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np.mean(np.sin(2*np.pi*abs(np.square(4.815105418662564)))+np.square(1.5058506568107224)*6.496182630523563+np.round(8.74037201392673)+array_x, axis=1)+10*(np.sin(2*np.pi*np.mean(np.sin(2*np.pi*abs(np.square(1.2501645370584071)))+np.square(4.681219670576118)*8.162293697778424+np.round(1.7803493136900874)+array_x, axis=1))) |
np.mean(np.log(abs(abs(np.sin(2*np.pi*6.028875769885846))-1/(np.sin(2*np.pi*np.cos(2*np.pi*np.exp(np.sqrt(abs(np.sqrt(abs(array_x)))))))))), axis=1) |
np.mean(1/(1.619743922073943)+np.round(array_x)-np.square(8.333049804412997), axis=1)+10*(np.sin(2*np.pi*np.mean(1/(7.391467109343429)+np.round(array_x)-np.square(8.895283993305046), axis=1))) |
np.mean(np.cos(2*np.pi*np.cos(2*np.pi*3.8182272092998177))+2.0543481090509266+(np.dot(array_x, np.array([[0.5814478700073347, 0.008586252693553398, 0.19882708445848063, 0.4629191088884751, 0.9229762289839231, 0.005595879216919841, 0.12891825078424146, 0.27191395974305066, 0.3353309261245674, 0.3896331059177678], [0.21452557642825587, 0.16777927061771014, 0.956081314507052, 0.823318164880534, 0.213447058699881, 0.8779303248646039, 0.3302256619179845, 0.34568048623713465, 0.1114383251912151, 0.24393805152149428], [0.15756126063628162, 0.9936516812463517, 0.10664057660706305, 0.28155060696770795, 0.7692894852614327, 0.33697837100177985, 0.8116178228333445, 0.4441443150277048, 0.7010659240259289, 0.6307601437781203], [0.10662236157343041, 0.7033011210935884, 0.12146206577974616, 0.005711666867850518, 0.6047342494924747, 0.3805653480227741, 0.4009186349811753, 0.4349753386969123, 0.008192155570115656, 0.08193739953541457], [0.45938729789057575, 0.8134932255374019, 0.8677943674189479, 0.7774396079094872, 0.3743709820226965, 0.28600203466479757, 0.8784537640938221, 0.005343796838849557, 0.37316089514860473, 0.641404876440069], [0.1313912261234078, 0.280354860170442, 0.18458981518156659, 0.8692700489828585, 0.15695134316790016, 0.8773683660644374, 0.38024691506591624, 0.3524314866685314, 0.4614199291740494, 0.7502348462048485], [0.6790618006078842, 0.5844470529844497, 0.37971796059787244, 0.8758920174397901, 0.042206619467776596, 0.34811582410116937, 0.5048752674648419, 0.008372663780340517, 0.46889268687781727, 0.5110285715646087], [0.46175353448707357, 0.17682509664602997, 0.9933584390765613, 0.3984467953270412, 0.8391449545639079, 0.7894764507067024, 0.38034541301070657, 0.31561240108613264, 0.4199808090470003, 0.5553927344992368], [0.9364771874701027, 0.07194818989856233, 0.3431238535469159, 0.4541863557753011, 0.20783038045208813, 0.26324024766201304, 0.10598835589345246, 0.6941158446802734, 0.3797189983721959, 0.44704204264518843], [0.2717262044884131, 0.08922516565665783, 0.25544024172220303, 0.6022565439288496, 0.6152839580991033, 0.7322173701178828, 0.672244741090676, 0.9331916289352802, 0.3976949938132971, 0.4251818575766192]])))*6.357292268392014+7.2820771757619545, axis=1) |
np.log(abs(1/(np.exp(np.mean(array_x, axis=1))*np.log(abs(np.round(2.8410252317912636))))))+10*(np.sin(2*np.pi*np.log(abs(1/(np.exp(np.mean(array_x, axis=1))*np.log(abs(np.round(2.110007759341573)))))))) |
np.mean(np.square(np.square(9.069097675291948)+np.sqrt(abs(10*((np.array(range(1, array_x.shape[1]+1)))*array_x)))-(np.array(range(1, array_x.shape[1]+1)))*array_x), axis=1) |
