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np.sum(array_x+9.05401256357436/np.square(np.cos(2*np.pi*4.378849967521912)+np.square(array_x)), axis=1) |
np.prod(np.exp(1.081495265460597)-1/(1.5185536576305902-(np.dot(array_x, np.array([[0.8004468488405659, 0.9938931895013084, 0.6007368104890023, 0.2763333786676515, 0.8191531790048203], [0.6860057984074471, 0.3088479153517395, 0.8407215983610116, 0.14669931574231643, 0.15401040137832334], [0.8386237022019658, 0.236443246185559, 0.7837047897368041, 0.6187631906234234, 0.9760764043878051], [0.8156958451075026, 0.5535374177182708, 0.4964118166549055, 0.5385709723969994, 0.6379599823004339], [0.9258305920677707, 0.3234007121446517, 0.8995083237011766, 0.7291879222747759, 0.948651227197831]])))), axis=1) |
np.mean(abs(np.square(np.square(np.sqrt(abs(7.892235959814728))-array_x))), axis=1) |
np.mean(np.square(10*(7.7099228531861215-array_x/np.sqrt(abs(5.227444514221901)))), axis=1)+np.sin(2*np.pi*np.mean(np.square(10*(5.798258456922669-array_x/np.sqrt(abs(6.550609077901962)))), axis=1)) |
np.mean(5.604139224778287/np.sqrt(abs(np.square(1.1565470829425448)-(np.array(range(1, array_x.shape[1]+1)))*array_x)), axis=1) |
np.mean(5.180908267567128-array_x, axis=1)+10*(np.sin(2*np.pi*np.mean(4.752328436855521-array_x, axis=1))) |
np.mean(10*(array_x+1.1226831255862662+(np.array(range(1, array_x.shape[1]+1)))*np.cos(2*np.pi*3.070357266294506)), axis=1) |
np.mean(np.square((np.dot(array_x, np.array([[0.9768842752099045, 0.16990090465164842, 0.7988436497574282, 0.7530773621498557, 0.9838659534449908], [0.18316225900908856, 0.5405405405800863, 0.028461461181775727, 0.10617773312350154, 0.6694748137033728], [0.9518634829672324, 0.9867655505922761, 0.7740129071743319, 0.8065040639138107, 0.7882130717778144], [0.023343662118280317, 0.1998141390004753, 0.7471004332277834, 0.5372108577999517, 0.1074893447974885], [0.7209966623676061, 0.48537044132312557, 0.3932204137308052, 0.6289762895382113, 0.2695677583509437]])))*2.3484996303191195*(np.dot(array_x, np.array([[0.1004837537743668, 0.8397554210609219, 0.6282537638592952, 0.1675345831406847, 0.1716498342521181], [0.03233322388146276, 0.11646940146553886, 0.9597990068957235, 0.8998883031044749, 0.8344672546058522], [0.28133148900706595, 0.791509561378755, 0.3853892338855135, 0.46085049728338956, 0.6032573965403489], [0.995471547633881, 0.5426438376762954, 0.41870577692806976, 0.9215476806548736, 0.7279725419131855], [0.109498237244682, 0.5101008694660735, 0.8355467332019811, 0.6811242376411202, 0.3991517570630172]])))+3.1588835619590614*(np.dot(array_x, np.array([[0.5820444158105298, 0.7839783209897554, 0.7254102462531686, 0.7285953909792582, 0.9119789944155079], [0.12262744559486127, 0.30374176364088346, 0.022389042352826793, 0.23380464032062265, 0.35070298981318215], [0.9818203796779869, 0.49063751416542745, 0.1775612563683513, 0.0231462734556398, 0.13852160359111598], [0.8928153996106369, 0.21185096316927587, 0.7053797348926938, 0.1686142875375568, 0.6074740498353789], [0.051525342690458054, 0.040670778851169764, 0.20714095532658328, 0.6482109429285607, 0.011564124384633945]])))-2.3083945118610254), axis=1) |
np.round(np.square(abs(np.sum(np.exp(3.714868925437465-array_x), axis=1)/10*(np.square(-(3.492141245144973)))))) |
