Datasets:

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BasStein

/

250000-randomfunctions-5d

like 0

np.mean(10*(3.0528033436019726+array_x)+2.33905066050525-np.cos(2*np.pi*array_x+np.exp(7.018687585995726)), axis=1)

np.mean(10*(np.round((np.dot(array_x, np.array([[0.6153151834287508, 0.008090320027399622, 0.6071627305901061, 0.11985927661811047, 0.5168350004983702], [0.9476108453936954, 0.7411286222123323, 0.5235088377015326, 0.7887566511508569, 0.2646895193841977], [0.8923413183935347, 0.6995657193531455, 0.32286181435084504, 0.8911260566592561, 0.3177640526093597], [0.5857348179069641, 0.3900028469991502, 0.29942549110265604, 0.09430938959221746, 0.2892846496941873], [0.018862356830840477, 0.13480827824687958, 0.8965776974142524, 0.514776388021829, 0.5229157910636739]]))))-2.697486678309404*(np.array(range(1, array_x.shape[1]+1)))/np.cos(2*np.pi*2.440077575465969)*abs(np.exp(2.9495194678501635+(np.dot(array_x, np.array([[0.8392227834247827, 0.3336579054534813, 0.49663646902556413, 0.8031991480999878, 0.9542240789427623], [0.7148342224995134, 0.8170446609429994, 0.2995320701623547, 0.010262818411111096, 0.17292533848893876], [0.05440285344207563, 0.5515351701045415, 0.9416755175749065, 0.6665038261412046, 0.11791159111836047], [0.43697940860497186, 0.6177599999960988, 0.38307407022363915, 0.39539732456927057, 0.6259996925012281], [0.4960116025569462, 0.7317112018616361, 0.004003040465059393, 0.366721410803857, 0.4379323524689165]])))))), axis=1)

abs(-(np.sum((np.dot(array_x, np.array([[0.11609433786147338, 0.9658426569634546, 0.25097150443059557, 0.827895335502521, 0.16848382167008735], [0.853856294118597, 0.29718324754761005, 0.9899540278603419, 0.6957657569901323, 0.7565897723513909], [0.02342129386885683, 0.28881692092941735, 0.10617365347017571, 0.7879030530618817, 0.11566950903319428], [0.08378585799361293, 0.7052528182600244, 0.8972555615294975, 0.6560283296112814, 0.3070544884389642], [0.20984730844590194, 0.05370826179811017, 0.7191533885568643, 0.3515201620849677, 0.23387247847750448]]))), axis=1)))-3.740569717907288

np.mean(-(np.square((np.array(range(1, array_x.shape[1]+1)))+array_x+1.2177057262976185))-3.347815621447321, axis=1)

np.round(np.mean(10*(10*(array_x+3.4879128453820227)), axis=1))

5.928147994768941-np.prod(5.792619517958515+array_x, axis=1)*np.log(abs(8.432333716087562))

np.mean(10*((np.dot(array_x, np.array([[0.16567028917388704, 0.3467433833404746, 0.8800017729364575, 0.5625406542766275, 0.9403615809200898], [0.4371217312283838, 0.15734764311795124, 0.9785170136285719, 0.4253120060329866, 0.7326990155047584], [0.34327824413456853, 0.8613671048130237, 0.6588015606989467, 0.648782437057968, 0.8157335106623522], [0.41059094894894177, 0.09363848430896149, 0.543724467334782, 0.7304564614864315, 0.746649337550929], [0.16686353924076336, 0.985104746070712, 0.538331613513914, 0.2707789789925388, 0.1909295862254322]])))*array_x)-np.cos(2*np.pi*6.378213697449697), axis=1)

np.prod(10*(9.430493499298636/np.round(9.530718945220684/np.sqrt(abs(array_x)))), axis=1)-np.sin(2*np.pi*8.902165313571004)

np.square(4.961979545956728-np.sqrt(abs(np.sum(6.674611617267268-(np.array(range(1, array_x.shape[1]+1)))*array_x*5.253452565199878/np.sin(2*np.pi*np.square(7.928213594596175)), axis=1))))

np.mean(10*(np.exp(7.311141974491347)*2.881852855246093-array_x-array_x+array_x/9.134168670201811), axis=1)

np.mean(np.sqrt(abs(6.800420881066761-array_x))*10*(np.cos(2*np.pi*np.square(array_x))/4.825465173420346)+np.square(5.894608972941576), axis=1)

