0PhddlZddlZddlZddlmZmZddlmZddlm Z m Z m Z m Z ddl mZddlmZmZddlmZmZmZmZmZmZmZmZmZmZmZmZddlm Z dd l!m"Z"m#Z#dd l$m%Z%e&ej'Z(d e(DZ)e)ed ed edededededededededg z Z)dZ* ddZ+dZ,ej-.de)e*dZ/egdej0 ej0gfegdej0 ej0gfegdej0 ej0gfegdej0 ej0gfeddgej0 d d!ej0gfeddgej0 d d!dej0gfed ddgej0 ej0gfedddgej0 ej0gfed"ddgej0 d d!ej0gfedddgej0 d d!dej0gfed#ddgej0 d d!dej0gfed ddgej0 ej0gfedgd$ej0 ej0gfed"ddgej0 d d!ej0gfedddgej0 d d!dej0gfed#ddgej0 d d!dej0gfegd%ej0 d&dej0gfegej0 d&d'ej0gfgZ1edggfedggfed gd(gfedd)dggfed"dggfed gd(gfedd)dggfed"dggfeddggfegd*gfg Z2egdgfed ggd+fedggd+fed"gdgfed ggd+fedgd+gfed"gdgfegddgfedd,gddgfg Z3ej-.d-e1e2zd.Z4ej-.d/e1e3zd0Z5ej-.d1ged2d3d4d5dfed2d3d6d2dfed, d2d3dd,dfed d2d3dddfed d3d2d2d7dfed,d#8d2d3d9ddfed,d#8d2dd:ddfed:ej6d5d5dej6d5zz dd5fed:ej6d5ej6d5d,zd,d,fed#d:ej6d5d;ddfedd:d6ddej6dzz ddfedd:d6ej6dd,z ddfed#d:d6ddddfed2d>d?d&dfed=d@dddfed2d@dAd&dfed2dBdCd&dfed2dDdEdFdGfed=dDgdGgd#zRed2dHdIdFdJfed=dHgdJgd#zRed=dKdEdLdGfed2dKgdMgd#zRed=dCdCddNfed=dOdOddPfed2d?dddfed=d?d?ddfed2dOddQdPfed2dRdddfed2dAdddfed=dAdAddfed#Sd=gdTegdTdUz ddfed#Sd2gdTegdTd,z ddfed#Sd:gdTegdTdVz ddfed#Sd:gdWegdWdXz ddfe*dYZ8ej-.de(ej-.dZd[d\gej-.d]ej9ej:gej-.d^ej9ej:gej-.d_ddgej-.d`ddgej-.daddgej-.dbddgdcZ;ej-.de)e*ej-.d_dddgdeZej-.de)e*djZ?ej-.de)e*ej-.d_dddgdkZ@ej-.de)e*ej-.d_dddgdlZAej-.dmgdne#doZBej-.de)e*ej-.d_dddgdpZCej-.dqeejDdrfeejEdrfed dsdrfeejDdtfeejDdufeejDdufeejDdvfgdwZFdxZGdyZHdzZIej-.d{ejJgd|ejJgd}fej-.d~ejJgdejJgdfdZKej-.de)e*dZLej-.de(ej-.d_dddgej-.dej9ej:fej-.dddZMej-.de(ej-.ddejNidejNdfgdZOej-.deddiePdfeddieQdfedd'ieQdfeddiePdfeddieQdfedd'ieQdfgdZRej-.de)e*dZSej-.dgddZTdS)N)assert_allcloseassert_array_equal)approx)LinearConstraintminimizeminimize_scalarnewton) logsumexp) IdentityLink_inclusive_low_high) _LOSSES AbsoluteErrorBaseLossHalfBinomialLoss HalfGammaLossHalfMultinomialLossHalfPoissonLossHalfSquaredErrorHalfTweedieLossHalfTweedieLossIdentity HuberLoss PinballLoss)assert_all_finite)create_memmap_backed_data skip_if_32bit)_IS_WASMc"g|] }| Sr).0losss ]/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/sklearn/_loss/tests/test_loss.py r"$s000T$$&&000g?)quantileg?power@crt|tr|}|jj}t|tr|d|jjdz }n[t|tr|d|jz }n8t|dr(t|jdr|d|jj dz }|St|S)Nz (quantile=)clossr'z(power=) isinstancer __class____name__rr-r$rhasattrr'str)paramr names r!loss_instance_namer55s%"" ~& dK ( ( 2 7!4777 7DD i ( ( 2 000 0DD T7 # # 2 G(D(D 2 1dj.111 1D 5zzr#d*ctj|}|jrtj||jf}||d|d||jz|jdd<tj| t|jz}n:t|j