vendor/lib_lightgbm.dylib in lightgbm-0.1.5 vs vendor/lib_lightgbm.dylib in lightgbm-0.1.6
- old
+ new
@@ -1,2812 +1,2619 @@
- x __TEXT __text __TEXT N __stubs __TEXT __stub_helper __TEXT d F d __gcc_except_tab__TEXT \9 __const __TEXT , N , __cstring __TEXT { v { __unwind_info __TEXT 0. __DATA __nl_symbol_ptr __DATA __got __DATA __la_symbol_ptr __DATA (' (' __mod_init_func __DATA 0- 0- __const __DATA P- P- __data __DATA B PG B __thread_vars __DATA @ h @ __thread_ptrs __DATA __thread_data __DATA \ __thread_bss __DATA 1 __bss __DATA H __common __DATA H __LINKEDIT
0 @rpath/lib_lightgbm.so " 0 H a y ش < G I P - E `> g E1.^a$
+ x __TEXT __text __TEXT % % __stubs __TEXT __stub_helper __TEXT ! x ! __gcc_except_tab__TEXT 0( S 0( __const __TEXT P{ 0Q P{ __cstring __TEXT 9 __unwind_info __TEXT N D1 N __DATA __nl_symbol_ptr __DATA __got __DATA 0 __la_symbol_ptr __DATA @ X @ __mod_init_func __DATA __const __DATA __data __DATA P G P __thread_vars __DATA @ h @ __thread_ptrs __DATA __thread_data __DATA \ __thread_bss __DATA 1 __common __DATA P __bss __DATA H __LINKEDIT 0 "
0 @rpath/lib_lightgbm.so " 0 P p ( 8 c P \ ~ K7 Tz$
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N HHK HH HKHHHKHSHPHHH[]fff. UHH
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HHK HH HKHHHKHSHPHHH[]fff. UHHm HHG HF HGHFHGHOHNHF]UH]f. UH]& f D UHHcHOH;O}HGHW ]W] UHH
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+AF(HEHtHC(HC( LH[A^]HHǃu$ AF(HHQHW$ HAF(u
+u9H8H= HexceptioHHHunknown Hf@n H$ {HIH= HH% &$ Yffff. UHAWAVSH8LHHLUHuH(H(H(LcI(Mc@(Lc[(HE H(A tIJ(1L}(H]H\$ HL$Ht$L\$L$ A HMt
+AG(HE HMHtHH(H@( LH8[A^A_]IHAuC# AG(HHQHX(# HAG(Au
+u9H8H= HexceptioHHHunknown Hf@n " wHIH=` HHw$ " Uf UHAWAVSHMHHыV(I(H(HE H(A tIp(1LuH]H\$H4$ E1I5@t
+AF(HEHtIG(IG( LH[A^A_]HHǃu" AF(HHQHW! HAF(u
+u9H8H=f HexceptioHHHunknown Hf@n ! yHIH=. HHE# ! W UHAWAVAUATSHHMLEIIHuLcr(MDuW)EHE tAL}Ey H}* N< L HEJ H]HL H]L}H=b H 1HMLELM 1C! HE A$ tI\$(HuUI(LEHLt
AG( HEHt
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-u1H0H= HexceptioHHHunknown Hf@n HIH= HH_$ " L}UHAWAVATSHMI E ]E E D'HHEH=-l HMLELMܺ" DjjP" H E9N؉]HcE9&IIH t,ΉtHcuHH9|H=k DQ" H[A\A^A_]fffff. UHAWAVAUATSHhIHIH(W)EHE )EHE HE# H}HH LxDULHALEIEHLpY LMLm1H}HuDҀHEIDAHIEHE1ffffff. B<0 tJT3IH9r J3H9vuIHLII HEH;Es0HMHHHMHUHPHW)EHE HELH}LMH}: LDULELMLJ\3AHHIEH9uII9vmH}HuHHI HEH;Es3HMHHHMHUHPHW)EHE HELmH}Hu覎H} H} W)EHE IE HMH)HHH~fLcA A HMIIBD1u
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-u1H0H= HexceptioHHHunknown Hf@n HIH= HH LHh[A\A]A^A_]ffff. UHAWAVAUATSHxMIIHIE A$ tI|$(1Hupt
AE( HcuH}b: L}H]Lc}MW)EHE Y Ey H}4 J< HEHEJ HMIOLHt!H H HEHHEIHuHrdffffff. H HEHHHMH@ HEHHHMH@ HEHHHMH@ HEHHEIuMԅ 1E1ff. H}H Ht HH)H w$H߾ H): H}M+ff. H tH H9t HD H HUJIHcHI9|A$ tI|$(1L}H]HuHUкt
AE( E;EtH= H5 1]HhHu= h$HiHExHHEpHH S(A9W(| I(LS Hh H}Ht#HEH9tHHH)HHHHM H]HtHHEH9t7 HHHMHxHtH9xtHx HMH9HuH]Ht LHx[A\A]A^A_]H= 1XIIHh
II} IIH}Ht#HEH9tHHH)HHHHM H]HtBHEH9t1HHHMHxHtH9xtHx HMH9HuH]H LAu7 AE(HHQHH= HHR HAE(Au
-u9HH=Q HexceptioHHHunknown Hf@n [ HIfffff. UHAWAVAUATSHh HIHH;C1 HL%S M|$@LLH)N HH@HHfHn)pLX HDžx EID$HEHpIhLLL LmH7S HHEHHDž HDž HDž HDž Dž W)EHE HuLGzLeL}H} LHH@HDŽ HL(L LLHHCH+HHr[A L- fffff. LLHN<L7 LLHcIHCH+HI9rH}HH譁HEHpHEHHEHHEHH HG H5.N HHp H Hh [A\A]A^A_]IE IE IE H5) 1LHh [A\A]A^A_] HH}H H< LNHNH` HHEHpHEHHEHHEHH Hh H5OM HHp9 H HQ UHAVSH tH(1H(tC(H[A^]IHAu C(HHQHO HC(Au
-u1H0H=a HexceptioHHHunknown Hf@n HIH=1 HH Q ef. UHAVSIH t$H{(Htt
-AF(HC( L[A^]HHǃu AF(HHQHT HAF(u
-u6H5H= HexceptioHHHunknown Hf@n HIH=Q HH q `f. UHAWAVAUATSHXMIIILmHcY(]I(H= L H= L H= L W)EHE t8y H} L, L HEH H]HL H]H=e\ HV 1HMLELM 1 A tI_(HUME1HLMtAF(H}H HEH9 HHH)HHHHM W)EHE t8y H} L, L HEH H]HL H]H=[ H 1HMLELM 1 A tI_(HUMA HLxtAF(H}Ht#HEH9tHHH)HHHHM LHX[A\A]A^A_]A tI(1I(A LL tAF(HIe HIH}HtTHEH9tFHHH)HHHHM/ HIH}Ht#HEH9tHHH)HHHHMj Lu7 AF(HHQHH=F HH f V HAF(u
-u9HH= HexceptioHHHunknown Hf@n HIf D UHAWAVATSHMI E ]E E D'HHEH=}Y HMLELMܺ" DjjP H E9N؉]HcE9$IIH t,tHcuHH9|H= Y D H[A\A^A_]UHAWAVATSHMI̋i E ]E E D7HHEH=X HMLELMܺ" DjjP3 H E9N؉]HcE9 M I$HcH9ILMII)I MIMI IIHHHr(fAD(fAL8fZfZffL 1HtWLH)HH|ILXfAfIfZfZfGfOfAf fZfZfGfH H@HuM9t LHȐAD0ZDHH9|H=W D H[A\A^A_]ff. UHAVSH0IHHUH(E E tH(1HUHMLEH許t
-AF(H= H t>H= H t+H= H tRHEHEH=V H HEHEH=V H HMLELM 13
LH0[A^]HEHEH=xV Hy HHǃu AF(HHQHWx HAF(u
-u9H8H=+ HexceptioHHHunknown Hf@n 5 cHIH= HH
Afff. UHAWAVATSHMI E ]E E D'HHEH={U HMLELMܺ" DjjP H E9N؉]HcE9&IIH D t+tt,HcuHH9|H=U D H[A\A^A_]fff. UHAWAVATSHMI̋ E ]E E D7HHEH=T HMLELMܺ" DjjP# H E9N؉]HcE9> M I$HcH9ILMII)I MI H|(H9HHMI\H9sHL0I<H9 MADHt=L|8I\H1ffffff. D ADA HHu1I reLH)HHʘ ILp AIGOAIGOAIGOA GHHHuM9t!LHf ILHL0HH9|H=/S D H[A\A^A_]fUHAWAVATSHMI̋Y E ]E E D7HHEH=R HMLELMܺ" DjjPC H E9N؉]HcE9 M I$HcH9ILMII)I MIMI IIHHHr AZAZLfD(L8 1HtKLH)HH|XILff. ZAZIGOZAZ fGH@H HuM9t'LH ADZD0HH9|H=Q D. H[A\A^A_]ff. UHAWAVSHIIHH(E E tH(1HUHMLEHǫt
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-u9H8H= HexceptioHHHunknown Hf@n yHIH=p HH3 Wf UHAVSH tH(1H(PtC(H[A^]IHAu8 C(HHQHO HC(Au
-u1H0H=ѥ HexceptioHHHunknown Hf@n HIH= HHd ef. UHAVSHIH tH(1Huit
-AF(EC(LH[A^]HHǃuU AF(HHQHW: HAF(u
-u9H8H= HexceptioHHHunknown Hf@n {HIH= HHx Yfffff. UHAVSHIH tH(1Hu艫t
-AF(EC(LH[A^]HHǃue AF(HHQHWJ HAF(u
-u9H8H= HexceptioHHHunknown Hf@n {HIH=ţ HH Yfffff. UHAVSIH t$H{(Ht萮t
-AF(HC( L[A^]HHǃu~ AF(HHQHTc HAF(u
-u6H5H= HexceptioHHHunknown Hf@n HIH= HH `f. UHAVSHIHHE tH(1H(HU轩t
-AF(HEHtHC(HC( LH[A^]HHǃux AF(HHQHW] HAF(u
-u9H8H= HexceptioHHHunknown Hf@n {HIH=ء HH YUHAVSHIHE HE H(HuHUt
-AF(HEHtHC(HC( LH[A^]HHǃuz AF(HHQHW_ HAF(u
-u9H8H= HexceptioHHHunknown Hf@n {HIH=ڠ HH Y UHAVSHIHE HE H(HuHU?t
-AF(HEHtHC(HC( LH[A^]HHǃuz AF(HHQHW_ HAF(u
-u9H8H= HexceptioHHHunknown Hf@n {HIH=ڟ HH Y UHAVSH tH(1 tHv(1tC(H[A^]IHAu C(HHQHO HC(Au
-u1H0H=8 HexceptioHHHunknown Hf@n HIH= HH ( eUHAVSH tH(1 tHv(1gtC(H[A^]IHAu C(HHQHO HC(Au
-u1H0H=h HexceptioHHHunknown Hf@n HIH=8 HH X eUHAVSH tH(1 tHv(1ǮtC(H[A^]IHAu C(HHQHO HC(Au
-u1H0H= HexceptioHHHunknown Hf@n HIH=h HH+ eUHAVSH tH(1H(tC(H[A^]IHAu8 C(HHQHO HC(Au
-u1H0H=ќ HexceptioHHHunknown Hf@n HIH= HHd ef. UHAVSHIH tH(1Hu詳t
-AF(EC(LH[A^]HHǃuU AF(HHQHW: HAF(u
-u9H8H= HexceptioHHHunknown Hf@n {HIH= HHx Yfffff. UHAVSHHE tH(1HuUtC(HH[A^]IHAui C(HHQHOO HC(Au
-u1H0H= HexceptioHHHunknown Hf@n HIH=Қ HH aff. UHAWAVATSHpMIHuHUE HcY(Hۉ]W)EHE y H} L< LX HEH H]HLS H]Hc]HW)EHE tFy H}D L< L HEH H]HL H] )EHE HEHD$HEH$H=C H 1HMLELM 16 A tI^(HuHUHMH谷t AD$(H}Ht#HEH9tHHH)HHHHMC H}Ht#HEH9tHHH)HHHHM LHp[A\A^A_]II2IIH}Ht#HEH9tHHH)HHHHM H}Ht#HEH9tHHH)HHHHM LAu AD$(HHQHQ HAD$(Au
-u1H0H=V HexceptioHHHunknown Hf@n HIH=& HH F ffffff. UHAWAVAUATSHMMI͋ E ]E E 7HHEH=A HMLELMԺ" ujjP H E9N؉]HcE9 HMM4$IU H1I?HcH9ILMII)I MI LH9ILMJLI9sNTH L9 LTL\8L|Md8J1 fADfA fZfZfADfA fADfA fZfZfADfA HI9uM9t6fffff. AD(ZD(ZH9H@|H=Y@ u H[A\A]A^A_]f. UHAVSH tH(1蔵tC(H[A^]IHAu, C(HHQHO HC(Au
-u1H0H=ŕ HexceptioHHHunknown Hf@n HIH= HHX efffff. UHAVSHIH tH(1Hut
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-u9H8H=ݔ HexceptioHHHunknown Hf@n {HIH= HHh Yfffff. UHAWAVAUATSHxMIIHIA$ tI|$(1Hut
AE( HcuH}Y L}H]Lc}MW)EHE ` Ey H}+ J< HEHEJ HMIOLHt(Hf. H HEHHEIHuHrdffffff. H HEHHHMH@ HEHHHMH@ HEHHHMH@ HEHHEIuMԅ 1E1ff. H}H Ht HH)Hw'H߾ H) H}M.fffff. H tHH9tHD f H HUJIHcHI9|A$ tI|$(1L}H]HUHu`t
AE( E;EtH=% H5щ 1MnHhHu-h$HiHExHHEpHH S(A9W(| I(LC Hh H}Ht#HEH9tHHH)HHHHM H]HtHHEH9t7 HHHMHxHtH9xtHx| HMH9HuH]Hd LHx[A\A]A^A_]H=p 1HmIIHh
II} IIH}Ht#HEH9tHHH)HHHHM H]HtBHEH9t1HHHMHxHtH9xtHx HMH9HuH]H LAu7 AE(HHQHH= HHB HAE(Au
-u9HH=A HexceptioHHHunknown Hf@n K HIfffff. UHAWAVATSHIIIH tH{(1Hu_t
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-u9H8H= HexceptioHHHunknown Hf@n uHIH=ڎ HH S UHAVSHIH tH(1v(HUt
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-u9H8H=( HexceptioHHHunknown Hf@n 2 {HIH= HH Yf UHAVSHHHH( tH(1v(HUHtC(HH[A^]IHAu C(HHQHO HC(Au
-u1H0H=< HexceptioHHHunknown Hf@n HIH= HH , a D UH1( ~( Dȃz( D]f UHAWAVSPMLu LM1y( LUAx( DA{( D tH(1L}(H(R(EB(I(I(L4$跹tAG(LH[A^A_]IHAuX AG(HHQHP= HAG(Au
-u1H0H= HexceptioHHHunknown Hf@n HIH= HH ] UHAVSH1z( y( DAx( DHE tH(1H]v(AI(LEjt C(
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-u9H8H=ۊ HexceptioHHHunknown Hf@n {HIH= HHf Yfff. UHAWAVATSHPIIHU8LU0H]@Lu1A~( Lu A~( DHE(x( DHMH(I(I(Mc@(McI(Lcq(H( tH(1L}HAJ(H(H\$8H]H\$0HT$(L$ D$Lt$LL$L$ A LMtAG(LHP[A\A^A_]IHAu AG(HHQHP HAG(Au
-u1H0H=N HexceptioHHHunknown Hf@n HIH= HH > [ UHAWAVATSH0ILu L]Le(1Ax( H]Ay( D{( D؋J(EB(H(I( tH(1L}0AC(I(Ld$ HUHT$Lt$D$ $ E1ltAG(LH0[A\A^A_]IHAuk AG(HHQHPP HAG(Au
-u1H0H= HexceptioHHHunknown Hf@n HIH=҇ HH [ff. UHAVSHH tH(1V(H(1HUtC(H[A^]IHAu C(HHQHOs HC(Au
-u1H0H=& HexceptioHHHunknown Hf@n HIH= HH effffff. UHAWAVAUATSH8LLEHMIIIHE Mcg(MW)EHE Ey H} Ly HHMHMHLHEID$LHt$Hff. HMHHMIHuHrH @ HEHHHM@ HEHHHM@ HEHHHM@ HMHHMIuA tI~(1AU(IcO(LMLE1wt!C(H}Ht\H9}tH} KIHUH uMH]HEP(A9W(|H}H(H HtH9]tH]HF LHH8[A\A]A^A_]H=O| 1L$a IHIH}HtH9}tH} Lu9 LC(HHQHH=ׄ HH y HAG(u
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HH9u} H9txHtru INHIFINHHt;H1ff. <9#t
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o H} k HPHu HH HEpH=n 1 H}~ HlLHHPi HPL H=n 1bH`Ht(L;LsH{(& L H< MLuHPHDžP Ht H H H;EuH [A\A]A^A_] HIH}KIH}H9t
Ht?HP(7HP /;9IH} .H! I IH}p HkIH`Ht5ffff. L#L{H{(6 L. HL MLuHPHDžP Ht( L @ UH]ff D UH]f D UHAWAVAUATSPIA$1 tS
- I$ IDŽ$ HtHPI$ IDŽ$ HtHPM$p M I$x L9to LkM$x L{MtGHCL9t.fHHHKHxH@ HtHPHCL9uM} L( M$x M9LuM$p L I$X HtOI$` H9t:fHHI$` HxH@ HtHPI$` H9uI$X H M$@ Mt]I$H L9tHf D HHI$H HXH@ HtH H_ I$H L9uM$@ LB I$8 IDŽ$8 HtHE H LH[A\A]A^A_]WHEI$ IDŽ$ HtHPI$ IDŽ$ HtHPM$p Mt~I$x L9tiLsM$x LkMtDHCL9t+HHHKHxH@ HtHPHCL9uM.LV M$x M9LuM$p L6 I$X HtMI$` H9t8HHI$` HxH@ HtHPI$` H9uI$X H M$@ Mt]I$H L9tHf D HHI$H HXH@ HtH H I$H L9uM$@ Lr I$8 IDŽ$8 HtHu HI L%H}
-fff. UH]&f D UHAWAVAUATSH W)E)pE ?HGH H}f D HELhH=qx L9; IMtQLH(HpH; IMH]Lmt[LeLDf(EA LH H=1x ML: He L* H* AD$(HKHH$IET$0It$8IHDH@IHxHH}g LOLH! I9M 1LHI HN0Eu D$uLHHUHRH]I(H9s uLc E LH HEL`8H}LuEuIGHF8HEL9 IM
AE(u I(IMm8H}HE uHIF LeAF(u Mf(IMf8IuHHEHX8LY9 H @(u H(HH@8HD$H $1H=h HuHULMM HN0uLHHUHRH9 IELEf0IHMLEqM9LIBHtSIEHE~8HuHuHƄHEq E} EHuЅt"y)HEHp 1H=g Y M9]uLkH}LuLeLeMl$8H}LuELmuMoLn8L(8 HHB A(u H(HHI8HE uL@MF IF(AV(uLHMN8uH{I|$8uHIF8HuLHD$H<$1H=f HpH3 HHu $ MM!HpJHj L(M^ LuAD$(II$MEt$0ME|$8MH. MM ID$HEffff. IEH9r1HHL9 AM% HfIuHDL9ukfu?A: $u]IM H)HEf D HG HH:H]H@t'ILI} LLW H}IL Mm MLL IEH9Lur1HHL92 AE HIEMH Mm Mu M ID$HEfIEL!L9 AM% HfIuHDL9ukfu9A: $u]IM H)HEH7 HH:H]H@t-LI} LLMMD MMIH] Mm MV9 @ IEL!L9u)AE HIEMH Mm MuLu MĿ@ IHI]H; H]MI(LHu$ MuIE HEHxH*HHH H*XHuHHH HxH* H*XMtY.wM H6Hr
+HMHA( HEH@( H}Ht#HEH9tHHH)HHHHM LHH[A\A]A^A_]L}HIH}Ht#HEH9tHHH)HHHHM Lu; L}AG(HHQHH=l HH! s HHE@(u
+u1H0H=, HexceptioHHHunknown Hf@n HIH= HH! ^ L}UHAWAVATSHMI E ]E E D'HHEH=m HMLELMܺ" DjjPE H E9N؉]HcE9&IIH t,ΉtHcuHH9|H= D H[A\A^A_]fffff. UHAWAVAUATSHhIHIH(W)EHE )EHE H H}HH LxDULHALEIEHLpY LMLm1H}HuDҀHEIDAHIEHE1ffffff. B<0 tJT3IH9r J3H9vuIHLII HEH;Es0HMHHHMHUHPHW)EHE HELH}L蝆H} LDULELMLJ\3AHHIEH9uII9vmH}HuHHIv HEH;Es3HMHHHMHUHPHW)EHE HELmH}HuH}) H} W)EHE IE HMH)HHH~fLcA A HMIIBD1u
JD1
+f D J1HEHMH;MsHHE @ H}Hus/M9|Hp tHx(1LxHutlAD$(H}Ht#HEH9tHHH)HHHHM] H]H I} H9 D HI} I} H9u^H}Ht#HEH9tHHH)HHHHM H]H I} H9t f D HI} I} H9uH]H j dII IIH}e IIH}T H]HLx H}H9 HH}& H}H9ueIIH}HLxt#HEH9tHHH)HHHHM H]Ht/I} H9t @ HI} I} H9uH]H LAu AD$(HHQHQ HAD$(Au
+u1H0H=Q HexceptioHHHunknown Hf@n HIH=! HH8 LHh[A\A]A^A_]ffff. UHAWAVAUATSHxMIIHIE A$ tI|$(1Hupt
AE( HcuH}b: L}H]Lc}MW)EHE Y Ey H} J< HEHEJ HMIOLHt!H H HEHHEIHuHrdffffff. H HEHHHMH@ HEHHHMH@ HEHHHMH@ HEHHEIuMԅ 1E1ff. H}H Ht HH)H w$H߾ H): H}M+ff. H tH H9t HD H HUJIHcHI9|A$ tI|$(1L}H]HuHUt
AE( E;EtH= H5 1McHhHu= h$HiHExHHEpHH S(A9W(| I(L Hha H}Ht#HEH9tHHH)HHHHMg H]HtHHEH9t7 HHHMHxHtH9xtHx. HMH9HuH]H LHx[A\A]A^A_]H= 1HbIIHh
II} IIH}Ht#HEH9tHHH)HHHHM H]HtBHEH9t1HHHMHxHtH9xtHxn HMH9HuH]HV LAu7x AE(HHQHH= HH Q A HAE(Au
+u9HH= HexceptioHHHunknown Hf@n HIfffff. UHAWAVAUATSHh HIHH;C1 HL%;{ M|$@LLHu HH@HHfHn)pL HDžx EID$HEHpIhLLLR LmHz HHEHHDž HDž HDž HDž Dž W)EHE HuLqLeL}H}R LHH@HDŽ HL(LD LLH wHCH+HHr[A L-% fffff. LLvHN<L LLHvIHCH+HI9rH}HHxHEHpHEHHEHHEHHa H H5u HHp H2 Hh [A\A]A^A_]IE IE IE H5 1LHh [A\A]A^A_] HH} H LNHNH` HHEHpHEHHEHHEHH H H5t HHp HS H UHAVSH tH(1H(tC(H[A^]IHAuj C(HHQHOP HC(Au
+u1H0H= HexceptioHHHunknown Hf@n HIH= HH ef. UHAVSIH t$H{(Htt
+AF(HC( L[A^]HHǃu AF(HHQHTu HAF(u
+u6H5H= HexceptioHHHunknown Hf@n 2 HIH= HH `f. UHAWAVAUATSHXMIIILmHcY(]I(H= Ls H= L\ H= LE W)EHE t8y H}i
L, L9 HEH H]HL4 H]H= HV 1HMLELM 1 A tI_(HUME1HL-tAF(H}H HEH9 HHH)HHHHM W)EHE t8y H} L, Lg HEH H]HLb H]H=Ӄ H 1HMLELM 1 A tI_(HUMA HLXtAF(H}Ht#HEH9tHHH)HHHHM
LHX[A\A]A^A_]A tI(1I(A LLtAF(HIe HIH}HtTHEH9tFHHH)HHHHM/ HIH}Ht#HEH9tHHH)HHHHM
Lu7/
AF(HHQHH= HH
HAF(u
+u9HH=i HexceptioHHHunknown Hf@n HIf D UHAWAVATSHMI E ]E E D'HHEH= HMLELMܺ" DjjP H E9N؉]HcE9$IIH t,tHcuHH9|H=] DE H[A\A^A_]UHAWAVATSHMI̋i E ]E E D7HHEH= HMLELMܺ" DjjP H E9N؉]HcE9 M I$HcH9ILMII)I MIMI IIHHHr(fAD(fAL8fZfZffL 1HtWLH)HH|ILXfAfIfZfZfGfOfAf fZfZfGfH H@HuM9t LHȐAD0ZDHH9|H= D
+ H[A\A^A_]ff. UHAVSH0IHHUH(E E tH(1HUHMLEH舓t
+AF(H=Խ H t>H=ǽ Hv t+H= Hc tRHEHEH=
+ H HEHEH=~ H HMLELM 1 LH0[A^]HEHEH=~ Hy HHǃu5 AF(HHQHW HAF(u
+u9H8H= HexceptioHHHunknown Hf@n cHIH=S HHj
+ Afff. UHAWAVATSHMI E ]E E D'HHEH=} HMLELMܺ" DjjP H E9N؉]HcE9&IIH D t+tt,HcuHH9|H=Y} DA H[A\A^A_]fff. UHAWAVATSHMI̋ E ]E E D7HHEH=| HMLELMܺ" DjjP H E9N؉]HcE9> M I$HcH9ILMII)I MI H|(H9HHMI\H9sHL0I<H9 MADHt=L|8I\H1ffffff. D ADA HHu1I reLH)HHʘ ILp AIGOAIGOAIGOA GHHHuM9t!LHf ILHL0HH9|H=o{ DW H[A\A^A_]fUHAWAVATSHMI̋Y E ]E E D7HHEH=
{ HMLELMܺ" DjjP H E9N؉]HcE9 M I$HcH9ILMII)I MIMI IIHHHr AZAZLfD(L8 1HtKLH)HH|XILff. ZAZIGOZAZ fGH@H HuM9t'LH ADZD0HH9|H=y D H[A\A^A_]ff. UHAWAVSHIIHH(E E tH(1HUHMLEH觍t
+AF(7H= H tH= H tEEȉEAG(LH[A^A_]HHǃu AF(HHQHW HAF(u
+u9H8H= HexceptioHHHunknown Hf@n T yHIH= HH 2 Wf UHAVSH tH(1H(PtC(H[A^]IHAu C(HHQHO HC(Au
+u1H0H=1 HexceptioHHHunknown Hf@n HIH= HH c ef. UHAVSHIH tH(1Huit
+AF(EC(LH[A^]HHǃu AF(HHQHW HAF(u
+u9H8H=M HexceptioHHHunknown Hf@n {HIH= HH, w Yfffff. UHAVSHIH tH(1Hu艍t
+AF(EC(LH[A^]HHǃu AF(HHQHW HAF(u
+u9H8H=] HexceptioHHHunknown Hf@n {HIH=% HH< Yfffff. UHAVSIH t$H{(Ht耑t
+AF(HC( L[A^]HHǃu AF(HHQHT HAF(u
+u6H5H=v HexceptioHHHunknown Hf@n HIH=A HHX `f. UHAVSHIHHE tH(1H(HU譌t
+AF(HEHtHC(HC( LH[A^]HHǃu AF(HHQHW HAF(u
+u9H8H=p HexceptioHHHunknown Hf@n {HIH=8 HHO YUHAVSHIHE HE H(HuHUߌt
+AF(HEHtHC(HC( LH[A^]HHǃu AF(HHQHW HAF(u
+u9H8H=r HexceptioHHHunknown Hf@n {HIH=: HHQ Y UHAVSHIHE HE H(HuHU/t
+AF(HEHtHC(HC( LH[A^]HHǃu AF(HHQHW HAF(u
+u9H8H=r HexceptioHHHunknown Hf@n {HIH=: HHQ Y UHAVSH tH(1 tHv(1tC(H[A^]IHAuA C(HHQHO' HC(Au
+u1H0H= HexceptioHHHunknown Hf@n HIH=h HH eUHAVSH tH(1 tHv(1WtC(H[A^]IHAuq C(HHQHOW HC(Au
+u1H0H= HexceptioHHHunknown Hf@n HIH= HH eUHAVSH tH(1 tHv(1跑tC(H[A^]IHAu C(HHQHO HC(Au
+u1H0H= HexceptioHHHunknown Hf@n HIH= HH * eUHAVSH tH(1H(tC(H[A^]IHAu C(HHQHO HC(Au
+u1H0H=1 HexceptioHHHunknown Hf@n HIH= HH c ef. UHAVSHIH tH(1Hu虖t
+AF(EC(LH[A^]HHǃu AF(HHQHW HAF(u
+u9H8H=M HexceptioHHHunknown Hf@n {HIH= HH, w Yfffff. UHAVSHHE tH(1HuEtC(HH[A^]IHAu C(HHQHO HC(Au
+u1H0H=b HexceptioHHHunknown Hf@n HIH=2 HHI aff. UHAWAVATSHpMIHuHUE HcY(Hۉ]W)EHE y H}* L< L HEH H]HL H]Hc]HW)EHE tFy H} L< L HEH H]HL H] )EHE HEHD$HEH$H=k H 1HMLELM 1 A tI^(HuHUHMH蠚t AD$(H}Ht#HEH9tHHH)HHHHM H}Ht#HEH9tHHH)HHHHM LHp[A\A^A_]II2IIH}Ht#HEH9tHHH)HHHHMm H}Ht#HEH9tHHH)HHHHMA LAu c AD$(HHQHQG HAD$(Au
+u1H0H= HexceptioHHHunknown Hf@n HIH= HH ffffff. UHAWAVAUATSHMMI͋ E ]E E 7HHEH=i HMLELMԺ" ujjP H E9N؉]HcE9 HMM4$IU H1I?HcH9ILMII)I MI LH9ILMJLI9sNTH L9 LTL\8L|Md8J1 fADfA fZfZfADfA fADfA fZfZfADfA HI9uM9t6fffff. AD(ZD(ZH9H@|H=h u H[A\A]A^A_]f. UHAVSH tH(1脘tC(H[A^]IHAu C(HHQHO HC(Au
+u1H0H=% HexceptioHHHunknown Hf@n HIH= HH W efffff. UHAVSHIH tH(1Hu٘t
+AF(EC(LH[A^]HHǃu AF(HHQHW HAF(u
+u9H8H== HexceptioHHHunknown Hf@n {HIH= HH g Yfffff. UHAWAVAUATSHxMIIHIA$ tI|$(1Huǚt
AE( HcuH}Y L}H]Lc}MW)EHE ` Ey H} J< HEHEJ HMIOLHt(Hf. H HEHHEIHuHrdffffff. H HEHHHMH@ HEHHHMH@ HEHHHMH@ HEHHEIuMԅ 1E1ff. H}H Ht HH)Hw'H߾ H) H}M.fffff. H tHH9tHD f H HUJIHcHI9|A$ tI|$(1L}H]HUHuPt
AE( E;EtH=8 H5 1=;HhHu-h$HiHExHHEpHH S(A9W(| I(L HhQ H}Ht#HEH9tHHH)HHHHMW H]HtHHEH9t7 HHHMHxHtH9xtHx HMH9HuH]H LHx[A\A]A^A_]H= 18:IIHh
II} IIH}Ht#HEH9tHHH)HHHHM H]HtBHEH9t1HHHMHxHtH9xtHx^ HMH9HuH]HF LAu7h AE(HHQHH=߹ HH A 1 HAE(Au
+u9HH= HexceptioHHHunknown Hf@n HIfffff. UHAWAVATSHIIIH tH{(1HuOt
+AF(QI( tH{(1Aw(HUL蠛t
+AF("E;EtH=[ H5 1`8LH[A\A^A_] HHǃu AF(HHQHW HAF(u
+u9H8H=r HexceptioHHHunknown Hf@n uHIH=: HHQ S UHAVSHIH tH(1v(HUt
+AF(HEHC(LH[A^]HHǃu2 AF(HHQHW HAF(u
+u9H8H= HexceptioHHHunknown Hf@n {HIH=P HHg Yf UHAVSHHHH( tH(1v(HUHtC(HH[A^]IHAuE C(HHQHO+ HC(Au
+u1H0H= HexceptioHHHunknown Hf@n HIH=l HH a D UH1( ~( Dȃz( D]f UHAWAVSPMLu LM1y( LUAx( DA{( D tH(1L}(H(R(EB(I(I(L4$觜tAG(LH[A^A_]IHAu AG(HHQHP HAG(Au