np.mean(10*(2.0726486618827256)/np.exp((np.array(range(1, array_x.shape[1]+1))))+np.exp(np.sin(2*np.pi*array_x))+np.cumsum(np.sqrt(abs((np.dot(array_x, np.array([[0.9821107847464255, 0.15732466190213024, 0.44229224010105583, 0.5880012845021865, 0.24340240895543852], [0.0443875957357438, 0.06819308459799378, 0.16955765272151446, 0.5104835839670961, 0.46138065650675963], [0.8078340408062554, 0.661793758893772, 0.6858126782779986, 0.5300165163819507, 0.917988272030802], [0.9655778040329426, 0.7845490573333785, 0.5945085608344142, 0.9090312660373345, 0.3137946696016295], [0.17304751155910825, 0.9547900498115983, 0.6670794202039234, 0.8693017421490029, 0.4512692905893967]])))-array_x-2.778456619870919)), axis=1), axis=1)+10*(np.sin(2*np.pi*np.mean(10*(2.9894329165007427)/np.exp((np.array(range(1, array_x.shape[1]+1))))+np.exp(np.sin(2*np.pi*array_x))+np.cumsum(np.sqrt(abs((np.dot(array_x, np.array([[0.800074709171988, 0.7036778266805254, 0.38560831336650014, 0.3105135498063569, 0.1824185594365716], [0.3136074763562974, 0.9780434958429532, 0.9614741636562405, 0.7145725192892544, 0.1275044674295125], [0.04086620456155565, 0.031814018399138, 0.123400505129029, 0.3219919252230232, 0.14227545744635095], [0.013375344568297032, 0.9764450522425714, 0.3177050290562573, 0.5076051518778207, 0.43530237353104595], [0.4785281234032541, 0.05649309374433131, 0.3607537031693412, 0.2430601877351617, 0.9969510510244012]])))-array_x-9.642680235974993)), axis=1), axis=1)))

np.mean(-(1.5602006181778838+np.square(8.125875980052946)+array_x), axis=1)+10*(np.sin(2*np.pi*np.mean(-(6.555219550536146+np.square(8.447511824794317)+array_x), axis=1)))

np.mean(np.square(np.round(abs(np.square(1.9357964183220022+np.exp(array_x))))), axis=1)

np.mean(8.706018930277935+np.square(10*(np.log(abs(np.sqrt(abs(np.sin(2*np.pi*4.102773228612449)-array_x)))))+2.600134909784534), axis=1)

np.mean(np.square(np.exp(3.8576307903986624*np.sin(2*np.pi*5.9180413284312925+np.round(array_x)))), axis=1)

np.sum(10*(np.sin(2*np.pi*array_x)+7.869516750380563-9.36134814730037+np.sin(2*np.pi*8.771880729196436)), axis=1)

np.mean(np.square(np.cumsum(np.cumsum(array_x*2.444745417742782+5.713270189941643+np.log(abs(np.sqrt(abs(8.704885055615202))))*10*(3.0944633566895945)-array_x, axis=1), axis=1)), axis=1)

np.prod(10*(array_x), axis=1)+np.sin(2*np.pi*7.559579109625924)

np.mean(8.789888531265849/-(array_x+7.57419606898479)+np.exp(8.261036044774801+array_x)+6.0369830150927974, axis=1)+np.sin(2*np.pi*np.mean(5.488959607445167/-(array_x+4.7367688814442115)+np.exp(2.6906747435477025+array_x)+6.768131627158333, axis=1))

np.mean(np.square(np.exp(np.exp(np.cos(2*np.pi*1.1670995260082906))/9.833144408909114-(np.dot(array_x, np.array([[0.03743491364083318, 0.4121110437500911, 0.38301878515855214, 0.5213571825871911, 0.04851723995453139], [0.05363287410296769, 0.8242589818021345, 0.6980407050448826, 0.7495394233502538, 0.014620113366806908], [0.4462678659897651, 0.711252318205743, 0.42761898573628887, 0.7848055557641793, 0.05482147226911782], [0.5759419479318075, 0.5703005434377643, 0.8943884636510261, 0.510498823574463, 0.5651410318072725], [0.8138117782070364, 0.33573033234558636, 0.7973650258781524, 0.2482067119286948, 0.3130282616796364]]))))+5.4318758321920875-array_x), axis=1)

np.mean(1/(8.395483542321486-8.496885484331646-np.exp((np.dot(array_x, np.array([[0.6415770343964936, 0.57459446320885, 0.4065434491164647, 0.9181652832867566, 0.7015142135044713], [0.4327439852051995, 0.36173875024067514, 0.28706627906353566, 0.3739891507271943, 0.26817377954112964], [0.18816299045337903, 0.1787877483339071, 0.8425845363000183, 0.48378075739989557, 0.6010218600350902], [0.5196815211515885, 0.663291005752975, 0.4279465590898722, 0.15030988577581184, 0.7649926528928183], [0.6864465044446071, 0.8049570581127707, 0.09356150064340674, 0.46983686538730063, 0.48385236038948076]]))))+abs(np.round(3.966065784568917))), axis=1)

np.sum(np.cos(2*np.pi*np.sqrt(abs(-(np.exp(8.369542312834394+array_x/5.424001441828391))+1/(3.8955064573427407-array_x)))), axis=1)

np.mean(np.cos(2*np.pi*np.square(array_x))-np.log(abs(9.531681637766031))+abs(4.0301090552250445)*9.780698274670192+10*(array_x), axis=1)

np.mean(np.cumsum(np.square((np.array(range(1, array_x.shape[1]+1)))-6.14206398101588+1.758532350754922-5.892311809960959/3.4314563856726323+abs(np.cos(2*np.pi*3.5704790158774578)-array_x)), axis=1), axis=1)