np.mean(np.cos(2*np.pi*array_x/6.779942060569983+array_x+np.cos(2*np.pi*9.841144308750724-array_x))-np.square(4.70206013912878)*array_x*8.638025396801854, axis=1) |
4.362422450835277+np.round(np.sum(1.737116212025241*array_x-9.54095211855305, axis=1)) |
np.mean(-(np.sqrt(abs(np.sqrt(abs(1.5750333171154185)))))/np.square(np.cos(2*np.pi*np.square(array_x)-8.256091303348985)), axis=1) |
np.sum(array_x-(np.dot(array_x, np.array([[0.996190136211392, 0.40893838926515647, 0.8056621719171361, 0.6694242458162799, 0.2041095952622478, 0.4954825970003225, 0.527449561489473, 0.37620964389061695, 0.5301917549208309, 0.8039755656496896], [0.60621078719428, 0.7686315873997026, 0.5711883701918279, 0.6133182947937854, 0.9059089165723616, 0.9366940827593373, 0.5377708234031752, 0.6962374426518255, 0.643729043668315, 0.6809124299043718], [0.18940090997925663, 0.7150673747322346, 0.46469875313361764, 0.3677642194486106, 0.9646999231839526, 0.9230501521877986, 0.8322441187283779, 0.6007110425289478, 0.7429979364634345, 0.7010231525832726], [0.22956505939862382, 0.5479198248873667, 0.35861548246952724, 0.28034522283820773, 0.03871940246872185, 0.6022616375956804, 0.20376252983840148, 0.684990258334426, 0.32053564454368944, 0.5276858214615852], [0.38413749759900495, 0.16578541043934591, 0.5111467636073013, 0.5676117107184552, 0.9478132052337057, 0.6237693376255293, 0.09178861187705456, 0.8310691986894686, 0.28787098712368864, 0.005992592368451066], [0.48128412285760647, 0.9223065417872505, 0.6059840681894709, 0.6781144015950403, 0.5939174825144437, 0.6215202934162602, 0.920957034277284, 0.30056734810547814, 0.21873874017699146, 0.08641220092902357], [0.5371915155518913, 0.01570968410169138, 0.42311791149513334, 0.4965519717631788, 0.3161487205976323, 0.7188114847536221, 0.6457605740223659, 0.6531211609685791, 0.49918513653584484, 0.7083347480726366], [0.6163800051559705, 0.9477226650385531, 0.23990371569529967, 0.30678230610240165, 0.016694692758780882, 0.9746205326194433, 0.2642607772576915, 0.05436373103925851, 0.44076667897947797, 0.28748779405104374], [0.985905822216059, 0.05297121838265728, 0.38046442269484004, 0.4524624205866785, 0.7843548863217511, 0.6820280055483477, 0.03207109735362401, 0.27977014511583886, 0.7007702962842894, 0.6057907838485443], [0.9202986601541606, 0.40378665434063843, 0.2568932719578594, 0.3392355443448879, 0.632984261116428, 0.3878964560511138, 0.6765999303733088, 0.3099117812283928, 0.18179356427852245, 0.8820146817549153]])))+9.295772346217673-np.square(7.17838535962287), axis=1) |
np.exp(np.amax(array_x*(np.dot(array_x, np.array([[0.8767908650649622, 0.7714607259253724, 0.9404227745332354, 0.8541972919751191, 0.47740905996598526, 0.02697672646511373, 0.9735557771174783, 0.6736840621142544, 0.02010828776955864, 0.5494301920549756], [0.9673523562531339, 0.16164087557229612, 0.1481851042401331, 0.8863859421980048, 0.1114358997258924, 0.4191794889660906, 0.3743895230965333, 0.5235031830428313, 0.27813847781358947, 0.35515335532446746], [0.4067975016695682, 0.21667006296385682, 0.5905268907040548, 0.05832175797335104, 0.545827282272113, 0.6321447450221338, 0.9758777623872849, 0.5705640295740115, 0.8048209534552697, 0.03033661287591838], [0.5495515783104435, 0.10371175957551737, 0.07165157022939961, 0.020590484119171304, 0.18245741340545418, 0.019073955316033175, 0.5058805108063857, 0.4769228719870514, 0.10979816350935068, 