eigen::Block<XprType, BlockRows, BlockCols, InnerPanel>::Block(XprType&, Eigen::Index) [with XprType = Eigen::Matrix<double, -1, -1>; int BlockRows = 1; int BlockCols = -1; bool InnerPanel = false; Eigen::Index = long int]void Eigen::DenseBase<Derived>::swap(const Eigen::DenseBase<OtherDerived>&) [with OtherDerived = Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, 1, true>; Derived = Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, 1, true>]void Eigen::DenseBase<Derived>::swap(const Eigen::DenseBase<OtherDerived>&) [with OtherDerived = Eigen::Block<Eigen::Matrix<double, -1, -1>, 1, -1, false>; Derived = Eigen::Block<Eigen::Matrix<double, -1, -1>, 1, -1, false>]typename Eigen::internal::traits<T>::Scalar Eigen::DenseBase<Derived>::maxCoeff(IndexType*, IndexType*) const [with IndexType = long int; Derived = Eigen::CwiseUnaryOp<Eigen::internal::scalar_score_coeff_op<double>, const Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false> >; typename Eigen::internal::traits<T>::Scalar = double]Eigen::MapBase<Derived, 0>::MapBase(Eigen::MapBase<Derived, 0>::PointerType, Eigen::Index, Eigen::Index) [with Derived = Eigen::Block<Eigen::Matrix<double, -1, -1>, -1, -1, false>; Eigen::MapBase<Derived, 0>::PointerType = double*; Eigen::Index = long int]Eigen::Block<XprType, BlockRows, BlockCols, InnerPanel>::Block(XprType&, Eigen::Index, Eigen::Index, Eigen::Index, Eigen::Index) [with XprType = Eigen::Matrix<double, -1, -1>; int BlockRows = -1; int BlockCols = -1; bool InnerPanel = false; Eigen::Index = long int]typename Eigen::internal::traits<T>::Scalar Eigen::DenseBase<Derived>::redux(const Func&) const [with BinaryOp = Eigen::internal::scalar_sum_op<double, double>; Derived = Eigen::Block<const Eigen::CwiseUnaryOp<Eigen::internal::scalar_abs_op<double>, const Eigen::Matrix<double, -1, -1> >, -1, 1, true>; typename Eigen::internal::traits<T>::Scalar = double]Eigen::internal::variable_if_dynamic<T, Value>::variable_if_dynamic(T) [with T = long int; int Value = 1]typename Eigen::internal::traits<T>::Scalar Eigen::DenseBase<Derived>::redux(const Func&) const [with BinaryOp = Eigen::internal::scalar_max_op<double, double>; Derived = Eigen::PartialReduxExpr<const Eigen::CwiseUnaryOp<Eigen::internal::scalar_abs_op<double>, const Eigen::Matrix<double, -1, -1> >, Eigen::internal::member_sum<double, double>, 0>; typename Eigen::internal::traits<T>::Scalar = double]void