np.mean(np.cos(2*np.pi*np.sqrt(abs((np.dot(array_x, np.array([[0.4038970966317208, 0.9921356329466571, 0.0355317894539805, 0.21903519803646898, 0.74202687269737], [0.39226536066470186, 0.2680319139010441, 0.20163378451131864, 0.7698597823840378, 0.011021897905535982], [0.9953852906347592, 0.4639965819545281, 0.43489346816152175, 0.30594820674065604, 0.6438971765404861], [0.13496664959258664, 0.484969218339805, 0.040411212990657086, 0.6027838415409225, 0.29301154129308804], [0.07071805248983165, 0.2118903091703085, 0.8016957596416596, 0.16151698982348262, 0.8040320811217486]])))))), axis=1)/8.213766576540214+np.sum((np.dot(array_x, np.array([[0.3384169366187624, 0.5343484795701818, 0.31474083855519974, 0.4481534508796766, 0.680575971911314], [0.4787419333227517, 0.6690259894470874, 0.08140742282225921, 0.9815622790871327, 0.3375989039265347], [0.6678571926301714, 0.5955702612647813, 0.6467736906801972, 0.8336381370079305, 0.17188002599681207], [0.009130803285503752, 0.09959778159916077, 0.8981180487488041, 0.7247354161205304, 0.9898540099621667], [0.33153284789841975, 0.7886768220167444, 0.46101489559987785, 0.20708255548146925, 0.9026406040537356]]))), axis=1)

np.round(np.mean(array_x*np.exp(8.010083112928502)+np.square(5.2478206974200186/4.136309299885756+array_x+array_x*6.643534997410222), axis=1))

np.mean(1/(np.log(abs(np.log(abs(1.3645777119646683))+array_x))), axis=1)

np.mean(10*(np.cos(2*np.pi*array_x*1.0655286316793569*5.01156114694867-10*(7.9371436874699)*array_x+8.287561494004876)), axis=1)

np.mean(np.square(abs(6.379773597830157*np.sqrt(abs(array_x+array_x))+1.280175674443532)), axis=1)

np.mean(abs(np.cos(2*np.pi*array_x-4.352267723649447/4.832936060020723)-np.sqrt(abs(8.71602586059555-array_x-2.484516734098353))*8.436008224866441*array_x+3.872353065440784*7.6722093487823075), axis=1)

np.mean(4.470038004964854-np.round(np.cumsum(np.square(array_x), axis=1))*np.square(2.1236581009352955), axis=1)

np.mean(10*(4.49092483463018-array_x/1.157415230370142)*array_x+np.exp(array_x)+1.4840276960768013, axis=1)

np.mean(np.cos(2*np.pi*4.963682364476082-array_x-np.cos(2*np.pi*array_x)/np.cos(2*np.pi*3.508288681592691)), axis=1)+10*(np.sin(2*np.pi*np.mean(np.cos(2*np.pi*4.313725517085698-array_x-np.cos(2*np.pi*array_x)/np.cos(2*np.pi*4.006286859252165)), axis=1)))

np.mean(9.469693726689488-9.86789790771579*np.square(array_x)-5.402611424521747, axis=1)

np.mean(np.cos(2*np.pi*np.round(4.648283641060398-np.exp(array_x))*array_x/9.03813084939286), axis=1)+10*(np.sin(2*np.pi*np.mean(np.cos(2*np.pi*np.round(9.99006324225483-np.exp(array_x))*array_x/7.6486854762767145), axis=1)))

np.sum(np.square(array_x), axis=1)-np.cos(2*np.pi*np.sqrt(abs(8.009422334108269)))+10*(np.sin(2*np.pi*np.sum(np.square(array_x), axis=1)-np.cos(2*np.pi*np.sqrt(abs(5.509164422451246)))))

np.mean(np.cos(2*np.pi*abs(5.03434767805687))/np.cos(2*np.pi*array_x*2.1913267006576236)/5.825172243824601-np.sin(2*np.pi*array_x), axis=1)

np.mean(np.square(10*(1.129624397533464)-1.0047481157906195*np.sqrt(abs((np.dot(array_x, np.array([[0.784631529357013, 0.4501499786210188, 0.6724843209609566, 0.7329099719306862, 0.6982065697405205], [0.24846434191270006, 0.10451989652469562, 0.37447990958347954, 0.9389991805307533, 0.4191248255233885], [0.18830889826977626, 0.4498361877268221, 0.3425368372625255, 0.9212002704641478, 0.649583888260163], [0.7749962665940261, 0.9865251142147989, 0.18437765468971046, 0.8278474960308275, 0.5532433927638816], [0.04417546706800268, 0.8907183297883234, 0.7236481639249764, 0.4225602073406869, 0.5672127838710176]])))))), axis=1)

np.mean(9.30911073085656-np.square((np.dot(array_x, np.array([[0.8730541557618168, 0.8488419137506963, 0.7489382902203182, 0.662140337880721, 0.6482694767855095], [0.6490078137225979, 0.4881059765421315, 0.8614586480588913, 0.6164076570475414, 0.6666060253649118], [0.45776597898199956, 0.8842772377676054, 0.24357109159784185, 0.18182913087615038, 0.7063799276552759], [0.18971117000572302, 0.5474503380987521, 0.559745744073461, 0.877643741977342, 0.9776133242677935], [0.11490511431281591, 0.6523053441124579, 0.9408660420576955, 0.7077981564462645, 0.2381514738921745]]))))+abs(8.175608854925407)-array_x, axis=1)+np.sin(2*np.pi*np.mean(3.5709163122431153-np.square((np.dot(array_x, np.array([[0.3824892430339639, 0.1775906177944102, 0.12995386981206902, 0.07895700408522022, 0.8083668623044633], [0.7025422142399979, 0.4422408858845891, 0.6057405404104219, 0.623175904671624, 0.7073731907831421], [0.8341883254304442, 0.10175530280356071, 0.4396160420781404, 0.4272542601565431, 0.2855322211489787], [0.2658720581224919, 0.3630082748386596, 0.0326210870030077, 0.09125612500640956, 0.17800698781869428], [0.22105441173686735, 0.5641616583765753, 0.20344594946240568, 0.3922841811811235, 0.34302657922576474]]))))+abs(7.281314799478311)-array_x, axis=1))