0.6509717586817181], [0.2425773010286566, 0.4223479672708883, 0.6774902870265335, 0.9122892907409568, 0.23594632019144557, 0.004677207329326416, 0.037488740120087516, 0.1601624897928977, 0.09845718093648659, 0.8932181843106302], [0.5202578881101574, 0.34985428880413405, 0.6953238054668577, 0.3577259767767915, 0.857052767763391, 0.5834670062226699, 0.506634908251377, 0.12345670537870501, 0.35635640052194273, 0.46559994011362527], [0.6056303452842066, 0.9723348272744726, 0.06655961083401307, 0.2192338564421592, 0.35171736920787555, 0.9953403657840728, 0.3809201043927657, 0.7931238330182148, 0.6775320225749519, 0.38762636760447], [0.38197278473621443, 0.018929926350830795, 0.7839458769442097, 0.20618520523093775, 0.45046730447349137, 0.030654675981330648, 0.5056000703524145, 0.2570644915629171, 0.17373755263736945, 0.6106428462400649], [0.2328464520714525, 0.26474968927976583, 0.8893390367923033, 0.8613337090232793, 0.48417778890697616, 0.8056607055222448, 0.633515118096404, 0.7149755612776287, 0.6669444110533438, 0.7711346144500497], [0.4274609028558176, 0.8082095188382465, 0.2888874923442346, 0.7388798603047969, 0.8990722553527911, 0.31233565973069677, 0.7485158013659283, 0.8564224924296613, 0.5963368251263248, 0.8247519726795582]])))+np.round(np.sin(2*np.pi*abs(6.845033346022824))), axis=1)) |
np.mean(np.exp(3.9612553335591305+array_x), axis=1) |
np.mean(np.exp(1.3947055366619776)-np.square(np.exp(3.8951783144559586)+array_x/7.540209407022264), axis=1) |
np.amax(np.sqrt(abs(3.9671561911015005*array_x/np.cos(2*np.pi*array_x-4.680827339752383)-9.020811186947752-np.round(5.052354060264283))), axis=1)+np.sin(2*np.pi*np.amax(np.sqrt(abs(2.0682427730462964*array_x/np.cos(2*np.pi*array_x-3.086246941766193)-8.036029737147787-np.round(8.394889105589483))), axis=1)) |
3.7911998078134515-np.square(np.amax((np.array(range(1, array_x.shape[1]+1)))*array_x*4.761023621517679/6.1218372708703415, axis=1)) |
np.mean(10*(abs((np.array(range(1, array_x.shape[1]+1)))*array_x-7.647621451511403))+8.15190310173402+(np.array(range(1, array_x.shape[1]+1)))*array_x-2.2379990658137805, axis=1) |
np.mean(np.cos(2*np.pi*array_x+9.839874708045194-5.108694159956044*np.cumsum(array_x, axis=1)*7.551397710605237-array_x)*np.log(abs(np.sqrt(abs(8.103574099855836))))+np.square(array_x*3.459272227695975), axis=1) |
np.prod(np.cos(2*np.pi*4.04425074225329)+np.square(abs(np.square(array_x))), axis=1)+np.sin(2*np.pi*np.prod(np.cos(2*np.pi*1.4158660771964557)+np.square(abs(np.square(array_x))), axis=1)) |
np.mean(np.log(abs(1/(np.sin(2*np.pi*(np.dot(array_x, np.array([[0.27809904979293565, 0.5101799727622417, 0.08000316250646711, 0.10962909261528586, 0.6810895588170124, 0.3839204787938255, 0.6523811604488964, 0.3641171970196596, 0.4518784849828231, 0.49956293975089217], [0.6659562892195634, 0.9354405804874905, 0.2409021824293005, 0.310032359716217, 0.46312275691470683, 0.8247795255733639, 0.17026745665657295, 0.31308029007169835, 0.46408046681329485, 0.22344798792987564], [0.4717526584823891, 0.7200606336267731, 0.7561774821161804, 0.1140764248956766, 0.18169211136786778, 0.03950302144140039, 0.8980552360847528, 0.9364751388528085, 0.15377341795622845, 0.29111951662750246], [0.6350431238881085, 0.6492619851678247, 0.9130586806704725, 0.00426613163830547, 0.3526313899114586, 0.5223331491586887, 0.9598882931502313, 