Q(h&dZddlZddlZddlZddlmZddlmZddl m Z ddl m Z m Z mZddl mZddlmZdd lmZmZd Zej.ej0dd d j2Zej.gd j2Zej8eej;dkDeZeej;Z ejBejDeZ#de#e dk<de#e dk\e dkz<de#e dkD<e ddZ$e de$e ddedde ddzgZ%e%Dcgc] }|e$k7s | c}Z&ejNjQde%dZ)dZ*ejNjQde&dZ+ejNjQde%dZ,ejNjQde%dZ-ejNjQde&d Z.ejNjQde%d!Z/d"Z0ejNjQde&d#Z1ejNjQde%d$Z2ejNjQde%d%Z3d&Z4ejNjQd'de die5d(fgd)Z6ycc}w)*z+Testing for Gaussian process classificationN) approx_fprime)ConvergenceWarning)GaussianProcessClassifier)RBFCompoundKernel WhiteKernel)ConstantKernel) MiniSeqKernel)assert_almost_equalassert_array_equalc,tj|S)N)npsin)xs e/var/www/skyplay_api_hub/venv/lib/python3.12/site-packages/sklearn/gaussian_process/tests/test_gpc.pyfrs 66!9 )@g@g@g@g@dtypegffffffֿgffffff??fixed length_scalelength_scale_boundsg?)r)MbP?@@g{Gz?Y@kernelct|jtt}t |j t|j tdddfdk\y)Nr$r?)rfitXyr predict predict_probar$gpcs rtest_predict_consistentr/2sF $6 2 6 6q! F #6 2 6 6q! KsI $6 2 6 6q! z#test_lml_gradient..ssC$?$?u$Mrg|=)rr(r)r*r8r:rr )r$rKrLlml_gradient_approxr.s @rtest_lml_gradientrSls] $6 2 6 6q! X$FwH %a &!5+  #a) (()9)9:X 4 8 88888rcfd}t||}|jtt|j |j j |j |j k\sJy)Nc `tjj }|||d}}tdD]q}tj|j tj d|dddftjd|dddf}||d}||ksn||}}s||fS)NF) eval_gradientrrr)rrZr[r` atleast_1duniformmaximumminimum) obj_func initial_thetarJrg theta_optfunc_min_r:rrds r optimizerz(test_custom_optimizer..optimizersii##$67+X . 8 r /AMM BJJr6!Q$<8"**QqRSt :UVEe4A8|&+Q8  /(""r)r$rx)rr(r)y_mcr8r9r:)r$rdrxr.s ` rtest_custom_optimizerrzs` # $6Y GCGGAt  & &   $ $V\\ 2 33 3rct|}|jtt|j t }t |jdd|jt }ttj|d|y)Nr&r) rr(r)ryr,X2r r]r+r rargmax)r$r.y_proby_preds rtest_multi_classrs` $6 2CGGAt   r "F 1 q) [[_Fryy+V4rc t|}|jttt|d}|jtt|j t }|j t }t ||y)Nr&r)r$n_jobs)rr(r)ryr,r|r )r$r.gpc_2r~y_prob_2s rtest_multi_class_n_jobsrs` $6 2CGGAt %VA >E IIa   r "F""2&H)rc8tddg}t|}d}tjt|5|j t tdddtddgtdd gz}t|}tjd 5}tjd |j t tt|d k(sJt|djtsJ|djj ddk(sJt|djtsJ|djj ddk(sJ dddt#j$t d }tddgddg}t|}tjd 5}tjd |j |tt|d k(sJt|djtsJ|djj ddk(sJt|djtsJ|djj ddk(sJ dddy#1swY%xYw#1swY*xYw#1swYyxYw)Ngh㈵>r )rr&zThe optimal value found for dimension 0 of parameter length_scale is close to the specified upper bound 0.001. Increasing the bound and calling fit again may find a better value.match)noise_level_boundsr!gj@T)recordalwaysrrzThe optimal value found for dimension 0 of parameter k1__noise_level is close to the specified upper bound 0.001. Increasing the bound and calling fit again may find a better value.rzThe optimal value found for dimension 0 of parameter k2__length_scale is close to the specified lower bound 1000.0. Decreasing the bound and calling fit again may find a better value.rrg$@r#rzThe optimal value found for dimension 0 of parameter length_scale is close to the specified upper bound 100.0. Increasing the bound and calling fit again may find a better value.zThe optimal value found for dimension 1 of parameter length_scale is close to the specified upper bound 100.0. Increasing the bound and calling fit again may find a better value.)rrpytestwarnsrr(r)r*rwarningscatch_warnings simplefilterlen issubclasscategorymessageargsrtile) r$r.warning_message kernel_sumgpc_sumrX_tile kernel_dimsgpc_dimss rtest_warning_boundsrs dD\ 2F #6 2C  ( @ 1 t = #JAJ(z:G   - h' Aq6{a&),,.@AAA 1I   " "1 %*1 1  1&),,.@AAA 1I   " "1 %*1 1  1% 4WWQ]FC:C:NK( rErMrSrjrzrrr ValueErrorrr&s0rrs1  (1> OJBMM+"++aR()++R]],-// BHHQqTZZ\A S)qTZZ\rxxs#R%Z$%bEkb4i !R$YA SSk:c;#3KPP   +2LV|5KVL7+J,J J#4567+,7+;,;#45 6 7+ >, >.#4536327+ 5, 57+ *, *F R!~a( )  /   SMs J< J<