np.mean(np.cos(2*np.pi*array_x)/np.square(6.89386729112439)+np.sqrt(abs((np.dot(array_x, np.array([[0.9197907018797865, 0.7241518233503562, 0.8412591810070557, 0.41712839451176287, 0.9412103331232964], [0.513808757893195, 0.7064573748887027, 0.1745760442223343, 0.6740828386519259, 0.5813551718367846], [0.7493266693696924, 0.8651436899595714, 0.683101885737313, 0.5619666757135445, 0.010483789112903374], [0.9644811099715618, 0.792022767791421, 0.9119476512896285, 0.1673241587575569, 0.26147711093917636], [0.9895595409477701, 0.4662946704443397, 0.59692797131348, 0.3707525132212759, 0.19334100652986985]])))+8.982957894300874)), axis=1)+10*(np.sin(2*np.pi*np.mean(np.cos(2*np.pi*array_x)/np.square(3.0856274597421134)+np.sqrt(abs((np.dot(array_x, np.array([[0.6284555337316436, 0.2152745794827925, 0.6950063990031111, 0.32484620015442256, 0.8571352324375628], [0.6308997054900856, 0.41899512002550676, 0.7425759654185451, 0.8892550295909104, 0.4843959257205963], [0.6470357362354883, 0.4746100365221536, 0.5238385202941062, 0.8016596279168601, 0.3126638736140921], [0.49387310584007327, 0.5026371322382819, 0.38392748942309673, 0.8999356698096781, 0.7578942258759521], [0.7331414594903127, 0.0384944559736734, 0.3067634457517586, 0.22292215735945875, 0.25793528614507055]])))+3.7009674617504853)), axis=1))) |
abs(3.6040708472673786-abs(np.sum(array_x+2.0309281227446743, axis=1)))+10*(np.sin(2*np.pi*abs(3.5634278903171306-abs(np.sum(array_x+6.994027347833364, axis=1))))) |
np.mean(1.368594233835446+np.square(array_x+3.402529679846899)*4.579384228863999, axis=1)+10*(np.sin(2*np.pi*np.mean(9.485600194162789+np.square(array_x+2.1964307392747475)*3.5440088213784233, axis=1))) |
np.square(8.146492055561207+8.998693171595288*2.0230915471693667-np.mean(array_x, axis=1)) |
np.mean(np.square(2.5626799255099364)-np.square(6.73404855596991)*array_x, axis=1) |
np.exp(np.round(8.458482891022985-np.sum(abs(array_x), axis=1)/np.sin(2*np.pi*8.803180055105834))) |
np.mean(8.040908365064391*np.square(np.exp(array_x))+5.061588416380951-7.2471901472334395*array_x, axis=1) |
np.mean(8.522234642063697/np.sqrt(abs(1.207643692671247))+np.sqrt(abs((np.array(range(1, array_x.shape[1]+1)))))-np.sin(2*np.pi*np.square(array_x)), axis=1)+10*(np.sin(2*np.pi*np.mean(3.117616974563235/np.sqrt(abs(4.0934500547193355))+np.sqrt(abs((np.array(range(1, array_x.shape[1]+1)))))-np.sin(2*np.pi*np.square(array_x)), axis=1))) |
np.round(1/(np.sin(2*np.pi*abs(np.mean(5.621249886445058-array_x/6.548096935043416, axis=1))))) |
np.mean(10*(np.square(1.2410078721515339)-array_x), axis=1) |
np.mean(10*(np.square((np.dot(array_x, np.array([[0.4573256286007382, 0.605704124987322, 0.2303416786068545, 0.1507562990126854, 0.8632073571911248], [0.2910390845858635, 0.9246621008622754, 0.5184523822820806, 0.5658117378217166, 0.17522660687421365], [0.8936924448204928, 0.9775894837766242, 0.030607556897163657, 0.986419756610196, 0.9363335559403223], [0.19908274310116736, 0.096775267070638, 0.7395985626063822, 0.1897396898923206, 0.9079012566838638], [0.2540459825235669, 0.4884257830854445, 0.620173029348316, 0.527089702024946, 0.9376777201641496]])))*5.231526633630203-5.278241025473518-array_x)), axis=1) |
np.mean(array_x*8.943648510060763-2.3379619365009447-array_x/8.511108361162226/(np.array(range(1, array_x.shape[1]+1)))*5.132592750692437+array_x-array_x, axis=1)+10*(np.sin(2*np.pi*np.mean(array_x*5.169932137260841-8.75473444830633-array_x/6.079810619056645/(np.array(range(1, array_x.shape[1]+1)))*4.112752472209381+array_x-array_x, axis=1))) |