trSt|j\}} tj||dg}tj| |dg} || f}||d|d|}t|j\}} t'||d}t)| |d} ||| |}|jjdkr|jjr d|dd|dz<|jjdkr|jjr d|dd|dz<||fS)z9Random generate y_true and raw_prediction in valid range.rr()lowhighsizeNr@)nprandom RandomState is_multiclassempty n_classesuniformflatarangeastypefloatr.linkr r interval_y_predamaxamininterval_y_truemaxminr> low_inclusiver?high_inclusive) r n_samplesy_bound raw_boundseedrngraw_predictiony_truer>r?s r!random_y_true_raw_predictionr^Ds )   % %C .9dn"=>>!$! 1T^+"-" " AAA 9%%,,U33dnD di . . $+D,@AAIC'3 ! -..C7D)A,/00Dd I! 9Q !!r#ctj||}||d|zz }|||z }|||z}||d|zz}| d|zzd|zz |zd|zz S)z2Helper function for numerical (first) derivatives.) fill_valuer)(@)rC full_like)funcxepsh f_minus_2h f_minus_1h f_plus_1h f_plus_2hs r!numerical_derivativerlis Q3'''Aa!a%iJa!eJQU IQQYI JY &Z 7* DPS TTr#r )idsc|jr0d}tjtjd|dz |d}n.t |j\}}tj||d}|jjrtj||jjf}|jj rtj||jj f}| |sJ|j d}t |j \}}|jrgtj||f}tj||||dddf<dd|dddfz z|dddf<dd|dddfz z|dddf<ntj|||}||sJ|j|}||| dS) z4Test interval ranges of y_true and y_pred in losses.rBrr(num N?r)r]r\)rFrCtilelinspacer rRrUr_r>rVr?in_y_true_rangeshaperOrGin_y_pred_rangerNr )r rHr]r>r?ny_predr\s r!test_loss_boundaryr|ws 0 Q A 9EEEqII'(<== TS$B/// )9vt3778 *:vt3889    ' '''' QA#D$899IC /1i.)){3!444qqq!t a&A,./qqq!t a&A,./qqq!t S$A...    ' ''''Y^^F++NIIVNI;;;;;r#)r7r皙?r8r}r8皙?rB)r~rrr}r8)r}rr?g?)r7rrr7)?r))r~rrrrz!loss, y_true_success, y_true_failc|D],}|tj|gsJ-|D],}|tj|grJ-dS)z-Test boundaries of y_true for loss functions.N)rwrCarray)r y_true_success y_true_failys r!test_loss_boundary_y_truerz 33##BHaSMM222222 77''! 66666677r#z!loss, y_pred_success, y_pred_failc|D],}|tj|gsJ-|D],}|tj|grJ-dS)z-Test boundaries of y_pred for loss functions.N)ryrCr)r y_pred_success y_pred_failrs r!test_loss_boundary_y_predrrr#zDloss, y_true, raw_prediction, loss_true, gradient_true, hessian_truerg@rag@gп)r$deltag@@gȿg?rg@xg@xDg@g@@gBgB@gB%ggT`Grc|tj|gtj|g}|tj|gtj|g}|tj|gtj|g\}} |tj|gtj|g\} } |t |ddksJ|t |ddksJ|H|t |ddksJ| t |ddksJ| t |ddksJ|| t |ddksJdSdS)z7Test losses, gradients and hessians at specific values.rsV瞯<)relabsN)rCrgradient loss_gradientgradient_hessianr) r r]r\ loss_true gradient_true hessian_trueloss1grad1loss2grad2grad3hesss r!test_loss_on_specific_valuesrsx D&**28^DT;U;U V V VE MMx!!"(N;K2L2L   E%%x!!"(N;K2L2L&LE5''x!!"