+u1H0H=O HexceptioHHHunknown Hf@n HIH= HH6 ] UHAVSH1z( y( DAx( DHE tH(1H]v(AI(LEZt C(
+HEMH(HH[A^]IHAu C(HHQHW HC(Au
+u9H8H=; HexceptioHHHunknown Hf@n {HIH= HH e Yfff. UHAWAVATSHPIIHU8LU0H]@Lu1A~( Lu A~( DHE(x( DHMH(I(I(Mc@(McI(Lcq(H( tH(1L}HAJ(H(H\$8H]H\$0HT$(L$ D$Lt$LL$L$ A LMtAG(LHP[A\A^A_]IHAuY AG(HHQHP> HAG(Au
+u1H0H= HexceptioHHHunknown Hf@n HIH=~ HH [ UHAWAVATSH0ILu L]Le(1Ax( H]Ay( D{( D؋J(EB(H(I( tH(1L}0AC(I(Ld$ HUHT$Lt$D$ $ E1̶tAG(LH0[A\A^A_]IHAu
AG(HHQHP HAG(Au
+u1H0H=b HexceptioHHHunknown Hf@n HIH=2 HHI [ff. UHAVSHH tH(1V(H(1HtC(H[A^]IHAu/ C(HHQHO HC(Au
+u1H0H= HexceptioHHHunknown Hf@n HIH=V HHm effffff. UHAWAVAUATSH8LLEHMIIIHE Mcg(MW)EHE Ey H}C L HHMHMHLHEID$LHt$Hff. HMHHMIHuHrH @ HEHHHM@ HEHHHM@ HEHHHM@ HMHHMIuA tI~(1AU(IcO(LMLE1t!C(H}Ht\H9}tH}2 KIHUH uMH]HEP(A9W(|H}H(Ha HtH9]tH]H LHH8[A\A]A^A_]H=b 1L. IHIH}HtH9}tH} Lu9 LC(HHQHH=7 HHN y HAG(u
+u1H0H= HexceptioHHHunknown Hf@n HIH=ʭ HH , L UHAWAVAUATSH8LLEHMIIIHE Mcg(MW)EHE Ey H} L HHMHMHLHEID$LHt$Hff. HMHHMIHuHrH @ HEHHHM@ HEHHHM@ HEHHHM@ HMHHMIuA tI~(1AU(IcO(LMLE1t!C(H}Ht\H9}tH} KIHUH uMH]HEP(A9W(|H}H(H HtH9]tH]Hh LHH8[A\A]A^A_]H= 1L+ IHIH}HtH9}tH} Lu9B LC(HHQHH= HH y HAG(u
+u1H0H=z HexceptioHHHunknown Hf@n HIH=J HHa L UHAWAVSPHIIF IF I H) H
+H9rLS H H<@" HINIH[HIFHCHHt0HfHA HA H INHINHHuH ff. HA HA H IFHHINH@( H@ H@ IFHHINH@( H@ H@ IFHHINH@( H@ H@ INHINHgH[A^A_]II HtHIFH9t7 HHINHxHtH9xtHx INH9HuI H L UHAWAVAUATSPIII^MnLH)L9sVIH)LyL IMnI)H?I9 MI9LBM~I)1M ID$LHtHf I^HI^IHuH
+ IFHHIN@ IFHHIN@ IFHHIN@ I^HI^Iu M~I)IL LIIL$LHtHL @ HIHuLHr% C fC C HIuHM&IVL)I)H~LIL LM>I^IFMtLH[A\A]A^A_] H[A\A]A^A_] UHAWAVAUATSHI։IM IDž IDž IDž IDž IDž IDž IDž IDž IDž IDž IDž IDž LL A ~ AEPuHIEXHuA}tH=\ 1>&1q H[A\A]A^A_]HEI IDž HtHPI IDž HtHPI HEH M L;utkM~M I^HtCIFH9t+HHINHxH@ HtHPIFH9uI H' M L;}MuI HEH} M MtII L9t5HHI HxH@ HtHPI L9uM L M Mt[I L9tG HHI HXH@ HtH x Hb I L9uM LG I $I$ HtHw H& L H}] UHAWAVAUATSH IIHg? H HEW)`)PDžp ?|,Lc LPf I4L4 HL9|HP W)HDž H5 H HPLLHL H W)EHE H5 H} HPHu HH HEpL1LU H]H H]H= HHEL}LHhZ H}H9LHt
HtHP( HEH}P LL M9 H]L}I > AuHHffff. IVH uIvHH IVIvH 1ffff. <>#t
HH9u} H9txHtru INHIFINHHt;H1ff. <9#t
HH9uHHHH)H9HHDHL LL% MHHHEEHt>HUHD @ HtKL HHȀ?wL!uHо H)H#HEHE(E)E ff. 1HL] MHHHuHDHEEHHt-I1f > H?wL!t
+HH9uHL1L HEHEHEHMHMHEW)EHE AufA ff. IF IF 1L HEIFHEHMINIW)EHE H} L AuHIFHt'uIvffff. IvHP@ IM9CYW)EHE H5 H} HPHu HH HEpH= 1H} H dLHHPi HPL:Y H=ه 1<ZH`Ht(L;LsH{( L H MLuHPHDžP Ht H-: H H;EuH [A\A]A^A_]M HIH}KIH}H9t
Ht?HP(7HP /;9IH}% .H! I IH} HcIH`Ht5ffff. L#L{H{( L H MLuHPHDžP Ht L
+ @ UH]f D UH]f D UHAWAVAUATSPIA$ t I$ IDŽ$ HtHPI$ IDŽ$ HtHPM$ M I$ L9to LkM$ L{MtGHCL9t.fHHHKHxH@ HtHPHCL9uM} L M$ M9LuM$ L I$ HtOI$ H9t:fHHI$ HxH@ HtHPI$ H9uI$ HN M$ Mt]I$ L9tHf D HHI$ HXH@ HtHo H I$ L9uM$ L I$ IDŽ$ HtHeo H LH[A\A]A^A_]HEI$ IDŽ$ HtHPI$ IDŽ$ HtHPM$ Mt~I$ L9tiLsM$ LkMtDHCL9t+HHHKHxH@ HtHPHCL9uM.L M$ M9LuM$ L I$ HtMI$ H9t8HHI$ HxH@ HtHPI$ H9uI$ H~ M$ Mt]I$ L9tHf D HHI$ HXH@ HtHm H1 I$ L9uM$ L I$ IDŽ$ HtHm H L%H}Lfff. UH]&f D UHAWAVAUATSH W)E)pE ?HGH H}f D HELhH= L: IMtQLH(HpH: IMH]Lmt[LeLDf(EA LH H=Ѡ ML>: He L* H* AD$(HKHH$IET$0It$8IHDH@IHxHH}g LOLH! I9M 1LHI HN0Eu D$uLHHUHRH]I(H9s uLc E LH HEL`8H}LuEuIGHF8HEL49 IM
AE(u I(IMm8H}HE uHIF LeAF(u Mf(IMf8IuHHEHX8L8 H @(u H(HH@8HD$H $1H=* HuHULMM HN0uLHHUHRH9 IELEf0IHMLEqM9LIBHtSIEHE~8HuHuHƄHEq E+ EHuЅt"y)HEHp 1H= M9]uLkH}LuLeLeMl$8H}LuELmuMoLn8L7 HHB A(u H(HHI8HE uL@MF IF(AV(uLHMN8uH{I|$8uHIF8HuLHD$H<$1H= 4HpH2 HHu $ MM!HpJHj L(M^ LuAD$(II$MEt$0ME|$8MH. MM ID$HEffff. IEH9r1HHL9 AM% HfIuHDL9ukfu?A: $u]IM H)HEf D HG HH:H]H@t'ILI} LL H}IL Mm MLL IEH9Lur1HHL92 AE HIEMH Mm Mu M ID$HEfIEL!L9 AM% HfIuHDL9ukfu9A: $u]IM H)HEH7 HH:H]H@t-LI} LLMM MMIH] Mm MV9 @ IEL!L9u)AE HIEMH Mm MuLu MĿ@ - IHI]H H]MI(LHu MuIE HEHxH*HHH H*XHuHHH HxH* H*XMtY.wM H6Hr
HNHH ^f:
-( \H,H H1H,.HCH9HCHp6 HxHFHtI9r1LHII!HpJ Ht HIE L)GHMIM LmHMJ IE Ht*H@HNHtH9r
1HHH!HpL,HE HEH H LuM L}IGHE HL!IHH L8M AFII$MEfMEn HE M IEHhH}LXffffff. IGL9r1IHH9& AO% HfIwHDL9utfu;A:M ufIO H)Hh H HH:H@t4I LLMML H}IMMLX> M?MD ffffff. IGL9r1IHH9V AG HIEOH M?Mu) M IEHhH}L`f. IGH!H9 AO% HfIwHDL9usfuFA:M ueIO H)Hhffff. HE 1HH@:0H@t(I LLM H}ML` M?MOMffff. IGH!H9u4AG HIEOH M?Mufff. H}MͿ@ II_HHu H]HuH(MI(L HEIGI H}HGHxH*HHH H*XLHDH MxI* H*XO tY.w H] KD- Ir
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HF IF8HE, HH C(uH(H H[8AuIM~ A$uIMe8L$$1H=u HuHUHMIMhM6ML}hLuMt7ffffff. I M~I~(蜽 L蔽 L达 HIuHpHDžp Ht蚾 HĨ [A\A]A^A_]ÿ 袾 HH5~m H輼 Ho% HHH5 H H袾 b HH5>m H| H/% HHH5 H Hb " HH5l H< H$ HHH5q Hj H" HH5l H H$ HHH51 H* H 袽 HH5~l H輻 Ho$ HHH5 H H袽 e IHo YTIL IH} IL /ILʻ IH}躻 IL IH]Ht)L#L{H{(茻 L脻 H讼 MLuHpHDžp Ht芼 Lʹ @ UHAWAVAUATSH(IHAU M}$IuIDHIEUH}IH]LsMP MFLL!tM9Lr1LIHLL!HEH HH HH LeAE II$MEeME}H MF H} HCL9r1IHH9 K% HfHsHDL9uKfu+A:u>H1 H93 T A:THItH{ LL萼 H} HHn M H}LE HCL!H9uzK% HfHsHDL9uMfu)A:u@H1ɐH9 T A:THIt H{ LL LEH}b HHzLe Leffff. HCL9r1IHH9umC HHEKH HHuDLeffff. HCL!H9u'C HHEKH HHuI@ ڹ HIEHC IE IMHKHCIE IE IE HC8 HC0 HC( LcH LmIEHxH*HHH H*XHSHs(LHDH MxI* H*XAM t
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INLH ^f:
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HtHP(HHP H H+HHHHHQH= H* HLH 18 HDž AP AEPuMFME`HL`LLt HL9t
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HtHP(
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H胝 Hpw W)PHDž` L}LuLL)t8HHHH< e H HPHXH`M9 ff. IGAIEGH t +t-uHfff. Hـ1Ҁ w*1ffff. ˍTQXHـЀ
-r uHffff. 8 H@t։pHXH;`s HHX"ffffff. HPHp
. IM9L}L K &Mt.H}L9t HH} H}L9uL}L H;HH8L<RtDKDHH9tHQH)HHHH HC HC H H8(PH`KD&H0H@B uLf D JL JD MHcEH9HRdIL IH} IHp H]HtH}H9 @ HH}ɚ H}H9unI8IHPHt)HXH9tHHH)HHHHX觛 H]H[H}H9t D HH}Y H}H9uH]Hj Lf D UHSHHW)EHE H5- H} 辙 u H HH H}/ H H HtHPH}这 H HP`H=3 1J'H[]HH}荙 H ffffff. UHSPHH{8 u H HH "/ H H Ht
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HtHP( HEH}P MA$ tTA$p 4LH HH4HLL LA$p A$ Mt$PAD$PuMFMD$`HXHpLc A$ AD$PuIMt$`A$ u
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HH @ HTI$ HX HPI$ I;$ s HDžP HHI$ fH HP]> A$ tI$ Hx1 I$ I;$ s-H@ H@ H I$ ff. H(TM$@ H8HI9 f LL5 LL( H`Ht{HHII$ HrVPHXHI$ HGH;Gs H`HDž` HHGHH`H`HDž` HtHPL觰 IL9NI$ HHPHDžP HtH>Q H蔱 IID$hIL$pH)HHHI9LhL@L(H L հ L)H*^% Y% H= f G8HX^ H}HpH9t
HtHP(HpHpP MtLLư H9 H H;EuHĘ [A\A]A^A_]Y IL蔰 1Hh LhDLhI 3IHXHDžX Ht HPIH dI_3IH`HDž` Ht HPIH ͮ IHPHDžP HtH}O Hӯ HX] H}HpH9uHP HtHP(HhHtHrH舯 LȬ fUHAWAVATSIIFM&I^L)HHL)IIL9y MtJ 1JHL)t?HHt9HHf H{HC H[HyHHuHM HH8 f HSHC HQHSHC HQHSHC HQHSHC HQHSHC HQHSHC HQHSHC HQHSHC H[HQHI9oM>MfIIFIFM9t5 I\$ID$ Md$HtHM H M9uMt L[A\A^A_]ܭ H [A\A^A_]H0UHAWAVATSIIFM&I^L)HHHHL)IILL9 Htx 1K HHL9t`f. HA HA HA CAHSHQHC HC HC H[HI9uM>MfMIIFIFM9td Mt$I\$HtFID$H9t4 HHIL$HxH@ HtHPID$H9uI H艬 M9MuMt L[A\A^A_]l H蔬 蕬 [A\A^A_]HUHAWAVAUATSH H LLHjCL
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HtHP(HHP H H+HHHHH8NH= H* HLH 1j HDž A AEPuMFME`HL`LLQ HL9t
HtHP(H`H`P AuIMu`A u I HI H1A LY M I MtLC L I ff. HPI H9t HHI HxH@ HuI} LT I I Ht
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HPH HH u H HH HHH[]f UHAWAVAUATSH(IHAU M}$IuIDHIEUH}QIH]LsMLeL MFLL!tM9Lr1LIHLL!HEH HH HH AE II$MEeME}H MF H} HCL9r1IHH9 K% HfHsHDL9uKfu+A:u>H1 H9 T A:THItH{ LLP H} HHn M H}LE HCL!H9uzK% HfHsHDL9uMfu)A:u@H1ɐH9p T A:THIt H{ LL谖 LEH}? HHzLe Leffff. HCL9r1IHH9umC HHEKH HHuDLeffff. HCL!H9u'C HHEKH HHuI@ 蚔 HH{H}L= HC8 HC0 HC( LcH LmIEHxH*HHH H*XII(LHDH MxI* H*XAM t
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INLH ^f:
-
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+ I Ht)I H9tHHH)HHHI a
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IM!HEH J Ht^Ht9f H HtHHAL9r1IHL9u29Yu H HtHAH!L9u 9Yu fLE + IA_HEIGI HEH@@HxH* HHH H*XLHDH MxI* H*XHEHHt
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+txID$H9tJ @ HHIL$HxHt(HHH9tHQH)HHHHPy IL$H9HuI $H` ID$ ID$ I$ 1L9u L^ I $ID$ LM|$I)Ot= ILL9HELF IHM9tI9tIu IUHHEL9H ID$I+$HHHH)LLHU}1HH)HHHTUUUUUUH9w)HHUL9H9ILBsu L职 LuIK vHM ID$I$HID$LLLHMH[A\A]A^A_]r ID$H9tJ HHIL$HxHt(HHH9tHQH)HHHHPɍ IL$H9HuH[A\A]A^A_]ffffff. UHAWAVAUATSPIIHUI9 I\$f. HC HC H MoM+/MItMLH>tHb L: ILkL+KD HCI7MwI)M~LLc MLkII\$HI\$L9}pH[A\A]A^A_]IH;Ht#HCH9tHHH)HHHHK行 L ff. UHAWAVAUATSHHuII
+II^H)HIIHL9v Ll IINH)HIHUUUUUUUH9Lus+HH9HBI^H)HI1HIA u&I^H)HIJ H<@܋ ILHEL<[K KD KD K HELhL+(MItWLH>tH諉 L胋 IOlL+KD KDHEH0LpI)M~O|LL行 MM/HEH@MHSHMHHqH9taff. HC HC HC FCH~H{HF HF HF HvHH9uL1HAIHHQLAL9t? HXHxHt#HHH9tHQH)HHHHPn I9HuMtLH[A\A]A^A_]K H[A\A]A^A_]IH;Ht(KDH9tKLHPH)HHHH L LA ff. UHAWAVATSH0IIII_H)HHHH=t L IIOMgI)LHHH9w
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+J JTHUHL) HEHHt7Hffffff. HsHC H[HpHEHHEHuHr~ D HKHC HHHEHHHMHKHC HHHEHHHMHKHC HHHEHHHMHKHC H[HHHEHHEI9uM'HUHEILeIGIWHEIGHMIOHELeH}jH8[A\A]A^A_]ff. UHAWAVAUATSPHuIM&I^L)HHHH=t L3 M&MnM)LHHH9s'II9LBI^IM)I1M u(I I^IM)IJ< 觀 LJLHMH1J4JDHL) HHt"HfHKHC H[HJHHuH8 f HKHC HJHKHC HJHKHC HJHKHC HJHKHC HJHKHC HJHKHC HJHKHC H[HJHI9oM&I^IIFMFL9t( H{HC H[HtHP I9uMtLH[A\A]A^A_]C H[A\A]A^A_]f D UHAWAVAUATSPIM.IFL)HHHH9 I^IM)HL<vL~ ILLL9tXfHA HA HA CAHSHQHC HC HC H[HI9uM>MnMIMfIFM9t? I]I}Ht#IEH9tHHH)HHHIM.~ I9IuMtLH[A\A]A^A_] ~ H[A\A]A^A_]fffff. UHAWAVAUATSHHuII
+II^H)HIIHL9v L{ IINH)HIHUUUUUUUH9Lus+HH9HBI^H)HI1HIA u&I^H)HIJ H<@<} ILHEL<[K KD KD K HELhL+(MItWLH=tH { L| IOlL+KD KDHEH0LpI)M~O|LL~ MM/HEH@MHSHMHHqH9taff. HC HC HC FCH~H{HF HF HF HvHH9uL1HAIHHQLAL9t? HXHxHt#HHH9tHQH)HHHHP{ I9HuMtLH[A\A]A^A_]{ H[A\A]A^A_]IH;Ht(KDH9tKLHPH)HHHHi{ La{ Lx ff. UHAWAVAUATSHIIM7MoM)I\$I+ $LIGL)H9L}sGH{ HN<(M~HLLIN| LHMHLyII_MtLz M,$Mt$M9 Luf D Ee I_I;_tD#IG D III)IyLx II_H)H?H9Des&HL9IBMwMI)HA u%MwMI)HHI"z LIJ IUB JT I)L)M~ HILHUL>{ HULHEHHPLxIHLutHy IM9H[A\A]A^A_]f UHAWAVAUATSHIIII_H)IIMnM+.IMIGH)HL9sOJ< HMCy N$N,H~HHuHHzz HIMgMoHEHHtx I MfL9tCLuff. H HMIGI;GtHHIG
fLLeHI9uH[A\A]A^A_]ffff. UHAWAVAUATSPHH3p CHSKLc(HC@ HC8 HC0 HC( CH ?LkPHǃ HCx HCp HCh HC` HCX HCP ǃ ?H HC C$L H[A\A]A^A_]IH{xHtL?w MLuH{hHCh Htw M} Mt+H{XL9tHH{XGv H{XL9uM} Ldw H{8Ht ffff. H Fw HHuI<$I$ Ht(w Lht fUHAWAVAUATSH HIW))pDž ?)PHDž` H5v HP Eu HLm IE HHI] HHH6u HHEW)EHE )0HDž@ H!x H0HHt 0HHL8IEHg L11L}L0$H@IDHIEHE1fff. B/< t<,tJD+IH9r J+H9vlLLHLMIt HEH;Es-HMHHHMHUHPHW)EHE HE H}LLt 0L8J\+HHIEH9!rII9vjH}H0HHIs HEH;Es-HMHHHMHUHPHW)EHE HE
H}Hu3H}fs H0Zs LeMt$PMtXM|$PI\$XI|$XL9t#fff. HH; s H;L9uM7L?t IG IG I I\$XHEID$PHEHHEID$`Hr I $ uHH( H HhHPr H HhH! Hhor H} H7PHHHEXH}HI+r HE@ LpPH@XL)HHHȅ] HHH]ڀH]HDLeHELeLDLcV HE1LEKTm A4HH@IELH9u=H I H@IELHL :uHHHu^ IM9|HuH]@ H u H HH0 A fffff. H t+t-uHA Hu Hˀ1 w!1 @ э4trHHʀЀ
+rAw fffff. H ttH=Jr 1HEЋp H=r 1 H
IE1f AFHH$IENH9u IvIE6LHr IIM9|HuH]LEID,HuH]HEDh IDExH=.q 1AH=7q 1QH}o LmIEPMuXL9 IcM H IH\L9 ffff. L{CufC fffff. HC HC 1L;o HCIGHHKIOIHC HC H HI9uMuXHL)Ht*H H IMuXLn MuXI9uIuPI9tJE1LpI1 LL HIuPIEXH)HIIH9rI] h uHHp H HHPMn Hh HHW H5n HE h H0PHHHEXH}HIm EHuHEuH"" LLM9Lm IE(HEIE8HxHLpLpffffff. HLum LHZHtELHj LcHELh0M IuHL!tDM9MrB1LII5 HHHD1H=Oo 艺 IM!HEH J Ht^Ht9f H HtHHAL9r1IHL9u29Yu H HtHAH!L9u 9Yu fLE m IA_HEIGI HEH@@HxH* HHH H*XLHDH MxI* H*XHEHHt
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IMLH ^f:
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1IHH!HMH L<HEH@@LpHHj IM9LmLp9LMt8HL9t$HHj HL9uLLk H}xj H0h u Hh HHx H HHHpH9o HEHH(HMH@8HxLLHi H HHD
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+rDffff. H tt1H=k öMcHEHX0H HsHH!t#I9Mr!1LHIfffff. IM!HEH J HtZHt5 D H HtHHAH9r1HHL9u2D9qu H HtHAH!L9u D9qu i IEt$Ml$I$ HEH@@HxH*"ffff. HHH H*XHHH HxH*
H*XHEHHt Y.wM ff. HHr
HKHH ^f:
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+rAw fffff. H ttH=g 1HEЋp H= h 1LmME(A] LcM}0M IwHL!tM9Mr1LIIIM!I J Ht]Ht:f. H HtFHAL9r1IHL9u09Yu H HtHAH!L9u
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1IHH!HL,LmIE@H(I] uHH H HHP H HH Hs Iu PHHHEXH(HI/ (H)HE8H m L HHpI9 IEhHEIExHxH@Lpfffff. HL LH?HtELH D(IcHELppM IvHL!tCL9IrA1HII4f@HPHAHD1H=
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1IHH!HL,LmIE@HI] uHH H HHP] HÀ HH H] Iu PHHHEXH(HIE] (H)HE8H m L HHpI9 IEhHEIExHxH@Lpfffff. HL\ LH|HtELH D(IcHELppM IvHL!tCL9IrA1HII4f@HPHAHD1H=a ٩ II!HEH J Ht^Ht9f H HtHHAL9r1IHL9u2D9iu H HtHAH!L9u D9iu H] ] HDkHEHCH LmI Hx H* D HHH H*XLHDH MxI* H*XA t
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INLH ^f:
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HH HpH9h IEhHEIExHxLXLH? XHhHYHD
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S IH Ht;HH9t' @ HHR HH9uH HS H(R sIIHXR HHtSH H9t.ff. HH lR H H9uHHS IHTHP4R HHt(fff. L;H{R H:S MLuHpHDžp HtS LVP UH]vf D UHAWAVSPIIxHtf. H R HHuIhIGh HtR MwPMt3IXL9t" HIX_Q IXL9uMwPL|R I8Ht D H fR HHuI(IG( HHt [A^A_]>R [A^A_]f UH]&f D UHSHHH uHHKH}1HIP DED% HDfHUHD;HH@HEsH9uOHs@H}HEsfHE}uBHtXAD:u'HMHƺ H)fHt9HH:HvtH}-P 1&HtR H}P u H}P H[]UHAWAVAUATSH(HILy$HqIDHHMHEQH}?rHMt$MH}- MNLL!tL9Ir1HIIMI!I$JH HH LeHUII$LEbLEzH M, D HCL9r1IHL9 K% HfHsHDL9uGfu+A:u:H1 H9 T A:THItH{ LL@Q HHr M LMHCL!L9urK% HfHsHDL9uIfu)A:u<H1ɐH9K T A:THItH{ LLP LM HHuLe Le D HCL9r1IHL9umC HHEKH HHuDLeffff. HCL!L9u'C HHEKH HHuͿ0 N HIILHuMM C( HEHCH ID$HxH*HHH H*XLHDH MxI* H*XAL$ t
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INLH ^f:
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+Huպ HH`w H5M H HxMc~IELeLu I I9 ~ x HHC H HL@LHHXHfo f0LHx HDž8 Dž@HEH0HEHL@Lw HEHHLHDž HDž HDž HDž Dž ff`HDžp H`LH`v HEH ZH5 Hy L@Hy LHHWHMH H H)HL9ICH A Le BZHH56 y E H@LIH y LHHIHMH H H)HL9ICI9rH}LHL HEH0HEHLeL@HEHHHmu Lu H5 HH0u H>v Hlv L(#ffEHE H5 # H}1t U$HMHuHDHHEUHxE
+HuӺ HH}t H5%
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+HuҺ H:H`:t H5 H II I HH)ff0HDž@ t`HH>t H0;s L< L u IL0I H@LLt H8L}I I E1II) Iv#LHtJI9A KH94 1))HQH)H)HtH >A HHuHrBH)H)ID HT>f. rprprp20HHHuIcWHH`H0> `$HpHaHDHHEhLLeE
+HuѺ HsH`sr H0Ht)H8H9tHHH)HHHH8ps H5 H HIwIcWHH`
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+HuϺ H4H`4q H5Ɔ H HIH IcWH` `$HpHaHDHHEhHE
+Huκ HH`p H5M H HI` IcWH`1 `$HpHaHDHHEhH?E
+Huͺ H*H`*p H5Յ H HIx IcWH`l `$HpHaHDHHEhHE
+Hu̺ HH`o H5\ H HI IcWHH` `$HpHaHDHHEhH2E
+Hu˺ H H` o H5 H HI IcWHH`| `$HpHaHDHHEhHE
+Huʺ HH`n H5m H qHI IcWHH` `$HpHaHDHHEhH"E
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1;4 TID AD drpL f D Hi QH%kd)H<B HA XHH' w*ffEHE H5} H}1&k w0 TH5# 2 1HPHpLRn HLHI I H)HL9ICH' A L= B ȃi Hl ; TID AD drMffff. Hi QH%kd)H4?B >XA XH' w D ʃ w 0 @ TB:B 9HPE HpHuHL1m HLHIHMH H H)HL9ICI9H}HxH*L}HEH`HEHHEHpHEHxHi H j H5 HH`i H[j Lj HxU$HuHEuHHEUH!E
+Huƺ H H}i L(LeH5
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+L HH5Z HHx\ LvL HLg HHEHHEH HEHHEHH[ H3\ H5 HH\ H \ L\ HY f. UHAWAVAUATSHX HIH IFI9 H} l\ IIE IE H L% M|$@LHHG HH@H HfHnf)[ HE EID$HEHIhLeL L}LH[ L%j ILLHDž HDž HDž HDž Dž fWf)EHE HuHQLeH}Z I H5"w L] LL] LLHϾIINH)HH9HCHrzA fffff. BLH5v \ E HHupIL\ LLHWIIINH)HH9HCI9rH}HHHEHHEH HEHHEHHX H}Y H50 HHNY H Y LY HX [A\A]A^A_]HG HG H H5 1HX [A\A]A^A_]8X L HH}mX LeX HX
+L HH5 HHX LvL HLg HHEHHEH HEHHEHHW HcX H5 HH4X H X LX H V f. UHAWAVAUATSHX IIH L=h I_@HLH HH@H HfHnf)L'X HE EIGHEHIhL HLW H]H ؾ HHHDž HDž HDž HDž Dž fWf)EHE HuLʵH}V LHH@HDŽ H5l
LOAvHV H53 H/E
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+HuϺ HH5|h H HD6R H5 HζE
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+Hu̺ H$H}H HM H5e H HMH HU Wf.z> f.s 8 _HN H5 H蠳E
+Huɺ H苳H5de H sE
+HuȺ H^/ L}H ,W)EHE LcH B B;L Mt$ XA A ffffff. H AABD)щJcB4)։ 9}FHH Hs1HEH;Et HHEH}H+QH} A vXH B; :H5c
H L(L}Lx HHEH;E HXLpL HH(Hp()LHM HDž Dž HEHLpL H&M HEHLHHDž` HDžX HDžP HDžH Džh W)0HDž@ H0HjH0*L L H H@HDŽ HE0LFL IHEH+EHHr@ L5b LLHE4LL HHEH+EHH9rH`LHHEHHxHpL}L HEHHH\K HK H5 HHK Hp-L HX*W)`HDžp H5 H`1J `$HaHEpHHEhHïH5
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+G I ( # HH`E HHE HhE LEF HH5 HHF L/ HL$ L}HHwE HHEHHxHpHEH HEHHH4E LE H5i HHE HpF 1HH}Ht,HEH9tHHH)HHHHMF L}HHEHHxHHEH HEH(HhD H(E H5è HHD H_E HB ffffff. UHAWAVAUATSHX AIH}H L= Mo@LHH[ HH@H HfHnf)HD HE EIGHEHIhL LmLH+D H HIHHDž HDž HDž HDž Dž fWf)EHE HuHsH}6C IcI t+$ I f.n HH5Z ЧLeI H C H5Z H褧LHHEHL HEHLHqB HB H5 HHB H >C LHX [A\A]A^A_]HLeuZ H5Y I H,B H5Y HЦH5Y H輦 H5Y 螦I HA H5Y HvH5NY `I HA H5iY H8H5mY H${H5X I HBA H5/Y H=HH}@ 1HDHVHHEHL HEHLH@ H A H5Ӥ HH@ H oA H> ffffff. UHAWAVAUATSHX IH}LcI F$(H L= I_@HHH` HH@H HfHn)@ LmHE EIGHEHIhL H]HHH)@ L- ILHDž HDž HDž HDž Dž W)EHE HuHvA H}5? HAuH5jW T ILH5W S ILI HM, H5W & LȣI Hcۋt+4L? H5W L蛣I 4H> H5W . LuH}HեHEHL HEHLHA> H> H5r HH> H ? HEHX [A\A]A^A_]*IHH}= H= LDHDHVHHEHL HEHLH= H> H5Ρ HH= H j> H; f. UHAWAVAUATSHh MЉUIH}HL%! Mt$@LLHg HH@HHfHnf)pL= HDžx EI\$HpIhLLL;= H]LuH HHEHHDž HDž HDž HDž Dž fWf)EHE HuLzH}=< LH5U LHNju_< EЄtH5 LH5gU LܠA@H5cU LIH H; H5DU H蚠 H5-U - L聠I I9 tU1L-_ Ht LLKI 4L; HI I H)HH9rH5T LLH5T LA ~H5T LϟEH}1Lk U$HuHEuHHEUL蘟H}: H5T L{E
+Hu HfH5S LRHNju: EЄtH5S L,H5 LH5
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A ~H5R LEH}1L U$HuHEuHHEUL踝H}8 H5R L蛝E