np.round(np.mean(np.exp(np.sqrt(abs(np.sqrt(abs(np.log(abs(5.924733237808933-array_x+3.2456597155728466))))))+9.771526958580425), axis=1)) |
np.sum(array_x*8.57955871352301*2.3433828121779032+array_x-np.square(1.2233574796536628), axis=1) |
np.mean(np.sqrt(abs(np.square(np.square(array_x*7.95262242874173))+6.760285565558096-array_x-6.79674132803442)), axis=1) |
np.round(np.prod(9.641340581924839/np.sqrt(abs(2.2276231347796815))+array_x, axis=1)) |
np.mean(-(np.square(np.exp(np.cos(2*np.pi*array_x*1.9355543376426478)))), axis=1) |
10*(10*(np.mean(np.square(np.sin(2*np.pi*np.square(5.857669967234537)))*(np.dot(array_x, np.array([[0.8742334822306147, 0.5256660112303754, 0.4307866062251421, 0.14848540598265547, 0.6030133498634314], [0.8435234392455967, 0.9333289524268754, 0.326608851070483, 0.7482582739148065, 0.130859633013801], [0.07024721288507052, 0.10838372034163823, 0.7071984060827677, 0.10483331475479762, 0.5332290394685923], [0.8631691742319637, 0.7254837091824633, 0.16405438284305995, 0.28717590006106597, 0.22602045953966465], [0.7279336225866395, 0.8106000578857107, 0.40096732895085496, 0.2644970771110401, 0.40538817462796395]])))-3.1973412745257814, axis=1))) |
np.mean(np.sin(2*np.pi*4.072428577104985)+array_x*np.round(8.330061561738173-array_x)+array_x+2.821136685374507-1/(1.6464785172086909), axis=1) |
np.round(np.sqrt(abs(np.sum(np.exp(7.8874335438035725)*9.703372926015158-array_x, axis=1)))/np.exp(np.amax(1.8097494822022924+np.sqrt(abs(array_x)), axis=1))) |
np.mean(10*(array_x+np.log(abs(3.5659433409736723)))/1.2582889872562775-np.sin(2*np.pi*array_x*8.5992983886369), axis=1)+np.sin(2*np.pi*np.mean(10*(array_x+np.log(abs(2.16762549891473)))/5.3707853373395045-np.sin(2*np.pi*array_x*7.6374448918560525), axis=1)) |
np.square(np.sum(4.998233813760671-array_x-5.7383302258934705, axis=1)) |
np.mean(np.cumsum(np.exp(7.274883279849301+array_x+4.873578008187653)*7.648631406213794, axis=1), axis=1)+np.sin(2*np.pi*np.mean(np.cumsum(np.exp(4.036745016041987+array_x+6.121815590952803)*1.9028741278379382, axis=1), axis=1)) |
np.round(np.mean(np.square(np.exp(7.654544288170937-array_x))/5.453819560207952+np.square(3.6939944673070455), axis=1)) |
np.mean(5.78822255379311-np.square(-(np.exp(array_x)))*6.59431213723985, axis=1)+10*(np.sin(2*np.pi*np.mean(8.086860372184988-np.square(-(np.exp(array_x)))*4.093780072298283, axis=1))) |
np.round(np.sqrt(abs(np.round(np.log(abs(3.737102156559148)))))-np.square(np.sum(2.6010083257998153*(np.array(range(1, array_x.shape[1]+1)))*array_x, axis=1))) |
np.mean(np.sin(2*np.pi*np.exp(array_x))/5.681273711846924-10*(6.531780303938176+array_x)+np.sqrt(abs(np.sqrt(abs(2.078746826284551)))), axis=1)+10*(np.sin(2*np.pi*np.mean(np.sin(2*np.pi*np.exp(array_x))/2.9093448617282176-10*(7.631053539157624+array_x)+np.sqrt(abs(np.sqrt(abs(6.799000435616902)))), axis=1))) |
np.round(np.mean(np.square(np.exp(array_x)+3.98329104180762/6.466158169330783), axis=1)) |
np.mean((np.array(range(1, array_x.shape[1]+1)))*array_x+(np.array(range(1, array_x.shape[1]+1)))+(np.array(range(1, array_x.shape[1]+1)))*array_x*8.307798670467712, axis=1) |