Eigen::FullPivLU<MatrixType>::computeInPlace() [with _MatrixType = Eigen::Matrix<double, -1, -1>]void Eigen::internal::resize_if_allowed(DstXprType&, const SrcXprType&, const Eigen::internal::assign_op<T1, T2>&) [with DstXprType = Eigen::Matrix<double, -1, -1>; SrcXprType = Eigen::Matrix<double, -1, -1>; T1 = double; T2 = double]void Eigen::PlainObjectBase<Derived>::resize(Eigen::Index) [with Derived = Eigen::Matrix<int, 1, -1>; Eigen::Index = long int]void Eigen::PlainObjectBase<Derived>::resize(Eigen::Index) [with Derived = Eigen::Matrix<int, -1, 1>; Eigen::Index = long int]Eigen::DenseCoeffsBase<Derived, 1>::Scalar& Eigen::DenseCoeffsBase<Derived, 1>::operator()(Eigen::Index, Eigen::Index) [with Derived = Eigen::Matrix<double, -1, -1>; Eigen::DenseCoeffsBase<Derived, 1>::Scalar = double; Eigen::Index = long int]void* Eigen::internal::aligned_malloc(std::size_t)void Eigen::PlainObjectBase<Derived>::resize(Eigen::Index, Eigen::Index) [with Derived = Eigen::Matrix<double, -1, -1>; Eigen::Index = long int]N8LightGBM17LinearTreeLearnerEj@<SerialTreeLearner::ConstructHistogramsSerialTreeLearner::BeforeFindBestSplit/__w/1/s/src/treelearner/serial_tree_learner.cppCheck failed: (num_machines) > (1) at %s, line %d . Check failed: (bag_cnt) == (num_data_) at %s, line %d . share_state_ Can't be NULL at %s, line %d . Check failed: (data_partition_->num_leaves()) >= (tree->num_leaves()) at %s, line %d . SerialTreeLearner::BeforeTrainInvalid categorical threshold split'Forced Split' will be ignored since the gain getting worse.cegb_penalty_feature_lazy should be the same size as feature number.cegb_penalty_feature_coupled should be the same size as feature number.SerialTreeLearner::FindBestSplitsFromHistogramsGet historical Histogram for leaf %d failed, will skip the ``RecomputeBestSplitForLeaf``Check failed: (best_split_info.left_count) > (0) at %s, line %d . Check failed: (best_split_info.right_count) > (0) at %s, line %d . @@ -5013,3452 +4991,3459 @@ You need to set `feature_pre_filter=false` to dynamically change the `min_data_in_leaf`.Cannot change linear_tree after constructed Dataset handle.Cannot change precise_float_parser after constructed Dataset handle.LGBM_GetSampleCount output is nullptrCheck failed: (num_used_row_indices) > (0) at %s, line %d . used_row_indices should be sorted in SubsetLGBM_SampleIndices output is nullptrThe number of columns should be greater than zero.The number of columns should be