0.8861448191430853, 0.7998416678155907, 0.8728012386404028], [0.16467846873003344, 0.5867444948297316, 0.6477685893542062, 0.1312123594598994, 0.784989654050409, 0.02782631572777683, 0.9522456923077454, 0.1609674044578886, 0.17350835950513555, 0.5362959028272127], [0.49798181445709244, 0.532115904732591, 0.7588780942528905, 0.9896926821088621, 0.25035100258338494, 0.7544838023764063, 0.8216690940025421, 0.3176978295362197, 0.8241791096854304, 0.02256553816740403], [0.6137576391994469, 0.04808124591542651, 0.4138245604226347, 0.17980989919262458, 0.09611398739184951, 0.00395483700481547, 0.2740958747526845, 0.374583667278388, 0.10617798770943154, 0.7068751251623585], [0.6567594214897671, 0.2292731379759244, 0.5710415050474466, 0.3011301806252209, 0.6195573430329239, 0.25764208958121493, 0.7378682385562491, 0.9126772838525915, 0.1900944695409552, 0.9146878227620909], [0.881215808993092, 0.6234404046347456, 0.4843901946259803, 0.48229544672032254, 0.17360365478589979, 0.034904346379938, 0.42298098213984014, 0.5620992255354542, 0.7512999766133656, 0.6984924910277764], [0.9618722830290519, 0.7179586278420236, 0.6546812753153657, 0.4989302215611171, 0.048709623524043955, 0.005110686343199888, 0.7837567474763538, 0.32857143589225135, 0.9650417965160759, 0.5275181142746976]]))))-9.32027317599238)-9.697800875620594))*np.square(10*(1.5591308004346285)+1/(2.2567263948325973)+(np.dot(array_x, np.array([[0.12867106398352557, 0.6237565283230223, 0.3028340905607523, 0.9755254679284757, 0.7868671541105754, 0.6034782481654063, 0.04203458095869883, 0.5156078501755293, 0.06634078825665246, 0.47228217312831844], [0.8251459891297902, 0.28775941004804884, 0.5745160094620544, 0.2962627166794415, 0.2815861914511908, 0.05041456470905148, 0.6387247992003452, 0.9523569381715505, 0.03755347227940442, 0.7264332463646389], [0.01619167720255088, 0.823308089367362, 0.22608432427323877, 0.7538541746566257, 0.04549522928975891, 0.5512551564701756, 0.5822742481940052, 0.49599678193044705, 0.8774677611754262, 0.052878052693287714], [0.27645055768990523, 0.9303182181571508, 0.8163015195703006, 0.5392764207223352, 0.9371108454991635, 0.9032335325992799, 0.666127516644988, 0.19552881712771641, 0.880918693835086, 0.4296926083054815], [0.9126343780759856, 0.7905058009241661, 0.5808083018124148, 0.32542563922773293, 0.5507833325802207, 0.021520244810514066, 0.4143044372130542, 0.37703459990813915, 0.7317029008688417, 0.942938456427319], [0.18963224917972044, 0.7036118061716604, 0.12319530457304395, 0.6372899462900117, 0.2650727235018394, 0.7220382822428552, 0.6780358723076662, 0.7276591898811869, 0.3371868291004636, 0.8131572536605014], [0.8520528187422978, 0.9393898024570033, 0.7694415387151547, 0.13588082758594655, 0.06134619436398825, 0.07223546562326355, 0.7377093003435776, 0.4018749675239439, 0.6664387908221224, 0.08045617799655946], [0.22443713234360074, 0.7985360803190126, 0.609741143336548, 0.6561043770506694, 0.548615699099652, 0.25848221272863336, 0.8132573330342063, 0.21627330425844482, 0.7399786571329877, 0.7250228866128033], [0.8832014984917866, 0.48908873189316726, 0.8523398498776614, 0.7579708519095606, 0.8602184009334887, 0.8483630801076442, 0.964985684836361, 0.2187976920753104, 0.8892543580359723, 0.36226234654114187], [0.08933298310386739, 0.7133913718542652, 0.03129040618778345, 0.7138178160960641, 0.3540900213532213, 0.18690524718035928, 0.7983911157501424, 0.09674132893559473, 0.3688617633813416, 0.40297165967032633]])))+1.9094674343503102), axis=1) |
