vendor/lib_lightgbm.dylib in lightgbm-0.1.6 vs vendor/lib_lightgbm.dylib in lightgbm-0.1.7
- old
+ new
@@ -1,2613 +1,2595 @@
- 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$
+ x __TEXT __text __TEXT % % __stubs __TEXT __stub_helper __TEXT x __gcc_except_tab__TEXT ' R ' __const __TEXT y 0Q y __cstring __TEXT P W P __unwind_info __TEXT N P1 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 @d P \ ~ zGY3y$
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C# HD] D UHH" ] UHAWAVAUATSH8IIIM. A I4$I|$ HH)HHI;V L}W)E)EE ?H~RLcE1 I4$A IHB9 6}(H}LMNl6LH} L(MIH}L9|I} HH HuHUH}Htf. H 6 HHuH}HE HD : HI L4@JH)H~^@u1LcA9}H~J< Ht7H)HHt>fHc~9}H^H Hc~9}H H H HuI} HHL I J<JtH)HHI$I\$H)HHH9vi t~pA u1Hc29}H HtDH)HHT2ffffff. Hcr9}H Hc29}H H HuH8[A\A]A^A_]IH}Ht<H 貽 HHu*IH}Ht fffff. H 膽 HHuH}HE Hth L診 fUHAWAVAUATSPID6IcMoM IMHL!tL9Ir1HIIII!IJ HtXHt4 @ HHtGHCL9r1IHL9u1D9su HHtHCH!L9u D9suo H}п 譼 HDsHC HEHCH IGHxH*HHH H*XLHDH MxI* H*XAO t
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+ HEHtuHHII4$VPHXHI$ I;$ s(HMHE HHI$ ff. H}HuC H}HE HtHPL HI9WH}N H([A\A]A^A_]HH}HE Ht HPHH}s H ffffff. UHAWAVATSIIFM&I^L)HHL)IIL9o MtZ 1JHL)t?HHt9HHf H{HC H[HyHHuHM HH8 f HSHC HQHSHC HQHSHC HQHSHC HQHSHC HQHSHC HQHSHC HQHSHC H[HQHI9oM>MfIIFIFM9t+ I|$ID$ Md$HtHPM9uMt L[A\A^A_] H [A\A^A_]Hf. UHAWAVAUATSPIIII_H)HHHH=t L IMoI)LHHH9s$II9LBMgI)I1M u%I MgI)IJ< > LJLII J NLI IwH9 HNH)ىHt/Hffffff. HFHF HvHBHHuH8 f HFHF HBHFHF HBHFHF HBHFHF HBHFHF HBHFHF HBHFHF HBHFHF HvHBHH9oM7I_IIMOMGL9t# H{HC H[HtHPI9uMtLH[A\A]A^A_] H[A\A]A^A_]f D UHAWAVAUATSPIII$I\$H)HHHH=t L I$Mt$I)LHHH9L}s(II9LBM|$MI)I1M u/I M|$MI)IJ< H HLJ H4HMH J NlI)L)M~ HIHuLL# HuLI $Ml$It$HtHH[A\A]A^A_] H[A\A]A^A_] @ UHAVSHL MtoH L9t[fffff. HHH HxHt*HHH9tHQH)HHHHP H L9HuL L HC@H{`H9t
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HtHP(HP LF MwIG MtEI^8IF8 HtHH I~ L9t
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+ LLMM MMLeȅ f HH$E1 E1LELMHCI9tHL!L9 I9 K% HfHsHDL9unH{HfHE{ u4MtfA: $uOH1f. H9tIT A:T HIt)Mt2LL LMLEtffff. HHDILH([A\A]A^A_]E1 UHAWAVAUATSI'1˗\ȥIi-8ߝIO@/;jH w H HH[A\A]A^A_] H@C H^H~LTIIHL 8I IIHFII9HII!MMMH\HH|L I HHH\HH9LHH!HHLHILIHHH/H1ILHH/H1I HsoH>LVHDLLLHL)HLH H <AHH LHHWePIL1H,HL)HH1IHH/H1H1IHH/H1IO H|LDLTLLI HDH1IHH/H1H1III/I1ML\IJ LH+O<K MMIMIHsoIN$)LLLdI <LLLILIMIH+IIHH@ H)H]HHUHH@Iso D H H]H@HUH@MLHLvHVLLLFLHHILNMLHIMH^LULHMIMIIMHIIM1I LILH+IIHH^L^LHMMLHIMH+IHUH1I1Hi-8ߝLLH/I1I1LLH/L1HHH/H1HsoHLHM1LLH/M1I1LLH/L1HHH1HHH/H1H1HHH/H1H[A\A]A^A_]f UHIi-8ߝH r=HT7H2HHH1IHH/H1H1IHH/H1IH1]Hr4HD7H1IHH/H1H1IHH/H1I]HO@/;jHt?HH HH T7HHWePIHH1HH/H1H] D UHAWAVAUATSPHUIIIE IE IE M H
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HtHP(HP L@L`H3 HH@LHHHPHHXH]Hf3 HHEHPLH|Q H}H9t
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v$ HHHKHSHPHHH[] UHHE$ HHHGHOHNHF]fff. UHHH]? ffffff. UHHNH1H;
S HD] D UHH ] UHAWAVAUATSH8IIIM.肽 A I4$I|$ HH)HHI;V L}W)E)EE ?H~RLcE1 I4$A IHB9 6}(H}LMNl6LH} L(MIH}L9|I} HH HuHUH}Htf. H & HHuH}HE HD : HI L4@JH)H~^@u1LcA9}H~J< Ht7H)HHt>fHc~9}H^H Hc~9}H H H HuI} HHL I J<JtH)HHI$I\$H)HHH9vY t~pA u1Hc29}H HtDH)HHT2ffffff. Hcr9}H Hc29}H H HuH8[A\A]A^A_]IH}Ht<H 袹 HHu*IH}Ht fffff. H v HHuH}HE HtX L蘶 fUHAWAVAUATSPID6IcMoM IMHL!tL9Ir1HIIII!IJ HtXHt4 @ HHtGHCL9r1IHL9u1D9su HHtHCH!L9u D9suo H}п 蝸 HDsHC HEHCH IGHxH*HHH H*XLHDH MxI* H*XAO t
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IMLH ^f:
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T (\H,H H1H,.HCH9HCL MoIELLetM9r1LIII!IJ Ht HHHAIOIWHI_J HHt&H@IMLtL9r
1IHH!IH IGHHH[A\A]A^A_]IH L_ f UHAWAVSPHIHu HCHt HZ HM~L9 Ir IGL1INHxH*HHH H*XA^F f:
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4 (\H,H H1H,.HCH9HCL MoIELLetM9r1LIII!IJ Ht HHHAIOIWHI_J HHt&H@IMLtL9r
1IHH!IH IGHHH[A\A]A^A_]IH LO f UHAWAVSPHIHu HCHt HJ HM~L9 Ir IGL1INHxH*HHH H*XA^F f:
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(\H,H H1H,.HCtHHρ A H诸 H9HBL9sLHH[A^A_] H[A^A_]ff. UHAWAVSPIHM J< H;HHtȹ LsMNL1Ht HH HH9uI LH)H1 H3HH H3HHD H3HHD H3HHD H3HHD H3HHD( H3HHD0 H3HHD8 HH9uL[M HKMSMM!t3M9r11LII$H;H Ht軸 HC I M!HJ IH3 MM LIH D HFL9r1IHL9tTH;H< H f HHHtN;JtIH H H HH HH0I7Hu HHIu f D Hffff. HFL!L9tKHH< tWH HH:HtN;OtI;H;H<H?H:HHH0I7Hu2HHIIu"H H LHHIIIH[A^A_]fff. UHSPH G H
HHHKHHH[]UHH HHHGHF] @ UHHH]? ffffff. UHHNH1H;
HD] D UHH ] UHAWAVAUATSPIIL/ HI L$@J4IIH)H~jA @u1LcA9}HzJ<ƿ Ht<H)HHT: Hcz9}HZH Hcz9}HH H HuI} HIM@L I J<JtH)HHMIWL)HHH9vڵ e~aA u1Ic9}H Ht5H)HITHcr9}H Hc29}H H HuH[A\A]A^A_] UHSPH 7 H
HHHKHSHPHHH[] UHHu HHHGHOHNHF]fff. UHHH]? ffffff. UHHNH1H;
HD] D UHH ] UHAWAVAUATSHHIHIM. A H3H]H{ HH)HHI;V L}W)E)EE ?H~OHcE1A "ffff. HEH0A IIB9 6}N|6LH}dL8I9|I} HH I@HuHULH}Ht @ H 膳 HHuH}HE HD d : HI L4@JH)H~^@u1LcA9}H~J< Ht7H)HHt>fHc~9}H^H Hc~9}H H H HuI} H IM@HL I J<JtH)HHHMHHYH)HHH9v賲 n~jA u1Hc29}H Ht>H)HHT2f Hcr9}H Hc29}H H HuHH[A\A]A^A_]IH}Ht<H HHu*IH}Ht fffff. H ֱ HHuH}HE Ht踱 L fUHSPH 觱 H
HHHKHSHPHHH[] UHH HHHGHOHNHF]fff. UHHH]? ffffff. UHHNH1H;
HD] D UHH= ] UHAWAVAUATSHHIHIM.R A H3H]H{ HH)HHI;V L}W)E)EE ?H~OHcE1A "ffff. HEH0A IIB9 6}N|6LH}L8I9|I} HH I@HuHULH}Ht @ H HHuH}HE HD ԯ : HI L4@JH)H~^@u1LcA9}H~J< Ht7H)HHt>fHc~9}H^H Hc~9}H H H HuI} H IM@HL I J<JtH)HHHMHHYH)HHH9v# n~jA u1Hc29}H Ht>H)HHT2f Hcr9}H Hc29}H H HuHH[A\A]A^A_]IH}Ht<H r HHu*IH}Ht fffff. H F HHuH}HE Ht( Lh fUHAWAVAUATSPID6IcMoM IMHL!tL9Ir1HIIII!IJ HtXHt4 @ HHtGHCL9r1IHL9u1D9su HHtHCH!L9u D9sun H}п m HDsC HEHCH IGHxH*HHH H*XLHDH MxI* H*XAO t
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HHHKHHH[]UHH HHHGHF] @ UHHH]? ffffff. UHHNH1H;
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HD] D UHH ] UHAWAVAUATSHHIHIM.ұ A H3H]H{ HH)HHI;V L}W)E)EE ?H~OHcE1A "ffff. HEH0A IIB9 6}N|6LH}dL8I9|I} HH I@HuHULH}Ht @ H v HHuH}HE HD T : HI L4@JH)H~^@u1LcA9}H~J< Ht7H)HHt>fHc~9}H^H Hc~9}H H H HuI} H IM@HL I J<JtH)HHHMHHYH)HHH9v裮 n~jA u1Hc29}H Ht>H)HHT2f Hcr9}H Hc29}H H HuHH[A\A]A^A_]IH}Ht<H HHu*IH}Ht fffff. H ƭ HHuH}HE Ht設 L fUHSPH 藭 H
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HD] D UHHM ] UHAWAVAUATSHHIHIM.B A H3H]H{ HH)HHI;V L}W)E)EE ?H~OHcE1A "ffff. HEH0A IIB9 6}N|6LH}L8I9|I} HH I@HuHULH}Ht @ H HHuH}HE HD ī : HI L4@JH)H~^@u1LcA9}H~J< Ht7H)HHt>fHc~9}H^H Hc~9}H H H HuI} H IM@HL I J<JtH)HHHMHHYH)HHH9v n~jA u1Hc29}H Ht>H)HHT2f Hcr9}H Hc29}H H HuHH[A\A]A^A_]IH}Ht<H b HHu*IH}Ht fffff. H 6 HHuH}HE Ht LX fUHAWAVAUATSPID6IcMoM IMHL!tL9Ir1HIIII!IJ HtXHt4 @ HHtGHCL9r1IHL9u1D9su HHtHCH!L9u D9sun H}п ] HDsC HEHCH IGHxH*HHH H*XLHDH MxI* H*XAO t
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(\H,H H1H,.HCH9HCL MoIELLetM9r1LIII!IJ Ht HHHAIOIWHI_J HHt&H@IMLtL9r
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-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: $u]IM H)HEH7 HH:H]H@t-LI} LLMM MMIH] Mm MV9 @ IEL!L9u)AE HIEMH Mm MuLu MĿ@ - IHI]H H]MI(LHu MuIE HEHxH*HHH H*XHuHHH HxH* H*XMtY.wM H6Hr
+HMHA( HEH@( H}Ht#HEH9tHHH)HHHHM LHH[A\A]A^A_]L}HIH}Ht#HEH9tHHH)HHHHM Lu; L}AG(HHQHH=| HHs s HHE@(u
+u1H0H=< HexceptioHHHunknown Hf@n HIH= HH N L}UHAWAVATSHMI E ]E E D'HHEH=} HMLELMܺ" DjjP5 H E9N؉]HcE9&IIH t,ΉtHcuHH9|H= D H[A\A^A_]fffff. UHAWAVAUATSHhIHIH(W)EHE )EHE H H}HHt LxDULHALEIEHLpY LMLm1H}HuDҀHEIDAHIEHE1ffffff. B<0 tJT3IH9r J3H9vuIHLII HEH;Es0HMHHHMHUHPHW)EHE HELH}L蝆H} LDULELMLJ\3AHHIEH9uII9vmH}HuHHIf HEH;Es3HMHHHMHUHPHW)EHE HELmH}HuH} H} W)EHE IE HMH)HHH~fLcA A HMIIBD1u
JD1
+f D J1HEHMH;MsHHE @ H}Hu/M9|Hp tHx(1LxHu踘tlAD$(H}Ht#HEH9tHHH)HHHHMM 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}U IIH}D H]HLx H}H9 HH} H}H9ueIIH}HLxt#HEH9tHHH)HHHHM H]Ht/I} H9t @ HI} I} H9uH]H LAu AD$(HHQHQ HAD$(Au
+u1H0H=a HexceptioHHHunknown Hf@n HIH=1 HH( s LHh[A\A]A^A_]ffff. UHAWAVAUATSHxMIIHIE A$ tI|$(1Hu萢t
AE( HcuH}b: L}H]Lc}MW)EHE Y Ey H} J< HEHEJ HMIOLHt!H H HEHHEIHuHrdffffff. H HEHHHMH@ HEHHHMH@ HEHHHMH@ HEHHEIuMԅ 1E1ff. H}H Ht HH)H w$H߾ H): H}M+ff. H tH H9t HD H HUJIHcHI9|A$ tI|$(1L}H]HuHUt
AE( E;EtH= H5 1_HhHu= h$HiHExHHEpHH S(A9W(| I(L HhQ H}Ht#HEH9tHHH)HHHHMW H]HtHHEH9t7 HHHMHxHtH9xtHx HMH9HuH]H LHx[A\A]A^A_]H= 1^IIHh
II} IIH}Ht#HEH9tHHH)HHHHM H]HtBHEH9t1HHHMHxHtH9xtHx^ HMH9HuH]HF LAu7h AE(HHQHH= HH A 1 HAE(Au
+u9HH= HexceptioHHHunknown Hf@n HIfffff. UHAWAVAUATSHh HIHH;C1 HL%Kx M|$@LLHr HH@HHfHn)pL HDžx EID$HEHpIhLLLB LmHw HHEHHDž HDž HDž HDž Dž W)EHE HuLqLeL}H}B LHH@HDŽ HL(L4 LLH wHCH+HHr[A L-1 fffff. LLvHN<L LLHvIHCH+HI9rH}HHxHEHpHEHHEHHEHHQ
H
H5r HHp
H" Hh [A\A]A^A_]IE IE IE H5 1LHh [A\A]A^A_] HH} H LNHNH` HHEHpHEHHEHHEHHv H H5q HHp HC
H
+ UHAVSH tH(1H( tC(H[A^]IHAuZ
C(HHQHO@
HC(Au
+u1H0H= HexceptioHHHunknown Hf@n HIH= HH ef. UHAVSIH t$H{(Ht t
+AF(HC( L[A^]HHǃu AF(HHQHTe HAF(u
+u6H5H= HexceptioHHHunknown Hf@n " HIH= HH
`f. UHAWAVAUATSHXMIIILmHcY(]I(H=ο Lc
H= LL
H= L5
W)EHE t8y H}Y L, L) HEH H]HL$ H]H= HV 1HMLELM 1v A tI_(HUME1HLMtAF(H}H HEH9 HHH)HHHHM W)EHE t8y H} L, LW
+ HEH H]HLR
+ 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)HHHHM Lu7 AF(HHQHH= HH
+ HAF(u
+u9HH=y HexceptioHHHunknown Hf@n HIf D UHAWAVATSHMI E ]E E D'HHEH=~ HMLELMܺ" DjjP H E9N؉]HcE9$IIH t,tHcuHH9|H=m~ D5 H[A\A^A_]UHAWAVATSHMI̋i E ]E E D7HHEH=
~ HMLELMܺ" DjjP H E9N؉]HcE9 M I$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訐t