np.mean(1.5918752750911085-array_x-array_x*9.275433299051924-abs(10*(4.805116320665608)), axis=1)

np.mean(abs(10*(array_x)+7.213857935232977)-9.659407036901714, axis=1)

np.sqrt(abs(5.538516742040612*1.84705510573652-np.sum(10*((np.dot(array_x, np.array([[0.4833868518009229, 0.8485592233581221, 0.004448563980470555, 0.07244943808939297, 0.3329337707031538], [0.9866419037659867, 0.3975841965983533, 0.9191062490786117, 0.6143330273019701, 0.9758564353105872], [0.4623746341648104, 0.23230359377227539, 0.22321280762196616, 0.32536269268760665, 0.9538121419307655], [0.7041746064935512, 0.23723252409370765, 0.616527210167677, 0.045545743523240256, 0.5696050526912554], [0.8980166339112033, 0.517831700998806, 0.3277991746416574, 0.45488016202830417, 0.8245426911371958]])))), axis=1)))

np.mean(np.square(7.282258493743548*array_x-7.926835101130511)/4.142084699316647, axis=1)+10*(np.sin(2*np.pi*np.mean(np.square(2.424961737340554*array_x-1.6869469061577855)/7.9329620747505185, axis=1)))

np.sum(np.sin(2*np.pi*(np.dot(array_x, np.array([[0.548610186572594, 0.3659321038970407, 0.09163375195276857, 0.04367094296868279, 0.46893260250916324], [0.48350983678097503, 0.4205748178930194, 0.7668230338297081, 0.6082228476125766, 0.24086376879199456], [0.3018687211562966, 0.7263382930933678, 0.6340979254298931, 0.5791376358634315, 0.05320585466038019], [0.058861922701755276, 0.9652980723997507, 0.28992265001354345, 0.5626494645623564, 0.9142896832544917], [0.9764440716597057, 0.938662444064687, 0.07820985117798518, 0.2393165685382337, 0.37227650006214386]])))*1.6442267331282836-np.exp(3.781997097478107)*abs(np.round(3.5363992432718647))), axis=1)

5.444395490895887-np.sum(7.118493899238214+4.374484314285324*array_x, axis=1)

np.mean(np.round(5.754247453081444+array_x)+np.cos(2*np.pi*2.4227978674118136), axis=1)+10*(np.sin(2*np.pi*np.mean(np.round(6.53924177022107+array_x)+np.cos(2*np.pi*1.8309958625905725), axis=1)))

-(np.sum(array_x, axis=1)-2.14134821881374*np.square(np.prod(-(6.6731910348467025/np.sqrt(abs(-(array_x+5.723430219619347)))), axis=1)))

np.mean(abs(array_x/np.sqrt(abs(2.310571200629383)))-2.988699517010544/3.039810143876237, axis=1)+10*(np.sin(2*np.pi*np.mean(abs(array_x/np.sqrt(abs(8.39449241987639)))-4.384057997974942/5.9778248948615875, axis=1)))

np.sum(np.square(8.812803922491074*array_x), axis=1)+9.507082243728528

np.mean(10*(2.9194805004386293*array_x-3.4791619780331375*array_x+2.3247411735181185), axis=1)+np.sin(2*np.pi*np.mean(10*(4.303617091716198*array_x-9.631383935394375*array_x+1.925743694771897), axis=1))

np.prod(array_x+3.4496566195456246-1.40103403806593+np.square(np.log(abs(2.144171768977733))), axis=1)

np.mean(6.537252172051684-array_x*np.square(3.5596812205322808)-1.708604200635846, axis=1)

np.mean(np.square(9.063303551276178-(np.array(range(1, array_x.shape[1]+1)))*4.440274693526947-np.sin(2*np.pi*8.265113441206957/np.log(abs(np.log(abs(1.1142086758107306))))/np.exp((np.array(range(1, array_x.shape[1]+1)))*array_x))), axis=1)

np.mean(9.892155507028946*np.cumsum(7.906854021980251-array_x, axis=1)-3.538491694966-array_x+abs(array_x*10*(array_x)), axis=1)