np.square(np.amax(np.sin(2*np.pi*(np.dot(array_x, np.array([[0.6407268569826872, 0.8957198783130246, 0.38713603573120814, 0.42793331460309025, 0.17122383368407657], [0.6957449309001731, 0.04290279584239454, 0.429474596281741, 0.2915185352463089, 0.40113662990366983], [0.12917852632766036, 0.054272233621873944, 0.23376806017714402, 0.8715318362996203, 0.9703821177034392], [0.8399575930632311, 0.06389279640770673, 0.8501066750080796, 0.7147862304294065, 0.5622259368868574], [0.06456706285120539, 0.6654422873888641, 0.7890352757958328, 0.8251015269193646, 0.8538055155040511]])))*1.0723038698564045), axis=1)+abs(np.square(2.3729542191210182))+np.prod((np.dot(array_x, np.array([[0.5924781600259701, 0.6722253631551096, 0.5665584866178347, 0.7840149334135592, 0.7236695473592955], [0.40034259709601216, 0.8418859036866886, 0.9715147670593419, 0.3202071873124991, 0.8630670549964881], [0.6169743015726927, 0.9220785652635386, 0.3784501980397633, 0.5009555291898848, 0.7825191409500425], [0.10880168212006869, 0.7210202555321138, 0.7012200941414217, 0.36967936364645915, 0.421580613370132], [0.41317988919459414, 0.9146796866195637, 0.6991368277381486, 0.8234479550247058, 0.911030787986152]])))-3.198603129361689/7.36255664018862, axis=1)+3.2000505704946143) |
np.mean(np.square(3.5912024894975714+np.exp(np.log(abs(array_x-7.542680983130444))*3.2875011573581734)), axis=1) |
np.mean(1/(np.cos(2*np.pi*np.sqrt(abs(3.5696821065436364-array_x)))), axis=1) |
np.mean(np.square(4.007622257789322+(np.dot(array_x, np.array([[0.27827526217983145, 0.6628018828367283, 0.7156983621974025, 0.9810075493995825, 0.7965093946384375], [0.27292285317361964, 0.21532179133749307, 0.4723154617025477, 0.9522582061751121, 0.5162881169371725], [0.5520859576049534, 0.6877542232955294, 0.12954803812217774, 0.005406524862650475, 0.15778261231608015], [0.06024180213080288, 0.7085304326775603, 0.6650045008829449, 0.8989841145544798, 0.5505722141743197], [0.28703164283796956, 0.32819009512397146, 0.04546261885998659, 0.12545006891201216, 0.23289285782630254]]))))-1.0617621438323885-(np.dot(array_x, np.array([[0.11893936500329505, 0.07282052426184393, 0.4511478361157738, 0.5790960203886123, 0.5938164786259554], [0.7359276186432283, 0.22674026248823376, 0.16802557496221193, 0.5829987722069846, 0.8802567434731557], [0.30571364470120654, 0.5564827094102529, 0.6935379073178132, 0.24344824776819096, 0.956776355798791], [0.675132313096752, 0.5819332219151012, 0.5473699138827742, 0.8283803371436103, 0.9502414397760839], [0.012975330555923814, 0.9151474119035378, 0.5139076654297574, 0.4725002076596406, 0.22421853890443844]])))*6.699158462537658, axis=1) |
np.mean(np.square(6.982214644843123*3.283754670731099-4.527413340339463*array_x), axis=1)+np.sin(2*np.pi*np.mean(np.square(1.7204954940977466*6.329526567031478-8.51023093817323*array_x), axis=1)) |
np.mean(np.exp(array_x/3.1382600455404965-3.760413396275938/5.802494667894683/4.749986397361743)+array_x+7.03854376137119*3.0744447525398035*(np.dot(array_x, np.array([[0.2406532100077674, 0.4662048610582791, 0.8089913191524879, 0.43641758189131397, 0.8192684019335879], [0.9120918295532521, 0.7245906632099941, 0.3739288229585995, 0.49549563781189754, 0.1387132461813816], [0.6214144182280328, 0.34497150699603696, 0.02066399250320372, 0.1568779247593346, 0.45334249413351624], [0.5048147962905953, 0.8910254635859994, 0.5500758952486192, 0.33549634381695503, 0.47782423776125615], [0.33149768631204146, 0.04994020517572473, 0.6082885513290306, 0.04820673713622714, 0.2397222651753742]]))), axis=1) |