+AF(H= Hy t>H= Hf t+H=ʹ HS tRHEHEH=| H HEHEH=| H HMLELM 1 LH0[A^]HEHEH={ Hy HHǃu% AF(HHQHW
+ HAF(u
+u9H8H= HexceptioHHHunknown Hf@n cHIH=c HHZ Afff. UHAWAVATSHMI E ]E E D'HHEH=z HMLELMܺ" DjjP H E9N؉]HcE9&IIH D t+tt,HcuHH9|H=iz D1 H[A\A^A_]fff. UHAWAVATSHMI̋ E ]E E D7HHEH=y HMLELMܺ" DjjP H E9N؉]HcE9> M I$HcH9ILMII)I MI H|(H9HHMI\H9sHL0I<H9 MADHt=L|8I\H1ffffff. D ADA HHu1I reLH)HHʘ ILp AIGOAIGOAIGOA GHHHuM9t!LHf ILHL0HH9|H=x DG H[A\A^A_]fUHAWAVATSHMI̋Y E ]E E D7HHEH= x HMLELMܺ" DjjP H E9N؉]HcE9 M I$HcH9ILMII)I MIMI IIHHHr AZAZLfD(L8 1HtKLH)HH|XILff. ZAZIGOZAZ fGH@H HuM9t'LH ADZD0HH9|H=v D H[A\A^A_]ff. UHAWAVSHIIHH(E E tH(1HUHMLEHNJt
+AF(7H= H tH= H tEEȉEAG(LH[A^A_]HHǃu AF(HHQHW HAF(u
+u9H8H= HexceptioHHHunknown Hf@n D yHIH= HH " Wf UHAVSH tH(1H(ptC(H[A^]IHAu C(HHQHO HC(Au
+u1H0H=A HexceptioHHHunknown Hf@n HIH= HH S ef. UHAVSHIH tH(1Hu艋t
+AF(EC(LH[A^]HHǃu AF(HHQHW HAF(u
+u9H8H=] HexceptioHHHunknown Hf@n {HIH=% HH g Yfffff. UHAVSHIH tH(1Hu詊t
+AF(EC(LH[A^]HHǃu AF(HHQHW HAF(u
+u9H8H=m HexceptioHHHunknown Hf@n {HIH=5 HH, w Yfffff. UHAVSIH t$H{(Ht蠎t
+AF(HC( L[A^]HHǃu AF(HHQHT HAF(u
+u6H5H= HexceptioHHHunknown Hf@n HIH=Q HHH `f. UHAVSHIHHE tH(1H(HU͉t
+AF(HEHtHC(HC( LH[A^]HHǃu
+ AF(HHQHW HAF(u
+u9H8H= HexceptioHHHunknown Hf@n {HIH=H HH? YUHAVSHIHE HE H(HuHUt
+AF(HEHtHC(HC( LH[A^]HHǃu AF(HHQHW HAF(u
+u9H8H= HexceptioHHHunknown Hf@n {HIH=J HHA Y UHAVSHIHE HE H(HuHUOt
+AF(HEHtHC(HC( LH[A^]HHǃu AF(HHQHW HAF(u
+u9H8H= HexceptioHHHunknown Hf@n {HIH=J HHA Y UHAVSH tH(1 tHv(1tC(H[A^]IHAu1 C(HHQHO HC(Au
+u1H0H= HexceptioHHHunknown Hf@n HIH=x HHo eUHAVSH tH(1 tHv(1wtC(H[A^]IHAua C(HHQHOG HC(Au
+u1H0H= HexceptioHHHunknown Hf@n HIH= HH eUHAVSH tH(1 tHv(17tC(H[A^]IHAu C(HHQHOw HC(Au
+u1H0H= HexceptioHHHunknown Hf@n HIH= HH eUHAVSH tH(1H(tC(H[A^]IHAu C(HHQHO HC(Au
+u1H0H=A HexceptioHHHunknown Hf@n HIH= HH S ef. UHAVSHIH tH(1Hut
+AF(EC(LH[A^]HHǃu AF(HHQHW HAF(u
+u9H8H=] HexceptioHHHunknown Hf@n {HIH=% HH g Yfffff. UHAVSHHE tH(1HuřtC(HH[A^]IHAu C(HHQHO HC(Au
+u1H0H=r HexceptioHHHunknown Hf@n HIH=B HH9 aff. UHAWAVATSHpMIHuHUE HcY(Hۉ]W)EHE y H} L< L HEH H]HL H]Hc]HW)EHE tFy H} L< L HEH H]HL H] )EHE HEHD$HEH$H=i H 1HMLELM 1 A tI^(HuHUHMH t AD$(H}Ht#HEH9tHHH)HHHHM H}Ht#HEH9tHHH)HHHHM LHp[A\A^A_]II2IIH}Ht#HEH9tHHH)HHHHM] H}Ht#HEH9tHHH)HHHHM1 LAu S AD$(HHQHQ7 HAD$(Au
+u1H0H=ƾ HexceptioHHHunknown Hf@n HIH= HH ffffff. UHAWAVAUATSHMMI͋ E ]E E 7HHEH=f HMLELMԺ" ujjP H E9N؉]HcE9 HMM4$IU H1I?HcH9ILMII)I MI LH9ILMJLI9sNTH L9 LTL\8L|Md8J1 fADfA fZfZfADfA fADfA fZfZfADfA HI9uM9t6fffff. AD(ZD(ZH9H@|H=e uq H[A\A]A^A_]f. UHAVSH tH(1tC(H[A^]IHAu C(HHQHO HC(Au
+u1H0H=5 HexceptioHHHunknown Hf@n HIH= HH G efffff. UHAVSHIH tH(1HuYt
+AF(EC(LH[A^]HHǃu AF(HHQHW HAF(u
+u9H8H=M HexceptioHHHunknown Hf@n y {HIH= HH W Yfffff. UHAWAVAUATSHxMIIHIA$ tI|$(1HuGt
AE( HcuH}Y L}H]Lc}MW)EHE ` Ey H} J< HEHEJ HMIOLHt(Hf. H HEHHEIHuHrdffffff. H HEHHHMH@ HEHHHMH@ HEHHHMH@ HEHHEIuMԅ 1E1ff. H}H Ht HH)Hw'H߾ H) H}M.fffff. H tHH9tHD f H HUJIHcHI9|A$ tI|$(1L}H]HUHuИt
AE( E;EtH=H H5 17HhHu-h$HiHExHHEpHH S(A9W(| I(L HhA H}Ht#HEH9tHHH)HHHHMG H]HtHHEH9t7 HHHMHxHtH9xtHx HMH9HuH]H LHx[A\A]A^A_]H= 16IIHh
II} IIH}Ht#HEH9tHHH)HHHHM H]HtBHEH9t1HHHMHxHtH9xtHxN HMH9HuH]H6 LAu7X AE(HHQHH= HH 1 ! HAE(Au
+u9HH= HexceptioHHHunknown Hf@n HIfffff. UHAWAVATSHIIIH tH{(1Huϔt
+AF(QI( tH{(1Aw(HUL t
+AF("E;EtH=k H5 14LH[A\A^A_] HHǃu AF(HHQHW HAF(u
+u9H8H= HexceptioHHHunknown Hf@n uHIH=J HHA S UHAVSHIH tH(1v(HUvt
+AF(HEHC(LH[A^]HHǃu" AF(HHQHW HAF(u
+u9H8H= HexceptioHHHunknown Hf@n {HIH=` HHW Yf UHAVSHHHH( tH(1v(HUHotC(HH[A^]IHAu5 C(HHQHO HC(Au
+u1H0H= HexceptioHHHunknown Hf@n HIH=| HHs a D UH1( ~( Dȃz( D]f UHAWAVSPMLu LM1y( LUAx( DA{( D tH(1L}(H(R(EB(I(I(L4$'tAG(LH[A^A_]IHAu AG(HHQHP HAG(Au
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+u1H0H= HexceptioHHHunknown Hf@n HIH= HH [ UHAWAVATSH0ILu L]Le(1Ax( H]Ay( D{( D؋J(EB(H(I( tH(1L}0AC(I(Ld$ HUHT$Lt$D$ $ E1\tAG(LH0[A\A^A_]IHAu AG(HHQHP HAG(Au
+u1H0H=r HexceptioHHHunknown Hf@n HIH=B HH9 [ff. UHAVSHH tH(1V(H(1HtC(H[A^]IHAu C(HHQHO HC(Au
+u1H0H= HexceptioHHHunknown Hf@n HIH=f HH] effffff. UHAWAVAUATSH8LLEHMIIIHE Mcg(MW)EHE Ey H}3 L HHMHMHLHEID$LHt$Hff. HMHHMIHuHrH @ HEHHHM@ HEHHHM@ HEHHHM@ HMHHMIuA tI~(1AU(IcO(LMLE1t!C(H}Ht\H9}tH}" KIHUH uMH]HEP(A9W(|H}H(HQ HtH9]tH]H LHH8[A\A]A^A_]H=n 1Lt* IHIH}HtH9}tH} Lu9 LC(HHQHH=G HH> yy HAG(u
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+u1H0H= HexceptioHHHunknown Hf@n HIH=Z HHQ L UHAWAVSPHIIF IF I H) H
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L IE1I M~LLL2 M'I_I_Mt#LH[A\A]A^A_] H H[A\A]A^A_]H UHAWAVAUATSHIIL$`ID$hH9tHPH)HHHIT$hIcD$H?HAD$x A |%1A fffff. EA9|AL$xMt$`EE E1ɾE1uED$@Mc D HcHquID$\D9}7IT$( ffff. < u~}|0H L9|ADMD9}dE9H]ff. ID$hI;D$ps$MЉDMDHHID$h fff. LHHU1 DMEE9~0AL$MA9}\EH]f. ED$@EADEID$hI;D$psD UԉPHID$hLHuH AL$A9|LH[A\A]A^A_]UHAVSHIHIv@ HPHIvIcV@HHPHH@Iv(IcV@HH[A^]UHHcG@HD]ÐUHAWAVAUATSHXHUHIHc;MnM9ntMnIN(IF0H9tHPH)HHHIV0M~IF(HEA~@L}k HHE1H] M;n H}tBD#AE IF I7LH)HyL H}IvMn I)H?I9s&MI9LBIVII)MA u1IVII)IHULH HUHH}MJ 8IHMBL!B 8N|8H)H)H~HILE1 LELH}I^M~MF HL}H]t H H}H4;IF0I;F8tHIF0 D H}H}IIcF@MnHI9I^ I9sAE IF II)IyL[ IVI^ H)H?H9s#HL9IBM~MI)E1H۸ u%M~MI)HHI LIJ (IB( JD(I)L)M~HILLI LLI^IFMf HL}tHz L账H};HELL@M9\ W)EHE E LL)H E1LeE1IcE E9}]Icv@9}UHcI~T H9}&IF( < uD| HAD H9|9}Af. EnE9|E9uRA;^@}LHcHHEHHEH;EsD
-HHHE"H}L HELL@ff. DUADULL)HA9"HuL H}Ht#HEH9tHHH)HHHHM HX[A\A]A^A_]LHX[A\A]A^A_] HH}Ht#HEH9tHHH)HHHHM H fff. UHAWAVAUATSHhAIH}Ht HR HR 1HH 1HEHuH^H9^tH^HN(HF0H9tHPH)HHHHV0E1E ~AE HMHʊJxHHJ`HRhH)HAH9s D$DTHELxHH(HMEL}HH HMIcHxE1I1Lmfff. EL E9HM}oHEHPHcx@f D McB\"IA9}+HEHp( fB< uBD"I ÃI9|A9}Af. HEDPE9|E9 IcHMHI(E HED)I^Mn L9M DU{ t EIF fffff. IH)Hy L HEHHEL(LuDUI)H?I9s%MI9LBMvMI)M u)MvMI)IHL; LDUHJ 8HƋMB 8N|8I)L)M~&HIHpLLQ HpLDULuI^M~Iv HL}t H DUE IF0I;F8s HIF0 ffff. H}HuDUԋMI^Mn L9M t(EIFLm f D E HM0 IH)Hy LG HEHHEL(LuDUI)H?I9s%MI9LBMvMI)M u)MvMI)IHL LDUHJ 8HƋMB 8N|8I)L)M~ HIHuLL HuLDULuI^M~Iv HL}Lmt HB DUIF0I;F8tMHIF0HMA H}HuDUHMA HH;xPI^HMIHH9s IF HMM'L)HyL M'HEH LuL)H?H9s&HH9HBMvLL)E1HIǸ u MvLL)IL` ML<I H\M)M)M~LLL LuM~I^HEL(ML}tL IF0I+F(HAF@L.H}L Hh[A\A]A^A_]H H H H H H fffff. UHAVSI HHLV H[A^]IHY L UH]. f D UHAWAVAUATSH(IIHP HIE AFAEMeIE IE IE I^I+^tIHyL H IM}M}LI] IvI^H)H~LH
- IM}LeIE(HEIE8 IE0 IE( I^0I+^(IItOLH>t H} H^ IM}0M}(KIE8Iv(I^0H)H~LH IM}0AF@AE@M}HIvHL IEp IEh IE` I^hI+^`IIt[L}IE`HELH=t H} H IM}hM}`KIEpIv`I^hH)H~LH IM}hAFxAExH([A\A]A^A_]II<$ IHEH8Ht#IEhH9tHHH)HHHIMh, HEHHthIEPH9tPf HHIMPHxHt'HHH9tHQH)HHHHP IMPH9HuHEHH IHEH8Ht#IE0H9tHHH)HHHIM0 HEH8HtI9}tI}n L UH G ]UH G ]UHAWAVATSHHHGL HHHsLsL{@HCP HCH HC@ HC8 HC0 HC( HC HC HC HcF@1M9}#H~(Hcfffff. < uHH9|y9|˅t$HcL\ HCHH;CPtM܉HHCH HuLH[A\A^A_]II?Ht#HCHH9tHHH)HHHHKH H{(Ht#HC0H9tHHH)HHHHK0 I>Ht#HCH9tHHH)HHHHK L ffff. UHSPHHJ HHH{@Ht#HCHH9tHHH)HHHHKHk H{(Ht#HC0H9tHHH)HHHHK0? H{Ht)HCH9tHHH)HHHHKH[]
H[] UHSPHHJ HHH{@Ht#HCHH9tHHH)HHHHKH H{(Ht#HC0H9tHHH)HHHHK0 H{Ht)HCH9tHHH)HHHHKH[]] H[] UHSPHH`I HHH{@Ht#HCHH9tHHH)HHHHKH H{(Ht#HC0H9tHHH)HHHHK0 H{Ht#HCH9tHHH)HHHHK HH[] UHAWAVAUATSHXIIHcffEHE H]Ut>y H} H< H}X ILmM| L}LHuN L}E1E1I~(HtII^(IF0H9tHHH)HHHIN0 HC HC H LmoE
-fInfpDEԅMn(AF0ffEHE tFy H} HEL< L ILmHEI\ H]LL H]1E1I~@HtII^@IFHH9tHHH)HHHINH+ HC HC H LmoE
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+H)HL9s^HUMfHMI)J< HJ N<#M~HHuLI LHEHM~INH}Htp IEHIMPH)HIIHrsA @ IvHHLL{ I}HH HD H9tHPH)HHHHT H IIEHIMPH)HIHI9rI>IvHU LL H[A\A]A^A_]fUHAWAVAUATSHIIIM)M II_MoLH)HI9Hu ML)HLHH=tLIp LHuMMoM)LHHH9LM II9LBHL)HE1M H II)LHI9L MI9I LIHHLM)DHHLt HHLNjWQHHHuM)Ir;GAGAG A GAGAGAG A H H H9uINl MoHuM H- I HL)HJ< I LMLHuMLI9My LaM)MIAPH HLffffff. A>;A~{HIHu IIJ IMI)MILL)H9 J HH)L]IIDHLHt'HLH HHHIOHIOHuIrfffffff. HHIGHHIOHNHHIGHHIOHNHHIGHHIOHNHHIOHIOH H9rMH} IDHt+H1 D D2AD5D2AD5HHuIHIrNI D BAEBAE BAEBAEBAEBAEBAEBHRAEIH9uM9tDIHL)H HH A60AvpIHHu H LIrDAAVSAVSAV S AVSAVSAVSAV S I H I9uOlHL)LUMI)H~ ILLL LILMHuN4I_H)H~LHi LMIM'MoMwMtL HEH[A\A]A^A_]HHsHf AANHANHAN H ANHANHANHAN H I H M9uH UHAWAVAUATSPIIGM7MoL)HM)LHH9v_Ht
L; IE1I M~LLLr M'I_I_Mt#LH[A\A]A^A_] H H[A\A]A^A_]H UHAWAVAUATSHHIIMoM9otMoIO(IG0H9tHPH)HHHIW0MwIG(HEI $I9L$L}Luu IO IG HE11LeًLMȉ)Ht UH]MgMo M9 L ft A $IG ffffff. II)IyL IHEL(]I)H?I9s.MM9MBMMI)MA u - MMI)ILHk HMJ 0IċMB 0Nt0I)L)M~HILL LL}I_MwMg HLu]tH E IG0I;G8s HIG0 @ H}HuMgMo M9 t"A $IGLeH]Dm f II)IyL IHEL(]I)H?I9s&MM9MBMwMI)MA u%MwMI)ILH# HMJ 8IċMB 8N|8I)L)M~HILLD LHEHXLxIMg HLuLeH]DmtH IG0I;G8tMȉHIG0
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L IE1I M~LLLB M'I_I_Mt#LH[A\A]A^A_] H H[A\A]A^A_]HX UHAWAVAUATSHIIL$`ID$hH9tHPH)HHHIT$hIcD$H?HAD$x A |%1A fffff. EA9|AL$xMt$`EE E1ɾE1uED$@Mc D HcHquID$\D9}7IT$( ffff. < u~}|0H L9|ADMD9}dE9H]ff. ID$hI;D$ps$MЉDMDHHID$h fff. LHHUA DMEE9~0AL$MA9}\EH]f. ED$@EADEID$hI;D$psD UԉPHID$hLHuH AL$A9|LH[A\A]A^A_]UHAVSHIHIv@ HPHIvIcV@HHPHH@Iv(IcV@HH[A^]UHHcG@HD]ÐUHAWAVAUATSHXHUHIHc;MnM9ntMnIN(IF0H9tHPH)HHHIV0M~IF(HEA~@L}k HHE1H] M;n H}tBD#AE IF I7LH)HyL H}IvMn I)H?I9s&MI9LBIVII)MA u1IVII)IHULH- HUHH}MJ 8IHMBL!B 8N|8H)H)H~HILEA LELH}I^M~MF HL}H]t H H}H4;IF0I;F8tHIF0 D H}H}IIcF@MnHI9I^ I9sAE IF II)IyLk IVI^ H)H?H9s#HL9IBM~MI)E1H۸ u%M~MI)HHI LIJ (IB( JD(I)L)M~HILLI LLI^IFMf HL}tH L账H};HELL@M9\ W)EHE E LL)H E1LeE1IcE E9}]Icv@9}UHcI~T H9}&IF( < uD| HAD H9|9}Af. EnE9|E9uRA;^@}LHcHHEHHEH;EsD