np.mean(7.0119871143294725+(np.array(range(1, array_x.shape[1]+1)))-4.529894794253259*array_x-(np.dot(array_x, np.array([[0.1273866400850432, 0.6079687505680014, 0.8693569182980287, 0.9474770669396791, 0.1831607531713415], [0.9354849380553785, 0.08500786728596721, 0.19286343072670364, 0.257782625814589, 0.7284203108820132], [0.05321483674283434, 0.9967906698215441, 0.7517697044071304, 0.8050862769076159, 0.8619233966484329], [0.22856193611759856, 0.60402215363093, 0.45638472879089786, 0.7895346672207585, 0.6764893246564312], [0.7680850812610888, 0.1887624865043077, 0.25177799588547445, 0.0030850642709691067, 0.4689380267163361]]))), axis=1)+10*(np.sin(2*np.pi*np.mean(7.373498941872775+(np.array(range(1, array_x.shape[1]+1)))-5.676948004957369*array_x-(np.dot(array_x, np.array([[0.38746216067233663, 0.6805677948285836, 0.9937559824638434, 0.6211722794626979, 0.7120692128403849], [0.6910043563986553, 0.33722424256583783, 0.7067010430218396, 0.8387439415338087, 0.4111665200433757], [0.8126369218806959, 0.8317479351803888, 0.8143105488994329, 0.2579482238777019, 0.17170577520640218], [0.9136046664894969, 0.2628195922326182, 0.23087023849071353, 0.9007152467208335, 0.3456385493571701], [0.06774413471540963, 0.9657570085552789, 0.9154975360041063, 0.045269877954371096, 0.8080916476064078]]))), axis=1)))

np.square(np.sum(np.cos(2*np.pi*np.cos(2*np.pi*1.0918423499656182*array_x))-np.cos(2*np.pi*4.313879995188434), axis=1))+np.sin(2*np.pi*np.square(np.sum(np.cos(2*np.pi*np.cos(2*np.pi*8.966727873061522*array_x))-np.cos(2*np.pi*5.226757125200887), axis=1)))

np.mean(10*(np.round(8.637793043806283)+array_x), axis=1)

np.mean(7.002258585632367/3.3592119741813953-10*(np.square((np.dot(array_x, np.array([[0.741524009022997, 0.1732180885222535, 0.8224812524566989, 0.3070583734174541, 0.8643381822237128], [0.19958670519986843, 0.7205334866169167, 0.8675454245113782, 0.3811494161225244, 0.33408888049997987], [0.301036726760624, 0.8148888189725445, 0.5587135918268947, 0.34263503476144486, 0.42092055706171483], [0.6771179150257846, 0.8323006775535094, 0.3452718757358594, 0.926147182722287, 0.4042705745476173], [0.955518612432849, 0.18263441975806516, 0.28764011043283677, 0.7541389645786701, 0.20917647554359642]])))))-6.501226370693555+(np.dot(array_x, np.array([[0.7357563729447824, 0.19157267387051957, 0.09422916433407769, 0.8179271459617131, 0.36380412828358144], [0.8786504515759981, 0.5464497506206096, 0.3352717368945022, 0.40385393475443077, 0.41809894873651343], [0.5941267261149714, 0.02796871003309065, 0.8464866637168637, 0.2080925689574632, 0.45242963114989354], [0.8940663046774681, 0.8426837240485503, 0.06289779995369904, 0.5358474504684496, 0.6726219538119717], [0.8903470216243271, 0.6690612877439217, 0.7343928998859641, 0.2923405520223794, 0.5104084060548258]]))), axis=1)

np.prod(np.cos(2*np.pi*np.square(array_x-np.cos(2*np.pi*5.628534700107518)))/np.cos(2*np.pi*8.661073475337284)-(np.dot(array_x, np.array([[0.4704625575160768, 0.43345273034009235, 0.2869050694450588, 0.0940514847970656, 0.9666952669621649], [0.9725975563029742, 0.17089594435818223, 0.8441950302291654, 0.62874849250335, 0.9451264542288187], [0.5036974570296281, 0.3773752082288261, 0.8349282894502712, 0.905898161234006, 0.999626907189228], [0.9296308203383965, 0.32974506853888164, 0.0002365290011043797, 0.3205895603276536, 0.17593090678552759], [0.9262300331111889, 0.42065301956745604, 0.6183181707181113, 0.9551007881948682, 0.8417992109452388]])))-(np.dot(array_x, np.array([[0.055041264999648676, 0.18848335120966475, 0.09358036831303163, 0.8675369248875875, 0.7914050363440294], [0.7057400828306803, 0.3451665174212094, 0.15976428429017742, 0.05335095985220628, 0.1705391018050495], [0.45744974767886637, 0.24121192011114578, 0.9622957939517527, 0.016799913578784853, 0.28245858204719865], [0.33063649253817684, 0.829257307771139, 0.8795023586751514, 0.8358522349505704, 0.5275927365656069], [0.1039319949351285, 0.5828208106064929, 0.8132245645336268, 0.3569060458680675, 0.6911597147901792]]))), axis=1)+10*(np.sin(2*np.pi*np.prod(np.cos(2*np.pi*np.square(array_x-np.cos(2*np.pi*3.659351175466911)))/np.cos(2*np.pi*8.653840033041462)-(np.dot(array_x, np.array([[0.4732977714344597, 0.8191751815432878, 0.8993094665248934, 0.07574057208884954, 0.1573811575284111], [0.44650980272309204, 0.423001431144576, 0.3175076596714521, 0.6682055549337607, 0.7884336091419157], [0.6935052779064889, 0.9617930370946254, 0.3507874150004401, 0.3679271276882564, 0.2533522257017955], [0.3737822042909743, 0.38324992652004164, 0.6298528033460317, 0.9229931037215819, 0.6838847999886304], [0.19012213265989664, 0.9548767257775491, 0.4862734433353687, 0.7614831245032979, 0.14638205363248924]])))-(np.dot(array_x, np.array([[0.2574668813083446, 0.9705204201951126, 0.9358645042441998, 0.6044739028899799, 0.52236590958435], [0.3802595840554004, 0.3285176315670507, 0.8094584381947798, 0.0011858004370443043, 0.6924148934258338], [0.1567041779901861, 0.1475034943311243, 0.12145764316936525, 0.446844923116569, 0.4195277828559757], [0.7548011319771236, 0.072818689104858, 0.26737959571221015, 0.4285196922682203, 0.27061967686018074], [0.43029890926943914, 0.28846859442881256, 0.4915504143566142, 0.7603133767027557, 0.22129510506917838]]))), axis=1)))