+Hu H膝H}LHEHpLHEHHEHHR8 L8 H5 HHp8 H#9 HEHh [A\A]A^A_];IH}8 0+IH}7 H7 LLILI^ IHEHpLHEHHEHH7 H8 H5̛ HHp7 Hh8 L5 UHAWAVAUATSHh MAHH}HL5! M~@LLHh HH@HHfHnf)pL7 H]HDžx EIFHEHpIhLuLLL17 H HHHDž HDž HDž HDž Dž fWf)EHE HuL|H]L}H}76 HHH@HDŽ E H5(P L}McHMHIPB4H,6 H5P HĚH]H B(uqH}HDcU$HuHEuHHEULLyoH5O HcL}AԋE HD5 H}HDU$HuHEuHHEULLH} 5 HCBEDH}HDU$H]HuHDHHEUL軙H}4 H5N
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+?;7IH}x3 ,'IH}h3 H\3 LLILI^IHEHpHEHHEHHEHH3 H3 H5C HHpa3 H3 LU1 ffffff. UHAWAVAUATSHh MAHH}HL5 M~@LLHؔ HH@HHfHnf)pLP3 H]HDžx EIFHEHpIhLuLLL2 H HHHDž HDž HDž HDž Dž fWf)EHE HuLH]L}H}1 HHH@HDŽ E H5K cL}McHMHIPB4H1 H5K H4HMHIPB4Ht1 H5K H H]H B(uqH}HDU$HuHEuHHEULLoH5MJ H諕L}AԋE HD0 H}HD*U$HuHEuHHEULLPH}S0 HCBEDH}HDiU$H]HuHDHHEULH}0 H5-J
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+ -+ HPLuLFKHLW+ HLmuH=E 1xffEHE H5 H}
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+ ( HPLuLIHL&) H ffEHE H5C H}
+ ( HPHumAUH}H+% IH HtGIP H9tHHH)HHHIP ) HH@ H@ H foEAH HEIX H}b( H=$C 1uffEHE H5EC H} ' HPLuLGHL( Ht[ffEHE H5B H} ' HPHulHH HExI }( H}' H ?I A} ffEHE H5B H}
+ $' HPLuL=GHLN' H] ffEHE H5BB H}
+ & HPHukMcuMfE& MHEHXffHDž E H'& N< L' ILKD HLLI' LA HEHEHDE1fHÊ< t <+t<-u
HÿHÊ1 w*1ffff. tPCHÉЀ
+rAt Hf. { H[tHL9uH=A 1^slE1E1H]H{HtAHCH9tHHH)HHHHK& HH@ H@ H LkL{L{H}o% IffEHE H5@ H} $ HPLuLEHL(% Hg ffEHE H5g@ H} $ HPHuiMcuMfE0 MHEHXffHDž E H$ N< L% ILKD HLLI% LA HEHEHDE1fff. HÊ< t <+t<-u
HÿHÊ1 w*1ffff. tPCHÉЀ
+rAt Hf. { H[tHL9uH= ? 1.qlE1E1H]H{ HtAHC(H9tHHH)HHHHK($ HH@ H@ H Lk L{(L{0H}?# IffEHE H5> H}
" HPLuLBHL" Hg ffEHE H5> H}
" HPHumgMcuMfE0 MHEHXffHDž E H! N< L# ILKD HLLI# LA HEHEHDE1fff. HÊ< t <+t<-u
HÿHÊ1 w*1ffff. tPCHÉЀ
+rAt Hf. { H[tHL9uH=<= 1nlE1E1H]H{PHtAHCXH9tHHH)HHHHKXt" H H@ H@ H LkPL{XL{`H}! IffEHE H5< H} HPLuL@HL H ffEHE H5< H} Q HPHu=eAUH}H I HtGI H9tHHH)HHHI t! H(H@ H@ H foEA HEI H} H=< 1vmffEHE H5!< H}
+ HPLuL?HL H% ffEHE H5; H}
+ 7 HPHu#dIcMLaE II LEhffHDž HM H N< LS ILK H LLE HHE L LHHDž LLp I ZADL9|LoOIcMHI I HH)HHH)K Hm fE1fHnfpDLmI HtQfEI H9tHHH)HHHI % HH@ H@ H foEM A H} ffEHE H59 H} I HPLuLb=HLs Hb ffEHE H59 H} HPHuaMcuMfET MHEHXff`HDžp E H`L N< L IL`KD HpLLI
+ LhA HEHEHDE1
HÊ< t <+t<-u
HÿHÊ1 w*1ffff. tPCHÉЀ
-rAt Hf. { H[tHL9uH= 1|lE1E1H]H{HtAHCH9tHHH)HHHHK H H@ H@ H LkL{L{H}Y IffEHE H5j H} HPLuLG$HL Hg ffEHE H5 H} HPHu @McuMfE0 MHEHXffHDž E H N< L ILKD HLLI LA HEHEHDE1fff. HÊ< t <+t<-u
HÿHÊ1 w*1ffff. tPCHÉЀ
-rAt Hf. { H[tHL9uH= 1zlE1E1H]H{ HtAHC(H9tHHH)HHHHK( HH@ H@ H Lk L{(L{0H}) IffEHE H5 H}
HPLuL"HL Hg ffEHE H5: H}
k HPHu=McuMfE0 MHEHXffpHDž E Hp N< L ILpKD HLLIm LxA HEHEHDE1fff. HÊ< t <+t<-u
HÿHÊ1 w*1ffff. tPCHÉЀ
-rAt Hf. { H[tHL9uH= 1xlE1E1H]H{PHtAHCXH9tHHH)HHHHKXR HH@ H@ H LkPL{XL{`H} IffEHE H5 H} HPLuL HL H ffEHE H5[ H} ; HPHu;AUH}H I HtGI H9tHHH)HHHI R HH@ H@ H foEA HEI H} H= 1wffEHE H5 H}
- n HPLuL HL H% ffEHE H5 H}
- ! HPHu:IcMLaE II LEhffHDž HM Hm N< L1 ILK H LL# HHE L LHHDž LL` IZADL9|LoOIcMHI I HH)HHH) H fE1fHnfpDLmI HtQfEI H9tHHH)HHHI HH@ H@ H foEM A H} ffEHE H5 H} 3 HPLuL HL] Hb ffEHE H5Z H} HPHuh8McuMfET MHEHXffPHDž` E HP6 N< L ILPKD H`LLI LXA HEHEHDE1
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HÿHÊ1 w*1ffff. tPCHÉЀ
-rAt Hf. { H[tHL9u?IcMHI I HH)HHH)G H5~E1E1H]H HtGH H9tHHH)HHHH HH@ H@ H L L L H}> IffEHE H5P H} HPLuL,HL H ffEHE H5 H} HPHu6IcMLaE II LEhffHDž HM H N< L ILK H LL HHE Lf D LHHDž LL IHIDL9|LoIcMHIx I HH)HHH)V H{ fE1>H H9HPH)HHHI fHnfpDLmIx HtQfEI H9tHHH)HHHI : HH@ H@ H foEMx A H} ffEHE H5 H}
- j HPLuLHL HY ffEHE H5 H}
- HPHu3McuML MHEHXff0HDž@ Ey H0q N< L5 IEL0Ot L@LL# L8E HEHEHD1 f D HÊ t +t-u
HþHÊ 1< w'1fff. THKHÉ<
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HPLuLoHL: H ffEHE H5` H}
HPHuE1MceMt$E\ HH HEXff0HDž@ E H0 L IL8L0LLH@INLH HLA L8IL8HHug IcEHXffEHE H} HP HHMHMHHHEHCHHt H HMHHMHHuH| HEHHHM@ HEHHHM@ HEHHHM@ HMHHMHu3 E1E11 ?H H92HPH)HHHI rH H9eHPH)HHHI HH H9HPH)HHHIh rLHr^A H8HQH8A H8HQH8A H8HQH8A L8IL8HuA E1 @ L0
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H ffEHE H5M7 H} HPHu_AUH}H@& I HtGI H9tHHH)HHHI HH@ H@ H foEA HEI H}G IcMHI I HH)HHH)vqH# MH H9@HPH)HHHI #H H9vHPH)HHHI Ys!H H9tHPH)HHHI ffEHE H55 H} HPLuL29HLC H ffEHE H55 H} HPHu]AUH}Hv$ I HtGI H9tHHH)HHHI HH@ H@ H foEA HEI H}} XIcMHI I HH)HHH)vH\#s!H H9tHPH)HHHI ffEHE H54 H} HPLuL7HL H ffEHE H5>4 H} h HPHuT\AUH}H# I` HtGIh H9tHHH)HHHIh HH@ H@ H foEA` HEIp H} UIcMI` Ih HH)HHH)vH#s!H H9tHPH)HHHIh ffEHE H583 H}
+ V HPLuLo6HL H_ ffEHE H52 H}
+ HPHuZMcuMR MHEHXff@HDžP Ey H@] N< L- IEL@Ot LPLL LHE HEHEHD1fff. HÊ t +t-u
HþHÊ 1< w'1fff. THKHÉ<
+rAT Hfff. { H[tHL9u<IcMIx I HH)HHH) H ~E1E1H]Hx HtGH H9tHHH)HHHH H H@ H@ H Lx L L H}g IffEHE H50 H}
HPLuL4HL H ffEHE H50 H}
HPHuXMceMt$E\ HH HEXff0HDž@ E H0 L IL8L0LLH@INLH, HLA L8IL8HHu IcEHXffEHE w H}j HB HHMHMHHHEHCHHt H HMHHMHHuH HEHHHM@ HEHHHM@ HEHHHM@ HMHHMHu E1E11e H H9HPH)HHHI LHr^A H8HQH8A H8HQH8A H8HQH8A L8IL8HuA E1 @ L0
H< t <+t <-u Hþffffff. HÊ1 w01ffff. P CHÉЀ
-rA< H D { H[tHL9ZL8H0L@I Ht3I9 tI HH@ H@ H I M M H}^ b1I Ht7I9 tI _ HH@ H@ H HMHEI I HEI A E ffEHE H5) H} ~ HPLuLHL HM ffEHE H5 H} 1 HPHu,Mc MfE MHEHXffEHE A H} N< LJ ILmKD HELLI> L}E HEHEHDE1HÊ< t <+t<-u
HÿHÊ1 w*1ffff. tPCHÉЀ
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+ HHHHH9tJ|#PHH #HHHAtJD#xHAdHIlJL#pJD#hHHt)H H9tHHH)HHHH a HxHt)HH9tHHH)HHHH` HcEI9Mm+ ` HMHHH5; H| ` ` HMHHH5 HP ` =HIHHt9H H9tHHH)HHHH 7` HI;HIHxHt)HH9tHHH)HHHH_ L` u-HHQH1H1uH}t_ LuH}t_ LuH= u ` H [A\A]A^A_]HH_ H_ HhHhff. UHAWAVAUATSHIIIHB HEF,^ A$AD$CAD$ CID$(HC AD$CID$HCID$ HC ID$0HC(ID$8HC0ID$@HC8ID$HHC@AD$hCHAD$iCIID$pHCJID$xHCRAD$CZH{^It$PIcT$H_ McAB+AGBD+AG BD+IG(JD+ AGBD+IGJD+IG JD+ IG0JD+(IG8JD+0IG@JD+8IGHJD+@AGhBD+HAGiBD+IIGpJD+JIGxJD+RAGBD+ZJ|+^IwPIcWH:_ HEЍ H HELEHDLҪLLG) MLL9) H[A\A]A^A_]f. UHAWAVATSMIAH IcH HTIc7H D Ic[A\A^A_]fUH1x
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+ HHHH9tK|&PIt PIT XID xKD&xID hIL pKL&pKD&hDžHDž HDž HDž HDž H HHDž0 HDž( HDž HDž HDž HDž HDž ƅ8ƅ9 f( f@Dž HDž, HDž$ HDž@ HDž8 HHHDž HDžx HDžp HDžh HDž` HDžX HDžP ƅƅ fM I HcHI
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H]HtbHEH9tQffff. HHHMHxHt'HHH9tHQH)HHHHP HMH9HuH]H L fff. UHAWAVAUATSH I` DžHDž HDž HDž HDž H H HDž8 HDž0 HDž( HDž HDž HDž HDž ƅ@ƅA (H HDžXHDžd HDž\ HDžx HDžp HEHE HE HE HE HE HE HE EE EM$ I$ HcHI H
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- @ HP HðuLf M ILJ MtYMt$ID$HtBHH fffff. I| ID H9tHtHP(
- @ HP HðuLrf I ILJ Ht\f I ILJ Ht@f I Ht)I H9tHHH)HHHI f I Ht)I H9tHHH)HHHI e I Ht)I H9tHHH)HHHI e I Ht)I H9tHHH)HHHI le I Ht)I H9tHHH)HHHI 7e Ix Ht&e I` Hte IH HtI9P tIP d I0 HtI98 tI8 d I lL[A\A^A_]+ff. UH]nb f D UHSPHZb HH[]d @ UHAWAVAUATSHIILum8 A$ JLc E( E EU E9~
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ff. 1@yH8 H;@ f(t I AI HQ I$LBH H Hc4I4Lc HvJ4v0q(HcuH0H9H@H= DZ H[A\A^A_] D UHAVSH葔L H L+( * ^ H LߖH Hc H H HH)HHH)vH 5#s!H H9tHPH)HHHH H` Hh H)t<H HHHH 1fffff. HHH0H9r[A^]fff. UHSPH負H H@H0 @H0 HH HR HELE HH H @ A( A(H H@ @ @(H H[]fUHAWAVAUATSH(AAHI HHD AHHD EM L HK(H{Hs HQA9 H}HuEe IcHq 4HcAuHHA8IE HE HE LULuH= L% 7 LELM 1LLARAV7X HHEIEHEIE( AE(L HC(HKHS HMHUE} IcHPHp 4Hc AuHHH8IM HE HE LELM 1H=` L 1) H}HuE} IcHq 4HcAuHHA8IE HE HE LULuH= L=6 LELM 1LLARAV3W HHEIEHEIE( AE(L HC(HKHS HMHUEe IcHPHp 4Hc AuHHH8IM HE HE LELM 1H=\ LLH]SAVV HHEIEHEIE( AE(H([A\A]A^A_]ffffff. UHAWAVAUATSH IHB H HEIc] HW)`HDžp y H`S HiU HHhH`HHHpHCHHt"H HhHHhHHuHrjfffff. HhHHHh@ HhHHHh@ HhHHHh@ HhHHhHuH= H6 H` 1LIU 1I}P ELH4Icu HhD|Icu LPL1|HE HE2HE HE HE HE HE HE HE HDž L}H LHBR H4R L|$Lt$HEH$H= H` L`Lh 1L+T HDž H]H Q H W) HDž0 )HDž A HhH A HPHIc HW)HDž y HQ H L4@LR HHHH[HHHCHHt[HH Hffff. H H~HF HHHu) HDž G HLH H H HFF F HF FHF F( F, HF0 F0HF8F@ FD HFH FHHFPHFX H`HvHIc HW) HDž o y H O H L4@LNQ HHH H[HHHSHHt@HH Hf H HxH@ HHHuHIH H ffffff. H HP@ @ H@ @HP @( @, H@0 @0HP8@@ @D H@H @HHPPH@X H`HvLMc M HH HHH1ۋFHB(HFHBFH AHG(HAHGAH HHHHHL9|I0 H(I0 1E 1E11ffff. HcHH\H\7H HTHT7H 7VI0 HcH HLHLH H\H\H 0IIc I0 HI9|I0 H(I0 1IH W)HDž )HDž A( A 1I L@E1 L1 D HDž@ Dž@LHHDžP HH;s9HPHHH@HHHPHHHH D HLA HIH IcHtHpH4HLHHHpHDž@ Dž@LHHDžP HH;s?HPHHH@HHHPHHHH!ff. HL@ HAL$IH HcHt
+HHHpA0A 9A L9`W)@HDžP )0HDž@ I 0HH@Lu I 0HH0LV H@H0LF H(H8A I` Mx M A D)H H H HT$$ SIE H H`1LH0Ht)H8H9tHHH)HHHH8# H@Ht)HHH9tHHH)HHHHH HHt;HH9t*HAH)HHHHHRHH觱 HHt;HH9t*HAH)HHHHHRHH` H Ht;HH9t*HAH)HHHHHRHH HHt;HH9t*HAH)HHHHHRHHҰ HHtmHH9tYfff. HHHHxHt*HHH9tHQH)HHHHPw HH9HuHHY H HtdH(H9tP HHH(HxHt*HHH9tHQH)HHHHP H(H9HuH H H}ұHPHtkHXH9tWf. HHHXHxHt*HHH9tHQH)HHHHP臯 HXH9HuHPHi HhHtdHpH9tP HHHpHxHt*HHH9tHQH)HHHHP HpH9HuHhH H`HtH9htHhخ HK H H;Eu-H [A\A]A^A_]H HuH H艮 P IH %I/ In I I IH0Ht)H8H9tHHH)HHHH8# H@Ht@HHH9tHHH)HHHHH I) I IHHt;HH9t*HAH)HHHHHRHH萭 HHt;HH9t*HAH)HHHHHRHHI H Ht;HH9t*HAH)HHHHHRHH HHt;HH9t*HAH)HHHHHRHH軬 HHtfHH9tR D HHHHxHt*HHH9tHQH)HHHHPg HH9HuHHI H HtdH(H9tP HHH(HxHt*HHH9tHQH)HHHHP H(H9HuH H٫ H}HPHtkHXH9tWf. HHHXHxHt*HHH9tHQH)HHHHPw HXH9HuHPHY HhHtdHpH9tP HHHpHxHt*HHH9tHQH)HHHHP HpH9HuHhH H`HtH9htHhȪ L fUHAWAVATSHIIA\$ E ]E E D?HHEH=. HMLELMܺ" DjjP H E9N؉]HcM9lHHHHfffff. IT$8|
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+]IT$p fffff. HcHHPH9|H=2. DJ H[A\A^A_] D UHAWAVAUATSH LMLEHMIAZ B E ]E E 7HEH$D$ D$ H=- HMLELMԺ" LUȉu跩 HuȋE9N؉]LceA9 H HHEfffff. HEH B< H~HFpH B MH AIQO<IJL8DHEL@pH HH@PHp(H@0H}L7MIK<.CD.hH K\.(LXP H08UH@HHHPL}J|;@H HH@0HPH $H0H8HML@LHHUKD.(H AY HEH MB (HuL MI I H HE8 toLvxK 9I) HNpLHH4HL1It6HcHH1f. )fAfNf\fNHHHH9|:A@AHH~HFxJLDLHELpxL APII@A@MH(Ip0H}H J<+BD+hH JL+(X3O HX`ULhHpHxK|@H HH@0HxH $HXH`HMLhLpHMJD+(I AY HEH MB (HuHcEI9Md$ HMHHH5d H ( HMHHH58 Hy uHHť u-HHQH1HܺH}C袥 HuWH},若 Hu@H=) uǥ H [A\A]A^A_]HHT HJ H~Hvf D UHAWAVAUATSH IAH}I] IEH9tE D HHIMHxHt'HHH9tHQH)HHHHP蚤 IMH9HuET DuHEL HPI9 L8HULm
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+ LLHIF` IFX IFP L}H]L)IIt[M~PH?I9vLr HJ HI^XI^PJIF`L}LmM)M~HLLo LI^XHEHHA(IFxHAHI INpIFhLmIm fffff. LLux L}Mt&HEL9tHHL)HHHHML蔢 IL9eL8!HEL HPELcL)HI9s/IUI+U H1Lu IMu IULLsv HĨ [A\A]A^A_]0HI?Ht(IFXH9tHHH)HHHINX HH}Ht#HEH9tHHH)HHHHM辡 H UHAWAVAUATSHhIIIIM IEH9tHPH)HHHIU IL E IFHcXLHfWf)EHE }y H}O H H<@ HHMHMH[HHEuH3A H HAHA HMHHMHs}tiH fffff. A HAHA HMHQHUAA HA HA( HMHHMHuIIOH9 W*A*^Zff. HcHxXHpHH W*YH^HuH<Rf.Lv+HHRHxH|H8HXH\H<HuLHH9ufWf)EHE Mc M H]L}L9 LuI1LuLe1ffffff. HHEHMHSHUHHsHuHUH9t(HMHHHMHUHPHHEf. LL赂 I9uH]HELu 11zHH)HHHI9sUHEH+EHiЫANH}1. HuHMKvHH9t'HAH)HHHHRHHUHuHUHxHHuH)IIHLE1H= A |GL5< E1Lfffff. H H<@L Hu
HA uIIHUH}HxLMj MtL˝ H]LuL9tf: f.CrHI9u@fffff. tIMI;Mt HIMLH؟(: H]Ht8HMH9t'HAH)HHHHHRHHEH H}Ht5HMH9t'HAH)HHHHHRHHEݜ Hh[A\A]A^A_]IUIMtL蹜 IH}Ht5HMH9t'HAH)HHHHHRHHEt H}Ht5HMH9t'HAH)HHHHHRHHE6 Lv UHAWAVAUATSHHHUHuIMcD$ M~{M$0 I$H Au1 A!' AtOfffff. HHHHI!4H!4HJHHHHI!4H!4HL9|HMHAH+HHMHQH+HHIc$ HUHD1HHEI$` ADŽ$ A$ p 111E1fH]HMH)HEH9HC1E1H] fff. IHIc$ I$ ID$xHL4J 0Jt0I)@Hc HID$xJ0@)IHHA$ IHuHUH]L} @ I9W HMHHIH)HH9 IL$HUHcHI0A$ Hc HH9u&I$0 HH HH 4I$ <IIIc$ I$ ID$pHL4J 0Jt0I)@Hc LID$pJ0@)IHHA$ HUHHL}H]I9sqHMHHIH)HI9HUIL$JcHI0A$ Hc HuH9LI$H HH HH 4I$ <!I$x <Ic$ HHH9}$I$` <|HA$ HHMLHH9HH[A\A]A^A_]fff. UHAWAVAUATSH IH H HEIc H88Ic L L"Ic_ HfWf)PHDž` y HPH H HHXHPHHH`HCHHt'H HXHHXHHuHrjfffff. HXHHHX@HXHHHX@HXHHHX@HXHHXHuIc_ HLpfWf)pHE y HpF H HxHpHHHMHKHHt+Hfff. HxHHxHHuH f. HxHHHx@HxHHHx@HxHHHx@HxHHxHuLpf)pHE I
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+ HHHH9tK|&PIt PIT X ID xKD&xID hIL pKL&pKD&hDžHDž HDž HDž HDž H HHDž HDž HDž HDž HDž HDž HDž ƅƅ f( : fDž HDž, HDž$ HDž@ HDž8 HHHDž HDžx HDžp HDžh HDž` HDžX HDžP ƅƅ fM I HcHI
+ HHHHH9tHIt PIT XJ LID xIEID hIL pIMIE I HcHxcM HI H
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+ HHHH9tK|&PHHDIEKD&xIE IMKL&pKD&h xtI HcHxeM HI
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+ э IHd I I I I}I I% IHpHt)HxH9tHHH)HHHHx蚌 HHt)HH9tHHH)HHHHe H}NHpHtH9xtHx; HPHtH9XtHX H HtaH(H9tMHHH(HxHt*HHH9tHQH)HHHHPˋ H(H9HuH H譋 H8HthH@H9tT HHH@HxHt*HHH9tHQH)HHHHPW H@H9HuH8H9 Ly UHAWAVAUATSHx LLHIA\$ E ]E E 7HEH$D$ D$ H=$ HMLELM̺" u1 E9N؉]LcmA9 DHpJ HELHHHEH HHxE1Lmffff. 迋 EKL5 ID$H HEBEI$0 II H?JH/ HDžHH@ H H@ H@ H HH@\ H@T H@L H@D H@< H@4 H@, ƅXƅY (1 `I$ I$ HMJc4I$ HUH H|HcHIH)HH@p I$M|$I$ AEAEH 1LI$ HUHLHpB42LEMͻM$ I$ HX@EI $H 0LI$ HQ(HI0ƅXH HEX0 HEHHHHHEI|@H HH@0HH $HHHMLLHEI@YMHc]HL8HA DʃD9fA.D (uz<upHH HEA< t[I
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+ @ HP HðuLD| M ILJ MtYMt$ID$HtBHH fffff. I| ID H9tHtHP(
+ @ HP HðuL{ I ILJ Ht{ I ILJ Ht{ I Ht)I H9tHHH)HHHI m{ I Ht)I H9tHHH)HHHI 8{ I Ht)I H9tHHH)HHHI { Ix Ht)I H9tHHH)HHHI z I` Ht)Ih H9tHHH)HHHIh z IH Htz I0 Htwz I HtI9 tI Vz I HtI9 tI 5z I( +vL[A\A^A_];rff. UH]w f D UHSPHw HH[]y @ UHAWAVAUATSHIILu v A Lc E E EU E9~
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ff. 1@yH8 H;@ f(t I AI HQ I$LBH H Hc4I4Lc HvJ4v0q(HcuH0H9H@H=2 DJp H[A\A^A_] D UHAVSHL( H Lk * ^ H@ L߂H Hc H H HH)HHH)vH N#s!H H9tHPH)HHHH H0 H8 H)t<H HHHH 1fffff. HHH0H9r[A^]fff. UHSPH貕H H@H @H H H HELE [H H @ A( A(H H@ @ @(H H[]fUHAWAVAUATSH(AAHI HHD AHHD EM L HK(H{Hs HQA9 H}HuEe IcHq 4HcAuHHA8IE HE HE LULuH= L%< LELM 1LLARAVm HHEIEHEIE(d AE(L HC(HKHS HMHUE} IcHPHp 4Hc AuHHH8IM HE HE LELM 1H= L 1) H}HuE} IcHq 4HcAuHHA8IE HE HE LULuH= L=; LELM 1LLARAVl HHEIEHEIE(` AE(L HC(HKHS HMHUEe IcHPHp 4Hc AuHHH8IM HE HE LELM 1H= LLH]SAVk HHEIEHEIE( AE(H([A\A]A^A_]ffffff. UHAWAVAUATSH IHz H HEIc] HW)`HDžp y H`h Hj HHhH`HHHpHCHHt"H HhHHhHHuHrjfffff. HhHHHh@ HhHHHh@ HhHHHh@ HhHHhHuH=e H6 H` 1LIwj 1I}h ELH$Icu HhvIcu LPLqvHE HE2HE HE HE HE HE HE HE HDž L}H LHg Hg L|$Lt$HEH$H= H` L`Lh 1Li HDž H]H 2g H W) HDž0 )HDž A HhH A HPHsIc HW)HDž y Hhf H L4@L4h HHHH[HHHCHHt[HH Hffff. H H~HF HHHu) HDž G HLH H H HFF F HF FHF F( F, HF0 F0HF8F@ FD HFH FHHFPHFX H`HvHIc HW) HDž o y H d H L4@Lf HHH H[HHHSHHt@HH Hf H HxH@ HHHuHIH H ffffff. H HP@ @ H@ @HP @( @, H@0 @0HP8@@ @D H@H @HHPPH@X H`HvLMc M HH HHH1ۋFHB(HFHBFH AHG(HAHGAH HHHHHL9|I H(I 1E 1E11ffff. HcHH\H\7H HTHT7H 7VI HcH HLHLH H\H\H 0IIc I HI9|I H(I 1I W)HDž )HDž A A 1I L@E1 L1 D HDž@ Dž@LHHDžP HH;s9HPHHH@HHHPHHHH D HLG HI IcHtHpH4HLHHHpHDž@ Dž@LHHDžP HH;s?HPHHH@HHHPHHHH!ff. HLQF HAL$I HcHt
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HA uIIHUH}HxLM= MtLP H]LuL9tf f.CrHI9u@fffff. tIMI;Mt HIMLH(Rx H]Ht8HMH9t'HAH)HHHHHRHHEHkO H}Ht5HMH9t'HAH)HHHHHRHHE-O Hh[A\A]A^A_]IUIMtL O IH}Ht5HMH9t'HAH)HHHHHRHHEN H}Ht5HMH9t'HAH)HHHHHRHHEN LK UHAWAVAUATSHHHUHuIMcD$ M~{M$0 I$H Au1 A!' AtOfffff. HHHHI!4H!4HJHHHHI!4H!4HL9|HMHAH+HHMHQH+HHIc$ HUHD1HHEI$` ADŽ$ A$ p 111E1fH]HMH)HEH9HC1E1H] fff. IHIc$ I$ ID$xHL4J 0Jt0I)@Hc^N HID$xJ0@)IHHA$ IHuHUH]L} @ I9W HMHHIH)HH9 IL$HUHcHI0A$ Hc HH9u&I$0 HH HH 4I$ <IIIc$ I$ ID$pHL4J 0Jt0I)@HcoM LID$pJ0@)IHHA$ HUHHL}H]I9sqHMHHIH)HI9HUIL$JcHI0A$ Hc HuH9LI$H HH HH 4I$ <!I$x <Ic$ HHH9}$I$` <|HA$ HHMLHH9HH[A\A]A^A_]fff. UHAWAVAUATSH IHJ H HEIc H8WIc L LrWIc_ HfWf)PHDž` y HPH HpJ HHXHPHHH`HCHHt'H HXHHXHHuHrjfffff. HXHHHX@HXHHHX@HXHHHX@HXHHXHuIc_ HLpfWf)pHE y HpG HnI HxHpHHHMHKHHt+Hfff. HxHHxHHuH f. HxHHHx@HxHHHx@HxHHHx@HxHHxHuLpf)pHE I f. IH@XH1LHPHt,H9XtHX6H fWf)PHDž` f(f)PHH`IH@XH1LHpHt/H9xtHxG ID$ ID$ I$ f(f)pHHEHE HE2HE HE HE HE HE HE HE HDž LeHLHE HE Ld$HpHD$L4$H=s H L8LP 1LG HDž H]H&E H 9 L8L@M9 M)II vH8 HL8pIrhLH1ۺ Af A(HH1A<ADAD9AfA.D(uzDHEHHI9u1M I Lc0IKD5 HI
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NSt3__110__function6__funcIZN8LightGBM9PredictorC1EPNS2_8BoostingEibbbbidEUlRKNS_6vectorINS_4pairIidEENS_9allocatorIS8_EEEEPdE2_NS9_ISF_EEFvSD_SE_EEE ZN8LightGBM9PredictorC1EPNS_8BoostingEibbbbidEUlRKNSt3__16vectorINS3_4pairIidEENS3_9allocatorIS6_EEEEPdE2_ NSt3__118basic_stringstreamIcNS_11char_traitsIcEENS_9allocatorIcEEEE NSt3__115basic_stringbufIcNS_11char_traitsIcEENS_9allocatorIcEEEE NSt3__110__function6__funcIZN8LightGBM9Predictor7PredictEPKcS5_bEUlS5_PNS_6vectorINS_4pairIidEENS_9allocatorIS8_EEEEE_NS9_ISD_EEFvS5_SC_EEE NSt3__110__function6__baseIFvPKcPNS_6vectorINS_4pairIidEENS_9allocatorIS6_EEEEEEE ZN8LightGBM9Predictor7PredictEPKcS2_bEUlS2_PNSt3__16vectorINS3_4pairIidEENS3_9allocatorIS6_EEEEE_ NSt3__110__function6__funcIZN8LightGBM9Predictor7PredictEPKcS5_bEUliRKNS_6vectorINS_12basic_stringIcNS_11char_traitsIcEENS_9allocatorIcEEEENSA_ISC_EEEEE_NSA_ISH_EEFviSG_EEE 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+ /Users/vsts/agent/2.155.1/work/1/s/src/c_api.cpp Check failed: num_used_row_indices > 0 at %s, line %d .
Used indices of subset Input data type error or field not found Field not found Unknown data type in RowFunctionFromDenseMatric Unknown data type in RowFunctionFromCSR Check failed: col_idx < ncol_ptr && col_idx >= 0 at %s, line %d .
Unknown data type in CSC matrix regression gbdt serial cpu LightGBM_model.txt LightGBM_predict_result.txt gbdt_prediction.cpp unordered_map::at: key not found Warning [LightGBM] [%s]
[LightGBM] [Fatal] %s