np.log(abs(np.exp(np.mean((np.array(range(1, array_x.shape[1]+1)))*array_x, axis=1))/8.001642945996835))*10*(9.683714702541298)-np.mean((np.array(range(1, array_x.shape[1]+1)))*array_x, axis=1) |
np.mean(np.exp(np.sqrt(abs(array_x-6.542927297855757+np.exp(4.821411555332281))))-2.1807110718628717, axis=1) |
np.mean(abs(array_x-2.0238431644890547*array_x-10*(np.cumsum(np.square(array_x-6.542107905951227), axis=1))), axis=1) |
np.mean(4.137700131931707*(np.array(range(1, array_x.shape[1]+1)))*array_x+(np.array(range(1, array_x.shape[1]+1)))*array_x-(np.array(range(1, array_x.shape[1]+1)))*array_x+7.451499165672182, axis=1) |
np.mean(1.3081152840931052-np.square(array_x*5.294742007939927-(np.array(range(1, array_x.shape[1]+1)))+8.944960724462302), axis=1) |
np.square(np.log(abs(2.942522934568855)))+np.sum(np.sin(2*np.pi*np.sqrt(abs((np.array(range(1, array_x.shape[1]+1)))*array_x)))+2.065861353637977, axis=1) |
np.mean(abs(array_x/7.863551029283847)+7.691892968539132+10*(array_x), axis=1) |
np.mean(np.round(np.square(7.901772757394372+array_x)), axis=1) |
np.mean(np.log(abs(3.4557134195847343))-np.square(array_x+5.394499315041223), axis=1) |
4.258482782294795+np.sum(np.cumsum(np.sqrt(abs(array_x-array_x*2.5541322630419905+np.round(9.331371716258428))), axis=1), axis=1)+10*(np.sin(2*np.pi*6.571061588852984+np.sum(np.cumsum(np.sqrt(abs(array_x-array_x*4.132667813109014+np.round(3.0142943861741265))), axis=1), axis=1))) |
abs(np.mean(4.55282617275768*array_x, axis=1)+3.719291940578435)+np.round(7.499629138937095)+10*(np.sin(2*np.pi*abs(np.mean(5.105589709736914*array_x, axis=1)+6.876125115158593)+np.round(5.5578387338797635))) |
np.exp(4.410645806670449*np.amax(np.cos(2*np.pi*np.square(array_x)), axis=1)) |
np.mean(np.exp(np.sqrt(abs(array_x))*np.exp(np.sqrt(abs(np.cos(2*np.pi*1.4982169744498022))))+2.7328288483812337), axis=1)+10*(np.sin(2*np.pi*np.mean(np.exp(np.sqrt(abs(array_x))*np.exp(np.sqrt(abs(np.cos(2*np.pi*9.615966270674411))))+6.198348348063018), axis=1))) |
np.mean(array_x*9.800975937466722-8.29001642487624+array_x, axis=1) |
np.mean(7.706841332997872+9.617523059437145+array_x-10*(array_x)-array_x*4.321242722355444-array_x-np.log(abs(7.024492634300063)), axis=1) |
np.mean(np.square(np.square(10*(5.346471812395445+array_x-8.593065141556224))), axis=1) |
np.mean(2.0076986545965045-array_x, axis=1)-np.sqrt(abs(np.round(np.cos(2*np.pi*np.amax(np.sqrt(abs(array_x))-10*(2.8701289002003882)-np.cumsum(array_x/4.274758085455488*3.485288185862788-1.0639459757390157, axis=1), axis=1)))))+10*(np.sin(2*np.pi*np.mean(6.062473604282806-array_x, axis=1)-np.sqrt(abs(np.round(np.cos(2*np.pi*np.amax(np.sqrt(abs(array_x))-10*(7.371948036936331)-np.cumsum(array_x/7.650039398309583*3.8009090801664827-8.54642086835499, axis=1), axis=1))))))) |
-(np.prod(10*(array_x)*np.log(abs(8.032738417119738))-abs(2.1808158271697278), axis=1)) |
np.mean(np.square(abs(3.5740760562447216))-np.cos(2*np.pi*(np.array(range(1, array_x.shape[1]+1)))*array_x)*np.square(3.1206502699016188), axis=1) |