np.mean(9.413053590756569/np.sqrt(abs(np.cos(2*np.pi*array_x-np.cos(2*np.pi*8.271186637115221)-array_x))), axis=1)

np.mean((np.array(range(1, array_x.shape[1]+1)))*array_x*5.536053700724095-np.round(8.307145143986885)*7.017245803901587+(np.array(range(1, array_x.shape[1]+1)))*array_x, axis=1)

np.mean(6.447468557382489+np.cos(2*np.pi*array_x)+np.square(5.547336245279518)*array_x, axis=1)

np.mean(2.9501930984428304-np.square(np.sin(2*np.pi*array_x)*6.43023476247903-(np.dot(array_x, np.array([[0.8615259945360401, 0.9914118667233034, 0.3671319057010006, 0.65589173427028, 0.29111194636813753], [0.9890695343871766, 0.41380472655101475, 0.14766844667623236, 0.9946864230078434, 0.8822212492782774], [0.34738710993262345, 0.2282041800834862, 0.4361480629272019, 0.40864256773204544, 0.3631660669185641], [0.3812307897747369, 0.6707689123284727, 0.46163270011745083, 0.5399838156752759, 0.47899797466349603], [0.04933822679976074, 0.7751078183045753, 0.32162321905268243, 0.8608367996406644, 0.11366218312332887]])))), axis=1)

np.mean(10*(np.cos(2*np.pi*9.872856255675108+array_x)/4.151590386563857), axis=1)+10*(np.sin(2*np.pi*np.mean(10*(np.cos(2*np.pi*2.9787280694276754+array_x)/3.5563683865725313), axis=1)))

np.mean(np.cumsum(abs(np.round(array_x+(np.dot(array_x, np.array([[0.786106780855998, 0.602439610957866, 0.16970090804334037, 0.9323521280082224, 0.6815890939949175], [0.44909145560604435, 0.43730061384902497, 0.14947960486382772, 0.4641265602969752, 0.094646042428297], [0.013659545397020634, 0.9124317706002342, 0.26177500683837174, 0.6169563002504886, 0.09963132049773649], [0.9181869701938598, 0.047365528704734206, 0.10084507874054882, 0.22544154240798453, 0.1443313339988357], [0.9575482708080915, 0.5191814166717769, 0.8536646013325849, 0.6174251441556516, 0.16052834203033128]])))-5.249369973162381)+9.912871843698008), axis=1), axis=1)

np.square(np.sum(array_x+1/(4.715156339039441)*array_x*9.346970830892213-9.426671938556275, axis=1))+np.sin(2*np.pi*np.square(np.sum(array_x+1/(4.692295155806179)*array_x*7.276522114445914-6.362998348024372, axis=1)))

np.mean(10*(np.exp(5.586154967955238*np.square(array_x)/9.33257473760736+8.735663652359879*array_x)-2.2049735936656853*array_x-5.567304313636858-(np.array(range(1, array_x.shape[1]+1)))), axis=1)

np.sum(np.cos(2*np.pi*np.cos(2*np.pi*(np.array(range(1, array_x.shape[1]+1)))*4.944815975726202-(np.dot(array_x, np.array([[0.5449965269319749, 0.4255623833909691, 0.0878086138815305, 0.4477596013617938, 0.9944826143286689], [0.5412714255683818, 0.11015148532148356, 0.9615998062418653, 0.6111286871583056, 0.09110923134745885], [0.48431572913276966, 0.02440853979236901, 0.055265261351315376, 0.024242717483041964, 0.39944035449801407], [0.21108930415888671, 0.6962653061165702, 0.05772286648081815, 0.7103644937903456, 0.7668800642545863], [0.617320907698557, 0.04297589924032641, 0.7802609516699212, 0.867400306788383, 0.17912444446462328]])))-9.527866409404938*5.998639756698863*np.round(1.9780773522287325)*array_x)), axis=1)

np.mean(np.square(np.exp(array_x))*1.6110229579602668-5.8422242182383854-array_x+array_x, axis=1)

np.mean(np.exp(np.cumsum(array_x, axis=1))+np.exp(np.cos(2*np.pi*3.2109169535327102)), axis=1)+10*(np.sin(2*np.pi*np.mean(np.exp(np.cumsum(array_x, axis=1))+np.exp(np.cos(2*np.pi*5.273577387064207)), axis=1)))

np.mean(array_x-9.504606341853158+9.22946303580624*array_x, axis=1)

np.mean(9.026168512364746/(np.array(range(1, array_x.shape[1]+1)))*array_x*6.850315877480047+np.cos(2*np.pi*7.5252075631168935), axis=1)