- Size of feature_names error, should equal with total number of features Find whitespaces in feature_names, replace with underlines Continued train from model is not supported for c_api,
+ Size of feature_names error, should equal with total number of features Do not support non-ascii characters in feature name. Find whitespaces in feature_names, replace with underlines Continued train from model is not supported for c_api,
please use continued train with input score feature Do not support feature parallel in c api Only find one worker, will switch to serial tree learner Using self-defined objective function num_class Cannot change num_class during training boosting Cannot change boosting during training metric Cannot change metric during training objective none Check failed: early_stop_freq > 0 at %s, line %d .
- /Users/vsts/agent/2.144.2/work/1/s/src/./application/predictor.hpp Check failed: early_stop_margin >= 0 at %s, line %d .
+ /Users/vsts/agent/2.155.1/work/1/s/src/application/predictor.hpp Check failed: early_stop_margin >= 0 at %s, line %d .
binary multiclass Prediction results file %s cannot be found Could not recognize the data format of data file %s Could not open %s Skipped header "%s" in file %s Debug Warning: last line of %s has no end of line, still using this line thread constructor failed Info [%s]: does not tolerate element [#%i = ] outside [ , ] Don't support large string in R-package Check failed: len == out_len at %s, line %d .
- /Users/vsts/agent/2.144.2/work/1/s/src/lightgbm_R.cpp group query init_score Check failed: out_len == len at %s, line %d .
- No training/prediction data, application quit config Config file %s doesn't exist, will ignore Finished loading parameters Finished loading data in %f seconds Finished initializing network Finished initializing training Started training... cpp Finished training Finished RefitTree Finished prediction Finished initializing prediction, total used %d iterations %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=%s Unknown parameter: %s %s is set=%s, %s=%s will be ignored. Current value: %s=%s /Users/vsts/agent/2.144.2/work/1/s/src/application/predictor.hpp Time for loading model: %f seconds dart goss rf tree Unknown boosting type %s Unknown model format or submodel type in model file %s /dev/urandom Check failed: data_idx >= 0 && data_idx <= static_cast<int>(valid_score_updater_.size()) at %s, line %d .
- /Users/vsts/agent/2.144.2/work/1/s/src/boosting/gbdt.h Check failed: tree_idx >= 0 && static_cast<size_t>(tree_idx) < models_.size() at %s, line %d .
+ /Users/vsts/agent/2.155.1/work/1/s/src/lightgbm_R.cpp group query init_score Check failed: out_len == len at %s, line %d .
+ No training/prediction data, application quit config Config file %s doesn't exist, will ignore Finished loading parameters Finished loading data in %f seconds Finished initializing network Finished initializing training Started training... cpp Finished training Finished RefitTree Finished prediction Finished initializing prediction, total used %d iterations %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=%s Unknown parameter: %s %s is set=%s, %s=%s will be ignored. Current value: %s=%s Time for loading model: %f seconds dart goss rf tree Unknown boosting type %s Unknown model format or submodel type in model file %s /dev/urandom Check failed: data_idx >= 0 && data_idx <= static_cast<int>(valid_score_updater_.size()) at %s, line %d .
+ /Users/vsts/agent/2.155.1/work/1/s/src/boosting/gbdt.h 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 .
Check failed: config_->top_rate + config_->other_rate <= 1.0f at %s, line %d .
- /Users/vsts/agent/2.144.2/work/1/s/src/boosting/goss.hpp Check failed: config_->top_rate > 0.0f && config_->other_rate > 0.0f at %s, line %d .
+ /Users/vsts/agent/2.155.1/work/1/s/src/boosting/goss.hpp Check failed: config_->top_rate > 0.0f && config_->other_rate > 0.0f at %s, line %d .
Cannot use bagging in GOSS Using GOSS Check failed: config->bagging_freq > 0 && config->bagging_fraction < 1.0f && config->bagging_fraction > 0.0f at %s, line %d .
- /Users/vsts/agent/2.144.2/work/1/s/src/boosting/rf.hpp Check failed: config->feature_fraction <= 1.0f && config->feature_fraction > 0.0f at %s, line %d .
+ /Users/vsts/agent/2.155.1/work/1/s/src/boosting/rf.hpp Check failed: config->feature_fraction <= 1.0f && config->feature_fraction > 0.0f at %s, line %d .
Check failed: train_data->metadata().init_score() == nullptr at %s, line %d .
Check failed: num_tree_per_iteration_ == num_class_ at %s, line %d .
Check failed: gradients == nullptr at %s, line %d .
Check failed: hessians == nullptr at %s, line %d .
- No object function provided Check failed: train_data != nullptr at %s, line %d .
- /Users/vsts/agent/2.144.2/work/1/s/src/boosting/gbdt.cpp Cannot add validation data, since it has different bin mappers with training data Check failed: cur_left_cnt == bag_data_cnt at %s, line %d .
+ RF mode do not support custom objective function, please use built-in objectives. Check failed: train_data != nullptr at %s, line %d .
+ /Users/vsts/agent/2.155.1/work/1/s/src/boosting/gbdt.cpp Cannot add validation data, since it has different bin mappers with training data No object function provided Check failed: cur_left_cnt == bag_data_cnt at %s, line %d .
Re-bagging, using %d data to train %f seconds elapsed, finished iteration %d .snapshot_iter_ Check failed: tree_leaf_prediction.size() > 0 at %s, line %d .
Check failed: static_cast<size_t>(num_data_) == tree_leaf_prediction.size() at %s, line %d .
Check failed: static_cast<size_t>(models_.size()) == tree_leaf_prediction[0].size() at %s, line %d .
Check failed: leaf_pred[i] < models_[model_index]->num_leaves() at %s, line %d .
Start training from score %lf regression_l1 quantile mape Disabling boost_from_average in %s may cause the slow convergence Stopped training because there are no more leaves that meet the split requirements Early stopping at iteration %d, the best iteration round is %d Output of best iteration round:
%s Iteration: , training : , valid_ Cannot reset training data, since new training data has different bin mappers Check failed: num_tree_per_iteration_ == objective_function_->NumModelPerIteration() at %s, line %d .
Use subset for bagging Number of class for initial score error : Check failed: max_depth_ >= 0 at %s, line %d .
- /Users/vsts/agent/2.144.2/work/1/s/include/LightGBM/tree.h r { "name":" ", "version":" "num_class": , "num_tree_per_iteration": "label_index": "max_feature_idx": "average_output": true false ,
+ /Users/vsts/agent/2.155.1/work/1/s/include/LightGBM/tree.h r { "name":" ", "version":" "num_class": , "num_tree_per_iteration": "label_index": "max_feature_idx": "average_output": true false ,
"objective":" ",
- "feature_names":[" "," "], "tree_info":[ "tree_index": } #include "gbdt.h" #include <LightGBM/utils/common.h> #include <LightGBM/objective_function.h> #include <LightGBM/metric.h> #include <LightGBM/prediction_early_stop.h> #include <ctime> #include <sstream> #include <chrono> #include <string> #include <vector> #include <utility> namespace LightGBM { double (*PredictTreePtr[])(const double*) = { , PredictTree }; 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); ++early_stop_round_counter; if (early_stop->round_period == early_stop_round_counter) { if (early_stop->callback_function(output, num_tree_per_iteration_)) return; early_stop_round_counter = 0; void GBDT::PredictRaw(const double* features, double *output, const PredictionEarlyStopInstance* early_stop) const { double (*PredictTreeByMapPtr[])(const std::unordered_map<int, double>&) = { ByMap 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); if (average_output_) { 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*) = { Leaf 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>&) = { LeafByMap void GBDT::PredictLeafIndexByMap(const std::unordered_map<int, double>& features, double* output) const { output[i] = (*PredictTreeLeafByMapPtr[i])(features); } // namespace LightGBM #define USE_HARD_CODE 0 #ifndef USE_HARD_CODE #else #endif version= num_class= num_tree_per_iteration= label_index= max_feature_idx= objective= average_output feature_names= feature_infos= Tree= tree_sizes= end of trees feature importances: =
-parameters: end of parameters feature_names Wrong line at model file: %s Model file doesn't specify the number of classes num_tree_per_iteration label_index Model file doesn't specify the label index max_feature_idx Model file doesn't specify max_feature_idx Wrong size of feature_names Model file doesn't contain feature_names feature_infos Wrong size of feature_infos Model file doesn't contain feature_infos tree_sizes Model format error, expect a tree here. met %s parameters: Unknown importance type: only support split=0 and gain=1 v2 w wb Unknown early stopping type: Multiclass early stopping needs predictions to be of length two or larger Binary early stopping needs predictions to be of length one Using OrderedSparseBin->ConstructHistogram() instead Check failed: max_bin > 0 at %s, line %d .
- /Users/vsts/agent/2.144.2/work/1/s/src/io/bin.cpp Check failed: right_max_bin > 0 at %s, line %d .
+ "feature_names":[" "," "], "monotone_constraints":[ ], "tree_info":[ "tree_index": } #include "gbdt.h" #include <LightGBM/utils/common.h> #include <LightGBM/objective_function.h> #include <LightGBM/metric.h> #include <LightGBM/prediction_early_stop.h> #include <ctime> #include <sstream> #include <chrono> #include <string> #include <vector> #include <utility> namespace LightGBM { double (*PredictTreePtr[])(const double*) = { , PredictTree }; 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); ++early_stop_round_counter; if (early_stop->round_period == early_stop_round_counter) { if (early_stop->callback_function(output, num_tree_per_iteration_)) return; early_stop_round_counter = 0; void GBDT::PredictRaw(const double* features, double *output, const PredictionEarlyStopInstance* early_stop) const { double (*PredictTreeByMapPtr[])(const std::unordered_map<int, double>&) = { ByMap 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); if (average_output_) { 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*) = { Leaf 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>&) = { LeafByMap void GBDT::PredictLeafIndexByMap(const std::unordered_map<int, double>& features, double* output) const { output[i] = (*PredictTreeLeafByMapPtr[i])(features); } // namespace LightGBM #define USE_HARD_CODE 0 #ifndef USE_HARD_CODE #else #endif version= num_class= num_tree_per_iteration= label_index= max_feature_idx= objective= average_output feature_names= monotone_constraints= feature_infos= Tree= tree_sizes= end of trees feature importances: =
+parameters: end of parameters feature_names monotone_constraints Wrong line at model file: %s Model file doesn't specify the number of classes num_tree_per_iteration label_index Model file doesn't specify the label index max_feature_idx Model file doesn't specify max_feature_idx Wrong size of feature_names Model file doesn't contain feature_names Wrong size of monotone_constraints feature_infos Wrong size of feature_infos Model file doesn't contain feature_infos tree_sizes Model format error, expect a tree here. met %s parameters: Unknown importance type: only support split=0 and gain=1 v3 w wb Unknown early stopping type: Multiclass early stopping needs predictions to be of length two or larger Binary early stopping needs predictions to be of length one Using OrderedSparseBin->ConstructHistogram() instead Check failed: max_bin > 0 at %s, line %d .
+ /Users/vsts/agent/2.155.1/work/1/s/src/io/bin.cpp 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 NaN Met categorical feature which contains sparse values. Consider renumbering to consecutive integers started from zero Check failed: default_bin_ > 0 at %s, line %d .
- Unknown parameter %s gbrt random_forest task train training predict prediction test convert_model refit refit_tree Unknown task type %s device_type gpu Unknown device type %s tree_learner feature_parallel data data_parallel voting voting_parallel Unknown tree learner type %s seed Check failed: valid.size() == valid_data_initscores.size() at %s, line %d .
- /Users/vsts/agent/2.144.2/work/1/s/src/io/config.cpp multiclassova softmax multiclass_ova ova ovr null custom na Number of classes should be specified and greater than 1 for multiclass training Number of classes must be 1 for non-multiclass training multi_logloss multi_error Multiclass objective and metrics don't match Histogram LRU queue was enabled (histogram_pool_size=%f).
+ Do not support non-ascii characters in config. Unknown parameter %s gbrt random_forest regression_l2 mean_squared_error mse l2 l2_root root_mean_squared_error rmse mean_absolute_error l1 mae softmax multiclassova multiclass_ova ova ovr xentropy cross_entropy xentlambda cross_entropy_lambda mean_absolute_percentage_error null custom na binary_logloss ndcg lambdarank map mean_average_precision multi_logloss kldiv kullback_leibler task train training predict prediction test convert_model refit refit_tree Unknown task type %s device_type gpu Unknown device type %s tree_learner feature_parallel data data_parallel voting voting_parallel Unknown tree learner type %s seed Check failed: valid.size() == valid_data_initscores.size() at %s, line %d .
+ /Users/vsts/agent/2.155.1/work/1/s/src/io/config.cpp Number of classes should be specified and greater than 1 for multiclass training Number of classes must be 1 for non-multiclass training multi_error Multiclass objective and metrics don't match Histogram LRU queue was enabled (histogram_pool_size=%f).
Will disable this to reduce communication costs Accuracy may be bad since you didn't set num_leaves and 2^max_depth > num_leaves [boosting: ]
[objective: [metric: [tree_learner: [device_type: Parameter %s should be of type int, got "%s" valid num_iterations Check failed: num_iterations >=0 at %s, line %d .
- /Users/vsts/agent/2.144.2/work/1/s/src/io/config_auto.cpp learning_rate Check failed: learning_rate >0.0 at %s, line %d .
+ /Users/vsts/agent/2.155.1/work/1/s/src/io/config_auto.cpp learning_rate Check failed: learning_rate >0.0 at %s, line %d .
num_leaves Check failed: num_leaves >1 at %s, line %d .
+ Check failed: num_leaves <=131072 at %s, line %d .
num_threads max_depth min_data_in_leaf Check failed: min_data_in_leaf >=0 at %s, line %d .
min_sum_hessian_in_leaf Check failed: min_sum_hessian_in_leaf >=0.0 at %s, line %d .
bagging_fraction Check failed: bagging_fraction >0.0 at %s, line %d .
Check failed: bagging_fraction <=1.0 at %s, line %d .
+ pos_bagging_fraction Check failed: pos_bagging_fraction >0.0 at %s, line %d .
+ Check failed: pos_bagging_fraction <=1.0 at %s, line %d .
+ neg_bagging_fraction Check failed: neg_bagging_fraction >0.0 at %s, line %d .
+ Check failed: neg_bagging_fraction <=1.0 at %s, line %d .
bagging_freq bagging_seed feature_fraction Check failed: feature_fraction >0.0 at %s, line %d .
Check failed: feature_fraction <=1.0 at %s, line %d .
- feature_fraction_seed early_stopping_round max_delta_step lambda_l1 Check failed: lambda_l1 >=0.0 at %s, line %d .
+ feature_fraction_bynode Check failed: feature_fraction_bynode >0.0 at %s, line %d .
+ Check failed: feature_fraction_bynode <=1.0 at %s, line %d .
+ feature_fraction_seed early_stopping_round first_metric_only max_delta_step lambda_l1 Check failed: lambda_l1 >=0.0 at %s, line %d .
lambda_l2 Check failed: lambda_l2 >=0.0 at %s, line %d .
min_gain_to_split Check failed: min_gain_to_split >=0.0 at %s, line %d .
drop_rate Check failed: drop_rate >=0.0 at %s, line %d .
Check failed: drop_rate <=1.0 at %s, line %d .
max_drop skip_drop Check failed: skip_drop >=0.0 at %s, line %d .
@@ -3345,39 +3181,53 @@
max_cat_threshold Check failed: max_cat_threshold >0 at %s, line %d .
cat_l2 Check failed: cat_l2 >=0.0 at %s, line %d .
cat_smooth Check failed: cat_smooth >=0.0 at %s, line %d .
max_cat_to_onehot Check failed: max_cat_to_onehot >0 at %s, line %d .
top_k Check failed: top_k >0 at %s, line %d .
- monotone_constraints feature_contri forcedsplits_filename refit_decay_rate Check failed: refit_decay_rate >=0.0 at %s, line %d .
+ feature_contri forcedsplits_filename forcedbins_filename refit_decay_rate Check failed: refit_decay_rate >=0.0 at %s, line %d .
Check failed: refit_decay_rate <=1.0 at %s, line %d .