np.mean(np.cumsum(np.cos(2*np.pi*np.sqrt(abs(7.247635201483208-9.793038863784034*(np.dot(array_x, np.array([[0.6972978979439038, 0.3639970822811972, 0.26877133623074556, 0.04946503737846142, 0.7679700190457646], [0.17528238292606757, 0.34703278321261755, 0.5756483407530052, 0.4670394271440492, 0.9103989650664213], [0.7386081917516516, 0.884108421439223, 0.2613340019165411, 0.09330919464273146, 0.32753020267184985], [0.5078373715091393, 0.6593477579564936, 0.29598405664388727, 0.8399900804307464, 0.4539156996059367], [0.7116152446514712, 0.3165376246562427, 0.132185913280904, 0.9410229239901189, 0.2758777786069426]])))))), axis=1)*np.exp(array_x/1.6060707346997436+3.5497622369877924), axis=1)+np.sin(2*np.pi*np.mean(np.cumsum(np.cos(2*np.pi*np.sqrt(abs(1.3810092552333697-9.366673831477625*(np.dot(array_x, np.array([[0.2545763619378305, 0.2996280070511659, 0.05079600766166681, 0.03843441020298355, 0.23228988063330325], [0.3403005773960325, 0.00020727381728957717, 0.9109910586690927, 0.43346121092428713, 0.16723282895080072], [0.2727603760797336, 0.5945023415662236, 0.019674030906454365, 0.640538388152986, 0.44302573434960635], [0.2913173296972835, 0.12144557140599899, 0.8174413249072947, 0.5945462973271712, 0.8841588208900012], [0.9999225838603116, 0.6349435965676352, 0.7475066715299785, 0.4958599243186691, 0.2743816358613663]])))))), axis=1)*np.exp(array_x/4.614748239440128+8.079762783648874), axis=1)) |
np.mean(9.450805082603182-array_x*8.438131353795685, axis=1) |
1/(10*(np.sin(2*np.pi*np.sum(5.988244545687982*np.cos(2*np.pi*2.8867184549943365*(np.array(range(1, array_x.shape[1]+1)))-array_x), axis=1))))+np.sin(2*np.pi*1/(10*(np.sin(2*np.pi*np.sum(3.607688246499979*np.cos(2*np.pi*9.198806656506095*(np.array(range(1, array_x.shape[1]+1)))-array_x), axis=1))))) |
np.round(np.mean(np.square(6.207571347550516*array_x+np.square(9.4545735607124)+array_x/9.86935032431388), axis=1)) |
np.mean(9.722727379202581-np.square(np.square(array_x)-10*(-(array_x))), axis=1) |
np.mean(10*((np.dot(array_x, np.array([[0.02452481883734825, 0.17816677770548106, 0.9045211300853483, 0.0008963944922391054, 0.15107117536464254], [0.484542560790611, 0.5089224287278213, 0.23366888444244294, 0.3381787108925559, 0.8644262357072909], [0.3920951765219284, 0.07883797350263066, 0.897296005858857, 0.18412633811890167, 0.585832224444148], [0.5399498608133749, 0.02841808360152187, 0.07825069907525295, 0.5880084673187552, 0.40030750604467213], [0.8620501872463497, 0.6482661410187547, 0.13652587086144807, 0.8083734125096242, 0.5944401569054737]])))/2.886825159267546/2.3796762015334663-np.cos(2*np.pi*array_x)+3.4589308748136265)*abs(np.square(array_x/5.9733515945792615/6.422284519728007-(np.array(range(1, array_x.shape[1]+1))))*5.749978511745395+np.sin(2*np.pi*(np.array(range(1, array_x.shape[1]+1))))), axis=1) |
np.mean(np.square(array_x+7.003893862211572/6.974449193340559*np.sin(2*np.pi*np.exp(1.9468810189298416))-10*(7.417958609641097)+np.round(array_x/7.828381778025069/1.8727425966412752)), axis=1) |
np.mean(6.034280527158351*10*(array_x)-np.round(array_x-3.1937831832120023)-5.389059399050268, axis=1)+np.sin(2*np.pi*np.mean(3.1042067594402614*10*(array_x)-np.round(array_x-2.49434695427645)-8.271258405879717, axis=1)) |
np.round(np.prod(np.sqrt(abs(7.893163427370547+(np.dot(array_x, np.array([[0.1678352043028255, 0.632248622719221, 0.8489040193236534, 0.6999292542548979, 0.9623695815476853], [0.4379927848757963, 0.03605828150227819, 0.2886998830776867, 0.6788019682454033, 0.06374902235257285], [0.7264941033797175, 0.5384744351084324, 0.8396104139516205, 0.6352264100428426, 0.19560101732679724], [0.08668096423431948, 0.745867299129871, 0.19833190236644316, 0.7230869808852738, 0.171336196367594], [0.6130372938307654, 0.09433361977477961, 0.7329785435745392, 0.016674833073840656, 0.6037465862669301]])))/8.263884714178147*array_x)), axis=1)) |