+HHHE"H}L HELL@ff. DUADULL)HA9"HuL( H}Ht#HEH9tHHH)HHHHM HX[A\A]A^A_]LHX[A\A]A^A_] HH}Ht#HEH9tHHH)HHHHM H fff. UHAWAVAUATSHhAIH}Ht H1 H2 1HH 1HEHuH^H9^tH^HN(HF0H9tHPH)HHHHV0E1E ~AE HMHʊJxHHJ`HRhH)HAH9s D$DTHELxHH(HMEL}HH HMIcHxE1I1Lmfff. EL E9HM}oHEHPHcx@f D McB\"IA9}+HEHp( fB< uBD"I ÃI9|A9}Af. HEDPE9|E9 IcHMHI(E HED)I^Mn L9M DU{ t EIF fffff. IH)Hy L HEHHEL(LuDUI)H?I9s%MI9LBMvMI)M u)MvMI)IHLK LDUHJ 8HƋMB 8N|8I)L)M~&HIHpLLa HpLDULuI^M~Iv HL}t H DUE IF0I;F8s HIF0 ffff. H}HuDUԋMI^Mn L9M t(EIFLm f D E HM0 IH)Hy LW HEHHEL(LuDUI)H?I9s%MI9LBMvMI)M u)MvMI)IHL LDUHJ 8HƋMB 8N|8I)L)M~ HIHuLL HuLDULuI^M~Iv HL}Lmt HR DUIF0I;F8tMHIF0HMA H}HucDUHMA HH;xPI^HMIHH9s IF HMM'L)HyL M'HEH LuL)H?H9s&HH9HBMvLL)E1HIǸ u MvLL)ILp ML<I H\M)M)M~LLL LuM~I^HEL(ML}tL IF0I+F(HAF@L.H}L Hh[A\A]A^A_]H H H H H H fffff. UHAVSI HHLf H[A^]IHi L UH]> f D UHAWAVAUATSH(IIH*0 HIE AFAEMeIE IE IE I^I+^tIHyL H IM}M}LI] IvI^H)H~LH IM}LeIE(HEIE8 IE0 IE( I^0I+^(IItOLH>t H} Hn IM}0M}(KIE8Iv(I^0H)H~LH IM}0AF@AE@M}HIvHL IEp IEh IE` I^hI+^`IIt[L}IE`HELH=t H} H IM}hM}`KIEpIv`I^hH)H~LH IM}hAFxAExH([A\A]A^A_]II<$ IHEH8Ht#IEhH9tHHH)HHHIMh< HEHHthIEPH9tPf HHIMPHxHt'HHH9tHQH)HHHHP IMPH9HuHEHH IHEH8Ht#IE0H9tHHH)HHHIM0 HEH8HtI9}tI}~ L UH G ]UH G ]UHAWAVATSHHHw+ HHHsLsL{@HCP HCH HC@ HC8 HC0 HC( HC HC HC HcF@1M9}#H~(Hcfffff. < uHH9|y9|˅t$HcLl HCHH;CPtM܉HHCH HuLH[A\A^A_]II?Ht#HCHH9tHHH)HHHHKH* H{(Ht#HC0H9tHHH)HHHHK0 I>Ht#HCH9tHHH)HHHHK L ffff. UHSPHH) HHH{@Ht#HCHH9tHHH)HHHHKH{ H{(Ht#HC0H9tHHH)HHHHK0O H{Ht)HCH9tHHH)HHHHKH[] H[] UHSPHH@) HHH{@Ht#HCHH9tHHH)HHHHKH H{(Ht#HC0H9tHHH)HHHHK0 H{Ht)HCH9tHHH)HHHHKH[]m H[] UHSPHH( HHH{@Ht#HCHH9tHHH)HHHHKH H{(Ht#HC0H9tHHH)HHHHK0 H{Ht#HCH9tHHH)HHHHK HH[] UHAWAVAUATSHXIIHcffEHE H]Ut>y H} H< H}h ILmM| L}LHu^ L}E1E1I~(HtII^(IF0H9tHHH)HHHIN0 HC HC H LmoE
+fInfpDEԅMn(AF0ffEHE tFy H}տ HEL< L ILmHEI\ H]LL H]1E1I~@HtII^@IFHH9tHHH)HHHINH; HC HC H LmoE
fHnfpDMn@AFHMMFMXIcx@ E1E1%IFFHcIP(
-BLIff. HcATH9}+I@( < uAtH H9|9| AIcA< t뉸E1E1 AINFHcIp(BTIHATH9}/IX( ff. < uAtH H9|9|EM HX[A\A]A^A_]HH}HtOHEH9tAHHH)HHHHM*HH}Ht#HEH9tHHH)HHHHM H ff. UHAWAVAUATSHcHw(H_@D4 B43 AA)AE)E9 uIcH}HGD)˃DH]fffff. LcLcdLclLcL\L4[\ L<[BfB:!ZfC \fXBfB:!| BfB:!B fB:! fC H [ZfC fXfC H<fAZfXfAfAZfXfACDCDADADHD9$HEȃAH}uA9}VH_IcHcHtH)fff. Hc~ H [f:!ZfA fXfA ADHHu[A\A]A^A_]ffff. UHAWAVAUATSHcHw(H_@D D GD DȃD)EA)E9 IcLoAA)AE D Ic\ Mc\MctMc|A\L$[B E\ ZBEtB BXE| ZO [BBX WZKvB XWZK X BDBDDDHD9LAEE9}KHwIcIcHtH)fffff. Hc~Z>H<XDHHu[A\A]A^A_]f. UHAWAVATSLcH_(HG@FB4F E~cIcHEEf. HwHc D8u*McN$\B\ B LH \D$AAHE9|H_(HG@HcD4E)E)F4D [A\A^A_]f D UHHcHO@]UH fG ]fffff. UH fG ]fffff. UHAWAVATSHHH7A HHHsLsL{@HCP HCH HC@ HC8 HC0 HC( HC HC HC HcN@19}H~(HcҐf<W uHH9|x9|ډE܅t$HcL HCHH;CPtM܉HHCH HuLH[A\A^A_]II?Ht#HCHH9tHHH)HHHHKH H{(Ht#HC0H9tHHH)HHHHK0 I>Ht#HCH9tHHH)HHHHK L f UHSPHH? HHH{@Ht#HCHH9tHHH)HHHHKH[ H{(Ht#HC0H9tHHH)HHHHK0/ H{Ht)HCH9tHHH)HHHHKH[] H[] UHSPHH? HHH{@Ht#HCHH9tHHH)HHHHKH H{(Ht#HC0H9tHHH)HHHHK0 H{Ht)HCH9tHHH)HHHHKH[]M H[] UHSPHH`> HHH{@Ht#HCHH9tHHH)HHHHKH H{(Ht#HC0H9tHHH)HHHHK0 H{Ht#HCH9tHHH)HHHHK HH[] UHAWAVAUATSHXIIHcffEHE H]Ut>y H}y H< H}H ILmM| L}LHu> L}E1E1I~(HtII^(IF0H9tHHH)HHHIN0 HC HC H LmoE
-fInfpDEԅMn(AF0ffEHE tFy H} HEL< L ILmHEI\ H]LLt H]1E1I~@HtII^@IFHH9tHHH)HHHINH HC HC H LmoE
-fHnfpDMn@AFHMMFMXIcx@ E1E1%IFFHcIP( JfBLIf HcATH9},I@( f<X uAtH H9|9| AIcA< t눸E1E1 AINFHcIp(VfBTIHATH9}.IX( f f<C uAtH H9|9|EM HX[A\A]A^A_]HH}HtOHEH9tAHHH)HHHHM*HH}Ht#HEH9tHHH)HHHHMp H f. UHAWAVAUATSHcHw(H_@D4 B43 AA)AE)E9 uIcH}HGD)˃DH]fffff. LcLcdLclLcL\L4[\ L<[BfB:!ZfC \fXBfB:!| BfB:!B fB:! fC H [ZfC fXfC H<fAZfXfAfAZfXfACDCDADADHD9$HEȃAH}uA9}VH_IcHcHtH)fff. Hc~ H [f:!ZfA fXfA ADHHu[A\A]A^A_]ffff. UHAWAVAUATSHcHw(H_@D D GD DȃD)EA)E9 IcLoAA)AE D Ic\ Mc\MctMc|A\L$[B E\ ZBEtB BXE| ZO [BBX WZKvB XWZK X BDBDDDHD9LAEE9}KHwIcIcHtH)fffff. Hc~Z>H<XDHHu[A\A]A^A_]f. UHAWAVATSLcH_(HG@FB4F E~eIcHEEf. HwHc D8u,McN$\fB\ B LH f\D$AAHE9|H_(HG@HcD4E)E)F4D [A\A^A_] @ UHHcHO@]UHH ] UHH ] UHAWAVATSHHH?6 HHHsLsL{@HCP HCH HC@ HC8 HC0 HC( HC HC HC HcF@1M9}#H~(Hcfffff. < uHH9|y9|˅t$HcL HCHH;CPtM܉HHCH HuLH[A\A^A_]II?Ht#HCHH9tHHH)HHHHKH
- H{(Ht#HC0H9tHHH)HHHHK0 I>Ht#HCH9tHHH)HHHHK L ffff. UHSPHH4 HHH{@Ht#HCHH9tHHH)HHHHKH[ H{(Ht#HC0H9tHHH)HHHHK0/ H{Ht)HCH9tHHH)HHHHKH[] H[] UHSPHH4 HHH{@Ht#HCHH9tHHH)HHHHKH H{(Ht#HC0H9tHHH)HHHHK0 H{Ht)HCH9tHHH)HHHHKH[]M H[] UHSPHHX3 HHH{@Ht#HCHH9tHHH)HHHHKH H{(Ht#HC0H9tHHH)HHHHK0 H{Ht#HCH9tHHH)HHHHK HH[] UHAWAVAUATSHXIIHcffEHE H]Ut>y H}y H< H}H ILmM| L}LHu> L}E1E1I~(HtII^(IF0H9tHHH)HHHIN0 HC HC H LmoE
-fInfpDEԅMn(AF0ffEHE tFy H} HEL< L ILmHEI\ H]LLt H]1E1I~@HtII^@IFHH9tHHH)HHHINH HC HC H LmoE
-fHnfpDMn@AFHMMFMXIcx@ E1E1%IFFHcIP( BLIff. HcATH9}+I@( < uAtH H9|9| AIcA< t뉸E1E1 AINFHcIp(BTIHATH9}/IX( ff. < uAtH H9|9|EM HX[A\A]A^A_]HH}HtOHEH9tAHHH)HHHHM*HH}Ht#HEH9tHHH)HHHHMq H ff. UHAWAVAUATSHcHw(H_@D4 B43 AA)AE)E9 uIcH}HGD)˃DH]fffff. LcLcdLclLcL\L4[\ L<[BfB:!ZfC \fXBfB:!| BfB:!B fB:! fC H [ZfC fXfC H<fAZfXfAfAZfXfACDCDADADHD9(HEȃAH}uA9}IH_IcHcHtH)Hc~ H [f:!ZfA fXfA ADHHu[A\A]A^A_]fffff. UHAWAVAUATSHcHw(H_@D D GD DȃD)EA)E9 IcLoAA)AE D Ic\ Mc\MctMc|A\L$[B E\ ZBEtB BXE| ZO [BBX WZKvB XWZK X BDBDDDHD9PAEE9}>HwIcIcHtH)fHc~Z>H<XDHHu[A\A]A^A_]ff. UHAWAVSLcH_(HG@FB4F E~=IcE H_Hc4D81uMcN<H4J4L<AHD9|H_(HG@HcD4E)E)F4D [A^A_]UHHcHO@]UHHG HG HG HGP HGH HG@ HG8 GX ?HGp HGh HG` ] UHHG HG HG HGP HGH HG@ HG8 GX ?HGp HGh HG` ] UHAWAVAUATSPIHLsHC HC HC L{8HCP HCH HC@ HC8 CX ?Lk`HCp HCh HC` I$H CAD$ C ID$(HC(AD$0C0t0L9tBIt$`IT$hLQ AD$XCXIt$H1LS L9tIt$IT$LO AD$xCxA$ H[A\A]A^A_]II} Ht#HChH9tHHH)HHHHKh H{HHtf L/ MLuI?I Htj I>Ht#HCH9tHHH)HHHHK? L f UH]Ff D UHAWAVAUATSPHL{HC HC HC Lc8HCP HCH HC@ HC8 CX ?Lk`HCp HCh HC` H[A\A]A^A_]II} Ht#HChH9tHHH)HHHHKhg H{HHtL/V MLuI<$I$ Ht8 I?Ht#HCH9tHHH)HHHHK
LM UHAWAVAUATSH8IIMc.E/AFAGAFAG IF IG(AFAG0INIOxIN I AN%A I)WtS)EHE E Ey H} N$ Lq HH]N,LmHLi Lm[)EHE E Ey H}N N$ L HH]N,LmHL Lm E11I`HtII_`IGhH9tHHH)HHHIOh HC HC H H]oE
-fInfpDI_`AGhIcHHL Mw8IP
IHHt fff. H F HHuIGH IG@H HPH1Ht fff. I>H HH9uH H)H1f. I6HH I6HHD I6HHD I6HHD I6HHD I6HHD( I6HHD0 I6HHD8 HH9uIGP A? E11f Iw`LL. HIcIH9|}E11IHtII_IGH9tHHH)HHHIO HC HC H H]oE
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HÿHÊ1 w*1ffff. tPCHÉЀ
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HÿHÊ1 w*1ffff. tPCHÉЀ
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+L HH5 HHW LvL HLg HHEHHEH HEHHEHHV HcW H56 HH4W H W LW H U f. UHAWAVAUATSHX HIH IFI9 H} W IIE IE H L%Q M|$@LHH HH@H HfHnf)V HE EID$HEHIhLeL L}LHAV L% ILLHDž HDž HDž HDž Dž fWf)EHE HuH葸LeH}@U I H5<s L4X LL7X LLHIINH)HH9HCHrzA fffff. BLH5r W E HHu谽ILW LLH藽IIINH)HH9HCI9rH}HHHEHHEH HEHHEHH+T HT H5 HH~T H T L*U HX [A\A]A^A_]HG HG H H5 1HX [A\A]A^A_]hS L HH}S LS HT
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+rAt Hf. { H[tHL9uH== 1nlE1E1H]H{HtAHCH9tHHH)HHHHK" HH@ H@ H LkL{L{H} IffEHE H5< H} . HPLuLCHLX Hg ffEHE H5< H} HPHuhMcuMfE0 MHEHXffHDž E H1 N< L! ILKD HLLI LA HEHEHDE1fff. HÊ< t <+t<-u
HÿHÊ1 w*1ffff. tPCHÉЀ
+rAt Hf. { H[tHL9uH=7; 1llE1E1H]H{ HtAHC(H9tHHH)HHHHK( HH@ H@ H Lk L{(L{0H}o IffEHE H5: H}
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HÿHÊ1 w*1ffff. tPCHÉЀ
+rAt Hf. { H[tHL9uH=V9 1jlE1E1H]H{PHtAHCXH9tHHH)HHHHKX H H@ H@ H LkPL{XL{`H}? IffEHE H5 9 H} HPLuL?HL H ffEHE H58 H} HPHu}dAUH}H I HtGI H9tHHH)HHHI H(H@ H@ H foEA HEI H}2 H=8 1iffEHE H5;8 H}
+ HPLuLm>HL H% ffEHE H57 H}
+ g HPHuccIcMLaE II LEhffHDž HM H N< L ILK H LLu HHE L LHHDž LLp I ZADL9|LoOIcMHI I HH)HHH)K Hm fE1fHnfpDLmI HtQfEI H9tHHH)HHHI U HH@ H@ H foEM A H} ffEHE H5 6 H} y HPLuL2<HL Hb ffEHE H55 H} , HPHu(aMcuMfET MHEHXff`HDžp E H`| N< LL IL`KD HpLLI: LhA HEHEHDE1
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HÿHÊ1 w*1ffff. tPCHÉЀ
-rAt Hf. { H[tHL9u?IcMHI I HH)HHH) H~E1E1H]H HtGH H9tHHH)HHHH HH@ H@ H L L L H}T IffEHE H57 H} HPLuL:HL
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}
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HPLuL4HL H ffEHE H50 H}
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+HHHpA0A 9A L9`W)@HDžP )0HDž@ I 0HH@Lu I 0HH0LV H@H0LF H(H8A I` Mx M A D)H3 H H HT$$ #IE 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}袵HPHtkHXH9tWf. 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}蒱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|
t4IT$hHt< t
ID
-]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
+]IT$p fffff. HcHHPH9|H=R/ 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\.(LXO 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+(X#N HX`ULhHpHxK|@H HH@0HxH $HXH`HMLhLpHMJD+(I AY HEH MB (HuHcEI9Md$ HMHHH5 H ( HMHHH5X H uHHť u-HHQH1H,H}蓺袥 HuWH}|若 Hu@H=* uǥ H [A\A]A^A_]HHT HJ HήHƮf D UHAWAVAUATSH IAH}I] IEH9tE D HHIMHxHt'HHH9tHQH)HHHHP蚤 IMH9HuET DuHEL HPI9 L8HULm
LLHHEH@ H@ H I\$XI+\$PIItWH?I9v H}6 H HH]H]JHEIt$PMt$XI)M~HL4 LH]ID$xHMHHB(ID$hIL$pHJ HBMuM;u
- 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
- f. IH@XH1LHPHt,H9XtHX fWf)PHDž` f(f)PHH`IH@XH1LHpHt/H9xtHx{ ID$ ID$ I$ f(f)pHHEHE HE2HE HE HE HE HE HE HE HDž LeHLH? H1 Ld$HpHD$L4$H=# H L8LP 1L1 HDž H]H֒ H 9 L8L@M9 M)II vH8jHL8pIrhLH1ۺ Af A(HH1A<ADAD9AfA.D(uzDHEHHI9u1M I Lc0IKD5 HI