(N;K2L2L(KE4 F9%U;;; ; ; ; ; F9%U;;; ; ; ; ; }%UCCCCCCC}%UCCCCCCC}%UCCCCCCCvl5AAAAAAA AAr#readonly_memmapFTdtype_in dtype_out sample_weightout1out2 n_threadsctr|rtjd|}d}t||ddd\} } | |} | |} |t jd g|z| }|t j| | }|t j| | }|r/t| } t| } |t|}| | | ||| } || |usnnJ| | | ||| } || |usnnJ| | | ||||\} } || |usnnJ|| |usnnJ||j rt j| | }| | | ||||\} } || |usnnJ|| |usnnJ|| | ||| ||| |t!|dr|| t!|dr3|| | ||||\} }|| |usnnJ|||usndSJdSdS)a0Test acceptance of dtypes, readonly and writeable arrays in loss functions. Check that loss accepts if all input arrays are either all float32 or all float64, and all output arrays are either all float32 or all float64. Also check that input arrays can be readonly, e.g. memory mapped. zmemmap not fully supported)reasonr;r6rqr<r rWrXrYrZNrdtype)r]r\rloss_outrT)r]r\r gradient_outr)r]r\rrrr)r]r\rr hessian_outrr]r\rr]r predict_proba)r\gradient_proba)r]r\rr proba_outr)rpytestxfailr^rLrCr empty_likerr rrrFrfit_intercept_onlyconstant_to_optimal_zeror1rr)r rrrrrrrrWr]r\lgrgps r!test_loss_dtyperwsb$:O: 89999 466DI9   FN]]8 $ $F#**844N #!2(CCC  }V9555 }^9===E*6222>BB  $5mDDM %#   A(1999999 %#   A(1999999   %#   DAq(1999999(1999999 D.}^9===  %# !  DAq(1999999(1999999D~]SSSS6GGG!!}!MMMt_%%: .999t%&& 7"")' #  1!,qDyyyyyy ,qDyyyyyyy 7 7yr#rangect|dddd\}}|dkr-tjd|jd|jd }tj|}tj|}tj|}tj|}tj|}tj|} ||||| |j|||| ft||||||| |j|||| t|||j ||||| |j ||||| t||t||| ||||| |j ||||| t||t|| dS)z:Test that Python and Cython functions return same results.r6rr<rrr(rror]r\rrr]r\rrr]r\rrrr]r\rrrN) r^rCrurxrr r-rrrr) r rr]r\out_l1out_l2out_g1out_g2out_h1out_h2s r!test_loss_same_as_C_functionsrs:   FN Av|AFLOLLL ]6 " "F ]6 " "F ]> * *F ]> * *F ]> * *F ]> * *FII%#   JOO%#  FF###MM%#   J%#  FF###J%#  J%# FF###FF###%#  J%#  FF###FF#####r#ct|ddd|\}}|dkr-tjd|jd|jd}tj|}tj|}tj|}tj|}tj|} tj|} ||||| } ||||| } |||||| \} }||||| | \}}t| | t| |tj | |sJt| |tj | |sJt| |t| |t| |tj | |sJt||tj ||sJt|| tj || sJt|d r|j sJtj|}tj|}||||||\}}t| |t| |t||ttj|ddddSdS)zTest that loss and gradient are the same across different functions. Also test that output arguments contain correct results. rr6rrrr(rrorrrrrr]r\rrraxisdy=)rtolN)r^rCrurxrr rrrrr shares_memoryr1rFrsum)r rglobal_random_seedr]r\rrrrout_g3out_h3l1g1l2g2g3h3out_g4 out_probag4probas r! test_loss_gradients_are_the_samer.s/:   FN Av|AFLOLLL ]6 " "F ]6 " "F ]> * *F ]> * *F ]> * *F ]> * *F %#   B %#   B   %#  FB " "%# #FBBr6""" B ' ''''r6""" B ' ''''BBr6""" B ' ''''r6""" B ' ''''r6""" B ' ''''t%&&>!!!!