10*(-(np.square(np.round(5.83408560377896))+np.mean(np.sqrt(abs(4.039218619921684))*np.square(array_x), axis=1))) |
np.square(np.sum(np.square(4.770425894320408)/np.cos(2*np.pi*(np.dot(array_x, np.array([[0.7668353612155254, 0.02902760338796262, 0.8756350286625422, 0.20492830619008007, 0.8944462627732421], [0.650627433387791, 0.06808382751807363, 0.5745025252547895, 0.6055671515659795, 0.9107743251647815], [0.8075812300508234, 0.9680745042619576, 0.9208286100539496, 0.22192759170073206, 0.8136484783257291], [0.701071757425639, 0.10436259212863963, 0.29566528545573934, 0.5803345997642392, 0.3066343806557793], [0.1818201672342502, 0.09507099654096274, 0.7149947234371671, 0.848058383077701, 0.5140109073354922]]))))/8.697586564199003, axis=1)) |
np.mean(7.353925980223409-10*(np.cumsum((np.array(range(1, array_x.shape[1]+1)))*array_x*5.369138579502164, axis=1))-np.cos(2*np.pi*1.9829063248413301), axis=1) |
np.mean(1.9449643200265272+10*(array_x*9.595554571169245), axis=1) |
np.mean(np.square(9.478198716473713-(np.array(range(1, array_x.shape[1]+1)))*array_x), axis=1) |
np.mean(7.284654119712221-abs(array_x+6.205232290713565)*abs(np.round(np.square(8.745338515875066))), axis=1) |
np.mean(np.sin(2*np.pi*10*(array_x-9.082980500165275)-1.9221798887006054)-np.square(4.061630017912066)/np.cos(2*np.pi*array_x/4.466870976617294), axis=1) |
np.exp(np.mean(2.577753499465434+array_x+1/(1.7032872971119848), axis=1))/2.989236859500548 |
np.mean(array_x/np.square(np.square((np.array(range(1, array_x.shape[1]+1)))))-np.cos(2*np.pi*array_x)/4.677005439673614+np.sin(2*np.pi*9.114116228773462)+np.round(np.square(array_x*np.exp(np.round(8.115484698244465-array_x)))), axis=1)+np.sin(2*np.pi*np.mean(array_x/np.square(np.square((np.array(range(1, array_x.shape[1]+1)))))-np.cos(2*np.pi*array_x)/8.05269271707876+np.sin(2*np.pi*4.70421876269827)+np.round(np.square(array_x*np.exp(np.round(8.78952113186376-array_x)))), axis=1)) |
np.mean(np.square(-(np.square(6.938200583525794)*np.sin(2*np.pi*array_x+3.958146124190492))), axis=1) |
np.prod(np.sqrt(abs(np.sqrt(abs(np.cos(2*np.pi*array_x/1.5497437131005283)))/np.log(abs(np.square(8.855851871021008)))*abs(9.136829506980344+np.sin(2*np.pi*array_x)))), axis=1) |
10*(10*(1.1222957216134057*np.round(4.935114668352172+array_x[:,0])))+np.prod(array_x+1/(9.104006059417879)-array_x, axis=1) |
np.mean(-((np.dot(array_x, np.array([[0.6730078674865005, 0.9311564361363307, 0.06402989781436763, 0.6731723229671298, 0.9265506645125191], [0.6895615002332344, 0.47275071115851464, 0.1305045220326756, 0.5862209108301536, 0.18132008783252274], [0.4555728725549937, 0.32549006949283943, 0.267899385447668, 0.36397332054670584, 0.7909100744531592], [0.9785380498180216, 0.646562106533744, 0.14667344915848668, 0.7501171415557475, 0.5722768888833275], [0.9169835637707971, 0.3887224392549544, 0.729674185115214, 0.11671170851589852, 0.30615560847570145]]))))*9.131747758621223*(np.dot(array_x, np.array([[0.6661865885725206, 0.9448962544302618, 0.7580790671363167, 0.2786903545270396, 0.8515858298558743], [0.33010281677386144, 0.2798877078741866, 0.7298505089001333, 0.1519868256106689, 0.47034862680720124], [0.597146443465972, 0.1290536069052155, 0.9150590962505318, 0.518891346009153, 0.3176125603305231], [0.34916589416563604, 0.8044731201385716, 0.14529145020923606, 0.695529946368256, 0.5350535819184417], [0.8879190651390019, 0.8931211609866145, 0.09219976226969273, 0.7780772138870268, 0.7755424170255406]])))-np.square(np.log(abs(8.758674698839975-1.3212122253575982*(np.array(range(1, array_x.shape[1]+1)))-1.2706307945374373))), axis=1) |