- verbosity max_bin Check failed: max_bin >1 at %s, line %d .
- min_data_in_bin Check failed: min_data_in_bin >0 at %s, line %d .
+ cegb_tradeoff Check failed: cegb_tradeoff >=0.0 at %s, line %d .
+ cegb_penalty_split Check failed: cegb_penalty_split >=0.0 at %s, line %d .
+ cegb_penalty_feature_lazy cegb_penalty_feature_coupled verbosity max_bin Check failed: max_bin >1 at %s, line %d .
+ max_bin_by_feature min_data_in_bin Check failed: min_data_in_bin >0 at %s, line %d .
bin_construct_sample_cnt Check failed: bin_construct_sample_cnt >0 at %s, line %d .
histogram_pool_size data_random_seed output_model snapshot_freq input_model output_result initscore_filename valid_data_initscores pre_partition enable_bundle max_conflict_rate Check failed: max_conflict_rate >=0.0 at %s, line %d .
Check failed: max_conflict_rate <1.0 at %s, line %d .
is_enable_sparse sparse_threshold Check failed: sparse_threshold >0.0 at %s, line %d .
Check failed: sparse_threshold <=1.0 at %s, line %d .
- use_missing zero_as_missing two_round save_binary enable_load_from_binary_file header label_column weight_column group_column ignore_column categorical_feature predict_raw_score predict_leaf_index predict_contrib num_iteration_predict pred_early_stop pred_early_stop_freq pred_early_stop_margin convert_model_language Check failed: num_class >0 at %s, line %d .
+ use_missing zero_as_missing two_round save_binary header label_column weight_column group_column ignore_column categorical_feature predict_raw_score predict_leaf_index predict_contrib num_iteration_predict pred_early_stop pred_early_stop_freq pred_early_stop_margin convert_model_language Check failed: num_class >0 at %s, line %d .
is_unbalance scale_pos_weight Check failed: scale_pos_weight >0.0 at %s, line %d .
sigmoid Check failed: sigmoid >0.0 at %s, line %d .
boost_from_average reg_sqrt alpha Check failed: alpha >0.0 at %s, line %d .
fair_c Check failed: fair_c >0.0 at %s, line %d .
poisson_max_delta_step Check failed: poisson_max_delta_step >0.0 at %s, line %d .
tweedie_variance_power Check failed: tweedie_variance_power >=1.0 at %s, line %d .
Check failed: tweedie_variance_power <2.0 at %s, line %d .
max_position Check failed: max_position >0 at %s, line %d .
- label_gain metric_freq Check failed: metric_freq >0 at %s, line %d .
- is_provide_training_metric eval_at num_machines Check failed: num_machines >0 at %s, line %d .
+ lambdamart_norm label_gain metric_freq Check failed: metric_freq >0 at %s, line %d .
+ is_provide_training_metric eval_at multi_error_top_k Check failed: multi_error_top_k >0 at %s, line %d .
+ num_machines Check failed: num_machines >0 at %s, line %d .
local_listen_port Check failed: local_listen_port >0 at %s, line %d .
time_out Check failed: time_out >0 at %s, line %d .
- machine_list_filename machines gpu_platform_id gpu_device_id gpu_use_dp [data: [valid: [num_iterations: [learning_rate: [num_leaves: [num_threads: [max_depth: [min_data_in_leaf: [min_sum_hessian_in_leaf: [bagging_fraction: [bagging_freq: [bagging_seed: [feature_fraction: [feature_fraction_seed: [early_stopping_round: [max_delta_step: [lambda_l1: [lambda_l2: [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: [feature_contri: [forcedsplits_filename: [refit_decay_rate: [verbosity: [max_bin: [min_data_in_bin: [bin_construct_sample_cnt: [histogram_pool_size: [data_random_seed: [output_model: [snapshot_freq: [input_model: [output_result: [initscore_filename: [valid_data_initscores: [pre_partition: [enable_bundle: [max_conflict_rate: [is_enable_sparse: [sparse_threshold: [use_missing: [zero_as_missing: [two_round: [save_binary: [enable_load_from_binary_file: [header: [label_column: [weight_column: [group_column: [ignore_column: [categorical_feature: [predict_raw_score: [predict_leaf_index: [predict_contrib: [num_iteration_predict: [pred_early_stop: [pred_early_stop_freq: [pred_early_stop_margin: [convert_model_language: [convert_model: [num_class: [is_unbalance: [scale_pos_weight: [sigmoid: [boost_from_average: [reg_sqrt: [alpha: [fair_c: [poisson_max_delta_step: [tweedie_variance_power: [max_position: [label_gain: [metric_freq: [is_provide_training_metric: [eval_at: [num_machines: [local_listen_port: [time_out: [machine_list_filename: [machines: [gpu_platform_id: [gpu_device_id: [gpu_use_dp: config_file task_type objective_type app application boosting_type boost train_data train_data_file data_filename valid_data valid_data_file test_data test_data_file valid_filenames num_iteration n_iter num_tree num_trees num_round num_rounds num_boost_round n_estimators shrinkage_rate eta num_leaf max_leaves max_leaf tree_type tree_learner_type num_thread nthread nthreads n_jobs device random_seed random_state min_data_per_leaf min_data min_child_samples min_sum_hessian_per_leaf min_sum_hessian min_hessian min_child_weight sub_row subsample bagging subsample_freq bagging_fraction_seed sub_feature colsample_bytree early_stopping_rounds early_stopping max_tree_output max_leaf_output reg_alpha reg_lambda lambda min_split_gain rate_drop topk mc monotone_constraint feature_contrib fc fp feature_penalty fs forced_splits_filename forced_splits_file forced_splits verbose subsample_for_bin hist_pool_size data_seed model_output model_out save_period model_input model_in predict_result prediction_result predict_name prediction_name pred_name name_pred init_score_filename init_score_file input_init_score valid_data_init_scores valid_init_score_file valid_init_score is_pre_partition is_enable_bundle bundle is_sparse enable_sparse sparse two_round_loading use_two_round_loading is_save_binary is_save_binary_file load_from_binary_file binary_load load_binary has_header label weight group_id query_column query_id ignore_feature blacklist cat_feature categorical_column cat_column is_predict_raw_score predict_rawscore raw_score is_predict_leaf_index leaf_index is_predict_contrib contrib convert_model_file num_classes unbalance unbalanced_sets metrics metric_types output_freq training_metric is_training_metric train_metric ndcg_eval_at ndcg_at map_eval_at map_at num_machine local_port port machine_list_file machine_list mlist workers nodes Parameter %s should be of type double, got "%s" nan inf infinity Unknown token %s in data file - + Parameter %s should be "true"/"+" or "false"/"-", got "%s" noname Check failed: num_data > 0 at %s, line %d .
- /Users/vsts/agent/2.144.2/work/1/s/src/io/dataset.cpp There are no meaningful features, as all feature values are constant. Check failed: static_cast<size_t>(num_total_features_) == io_config.monotone_constraints.size() at %s, line %d .
+ machine_list_filename machines gpu_platform_id gpu_device_id gpu_use_dp [data: [valid: [num_iterations: [learning_rate: [num_leaves: [num_threads: [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: [early_stopping_round: [first_metric_only: [max_delta_step: [lambda_l1: [lambda_l2: [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: [feature_contri: [forcedsplits_filename: [forcedbins_filename: [refit_decay_rate: [cegb_tradeoff: [cegb_penalty_split: [cegb_penalty_feature_lazy: [cegb_penalty_feature_coupled: [verbosity: [max_bin: [max_bin_by_feature: [min_data_in_bin: [bin_construct_sample_cnt: [histogram_pool_size: [data_random_seed: [output_model: [snapshot_freq: [input_model: [output_result: [initscore_filename: [valid_data_initscores: [pre_partition: [enable_bundle: [max_conflict_rate: [is_enable_sparse: [sparse_threshold: [use_missing: [zero_as_missing: [two_round: [save_binary: [header: [label_column: [weight_column: [group_column: [ignore_column: [categorical_feature: [predict_raw_score: [predict_leaf_index: [predict_contrib: [num_iteration_predict: [pred_early_stop: [pred_early_stop_freq: [pred_early_stop_margin: [convert_model_language: [convert_model: [num_class: [is_unbalance: [scale_pos_weight: [sigmoid: [boost_from_average: [reg_sqrt: [alpha: [fair_c: [poisson_max_delta_step: [tweedie_variance_power: [max_position: [lambdamart_norm: [label_gain: [metric_freq: [is_provide_training_metric: [eval_at: [multi_error_top_k: [num_machines: [local_listen_port: [time_out: [machine_list_filename: [machines: [gpu_platform_id: [gpu_device_id: [gpu_use_dp: config_file task_type objective_type app application boosting_type boost train_data train_data_file data_filename valid_data valid_data_file test_data test_data_file valid_filenames num_iteration n_iter num_tree num_trees num_round num_rounds num_boost_round n_estimators shrinkage_rate eta num_leaf max_leaves max_leaf tree_type tree_learner_type num_thread nthread nthreads n_jobs device random_seed random_state min_data_per_leaf min_data min_child_samples min_sum_hessian_per_leaf min_sum_hessian min_hessian min_child_weight sub_row subsample bagging pos_sub_row pos_subsample pos_bagging neg_sub_row neg_subsample neg_bagging subsample_freq bagging_fraction_seed sub_feature colsample_bytree sub_feature_bynode colsample_bynode early_stopping_rounds early_stopping n_iter_no_change max_tree_output max_leaf_output reg_alpha reg_lambda lambda min_split_gain rate_drop topk mc monotone_constraint feature_contrib fc fp feature_penalty fs forced_splits_filename forced_splits_file forced_splits verbose subsample_for_bin hist_pool_size data_seed model_output model_out save_period model_input model_in predict_result prediction_result predict_name prediction_name pred_name name_pred init_score_filename init_score_file input_init_score valid_data_init_scores valid_init_score_file valid_init_score is_pre_partition is_enable_bundle bundle is_sparse enable_sparse sparse two_round_loading use_two_round_loading is_save_binary is_save_binary_file has_header label weight group_id query_column query_id ignore_feature blacklist cat_feature categorical_column cat_column is_predict_raw_score predict_rawscore raw_score is_predict_leaf_index leaf_index is_predict_contrib contrib convert_model_file num_classes unbalance unbalanced_sets metrics metric_types output_freq training_metric is_training_metric train_metric ndcg_eval_at ndcg_at map_eval_at map_at num_machine local_port port machine_list_file machine_list mlist workers nodes Parameter %s should be of type double, got "%s" nan inf infinity Unknown token %s in data file - + Parameter %s should be "true"/"+" or "false"/"-", got "%s" noname Check failed: num_data > 0 at %s, line %d .
+ /Users/vsts/agent/2.155.1/work/1/s/src/io/dataset.cpp There are no meaningful features, as all feature values are constant. Check failed: static_cast<size_t>(num_total_features_) == io_config.monotone_constraints.size() at %s, line %d .
Check failed: static_cast<size_t>(num_total_features_) == io_config.feature_contri.size() at %s, line %d .
- Cannot change max_bin after constructed Dataset handle. Cannot change bin_construct_sample_cnt after constructed Dataset handle. Cannot change min_data_in_bin after constructed Dataset handle. Cannot change use_missing after constructed Dataset handle. Cannot change zero_as_missing after constructed Dataset handle. Cannot change sparse_threshold after constructed Dataset handle. Check failed: num_used_indices == num_data_ at %s, line %d .
- target weights Bianry file %s already exists .bin File %s exists, cannot save binary to it Cannot write binary data to %s Saving data to binary file %s ______LightGBM_Binary_File_Token______
- Check failed: static_cast<int>(bin_mappers.size()) == num_feature at %s, line %d .
- /Users/vsts/agent/2.144.2/work/1/s/include/LightGBM/feature_group.h name: Using column %s as label Could not find label column %s in data file
+ Check failed: static_cast<size_t>(num_total_features_) == io_config.max_bin_by_feature.size() at %s, line %d .
+ Check failed: *(std::min_element(io_config.max_bin_by_feature.begin(), io_config.max_bin_by_feature.end())) > 1 at %s, line %d .
+ Cannot change max_bin after constructed Dataset handle. Cannot change max_bin_by_feature after constructed Dataset handle. Cannot change bin_construct_sample_cnt after constructed Dataset handle. Cannot change min_data_in_bin after constructed Dataset handle. Cannot change use_missing after constructed Dataset handle. Cannot change zero_as_missing after constructed Dataset handle. Cannot change sparse_threshold after constructed Dataset handle. Cannot change forced bins after constructed Dataset handle. Check failed: num_used_indices == num_data_ at %s, line %d .
+ target weights Bianry file %s already exists .bin File %s exists, cannot save binary to it Cannot write binary data to %s Saving data to binary file %s wt num_features: %d
+ num_total_features: %d
+ num_groups: %d
+ num_data: %d
+ feature_names: %s,
+monotone_constraints: %d,
+feature_penalty: %lf,
+max_bin_by_feature:
+forced_bins:
+feature %d: NA, Cannot add features from other Dataset with a different number of rows ______LightGBM_Binary_File_Token______
+ Check failed: static_cast<int>(bin_mappers->size()) == num_feature at %s, line %d .
+ /Users/vsts/agent/2.155.1/work/1/s/include/LightGBM/feature_group.h name: Using column %s as label Could not find label column %s in data file
or data file doesn't contain header label_column is not a number,
if you want to use a column name,
please add the prefix "name:" to the column name Using column number %d as label Could not find ignore column %s in data file ignore_column is not a number,
if you want to use a column name,
please add the prefix "name:" to the column name Using column %s as weight Could not find weight column %s in data file weight_column is not a number,
@@ -3386,651 +3236,404 @@
if you want to use a column name,
please add the prefix "name:" to the column name Using column number %d as group/query id Could not find categorical_feature %s in data file categorical_feature is not a number,
if you want to use a column name,
please add the prefix "name:" to the column name Using a query id without pre-partitioning the data file is not supported for parallel training.
Please use an additional query file or pre-partition the data Could not recognize data format of %s Could not read binary data from %s Binary file error: token has the wrong size Input file is not LightGBM binary file Binary file error: header has the wrong size Binary file error: header is incorrect Check failed: static_cast<size_t>(dataset->num_total_features_) == config_.monotone_constraints.size() at %s, line %d .
- /Users/vsts/agent/2.144.2/work/1/s/src/io/dataset_loader.cpp Check failed: static_cast<size_t>(dataset->num_total_features_) == config_.feature_contri.size() at %s, line %d .
+ /Users/vsts/agent/2.155.1/work/1/s/src/io/dataset_loader.cpp Check failed: static_cast<size_t>(dataset->num_total_features_) == config_.feature_contri.size() at %s, line %d .
+ Check 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 size Binary file error: meta data is incorrect Current query exceeds the range of the query file,
-please ensure the query file is correct Binary file error: feature %d has the wrong size Binary file error: feature %d is incorrect, read count: %d Column_ Data file %s is empty Size of feature name error, should be %d, got %d Features in dataset should be ordered by group Check failed: label_idx_ >= 0 && label_idx_ <= dataset->num_total_features_ at %s, line %d .
+please ensure the query file is correct Binary file error: feature %d has the wrong size Binary file error: feature %d is incorrect, read count: %d Column_ Check failed: static_cast<size_t>(num_col) == config_.max_bin_by_feature.size() at %s, line %d .
+ The output cannot be monotone with respect to categorical features Check failed: buffer_size >= 0 at %s, line %d .
+ Data file %s is empty Size of feature name error, should be %d, got %d Features in dataset should be ordered by group Check failed: label_idx_ >= 0 && label_idx_ <= dataset->num_total_features_ at %s, line %d .
Check failed: weight_idx_ < 0 || weight_idx_ < dataset->num_total_features_ at %s, line %d .
Check failed: group_idx_ < 0 || group_idx_ < dataset->num_total_features_ at %s, line %d .
- Cannot open data file %s Query id exceeds the range of the query file, please ensure the query file is correct HDFS support is not enabled rb hdfs:// Unexpected trailing Expected JSON object, got Bad type for in Exceeded maximum nesting depth Expected '"' in object, got Expected ':' in object, got Expected ',' in object, got Expected ',' in list, got Expected value, got Unexpected end of input Leading 0s not permitted in numbers Invalid in number At least one digit required in fractional part At least one digit required in exponent expect /Users/vsts/agent/2.144.2/work/1/s/src/io/json11.cpp i != 0 Parse error: expected , got Unexpected end of input in string Unescaped in string Bad \u escape: Invalid escape character Unexpected end of input after start of comment Unexpected end of input inside multi-line comment Malformed comment '%c' (%d) (%d) %.17g %d \\ \" \b \f \n \r \t \u%04x \u2028 \u2029 [ : Using weights in data file, ignoring the additional weights file Using query id in data file, ignoring the additional query file Data partition error, data didn't match queries Weights size doesn't match data size Query size doesn't match data size Initial score size doesn't match data size Cannot used query_id for parallel training label cannot be nullptr Length of label is not same with #data Length of weights is not same with #data Sum of query counts is not same with #data .weight Loading weights... .init Loading initial scores... Invalid initial score file. Redundant or insufficient columns .query Loading query boundaries... Loading query weights... Data file %s doesn't exist Data file %s couldn't be read Data file %s should have at least one line Data file %s only has one line Unknown format of training data Data file %s doesn't contain a label column Label should be the first column in a LibSVM file Input format error when parsing as LibSVM Input format error when parsing as TSV Input format error when parsing as CSV num_leaves= num_cat= split_feature= split_gain= threshold= decision_type= left_child= right_child= leaf_value= leaf_count= internal_value= internal_count= cat_boundaries= cat_threshold= shrinkage= "num_leaves": "num_cat": "shrinkage": "tree_structure":{ "leaf_value": "tree_structure": "split_index": "split_feature": "split_gain": "threshold":" || "decision_type":"==", "threshold": "decision_type":"<=", "default_left":true, "default_left":false, "missing_type":"None", "missing_type":"Zero", "missing_type":"NaN", "internal_value": "internal_count": "left_child": "right_child": "leaf_index": "leaf_count": if (fval <= ) { || Tree::IsZero(fval) || std::isnan(fval)) { && !Tree::IsZero(fval) && !std::isnan(fval)) { if (std::isnan(fval)) { int_fval = -1; } else { int_fval = static_cast<int>(fval); } if (std::isnan(fval)) { int_fval = 0; } else { int_fval = static_cast<int>(fval); } if (int_fval >= 0 && int_fval < 32 * ( ) && (((cat_threshold[ + int_fval / 32] >> (int_fval & 31)) & 1))) { double PredictTree (const double* arr) { return ; const std::vector<uint32_t> cat_threshold = { }; double fval = 0.0f; int int_fval = 0; } (const std::unordered_map<int, double>& arr) { fval = arr[ ]; } else { fval = arr.count( ) > 0 ? arr.at( ) : 0.0f; Tree model should contain num_leaves field num_cat Tree model should contain num_cat field leaf_value Tree model string format error, should contain leaf_value field left_child Tree model string format error, should contain left_child field right_child Tree model string format error, should contain right_child field split_feature Tree model string format error, should contain split_feature field threshold Tree model string format error, should contain threshold field split_gain internal_count internal_value leaf_count decision_type cat_boundaries Tree model should contain cat_boundaries field. cat_threshold Tree model should contain cat_threshold field shrinkage %g Check failed: strs.size() == static_cast<size_t>(n) at %s, line %d .
- /Users/vsts/agent/2.144.2/work/1/s/include/LightGBM/utils/common.h Check failed: eval_at->at(i) > 0 at %s, line %d .
- /Users/vsts/agent/2.144.2/work/1/s/src/metric/dcg_calculator.cpp label should be int type (met %f) for ranking task,
-for the gain of label, please set the label_gain parameter label (%d) excel the max range %d regression_l2 l2 mean_squared_error mse l2_root root_mean_squared_error rmse l1 mean_absolute_error mae huber fair poisson binary_logloss binary_error auc ndcg lambdarank map mean_average_precision xentropy cross_entropy xentlambda cross_entropy_lambda kldiv kullback_leibler mean_absolute_percentage_error gamma gamma_deviance tweedie ndcg@ The NDCG metric requires query information map@ For MAP metric, there should be query information Total groups: %d, total data: %d label_ Can't be NULL at %s, line %d .
- /Users/vsts/agent/2.144.2/work/1/s/src/metric/xentropy_metric.hpp [%s:%s]: (metric) labels passed interval [0, 1] check Init [%s:%s]: (metric) weights not allowed to be negative [%s:%s]: sum-of-weights = %f is non-positive [%s:%s]: sum-of-weights = %f [%s:%s]: (metric) all weights must be positive [%s:%s]: (metric) at least one weight is negative %s offset term = %f Check failed: label > 0 at %s, line %d .
- /Users/vsts/agent/2.144.2/work/1/s/src/metric/regression_metric.hpp gamma-deviance Machine list file doesn't contain the local machine Finished linking network in %f seconds Machine list file %s doesn't exist machine_list size is larger than the parameter num_machines, ignoring redundant entries Cannot find any ip and port.
+ Cannot open data file %s Could 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. bin_upper_bound Query id exceeds the range of the query file, please ensure the query file is correct HDFS support is not enabled rb hdfs:// Unexpected trailing Expected JSON object, got Bad type for in Exceeded maximum nesting depth Expected '"' in object, got Expected ':' in object, got Expected ',' in object, got Expected ',' in list, got Expected value, got Unexpected end of input Leading 0s not permitted in numbers Invalid in number At least one digit required in fractional part At least one digit required in exponent expect /Users/vsts/agent/2.155.1/work/1/s/src/io/json11.cpp i != 0 Parse error: expected , got Unexpected end of input in string Unescaped in string Bad \u escape: Invalid escape character Unexpected end of input after start of comment Unexpected end of input inside multi-line comment Malformed comment '%c' (%d) (%d) %.17g %d \\ \" \b \f \n \r \t \u%04x \u2028 \u2029 [ : Using weights in data file, ignoring the additional weights file Using query id in data file, ignoring the additional query file Data partition error, data didn't match queries Weights size doesn't match data size Query size doesn't match data size Initial score size doesn't match data size Cannot used query_id for parallel training label cannot be nullptr Length of label is not same with #data Length of weights is not same with #data Sum of query counts is not same with #data .weight Loading weights... .init Loading initial scores... Invalid initial score file. Redundant or insufficient columns .query Loading query boundaries... Loading query weights... Data file %s doesn't exist Data file %s couldn't be read Data file %s should have at least one line Data file %s only has one line Unknown format of training data Data file %s doesn't contain a label column Label should be the first column in a LibSVM file Input format error when parsing as LibSVM Input format error when parsing as TSV Input format error when parsing as CSV num_leaves= num_cat= split_feature= split_gain= threshold= decision_type= left_child= right_child= leaf_value= leaf_weight= leaf_count= internal_value= internal_weight= internal_count= cat_boundaries= cat_threshold= shrinkage= "num_leaves": "num_cat": "shrinkage": "tree_structure":{ "leaf_value": "tree_structure": "split_index": "split_feature": "split_gain": "threshold":" || "decision_type":"==", "threshold": "decision_type":"<=", "default_left":true, "default_left":false, "missing_type":"None", "missing_type":"Zero", "missing_type":"NaN", "internal_value": "internal_weight": "internal_count": "left_child": "right_child": "leaf_index": "leaf_weight": "leaf_count": if (fval <= ) { || Tree::IsZero(fval) || std::isnan(fval)) { && !Tree::IsZero(fval) && !std::isnan(fval)) { if (std::isnan(fval)) { int_fval = -1; } else { int_fval = static_cast<int>(fval); } if (std::isnan(fval)) { int_fval = 0; } else { int_fval = static_cast<int>(fval); } if (int_fval >= 0 && int_fval < 32 * ( ) && (((cat_threshold[ + int_fval / 32] >> (int_fval & 31)) & 1))) { double PredictTree (const double* arr) { return ; const std::vector<uint32_t> cat_threshold = { }; double fval = 0.0f; int int_fval = 0; } (const std::unordered_map<int, double>& arr) { fval = arr[ ]; } else { fval = arr.count( ) > 0 ? arr.at( ) : 0.0f; Tree model should contain num_leaves field num_cat Tree model should contain num_cat field leaf_value Tree model string format error, should contain leaf_value field shrinkage left_child Tree model string format error, should contain left_child field right_child Tree model string format error, should contain right_child field split_feature Tree model string format error, should contain split_feature field threshold Tree model string format error, should contain threshold field split_gain internal_count internal_value internal_weight leaf_weight leaf_count decision_type cat_boundaries Tree model should contain cat_boundaries field. cat_threshold Tree model should contain cat_threshold field %g Check failed: strs.size() == static_cast<size_t>(n) at %s, line %d .
+ /Users/vsts/agent/2.155.1/work/1/s/include/LightGBM/utils/common.h Check failed: eval_at->at(i) > 0 at %s, line %d .
+ /Users/vsts/agent/2.155.1/work/1/s/src/metric/dcg_calculator.cpp label should be int type (met %f) for ranking task,
+for the gain of label, please set the label_gain parameter label (%d) excel the max range %d huber fair poisson binary_error auc gamma gamma_deviance tweedie ndcg@ The NDCG metric requires query information map@ For MAP metric, there should be query information Total groups: %d, total data: %d multi_error@ label_ Can't be NULL at %s, line %d .
+ /Users/vsts/agent/2.155.1/work/1/s/src/metric/xentropy_metric.hpp [%s:%s]: (metric) labels passed interval [0, 1] check Init [%s:%s]: (metric) weights not allowed to be negative [%s:%s]: sum-of-weights = %f is non-positive [%s:%s]: sum-of-weights = %f [%s:%s]: (metric) all weights must be positive [%s:%s]: (metric) at least one weight is negative %s offset term = %f Check failed: label > 0 at %s, line %d .
+ /Users/vsts/agent/2.155.1/work/1/s/src/metric/regression_metric.hpp Machine list file doesn't contain the local machine Finished linking network in %f seconds Machine list file %s doesn't exist machine_list size is larger than the parameter num_machines, ignoring redundant entries Cannot find any ip and port.
Please check machine_list_filename or machines parameter World size is larger than the machine_list size, change world size to %d Trying to bind port %d... Binding port %d succeeded Binding port %d failed Listening... Connecting to rank %d failed, waiting for %d milliseconds Connected to rank %d Socket construction error Set SO_RCVBUF failed, please increase your net.core.rmem_max to 100k at least Set SO_SNDBUF failed, please increase your net.core.wmem_max to 100k at least Set TCP_NODELAY failed 0.0.0.0 Socket accept error, code: %d Socket recv error, code: %d Socket send error, code: %d Local rank: %d, total number of machines: %d Please initilize the network interface first Unknown objective type name: %s sqrt Check failed: threshold >= weighted_cdf[pos - 1] at %s, line %d .
- /Users/vsts/agent/2.144.2/work/1/s/src/objective/regression_objective.hpp Check failed: threshold < weighted_cdf[pos] at %s, line %d .
+ /Users/vsts/agent/2.155.1/work/1/s/src/objective/regression_objective.hpp Check failed: threshold < weighted_cdf[pos] at %s, line %d .
Check failed: alpha_ > 0 && alpha_ < 1 at %s, line %d .
Cannot use sqrt transform in %s Regression, will auto disable it [%s]: at least one target label is negative [%s]: sum of labels is zero Sigmoid parameter %f should be greater than zero Cannot set is_unbalance and scale_pos_weight at the same time Starting from the 2.1.2 version, default value for the "boost_from_average" parameter in "binary" objective is true.
This may cause significantly different results comparing to the previous versions of LightGBM.
-Try to set boost_from_average=false, if your old models produce bad results Contains only one class Number of positive: %d, number of negative: %d [%s:%s]: pavg=%f -> initscore=%f BoostFromScore sigmoid: Sigmoid param %f should be greater than zero Lambdarank tasks require query information Label must be in [0, %d), but found %d in label num_class: /Users/vsts/agent/2.144.2/work/1/s/src/objective/xentropy_objective.hpp [%s:%s]: (objective) labels passed interval [0, 1] check [%s]: at least one weight is negative [%s]: sum of weights is zero [%s:%s]: pavg = %f -> initscore = %f [%s]: at least one weight is non-positive [%s:%s]: min, max weights = %f, %f; ratio = %f [%s:%s]: havg = %f -> initscore = %f Met 'abs(label) < 1', will convert them to '1' in MAPE objective and metric sqrt Objective should contain num_class field GPU Tree Learner was not enabled in this build.