- HHHH9tK|5PIt PIT X* ID xKD5xID hIL pKL5pKD5hI H Lc M L L(M9 M)II vH iHL oIrgLH1ۺ A A(HH1A| ADAD9AfA.D(uzDHEHHI9u1M IK&HIT
- 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
- HHHH9tHpIt PIT XID xHHHBID hIL pHJHI I D| HH HH0I Lc M IK&
- HHHH9tK|&PHHDIEKD&xIE IMKL&pKD&h xtI HcHxeM HI
- HH HHH9tI| PHpHxHHHAID xHHIIL pID hHpHt)HxH9tHHH)HHHHxŎ HHt)HH9tHHH)HHHH萎 H}yHpHtH9xtHxf HPHtH9XtHXE H HtpH(H9t\ffffff. HHH(HxHt*HHH9tHQH)HHHHP H(H9HuH Hɍ H8HtdH@H9tP HHH@HxHt*HHH9tHQH)HHHHPw H@H9HuH8HY H H H;Eu-H [A\A]A^A_]HHuHz H
- 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
- HHHHH9tI| PH@HHHHHAtID xHAdHIlIL pID hH@Ht)HHH9tHHH)HHHHHdž HI$H JLm DžHH@ H H@ H@ H HH@\ H@T H@L H@D H@< H@4 H@, ƅXƅY (. `I$ I$ HMJc4I$ HUH H|HcHIH)HH@2 I$M|$I$ AEAEH 1LI$ HUHLHpB42LEM菸M$ I$ HX@EI $H 0LI$ HQ(HI0ƅXH HEX- HEHHHHHEI|@H HH@0HH $HHHMLLHEI@YUHc]HL8HA DуD9fA.D (uz<ulHEH LA< t[I
- HHHHH9tI| PH@HHHHHAtID xHAdHIlIL pID hH@Ht)HHH9tHHH)HHHHH荃 HcEKL5IHHEPH92 腃 HxHHH5 H 蛃 V HxHHH5 H l HI= HIH@Ht)HHH9tHHH)HHHHHȂ L u-HHQH1HH}n͂ LmH}W趂 LmH= u Hx [A\A]A^A_]HH Hu H詋H衋UHAWAVATSMIωIHcI$ HTIc7I$ T Ic6I$ t;t M$ Ic?I\$(D0:LKHs 4Ic<rHH{8Hz Bf(* fB(I$ Ic>MT$(L@>MJIZ Ic<^HIz8H~ NfF( Ic6I|$(D@2LGH_ Ic4ZHHw8Hr Bf() fB(I$ Ic7I|$(L02LGH_ Ic4ZHHw8Hr JfB(M$ I$ It$Hc<LNH^0Hc<H H Hc I Hc4H{H4H\pH|x~0 HZ(Hz0IX(Ix0t [A\A^A_]DXD Y DixtHZ(B0A@(Ix0B(Hz0IX(A@0 UH1x
HcH ] UHAVSHsH2 HHH( nH Hǃ@ Hǃ8 Hǃ0 Hǃ( Hǃ Hǃ Hǃ Hǃ Hǃ Hǃ Hǃ Hǃ Hǃ Hǃ Hǃ Hǃ H [A^]IHwL!| ff. UHAVSHrH HHH( mH ~ Hǃ@ Hǃ8 Hǃ0 Hǃ( Hǃ Hǃ Hǃ Hǃ Hǃ Hǃ Hǃ Hǃ Hǃ Hǃ Hǃ Hǃ H [A^]IHuL{ ff. UHAWAVATSIH HII0 Ht>I8 H9t-HAH)HHHHHRHHI8 H} I Ht;I H9t*HAH)HHHHHRHI } I Ht;I H9t*HAH)HHHHHRHI | M ILJ MtYMt$ID$HtBHH fffff. I| ID H9tHtHP(
+ 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]LuL9tfp9 f.CrHI9u@fffff. tIMI;Mt HIMLH訣9 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 f. IH@XH1LHPHt,H9XtHX fWf)PHDž` f(f)PHH`IH@XH1LHpHt/H9xtHx{ ID$ ID$ I$ f(f)pHHEHE HE2HE HE HE HE HE HE HE HDž LeHLH? H1 Ld$HpHD$L4$H=C H L8LP 1L1 HDž H]H֒ H 9 L8L@M9 M)II vH8lHL8pIrhLH1ۺ Af A(HH1A<ADAD9AfA.D(uzDHEHHI9u1M I Lc0IKD5 HI
+ HHHH9tK|5PIt PIT X*AID xKD5xID hIL pKL5pKD5hI H Lc M L L(M9 M)II vH ]kHL oIrgLH1ۺ A A(HH1A| ADAD9AfA.D(uzDHEHHI9u1M IK&HIT
+ HHHH9tK|&PIt PIT X@ID xKD&xID hIL pKL&pKD&hDžHDž HDž HDž HDž H HHDž HDž HDž HDž HDž HDž HDž ƅƅ f(8 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
+ HHHH9tHpIt PIT X=ID xHHHBID hIL pHJHI I D| HH HHX2I Lc M IK&
+ HHHH9tK|&PHHD=IEKD&xIE IMKL&pKD&h xtI HcHxeM HI
+ HH HHH9tI| PHpHx<HHHAID xHHIIL pID hHpHt)HxH9tHHH)HHHHxŎ HHt)HH9tHHH)HHHH萎 H}IHpHtH9xtHxf HPHtH9XtHXE H HtpH(H9t\ffffff. HHH(HxHt*HHH9tHQH)HHHHP H(H9HuH Hɍ H8HtdH@H9tP HHH@HxHt*HHH9tHQH)HHHHPw H@H9HuH8HY H H H;Eu-H [A\A]A^A_]HHuHz H
+ э IHd I I I I}I I% IHpHt)HxH9tHHH)HHHHx蚌 HHt)HH9tHHH)HHHHe H} HpHtH9xtHx; 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=D HMLELM̺" u1 E9N؉]LcmA9 DHpJ HELHHHEH7 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 (0 `I$ I$ HMJc4I$ HUH H|HcHIH)HH@p I$M|$I$ AEAEH 1LI$ HUHLHpB42LEMM$ I$ HX@EI $H 0LI$ HQ(HI0ƅXH HEX/ HEHHHHHEI|@H HH@0HH $HHHMLLHEI@YMHc]HL8HA DʃD9fA.D (uz<upHH HEA< t[I
+ HHHHH9tI| PH@HH4HHHAtID xHAdHIlIL pID hH@Ht)HHH9tHHH)HHHHHdž HI$H JLm DžHH@ H H@ H@ H HH@\ H@T H@L H@D H@< H@4 H@, ƅXƅY (- `I$ I$ HMJc4I$ HUH H|HcHIH)HH@2 I$M|$I$ AEAEH 1LI$ HUHLHpB42LEMOM$ I$ HX@EI $H 0LI$ HQ(HI0ƅXH HEX, HEHHHHHEI|@H HH@0HH $HHHMLLHEI@YUHc]HL8HA DуD9fA.D (uz<ulHEH LA< t[I
+ HHHHH9tI| PH@HH1HHHAtID xHAdHIlIL pID hH@Ht)HHH9tHHH)HHHHH荃 HcEKL5IHHEPH92 腃 HxHHH5 H8 蛃 V HxHHH5 H l HI= HIH@Ht)HHH9tHHH)HHHHHȂ L u-HHQH1HWH}辗͂ LmH}觗趂 LmH= u Hx [A\A]A^A_]HH Hu HHUHAWAVATSMIωIEHcI$ HTIc7I$ T Ic6I$ t;t M$ Ic?I\$(D0:LKHs 4Ic<rHH{8Hz Bf() fB(I$ Ic>MT$(L@>MJIZ Ic<^HIz8H~ NfF( Ic6I|$(D@2LGH_ Ic4ZHHw8Hr Bf(( fB(I$ Ic7I|$(L02LGH_ Ic4ZHHw8Hr JfB(M$ I$ It$Hc<LNH^0Hc<H H Hc I Hc4H{H4H\pH|x~0 HZ(Hz0IX(Ix0t [A\A^A_]DXD Ys DixtHZ(B0A@(Ix0B(Hz0IX(A@0 UH1x
HcH ] UHAVSHquHR HHH( rH Hǃ@ Hǃ8 Hǃ0 Hǃ( Hǃ Hǃ Hǃ Hǃ Hǃ Hǃ Hǃ Hǃ Hǃ Hǃ Hǃ Hǃ H [A^]IHxL!| ff. UHAVSHQtH2 HHH( qH ~ Hǃ@ Hǃ8 Hǃ0 Hǃ( Hǃ Hǃ Hǃ Hǃ Hǃ Hǃ Hǃ Hǃ Hǃ Hǃ Hǃ Hǃ H [A^]IHwL{ ff. UHAWAVATSIH HII0 Ht>I8 H9t-HAH)HHHHHRHHI8 H} I Ht;I H9t*HAH)HHHHHRHI } I Ht;I H9t*HAH)HHHHHRHI | M ILJ MtYMt$ID$HtBHH fffff. I| ID H9tHtHP(
@ 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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+ @ 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( yL[A\A^A_] tff. UH]w f D UHSPHw HH[]y @ UHAWAVAUATSHIILuwX A jLc E E EU E9~
+E E1E McIELxH H Au11 HcIHc H@Hȋ1I AtAHcIHc4HRH9LHcTIHctHRH9LHL9|EIcHHH @IK@H9HBHI I HH)HH)vI sHH9tI I I HH)HH)vI ɟsHH9tI I0 Icu 1IH Icu 1A HcIh I` HH)HHH)vI` \A #s!HH9tHKH)HHHIh HcIx I HH)HHH)vIx a\#s!H H9tHPH)HHHI AE HcI I HH)HHH)vI \AE #s!HH9tHKH)HHHI HcI I HH)HHH)vI [#s!H H9tHPH)HHHI I Hc I I HH)HHH)vI R[#s!H H9tHPH)HHHI 8 mv I]KPHHH@ I I Ht 4v I]8 2v KPHHH@ I I Htu M( I LɮA A A *A ^A I@ L踋Mc} P LHPHXHHHBPHXHAtu HHMLxt0HLHH @ @H@8 H HPH°uM I MtXM|$ID$Ht=HH f I| ID H9tHtHP(
@ HP HðuLt E} McP LHPHXHHHBPHXHAt LxHHEt1HIK @H@8 H HPH°uM I MtTM|$ID$Ht=HH f I| ID H9tHtHP(
- @ HP HðuLs IEH HXI I HH)HIIHH)vI /s-H[HH9t HAH)IHHHRHI I I HH)HIHH)vI R /s-H[HH9t HAH)IHHHRHI IcFHI0 I8 HH)HIHH)vI0 5s3H@HHH9t#HAH)IHHHRHHI8 H= H HMȾ 1M s HEȃxH L H E1E1E1E1HMffff. B<I H LJ H @H HH0 LhHEH LHEH HEH0 LH9HMHQH H Jc4HJc4HRH HcH IIHcIHIPI0I9HMBH[A\A]A^A_] UHAWAVATSHIII$XH E ]E E D?HHEH= HMLELMܺ" DjjPq H E9N؉]HcE9& I$LBH H H @HI0 b f. Hc4I4Lc HvJ461Hc4I4Lc HvJ4 q Hc4I4Hc<HvH4~yH H;( t@<
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
+ @ HP HðuLs IEH HXI I HH)HIIHH)vI /s-H[HH9t HAH)IHHHRHI I I HH)HIHH)vI R /s-H[HH9t HAH)IHHHRHI IcFHI0 I8 HH)HIHH)vI0 5s3H@HHH9t#HAH)IHHHRHHI8 H=' H HMȾ 1M s HEȃxH L H E1E1E1E1HMffff. B<I H LJ H @H HH0 LhHEH LHEH HEH0 LH9HMHQH H Jc4HJc4HRH HcH IIHcIHIPI0I9HMBH[A\A]A^A_] UHAWAVATSHIII$XH E ]E E D?HHEH= HMLELMܺ" DjjPq H E9N؉]HcE9& I$LBH H H @HI0 R f. Hc4I4Lc HvJ461Hc4I4Lc HvJ4 q Hc4I4Hc<HvH4~yH H;( t@<
ff. 1@yH8 H;@ f(t I AI HQ I$LBH H Hc4I4Lc HvJ4v0q(HcuH0H9H@H=R DJp H[A\A^A_] D UHAVSH聁L( H Lk * ^ H@ L_H Hc H H HH)HHH)vH S#s!H H9tHPH)HHHH H0 H8 H)t<H HHHH 1fffff. HHH0H9r[A^]fff. UHSPH2H H@H @H H H HELE +H H @ A( A(H H@ @ @(H H[]fUHAWAVAUATSH(AAHqI HHD AHHD EM L HK(H{Hs HQA9 H}HuEe IcHq 4HcAuHHA8IE HE HE LULuH= L%< LELM 1LLARAVm HHEIEHEIE(T 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(P AE(L HC(HKHS HMHUEe IcHPHp 4Hc AuHHH8IM HE HE LELM 1H= LLH]SAVk HHEIEHEIE( AE(H([A\A]A^A_]ffffff. UHAWAVAUATSH IH H HEIc] HW)`HDžp y H`h Hj HHhH`HHHpHCHHt"H HhHHhHHuHrjfffff. HhHHHh@ HhHHHh@ HhHHHh@ HhHHhHuH= H6 H` 1LIwj 1I}h ELH褮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 ȹW)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
HpH4
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-HHHpA0A 9A L9`W)@HDžP )0HDž@ I 0HH@L_ I 0HH0Ln_ H@H0L^_ H(H8A I` Mx M A D)H H H HT$$ SIE H H`1LH0Ht)H8H9tHHH)HHHH8#a H@Ht)HHH9tHHH)HHHHH` HHt;HH9t*HAH)HHHHHRHH` HHt;HH9t*HAH)HHHHHRHH`` H Ht;HH9t*HAH)HHHHHRHH` HHt;HH9t*HAH)HHHHHRHH_ HHtmHH9tYfff. HHHHxHt*HHH9tHQH)HHHHPw_ HH9HuHHY_ H HtdH(H9tP HHH(HxHt*HHH9tHQH)HHHHP_ H(H9HuH H^ H}`HPHtkHXH9tWf. HHHXHxHt*HHH9tHQH)HHHHP^ HXH9HuHPHi^ HhHtdHpH9tP HHHpHxHt*HHH9tHQH)HHHHP^ HpH9HuHhH] H`HtH9htHh] HK H H;Eu-H [A\A]A^A_]H HuH[ H] P^ IH [ %I/ In I I IH0Ht)H8H9tHHH)HHHH8#] H@Ht@HHH9tHHH)HHHHH\ I) I IHHt;HH9t*HAH)HHHHHRHH\ HHt;HH9t*HAH)HHHHHRHHI\ H Ht;HH9t*HAH)HHHHHRHH\ HHt;HH9t*HAH)HHHHHRHH[ HHtfHH9tR D HHHHxHt*HHH9tHQH)HHHHPg[ HH9HuHHI[ H HtdH(H9tP HHH(HxHt*HHH9tHQH)HHHHPZ H(H9HuH HZ H}\HPHtkHXH9tWf. HHHXHxHt*HHH9tHQH)HHHHPwZ HXH9HuHPHYZ HhHtdHpH9tP HHHpHxHt*HHH9tHQH)HHHHPZ HpH9HuHhHY H`HtH9htHhY LW fUHAWAVATSHIIA\$ E ]E E D?HHEH= HMLELMܺ" DjjPY H E9N؉]HcM9lHHHHfffff. IT$8|
+HHHpA0A 9A L9`W)@HDžP )0HDž@ I 0HH@L_ I 0HH0Ln_ H@H0L^_ H(H8A I` Mx M A D)H3 H H HT$$ #IE H H`1LH0Ht)H8H9tHHH)HHHH8#a H@Ht)HHH9tHHH)HHHHH` HHt;HH9t*HAH)HHHHHRHH` HHt;HH9t*HAH)HHHHHRHH`` H Ht;HH9t*HAH)HHHHHRHH` HHt;HH9t*HAH)HHHHHRHH_ HHtmHH9tYfff. HHHHxHt*HHH9tHQH)HHHHPw_ HH9HuHHY_ H HtdH(H9tP HHH(HxHt*HHH9tHQH)HHHHP_ H(H9HuH H^ H}dHPHtkHXH9tWf. HHHXHxHt*HHH9tHQH)HHHHP^ HXH9HuHPHi^ HhHtdHpH9tP HHHpHxHt*HHH9tHQH)HHHHP^ HpH9HuHhH] H`HtH9htHh] Hk H H;Eu-H [A\A]A^A_]H HuH[ H] P^ IH [ %I/ In I I IH0Ht)H8H9tHHH)HHHH8#] H@Ht@HHH9tHHH)HHHHH\ I) I IHHt;HH9t*HAH)HHHHHRHH\ HHt;HH9t*HAH)HHHHHRHHI\ H Ht;HH9t*HAH)HHHHHRHH\ HHt;HH9t*HAH)HHHHHRHH[ HHtfHH9tR D HHHHxHt*HHH9tHQH)HHHHPg[ HH9HuHHI[ H HtdH(H9tP HHH(HxHt*HHH9tHQH)HHHHPZ H(H9HuH HZ H}`HPHtkHXH9tWf. HHHXHxHt*HHH9tHQH)HHHHPwZ HXH9HuHPHYZ HhHtdHpH9tP HHHpHxHt*HHH9tHQH)HHHHPZ HpH9HuHhHY H`HtH9htHhY LW fUHAWAVATSHIIA\$ E ]E E D?HHEH= HMLELMܺ" DjjPY H E9N؉]HcM9lHHHHfffff. IT$8|
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-]IT$p fffff. HcHHPH9|H=2 DJY H[A\A^A_] D UHAWAVAUATSH LMLEHMIAZ B E ]E E 7HEH$D$ D$ H= HMLELMԺ" LUȉuX HuȋE9N؉]LceA9 H HHEfffff. HEH B< H~HFpH B MH AIQO<IJL8D迊HEL@pH HH@PHp(H@0H}L7MIK<.CD.hH K\.(LX 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+(X3 HX`ULhHpHxK|@H HH@0HxH $HXH`HMLhLpHMJD+(I AY HEH MB (HuHcEI9Md$ U HMHHH5d H (U T HMHHH58 Hy T uHHT u-HHQH1HiH}CiT HuWH},iT Hu@H= uT H [A\A]A^A_]HHTT HJT H~]Hv]f D UHAWAVAUATSHhIIIIM IEH9tHPH)HHHIU IL E IFHcXLHfWf)EHE }y H}Q H H<@nS 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. LL5 I9uH]HELu 11zHH)HHHI9sUHEH+EHiЫANH}1~8 HuHMKvHH9t'HAH)HHHHRHHUHuHUHxHHuH)IIHLE1H= A |GL5 E1Lfffff. H H<@LlP Hu