np.mean(10*(np.sin(2*np.pi*array_x+np.square(1.2182788975956242-(np.dot(array_x, np.array([[0.49734600649174754, 0.6830522757927615, 0.2760879144965085, 0.010041233867607469, 0.8011835055996913], [0.8302797043622419, 0.43294036814233217, 0.11230929989737792, 0.1672215870770387, 0.7103383834765032], [0.8874029428168702, 0.8315368205235484, 0.9356425942409365, 0.936652726861084, 0.17150006018869113], [0.21729780856232417, 0.6992611772602598, 0.05871824181293639, 0.46490929082375254, 0.2789451233295571], [0.692034417296345, 0.26639670018371386, 0.4810931802403706, 0.6652481781095327, 0.25179576580609697]])))))), axis=1)

np.mean(np.cumsum(array_x-2.733853656525993, axis=1)*-(3.8542141568701624), axis=1)

np.mean(np.square(8.161417837815106+array_x+np.sqrt(abs(10*(1.3126828633114171+(np.dot(array_x, np.array([[0.9020120863989092, 0.35187751527433975, 0.4366836017277197, 0.14942919687316847, 0.21634332196895545], [0.7038073051010026, 0.940040884188014, 0.2483083966570132, 0.11135566340181458, 0.4299062621776698], [0.5764735869354025, 0.022083080379998954, 0.45026052939640304, 0.5838844343620619, 0.6948167038848603], [0.6627098962737777, 0.9634706744283372, 0.05774728136165874, 0.028646142518356066, 0.6338105090494852], [0.11951282909049554, 0.32287048822388276, 0.1047781568223961, 0.5520227702387994, 0.18527106038861785]])))/1.6938171698028852)))-array_x/3.9741661502707166), axis=1)

np.mean(np.square(array_x-10*(array_x))-6.943803129237153, axis=1)+np.sin(2*np.pi*np.mean(np.square(array_x-10*(array_x))-4.320248821228304, axis=1))

np.sqrt(abs(np.amax(np.sin(2*np.pi*6.462110907343786)-array_x, axis=1)))+10*(np.sin(2*np.pi*np.sqrt(abs(np.amax(np.sin(2*np.pi*9.93373913884971)-array_x, axis=1)))))

np.amax(8.104621943364691*array_x-2.15849749275131*7.59057026914896+2.8058421916211285*(np.dot(array_x, np.array([[0.6729416780442581, 0.1799611587492631, 0.20993115967920406, 0.027666315427728794, 0.219107775952172], [0.0681089312495865, 0.212806306675262, 0.8794927553782483, 0.41774523779237327, 0.06955386705844802], [0.26296404523015515, 0.8211734640618604, 0.5203135394572, 0.0022302796801135782, 0.88104172239979], [0.008826330559218998, 0.6163530555524802, 0.5987746623438444, 0.7810776795602162, 0.646711384309851], [0.059307800708074065, 0.3702370165514792, 0.8292776914018491, 0.8111177322905454, 0.7402335982275788]]))), axis=1)

np.mean(np.square(abs(2.797528375387185/np.sin(2*np.pi*7.915271120526766+array_x))), axis=1)

np.mean(np.log(abs(3.960891263362745))+1.5336362721545165*array_x*9.626759201053341+array_x, axis=1)+np.sin(2*np.pi*np.mean(np.log(abs(6.667372199449421))+7.56774789203004*array_x*4.627455429310022+array_x, axis=1))

np.sqrt(abs(3.700117904071794))/np.cos(2*np.pi*10*(np.amax(array_x, axis=1)-8.065770776959138))+np.sin(2*np.pi*np.sqrt(abs(9.12181267322848))/np.cos(2*np.pi*10*(np.amax(array_x, axis=1)-8.44911893046071)))

np.mean(np.square(10*(array_x)-3.0220274333120054/7.816050441410092+array_x*np.sin(2*np.pi*1.693595638534175))+np.log(abs(10*(4.879131339544639))), axis=1)

np.mean(np.exp(np.log(abs(1.5671454869844073/2.3303565812608804-array_x))+8.775205061545902), axis=1)

np.mean(array_x-np.sin(2*np.pi*5.433963389274569+array_x)+10*(array_x+3.5060400872859763+7.162018832973679), axis=1)

np.mean(10*(abs(4.2528860619839)-(np.dot(array_x, np.array([[0.03421257776196407, 0.8728688665247089, 0.21619882676021418, 0.5128378779844573, 0.6342671694299771], [0.6648583743491916, 0.22745912182595895, 0.5819080748729458, 0.4308927229237782, 0.7440321552836782], [0.5746149966617716, 0.3355609016140749, 0.7619814414629338, 0.4785987426488879, 0.7552795302847396], [0.1478439303343968, 0.042770317983011896, 0.7346098167027689, 0.5677043816476258, 0.6082665269551062], [0.6556330670156815, 0.5544136833552665, 0.1422477080823472, 0.9258663707818009, 0.752381786121263]])))/np.sqrt(abs(1/((np.dot(array_x, np.array([[0.23760558348871774, 0.8120910452895267, 0.08276169363749863, 0.15524763977568912, 0.815386420062231], [0.8620765439950357, 0.767835844740433, 0.8100136304027324, 0.572536440045314, 0.05874944461932308], [0.10386397545568771, 0.2127130544041127, 0.523640923615653, 0.4492209038427014, 0.035557121669438585], [0.5195870146110265, 0.1633959449426161, 0.2182632480871679, 0.2136788094573372, 0.5512337231471879], [0.9784351816888495, 0.7334019845800697, 0.037580751338573304, 0.8872480620738838, 0.7690303588619717]]))))))), axis=1)