np.mean(np.round(4.400079793198584)+(np.array(range(1, array_x.shape[1]+1)))*array_x/2.015854402495618*np.exp(7.214176005043959), axis=1) |
np.mean(np.square(array_x+5.9538420379398636+np.square(2.13930802967464-array_x+7.089793193563268))-np.log(abs(5.220586810493595)), axis=1)+np.sin(2*np.pi*np.mean(np.square(array_x+2.366344886727688+np.square(2.275332247693603-array_x+6.4323676906557425))-np.log(abs(5.162467115996167)), axis=1)) |
np.mean(np.square(np.square(5.5245090642965895)+np.sqrt(abs(6.62498476319934))/(np.array(range(1, array_x.shape[1]+1)))*4.273508702843164+array_x+1.9962817209356492), axis=1)+np.sin(2*np.pi*np.mean(np.square(np.square(5.407538688567824)+np.sqrt(abs(9.84432085137814))/(np.array(range(1, array_x.shape[1]+1)))*8.343858793140807+array_x+7.605568729377627), axis=1)) |
np.mean(np.round(1.9985878326656623)/np.cos(2*np.pi*4.007493548978904*np.square(5.713859345175244*array_x*array_x)+6.217514402484774), axis=1)+np.sin(2*np.pi*np.mean(np.round(6.714939728545413)/np.cos(2*np.pi*7.080448013535477*np.square(8.193609820452092*array_x*array_x)+4.25531050076134), axis=1)) |
np.mean(np.square(np.square((np.dot(array_x, np.array([[0.2855392855974376, 0.8649888860710838, 0.4125908930887022, 0.9962711303843125, 0.5863072715426509, 0.3451988658810011, 0.7390178667395495, 0.520913297070469, 0.5786368201374692, 0.5358802942433213], [0.9156600308078271, 0.9981324433183087, 0.8940928943751234, 0.7613780487441345, 0.20782689997049608, 0.10300840732223715, 0.17300890097419575, 0.15924513235746818, 0.7340479469895461, 0.6421279651066567], [0.5042907239589596, 0.725224275148622, 0.3458619514851574, 0.3688908687739808, 0.01502963842636551, 0.7202358072644542, 0.1682098800921773, 0.6411388395108549, 0.3946200424876243, 0.5069465488184147], [0.40317727704410977, 0.26824564473646373, 0.6499039666082285, 0.7945340174182057, 0.6918807317682255, 0.9668471286888644, 0.5191702847039382, 0.6133509775914296, 0.29554571037962774, 0.28953767400258223], [0.6444965005105153, 0.6488472387617247, 0.9952264926653255, 0.6627366562025557, 0.5302453507735523, 0.7096542275186765, 0.277997359711337, 0.3091945153138954, 0.1715465483751777, 0.45486066931245606], [0.16577694966586065, 0.5500642895383517, 0.06674803465926105, 0.21510105382742783, 0.3624702400147537, 0.49035017148228865, 0.5890209120877455, 0.7625257185711797, 0.19254161287998361, 0.5176677430302713], [0.9645507623558608, 0.01592047333829183, 0.5499781663378107, 0.8135734058600641, 0.4505659978510652, 0.670264989095557, 0.07652770783148799, 0.9107648455343085, 0.8747345937540681, 0.09485994842610568], [0.06587416466745144, 0.7982133020096187, 0.3165607069864801, 0.15697764805683512, 0.7087203015178705, 0.877174859528306, 0.349825910080776, 0.9319845138340866, 0.12307598137751774, 0.7797743794384049], [0.6182343139025918, 0.17953591572339656, 0.5389848713452683, 0.9993694147205923, 0.5447471773906096, 0.28948045124076993, 0.9543368507361255, 0.2187945354749392, 0.4089051490045418, 0.9317274019319323], [0.9380413731344002, 0.31540840119712177, 0.5582552458466735, 0.25322348494498204, 0.3240727310047904, 0.860672477283309, 0.2590133137813091, 0.7484180074090041, 0.8476806948009457, 