~..M.11 '')' (  E F###By)))u1---qu======>>r#onesrDc d}t||dd|\}}|dkr!tj|tj}nRtj|}||tj}t| ||||| ||d z| ||d \}}| |||\} } t||z| |j st||z| nt||d d d fz| | ||d \}} | |||\} } |j s(t||z| t| |z| d St||d d d fz| t| |d d d fz| d S) zTest sample weights in loss, gradients and hessians. Make sure that passing sample weights to loss, gradient and hessian computation methods is equivalent to multiplying by the weights. r8r6r9rr)rxrrArN) r^rCrfloat64rDrEnormalrLrr rrFr) r rrrWr]r\r[lossesr losses_sw gradient_swhessian hessian_sws r!test_sample_weight_multipliesrstI9   FNirzBBB i##$677 2299"*EE  )'    )))      ))%*FH "//%#0I{ F]*I666  H=0+>>>>=D#99;GGG--%.Hg #33%#4K  F=0+>>>-/<<<<<=D#99;GGG-4"88*EEEEEr#c6t|dddd\}}|jdkr|dddf}t||||||t||||||t||||||t||||||dSdS) z5Test that reshaped raw_prediction gives same results.rr6rr<rr(Nrs)r^ndimrr rrr)r r]r\raw_prediction_2ds r!test_graceful_squeezingrsW:   FNa*111d73 IIV4EI F F IIVNI C C       f=N  O O   f^  L L     MM8IM J J MMM G G      ! !@Q ! R R  ! ! ! O O      r#c`|jstjgd}t|jt rRd}|jj}|jjs||z}|jj }|jj s||z }tj |||}|j |}ntj |jt }tj|j|jftjd t }tjd|jdd|jdz<|dkr-tjd|jd|jd }|||| }||| }t1|| d d dS)z~Test value of perfect predictions. Loss of y_pred = y_true plus constant_to_optimal_zero should sums up to zero. )rrrr}rBrq绽|=rq)rxr`rNr(rrrorr+=r)atolr)rFrCrr.rNr rOr>rUr?rVclipinverserKrHrLrMfullexprJrurxr rr) r rr\rfr>r?r] loss_value constant_terms r!test_loss_of_perfect_predictionrs  @"<"<"<== di . . @C&*C'5 Ci',D'6 "czW^S$??N"">224>**11%88>4>2r {    68VBZZ11t~112 Av|AFLOLLL %#J 11]2M JUGGGGGGr#c@ d}t|dd|\ dkr-tjd jd jd  \}}|j jksJ|j jksJjsc fd }t | d }t||d d fd}t | d } jrdSt|| d ddStj D]  fd}t | dd fd }t|dd f|d d  fd}t | dd fd } jrqt|dd f| d ddS)zTest gradients and hessians with numerical derivatives. Gradient should equal the numerical derivatives of the loss function. Hessians should equal the numerical derivatives of gradients. rr6r9rrr(rrorc4|SNr)r rer rr]s r! loss_funcz6test_gradients_hessians_numerically..loss_func6s'99 + r#gư>)rfh㈵>rrrc4|Srrrs r! grad_funcz6test_gradients_hessians_numerically..grad_func@s'== +! r#cn}||ddf<|Sr)copyr rerawkr r\rr]s r!r z6test_gradients_hessians_numerically..loss_funcUsI$))++AAAqD yy!#&"/!r#Ngh㈵>c}||ddf<|ddfSr)rrrs r!rz6test_gradients_hessians_numerically..grad_funcas[$))++AAAqD }}!#&"/%!!Q$ r#) r^rCrurxrrFrlrapprox_hessianrrH) r rrrWrrgr  g_numericr h_numericrr\r]s `` @@@r!#test_gradients_hessians_numericallyrsI9   FN Av|AFLOLLL  %# !  