6.487251236127416*1.1406306187173203*np.sum((np.array(range(1, array_x.shape[1]+1)))*array_x, axis=1)-1.0141292797756276 |
8.147838849246526-array_x[:,0]/4.385454118741325-np.exp(np.sum(np.exp(np.sin(2*np.pi*array_x)), axis=1)) |
np.mean(np.cumsum(10*(2.0661319970562624*array_x-7.2913823850717465)+array_x+8.747568689575935/5.270094686308533-np.square(8.639644118975218), axis=1), axis=1) |
np.sqrt(abs(np.sqrt(abs(np.prod(array_x-4.111019762616267, axis=1)))))-np.mean(array_x-8.904267465786216, axis=1)*np.mean(4.567717066929385*array_x, axis=1) |
np.mean(10*(np.cos(2*np.pi*np.sqrt(abs(-(np.sin(2*np.pi*np.exp(array_x)))-np.cos(2*np.pi*5.539372485671041)-6.726224699111587+array_x)))), axis=1) |
np.mean((np.array(range(1, array_x.shape[1]+1)))*array_x+3.856457442284751-4.660375942681207*1.8118836426512566-2.5358093932013084*(np.array(range(1, array_x.shape[1]+1)))*array_x-(np.array(range(1, array_x.shape[1]+1)))*array_x-6.940810332217888/5.163461158338852*(np.array(range(1, array_x.shape[1]+1)))*array_x, axis=1) |
np.round(np.square(3.592930330673544)+np.mean(array_x, axis=1)*8.813744251554132) |
np.mean(6.686542595692369+np.sqrt(abs(1.8410174597706133))-10*(array_x), axis=1) |
np.mean(np.square(array_x+7.374444924755222)+np.log(abs(7.887038614585726))/np.square(np.log(abs((np.array(range(1, array_x.shape[1]+1)))+1.3449274440211219))), axis=1) |
np.mean(6.3923212029868255+np.sqrt(abs((np.array(range(1, array_x.shape[1]+1)))*array_x*(np.array(range(1, array_x.shape[1]+1)))*array_x))-abs(4.587069181900021*(np.array(range(1, array_x.shape[1]+1)))*array_x)*(np.dot(array_x, np.array([[0.46248589453742106, 0.7486317805551918, 0.624620242964814, 0.6507257762220858, 0.35470788389076524], [0.44334695537227986, 0.5715450709534793, 0.47618652831915764, 0.7346361387474399, 0.12554691892382575], [0.66371662712934, 0.5465577936049142, 0.10262895428054408, 0.523777416832871, 0.11790285925581889], [0.7221604176153827, 0.5949108525169396, 0.5612893837344816, 0.273551829829892, 0.22706257585017087], [0.08181059198132401, 0.16440150000734055, 0.532001848981275, 0.16731540228191, 0.885365952888682]])))+5.379202005482144/(np.array(range(1, array_x.shape[1]+1)))*array_x+(np.array(range(1, array_x.shape[1]+1)))*array_x+np.round(np.log(abs(8.792206908543058))), axis=1) |
np.square(10*(np.sum(array_x+3.169466903444494*8.813811665729157, axis=1))-2.206460919915756*2.5693137139406703) |
np.mean(10*(9.138893088235967)/np.sin(2*np.pi*2.4931641049593614+array_x), axis=1) |
np.mean(np.exp(8.295286270719117+array_x*9.855078908310375), axis=1) |
np.mean(np.round(array_x)/6.843725023998446-(np.array(range(1, array_x.shape[1]+1)))/np.cos(2*np.pi*2.1761610639142592+array_x), axis=1)+np.sin(2*np.pi*np.mean(np.round(array_x)/8.612749785494351-(np.array(range(1, array_x.shape[1]+1)))/np.cos(2*np.pi*7.271966307792704+array_x), axis=1)) |
np.mean(-(np.cumsum(10*(5.590143384554842*array_x*1.5596546250513121-np.sin(2*np.pi*3.5393815042117374)), axis=1)), axis=1) |