0.5606289598743163]]))))-7.944247511019224)*(np.dot(array_x, np.array([[0.672508250171695, 0.9913388360428479, 0.6617611377753388, 0.6490260789897577, 0.9090772244309606, 0.3871236838508002, 0.06736044455484835, 0.357785470538727, 0.23599133582627485, 0.08243100472816578], [0.7670436644683474, 0.5844622556416742, 0.05747456415562768, 0.35020164451657243, 0.3887068705720097, 0.1427156595584944, 0.5706006984864916, 0.643523814853755, 0.9583602635808424, 0.9464084173926617], [0.49715242388708036, 0.4834988453702903, 0.24031626405739848, 0.7439619918604335, 0.06188252752514278, 0.9093884537330985, 0.6830521718944959, 0.16060376102177332, 0.4543159490711287, 0.26628333106486457], [0.02112083472333537, 0.40030703574403204, 0.580753415696, 0.28948547781688894, 0.8213208050604024, 0.240010130678614, 0.7579956263213694, 0.7275481617173953, 0.18104783122458945, 0.4880893739846226], [0.7593865086389179, 0.331266721834896, 0.05792836288096426, 0.7169117548343413, 0.08202749828258349, 0.3568516364967549, 0.25977300894562183, 0.02302250338306544, 0.8540395204037432, 0.1503586188575834], [0.798719366534033, 0.7283109240736371, 0.745511504001573, 0.15738716964661936, 0.08436416577038808, 0.2819076049182291, 0.007191006601432792, 0.457758363745007, 0.478688629442623, 0.23974310470183413], [0.8903782280474721, 0.9443597359943776, 0.4913213498143221, 0.733655066227736, 0.18910248451441414, 0.7621541006682628, 0.15875335774525934, 0.9523231729621571, 0.7864927006350073, 0.8240676854231487], [0.16329253030300828, 0.6814787864301269, 0.9987529950619127, 0.7043623583066133, 0.2749195027070339, 0.9989749030489717, 0.013483042544740531, 0.9732652484099198, 0.2197805492657845, 0.23358117692407543], [0.5692216828207386, 0.5997211745741917, 0.8171975992200657, 0.26479783216357444, 0.5761484081576232, 0.8445804982337282, 0.764564137994667, 0.4683875116536188, 0.9292731878416216, 0.7180902418187948], [0.03750788265065308, 0.3981567059480833, 0.899518347815192, 0.22067973194731416, 0.4487675271595891, 0.8141420357591889, 0.1787123177309563, 0.40256405071740986, 0.25530149795691903, 0.9342308172164362]])))+9.991699307181412, axis=1) |
np.log(abs(np.square(np.sum(np.square(array_x)+4.571195303155381-(np.dot(array_x, np.array([[0.27945232065327885, 0.2993582975087479, 0.8417344715483137, 0.5663659752003871, 0.32125299370446503, 0.7819963099439678, 0.9395218613692604, 0.15137076409607064, 0.9493797627330738, 0.3545000932287411], [0.5680017158953354, 0.044896487087484616, 0.753799699752091, 0.9027776180220873, 0.46956952779537664, 0.8790409575390675, 0.5872451825772067, 0.8665631628996905, 0.3048448876233808, 0.014494748418926395], [0.6466025616921632, 0.4923379822199613, 0.5644225735193341, 0.6602728267900919, 0.20082683384725808, 0.2530486129907763, 0.6298679023679387, 0.34083017886520484, 0.36278800676157597, 0.5978177299649227], [0.37339497122786236, 0.04356616600940422, 0.1378705457930336, 0.07317481411127613, 0.062415140154869486, 0.0332892852597636, 0.8343705338279874, 0.8509369809181242, 0.19457826232851927, 0.10881733761624035], [0.13944002747535456, 0.6854326779680984, 0.3181097280376942, 0.5985279780112701, 0.04585399512163124, 0.2751788953655513, 0.6642700834970696, 0.6447396862895045, 0.1971091321975511, 0.3043754778137133], [0.6543788628797678, 0.43520507172030387, 0.3634165740595402, 0.4533726058057814, 0.43243837882864655, 0.5672429727200938, 