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$H=s H L8LP 1LG HDž H]H&E H 9 L8L@M9 M)II vH8 HL8pIrhLH1ۺ Af A(HH1A<ADAD9AfA.D(uzDHEHHI9u1M I Lc0IKD5 HI
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- HHHH9tK|&PIt PIT XSID xKD&xID hIL pKL&pKD&hDžHDž HDž HDž HDž H HHDž HDž HDž HDž HDž HDž HDž ƅƅ f([ fDž HDž, HDž$ HDž@ HDž8 HHHDž HDžx HDžp HDžh HDž` HDžX HDžP ƅƅ fM I HcHI
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C 8 _NSt3__112basic_stringIcNS_11char_traitsIcEENS_9allocatorIcEEEE NSt3__121__basic_string_commonILb1EEE NSt3__110__function6__funcIZN8LightGBM9PredictorC1EPNS2_8BoostingEibbbbidEUlRKNS_6vectorINS_4pairIidEENS_9allocatorIS8_EEEEPdE_NS9_ISF_EEFvSD_SE_EEE NSt3__110__function6__baseIFvRKNS_6vectorINS_4pairIidEENS_9allocatorIS4_EEEEPdEEE ZN8LightGBM9PredictorC1EPNS_8BoostingEibbbbidEUlRKNSt3__16vectorINS3_4pairIidEENS3_9allocatorIS6_EEEEPdE_ NSt3__110__function6__funcIZN8LightGBM9PredictorC1EPNS2_8BoostingEibbbbidEUlRKNS_6vectorINS_4pairIidEENS_9allocatorIS8_EEEEPdE0_NS9_ISF_EEFvSD_SE_EEE ZN8LightGBM9PredictorC1EPNS_8BoostingEibbbbidEUlRKNSt3__16vectorINS3_4pairIidEENS3_9allocatorIS6_EEEEPdE0_ NSt3__110__function6__funcIZN8LightGBM9PredictorC1EPNS2_8BoostingEibbbbidEUlRKNS_6vectorINS_4pairIidEENS_9allocatorIS8_EEEEPdE1_NS9_ISF_EEFvSD_SE_EEE ZN8LightGBM9PredictorC1EPNS_8BoostingEibbbbidEUlRKNSt3__16vectorINS3_4pairIidEENS3_9allocatorIS6_EEEEPdE1_ 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 NSt3__110__function6__baseIFviRKNS_6vectorINS_12basic_stringIcNS_11char_traitsIcEENS_9allocatorIcEEEENS6_IS8_EEEEEEE NSt3__117bad_function_callE ZN8LightGBM9Predictor7PredictEPKcS2_bEUliRKNSt3__16vectorINS3_12basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEENS8_ISA_EEEEE_ NSt3__110__function6__funcIZN8LightGBM10TextReaderIiE35ReadAllAndProcessParallelWithFilterERKNS_8functionIFviRKNS_6vectorINS_12basic_stringIcNS_11char_traitsIcEENS_9allocatorIcEEEENSA_ISC_EEEEEEERKNS5_IFbiiEEEEUlPKcmE_NSA_ISR_EEFmSQ_mEEE NSt3__110__function6__baseIFmPKcmEEE ZN8LightGBM10TextReaderIiE35ReadAllAndProcessParallelWithFilterERKNSt3__18functionIFviRKNS2_6vectorINS2_12basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEENS8_ISA_EEEEEEERKNS3_IFbiiEEEEUlPKcmE_ NSt3__110__function6__funcIZN8LightGBM10TextReaderIiE25ReadAllAndProcessParallelERKNS_8functionIFviRKNS_6vectorINS_12basic_stringIcNS_11char_traitsIcEENS_9allocatorIcEEEENSA_ISC_EEEEEEEEUliiE_NSA_ISL_EEFbiiEEE NSt3__110__function6__baseIFbiiEEE ZN8LightGBM10TextReaderIiE25ReadAllAndProcessParallelERKNSt3__18functionIFviRKNS2_6vectorINS2_12basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEENS8_ISA_EEEEEEEEUliiE_ N8LightGBM8BoostingE N8LightGBM8GBDTBaseE NSt3__119basic_ostringstreamIcNS_11char_traitsIcEENS_9allocatorIcEEEE NSt3__110__function6__funcIZ25LGBM_BoosterPredictForCSCE3$_0NS_9allocatorIS2_EEFNS_6vectorINS_4pairIidEENS3_IS7_EEEEiEEE NSt3__110__function6__baseIFNS_6vectorINS_4pairIidEENS_9allocatorIS4_EEEEiEEE Z25LGBM_BoosterPredictForCSCE3$_0 NSt3__110__function6__funcIZ26RowFunctionFromDenseMatricPKviiiiE3$_1NS_9allocatorIS4_EEFNS_6vectorIdNS5_IdEEEEiEEE NSt3__110__function6__baseIFNS_6vectorIdNS_9allocatorIdEEEEiEEE Z26RowFunctionFromDenseMatricPKviiiiE3$_1 NSt3__110__function6__funcIZ26RowFunctionFromDenseMatricPKviiiiE3$_2NS_9allocatorIS4_EEFNS_6vectorIdNS5_IdEEEEiEEE Z26RowFunctionFromDenseMatricPKviiiiE3$_2 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+ /Users/runner/runners/2.160.1/work/1/s/src/c_api.cpp Check failed: num_used_row_indices > 0 at %s, line %d .
+ Used indices of subset used_row_indices should be sorted in 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 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,
+ Size of feature_names error, should equal with total number of features Do not support non-ASCII characters in feature name. Do not support special JSON 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/2.155.1/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/2.155.1/work/1/s/src/lightgbm_R.cpp group query init_score Check failed: out_len == len at %s, line %d .
+ /Users/runner/runners/2.160.1/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 The number of features in data (%d) is not the same as it was in training data (%d). 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/runner/runners/2.160.1/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 .
+ /Users/runner/runners/2.160.1/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/2.155.1/work/1/s/src/boosting/goss.hpp Check failed: config_->top_rate > 0.0f && config_->other_rate > 0.0f at %s, line %d .
+ /Users/runner/runners/2.160.1/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/2.155.1/work/1/s/src/boosting/rf.hpp Check failed: config->feature_fraction <= 1.0f && config->feature_fraction > 0.0f at %s, line %d .
+ /Users/runner/runners/2.160.1/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 .
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 .
+ /Users/runner/runners/2.160.1/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/2.155.1/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 ,
+ /Users/runner/runners/2.160.1/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":[" "," "], "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 .
+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 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 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/runner/runners/2.160.1/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 .
- 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).
+ Do not support non-ASCII characters in config. 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/runner/runners/2.160.1/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_logloss 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/2.155.1/work/1/s/src/io/config_auto.cpp learning_rate Check failed: learning_rate >0.0 at %s, line %d .
+ [objective: [metric: [tree_learner: [device_type: binary_logloss ndcg lambdarank map mean_average_precision kldiv kullback_leibler Parameter %s should be of type int, got "%s" valid num_iterations Check failed: num_iterations >=0 at %s, line %d .
+ /Users/runner/runners/2.160.1/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 .
@@ -3207,11 +3175,12 @@
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: [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 .
+ /Users/runner/runners/2.160.1/work/1/s/src/io/dataset.cpp Check failed: num_total_features_ == static_cast<int>(bin_mappers->size()) at %s, line %d .
+ 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 .
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
@@ -3223,11 +3192,11 @@
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
+ /Users/runner/runners/2.160.1/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,
@@ -3236,253 +3205,250 @@
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/2.155.1/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 .
+ /Users/runner/runners/2.160.1/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_ 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 .
+ The output cannot be monotone with respect to categorical features 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: dataset->num_total_features_ == static_cast<int>(feature_names_.size()) at %s, line %d .
+ 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 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,
+ 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/runner/runners/2.160.1/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/runner/runners/2.160.1/work/1/s/include/LightGBM/utils/common.h Check failed: eval_at->at(i) > 0 at %s, line %d .
+ /Users/runner/runners/2.160.1/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.
+ /Users/runner/runners/2.160.1/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/runner/runners/2.160.1/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/2.155.1/work/1/s/src/objective/regression_objective.hpp Check failed: threshold < weighted_cdf[pos] at %s, line %d .
+ /Users/runner/runners/2.160.1/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/2.155.1/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.
+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/runner/runners/2.160.1/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/2.155.1/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 .
+ /Users/runner/runners/2.160.1/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/runner/runners/2.160.1/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/2.155.1/work/1/s/src/treelearner/feature_histogram.hpp Check failed: cache_size_ >= 2 at %s, line %d .