DAq 7n* * * * * 7n* * * * *  ;K       )NMMM 94e<<<<       )NMMM   A D Ayt% @ @ @ @ @ @t~&& K KA         -Yqqq!t8LRVWWWI AaaadGYT F F F F         -Yqqq!t8LRVWWWI" K!!!Q$EJJJJJ9 K Kr#zloss, x0, y_true) ) squared_errorgr<)rg@]@g?)rrr) binomial_lossrr})rir)rr) poisson_lossrbr)rrr)rg6g$@ctdtjgtjtj|gtj}dtjdtjffd }dtjdtjffd }dtjdtjffd }t ||||d d }|}tj |t||d d t |d ddS)acTest that gradients are zero at the minimum of the loss. We check this on a single value/sample using Halley's method with the first and second order derivatives computed by the Loss instance. Note that methods of Loss instances operate on arrays while the newton root finder expects a scalar or a one-element array for this purpose. Nrrrereturnc`|zS)zCompute loss plus constant term. The constant term is such that the minimum function value is zero, which is required by the Newton method. rsr])r rrer r]s r!rdztest_derivatives..funcs> yy!   ) ) ) 8 89 9r#c2|S)Nrsr r"s r!fprimez test_derivatives..fprimes}}F1}===r#c>|dS)Nrsr()rr"s r!fprime2z!test_derivatives..fprime2s!$$F1$EEaHHr#r8gHj>)x0r$r&maxitertolrrrrsgƠ>) r rCrrndarrayr ravelrrNrr)r r'r]rdr$r&optimums` ` r!test_derivativesr.rs4 4=t , , ,D Xvhbj 1 1 1F 2$bj ) ) )B9 9rz9999999>"*>>>>>>>>I2:I"*IIIIIII   G\\^^FmmooGDI%%g..777DDMM151111DMMMHH!RVWWWWWWr#c djs0jtjddnAtjtjjzdddd<dkrtjd d   }fd }jst|d ddi} tj |}|j tksJ|jjksJt!||t#|jd k|t#ddkdSt)|tjjdddidt-tjdjfdd} tj|df}|jjksJt!|t3||jddt3|ddddS)zzTest that fit_intercept_only returns the argmin of the loss. Also test that the gradient is zero at the minimum. 2rrorNr;rr}r)rcjstj|}n/tjtj|jf}|S)N)rxr`rxr)rFrCrascontiguousarray broadcast_torH)rer\r rWrr]s r!funz%test_loss_intercept_only..funss! WI1EEENN1)T^)DEEENt)'    r#gHz>r(r8)r)optionsrrg-q=)rgvIh%<=SLSQPr()r)r7method constraintsr r rr*)rFrNrrCrurKrLrrHrrrrcrxtuplerrrrerrzerosrrrtr)r rar6optgradrWr]s`` @@r!test_loss_intercept_onlyrAsI  ""2;r1)#D#D#DEE9%%,,RZ884>Isss  C :::  v]KKA           9cti5EFFF}}<22'   w%''!!!!w&,&&&&! VCEt $ $ $$$  fQE*******  Hdn & &$(!T^1D)E)Eq!LL    }}71y!n55'   w&,&&&&!35t%8888a((!%888888r#zloss, func, random_distrc.tj|dS)N)q)rC percentile)res r!rFsr}Q"/E/E/Er#poisson exponentialbinomialctj|}|dkr|ddd}nt ||d}||}t ||t|j||ksJ|j |t||ksJt|tr(t|j |||j jrD||j j||}t ||j jrF||j j||}t |dSdS)zTest that fit_intercept_only returns the correct functional. We test the functional for specific, meaningful distributions, e.g. squared error estimates the expectation of a probability distribution. rIr(rrr8rAr!N)rCrDrErIgetattrrrrrNrr.r rrRrUfillr>rVr?)r rd random_distrr[y_trainbaseline_predictions r! test_specific_fit_intercept_onlyrPs$ )   2 3 3Cj  ,,q#C,00+'#{++555111AA)*** &W )F)F"G"G G G G G 9  0 1 1VDDMM5J5J J J J J$ %%U ))*=>>@STTT )/ T)-..."55W5EE-... */ T).