4.531359975441857+abs(np.mean(np.exp(7.474274512706619)*10*(np.round(array_x)), axis=1))

np.mean(np.exp((np.array(range(1, array_x.shape[1]+1)))*array_x+1.5706366708817843)-2.829160652876052, axis=1)

np.mean(3.6008766959168583+7.306360534894594/np.exp(array_x)*np.round(-(9.309208040361206+array_x))+1.3005249051333418, axis=1)

np.mean(np.cumsum(3.00977465614192-(np.dot(array_x, np.array([[0.08602648796857915, 0.27187366833655047, 0.7627354872464371, 0.1544739786805288, 0.46120582282282696], [0.14507265184948437, 0.14854705370357568, 0.32007991897105526, 0.2554916884254287, 0.03798808059690573], [0.9239196366927026, 0.7778590517576423, 0.8301736222137244, 0.9254436340557894, 0.8313287536789461], [0.777179663154786, 0.8299146820270896, 0.9734013154754659, 0.7418596890315032, 0.6670433706747467], [0.04613621652410005, 0.1456219251805142, 0.7342482294949529, 0.9894024182130232, 0.5161248855565257]])))/np.square(5.101923201231296-10*(array_x)), axis=1), axis=1)+10*(np.sin(2*np.pi*np.mean(np.cumsum(5.827251619224752-(np.dot(array_x, np.array([[0.6199122144716822, 0.4648925725785771, 0.8191264042171463, 0.7601309477353667, 0.8359253768691576], [0.7011643849213228, 0.5247627734407743, 0.5491560323985097, 0.7220516052613278, 0.5772133142899895], [0.1267474024585069, 0.47251538346965216, 0.9859871215951685, 0.5300539375209907, 0.21951975332659257], [0.8156117318083735, 0.16336659934154052, 0.18485161925731075, 0.13723577499013873, 0.7340377611244382], [0.32360306661046934, 0.5581688289866631, 0.41717901587860606, 0.7091031128399653, 0.21108932742863862]])))/np.square(8.548288665561312-10*(array_x)), axis=1), axis=1)))

np.round(np.mean(5.032198298018487-np.sqrt(abs(array_x-np.exp(3.9724905169653413)-array_x))+np.square(7.887699424329585*array_x+np.sqrt(abs(6.6928840878977764))), axis=1))

np.mean(np.square(array_x)*9.901758876974814*9.779685618744208+2.98976449074357, axis=1)+10*(np.sin(2*np.pi*np.mean(np.square(array_x)*5.577163677961923*5.3407879501840325+9.5468629411287, axis=1)))

np.mean(2.13501874706865*array_x*2.780310063506387+4.375314770656268, axis=1)+np.sin(2*np.pi*np.mean(5.155045505171758*array_x*1.8381791592678525+2.1789627272440573, axis=1))

np.mean(np.log(abs(np.cos(2*np.pi*array_x-1.3491707449694483+np.cos(2*np.pi*array_x/7.949084201637371)-array_x))), axis=1)+10*(np.sin(2*np.pi*np.mean(np.log(abs(np.cos(2*np.pi*array_x-9.732424767129734+np.cos(2*np.pi*array_x/6.026091263855973)-array_x))), axis=1)))

np.mean(np.log(abs(8.758168944523394))+4.839673087310851*(np.array(range(1, array_x.shape[1]+1)))*array_x+array_x-array_x-1.0079395215015332*array_x, axis=1)

10*(-(np.round(np.sum(np.sqrt(abs(array_x)), axis=1)+9.628207218938325)))

np.mean(np.round(1.9227658175889837+array_x)*array_x-9.98007266501969-np.sin(2*np.pi*array_x), axis=1)+np.sin(2*np.pi*np.mean(np.round(8.784466206513503+array_x)*array_x-5.007860526905133-np.sin(2*np.pi*array_x), axis=1))

np.mean(10*(array_x+array_x+8.0774660086712/1.9966081695851925)-10*(array_x)/7.194451631483076-np.sin(2*np.pi*10*(7.8147380675727955)+1/(array_x-6.284032636107717+8.590109891362866)), axis=1)

np.mean(8.142412240287292*array_x+6.991104828560233, axis=1)/np.log(abs(np.log(abs(np.mean(array_x, axis=1)))))+9.323831133655554+10*(np.sin(2*np.pi*np.mean(7.61356858123794*array_x+4.823948288959761, axis=1)/np.log(abs(np.log(abs(np.mean(array_x, axis=1)))))+6.715713539589762))

np.mean(array_x-3.1769013783067535/9.125616101410188+array_x*7.174050599645498*8.698313964273535-5.789992278169997-array_x*np.sin(2*np.pi*7.358447752359642/(np.array(range(1, array_x.shape[1]+1))))*np.round(-(6.918283099340331)), axis=1)+np.sin(2*np.pi*np.mean(array_x-5.214685647578458/4.083138432112922+array_x*2.2152233519245295*7.513866525437132-7.365034497656295-array_x*np.sin(2*np.pi*4.939079355557009/(np.array(range(1, array_x.shape[1]+1))))*np.round(-(7.770757265109968)), axis=1))