+Try to set boost_from_average=false, if your old models produce bad results Contains only one class Number of positive: %d, number of negative: %d [%s:%s]: pavg=%f -> initscore=%f BoostFromScore sigmoid: Sigmoid param %f should be greater than zero Lambdarank tasks require query information Label must be in [0, %d), but found %d in label num_class: /Users/vsts/agent/2.155.1/work/1/s/src/objective/xentropy_objective.hpp [%s:%s]: (objective) labels passed interval [0, 1] check [%s]: at least one weight is negative [%s]: sum of weights is zero [%s:%s]: pavg = %f -> initscore = %f [%s]: at least one weight is non-positive [%s:%s]: min, max weights = %f, %f; ratio = %f [%s:%s]: havg = %f -> initscore = %f Met 'abs(label) < 1', will convert them to '1' in MAPE objective and metric sqrt Objective should contain num_class field GPU Tree Learner was not enabled in this build.
Please recompile with CMake option -DUSE_GPU=1 Check failed: tree->num_leaves() <= data_partition_->num_leaves() at %s, line %d .
- /Users/vsts/agent/2.144.2/work/1/s/src/treelearner/serial_tree_learner.h Number of data: %d, number of used features: %d Check failed: num_features_ == train_data_->num_features() at %s, line %d .
- /Users/vsts/agent/2.144.2/work/1/s/src/treelearner/serial_tree_learner.cpp No further splits with positive gain, best gain: %f Trained a tree with leaves = %d and max_depth = %d Check failed: data_partition_->num_leaves() >= tree->num_leaves() at %s, line %d .
+ /Users/vsts/agent/2.155.1/work/1/s/src/treelearner/serial_tree_learner.h Number of data: %d, number of used features: %d Check failed: num_features_ == train_data_->num_features() at %s, line %d .
+ /Users/vsts/agent/2.155.1/work/1/s/src/treelearner/serial_tree_learner.cpp No further splits with positive gain, best gain: %f Trained a tree with leaves = %d and max_depth = %d Check failed: data_partition_->num_leaves() >= tree->num_leaves() at %s, line %d .
Check failed: inner_feature_index >= 0 at %s, line %d .
left right Check failed: bag_cnt == num_data_ at %s, line %d .
Check failed: num_machines > 1 at %s, line %d .
Total Bins %d Check failed: offset == num_total_bin at %s, line %d .
- /Users/vsts/agent/2.144.2/work/1/s/src/treelearner/feature_histogram.hpp Check failed: cache_size_ >= 2 at %s, line %d .
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SaveModelToFileEiiPKc /PredictRawByMapERKNSt3__113unordered_mapIidNS1_4hashIiEENS1_8equal_toIiEENS1_9allocatorINS1_4pairIKidEEEEEEPdPKNS_27PredictionEarlyStopInstanceE / 3CreatePredictionEarlyStopInstanceERKNSt3__112basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEERKNS_25PredictionEarlyStopConfigE 1Bin c hE 1tE 7jE < C 1D 24PushEiij 26ReSizeEi 21 6 2Ei 11Ei 1 2Ev 21Ev 20Ev 2 DenseBinI 2Metadata1 ˞ hE 2tE 8jE = 1 35SplitEjjjNS_11MissingTypeEbjPiiS3_S3_ 58num_dataEv 6 1 38ConstructHistogramE 36 5 GetIteratorEjjj 3SizesInByteEv 7 PKiiPKf 3iPKf 4 S5_PNS_17HistogramBinEntryE 4PNS_17HistogramBinEntryE 4 S3_PNS_17HistogramBinEntryE 4PNS_17HistogramBinEntryE 5 S 5CreateOrderedBinEv 6 plitCategoricalEjjjPKjiPiiS4_S4_ 5aveBinaryToFileEPKNS_17VirtualFileWriterE 7 0 64LoadFromMemoryEPKvRKNSt3__16vectorIiNS4_9allocatorIiEEEE 7 FinishLoadEv 6CopySubsetEPKNS_3BinEPKii 7Ќ C 7D 74PushEiij 86ReSizeEi 81 ; 2Ei 71Ei 7 2Ev 71Ev 70Ev 7 1 85SplitEjjjNS_11MissingTypeEbjPiiS3_S3_ :8num_dataEv ; 1 88ConstructHistogramE 96 : GetIteratorEjjj 9SizesInByteEv < PKiiPKf 9iPKf 9 S5_PNS_17HistogramBinEntryE 9PNS_17HistogramBinEntryE : S3_PNS_17HistogramBinEntryE :PNS_17HistogramBinEntryE : S :CreateOrderedBinEv ; plitCategoricalEjjjPKjiPiiS4_S4_ ;aveBinaryToFileEPKNS_17VirtualFileWriterE <Х Ч 0 ;4LoadFromMemoryEPKvRKNSt3__16vectorIiNS4_9allocatorIiEEEE < FinishLoadEv <CopySubsetEPKNS_3BinEPKii < C <D =4PushEiij =6ReSizeEi =1 @ 2Ei <1Ei = 2Ev =1Ev =0Ev = б 1 =5SplitEjjjNS_11MissingTypeEbjPiiS3_S3_ ?8num_dataEv @ 1 >8ConstructHistogramE >6 ? GetIteratorEjjj >SizesInByteEv A PKiiPKf >iPKf ? S5_PNS_17HistogramBinEntryE ?PNS_17HistogramBinEntryE ? S3_PNS_17HistogramBinEntryE ?PNS_17HistogramBinEntryE ? п S @CreateOrderedBinEv @ plitCategoricalEjjjPKjiPiiS4_S4_ @aveBinaryToFileEPKNS_17VirtualFileWriterE A 0 A4LoadFromMemoryEPKvRKNSt3__16vectorIiNS4_9allocatorIiEEEE A FinishLoadEv ACopySubsetEPKNS_3BinEPKii A SparseBinI BBinMapper ^ hE BtE JjE Q C BD B6ReSizeEi C4PushEiij C1 H 2Ei B1Ei B 2Ev B1Ev B0Ev C SparseBinI CBinMapper b hE CtE KjE R 1 C5SplitEjjjNS_11MissingTypeEbjPiiS3_S3_ F8num_dataEv F 1 D8ConstructHistogramE D6 F GetIteratorEjjj DNextNonzeroEPiS2_ ESizesInByteEv J PKiiPKf DiPKf E S5_PNS_17HistogramBinEntryE EPNS_17HistogramBinEntryE E S3_PNS_17HistogramBinEntryE EPNS_17HistogramBinEntryE E S FCreateOrderedBinEv F plitCategoricalEjjjPKjiPiiS4_S4_ FaveBinaryToFileEPKNS_17VirtualFileWriterE J OrderedSparseBinI GGet iFeatureHistogram2 hE GtE NjE V C G10SparsePairC XD Y4InitEPKci Y5SplitEiiPKcc Z 1EPKNS_9SparseBinIhEE H2EPKNS_9SparseBinIhEE X 0 H2 I4LoadFromMemoryEPKvRKNSt3__16vectorIiNS4_9allocatorIiEEEE J FinishLoadEv ICopySubsetEPKNS_3BinEPKii J LoadFromPairERKNSt3__16vectorINS2_4pairIihEENS2_9allocatorIS5_EEEE JGetFastIndexEv J C JD J6ReSizeEi K4PushEiij K1 O 2Ei J1Ei J Р 2Ev J1Ev K0Ev K 1 K5SplitEjjjNS_11MissingTypeEbjPiiS3_S3_ M8num_dataEv N 1 K8ConstructHistogramE L6 M GetIteratorEjjj LNextNonzeroEPiS2_ MSizesInByteEv Q PKiiPKf LiPKf M S5_PNS_17HistogramBinEntryE MPNS_17HistogramBinEntryE M S3_PNS_17HistogramBinEntryE MPNS_17HistogramBinEntryE M S NCreateOrderedBinEv N plitCategoricalEjjjPKjiPiiS4_S4_ NaveBinaryToFileEPKNS_17VirtualFileWriterE Q C O10SparsePairC ZD [4InitEPKci [5SplitEiiPKcc \ 1EPKNS_9SparseBinItEE O2EPKNS_9SparseBinItEE [ 0 P2 P4LoadFromMemoryEPKvRKNSt3__16vectorIiNS4_9allocatorIiEEEE Q FinishLoadEv PCopySubsetEPKNS_3BinEPKii Q LoadFromPairERKNSt3__16vectorINS2_4pairIitEENS2_9allocatorIS5_EEEE QGetFastIndexEv Q C QD R6ReSizeEi R4PushEiij R1 V 2Ei Q1Ei R 2Ev R1Ev R0Ev R 1 R5SplitEjjjNS_11MissingTypeEbjPiiS3_S3_ T8num_dataEv U 1 S8ConstructHistogramE S6 U GetIteratorEjjj SNextNonzeroEPiS2_ TSizesInByteEv X PKiiPKf SiPKf T S5_PNS_17HistogramBinEntryE TPNS_17HistogramBinEntryE T S3_PNS_17HistogramBinEntryE TPNS_17HistogramBinEntryE T S UCreateOrderedBinEv U plitCategoricalEjjjPKjiPiiS4_S4_ UaveBinaryToFileEPKNS_17VirtualFileWriterE X C V10SparsePairC \D \4InitEPKci ]5SplitEiiPKcc ^ 1EPKNS_9SparseBinIjEE V2EPKNS_9SparseBinIjEE \ 0 W2 W4LoadFromMemoryEPKvRKNSt3__16vectorIiNS4_9allocatorIiEEEE X FinishLoadEv WCopySubsetEPKNS_3BinEPKii X LoadFromPairERKNSt3__16vectorINS2_4pairIijEENS2_9allocatorIS5_EEEE XGetFastIndexEv X 2Ev X1Ev X 2Ev Y1Ev Y0Ev Y ж 6OrderedSparseBinI Y7SerialTreeLearner1 hE1 YtE1 [jE1 ] 8ConstructHistogramEiPKf Z2NonZeroCountEi Z S3_PNS_17HistogramBinEntryE ZPNS_17HistogramBinEntryE Z 2Ev [1Ev [ 2Ev [1Ev [0Ev [ 8ConstructHistogramEiPKf [2NonZeroCountEi \ S3_PNS_17HistogramBinEntryE \PNS_17HistogramBinEntryE \ 2Ev \1Ev \ 2Ev \1Ev \0Ev ] 8ConstructHistogramEiPKf ]2NonZeroCountEi ^ S3_PNS_17HistogramBinEntryE ]PNS_17HistogramBinEntryE ] C ^8CopyFromEPKc _D _7FindBinEPdimiiiNS_7BinTypeEbb b18SizeForSpecificBinEi b 2E ^1E _ v _RKS0_ _PKv _ v _RKS0_ _PKv _ 2Ev _1Ev _ NeedFilterERKNSt3__16vectorIiNS0_9allocatorIiEEEEiiNS_7BinTypeE aFindGroupsERKNSt3__16vectorINS0_10unique_ptrINS_9BinMapperENS0_14default_deleteIS3_EEEENS0_9allocatorIS6_EEEERKNS1_IiNS7_IiEEEEPPiPKimiiib t G aD z reedyFindBinEPKdPKiiimi aet k FindBinWithZeroAsOneBinEPKdPKiiimi bDataParallelTreeLearnerINS_1 6CopyToEPc b1 b 6SaveBinaryToFileEPKNS_17VirtualFileWriterE c1SizesInByteEv c 9CreateBinEiidbdPb c1 c 5CreateSparseBinEii c4CreateDenseBinEii c Config eParser12CreateParserEPKcbii РMetric12CreateMetricERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEERKNS_6ConfigE Ѫ 6KV2MapERNSt3__113unordered_mapINS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEES8_NS1_4hashIS8_EENS1_8equal_toIS8_EENS6_INS1_4pairIKS8_S8_EEEEEEPKc i7Str2MapEPKc i3SetERKNSt3__113unordered_mapINS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEES8_NS1_4hashIS8_EENS1_8equal_toIS8_EENS6_INS1_4pairIKS8_S8_EEEEEE n1 n20GetMembersFromStringERKNSt3__113unordered_mapINS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEES8_NS1_4hashIS8_EENS1_8equal_toIS8_EENS6_INS1_4pairIKS8_S8_EEEEEE o ObjectiveTypeERKNSt3__113unordered_mapINS0_12basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEES7_NS0_4hashIS7_EENS0_8equal_toIS7_EENS5_INS0_4pairIKS7_S7_EEEEEEPS7_ kConfilctCountERKNSt3__16vectorIbNS0_9allocatorIbEEEEPKiii s MetricTypeERKNSt3__113unordered_mapINS0_12basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEES7_NS0_4hashIS7_EENS0_8equal_toIS7_EENS5_INS0_4pairIKS7_S7_EEEEEEPNS0_6vectorIS7_NS5_IS7_EEEE nDeviceTypeERKNSt3__113unordered_mapINS0_12basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEES7_NS0_4hashIS7_EENS0_8equal_toIS7_EENS5_INS0_4pairIKS7_S7_EEEEEEPS7_ n 8CheckParamConflictEv n1alias_tableE 3parameter_setE 8ToStringEv o19SaveMembersToStringEv o Dataset pN rgetlineERNSt3__118basic_stringstreamIcNS0_11char_traitsIcEENS0_9allocatorIcEEEERNS0_12basic_stringIcS3_S5_EEPKNS_17VirtualFileReaderERNS0_6vectorIcS5_EEm ʠLinkers ͬ C rD r9ConstructERNSt3__16vectorINS1_10unique_ptrINS_9BinMapperENS1_14default_deleteIS4_EEEENS1_9allocatorIS7_EEEEPPiPKimRKNS_6ConfigE u1 u21CopyFeatureMapperFromEPKS0_ v6ReSizeEi w 2E r1E r v ri r v ri r 2Ev r1Ev r oGroupERKNSt3__16vectorIiNS0_9allocatorIiEEEE setwork arkUsedERNSt3__16vectorIbNS0_9allocatorIbEEEEPKii setadata FastFeatureBundlingERNSt3__16vectorINS0_10unique_ptrINS_9BinMapperENS0_14default_deleteIS3_EEEENS0_9allocatorIS6_EEEEPPiPKimRKNS1_IiNS7_IiEEEEdiidbb uRecursiveHalvingMap 1 u0 v3 w4 w7binary_file_tokenE ResetConfigEPKc vCreateValidEPKS0_ vSetIntFieldEPKcPKii xGetIntFieldEPKcPiPPKi x FinishLoadEv vCopySubsetEPKS0_PKiib w Ѐ SetFloatFieldEPKcPKfi wGetFloatFieldEPKcPiPPKf x S wGetDoubleFieldEPKcPiPPKd x etDoubleFieldEPKcPKdi xaveBinaryFileEPKc x 9ConstructHistogramsERKNSt3__16vectorIaNS1_9allocatorIaEEEEPKiiiRNS2_INS1_10unique_ptrINS_10OrderedBinENS1_14default_deleteISB_EEEENS3_ISE_EEEEPKfSJ_PfSK_bPNS_17HistogramBinEntryE z2FixHistogramEiddiPNS_17HistogramBinEntryE z atasetLoader zCGCalculator C z9SetHeaderEPKc |D |1 |2 ~3 2ERKNS_6ConfigERKNSt3__18functionIFvRKNS4_6vectorINS4_4pairIidEENS4_9allocatorIS8_EEEEPdEEEiPKc |1ERKNS_6ConfigERKNSt3__18functionIFvRKNS4_6vectorINS4_4pairIidEENS4_9allocatorIS8_EEEEPdEEEiPKc | 2Ev |1Ev | 2 }9CheckCanLoadFromBinEPKc ~5LoadFromBinFileEPKcS2_iiPiPNSt3__16vectorIiNS4_9allocatorIiEEEE ӄ LoadFromFileEPKcS2_ii ~CheckDatasetEPKNS_7DatasetE ل 0LoadTextDataToMemoryEPKcRKNS_8MetadataEiiPiPNSt3__16vectorIiNS7_9allocatorIiEEEE 4SampleTextDataFromMemoryERKNSt3__16vectorINS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEENS6_IS8_EEEE ށ5ExtractFeaturesFromMemoryERNSt3__16vectorINS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEENS6_IS8_EEEEPKNS_6ParserEPNS_7DatasetE 2 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VirtualFile ÆGetLabelIdxFor ObjectiveFunction23CreateObjectiveFunctionERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE HistogramBinEntry10SumReducerEPKcPcii SerialTreeLearner Reader4MakeERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE Writer С" 4MakeERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE È6ExistsERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE Ɉ " " 4Json 9Json 10JsonObjectixERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE 4 1 9 ixE eqERKS0_ ltERKS0_ dumpERNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE typeEv " C 5parseERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEERS7_NS_9JsonParseE 11parse_multiERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEERmRS7_NS_9JsonParseE 2E 1E v ܌Dn d i b ȍRKNSt3__1 ԍONSt3__1 PKc ̔ " v Dn d i b RKNSt3__1 ONSt3__1 PKc Ҕ " " " " " " " " " 12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE 6vectorIS0_NS1_9allocatorIS0_EEEE ؔ3mapINS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEES0_NS1_4lessIS8_EENS6_INS1_4pairIKS8_S0_EEEEEE " 12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE 6vectorIS0_NS1_9allocatorIS0_EEEE ޔ3mapINS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEES0_NS1_4lessIS8_EENS6_INS1_4pairIKS8_S0_EEEEEE " 12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE 6vectorIS0_NS1_9allocatorIS0_EEEE 3mapINS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEES0_NS1_4lessIS8_EENS6_INS1_4pairIKS8_S0_EEEEEE " 12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE Ɣ6vectorIS0_NS1_9allocatorIS0_EEEE 3mapINS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEES0_NS1_4lessIS8_EENS6_INS1_4pairIKS8_S0_EEEEEE " " " " " " " " " " " " 2 0bool_valueEv 1array_itemsEv number_valueEv string_valueEv object_itemsEv " int_valueEv has_shapeERKSt16initializer_listINSt3__14pairINS2_12basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEENS0_4TypeEEEERS9_ " " " " " m RKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE " " Value ArrayixEm 1 9int_valueEv ixE 2 ޘ0bool_valueEv 1array_itemsEv number_valueEv string_valueEv object_itemsEv " " " " " " m RKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE " " " " " " " # Ѝ# C 4InitE 1 D 8Set 2Ev 1Ev # # PKcS2_ iii RKS0_PKii # 9LoadQueryBoundariesEv 1LoadWeightsEv 6 4 2SetInitScoreEPKdi 0SetWeightsEPKfi # $ Load ֜CheckOrPartitionEiRKNSt3__16vectorIiNS1_9allocatorIiEEEE QueryWeightsEv InitialScoreEPKc $ $ 2Ev 1Ev Ж$ $ $ $ PartitionLabelERKNSt3__16vectorIiNS1_9allocatorIiEEEE LoadFromMemoryEPKv Ş й$ $ $ LabelEPKfi QueryEPKii $ $ $ % 6SaveBinaryToFileEPKNS_17VirtualFileWriterE 1SizesInByteEv % % % % TSVERNSt3__112basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEEii CSVERNSt3__112basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEEii Ġ г% % % % C D ȡ5SplitEiiijdddiifNS_11MissingTypeEb 1 2E 1E i PKcPm % i ¡PKcPm & 2Ev ء1Ev ޡ & & Ћ& 6SplitCategoricalEiiiPKjiS2_iddiifNS_11MissingTypeE 0 4UnwoundPathSumEPKNS0_11PathElementEii ɦ7RecomputeMaxDepthEv ۦ & 2 8T 6ToJSONEv 1 0AddPredictionToScoreEPKNS_7DatasetE 3NumericalDecisionIfElseEi ǥ5CategoricalDecisionIfElseEi ͥ iPd PKiiPd & & o ƤreeSHAPEPKdPdiiPNS0_11PathElementEddi Ϧ StringEv IfElseEib ӥ & & 0NodeToJSONEi 2NodeToIfElseEib ٥7NodeToIfElseByMapEib ߥ3ExpectedValueEv զ & З' ' ' ' к' ' г( ExtendPathEPNS0_11PathElementEiddi UnwindPathEPNS0_11PathElementEii æ ( ( ( ( ( ( 1 4InitERKNSt3__16vectorIdNS1_9allocatorIdEEEE 9 ǩ6CalDCGERKNSt3__16vectorIiNS1_9allocatorIiEEEEPKfPKdiPNS2_IdNS3_IdEEEE Ū 3DefaultEvalAtEPNSt3__16vectorIiNS1_9allocatorIiEEEE 6DefaultLabelGainEPNSt3__16vectorIdNS1_9allocatorIdEEEE 2 0CheckLabelEPKfi ˪1label_gain_E ) ) ) CalMaxDCGAtKEiPKfi kMaxPositionE ɑ ) Cal ީdiscount_E MaxDCGERKNSt3__16vectorIiNS1_9allocatorIiEEEEPKfiPNS2_IdNS3_IdEEEE DCGAtKEiPKfPKdi ) ) ) ) * C 9ConstructEii 2E 1E v i 0 v i 0 0 0 0 C ˫9ConstructEii Ǭ 2E ٫1E v iNS_24RecursiveHalvingNodeTypeEb 0 v iNS_24RecursiveHalvingNodeTypeEb 0 0 0 0 C 1 7TryBindEi 9 D 2ENS_6ConfigE 1ENS_6ConfigE Ѯ 0 6ParseMachineListERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEES9_ 2 1CheckLinkerEi 0 1 ConstructEv ˮSetLinkerEiRKNS_9TcpSocketE 1 1 2Ev 1Ev С1 1 1 ListenThreadEi PrintLinkersEv 1 1 1 1 4InitE 7DisposeEv 9All 2 Ƴ1 NS_6ConfigE iiPFvPciiPKiS3_iS1_iRKPFvPKcS1_iiEEPFvS1_iS3_S3_iS1_iE 1 WN8LightGBM7Network ݰS VN I 8linkers_E 5rank_E 1 2 7buffer_E 1 1 3num_machines_E Ա0b ڱ2b 8allgather_ext_fun_E 1 ruck_map_E lock_len_E 1 2recursive_halving_map_E 3reduce_scatter_ext_fun_E 1 lock_start_E uffer_size_E 1 1 1 1 1 1 1 1 reduceEPciiS1_RKPFvPKcS1_iiE gatherEPc 1 0AllreduceByAllGatherEPciiS1_RKPFvPKcS1_iiE 6AllgatherRecursiveDoublingEPcPKiS3_S1_i 9ReduceScatterRecursiveHalvingEPciiPKiS3_S1_iRKPFvPKcS1_iiE ̶ 1 3 µ4AllgatherBruckEPcPKiS3_S1_i ƶ7ReduceScatterRingEPciiPKiS3_S1_iRKPFvPKcS1_iiE Ҷ ReduceScatterEPciiPKiS3_S1_iRKPFvPKcS1_iiE AllgatherRingEPcPKiS3_S1_i 1 PKiS3_S1_i iS1_ 1 1 1 1 1 1 2 2 RKNS_6ConfigE 2 2 4GPUTreeLearnerEE 7SerialTreeLearnerEE Ż C D 4InitEPKNS_7DatasetEb 1 28FindBestSplitsFromHistogramsERKNSt3__16vectorIaNS3_9allocatorIaEEEEb 5SplitEPNS_4TreeEiPiS5_ 2EPKNS_6ConfigE 1EPKNS_6ConfigE : : 2Ev 1Ev 0Ev : з: : : 1 4FindBestSplitsEv ResetConfigEPKNS_6ConfigE BeforeTrainEv : : : : : ; 3DataParallelTreeLearnerINS_1 غ5VotingParallelTreeLearnerINS_1 4GPUTreeLearnerEE24GetGlobalDataCountInLeafEi 7SerialTreeLearnerEE24GetGlobalDataCountInLeafEi ; C ԼD 4InitEPKNS_7DatasetEb 1 28FindBestSplitsFromHistogramsERKNSt3__16vectorIaNS3_9allocatorIaEEEEb 5SplitEPNS_4TreeEiPiS5_ 2EPKNS_6ConfigE 1EPKNS_6ConfigE ; ; 2Ev 1Ev 0Ev ; Ј; ; ; 1 ӽ4FindBestSplitsEv ResetConfigEPKNS_6ConfigE BeforeTrainEv ; ; ; ; ; ; 3DataParallelTreeLearnerINS_1 1SyncUpGlobalBestSplitEPcS0_PNS_9SplitInfoES2_iENUlPKcS0_iiE_8__invokeES4_S0_ii 5VotingParallelTreeLearnerINS_1 4GPUTreeLearnerEE11BeforeTrainEvENUlPKcPciiE_8__invokeES4_S5_ii 7SerialTreeLearnerEE11BeforeTrainEvENUlPKcPciiE_8__invokeES4_S5_ii ; ; ; 4GPUTreeLearnerEE 7SerialTreeLearnerEE C D 4InitEPKNS_7DatasetEb 11BeforeTrainEv 28FindBestSplitsFromHistogramsERKNSt3__16vectorIaNS3_9allocatorIaEEEEb 2EPKNS_6ConfigE 1EPKNS_6ConfigE ; ; 2Ev 1Ev 0Ev ; ; ; ; ; ; C D 4InitEPKNS_7DatasetEb 11BeforeTrainEv 28FindBestSplitsFromHistogramsERKNSt3__16vectorIaNS3_9allocatorIaEEEEb 2EPKNS_6ConfigE 1EPKNS_6ConfigE < < 2Ev 1Ev 0Ev < < І< < < < C D 4InitEPKNS_7DatasetEb 1 5 28FindBestSplitsFromHistogramsERKNSt3__16vectorIaNS1_9allocatorIaEEEEb 2EPKNS_6ConfigE 1EPKNS_6ConfigE < < 2Ev 1Ev 0Ev < Ъ< < < 7 1 9 4FindBestSplitsEv ResetTrainingDataEPKNS_7DatasetE FitByExistingTreeEPKNS_4TreeERKNSt3__16vectorIiNS4_9allocatorIiEEEEPKfSC_ < ResetConfigEPKNS_6ConfigE BeforeTrainEv ForceSplitsEPNS_4TreeERN6json114JsonEPiS6_S6_Pb < TrainEPKfS2_bRN6json114JsonE SplitEPNS_4TreeEiPiS3_ < 7FitByExistingTreeEPKNS_4TreeEPKfS5_ 5RenewTreeOutputEPNS_4TreeEPKNS_17ObjectiveFunctionE < < < BeforeFindBestSplitEPKNS_4TreeEii ConstructHistogramsERKNSt3__16vectorIaNS1_9allocatorIaEEEEb = И= = = = > PKdiPKii diPKii > > 6FindBestThresholdNumericalEddiddPNS_9SplitInfoE 8FindBestThresholdCategoricalEddiddPNS_9SplitInfoE > > dEEvRNSt3__16vectorIT_NS2_9allocatorIS4_EEEEENUlPKcPciiE_8__invokeESA_SB_ii