- cegb_penalty_feature_coupled should be the same size as feature number. cegb_penalty_feature_lazy should be the same size as feature number. 'Forced Split' will be ignored since the gain getting worse. Invalid categorical threshold split H L XQ X ! Q! Q aaQ Q % p в D! `
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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 4GPUTreeLearnerEE11BeforeTrainEvENUlPKcPciiE_8__invokeES4_S5_ii 7SerialTreeLearnerEE11BeforeTrainEvENUlPKcPciiE_8__invokeES4_S5_ii G G N ZN8LightGBM St3__1 8LightGBM 6json11 З 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 M 1__basic_string_commonILb1EEE 0__shared_ptr_emplaceIN6json11 M funcI baseIF ZN8LightGBM NS_6__bindIMN8LightGBM16FeatureHistogramEFvddiddPNS3_9SplitInfoEEJPS4_RNS_12placeholders4__phILi1EEERNSB_ILi2EEERNSB_ILi3EEERNSB_ILi4EEERNSB_ILi5EEERNSB_ILi6EEEEEENS_9allocatorISO_EEFvddiddS6_EEE ի 9Predictor 1 2RF12TrainOneIterEPKfS5_EUlS5_iE_NS_9allocatorIS6_EEFdS5_iEEE 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 M v mPKcmEEE b NS_ dPKfiEEE RKNS_6vectorINS_4pairIidEENS_9allocatorIS4_EEEEPdEEE PKcPNS_6vectorINS_4pairIidEENS_9allocatorIS6_EEEEEEE i mPKcmEEE ddiddPN8LightGBM9SplitInfoEEEE ۫M 9Predictor 1 2RF12TrainOneIterEPKfS2_EUlS2_iE_ C1EPNS_8BoostingEibbbbidEUlRKNSt3__16vectorINS3_4pairIidEENS3_9allocatorIS6_EEEEPdE 7PredictEPKcS2_bEUl _ 0_ 1_ 2_ M M M M M N N basic_stringstreamIcNS_11char_traitsIcEENS_9allocatorIcEEEE __weak_result_typeIMN8LightGBM16FeatureHistogramEFvddiddPNS1_9SplitInfoEEEE N N S5_PNS_6vectorINS_4pairIidEENS_9allocatorIS8_EEEEE_NS9_ISD_EEFvS5_SC_EEE iRKNS_6vectorINS_12basic_stringIcNS_11char_traitsIcEENS_9allocatorIcEEEENSA_ISC_EEEEE_NSA_ISH_EEFviSG_EEE N ЄN S2_PNSt3__16vectorINS3_4pairIidEENS3_9allocatorIS6_EEEEE_ 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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 ܹNS_6__bindIMN8LightGBM16FeatureHistogramEFvddiddPNS3_9SplitInfoEEJPS4_RNS_12placeholders4__phILi1EEERNSB_ILi2EEERNSB_ILi3EEERNSB_ILi4EEERNSB_ILi5EEERNSB_ILi6EEEEEENS_9allocatorISO_EEFvddiddS6_EEE 9Predictor 1 2RF12TrainOneIterEPKfS5_EUlS5_iE_NS_9allocatorIS6_EEFdS5_iEEE C1EPNS2_8BoostingEibbbbidEUlRKNS_6vectorINS_4pairIidEENS_9allocatorIS8_EEEEPdE 7PredictEPKcS5_bEUl _NS9_ISF_EEFvSD_SE_EEE 0_NS9_ISF_EEFvSD_SE_EEE ٽ1_NS9_ISF_EEFvSD_SE_EEE 2_NS9_ISF_EEFvSD_SE_EEE R 9Predictor ʼ1 2RF12TrainOneIterEPKfS2_EUlS2_iE_ C1EPNS_8BoostingEibbbbidEUlRKNSt3__16vectorINS3_4pairIidEENS3_9allocatorIS6_EEEEPdE 7PredictEPKcS2_bEUl _ ӽ0_ ߽1_ 2_ R R R R R R R basic_stringstreamIcNS_11char_traitsIcEENS_9allocatorIcEEEE __weak_result_typeIMN8LightGBM16FeatureHistogramEFvddiddPNS1_9SplitInfoEEEE R R R S5_PNS_6vectorINS_4pairIidEENS_9allocatorIS8_EEEEE_NS9_ISD_EEFvS5_SC_EEE iRKNS_6vectorINS_12basic_stringIcNS_11char_traitsIcEENS_9allocatorIcEEEENSA_ISC_EEEEE_NSA_ISH_EEFviSG_EEE R S2_PNSt3__16vectorINS3_4pairIidEENS3_9allocatorIS6_EEEEE_ iRKNSt3__16vectorINS3_12basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEENS8_ISA_EEEEE_ R RKNS_6vectorINS_12basic_stringIcNS_11char_traitsIcEENS_9allocatorIcEEEENS6_IS8_EEEEEEE PKcmEEE iiEEE R R R R 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 R 0TextReaderI 3 iE mE1 35ReadAllAndProcessParallelWithFilterERKNSt3__18functionIFviRKNS2_6vectorINS2_12basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEENS8_ISA_EEEEEEERKNS3_IFbiiEEEEUlPKcmE_ 2 1 9CountLineEvEUliPKcmE_ R i PKdiEEE fEEE iEEE EEE R 5ReadAllAndProcessParallelERKNS_8functionIFviRKNS_6vectorINS_12basic_stringIcNS_11char_traitsIcEENS_9allocatorIcEEEENSA_ISC_EEEEEEEEUliiE_NSA_ISL_EEFbiiEEE 3SampleAndFilterFromFileERKNS_8functionIFbiEEEPNS_6vectorIiNS_9allocatorIiEEEEPNS2_6RandomEiPNSA_INS_12basic_stringIcNS_11char_traitsIcEENSB_IcEEEENSB_ISL_EEEEEUliPKcmE_NSB_ISR_EEFviSQ_mEEE 6ReadPartAndProcessParallelERKNS_6vectorIiNS_9allocatorIiEEEERKNS_8functionIFviRKNS5_INS_12basic_stringIcNS_11char_traitsIcEENS6_IcEEEENS6_ISG_EEEEEEEEUliiE_NS6_ISP_EEFbiiEEE R 5ReadAllAndProcessParallelERKNSt3__18functionIFviRKNS2_6vectorINS2_12basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEENS8_ISA_EEEEEEEEUliiE_ 3SampleAndFilterFromFileERKNSt3__18functionIFbiEEEPNS2_6vectorIiNS2_9allocatorIiEEEEPNS_6RandomEiPNS8_INS2_12basic_stringIcNS2_11char_traitsIcEENS9_IcEEEENS9_ISJ_EEEEEUliPKcmE_ 6ReadPartAndProcessParallelERKNSt3__16vectorIiNS2_9allocatorIiEEEERKNS2_8functionIFviRKNS3_INS2_12basic_stringIcNS2_11char_traitsIcEENS4_IcEEEENS4_ISE_EEEEEEEEUliiE_ R GBDTBaseE BoostingE DenseBinI L R R 6vectorI 4pairIidEEiEEE NS_4pairIidEENS_9allocatorIS4_EEEEiEEE dNS_9allocatorIdEEEEiEEE R R R R 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 7JsonIntENS_9allocatorIS2_EEEE 9JsonArrayENS_9allocatorIS2_EEEE S S S 1JsonBooleanENS_9allocatorIS2_EEEE 0Json S S S DoubleENS_9allocatorIS2_EEEE StringENS_9allocatorIS2_EEEE ObjectENS_9allocatorIS2_EEEE S dEE iEE S ЏS S S S S ГS S S S ParserE MetricE S LibSVMParserE BinaryMetricINS_1 CrossEntropyE ЗS S S S S MetricINS_ L 8L 1 2MetricEEE 1MetricEEE S 2MetricE 1MetricE S 0 4 5HuberLossMetricEEE 3 1GammaMetricEEE 9GammaDevianceMetricEEE RMSEMetricEEE MAPEMetricEEE S ЛS S S QuantileMetricEEE FairLossMetricEEE S QuantileMetricE FairLossMetricE LambdarankNDCGE GPUTreeLearnerE ОS S S S СS PoissonMetricEEE TweedieMetricEEE S S 9BinaryLoglossMetricEEE 7BinaryErrorMetricEEE S BinaryLoglossMetricE GammaDevianceMetricE Regression ФS S S S S S classMetricINS_ ErrorMetricE 25MultiSoftmaxLoglossMetricEEE 16MultiErrorMetricEEE ЩS MultiSoftmaxLoglossMetricE KullbackLeiblerDivergenceE VotingParallelTreeLearnerINS_1 S S S CrossEntropy Regression MetricE LambdaE ЬS S S S S S аS S S S гS ȵS 2lossE 1lossE S S иS HuberLossE GammaLossE S FairLossE MAPELOSSE S PoissonLossE TweedieLossE S S S BinaryLoglossC1ERKNS2_6ConfigENS_8functionIFbfEEEEUlfE_NS_9allocatorISA_EES8_EE MulticlassOVAC1ERKNS2_6ConfigEEUlfE_NS_9allocatorIS7_EEFbfEEE S BinaryLoglossC1ERKNS_6ConfigENSt3__18functionIFbfEEEEUlfE_ MulticlassOVAC1ERKNS_6ConfigEEUlfE_ S S S S S S S S S S S 3DataParallelTreeLearnerINS_1 6FeatureParallelTreeLearnerINS_1 5VotingParallelTreeLearnerINS_1 4GPUTreeLearnerEEE 7SerialTreeLearnerEEE S S S 4GPUTreeLearnerEEE 7SerialTreeLearnerEEE S S 4GPUTreeLearnerEEE 7SerialTreeLearnerEEE S S 4GPUTreeLearnerEEE 7SerialTreeLearnerEEE S S S S S S 4GPUTreeLearnerEEE 7SerialTreeLearnerEEE S S 4GPUTreeLearnerEEE 7SerialTreeLearnerEEE S S S S S S S S S K@P*"! 0p00 `0@`@0 0 @0 @0 0@0P`P@ @0 P@ p@ @ @0 @ @0 @P`0@ @ @0 @0 @0 @0 P0@ P0@ P00 P00 @p
- 0 " ` @0 0 p pp@0 0 P0 0 P`P `0#
+Get UpdateOneIter Num PredictFor
DumpModel reate alcNumPredict FromModelfile _R _R _R ree
+eatureImportance _R huffleModels
+aveModel etLeafValue _R _R e
+ollbackOneIter set fit TrainingData Parameter _R _R Num CurrentIteration Eval
FeatureNames
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_R Custom _R _R _R _R ModelPerIteration
berOfTotalModel
Counts
Names
И_R _R _R Ч_R File CS Mat Щ_R _R R C SingleRow _R SingleRow s _R Ё ToString _R _R _R Init Free WithFunctions Й ZN8LightGBM MN8LightGBM7LinkersEFviEPS3_iEEEEEPvS8_ 14PipelineReader4ReadEPKciRKNS_8functionIFmS5_mEEEEUlvE_EEEEEPvSD_ 7Linkers8SendRecvEiPciiS4_iEUlvE_EEEEEPvS7_ Ђ Ј Х 1 2 8 4 3 49 G6 j7 x 1 0 /6 12 23 f5GetBoostingTypeERKNSt3__113unordered_mapINS0_12basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEES7_NS0_4hashIS7_EENS0_8equal_toIS7_EENS5_INS0_4pairIKS7_S7_EEEEEEPS7_ p8GetTreeLearnerTypeERKNSt3__113unordered_mapINS0_12basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEES7_NS0_4hashIS7_EENS0_8equal_toIS7_EENS5_INS0_4pairIKS7_S7_EEEEEEPS7_ w9 }7 Application Get tTreeLearner17CreateTreeLearnerERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEES9_PKNS_6ConfigE C 1 D 8LoadDataEv 9InitTrainEv 5TrainEv 7PredictEv 2EiPPc 1EiPPc 4LoadParametersEiPPc 1InitPredictEv 2ConvertModelEv 2Ev 1Ev Щ З
+
+ 7Network 2 3Log8GetLevelEvE5level 17GlobalSyncUpBy 9GlobalSumI M SumI , inI axIiEET_S2_ENUlPKcPciiE_8__invokeES4_S5_ii iEET_S2_ENUlPKcPciiE_8__invokeES4_S5_ii dEET_S2_ENUlPKcPciiE_8__invokeES4_S5_ii
+
+ 8GetBoostingTypeFromModelFileEPKc 7ObtainAutomaticInitialScoreEPKNS_17ObjectiveFunctionEi %4 f3 h0GetLabelIdxForLibsvmERKNSt3__112basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEEii 6FeatureParallelTreeLearnerINS_1 5VotingParallelTreeLearnerINS_1
+ B DenseBinI 5M | oosting1 ruckMap ζ 8LoadFileToBoostingEPS0_PKc 4CreateBoostingERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEPKc
+
+ GBDT Tree ɬ C !D !4InitEPKNS_6ConfigEPKNS_7DatasetEPKNS_17ObjectiveFunctionERKNSt3__16vectorIPKNS_6MetricENSA_9allocatorISE_EEEE !1 !8BoostingEv $2 $7BaggingEi %5TrainEiRKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE %9RefitTreeERKNSt3__16vectorINS2_IiNS1_9allocatorIiEEEENS3_IS5_EEEE % 2Ev !1Ev ! 2Ev !1Ev !0Ev ! 8ResetBaggingConfigEPKNS_6ConfigEb #5 #3BaggingHelperEPNS_6RandomEiiPi $6 %2 %1 &7ResetTrainingDataEPKNS_7DatasetEPKNS_17ObjectiveFunctionERKNSt3__16vectorIPKNS_6MetricENS7_9allocatorISB_EEEE ,9LoadModelFromStringEPKcm /
AddValidDatasetEPKNS_7DatasetERKNSt3__16vectorIPKNS_6MetricENS4_9allocatorIS8_EEEE $RollbackOneIterEv &
1BalancedBaggingHelperEPNS_6RandomEiiPi %5EvalAndCheckEarlyStoppingEv & д
ж
BoostFromAverageEib %GetTrainingScoreEPx ,
TrainOneIterEPKfS2_ &OutputMetricEi &GetPredictAtEiPdPx ,
UpdateScoreEPKNS_4TreeEi 'ResetConfigEPKNS_6ConfigE , 8LightGBM '6json11 4 '8 69 H1 _6Config w7Dataset1 2 GBDT 'Tree 1 )9 +7PredictEPKdPdPKNS_27PredictionEarlyStopInstanceE /21PredictLeafIndexByMapERKNSt3__113unordered_mapIidNS1_4hashIiEENS1_8equal_toIiEENS1_9allocatorINS1_4pairIKidEEEEEEPd / 3 +4PredictContribEPKdPdPKNS_27PredictionEarlyStopInstanceE ,7 -5 -0PredictRawEPKdPdPKNS_27PredictionEarlyStopInstanceE /2PredictByMapERKNSt3__113unordered_mapIidNS1_4hashIiEENS1_8equal_toIiEENS1_9allocatorINS1_4pairIKidEEEEEEPdPKNS_27PredictionEarlyStopInstanceE /6PredictLeafIndexEPKdPd / EvalOneMetricEPKNS_6MetricEPKd +ModelToIfElseEi - GetEvalAtEi ,DumpModelEii - У Щ о dEET_S2_ENUlPKcPciiE_8__invokeES4_S5_ii -iEET_S2_ENUlPKcPciiE_8__invokeES4_S5_ii SaveModelTo -FeatureImportanceEii / IfElseEiPKc -StringEii - Ѝ М SaveModelToFileEiiPKc /PredictRawByMapERKNSt3__113unordered_mapIidNS1_4hashIiEENS1_8equal_toIiEENS1_9allocatorINS1_4pairIKidEEEEEEPdPKNS_27PredictionEarlyStopInstanceE / д CreateNoneERKNS_25PredictionEarlyStopConfigE 1NeedFilterERKNSt3__16vectorIiNS0_9allocatorIiEEEEiiNS_7BinTypeE fFindGroupsERKNSt3__16vectorINS0_10unique_ptrINS_9BinMapperENS0_14default_deleteIS3_EEEENS0_9allocatorIS6_EEEERKNS1_IiNS7_IiEEEEPPiPKiimiiib }kHdfsProtoE CreateMulticlassERKNS_25PredictionEarlyStopConfigE 2OrderedSparseBinI LGet pFeatureHistogram2 CreateBinaryERKNS_25PredictionEarlyStopConfigE 4ParseMetricsERKNSt3__112basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEEPNS0_6vectorIS6_NS4_IS6_EEEE pGetStatisticEPKcPiS2_S2_ 3CreatePredictionEarlyStopInstanceERKNSt3__112basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEERKNS_25PredictionEarlyStopConfigE 5Bin j hE 5tE ;jE A C 5D 64PushEiij 66ReSizeEi 61 :5CloneEv ; 2E 61E 6 i 6RKS1_ ; i 6RKS1_ ; 2Ev 61Ev 60Ev 6 DenseBinI 6Metadata1 hE 7tE <jE B 1 75SplitEjjjNS_11MissingTypeEbjPiiS3_S3_ 98num_dataEv : 1 78ConstructHistogramE 86 9 GetIteratorEjjj 8SizesInByteEv ; PKiiPKf 8iPKf 8 S5_PNS_17HistogramBinEntryE 8PNS_17HistogramBinEntryE 9 