smaller than INT32_MAX.Check failed: (inner_data.first) < (num_col) at %s, line %d . Using self-defined objective functionContinued train from model is not supported for c_api, please use continued train with input scoreDo not support feature parallel in c apiOnly find one worker, will switch to serial tree learnerPrediction results file %s cannot be createdCould not recognize the data format of data file %sCannot change num_class during trainingCannot change boosting during trainingCannot change metric during training/__w/1/s/src/application/predictor.hppCheck failed: (early_stop_freq) > (0) at %s, line %d . Check failed: (early_stop_margin) >= (0) at %s, line %d . -Predicting SHAP feature contributions is not implemented for linear trees.Unknown data type in PredictSparseUnknown col_ptr type in PredictSparseCSCUnknown indptr type in PredictSparseCSRUnknown matrix type in LGBM_BoosterPredictSparseOutputstatic bool yamc::rwlock::ReaderPrefer::release_rlock(yamc::rwlock::ReaderPrefer::state*)yamc::shared_lock<Mutex>::~shared_lock() [with Mutex = yamc::alternate::basic_shared_mutex<yamc::rwlock::ReaderPrefer>]static void yamc::rwlock::ReaderPrefer::acquire_rlock(yamc::rwlock::ReaderPrefer::state*)static void yamc::rwlock::ReaderPrefer::release_wlock(yamc::rwlock::ReaderPrefer::state*)*Z31LGBM_BoosterPredictSparseOutputEUllE_*Z25LGBM_BoosterPredictForCSCEUliE_*Z30RowPairFunctionFromDenseMatricPKviiiiEUliE_*Z28RowPairFunctionFromDenseRowsPPKviiEUliE_SsZN8LightGBM9Predictor7PredictEPKcS2_bbbEUliRKSt6vectorISsSaISsEEE0_ZN8LightGBM9PredictorC1EPNS_8BoostingEiibbbbidEUlRKSt6vectorISt4pairIidESaIS5_EEPdE_ZN8LightGBM9PredictorC1EPNS_8BoostingEiibbbbidEUlRKSt6vectorISt4pairIidESaIS5_EEPdE0_ZN8LightGBM9PredictorC1EPNS_8BoostingEiibbbbidEUlRKSt6vectorISt4pairIidESaIS5_EEPS3_ISt13unordered_mapIidSt4hashIiESt8equal_toIiESaIS4_IKidEEESaISI_EEE1_ZN8LightGBM9PredictorC1EPNS_8BoostingEiibbbbidEUlRKSt6vectorISt4pairIidESaIS5_EEPdE2_ZN8LightGBM9PredictorC1EPNS_8BoostingEiibbbbidEUlRKSt6vectorISt4pairIidESaIS5_EEPdE3_ZN8LightGBM9Predictor7PredictEPKcS2_bbbEUlS2_PSt6vectorISt4pairIidESaIS5_EEE_Z33RowFunctionFromDenseMatric_helperIfESt8functionIFSt6vectorIdSaIdEEiEEPKviiiEUliE_Z33RowFunctionFromDenseMatric_helperIfESt8functionIFSt6vectorIdSaIdEEiEEPKviiiEUliE0_Z33RowFunctionFromDenseMatric_helperIdESt8functionIFSt6vectorIdSaIdEEiEEPKviiiEUliE_Z33RowFunctionFromDenseMatric_helperIdESt8functionIFSt6vectorIdSaIdEEiEEPKviiiEUliE0_Z29IterateFunctionFromCSC_helperIfiESt8functionIFSt4pairIidEiEEPKvPKiS6_iEUliE_Z29IterateFunctionFromCSC_helperIflESt8functionIFSt4pairIidEiEEPKvPKiS6_iEUliE_Z29IterateFunctionFromCSC_helperIdiESt8functionIFSt4pairIidEiEEPKvPKiS6_iEUliE_Z29IterateFunctionFromCSC_helperIdlESt8functionIFSt4pairIidEiEEPKvPKiS6_iEUliE_Z25RowFunctionFromCSR_helperIifiESt8functionIFSt6vectorISt4pairIidESaIS3_EET_EEPKvPKiSA_EUliE_Z25RowFunctionFromCSR_helperIiflESt8functionIFSt6vectorISt4pairIidESaIS3_EET_EEPKvPKiSA_EUliE_Z25RowFunctionFromCSR_helperIidiESt8functionIFSt6vectorISt4pairIidESaIS3_EET_EEPKvPKiSA_EUliE_Z25RowFunctionFromCSR_helperIidlESt8functionIFSt6vectorISt4pairIidESaIS3_EET_EEPKvPKiSA_EUliE_Z25RowFunctionFromCSR_helperIlfiESt8functionIFSt6vectorISt4pairIidESaIS3_EET_EEPKvPKiSA_EUllE_Z25RowFunctionFromCSR_helperIlflESt8functionIFSt6vectorISt4pairIidESaIS3_EET_EEPKvPKiSA_EUllE_Z25RowFunctionFromCSR_helperIldiESt8functionIFSt6vectorISt4pairIidESaIS3_EET_EEPKvPKiSA_EUllE_Z25RowFunctionFromCSR_helperIldlESt8functionIFSt6vectorISt4pairIidESaIS3_EET_EEPKvPKiSA_EUllE_{Gz?;Dsg`sHz, X{H ^ H (yKTT\5N`gx0z@Xp.NnȰxhXHxP!!hK$xT(Y(+H.h1h11h48(;H>A WDXH\(KdHNШhQhTWZ]`a@(d 8huk0vmpv(rvv0wxpwHzwy8|(0hs(sHsXshstt0tHtȱ`txttt(tHthtu(uȲHu`uuHuux(x(xxxhyHyyxyzh zzzXz{X8{p{{ |@|h`||X}`}}H~xh~4~XS((]xx^X_ dpfi(mq`htH(88xX(8hPp"X#؇I(JLLMH(MxOЉQ(QHTWȊ[]X8bHd(f(HhhhHixi(jXlHpr(xЎxzh{}H~hȊXp@X8((HHxx`h 88`HxXhx0`x`X8ؒ `X$%%'`,X.@PhB(DJxVȖWhZ\H]8bdȘHlhqrPsxyyPHHHx`(hXП(xh8ش 8ظ88`x0xؤ(8Xx19XYh[Ю[Xpq؈hȊȡ@H0X0HH8HH(hhPHX`hЬ8 hphh -H xphxȲhH(@88#8$X$%P&X(.@3890(;<h> Ap8EHIKMHT0[P[P]_ha@Xc8ewH((`(x8ش0(px0hH((x8 8  0 - 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BBBG T^[BEB B(A0D8J +8A0A(B BBBA TL_keSBBB L(D0A8G +8A0A(B BBBB T_ p !BEB B(A0A8J -8A0A(B BBBH lcKLc@BBB E(A0A8D`8A0A(B BBB$c/AGG YAALdȂ:BEA D(G0 -(D ABBG `(D ABBT`BBB E(D0D8D -8A0A(B BBBK dIdIdIdI eI$eI<eITeIleIeIeIeIeI#eIeIfI,fIDfI\fItfXJ'fpJ'fJfJfxJf@LgHNgP(4g(R<Lg R:BBA A(D0)(A ABBg Sg(SLg SBEE E(D0D8GP -8A0A(B BBBK  hS $hS<hSThSlhS*hS$DhT BGG B(A0A8 -0A(B BBBD DhaBKG B(A0A8 -0A(B BBBK D,ii BQG B(A0A8 -0A(B BBBE DtisBQG B(A0A8R -0A(B BBBA i`uihu i`ujhujpu4jxuLju"dju|ju9AwjAM -B HDjPBBE A(A0D@* -0A(A BBBE Lk^BBB B(A0A8DP% -8A0A(B BBBC LTkBBB E(A0A8D`8A0A(B BBBDkHFBBB D(A0D@  -0A(A BBBE <kPBBB A(D0 -(A BBBG ,,lBAA AB<\lBBA A(G0(A ABB<ltBBA A(G0`(A ABBLl@BBB B(A0A8GP8A0A(B BBBL,mBBB B(A0A8G@ -8A0A(B BBBA |mr(mX mr-BEB A(A0D` +8A0A(B BBBH czKLcH}BBB