iEEvRNSt3__16vectorIT_NS2_9allocatorIS4_EEEEENUlPKcPciiE_8__invokeESA_SB_ii @ @ @ 4GPUTreeLearnerEE 7SerialTreeLearnerEE C D 4InitEPKNS_7DatasetEb 1 28FindBestSplitsFromHistogramsERKNSt3__16vectorIaNS3_9allocatorIaEEEEb 5SplitEPNS_4TreeEiPiS5_ 2EPKNS_6ConfigE 1EPKNS_6ConfigE @ @ 2Ev 1Ev 0Ev @ @ @ @ 1 9BeforeFindBestSplitEPKNS_4TreeEii 4FindBestSplitsEv 2GlobalVotingEiRKNSt3__16vectorINS_14LightSplitInfoENS3_9allocatorIS5_EEEEPNS4_IiNS6_IiEEEE 8CopyLocalHistogramERKNSt3__16vectorIiNS3_9allocatorIiEEEES9_ ResetConfigEPKNS_6ConfigE BeforeTrainEv A ЊA A A A A A A 4GPUTreeLearnerEE24GetGlobalDataCountInLeafEi 7SerialTreeLearnerEE24GetGlobalDataCountInLeafEi A C D 4InitEPKNS_7DatasetEb 1 28FindBestSplitsFromHistogramsERKNSt3__16vectorIaNS3_9allocatorIaEEEEb 5SplitEPNS_4TreeEiPiS5_ 2EPKNS_6ConfigE 1EPKNS_6ConfigE A B 2Ev 1Ev 0Ev B B ЋB B 1 9BeforeFindBestSplitEPKNS_4TreeEii 4FindBestSplitsEv 2GlobalVotingEiRKNSt3__16vectorINS_14LightSplitInfoENS3_9allocatorIS5_EEEEPNS4_IiNS6_IiEEEE 8CopyLocalHistogramERKNSt3__16vectorIiNS3_9allocatorIiEEEES9_ ResetConfigEPKNS_6ConfigE BeforeTrainEv B B B B B B B C C 4GPUTreeLearnerEE11BeforeTrainEvENUlPKcPciiE_8__invokeES4_S5_ii 7SerialTreeLearnerEE11BeforeTrainEvENUlPKcPciiE_8__invokeES4_S5_ii C C N ZN8LightGBM St3__1 8LightGBM 6json11 1 2 6__bindIMN8LightGBM16FeatureHistogramEFvddiddPNS1_9SplitInfoEEJPS2_RNS_12placeholders4__phILi1EEERNS9_ILi2EEERNS9_ILi3EEERNS9_ILi4EEERNS9_ILi5EEERNS9_ILi6EEEEEE 2basic_stringIcNS_11char_traitsIcEENS_9allocatorIcEEEE 0__function6__ 8 5basic_stringbufIcNS_11char_traitsIcEENS_9allocatorIcEEEE 7bad_function_callE 9basic_ostringstreamIcNS_11char_traitsIcEENS_9allocatorIcEEEE 4basic_ 3basic_filebufIcNS_11char_traitsIcEEEE ɂH 1__basic_string_commonILb1EEE 0__shared_ptr_emplaceIN6json11 H funcI baseIF ZN8LightGBM NS_6__bindIMN8LightGBM16FeatureHistogramEFvddiddPNS3_9SplitInfoEEJPS4_RNS_12placeholders4__phILi1EEERNSB_ILi2EEERNSB_ILi3EEERNSB_ILi4EEERNSB_ILi5EEERNSB_ILi6EEEEEENS_9allocatorISO_EEFvddiddS6_EEE 9Predictor 1 C1EPNS2_8BoostingEibbbbidEUlRKNS_6vectorINS_4pairIidEENS_9allocatorIS8_EEEEPdE 7PredictEPKcS5_bEUl _NS9_ISF_EEFvSD_SE_EEE 0_NS9_ISF_EEFvSD_SE_EEE 1_NS9_ISF_EEFvSD_SE_EEE 2_NS9_ISF_EEFvSD_SE_EEE H v mPKcmEEE b NS_ RKNS_6vectorINS_4pairIidEENS_9allocatorIS4_EEEEPdEEE PKcPNS_6vectorINS_4pairIidEENS_9allocatorIS6_EEEEEEE i mPKcmEEE ddiddPN8LightGBM9SplitInfoEEEE H 9Predictor 1 C1EPNS_8BoostingEibbbbidEUlRKNSt3__16vectorINS3_4pairIidEENS3_9allocatorIS6_EEEEPdE 7PredictEPKcS2_bEUl _ 0_ 1_ 2_ H H H H H H H basic_stringstreamIcNS_11char_traitsIcEENS_9allocatorIcEEEE __weak_result_typeIMN8LightGBM16FeatureHistogramEFvddiddPNS1_9SplitInfoEEEE H H S5_PNS_6vectorINS_4pairIidEENS_9allocatorIS8_EEEEE_NS9_ISD_EEFvS5_SC_EEE iRKNS_6vectorINS_12basic_stringIcNS_11char_traitsIcEENS_9allocatorIcEEEENSA_ISC_EEEEE_NSA_ISH_EEFviSG_EEE H H S2_PNSt3__16vectorINS3_4pairIidEENS3_9allocatorIS6_EEEEE_ iRKNSt3__16vectorINS3_12basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEENS8_ISA_EEEEE_ H H RKNS_6vectorINS_12basic_stringIcNS_11char_traitsIcEENS_9allocatorIcEEEENS6_IS8_EEEEEEE PKcmEEE iiEEE ÑH H H 0TextReaderI 3 iE mE1 35ReadAllAndProcessParallelWithFilterERKNS_8functionIFviRKNS_6vectorINS_12basic_stringIcNS_11char_traitsIcEENS_9allocatorIcEEEENSA_ISC_EEEEEEERKNS5_IFbiiEEEEUlPKcmE_NSA_ISR_EEFmSQ_mEEE 2 1 9CountLineEvEUliPKcmE_NS_9allocatorIS7_EEFviS6_mEEE H H 0TextReaderI 3 ٛ iE mE1 35ReadAllAndProcessParallelWithFilterERKNSt3__18functionIFviRKNS2_6vectorINS2_12basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEENS8_ISA_EEEEEEERKNS3_IFbiiEEEEUlPKcmE_ 2 1 9CountLineEvEUliPKcmE_ H 5ReadAllAndProcessParallelERKNS_8functionIFviRKNS_6vectorINS_12basic_stringIcNS_11char_traitsIcEENS_9allocatorIcEEEENSA_ISC_EEEEEEEEUliiE_NSA_ISL_EEFbiiEEE 3SampleAndFilterFromFileERKNS_8functionIFbiEEEPNS_6vectorIiNS_9allocatorIiEEEERNS2_6RandomEiPNSA_INS_12basic_stringIcNS_11char_traitsIcEENSB_IcEEEENSB_ISL_EEEEEUliPKcmE_NSB_ISR_EEFviSQ_mEEE lj6ReadPartAndProcessParallelERKNS_6vectorIiNS_9allocatorIiEEEERKNS_8functionIFviRKNS5_INS_12basic_stringIcNS_11char_traitsIcEENS6_IcEEEENS6_ISG_EEEEEEEEUliiE_NS6_ISP_EEFbiiEEE ӉH i PKdiEEE ՂfEEE ӛ iEEE EEE H 5ReadAllAndProcessParallelERKNSt3__18functionIFviRKNS2_6vectorINS2_12basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEENS8_ISA_EEEEEEEEUliiE_ 3SampleAndFilterFromFileERKNSt3__18functionIFbiEEEPNS2_6vectorIiNS2_9allocatorIiEEEERNS_6RandomEiPNS8_INS2_12basic_stringIcNS2_11char_traitsIcEENS9_IcEEEENS9_ISJ_EEEEEUliPKcmE_ ͉6ReadPartAndProcessParallelERKNSt3__16vectorIiNS2_9allocatorIiEEEERKNS2_8functionIFviRKNS3_INS2_12basic_stringIcNS2_11char_traitsIcEENS4_IcEEEENS4_ISE_EEEEEEEEUliiE_ ىH 8 4 2 ۀ3BinE ۂ9 1 6 BoostingE GBDTBaseE DenseBinI L ϑH H H 6vectorI 4pairIidEEiEEE NS_4pairIidEENS_9allocatorIS4_EEEEiEEE dNS_9allocatorIdEEEEiEEE H H I 7ReadAllAndProcessERKNS_8functionIFvmPKcmEEEEUlS7_mE_NS_9allocatorISC_EEFmS7_mEEE 2ReadAllLinesEvEUlmPKcmE_NS_9allocatorIS7_EEFvmS6_mEEE ЈI 7ReadAllAndProcessERKNSt3__18functionIFvmPKcmEEEEUlS5_mE_ 2ReadAllLinesEvEUlmPKcmE_ I I I I 7ReadAllAndProcessERKNS_8functionIFviPKcmEEEEUlS7_mE_NS_9allocatorISC_EEFmS7_mEEE 2ReadAllLinesEvEUliPKcmE_NS_9allocatorIS7_EEFviS6_mEEE 8ReadAndFilterLinesERKNS_8functionIFbiEEEPNS_6vectorIiNS_9allocatorIiEEEEEUliPKcmE_NSB_ISH_EEFviSG_mEEE 4SampleFromFileERNS2_6RandomEiPNS_6vectorINS_12basic_stringIcNS_11char_traitsIcEENS_9allocatorIcEEEENSB_ISD_EEEEEUliPKcmE_NSB_ISJ_EEFviSI_mEEE I 7ReadAllAndProcessERKNSt3__18functionIFviPKcmEEEEUlS5_mE_ 2ReadAllLinesEvEUliPKcmE_ 8ReadAndFilterLinesERKNSt3__18functionIFbiEEEPNS2_6vectorIiNS2_9allocatorIiEEEEEUliPKcmE_ 4SampleFromFileERNS_6RandomEiPNSt3__16vectorINS4_12basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEENS9_ISB_EEEEEUliPKcmE_ I I I I DARTE G ÀI OSSE ՀBDTE I RFE 1 ߆5 4CrossEntropyLambdaMetricE ٘2RegressionQuantilelossE 3DataParallelTreeLearnerINS_1 6FeatureParallelTreeLearnerINS_1 ŝI I ifstreamIcNS_11char_traitsIcEEEE ÂofstreamIcNS_11char_traitsIcEEEE ςI I I I I hEE tEE jEE I I I SparseBinI LocalFileE ߉TSVParserE CSVParserE AUCMetricE MapMetricE hEE tEE jEE I I I
-6 ̈́0 3 1 7 2 ڐ4 ͓5HuberLossMetricE Ӕ9 8 OrderedSparseBinI DenseBinIteratorI LJRegression Multi hEE tEE jEE КI OrderedBinE RMSEMetricE NDCGMetricE MAPEMetricE I I ЛI Dense4bitsBinE نPoissonMetricE TweedieMetricE BinaryLoglossE MulticlassOVAE ҜI Dense4bitsBinIteratorE Regression I BinIteratorE GammaMetricE TreeLearnerE МI hEE އtEE jEE I I НI S ȈVirtualFile BinaryErrorMetricE ObjectiveFunctionE MulticlassSoftmaxE ̜ parseBinIteratorI erialTreeLearnerE hEE tEE jEE I I I I I I I I I I I I I I I ReaderE WriterE I I 9Json Ί5ValueILNS_4Json4TypeE 1 8JsonNullE 7JsonIntE ValueE ArrayE I I 4ENSt3__16vectorIS1_NS3_9allocatorIS1_EEEEEE 5ENSt3__13mapINS3_12basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEES1_NS3_4lessISA_EENS8_INS3_4pairIKSA_S1_EEEEEEEE ۍ0ENS_10NullStructEEE ݎ2EbEE 1E 3ENSt3__112basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEEEE ȐI 0Json 1JsonBooleanE ObjectE ՍDoubleE StringE лI I 8JsonNullENS_9allocatorIS2_EEEE ю1 7JsonIntENS_9allocatorIS2_EEEE 9JsonArrayENS_9allocatorIS2_EEEE ΐI I I 1JsonBooleanENS_9allocatorIS2_EEEE 0Json I I I DoubleENS_9allocatorIS2_EEEE StringENS_9allocatorIS2_EEEE ObjectENS_9allocatorIS2_EEEE ԐI I dEE iEE I I I I I I I I I LibSVMParserE BinaryMetricINS_1 CrossEntropyE I ParserE MetricE I I I I I 2MetricE 1MetricE I MetricINS_ L 8L 1 2MetricEEE 1MetricEEE ǓI I I 0 4 5HuberLossMetricEEE ٔ3 1GammaMetricEEE 9GammaDevianceMetricEEE RMSEMetricEEE MAPEMetricEEE I I I QuantileMetricE FairLossMetricE ߔLambdarankNDCGE ƜGPUTreeLearnerE I QuantileMetricEEE ͔FairLossMetricEEE I I I I I I PoissonMetricEEE TweedieMetricEEE I BinaryLoglossMetricE GammaDevianceMetricE Regression ÙI 9BinaryLoglossMetricEEE 7BinaryErrorMetricEEE I I I I I I MultiSoftmaxLoglossMetricE KullbackLeiblerDivergenceE ߘVotingParallelTreeLearnerINS_1 I classMetricINS_ ̗ErrorMetricE 25MultiSoftmaxLoglossMetricEEE 16MultiErrorMetricEEE I I I CrossEntropy Regression MetricE ӘLambdaE I I I I I I I I I I I 2lossE 1lossE I I I I HuberLossE GammaLossE I FairLossE MAPELOSSE I PoissonLossE TweedieLossE I I BinaryLoglossC1ERKNS2_6ConfigENS_8functionIFbfEEEEUlfE_NS_9allocatorISA_EES8_EE ͛MulticlassOVAC1ERKNS2_6ConfigEEUlfE_NS_9allocatorIS7_EEFbfEEE I I BinaryLoglossC1ERKNS_6ConfigENSt3__18functionIFbfEEEEUlfE_ MulticlassOVAC1ERKNS_6ConfigEEUlfE_ ޜI I I I I I I I I I I 4GPUTreeLearnerEEE 7SerialTreeLearnerEEE I I I 4GPUTreeLearnerEEE 7SerialTreeLearnerEEE I I I I I I I I 4GPUTreeLearnerEEE מ7SerialTreeLearnerEEE ݞI I 8LightGBM 6json11 4GBDTE 1 8DenseBinI 9SparseBinI 2 L N ̟ZN8LightGBM 8LightGBM 6json11 St3__1 4 1 8 2 3BinE 9 6 G DARTE BDTE OSSE L 9Json ݠ10JsonObjectE ValueE ArrayE ڡ L 9Json 1 5ValueILNS_4Json4TypeE 8JsonNullE 7JsonIntE ValueE ԡArrayE L L L L 0Json 1JsonBooleanE ObjectE DoubleE StringE L 7SerialTreeLearnerE 6OrderedSparseBinI M
-7 0 6 3 1 2 4 5HuberLossMetricE 9 8 S VirtualFile BinaryErrorMetricE ObjectiveFunctionE MulticlassSoftmaxE erialTreeLearnerE parseBinIteratorI M 2 1 6__bindIMN8LightGBM16FeatureHistogramEFvddiddPNS1_9SplitInfoEEJPS2_RNS_12placeholders4__phILi1EEERNS9_ILi2EEERNS9_ILi3EEERNS9_ILi4EEERNS9_ILi5EEERNS9_ILi6EEEEEE 1__basic_string_commonILb1EEE 0__shared_ptr_emplaceIN6json11 M 2basic_stringIcNS_11char_traitsIcEENS_9allocatorIcEEEE ̨0__function6__ Ҩ8 į5basic_stringbufIcNS_11char_traitsIcEENS_9allocatorIcEEEE ڰ7bad_function_callE ɴ9basic_ostringstreamIcNS_11char_traitsIcEENS_9allocatorIcEEEE 4basic_ 3basic_filebufIcNS_11char_traitsIcEEEE M baseIF funcI v b NS_ mPKcmEEE RKNS_6vectorINS_4pairIidEENS_9allocatorIS4_EEEEPdEEE PKcPNS_6vectorINS_4pairIidEENS_9allocatorIS6_EEEEEEE i ͳmPKcmEEE ddiddPN8LightGBM9SplitInfoEEEE M ZN8LightGBM 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UpdateScoreEPKNS_4TreeEi (ResetConfigEPKNS_6ConfigE - 8LightGBM (6json11 4 )8 89 J1 a6Config y7Dataset1 2 GBDT )Tree 1 *9 -7PredictEPKdPdPKNS_27PredictionEarlyStopInstanceE 021PredictLeafIndexByMapERKNSt3__113unordered_mapIidNS1_4hashIiEENS1_8equal_toIiEENS1_9allocatorINS1_4pairIKidEEEEEEPd 1 3 ,4PredictContribEPKdPdPKNS_27PredictionEarlyStopInstanceE -7 .5 /0PredictRawEPKdPdPKNS_27PredictionEarlyStopInstanceE 02PredictByMapERKNSt3__113unordered_mapIidNS1_4hashIiEENS1_8equal_toIiEENS1_9allocatorINS1_4pairIKidEEEEEEPdPKNS_27PredictionEarlyStopInstanceE 06PredictLeafIndexEPKdPd 0 EvalOneMetricEPKNS_6MetricEPKd -ModelToIfElseEi . GetEvalAtEi -DumpModelEii . Х dEET_S2_ENUlPKcPciiE_8__invokeES4_S5_ii .iEET_S2_ENUlPKcPciiE_8__invokeES4_S5_ii SaveModelTo /FeatureImportanceEii 0 IfElseEiPKc /StringEii / SaveModelToFileEiiPKc 0PredictRawByMapERKNSt3__113unordered_mapIidNS1_4hashIiEENS1_8equal_toIiEENS1_9allocatorINS1_4pairIKidEEEEEEPdPKNS_27PredictionEarlyStopInstanceE 0 CreateNoneERKNS_25PredictionEarlyStopConfigE 3NeedFilterERKNSt3__16vectorIiNS0_9allocatorIiEEEEiiNS_7BinTypeE hFindGroupsERKNSt3__16vectorINS0_10unique_ptrINS_9BinMapperENS0_14default_deleteIS3_EEEENS0_9allocatorIS6_EEEERKNS1_IiNS7_IiEEEEPPiPKimiiib kHdfsProtoE CreateMulticlassERKNS_25PredictionEarlyStopConfigE 4OrderedSparseBinI NParseMetricAliasERKNSt3__112basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEE tGet tFeatureHistogram2 CreateBinaryERKNS_25PredictionEarlyStopConfigE 6ParseMetricsERKNSt3__112basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEEPNS0_6vectorIS6_NS4_IS6_EEEE tGetStatisticEPKcPiS2_S2_ 3CreatePredictionEarlyStopInstanceERKNSt3__112basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEERKNS_25PredictionEarlyStopConfigE 7Bin l hE 7tE =jE C C 7D 84PushEiij 86ReSizeEi 81 <5CloneEv = 2E 81E 8 i 8RKS1_ = i 8RKS1_ = 2Ev 81Ev 80Ev 8К DenseBinI 8Metadata1 ߪ hE 9tE >jE D 1 95SplitEjjjNS_11MissingTypeEbjPiiS3_S3_ ;8num_dataEv < 1 98ConstructHistogramE :6 ; GetIteratorEjjj :SizesInByteEv =М PKiiPKf :iPKf : S5_PNS_17HistogramBinEntryE :PNS_17HistogramBinEntryE ; S3_PNS_17HistogramBinEntryE ;PNS_17HistogramBinEntryE ; S ;CreateOrderedBinEv < plitCategoricalEjjjPKjiPiiS4_S4_ <aveBinaryToFileEPKNS_17VirtualFileWriterE = 0 <4LoadFromMemoryEPKvRKNSt3__16vectorIiNS4_9allocatorIiEEEE = FinishLoadEv =CopySubsetEPKNS_3BinEPKii =Ю C >D >4PushEiij >6ReSizeEi >1 B5CloneEv C 2E >1E > i >RKS1_ Cж i >RKS1_ C 2Ev >1Ev >0Ev >й л 1 ?5SplitEjjjNS_11MissingTypeEbjPiiS3_S3_ A8num_dataEv B 1 ?8ConstructHistogramE ?6 A GetIteratorEjjj ?SizesInByteEv Cм PKiiPKf @iPKf @ S5_PNS_17HistogramBinEntryE @PNS_17HistogramBinEntryE A S3_PNS_17HistogramBinEntryE APNS_17HistogramBinEntryE A S ACreateOrderedBinEv B plitCategoricalEjjjPKjiPiiS4_S4_ BaveBinaryToFileEPKNS_17VirtualFileWriterE C 0 B4LoadFromMemoryEPKvRKNSt3__16vectorIiNS4_9allocatorIiEEEE C FinishLoadEv CCopySubsetEPKNS_3BinEPKii C C CD D4PushEiij D6ReSizeEi D1 H5CloneEv I 2E C1E D i DRKS1_ I i DRKS1_ I 2Ev D1Ev D0Ev D 1 E5SplitEjjjNS_11MissingTypeEbjPiiS3_S3_ F8num_dataEv G 1 E8ConstructHistogramE E6 G GetIteratorEjjj ESizesInByteEv I PKiiPKf EiPKf F S5_PNS_17HistogramBinEntryE FPNS_17HistogramBinEntryE F S3_PNS_17HistogramBinEntryE FPNS_17HistogramBinEntryE F S GCreateOrderedBinEv G plitCategoricalEjjjPKjiPiiS4_S4_ GaveBinaryToFileEPKNS_17VirtualFileWriterE I 0 H4LoadFromMemoryEPKvRKNSt3__16vectorIiNS4_9allocatorIiEEEE H FinishLoadEv HCopySubsetEPKNS_3BinEPKii H SparseBinI IBinMapper f hE ItE QjE Y C ID J6ReSizeEi J4PushEiij J1 O5CloneEv Q 2E J1E J i JRKS1_ Q i JRKS1_ Q 2Ev J1Ev J0Ev J SparseBinI KBinMapper k hE KtE RjE Z 1 K5SplitEjjjNS_11MissingTypeEbjPiiS3_S3_ M8num_dataEv N 1 K8ConstructHistogramE L6 M GetIteratorEjjj LNextNonzeroEPiS2_ MSizesInByteEv Q PKiiPKf LiPKf M S5_PNS_17HistogramBinEntryE LPNS_17HistogramBinEntryE M S3_PNS_17HistogramBinEntryE MPNS_17HistogramBinEntryE M S MCreateOrderedBinEv N plitCategoricalEjjjPKjiPiiS4_S4_ NaveBinaryToFileEPKNS_17VirtualFileWriterE Q hE NtE VjE ^ C O10SparsePairC aD a4InitEPKci a5SplitEiiPKcc c 1EPKNS_9SparseBinIhEE O2EPKNS_9SparseBinIhEE aЊ 0 P2 P4LoadFromMemoryEPKvRKNSt3__16vectorIiNS4_9allocatorIiEEEE Q FinishLoadEv PCopySubsetEPKNS_3BinEPKii Q LoadFromPairERKNSt3__16vectorINS2_4pairIihEENS2_9allocatorIS5_EEEE QGetFastIndexEv Q C RD R6ReSizeEi R4PushEiij R1 W5CloneEv Y 2E R1E R i RRKS1_ Y i RRKS1_ Y 2Ev R1Ev R0Ev R 1 S5SplitEjjjNS_11MissingTypeEbjPiiS3_S3_ U8num_dataEv V 1 S8ConstructHistogramE T6 U GetIteratorEjjj TNextNonzeroEPiS2_ USizesInByteEv Y PKiiPKf TiPKf T S5_PNS_17HistogramBinEntryE TPNS_17HistogramBinEntryE U S3_PNS_17HistogramBinEntryE UPNS_17HistogramBinEntryE U S UCreateOrderedBinEv V plitCategoricalEjjjPKjiPiiS4_S4_ VaveBinaryToFileEPKNS_17VirtualFileWriterE Y C V10SparsePairC cD c4InitEPKci c5SplitEiiPKcc d 1EPKNS_9SparseBinItEE W2EPKNS_9SparseBinItEE c 0 W2 X4LoadFromMemoryEPKvRKNSt3__16vectorIiNS4_9allocatorIiEEEE Y FinishLoadEv XCopySubsetEPKNS_3BinEPKii Y LoadFromPairERKNSt3__16vectorINS2_4pairIitEENS2_9allocatorIS5_EEEE XGetFastIndexEv Y C YD Z6ReSizeEi Z4PushEiij Z1 _5CloneEv ` 2E Y1E Z i YRKS1_ aО i ZRKS1_ ` 2Ev Z1Ev Z0Ev Z 1 [5SplitEjjjNS_11MissingTypeEbjPiiS3_S3_ ]8num_dataEv ^ 1 [8ConstructHistogramE [6 ] GetIteratorEjjj [NextNonzeroEPiS2_ ]SizesInByteEv ` PKiiPKf [iPKf \ S5_PNS_17HistogramBinEntryE \PNS_17HistogramBinEntryE \ S3_PNS_17HistogramBinEntryE \PNS_17HistogramBinEntryE ] S ]CreateOrderedBinEv ^ plitCategoricalEjjjPKjiPiiS4_S4_ ]aveBinaryToFileEPKNS_17VirtualFileWriterE ` C ^10SparsePairC dD e4InitEPKci e5SplitEiiPKcc f 1EPKNS_9SparseBinIjEE ^2EPKNS_9SparseBinIjEE d 0 _2 _4LoadFromMemoryEPKvRKNSt3__16vectorIiNS4_9allocatorIiEEEE ` FinishLoadEv _CopySubsetEPKNS_3BinEPKii ` LoadFromPairERKNSt3__16vectorINS2_4pairIijEENS2_9allocatorIS5_EEEE `GetFastIndexEv ` 2Ev a1Ev a 2Ev a1Ev a0Ev a 6OrderedSparseBinI b7SerialTreeLearner1 hE1 btE1 cjE1 e 8ConstructHistogramEiPKf b2NonZeroCountEi c S3_PNS_17HistogramBinEntryE bPNS_17HistogramBinEntryE c 2Ev c1Ev cЄ 2Ev c1Ev c0Ev c 8ConstructHistogramEiPKf d2NonZeroCountEi d S3_PNS_17HistogramBinEntryE dPNS_17HistogramBinEntryE dВ К 2Ev d1Ev d 2Ev e1Ev e0Ev e 8ConstructHistogramEiPKf e2NonZeroCountEi f S3_PNS_17HistogramBinEntryE fPNS_17HistogramBinEntryE fШ C g8CopyFromEPKc gD h7FindBinEPdimiiiNS_7BinTypeEbbRKNSt3__16vectorIdNS3_9allocatorIdEEEE k18SizeForSpecificBinEi k 2E g1E g v gRKS0_ gPKv g v gRKS0_ gPKv h б 2Ev h1Ev h G hD kModelVersionE reedyFindBinEPKdPKiiimi het v FindBinWithPredefinedBinEPKdPKiiimiRKNSt3__16vectorIdNS4_9allocatorIdEEEE jCheckMultiClassObjectiveERKNSt3__112basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEE y FindBinWithZeroAsOneBinEPKdPKiiimi jDataParallelTreeLearnerINS_1 RKNSt3__16vectorIdNS4_9allocatorIdEEEE k 6CopyToEPc k1 k 6SaveBinaryToFileEPKNS_17VirtualFileWriterE l1SizesInByteEv l 9CreateBinEiidbdPb l1 l 5CreateSparseBinEii l4CreateDenseBinEii l Config mParser12CreateParserEPKcbii Metric12CreateMetricERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEERKNS_6ConfigE 6KV2MapEPNSt3__113unordered_mapINS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEES8_NS1_4hashIS8_EENS1_8equal_toIS8_EENS6_INS1_4pairIKS8_S8_EEEEEEPKc r7Str2MapEPKc r3SetERKNSt3__113unordered_mapINS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEES8_NS1_4hashIS8_EENS1_8equal_toIS8_EENS6_INS1_4pairIKS8_S8_EEEEEE y1 y20GetMembersFromStringERKNSt3__113unordered_mapINS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEES8_NS1_4hashIS8_EENS1_8equal_toIS8_EENS6_INS1_4pairIKS8_S8_EEEEEE z ParseObjectiveAliasERKNSt3__112basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEE tFastFeatureBundlingERKNSt3__16vectorINS0_10unique_ptrINS_9BinMapperENS0_14default_deleteIS3_EEEENS0_9allocatorIS6_EEEEPPiPKimRKNS1_IiNS7_IiEEEEdiidbb RecursiveHalvingMap ͷ ObjectiveTypeERKNSt3__113unordered_mapINS0_12basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEES7_NS0_4hashIS7_EENS0_8equal_toIS7_EENS5_INS0_4pairIKS7_S7_EEEEEEPS7_ vConfilctCountERKNSt3__16vectorIbNS0_9allocatorIbEEEEPKiii ~ MetricTypeERKNSt3__113unordered_mapINS0_12basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEES7_NS0_4hashIS7_EENS0_8equal_toIS7_EENS5_INS0_4pairIKS7_S7_EEEEEEPNS0_6vectorIS7_NS5_IS7_EEEE yDeviceTypeERKNSt3__113unordered_mapINS0_12basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEES7_NS0_4hashIS7_EENS0_8equal_toIS7_EENS5_INS0_4pairIKS7_S7_EEEEEEPS7_ y 8CheckParamConflictEv y1alias_tableE 3parameter_setE 8ToStringEv z19SaveMembersToStringEv z Ш Dataset {N ~GetLineEPNSt3__118basic_stringstreamIcNS0_11char_traitsIcEENS0_9allocatorIcEEEEPNS0_12basic_stringIcS3_S5_EEPKNS_17VirtualFileReaderEPNS0_6vectorIcS5_EEm Linkers C }D ~9ConstructEPNSt3__16vectorINS1_10unique_ptrINS_9BinMapperENS1_14default_deleteIS4_EEEENS1_9allocatorIS7_EEEERKNS2_INS2_IdNS8_IdEEEENS8_ISD_EEEEPPiPKimRKNS_6ConfigE 1 21CopyFeatureMapperFromEPKS0_ 6ReSizeEi 2E }1E } v }i } v }i } 2Ev ~1Ev ~ oGroupERKNSt3__16vectorIiNS0_9allocatorIiEEEE ~etwork ѻ arkUsedEPNSt3__16vectorIbNS0_9allocatorIbEEEEPKii etadata Ѧ Ђ е 1 0 3 4 2 5addFeaturesFromEPS0_ 7binary_file_tokenE ResetConfigEPKc CreateValidEPKS0_ SetIntFieldEPKcPKii ȂGetIntFieldEPKcPiPPKi ڂ FinishLoadEv CopySubsetEPKS0_PKiib Ѕ SetFloatFieldEPKcPKfi GetFloatFieldEPKcPiPPKf Ъ S GetDoubleFieldEPKcPiPPKd Ԃ etDoubleFieldEPKcPKdi aveBinaryFileEPKc GetInt8FieldEPKcPiPPKa DumpTextFileEPKc 9ConstructHistogramsERKNSt3__16vectorIaNS1_9allocatorIaEEEEPKiiiPNS2_INS1_10unique_ptrINS_10OrderedBinENS1_14default_deleteISB_EEEENS3_ISE_EEEEPKfSJ_PfSK_bPNS_17HistogramBinEntryE 2FixHistogramEiddiPNS_17HistogramBinEntryE Х! Ч! atasetLoader CGCalculator C 9SetHeaderEPKc D ć1 2 3 2ERKNS_6ConfigERKNSt3__18functionIFvRKNS4_6vectorINS4_4pairIidEENS4_9allocatorIS8_EEEEPdEEEiPKc 1ERKNS_6ConfigERKNSt3__18functionIFvRKNS4_6vectorINS4_4pairIidEENS4_9allocatorIS8_EEEEPdEEEiPKc " " # 2Ev ԇ1Ev ڇ # # 2 ډ9CheckCanLoadFromBinEPKc 5LoadFromBinFileEPKcS2_iiPiPNSt3__16vectorIiNS4_9allocatorIiEEEE 3GetForcedBinsENSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEiRKNS1_13unordered_setIiNS1_4hashIiEENS1_8equal_toIiEENS5_IiEEEE LoadFromFileEPKcS2_ii CheckDatasetEPKNS_7DatasetE # # 0LoadTextDataToMemoryEPKcRKNS_8MetadataEiiPiPNSt3__16vectorIiNS7_9allocatorIiEEEE 4SampleTextDataFromMemoryERKNSt3__16vectorINS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEENS6_IS8_EEEE 5ExtractFeaturesFromMemoryEPNSt3__16vectorINS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEENS6_IS8_EEEEPKNS_6ParserEPNS_7DatasetE Џ2 ֏3ExtractFeaturesFromFileEPKcPKNS_6ParserERKNSt3__16vectorIiNS6_9allocatorIiEEEEPNS_7DatasetE ې # # 1ConstructBinMappersFromTextDataEiiRKNSt3__16vectorINS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEENS6_IS8_EEEEPKNS_6ParserEPNS_7DatasetE ʏ3LoadFromFileAlignWithOtherDatasetEPKcS2_PKNS_7DatasetE # $ SampleTextDataFromFileEPKcRKNS_8MetadataEiiPiPNSt3__16vectorIiNS7_9allocatorIiEEEE ՐCostructFromSampleDataEPPdPPiiPKimi $ $ $ $ $ $ % & VirtualFile גGetLabelIdxFor ObjectiveFunction23CreateObjectiveFunctionERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE HistogramBinEntry10SumReducerEPKcPcii SerialTreeLearner Reader4MakeERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE Writer о& 4MakeERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE ה6ExistsERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE ݔ & & 4Json ̕9Json 10JsonObjectixERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE 4 1 9 ixE eqERKS0_ ltERKS0_ dumpERNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE ҖtypeEv & C 5parseERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEERS7_NS_9JsonParseE 11parse_multiERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEERmRS7_NS_9JsonParseE Ŧ 2E 1E v Dn d ęi Йb ܙRKNSt3__1 ONSt3__1 PKc & v Dn d ʙi ֙b RKNSt3__1 ƛONSt3__1 PKc & & & & & & & & & 12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE 6vectorIS0_NS1_9allocatorIS0_EEEE 3mapINS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEES0_NS1_4lessIS8_EENS6_INS1_4pairIKS8_S0_EEEEEE & 12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE 6vectorIS0_NS1_9allocatorIS0_EEEE 3mapINS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEES0_NS1_4lessIS8_EENS6_INS1_4pairIKS8_S0_EEEEEE & 12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE 6vectorIS0_NS1_9allocatorIS0_EEEE 3mapINS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEES0_NS1_4lessIS8_EENS6_INS1_4pairIKS8_S0_EEEEEE & 12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE ڠ6vectorIS0_NS1_9allocatorIS0_EEEE 3mapINS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEES0_NS1_4lessIS8_EENS6_INS1_4pairIKS8_S0_EEEEEE & & & & & & & & & & & & 2 ̡0bool_valueEv 1array_itemsEv number_valueEv string_valueEv object_itemsEv & int_valueEv has_shapeERKSt16initializer_listINSt3__14pairINS2_12basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEENS0_4TypeEEEERS9_ ˦ & & & & & m RKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE & & Value ArrayixEm 1 Ȥ9int_valueEv ixE Υ 2 0bool_valueEv 1array_itemsEv ¥ number_valueEv string_valueEv object_itemsEv ȥ & & & & & & m RKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE & & & & & & & ' ' C 4InitE 1 D 8Set 2Ev 1Ev ( ( PKcS2_ iii RKS0_PKii ( 9LoadQueryBoundariesEv 1LoadWeightsEv 6 4 Ʃ2SetInitScoreEPKdi 0SetWeightsEPKfi ͪ ( ( Load CheckOrPartitionEiRKNSt3__16vectorIiNS1_9allocatorIiEEEE QueryWeightsEv InitialScoreEPKc ( ( 2Ev 1Ev ( ( ( ( PartitionLabelERKNSt3__16vectorIiNS1_9allocatorIiEEEE LoadFromMemoryEPKv ٪ ( ( ( LabelEPKfi ǪQueryEPKii Ӫ ( ) ) ) 6SaveBinaryToFileEPKNS_17VirtualFileWriterE 1SizesInByteEv ) ) ) ) TSVERKNSt3__112basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEEii ԬCSVERKNSt3__112basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEEii ڬ ) ) ) ) C D 5SplitEiiijdddiiddfNS_11MissingTypeEb 1 2E 1E ʭ i ĭPKcPm * i ڭPKcPm * 2Ev 1Ev * * Ф* 6SplitCategoricalEiiiPKjiS2_iddiiddfNS_11MissingTypeE 0 4UnwoundPathSumEPKNS0_11PathElementEii 7RecomputeMaxDepthEv * 2 8T 6ToJSONEv 1 0AddPredictionToScoreEPKNS_7DatasetE 3NumericalDecisionIfElseEi 5CategoricalDecisionIfElseEi iPd PKiiPd * * o reeSHAPEPKdPdiiPNS0_11PathElementEddi StringEv IfElseEib * + 0NodeToJSONEi ۱2NodeToIfElseEib 7NodeToIfElseByMapEib 3ExpectedValueEv + + + + + + + , ExtendPathEPNS0_11PathElementEiddi ײUnwindPathEPNS0_11PathElementEii ݲ , , , , , , 1 4InitERKNSt3__16vectorIdNS1_9allocatorIdEEEE 9 6CalDCGERKNSt3__16vectorIiNS1_9allocatorIiEEEEPKfPKdiPNS2_IdNS3_IdEEEE ߶ 3DefaultEvalAtEPNSt3__16vectorIiNS1_9allocatorIiEEEE 6DefaultLabelGainEPNSt3__16vectorIdNS1_9allocatorIdEEEE 2 0CheckLabelEPKfi 1label_gain_E - - л- CalMaxDCGAtKEiPKfi ۵kMaxPositionE - Cal discount_E MaxDCGERKNSt3__16vectorIiNS1_9allocatorIiEEEEPKfiPNS2_IdNS3_IdEEEE ӶDCGAtKEiPKfPKdi ٶ - - - - . C 9ConstructEii Ƿ 2E 1E v i 4 v i 4 4 4 4 C 9ConstructEii 2E 1E v iNS_24RecursiveHalvingNodeTypeEb ո 4 v ϸiNS_24RecursiveHalvingNodeTypeEb ۸ 4 4 4 4 C 1 7TryBindEi 9 D 2ENS_6ConfigE 1ENS_6ConfigE 4 6ParseMachineListERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEES9_ 2 1CheckLinkerEi Ż 4 5 ConstructEv SetLinkerEiRKNS_9TcpSocketE 5 5 2Ev 1Ev 5 5 5 ListenThreadEi PrintLinkersEv г5 5 5 5 4InitE 7DisposeEv 9All 2 1 NS_6ConfigE ȼiiPFvPciiPKiS3_iS1_iRKPFvPKcS1_iiEEPFvS1_iS3_S3_iS1_iE 5 WN8LightGBM7Network S VN I 8linkers_E 5rank_E 1 2 7buffer_E 5 5 3num_machines_E 0b 2b پ8allgather_ext_fun_E 5 ruck_map_E lock_len_E 5 2recursive_halving_map_E Ӿ3reduce_scatter_ext_fun_E 5 lock_start_E uffer_size_E 5 5 5 5 5 5 5 5 reduceEPciiS1_RKPFvPKcS1_iiE ڿgatherEPc 5 0AllreduceByAllGatherEPciiS1_RKPFvPKcS1_iiE 6AllgatherRecursiveDoublingEPcPKiS3_S1_i 9ReduceScatterRecursiveHalvingEPciiPKiS3_S1_iRKPFvPKcS1_iiE 5 3 4AllgatherBruckEPcPKiS3_S1_i 7ReduceScatterRingEPciiPKiS3_S1_iRKPFvPKcS1_iiE ReduceScatterEPciiPKiS3_S1_iRKPFvPKcS1_iiE AllgatherRingEPcPKiS3_S1_i 5 PKiS3_S1_i iS1_ 5 5 5 5 5 5 6 6 RKNS_6ConfigE Ў6 6 : 4GPUTreeLearnerEE 7SerialTreeLearnerEE C D 4InitEPKNS_7DatasetEb 1 28FindBestSplitsFromHistogramsERKNSt3__16vectorIaNS3_9allocatorIaEEEEb 5SplitEPNS_4TreeEiPiS5_ 2EPKNS_6ConfigE 1EPKNS_6ConfigE Ы> > 2Ev 1Ev 0Ev > > > > 1 4FindBestSplitsEv ResetConfigEPKNS_6ConfigE BeforeTrainEv > > > > > ? 3DataParallelTreeLearnerINS_1 5VotingParallelTreeLearnerINS_1 4GPUTreeLearnerEE24GetGlobalDataCountInLeafEi 7SerialTreeLearnerEE24GetGlobalDataCountInLeafEi ? C D 4InitEPKNS_7DatasetEb 1 28FindBestSplitsFromHistogramsERKNSt3__16vectorIaNS3_9allocatorIaEEEEb 5SplitEPNS_4TreeEiPiS5_ 2EPKNS_6ConfigE 1EPKNS_6ConfigE ? ? 2Ev 1Ev 0Ev ? ? ? ? 1 4FindBestSplitsEv ResetConfigEPKNS_6ConfigE BeforeTrainEv ? ? ? ? ? ? 3DataParallelTreeLearnerINS_1 1SyncUpGlobalBestSplitEPcS0_PNS_9SplitInfoES2_iENUlPKcS0_iiE_8__invokeES4_S0_ii 5VotingParallelTreeLearnerINS_1 4GPUTreeLearnerEE11BeforeTrainEvENUlPKcPciiE_8__invokeES4_S5_ii 7SerialTreeLearnerEE11BeforeTrainEvENUlPKcPciiE_8__invokeES4_S5_ii ? ? ? 4GPUTreeLearnerEE 7SerialTreeLearnerEE C D 4InitEPKNS_7DatasetEb 11BeforeTrainEv 28FindBestSplitsFromHistogramsERKNSt3__16vectorIaNS3_9allocatorIaEEEEb 2EPKNS_6ConfigE 1EPKNS_6ConfigE ? ? 2Ev 1Ev 0Ev ? ? ? ? ? @ C D 4InitEPKNS_7DatasetEb 11BeforeTrainEv 28FindBestSplitsFromHistogramsERKNSt3__16vectorIaNS3_9allocatorIaEEEEb 2EPKNS_6ConfigE 1EPKNS_6ConfigE @ @ 2Ev 1Ev 0Ev @ @ @ @ @ @ C D 4InitEPKNS_7DatasetEb 1 5 28FindBestSplitsFromHistogramsERKNSt3__16vectorIaNS1_9allocatorIaEEEEb 2EPKNS_6ConfigE 1EPKNS_6ConfigE @ Ы@ 2Ev 1Ev 0Ev Я@ @ @ @ 7 1 5GetUsedFeaturesEb 9 4FindBestSplitsEv ResetTrainingDataEPKNS_7DatasetE FitByExistingTreeEPKNS_4TreeERKNSt3__16vectorIiNS4_9allocatorIiEEEEPKfSC_ @ ResetConfigEPKNS_6ConfigE BeforeTrainEv ForceSplitsEPNS_4TreeERKN6json114JsonEPiS7_S7_Pb @ TrainEPKfS2_bRKN6json114JsonE SplitEPNS_4TreeEiPiS3_ @ 7FitByExistingTreeEPKNS_4TreeEPKfS5_ 5RenewTreeOutputEPNS_4TreeEPKNS_17ObjectiveFunctionENSt3__18functionIFdPKfiEEEiPKii @ @ @ A BeforeFindBestSplitEPKNS_4TreeEii ConstructHistogramsERKNSt3__16vectorIaNS1_9allocatorIaEEEEb A A A A A B B 6FindBestThresholdNumericalEddiddPNS_9SplitInfoE 8FindBestThresholdCategoricalEddiddPNS_9SplitInfoE C ФC dEENSt3__16vectorIT_NS2_9allocatorIS4_EEEEPS7_ENUlPKcPciiE_8__invokeESA_SB_ii iEENSt3__16vectorIT_NS2_9allocatorIS4_EEEEPS7_ENUlPKcPciiE_8__invokeESA_SB_ii D D D 4GPUTreeLearnerEE 7SerialTreeLearnerEE C D 4InitEPKNS_7DatasetEb 1 28FindBestSplitsFromHistogramsERKNSt3__16vectorIaNS3_9allocatorIaEEEEb 5SplitEPNS_4TreeEiPiS5_ 2EPKNS_6ConfigE 1EPKNS_6ConfigE E E 2Ev 1Ev 0Ev E E E E 1 9BeforeFindBestSplitEPKNS_4TreeEii 4FindBestSplitsEv 2GlobalVotingEiRKNSt3__16vectorINS_14LightSplitInfoENS3_9allocatorIS5_EEEEPNS4_IiNS6_IiEEEE 8CopyLocalHistogramERKNSt3__16vectorIiNS3_9allocatorIiEEEES9_ ResetConfigEPKNS_6ConfigE BeforeTrainEv E E E E E F F F 4GPUTreeLearnerEE24GetGlobalDataCountInLeafEi 7SerialTreeLearnerEE24GetGlobalDataCountInLeafEi F C D 4InitEPKNS_7DatasetEb 1 28FindBestSplitsFromHistogramsERKNSt3__16vectorIaNS3_9allocatorIaEEEEb 5SplitEPNS_4TreeEiPiS5_ 2EPKNS_6ConfigE 1EPKNS_6ConfigE F F 2Ev 1Ev 0Ev F F F F 1 9BeforeFindBestSplitEPKNS_4TreeEii 4FindBestSplitsEv 2GlobalVotingEiRKNSt3__16vectorINS_14LightSplitInfoENS3_9allocatorIS5_EEEEPNS4_IiNS6_IiEEEE 8CopyLocalHistogramERKNSt3__16vectorIiNS3_9allocatorIiEEEES9_ ResetConfigEPKNS_6ConfigE BeforeTrainEv F F F F G G G G G 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NS_4pairIidEENS_9allocatorIS4_EEEEiEEE ܅dNS_9allocatorIdEEEEiEEE N N N 7ReadAllAndProcessERKNS_8functionIFvmPKcmEEEEUlS7_mE_NS_9allocatorISC_EEFmS7_mEEE 2ReadAllLinesEvEUlmPKcmE_NS_9allocatorIS7_EEFvmS6_mEEE ШN 7ReadAllAndProcessERKNSt3__18functionIFvmPKcmEEEEUlS5_mE_ 2ReadAllLinesEvEUlmPKcmE_ N N N N 7ReadAllAndProcessERKNS_8functionIFviPKcmEEEEUlS7_mE_NS_9allocatorISC_EEFmS7_mEEE 2ReadAllLinesEvEUliPKcmE_NS_9allocatorIS7_EEFviS6_mEEE Ѝ8ReadAndFilterLinesERKNS_8functionIFbiEEEPNS_6vectorIiNS_9allocatorIiEEEEEUliPKcmE_NSB_ISH_EEFviSG_mEEE 4SampleFromFileEPNS2_6RandomEiPNS_6vectorINS_12basic_stringIcNS_11char_traitsIcEENS_9allocatorIcEEEENSB_ISD_EEEEEUliPKcmE_NSB_ISJ_EEFviSI_mEEE N 7ReadAllAndProcessERKNSt3__18functionIFviPKcmEEEEUlS5_mE_ ʍ2ReadAllLinesEvEUliPKcmE_ ܍8ReadAndFilterLinesERKNSt3__18functionIFbiEEEPNS2_6vectorIiNS2_9allocatorIiEEEEEUliPKcmE_ 4SampleFromFileEPNS_6RandomEiPNSt3__16vectorINS4_12basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEENS9_ISB_EEEEEUliPKcmE_ N N N N DARTE G N OSSE BDTE N RFE 1 5 4CrossEntropyLambdaMetricE 2RegressionQuantilelossE 3DataParallelTreeLearnerINS_1 ɪ6FeatureParallelTreeLearnerINS_1 N N N N N ifstreamIcNS_11char_traitsIcEEEE ofstreamIcNS_11char_traitsIcEEEE N N N N N hEE tEE ŐjEE ːN N N SparseBinI LocalFileE TSVParserE CSVParserE AUCMetricE MapMetricE hEE tEE đjEE ʑN N N
+6 0 3 ē1 ֔7 2 4 5HuberLossMetricE 9 8 OrderedSparseBinI גDenseBinIteratorI Regression Multi hEE tEE jEE N OrderedBinE RMSEMetricE NDCGMetricE MAPEMetricE N N N Dense4bitsBinE PoissonMetricE TweedieMetricE ЦBinaryLoglossE MulticlassOVAE N Dense4bitsBinIteratorE ДRegression ЧN BinIteratorE GammaMetricE TreeLearnerE ϫN hEE tEE jEE N N N S VirtualFile BinaryErrorMetricE ObjectiveFunctionE MulticlassSoftmaxE parseBinIteratorI erialTreeLearnerE ɫ hEE ҖtEE ؖjEE ޖN N N N N N N N N N N N N N N ReaderE ėWriterE ʗN N 9Json 5ValueILNS_4Json4TypeE 1 ݚ8JsonNullE 7JsonIntE ValueE ArrayE N N 4ENSt3__16vectorIS1_NS3_9allocatorIS1_EEEEEE ך5ENSt3__13mapINS3_12basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEES1_NS3_4lessISA_EENS8_INS3_4pairIKSA_S1_EEEEEEEE 0ENS_10NullStructEEE 2EbEE 1E 3ENSt3__112basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEEEE N 0Json 1JsonBooleanE ObjectE DoubleE ՝StringE N N 8JsonNullENS_9allocatorIS2_EEEE 1 7JsonIntENS_9allocatorIS2_EEEE 9JsonArrayENS_9allocatorIS2_EEEE N N N 1JsonBooleanENS_9allocatorIS2_EEEE ۜ0Json N N N DoubleENS_9allocatorIS2_EEEE ϝStringENS_9allocatorIS2_EEEE ObjectENS_9allocatorIS2_EEEE N N dEE iEE N N N N N N N N N LibSVMParserE ڞBinaryMetricINS_1 CrossEntropyE N ParserE MetricE N N N N N 2MetricE 1MetricE N MetricINS_ ˟L ܦ 8L ؟1 2MetricEEE 1MetricEEE N N N 0 ޠ4 5HuberLossMetricEEE 3 1GammaMetricEEE 9GammaDevianceMetricEEE ʦ RMSEMetricEEE MAPEMetricEEE N N N QuantileMetricE FairLossMetricE LambdarankNDCGE GPUTreeLearnerE N QuantileMetricEEE FairLossMetricEEE N N N N N N PoissonMetricEEE TweedieMetricEEE ֦N BinaryLoglossMetricE GammaDevianceMetricE ĦRegression N 9BinaryLoglossMetricEEE 7BinaryErrorMetricEEE N N N O O O MultiSoftmaxLoglossMetricE KullbackLeiblerDivergenceE VotingParallelTreeLearnerINS_1 O classMetricINS_ ErrorMetricE ֥ 25MultiSoftmaxLoglossMetricEEE Х16MultiErrorMetricEEE ܥO O O CrossEntropy Regression MetricE LambdaE ЂO O O ЃO O O O O O O O 2lossE 1lossE O ЈO O O HuberLossE GammaLossE O FairLossE ʧMAPELOSSE O PoissonLossE TweedieLossE êO O BinaryLoglossC1ERKNS2_6ConfigENS_8functionIFbfEEEEUlfE_NS_9allocatorISA_EES8_EE MulticlassOVAC1ERKNS2_6ConfigEEUlfE_NS_9allocatorIS7_EEFbfEEE O O BinaryLoglossC1ERKNS_6ConfigENSt3__18functionIFbfEEEEUlfE_ MulticlassOVAC1ERKNS_6ConfigEEUlfE_ O O O O O O O O O ЏO O 4GPUTreeLearnerEEE 7SerialTreeLearnerEEE АO O O 4GPUTreeLearnerEEE 7SerialTreeLearnerEEE ëO O O O ГO O O O 4GPUTreeLearnerEEE 7SerialTreeLearnerEEE O O 8LightGBM Ĭ6json11 4GBDTE 1 8DenseBinI 9SparseBinI 2 R N ZN8LightGBM 8LightGBM 6json11 St3__1 4 1 8 2 3BinE 9 6 G DARTE BDTE OSSE R 9Json 10JsonObjectE ValueE ArrayE иR 9Json 1 5ValueILNS_4Json4TypeE 8JsonNullE 7JsonIntE ValueE ArrayE йR R R R 0Json ϯ1JsonBooleanE ObjectE DoubleE StringE R 7SerialTreeLearnerE 6OrderedSparseBinI R
+7 0 6 3 1 2 4 5HuberLossMetricE 9 8 S ɱVirtualFile BinaryErrorMetricE ObjectiveFunctionE MulticlassSoftmaxE erialTreeLearnerE parseBinIteratorI R 2 1 6__bindIMN8LightGBM16FeatureHistogramEFvddiddPNS1_9SplitInfoEEJPS2_RNS_12placeholders4__phILi1EEERNS9_ILi2EEERNS9_ILi3EEERNS9_ILi4EEERNS9_ILi5EEERNS9_ILi6EEEEEE 1__basic_string_commonILb1EEE 0__shared_ptr_emplaceIN6json11 R 2basic_stringIcNS_11char_traitsIcEENS_9allocatorIcEEEE 0__function6__ 8 5basic_stringbufIcNS_11char_traitsIcEENS_9allocatorIcEEEE 7bad_function_callE 9basic_ostringstreamIcNS_11char_traitsIcEENS_9allocatorIcEEEE 4basic_ 3basic_filebufIcNS_11char_traitsIcEEEE R baseIF funcI v ٶb NS_ mPKcmEEE dPKfiEEE RKNS_6vectorINS_4pairIidEENS_9allocatorIS4_EEEEPdEEE PKcPNS_6vectorINS_4pairIidEENS_9allocatorIS6_EEEEEEE i mPKcmEEE ddiddPN8LightGBM9SplitInfoEEEE R ZN8LightGBM 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7ReadAllAndProcessERKNS_8functionIFvmPKcmEEEEUlS7_mE_NS_9allocatorISC_EEFmS7_mEEE 2ReadAllLinesEvEUlmPKcmE_NS_9allocatorIS7_EEFvmS6_mEEE R 7ReadAllAndProcessERKNSt3__18functionIFvmPKcmEEEEUlS5_mE_ 2ReadAllLinesEvEUlmPKcmE_ R R R R 7ReadAllAndProcessERKNS_8functionIFviPKcmEEEEUlS7_mE_NS_9allocatorISC_EEFmS7_mEEE 2ReadAllLinesEvEUliPKcmE_NS_9allocatorIS7_EEFviS6_mEEE 8ReadAndFilterLinesERKNS_8functionIFbiEEEPNS_6vectorIiNS_9allocatorIiEEEEEUliPKcmE_NSB_ISH_EEFviSG_mEEE 4SampleFromFileEPNS2_6RandomEiPNS_6vectorINS_12basic_stringIcNS_11char_traitsIcEENS_9allocatorIcEEEENSB_ISD_EEEEEUliPKcmE_NSB_ISJ_EEFviSI_mEEE R 7ReadAllAndProcessERKNSt3__18functionIFviPKcmEEEEUlS5_mE_ 2ReadAllLinesEvEUliPKcmE_ 8ReadAndFilterLinesERKNSt3__18functionIFbiEEEPNS2_6vectorIiNS2_9allocatorIiEEEEEUliPKcmE_ 4SampleFromFileEPNS_6RandomEiPNSt3__16vectorINS4_12basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEENS9_ISB_EEEEEUliPKcmE_ R R R R R R RFE 1 5 4CrossEntropyLambdaMetricE 2RegressionQuantilelossE 3DataParallelTreeLearnerINS_1 6FeatureParallelTreeLearnerINS_1 R R R R R ifstreamIcNS_11char_traitsIcEEEE ofstreamIcNS_11char_traitsIcEEEE R R R R R hEE tEE jEE R R hEE tEE jEE R R R R R hEE tEE jEE R SparseBinI LocalFileE TSVParserE CSVParserE AUCMetricE MapMetricE hEE tEE jEE R R R R R hEE tEE jEE R R R OrderedBinE RMSEMetricE NDCGMetricE MAPEMetricE R OrderedSparseBinI DenseBinIteratorI Regression Multi hEE tEE jEE R R R Dense4bitsBinE PoissonMetricE TweedieMetricE BinaryLoglossE MulticlassOVAE R BinIteratorE GammaMetricE TreeLearnerE R Dense4bitsBinIteratorE Regression R hEE tEE jEE R R R hEE tEE jEE R R R R S S S S S S S S S S S S ReaderE WriterE S S S 4ENSt3__16vectorIS1_NS3_9allocatorIS1_EEEEEE 5ENSt3__13mapINS3_12basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEES1_NS3_4lessISA_EENS8_INS3_4pairIKSA_S1_EEEEEEEE 0ENS_10NullStructEEE 2EbEE 1E 3ENSt3__112basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEEEE S S 8JsonNullENS_9allocatorIS2_EEEE 1 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