///"55W5EE-.....//r#c tjd}d}t|}|d|dzdtj}||}|j|fksJtj ||j }t|D]}||k ||<t|tj|tj tj|z t|d d d f|j|d d d ftj d tjd fD]X}|tj}||}|j |j ksJt#|Yd S) zATest that fit_intercept_only returns the mean functional for CCE.rrrr(r8rAr!rNrqr3)rCrDrErrandintrLrrrxr=rrmeanrlogrNrr)r[rHr rNrOrrs r!(test_multinomial_loss_fit_intercept_onlyrU s )   " "CI  3 3 3Dkk!Y]k55<.alt_lossws> EAIwrva"&!h"7"7788999r#cNd|zdz }| dtj||zzz Sra)rCrrbs r! alt_gradientz>test_binomial_vs_alternative_formulation..alt_gradient{s/ EAIrQH ---..r#N)rrLrrr)r]r{ global_dtyperergbin_lossdatums r!(test_binomial_vs_alternative_formulationrkfs":::///  !!H ]]< ( (F ]]< ( (F V E 8U vhh&677 7 7 7 7%H%u-||U/CDDDDDr#c d}t||dd|\}}t|drT||}|j||jfksJt j|dtdd ksJt|d rd d t j|ft j|d ft j|t j|ffD]\}}| ||d || \}}|j||jfksJt j|dtdd ksJt|| ||d d d Sd S)z22{y$.99999ve!$$$qe(<(<(<<<<<t%&&  2=00 1 ]> * *D 1 ]> * *BM.,I,I J    KD% ---"! .KD%;9dn"=====6%a(((F1%,@,@,@@@@@  !#1"&!%     !  r#rorder)CFcd}|dkrtj|}||}||||\}}|jr|j|fksJ|jdksJnL|jr)|j||jfksJ|j||jfksJn|j|fksJ|j|fksJ|j|ksJ|j|ksJ|dkr|jj sJ|jj sJdS|jj sJ|jj sJdS)zTest that init_gradient_and_hessian works as expected. passing sample_weight to a loss correctly influences the constant_hessian attribute, and consequently the shape of the hessian array. r;rr)rWrrn)r(roN) rCrinit_gradient_and_hessianconstant_hessianrxrFrHrflags c_contiguous f_contiguous)r rrrnrWrrs r!test_init_gradient_and_hessiansrwsI ** 4m , , ,D667Hg  -~)----}$$$$$ -~)T^!<<<<<}DN ;;;;;;} ,,,,} ,,,, >U " " " " =E ! ! ! ! ||~****}))))))~****}))))))r#zparams, err_msgz+Valid options for 'dtype' are .* Got dtype=z instead.c|}tjttf|5|jdddi|\}}ddddS#1swxYwYdS)zDTest that init_gradient_and_hessian raises errors for invalid input.matchrWr;Nr)rraises ValueError TypeErrorrr)r paramserr_msgrrs r!%test_init_gradient_and_hessian_raisesrs 466D  I.g > > >RR:D:QQQQ&QQ'RRRRRRRRRRRRRRRRRRsA  AAzloss, params, err_type, err_msgr$z4quantile must be an instance of float, not NoneType.zquantile == 0, must be > 0.zquantile == 1.1, must be < 1.cttj||5|di|ddddS#1swxYwYdS)z/Test that loss raises errors for invalid input.ryNr)rr{)r r~err_typers r!