E(A0A8D`8A0A(B BBB$4d/AGG YAAL\d:BEA D(G0 +(D ABBG `(D ABBT`BBB E(D0D8D +8A0A(B BBBK eGeG4eFLeFdeF|eFeFeFeFeFeF fF$fF#<fFTfFlfFfFfFfFfG'fG'fGgG,gGDgxI\gKtgHM(g`O<gXO:BBA A(D0)(A ABBgXPg`PLhXPBEE E(D0D8GP +8A0A(B BBBK dhQ |hQhQhQhQ*h Q$Dh8Q BGG B(A0A8 +0A(B BBBD D<i_BKG B(A0A8 +0A(B BBBK Dif BQG B(A0A8 +0A(B BBBE Di@pBQG B(A0A8R +0A(B BBBA jr,jr Djr\jrtjrjrjr"jrjr9AwjAM +B HDkPBBE A(A0D@* +0A(A BBBE L\k^BBB B(A0A8DP% +8A0A(B BBBC LkBBB E(A0A8D`8A0A(B BBBDkFBBB D(A0D@  +0A(A BBBE <DlBBB A(D0 +(A BBBG ,lBAA AB<lBBA A(G0(A ABB<l8tBBA A(G0`(A ABBL4mxBBB B(A0A8GP8A0A(B BBBLmHBBB B(A0A8G@ +8A0A(B BBBA mp(mnp-BEB A(A0D` 0A(A BBBJ N 0A(A BBBJ v 0A(A BBBB  -0A(A BBBI L4nxtAAG e +0A(A BBBI LnqAAG e CAF { ACI R -CAJ LAAnZn@ n Yn Yn kn0 -fo -,o -iDoH \o LtosAAG ] +CAJ LAAn0Znx oY$oY<okTohfloo(iooLo qAAG ] CAF k ACA R -CAJ LAALo(tAAG ] +CAJ LAALp`qAAG ] CAF [ ACA R -CAJ LAALpXtAAG ] +CAJ LAALlpqAAG ] CAF [ ACA R -CAJ LAALdptAAG ] +CAJ LAALpqAAG ] CAF [ ACA R -CAJ LAALptAAG ] +CAJ LAAL qqAAG ] CAF [ ACA R -CAJ LAALqtAAG ] +CAJ LAAL\q rAAG ] CAF [ ACA R -CAJ LAALTquAAG ] +CAJ LAALqPrAAG ] CAF [ ACA R -CAJ LAALqHuAAG ] +CAJ LAALqrAAG ] CAF [ ACA R -CAJ LAALqxuAAG ] +CAJ LAALLrrAAG ] CAF [ ACA R -CAJ LAALDruAAG ] +CAJ LAALrrAAG ] CAF [ ACA R -CAJ LAALruAAG ] +CAJ LAALrsAAG ] CAF [ ACA R -CAJ LAALrvAAG ] +CAJ LAAL<s@sAAG ] CAF [ ACA R -CAJ LAAL4s8vAAG ] +CAJ LAALspsAAG ] CAF [ ACA R -CAJ LAALshvAAG ] +CAJ LAALssAAG ] CAF [ ACA R -CAJ LAALsvAAG ] +CAJ LAAL,tsAAG ] CAF [ ACA R -CAJ LAAL$tvAAG ] +CAJ LAAL|ttAAG ] CAF [ ACA R -CAJ LAALttvAAG ] +CAJ LAALt0tAAG ] CAF [ ACA R -CAJ LAALt(wAAG ] +CAJ LAALu`tAAG ] CAF [ ACA R -CAJ LAALuXwAAG ] +CAJ LAALlutAAG ] CAF [ ACA R -CAJ LAALduwAAG ] +CAJ LAALutAAG ] CAF [ ACA R -CAJ LAALuwAAG ] +CAJ LAAL vtAAG ] CAF [ ACA R -CAJ LAALvwAAG ] +CAJ LAAL\v uAAG ] CAF [ ACA R -CAJ LAALTvxAAG ] +CAJ LAALvPuAAG ] CAF [ ACA R -CAJ LAALvHxAAG ] +CAJ LAALvuAAG ] CAF [ ACA R -CAJ LAALvxxAAG ] +CAJ LAALLwuAAG ] CAF [ ACA R -CAJ LAALDwxAAG ] +CAJ LAALwuAAG ] CAF [ ACA R -CAJ LAALwxAAG ] +CAJ LAALwvAAG ] CAF [ ACA R -CAJ LAALwyAAG ] +CAJ LAAL<x@vAAG ] CAF [ ACA R -CAJ LAAL4x8yAAG ] +CAJ LAALxpvAAG ] CAF [ ACA R -CAJ LAALxhyAAG ] +CAJ LAALxvAAG ] CAF [ ACA R -CAJ LAALxyAAG ] +CAJ LAAL,yvAAG ] CAF [ ACA R -CAJ LAAL$yyAAG ] +CAJ LAAL|ywAAG ] CAF [ ACA R -CAJ LAALtyyAAG ] +CAJ LAALy0wAAG ] CAF [ ACA R -CAJ LAALy(zAAG ] +CAJ LAALz`wAAG ] CAF [ ACA R -CAJ LAALzXzAAG ] +CAJ LAALlzwAAG ] CAF [ ACA R -CAJ LAALdzzAAG ] +CAJ LAALzwAAG ] CAF [ ACA R -CAJ LAALzzAAG ] +CAJ LAAL {wAAG ] CAF [ ACA R -CAJ LAAL{zAAG ] +CAJ LAAL\{ xAAG ] CAF [ ACA R -CAJ LAALT{{AAG ] +CAJ LAAL{PxAAG ] CAF [ ACA R -CAJ LAAL{H{AAG ] +CAJ LAAL{xAAG ] CAF [ ACA R -CAJ LAAL{x{AAG ] +CAJ LAALL|xAAG ] CAF [ ACA R -CAJ LAALD|{AAG ] +CAJ LAAL|xAAG ] CAF [ ACA R -CAJ LAAL|{AAG ] +CAJ LAAL|yAAG ] CAF [ ACA R -CAJ LAAL||AAG ] +CAJ LAAL<}@yAAG ] CAF [ ACA R -CAJ LAAL4}8|AAG ] +CAJ LAAL}pyAAG ] CAF [ ACA R -CAJ LAAL}h|AAG ] +CAJ LAAL}yAAG ] CAF [ ACA R -CAJ LAAL}|AAG ] +CAJ LAAL,~yAAG ] CAF [ ACA R -CAJ LAAL$~|AAG ] +CAJ LAAL|~zAAG ] CAF [ ACA R -CAJ LAALt~|AAG ] +CAJ LAAL~0zAAG ] CAF [ ACA R -CAJ LAAL~(}AAG ] +CAJ LAAL`zAAG ] CAF [ ACA R -CAJ LAALX}AAG ] +CAJ LAALlzAAG ] CAF [ ACA R -CAJ LAALd}AAG ] +CAJ LAALzAAG ] CAF [ ACA R -CAJ LAAL}AAG ] +CAJ LAAL zAAG ] CAF [ ACA R -CAJ LAAL}AAG ] +CAJ LAAL\ {AAG ] CAF [ ACA R -CAJ LAALT~AAG ] +CAJ LAALP{AAG ] CAF [ ACA R -CAJ LAALH~AAG ] +CAJ LAAL{AAG ] CAF [ ACA R -CAJ LAALx~AAG ] +CAJ LAALL{AAG ] CAF [ ACA R -CAJ LAALD~AAG ] +CAJ LAAL{AAG ] CAF [ ACA R -CAJ LAAL~AAG ] +CAJ LAAL|AAG ] CAF [ ACA R -CAJ LAALAAG ] +CAJ LAAL<@|AAG ] CAF [ ACA R -CAJ LAAL48AAG ] +CAJ LAALp|AAG ] CAF [ ACA R -CAJ LAALhAAG ] +CAJ LAAL܂|AAG ] CAF [ ACA R -CAJ LAALԂAAG ] +CAJ LAAL,|AAG ] CAF [ ACA R -CAJ LAAL$AAG ] +CAJ LAAL|}AAG ] CAF [ ACA R -CAJ LAALtAAG ] +CAJ LAAL̃0}AAG ] CAF [ ACA R -CAJ LAALă(AAG ] +CAJ LAAL`}AAG ] CAF [ ACA R -CAJ LAALXAAG ] +CAJ LAALl}AAG ] CAF [ ACA R -CAJ LAALdAAG ] +CAJ LAAL}AAG ] CAF [ ACA R -CAJ LAALAAG ] +CAJ LAAL }AAG ] CAF [ ACA R -CAJ LAALAAG ] +CAJ LAAL\ ~AAG ] CAF [ ACA R -CAJ LAALTAAG ] +CAJ LAALP~AAG ] CAF [ ACA R -CAJ LAALHAAG ] +CAJ LAAL~AAG ] CAF [ ACA R -CAJ LAALxAAG ] +CAJ LAALL~AAG ] CAF [ ACA R -CAJ LAALDAAG ] +CAJ LAAL~AAG ] CAF [ ACA R -CAJ LAAL؁AAG ] +CAJ LAALAAG ] CAF [ ACA R -CAJ LAALAAG ] +CAJ LAAL<@AAG ] CAF [ ACA R -CAJ LAAL48AAG ] +CAJ LAALpAAG ] CAF [ ACA R -CAJ LAALhAAG ] +CAJ LAAL܇AAG ] CAF [ ACA R -CAJ LAALԇAAG ] +CAJ LAAL,AAG ] CAF [ ACA R -CAJ LAAL$ȂAAG ] +CAJ LAAL|AAG ] CAF [ ACA R -CAJ LAALtAAG ] +CAJ LAAL̈0AAG ] CAF [ ACA R -CAJ LAALĈ(AAG ] +CAJ LAAL`AAG ] CAF [ ACA R -CAJ LAALXAAG ] +CAJ LAALlAAG ] CAF [ ACA R -CAJ LAALdAAG ] +CAJ LAALAAG ] CAF [ ACA R -CAJ LAALAAG ] +CAJ LAAL AAG ] CAF [ ACA R -CAJ LAALAAG ] +CAJ LAAL\ AAG ] CAF [ ACA R -CAJ LAALTAAG ] +CAJ LAALPAAG ] CAF [ ACA R -CAJ LAALHAAG ] +CAJ LAALAAG ] CAF [ ACA R -CAJ LAALxAAG ] +CAJ LAALLAAG ] CAF [ ACA R -CAJ LAALDAAG ] +CAJ LAALAAG ] CAF [ ACA R -CAJ LAAL؄AAG ] +CAJ LAALAAG ] CAF [ ACA R -CAJ LAALAAG ] +CAJ LAAL<@AAG ] CAF [ ACA R -CAJ LAAL48AAG ] +CAJ LAALpAAG ] CAF [ ACA R -CAJ LAALhAAG ] +CAJ LAAL܌AAG ] CAF [ ACA R -CAJ LAALԌAAG ] +CAJ LAAL,ЂAAG ] CAF [ ACA R -CAJ LAAL$ȅAAG ] +CAJ LAAL|AAG ] CAF [ ACA R -CAJ LAALtAAG ] +CAJ LAAL̍0AAG ] CAF [ ACA R -CAJ LAALč(AAG ] +CAJ LAAL`AAG ] CAF [ ACA R -CAJ LAALXAAG ] +CAJ LAALlAAG ] CAF [ ACA R -CAJ LAALdAAG ] +CAJ LAALAAG ] CAF [ ACA R -CAJ LAALAAG ] +CAJ LAAL AAG ] CAF [ ACA R -CAJ LAALAAG ] +CAJ LAAL\ AAG ] CAF [ ACA R -CAJ LAALTAAG ] +CAJ LAALPAAG ] CAF [ ACA R -CAJ LAALHAAG ] +CAJ LAALAAG ] CAF [ ACA R -CAJ LAALxAAG ] +CAJ LAALLAAG ] CAF [ ACA R -CAJ LAALDAAG ] +CAJ LAALAAG ] CAF [ ACA R -CAJ LAAL؇AAG ] +CAJ LAALAAG ] CAF [ ACA R -CAJ LAALAAG ] +CAJ LAAL<@AAG ] CAF [ ACA R -CAJ LAAL48AAG ] +CAJ LAALpAAG ] CAF [ ACA R -CAJ LAALhAAG ] +CAJ LAALܑAAG ] CAF [ ACA R -CAJ LAALԑAAG ] +CAJ LAAL,ЅAAG ] CAF [ ACA R -CAJ LAAL$ȈAAG ] +CAJ LAAL|AAG ] CAF [ ACA R -CAJ LAALtAAG ] +CAJ LAAL̒0AAG ] CAF [ ACA R -CAJ LAALĒ(AAG ] +CAJ LAAL`AAG ] CAF [ ACA R -CAJ LAALXAAG ] +CAJ LAALlAAG ] CAF [ ACA R -CAJ LAALdAAG ] +CAJ LAALAAG ] CAF [ ACA R -CAJ LAALAAG ] +CAJ LAAL AAG ] CAF [ ACA R -CAJ LAALAAG ] +CAJ LAAL\ AAG ] CAF [ ACA R -CAJ LAALTAAG ] +CAJ LAALPAAG ] CAF [ ACA R -CAJ LAALHAAG ] +CAJ LAALAAG ] CAF [ ACA R -CAJ LAALxAAG ] +CAJ LAALLAAG ] CAF [ ACA R -CAJ LAALDAAG ] +CAJ LAALAAG ] CAF [ ACA R -CAJ LAAL؊AAG ] +CAJ LAALAAG ] CAF [ ACA R -CAJ LAALAAG ] +CAJ LAAL<@AAG ] CAF [ ACA R -CAJ LAAL48AAG ] +CAJ LAALpAAG ] CAF [ ACA R -CAJ LAALhAAG ] +CAJ LAALܖAAG ] CAF [ ACA R -CAJ LAALԖAAG ] +CAJ LAAL,ЈAAG ] CAF [ ACA R -CAJ LAAL$ȋAAG ] +CAJ LAAL|AAG ] CAF [ ACA R -CAJ LAALtAAG ] +CAJ LAAL̗0AAG ] CAF [ ACA R -CAJ LAAė#AGPJ4(BDC GpR IXB$AG`F -Iq $DHHADG yAA$lpHADG yAAX`p$ĘhD -I L"BBB E(A0A8DP8A0A(B BBB<<@BBB A(D0(A BBB<BDD A(G` -(A ABBA <MBBD A(D0/ -(A ABBF <UBBD A(D0/ -(D ABBK <<BBD A(D0m -(A ABBH D|BGG B(A0A8 -0A(B BBBF ĚX7ܚxHH<4ȗBII A(A0 -(A BJBF <tXBIF D(A0 -(A MBBM D +CAJ LAAP#AGPJ4<`BDC GpR IXB$tAG`F +Iq $HADG yAA$ĘHADG yAAp$D +I LD8"BBB E(A0A8DP8A0A(B BBB<xBBB A(D0(A BBB< BDD A(G` +(A ABBA <ȊMBBD A(D0/ +(A ABBF <T،UBBD A(D0/ +(D ABBK <BBD A(D0m +(A ABBH DԚHBGG B(A0A8 +0A(B BBBF 74LHlPH<BII A(A0 +(A BJBF <̛BIF D(A0 +(A MBBM D 0 BNB B(A0A8t -0A(B BBBE <BBD A(D0m -(A ABBH 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