S3_PNS_17HistogramBinEntryE 9PNS_17HistogramBinEntryE 9 S 9CreateOrderedBinEv : plitCategoricalEjjjPKjiPiiS4_S4_ :aveBinaryToFileEPKNS_17VirtualFileWriterE ; 0 :4LoadFromMemoryEPKvRKNSt3__16vectorIiNS4_9allocatorIiEEEE ; FinishLoadEv ;CopySubsetEPKNS_3BinEPKii ; C <D <4PushEiij <6ReSizeEi <1 @5CloneEv A 2E <1E < i <RKS1_ A i <RKS1_ A 2Ev <1Ev <0Ev < 1 =5SplitEjjjNS_11MissingTypeEbjPiiS3_S3_ ?8num_dataEv @ 1 =8ConstructHistogramE =6 ? GetIteratorEjjj =SizesInByteEv A PKiiPKf >iPKf > S5_PNS_17HistogramBinEntryE >PNS_17HistogramBinEntryE ? S3_PNS_17HistogramBinEntryE ?PNS_17HistogramBinEntryE ? Њ S ?CreateOrderedBinEv @ plitCategoricalEjjjPKjiPiiS4_S4_ @aveBinaryToFileEPKNS_17VirtualFileWriterE A 0 @4LoadFromMemoryEPKvRKNSt3__16vectorIiNS4_9allocatorIiEEEE A FinishLoadEv ACopySubsetEPKNS_3BinEPKii AА Ж C AD B4PushEiij B6ReSizeEi B1 F5CloneEv G 2E A1E B i BRKS1_ GИ i BRKS1_ G 2Ev B1Ev B0Ev BЛ Н 1 C5SplitEjjjNS_11MissingTypeEbjPiiS3_S3_ D8num_dataEv E 1 C8ConstructHistogramE C6 E GetIteratorEjjj CSizesInByteEv G PKiiPKf CiPKf D S5_PNS_17HistogramBinEntryE DPNS_17HistogramBinEntryE D S3_PNS_17HistogramBinEntryE DPNS_17HistogramBinEntryE D S ECreateOrderedBinEv E plitCategoricalEjjjPKjiPiiS4_S4_ EaveBinaryToFileEPKNS_17VirtualFileWriterE G 0 F4LoadFromMemoryEPKvRKNSt3__16vectorIiNS4_9allocatorIiEEEE F FinishLoadEv FCopySubsetEPKNS_3BinEPKii Fа б г SparseBinI GBinMapper d hE GtE OjE W C GD H6ReSizeEi H4PushEiij H1 M5CloneEv O 2E H1E H i HRKS1_ O i HRKS1_ O 2Ev H1Ev H0Ev H н SparseBinI HBinMapper i hE ItE PjE X 1 I5SplitEjjjNS_11MissingTypeEbjPiiS3_S3_ K8num_dataEv L 1 I8ConstructHistogramE J6 K GetIteratorEjjj JNextNonzeroEPiS2_ KSizesInByteEv O PKiiPKf JiPKf J S5_PNS_17HistogramBinEntryE JPNS_17HistogramBinEntryE K S3_PNS_17HistogramBinEntryE KPNS_17HistogramBinEntryE Kп S KCreateOrderedBinEv L plitCategoricalEjjjPKjiPiiS4_S4_ LaveBinaryToFileEPKNS_17VirtualFileWriterE O hE LtE TjE \ C M10SparsePairC _D _4InitEPKci _5SplitEiiPKcc a 1EPKNS_9SparseBinIhEE M2EPKNS_9SparseBinIhEE _ 0 N2 N4LoadFromMemoryEPKvRKNSt3__16vectorIiNS4_9allocatorIiEEEE O FinishLoadEv NCopySubsetEPKNS_3BinEPKii O LoadFromPairERKNSt3__16vectorINS2_4pairIihEENS2_9allocatorIS5_EEEE OGetFastIndexEv O C PD P6ReSizeEi P4PushEiij P1 U5CloneEv W 2E P1E P i PRKS1_ W i PRKS1_ W 2Ev P1Ev P0Ev P З 1 Q5SplitEjjjNS_11MissingTypeEbjPiiS3_S3_ S8num_dataEv T 1 Q8ConstructHistogramE R6 S GetIteratorEjjj RNextNonzeroEPiS2_ SSizesInByteEv W PKiiPKf RiPKf R S5_PNS_17HistogramBinEntryE RPNS_17HistogramBinEntryE S S3_PNS_17HistogramBinEntryE SPNS_17HistogramBinEntryE SЙ S SCreateOrderedBinEv T plitCategoricalEjjjPKjiPiiS4_S4_ TaveBinaryToFileEPKNS_17VirtualFileWriterE W Х C T10SparsePairC aD a4InitEPKci a5SplitEiiPKcc b 1EPKNS_9SparseBinItEE U2EPKNS_9SparseBinItEE a 0 U2 V4LoadFromMemoryEPKvRKNSt3__16vectorIiNS4_9allocatorIiEEEE W FinishLoadEv VCopySubsetEPKNS_3BinEPKii W LoadFromPairERKNSt3__16vectorINS2_4pairIitEENS2_9allocatorIS5_EEEE VGetFastIndexEv W C WD X6ReSizeEi X4PushEiij X1 \5CloneEv ^ 2E W1E X i WRKS1_ ^ i XRKS1_ ^ 2Ev X1Ev X0Ev X 1 Y5SplitEjjjNS_11MissingTypeEbjPiiS3_S3_ [8num_dataEv \ 1 Y8ConstructHistogramE Y6 [ GetIteratorEjjj YNextNonzeroEPiS2_ [SizesInByteEv ^ PKiiPKf YiPKf Z S5_PNS_17HistogramBinEntryE ZPNS_17HistogramBinEntryE Z S3_PNS_17HistogramBinEntryE ZPNS_17HistogramBinEntryE Z S [CreateOrderedBinEv \ plitCategoricalEjjjPKjiPiiS4_S4_ [aveBinaryToFileEPKNS_17VirtualFileWriterE ^ C \10SparsePairC bD c4InitEPKci c5SplitEiiPKcc d 1EPKNS_9SparseBinIjEE \2EPKNS_9SparseBinIjEE b 0 ]2 ]4LoadFromMemoryEPKvRKNSt3__16vectorIiNS4_9allocatorIiEEEE ^ FinishLoadEv ]CopySubsetEPKNS_3BinEPKii ^ LoadFromPairERKNSt3__16vectorINS2_4pairIijEENS2_9allocatorIS5_EEEE ^GetFastIndexEv ^Ѕ Д 2Ev _1Ev _ 2Ev _1Ev _0Ev _г 6OrderedSparseBinI _7SerialTreeLearner1 hE1 `tE1 ajE1 c 8ConstructHistogramEiPKf `2NonZeroCountEi a S3_PNS_17HistogramBinEntryE `PNS_17HistogramBinEntryE a 2Ev a1Ev a 2Ev a1Ev a0Ev a 8ConstructHistogramEiPKf b2NonZeroCountEi b S3_PNS_17HistogramBinEntryE bPNS_17HistogramBinEntryE b 2Ev b1Ev b 2Ev c1Ev c0Ev c 8ConstructHistogramEiPKf c2NonZeroCountEi d S3_PNS_17HistogramBinEntryE dPNS_17HistogramBinEntryE d C e8CopyFromEPKc eD f7FindBinEPdimiiiNS_7BinTypeEbbRKNSt3__16vectorIdNS3_9allocatorIdEEEE i18SizeForSpecificBinEi i 2E e1E e v eRKS0_ ePKv e v eRKS0_ ePKv f 2Ev f1Ev f G fD фkModelVersionE reedyFindBinEPKdPKiiimi fet r FindBinWithPredefinedBinEPKdPKiiimiRKNSt3__16vectorIdNS4_9allocatorIdEEEE hCheckMultiClassObjectiveERKNSt3__112basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEE w Г FindBinWithZeroAsOneBinEPKdPKiiimi hDataParallelTreeLearnerINS_1 RKNSt3__16vectorIdNS4_9allocatorIdEEEE i 6CopyToEPc i1 i 6SaveBinaryToFileEPKNS_17VirtualFileWriterE j1SizesInByteEv j 9CreateBinEiidbdPb j1 j 5CreateSparseBinEii j4CreateDenseBinEii j Config kParser12CreateParserEPKcbii ìMetric12CreateMetricERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEERKNS_6ConfigE ȶ 6KV2MapEPNSt3__113unordered_mapINS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEES8_NS1_4hashIS8_EENS1_8equal_toIS8_EENS6_INS1_4pairIKS8_S8_EEEEEEPKc p7Str2MapEPKc p3SetERKNSt3__113unordered_mapINS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEES8_NS1_4hashIS8_EENS1_8equal_toIS8_EENS6_INS1_4pairIKS8_S8_EEEEEE w1 w20GetMembersFromStringERKNSt3__113unordered_mapINS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEES8_NS1_4hashIS8_EENS1_8equal_toIS8_EENS6_INS1_4pairIKS8_S8_EEEEEE x Ъ ObjectiveTypeERKNSt3__113unordered_mapINS0_12basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEES7_NS0_4hashIS7_EENS0_8equal_toIS7_EENS5_INS0_4pairIKS7_S7_EEEEEEPS7_ rConfilctCountERKNSt3__16vectorIbNS0_9allocatorIbEEEEPKiii | Д MetricTypeERKNSt3__113unordered_mapINS0_12basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEES7_NS0_4hashIS7_EENS0_8equal_toIS7_EENS5_INS0_4pairIKS7_S7_EEEEEEPNS0_6vectorIS7_NS5_IS7_EEEE tDeviceTypeERKNSt3__113unordered_mapINS0_12basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEES7_NS0_4hashIS7_EENS0_8equal_toIS7_EENS5_INS0_4pairIKS7_S7_EEEEEEPS7_ w К TaskTypeERKNSt3__113unordered_mapINS0_12basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEES7_NS0_4hashIS7_EENS0_8equal_toIS7_EENS5_INS0_4pairIKS7_S7_EEEEEEPNS_8TaskTypeE wDataTypeERKNSt3__16vectorINS0_12basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEENS5_IS7_EEEEPi 8CheckParamConflictEv w1alias_tableE 3parameter_setE 8ToStringEv x19SaveMembersToStringEv x Dataset yN |GetLineEPNSt3__118basic_stringstreamIcNS0_11char_traitsIcEENS0_9allocatorIcEEEEPNS0_12basic_stringIcS3_S5_EEPKNS_17VirtualFileReaderEPNS0_6vectorIcS5_EEm Linkers ĸ C {D |9ConstructEPNSt3__16vectorINS1_10unique_ptrINS_9BinMapperENS1_14default_deleteIS4_EEEENS1_9allocatorIS7_EEEEiRKNS2_INS2_IdNS8_IdEEEENS8_ISD_EEEEPPiPKiimRKNS_6ConfigE ~1 ~21CopyFeatureMapperFromEPKS0_ р6ReSizeEi ݀ 2E {1E { v {i { v {i | 2Ev |1Ev | Ђ oGroupERKNSt3__16vectorIiNS0_9allocatorIiEEEE |etwork Ј arkUsedEPNSt3__16vectorIbNS0_9allocatorIbEEEEPKii }etadata FastFeatureBundlingERKNSt3__16vectorINS0_10unique_ptrINS_9BinMapperENS0_14default_deleteIS3_EEEENS0_9allocatorIS6_EEEEPPiPKiimRKNS1_IiNS7_IiEEEEdiidbb ~RecursiveHalvingMap 1 0 3 4 2 5addFeaturesFromEPS0_ ˄7binary_file_tokenE ResetConfigEPKc CreateValidEPKS0_ ׀SetIntFieldEPKcPKii GetIntFieldEPKcPiPPKi FinishLoadEv ˀCopySubsetEPKS0_PKiib Ћ Б SetFloatFieldEPKcPKfi GetFloatFieldEPKcPiPPKf ж S ȁGetDoubleFieldEPKcPiPPKd etDoubleFieldEPKcPKdi aveBinaryFileEPKc ̂ GetInt8FieldEPKcPiPPKa ƂDumpTextFileEPKc ҂ 9ConstructHistogramsERKNSt3__16vectorIaNS1_9allocatorIaEEEEPKiiiPNS2_INS1_10unique_ptrINS_10OrderedBinENS1_14default_deleteISB_EEEENS3_ISE_EEEEPKfSJ_PfSK_bPNS_17HistogramBinEntryE 2FixHistogramEiddiPNS_17HistogramBinEntryE ń ! ! ! atasetLoader CGCalculator ز C 9SetHeaderEPKc D 1 2 ։3 2ERKNS_6ConfigERKNSt3__18functionIFvRKNS4_6vectorINS4_4pairIidEENS4_9allocatorIS8_EEEEPdEEEiPKc 1ERKNS_6ConfigERKNSt3__18functionIFvRKNS4_6vectorINS4_4pairIidEENS4_9allocatorIS8_EEEEPdEEEiPKc " " # 2Ev 1Ev Т# # 2 9CheckCanLoadFromBinEPKc Љ5LoadFromBinFileEPKcS2_iiPiPNSt3__16vectorIiNS4_9allocatorIiEEEE 3GetForcedBinsENSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEiRKNS1_13unordered_setIiNS1_4hashIiEENS1_8equal_toIiEENS5_IiEEEE LoadFromFileEPKcS2_ii ʉCheckDatasetEPKNS_7DatasetE г# # 0LoadTextDataToMemoryEPKcRKNS_8MetadataEiiPiPNSt3__16vectorIiNS7_9allocatorIiEEEE 4SampleTextDataFromMemoryERKNSt3__16vectorINS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEENS6_IS8_EEEE 5ExtractFeaturesFromMemoryEPNSt3__16vectorINS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEENS6_IS8_EEEEPKNS_6ParserEPNS_7DatasetE 2 3ExtractFeaturesFromFileEPKcPKNS_6ParserERKNSt3__16vectorIiNS6_9allocatorIiEEEEPNS_7DatasetE # # 1ConstructBinMappersFromTextDataEiiRKNSt3__16vectorINS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEENS6_IS8_EEEEPKNS_6ParserEPNS_7DatasetE 3LoadFromFileAlignWithOtherDatasetEPKcS2_PKNS_7DatasetE # И$ SampleTextDataFromFileEPKcRKNS_8MetadataEiiPiPNSt3__16vectorIiNS7_9allocatorIiEEEE CostructFromSampleDataEPPdPPiiPKimi $ Ц$ $ $ $ $ % & VirtualFile GetLabelIdxFor ReadKLineFromFileEPKcbi ObjectiveFunction23CreateObjectiveFunctionERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE HistogramBinEntry10SumReducerEPKcPcii SerialTreeLearner Reader4MakeERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE Writer & 4MakeERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE 6ExistsERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE & & 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 ǘ v Dn d i b RKNSt3__1 ONSt3__1 PKc & v Dn d i b RKNSt3__1 ONSt3__1 ӞPKc & & & & & & & & & 12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE 6vectorIS0_NS1_9allocatorIS0_EEEE 3mapINS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEES0_NS1_4lessIS8_EENS6_INS1_4pairIKS8_S0_EEEEEE ՠ & 12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE 6vectorIS0_NS1_9allocatorIS0_EEEE à3mapINS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEES0_NS1_4lessIS8_EENS6_INS1_4pairIKS8_S0_EEEEEE ۠ & 12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE ͞6vectorIS0_NS1_9allocatorIS0_EEEE ɠ3mapINS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEES0_NS1_4lessIS8_EENS6_INS1_4pairIKS8_S0_EEEEEE & 12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE 6vectorIS0_NS1_9allocatorIS0_EEEE Ϡ3mapINS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEES0_NS1_4lessIS8_EENS6_INS1_4pairIKS8_S0_EEEEEE & & & & & & & & & & & & 2 0bool_valueEv 1array_itemsEv number_valueEv աstring_valueEv object_itemsEv & int_valueEv has_shapeERKSt16initializer_listINSt3__14pairINS2_12basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEENS0_4TypeEEEERS9_ & & & & & m ףRKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE ݣ & & Value ArrayixEm 1 9int_valueEv ixE 2 ä0bool_valueEv 1array_itemsEv number_valueEv string_valueEv object_itemsEv & & & & & & m RKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE & & & & & & & ' ' C Ŧ4InitE 1 D 8Set 2Ev զ1Ev ۦ ( ( PKcS2_ iii RKS0_PKii ( 9LoadQueryBoundariesEv 1LoadWeightsEv 6 4 2SetInitScoreEPKdi 0SetWeightsEPKfi А( З( Load CheckOrPartitionEiRKNSt3__16vectorIiNS1_9allocatorIiEEEE QueryWeightsEv InitialScoreEPKc ( ( 2Ev 1Ev ( ( ( ( PartitionLabelERKNSt3__16vectorIiNS1_9allocatorIiEEEE LoadFromMemoryEPKv ( ( ( LabelEPKfi QueryEPKii ( ( ) ) 6SaveBinaryToFileEPKNS_17VirtualFileWriterE 1SizesInByteEv ) ) ) ) TSVERKNSt3__112basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEEii CSVERKNSt3__112basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEEii ) ) ) ) ) ) C D 5SplitEiiijdddiiddfNS_11MissingTypeEb ٭1 ߭ 2E 1E i PKcPm ܱ Ѝ* i PKcPm * 2Ev ͭ1Ev ӭ * * * 6SplitCategoricalEiiiPKjiS2_iddiiddfNS_11MissingTypeE 0 4UnwoundPathSumEPKNS0_11PathElementEii 7RecomputeMaxDepthEv Ҳ * 2 8T 6ToJSONEv ް1 0AddPredictionToScoreEPKNS_7DatasetE 3NumericalDecisionIfElseEi 5CategoricalDecisionIfElseEi ı iPd PKiiPd * * o reeSHAPEPKdPdiiPNS0_11PathElementEddi Ʋ StringEv ذIfElseEib ʱ * + 0NodeToJSONEi 2NodeToIfElseEib б7NodeToIfElseByMapEib ֱ3ExpectedValueEv ̲ + + + + + + + , ExtendPathEPNS0_11PathElementEiddi UnwindPathEPNS0_11PathElementEii , , , , , , 1 4InitERKNSt3__16vectorIdNS1_9allocatorIdEEEE 9 6CalDCGERKNSt3__16vectorIiNS1_9allocatorIiEEEEPKfPKdiPNS2_IdNS3_IdEEEE 3DefaultEvalAtEPNSt3__16vectorIiNS1_9allocatorIiEEEE 6DefaultLabelGainEPNSt3__16vectorIdNS1_9allocatorIdEEEE 2 0CheckLabelEPKfi ¶1label_gain_E - - - CalMaxDCGAtKEiPKfi kMaxPositionE - Cal յdiscount_E MaxDCGERKNSt3__16vectorIiNS1_9allocatorIiEEEEPKfiPNS2_IdNS3_IdEEEE DCGAtKEiPKfPKdi - - - - г. C 9ConstructEii 2E 1E v i 4 v i 4 4 4 4 C ·9ConstructEii 2E з1E v iNS_24RecursiveHalvingNodeTypeEb 4 v iNS_24RecursiveHalvingNodeTypeEb 4 4 4 4 C 1 7TryBindEi 9 D κ 2ENS_6ConfigE 1ENS_6ConfigE Ⱥ 4 6ParseMachineListERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEES9_ 2 1CheckLinkerEi 4 5 ConstructEv ºSetLinkerEiRKNS_9TcpSocketE 5 5 2Ev 1Ev 5 5 г5 ListenThreadEi PrintLinkersEv 5 5 к5 5 4InitE ڻ7DisposeEv 9All 2 1 NS_6ConfigE iiPFvPciiPKiS3_iS1_iRKPFvPKcS1_iiEEPFvS1_iS3_S3_iS1_iE 5 WN8LightGBM7Network ԼS VN I ڬ 8linkers_E 5rank_E 1 2 7buffer_E 5 5 3num_machines_E ˽0b ѽ2b 8allgather_ext_fun_E 5 ruck_map_E lock_len_E 5 2recursive_halving_map_E 3reduce_scatter_ext_fun_E 5 lock_start_E ؾuffer_size_E 5 5 5 5 5 5 5 5 reduceEPciiS1_RKPFvPKcS1_iiE gatherEPc 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 6 з6 : 4GPUTreeLearnerEE 7SerialTreeLearnerEE C D 4InitEPKNS_7DatasetEb 1 28FindBestSplitsFromHistogramsERKNSt3__16vectorIaNS3_9allocatorIaEEEEb 5SplitEPNS_4TreeEiPiS5_ 2EPKNS_6ConfigE 1EPKNS_6ConfigE > > 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 І? ? 2Ev 1Ev 0Ev ? ? ? Ћ? 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 ? ? ? ? ? Ѕ@ C D 4InitEPKNS_7DatasetEb 11BeforeTrainEv 28FindBestSplitsFromHistogramsERKNSt3__16vectorIaNS3_9allocatorIaEEEEb 2EPKNS_6ConfigE 1EPKNS_6ConfigE @ Ў@ 2Ev 1Ev 0Ev @ @ @ @ @ @ C D 4InitEPKNS_7DatasetEb 1 5 28FindBestSplitsFromHistogramsERKNSt3__16vectorIaNS1_9allocatorIaEEEEb 2EPKNS_6ConfigE 1EPKNS_6ConfigE @ @ 2Ev 1Ev 0Ev @ @ @ @ 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 C 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 4GPUTreeLearnerEE11BeforeTrainEvENUlPKcPciiE_8__invokeES4_S5_ii 7SerialTreeLearnerEE11BeforeTrainEvENUlPKcPciiE_8__invokeES4_S5_ii G G 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 M 1__basic_string_commonILb1EEE 0__shared_ptr_emplaceIN6json11 M funcI baseIF ZN8LightGBM 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7ReadAllAndProcessERKNSt3__18functionIFviPKcmEEEEUlS5_mE_ 2ReadAllLinesEvEUliPKcmE_ 8ReadAndFilterLinesERKNSt3__18functionIFbiEEEPNS2_6vectorIiNS2_9allocatorIiEEEEEUliPKcmE_ Ӗ4SampleFromFileEPNS_6RandomEiPNSt3__16vectorINS4_12basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEENS9_ISB_EEEEEUliPKcmE_ N N ЪN N DARTE ύG ՍN OSSE BDTE ЬN RFE 1 5 4CrossEntropyLambdaMetricE 2RegressionQuantilelossE 3DataParallelTreeLearnerINS_1 6FeatureParallelTreeLearnerINS_1 N N N N N ifstreamIcNS_11char_traitsIcEEEE ofstreamIcNS_11char_traitsIcEEEE N N N N N hEE tEE jEE N N зN SparseBinI LocalFileE TSVParserE ۞CSVParserE AUCMetricE ӣMapMetricE ߣ hEE tEE jEE N N N
+6 0 ђ3 1 7 2 4 5HuberLossMetricE 9 Ţ8 OrderedSparseBinI DenseBinIteratorI Regression Multi ˤ hEE ˒tEE jEE N OrderedBinE RMSEMetricE ߟNDCGMetricE ٣MAPEMetricE N N N Dense4bitsBinE PoissonMetricE TweedieMetricE BinaryLoglossE էMulticlassOVAE N Dense4bitsBinIteratorE Regression N BinIteratorE GammaMetricE TreeLearnerE N hEE tEE jEE N N N S VirtualFile BinaryErrorMetricE ǣObjectiveFunctionE զMulticlassSoftmaxE parseBinIteratorI erialTreeLearnerE hEE tEE jEE N N N N N N N N N N N N N N N ReaderE WriterE N N 9Json 5ValueILNS_4Json4TypeE 1 8JsonNullE 7JsonIntE ԝ ValueE ArrayE N N 4ENSt3__16vectorIS1_NS3_9allocatorIS1_EEEEEE 5ENSt3__13mapINS3_12basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEES1_NS3_4lessISA_EENS8_INS3_4pairIKSA_S1_EEEEEEEE 0ENS_10NullStructEEE 2EbEE Ĝ1E 3ENSt3__112basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEEEE N 0Json ֚1JsonBooleanE ObjectE DoubleE StringE N N 8JsonNullENS_9allocatorIS2_EEEE 1 7JsonIntENS_9allocatorIS2_EEEE Ν9JsonArrayENS_9allocatorIS2_EEEE N N N 1JsonBooleanENS_9allocatorIS2_EEEE 0Json ʜN N N DoubleENS_9allocatorIS2_EEEE StringENS_9allocatorIS2_EEEE ObjectENS_9allocatorIS2_EEEE N N dEE ȝiEE ڝN N N N N N N N N LibSVMParserE BinaryMetricINS_1 CrossEntropyE N ParserE ՞MetricE ٟN N N N N 2MetricE 1MetricE N MetricINS_ L 8L 1 2MetricEEE ӟ1MetricEEE N N N 0 4 š5HuberLossMetricEEE 3 1GammaMetricEEE 9GammaDevianceMetricEEE RMSEMetricEEE ߠMAPEMetricEEE N N N QuantileMetricE FairLossMetricE LambdarankNDCGE GPUTreeLearnerE ݪN QuantileMetricEEE FairLossMetricEEE N N N N N N PoissonMetricEEE TweedieMetricEEE N BinaryLoglossMetricE GammaDevianceMetricE Regression N 9BinaryLoglossMetricEEE 7BinaryErrorMetricEEE ͣN N N N N N MultiSoftmaxLoglossMetricE ŤKullbackLeiblerDivergenceE VotingParallelTreeLearnerINS_1 ʫN classMetricINS_ ErrorMetricE 25MultiSoftmaxLoglossMetricEEE 16MultiErrorMetricEEE N N N CrossEntropy ߥRegression MetricE LambdaE N N N N N N O ЀO O O O 2lossE Ϧ1lossE ۦO O O O HuberLossE GammaLossE O FairLossE MAPELOSSE O PoissonLossE ϧTweedieLossE O O BinaryLoglossC1ERKNS2_6ConfigENS_8functionIFbfEEEEUlfE_NS_9allocatorISA_EES8_EE MulticlassOVAC1ERKNS2_6ConfigEEUlfE_NS_9allocatorIS7_EEFbfEEE O O BinaryLoglossC1ERKNS_6ConfigENSt3__18functionIFbfEEEEUlfE_ MulticlassOVAC1ERKNS_6ConfigEEUlfE_ O O ЈO O O O O O O O ЋO 4GPUTreeLearnerEEE ת7SerialTreeLearnerEEE O O O 4GPUTreeLearnerEEE 7SerialTreeLearnerEEE O O O O O O O O 4GPUTreeLearnerEEE 7SerialTreeLearnerEEE ГO O 8LightGBM 6json11 4GBDTE Ԭ1 կ8DenseBinI 9SparseBinI 2 R N ZN8LightGBM 8LightGBM 6json11 St3__1 ر 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 1JsonBooleanE ObjectE ϯDoubleE StringE R 7SerialTreeLearnerE 6OrderedSparseBinI R
+7 ΰ0 6 3 1 2 4 5HuberLossMetricE 9 8 S VirtualFile BinaryErrorMetricE ObjectiveFunctionE MulticlassSoftmaxE erialTreeLearnerE ұparseBinIteratorI R 2 1 ӳ6__bindIMN8LightGBM16FeatureHistogramEFvddiddPNS1_9SplitInfoEEJPS2_RNS_12placeholders4__phILi1EEERNS9_ILi2EEERNS9_ILi3EEERNS9_ILi4EEERNS9_ILi5EEERNS9_ILi6EEEEEE 1__basic_string_commonILb1EEE ͳ0__shared_ptr_emplaceIN6json11 R 2basic_stringIcNS_11char_traitsIcEENS_9allocatorIcEEEE 0__function6__ 8 ڽ5basic_stringbufIcNS_11char_traitsIcEENS_9allocatorIcEEEE 7bad_function_callE 9basic_ostringstreamIcNS_11char_traitsIcEENS_9allocatorIcEEEE 4basic_ 3basic_filebufIcNS_11char_traitsIcEEEE R baseIF funcI v b NS_ mPKcmEEE dPKfiEEE RKNS_6vectorINS_4pairIidEENS_9allocatorIS4_EEEEPdEEE ۷PKcPNS_6vectorINS_4pairIidEENS_9allocatorIS6_EEEEEEE i mPKcmEEE ddiddPN8LightGBM9SplitInfoEEEE R ZN8LightGBM NS_6__bindIMN8LightGBM16FeatureHistogramEFvddiddPNS3_9SplitInfoEEJPS4_RNS_12placeholders4__phILi1EEERNSB_ILi2EEERNSB_ILi3EEERNSB_ILi4EEERNSB_ILi5EEERNSB_ILi6EEEEEENS_9allocatorISO_EEFvddiddS6_EEE 9Predictor 1 2RF12TrainOneIterEPKfS5_EUlS5_iE_NS_9allocatorIS6_EEFdS5_iEEE 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 ½2_NS9_ISF_EEFvSD_SE_EEE νR 9Predictor 1 2RF12TrainOneIterEPKfS2_EUlS2_iE_ C1EPNS_8BoostingEibbbbidEUlRKNSt3__16vectorINS3_4pairIidEENS3_9allocatorIS6_EEEEPdE 7PredictEPKcS2_bEUl _ 0_ 1_ Ƚ2_ ԽR R R R R R R basic_stringstreamIcNS_11char_traitsIcEENS_9allocatorIcEEEE __weak_result_typeIMN8LightGBM16FeatureHistogramEFvddiddPNS1_9SplitInfoEEEE R R R S5_PNS_6vectorINS_4pairIidEENS_9allocatorIS8_EEEEE_NS9_ISD_EEFvS5_SC_EEE iRKNS_6vectorINS_12basic_stringIcNS_11char_traitsIcEENS_9allocatorIcEEEENSA_ISC_EEEEE_NSA_ISH_EEFviSG_EEE R 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3SampleAndFilterFromFileERKNS_8functionIFbiEEEPNS_6vectorIiNS_9allocatorIiEEEEPNS2_6RandomEiPNSA_INS_12basic_stringIcNS_11char_traitsIcEENSB_IcEEEENSB_ISL_EEEEEUliPKcmE_NSB_ISR_EEFviSQ_mEEE 6ReadPartAndProcessParallelERKNS_6vectorIiNS_9allocatorIiEEEERKNS_8functionIFviRKNS5_INS_12basic_stringIcNS_11char_traitsIcEENS6_IcEEEENS6_ISG_EEEEEEEEUliiE_NS6_ISP_EEFbiiEEE R 5ReadAllAndProcessParallelERKNSt3__18functionIFviRKNS2_6vectorINS2_12basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEENS8_ISA_EEEEEEEEUliiE_ 3SampleAndFilterFromFileERKNSt3__18functionIFbiEEEPNS2_6vectorIiNS2_9allocatorIiEEEEPNS_6RandomEiPNS8_INS2_12basic_stringIcNS2_11char_traitsIcEENS9_IcEEEENS9_ISJ_EEEEEUliPKcmE_ 6ReadPartAndProcessParallelERKNSt3__16vectorIiNS2_9allocatorIiEEEERKNS2_8functionIFviRKNS3_INS2_12basic_stringIcNS2_11char_traitsIcEENS4_IcEEEENS4_ISE_EEEEEEEEUliiE_ R GBDTBaseE BoostingE DenseBinI L R R 6vectorI 4pairIidEEiEEE NS_4pairIidEENS_9allocatorIS4_EEEEiEEE dNS_9allocatorIdEEEEiEEE R R R R 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 7JsonIntENS_9allocatorIS2_EEEE 9JsonArrayENS_9allocatorIS2_EEEE S S S 1JsonBooleanENS_9allocatorIS2_EEEE 0Json S S S DoubleENS_9allocatorIS2_EEEE StringENS_9allocatorIS2_EEEE ObjectENS_9allocatorIS2_EEEE S dEE iEE S ЏS S S S S ГS S S S ParserE MetricE S LibSVMParserE BinaryMetricINS_1 CrossEntropyE ЗS S S S S MetricINS_ L 8L 1 2MetricEEE 1MetricEEE S 2MetricE 1MetricE S 0 4 5HuberLossMetricEEE 3 1GammaMetricEEE 9GammaDevianceMetricEEE RMSEMetricEEE MAPEMetricEEE S ЛS S S QuantileMetricEEE FairLossMetricEEE S QuantileMetricE FairLossMetricE LambdarankNDCGE GPUTreeLearnerE ОS S S S СS PoissonMetricEEE TweedieMetricEEE S S 9BinaryLoglossMetricEEE 7BinaryErrorMetricEEE S BinaryLoglossMetricE GammaDevianceMetricE Regression ФS S S S S S classMetricINS_ ErrorMetricE 25MultiSoftmaxLoglossMetricEEE 16MultiErrorMetricEEE ЩS MultiSoftmaxLoglossMetricE KullbackLeiblerDivergenceE VotingParallelTreeLearnerINS_1 S S S CrossEntropy Regression MetricE LambdaE ЬS S S S S S аS S S S гS ȵS 2lossE 1lossE S S иS HuberLossE GammaLossE S FairLossE MAPELOSSE S PoissonLossE TweedieLossE S S S BinaryLoglossC1ERKNS2_6ConfigENS_8functionIFbfEEEEUlfE_NS_9allocatorISA_EES8_EE MulticlassOVAC1ERKNS2_6ConfigEEUlfE_NS_9allocatorIS7_EEFbfEEE S BinaryLoglossC1ERKNS_6ConfigENSt3__18functionIFbfEEEEUlfE_ MulticlassOVAC1ERKNS_6ConfigEEUlfE_ S S S S S S S S S S S 3DataParallelTreeLearnerINS_1 6FeatureParallelTreeLearnerINS_1 5VotingParallelTreeLearnerINS_1 4GPUTreeLearnerEEE 7SerialTreeLearnerEEE S S S 4GPUTreeLearnerEEE 7SerialTreeLearnerEEE S S 4GPUTreeLearnerEEE 7SerialTreeLearnerEEE S S 4GPUTreeLearnerEEE 7SerialTreeLearnerEEE S S S S S S 4GPUTreeLearnerEEE 7SerialTreeLearnerEEE S S 4GPUTreeLearnerEEE 7SerialTreeLearnerEEE S S S S S S S S S J@P*"! 0p00 `0@`@0 0 @0 @0 0@0P`P@ @0 P@ p@ @ @0 @ @0 @P`0@ @ @0 @0 @0 @0 P0@ P0@ P00 P00 @p
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