#test_loss_init_parameter_validationrsB xw / / / vs -11cd}t||ddd\}}tj|}tj|}|||t |||ksJdS)z Test that losses can be pickled.rr6r9r<rrsN)r^pickledumpsloadsr)r rWr]r\ pickled_lossunpickled_losss r!test_loss_picklersI9   FN<%%L\,//N 4vn = = =f^DDDBB      r#r)r%rr(rr)rBcBt|}t|}d}t||d\}}|j|}|||||z}|||||z}t|||||\} } |||\} } t| || zt| || z|dz| zzdS)zCTest for identical losses when only the link function is different.r&rqr<rWrsr)N) rrr^rNrr rrr) rhalf_tweedie_loghalf_tweedie_identityrWr]r\r{loss_log loss_identity gradient_log hessian_loggradient_identityhessian_identitys r!%test_tweedie_log_identity_consistencyr&s'Q///3!<<<I9 FN " * *> : :F $$n%11&99:H*..f/66v>>?MHm,,,!1 A An!B!!L++@*P*Pf+Q++''L&+<"<===V//&!)>N2NNr#)r6r9r<)UrnumpyrCr numpy.testingrrrscipy.optimizerrrr scipy.specialr sklearn._loss.linkr r sklearn._loss.lossr rrrrrrrrrrr sklearn.utilsrsklearn.utils._testingrrsklearn.utils.fixesrlistvalues ALL_LOSSESLOSS_INSTANCESr5r^rlmark parametrizer|infY_COMMON_PARAMS Y_TRUE_PARAMS Y_PRED_PARAMSrrrTlog1prfloat32rrrrrrrrr.rArSmedianrPrUrYr^rrkrmrwint64rr}r|rrrrr#r!rs  ======== $#####@@@@@@@@                            ,+++++KKKKKKKK(((((( T.'."" # # 00Z000K ItO$O!O!O!O#!$$$!$$$!$$$#&&&      CE""""""""J U U U5GHH < <IH _1SzRVGRV+<=_3#srvgr4-HI_1SzRVGRq"&+IJ_1SzRVGRq"&+IJ2&&&c bfWbf4EF1%%%'>'>'>"&"&@QR3'''#srvgr45PQ1%%%SzRVGRq"&3QR1%%%SzRVGRq"&3QRBF7B26*BCB"&"c26 :;'2_R Y[[1#r_24_1ay"-_3!b)2&&&<1%%%ay"53'''!b1!Q$MMM2.  _QC _2MMM2_1r===1_3aS)2&&&MMM:1%%%}}}b93'''aS1aV$S#JA/  '=)H777'=)H777Js    S#q!Q/s #sCd3s c " " "Cad;s d # # #S#~xN s d # # #S#xE s Cq ) ) )3_dDQ s Cq ) ) )3ZtLs   CAFBF1II ,=uaHs #vrvayy&"&))e*;YNs q ! ! !3q 3DdDQs ! q ) ) )3QVRVAYY5FdSs ! q ) ) )3VRVAYY5FdSs $ #! , , ,   )    s,      BF1II BHQKK$* *    +sr    S%Aq1sst    S%r15usv    S$1a0wsx    S$R3ysz    S%r15{s|    S%Y8MN}s~    S%F+@*AA*EFFs@    S%y:OPAsB    S%F+@*AA*EFFCsD    S$I7LMEsF    S$E*?)@1)DEEGsH    S$a1FGIsJ    S%3JKKsL    S$1a0MsN    S$a3OsR       $ #  Qs`    S%Aq1asb    S#q!Q/csd    S#sAq1esh ! , , ,  OO Iooo & & ,    gsx ! , , ,  OO Iooo & & ,    wsH ! , , ,  OO Iooo & & ,    GsX ! , , ,  NN Innn % % .    Wsh mwwpBBqwwpB6,,*UDM::bj"*%=>>rz2:&>??4)44$++$++q!f--\7\7.-,+,+54@??>;:-,\7~5GHH4/::K$K$;:IHK$\5GHH4/::L>L>;:IHL>^5GHH68*<==BFBF>=IHBFJ5GHH  IH <5GHH4/::-H-H;:IH-H`5GHH4/::VKVK;:IHVKr   "+X+X#$+X\5GHH4/::>9>9;:IH>9B    RWh/ ")X. d # # #%E%ExP   BGY/ "'=1   BG]3    RWj1  //  />///."""<"HBH[[[$9$9828KKK;P;P#QRRHBH^^^$<$S>S#TUUEEVUSRED5GHH((IH(V,,4/::2:rz":;;*--!*!*.-<;;:-,!*H,,bh  M"( M M M RR-,R%     B    O  )  z3'5TU     B    O  )  Z%z3RS5>?> 5GHHIH"55566))76)))r#