0.7041624850132491, 0.29409277850685234, 0.6182736133706563, 0.07090116234949839], [0.0534370317034063, 0.6628187903290922, 0.1785662896264495, 0.3588599651258694, 0.4995798675339592, 0.572479894718666, 0.24037752167238546, 0.34162392158674937, 0.08793209441744809, 0.36028173849397604], [0.12271362076443038, 0.21531745552145654, 0.7895220739092671, 0.5217442567126054, 0.17555010570616314, 0.8010349965335993, 0.20535202823666832, 0.8438121070409025, 0.4734519093195433, 0.6500584279587119], [0.4404919825361805, 0.8999513340593138, 0.9189513914180892, 0.9286692508703202, 0.34023432731817105, 0.5213717963668704, 0.8184055466597167, 0.1354473775072499, 0.6288703222853775, 0.5460709821961701], [0.38413003473828666, 0.6672165920161579, 0.8730068380755178, 0.3168793179065088, 0.8186674454359101, 0.14299531732862725, 0.9965146115924839, 0.014898437743521953, 0.44440707468449636, 0.36744046590834145]]))), axis=1))))+10*(np.sin(2*np.pi*np.log(abs(np.square(np.sum(np.square(array_x)+2.57832131666668-(np.dot(array_x, np.array([[0.9620275667399499, 0.479393391529487, 0.05586951403711815, 0.18342971272382358, 0.42318626160793216, 0.5582039629605491, 0.658988327424158, 0.18780256162554032, 0.29573895845651577, 0.3937663770461062], [0.6658957736415948, 0.5893769816864661, 0.49714332992449006, 0.7656830420054479, 0.16723398720487537, 0.31549462539341033, 0.8907196784894275, 0.3418723779989876, 0.26766039898091176, 0.015313630876731765], [0.37681074012963955, 0.4522091301400689, 0.5559988688510702, 0.36092345885961796, 0.8818800234781095, 0.6650182081941367, 0.27736090579305517, 0.6858942411326914, 0.9079773920148787, 0.5145625741250743], [0.42513854515036653, 0.46373231502170786, 0.8321847803580028, 0.02004838639137585, 0.5633583618403252, 0.8104970742427061, 0.06515403165865763, 0.7376503855619821, 0.8722871314382699, 0.35882100624915936], [0.433908015104974, 0.3685878255535895, 0.02274371060852043, 0.37414607163497304, 0.71142921885247, 0.32911085875121393, 0.3477562031426298, 0.4807564967744, 0.1769735049915312, 0.037373392596697474], [0.6509971125327644, 0.042346087112692055, 0.38595356758547916, 0.7090736945543444, 0.8841386609376827, 0.9010200682282248, 0.24626523664420907, 0.4160302424427651, 0.4678126320612094, 0.2605023276765732], [0.9895782562135176, 0.11293582479670772, 0.6558653203511134, 0.11468577277924241, 0.28501618640955306, 0.612316679045988, 0.9712437062184568, 0.18499084758567297, 0.05036128124602046, 0.9261368299116266], [0.890843594868057, 0.19224153321136628, 0.06692964818575053, 0.9501804712856771, 0.9751782156767687, 0.9528683568810965, 0.21848649329281444, 0.7804451405563755, 0.14173392735199242, 0.2792580636291184], [0.40052854866640697, 0.05574119065773764, 0.6507105073361592, 0.9040860857219805, 0.48961127432740237, 0.010712577428726222, 0.41714817426551454, 0.4631680211881598, 0.4826016528258066, 0.7872238850866928], [0.1746116251336155, 0.266341932781324, 0.6034805764282937, 0.7262784255238143, 0.4642006282907708, 0.18314838305369474, 0.7107780856316779, 0.8242458643094929, 0.2260763122849514, 0.5700060765635228]]))), axis=1)))))) |
np.square(7.879357778430189-np.mean(np.square(7.606346517634445*array_x), axis=1)) |
np.mean(10*(np.cos(2*np.pi*np.sqrt(abs(1.6276809840957482))))+10*(np.sin(2*np.pi*5.865611779029246+(np.dot(array_x, np.array([[0.42226548752541493, 0.4881235773224514, 0.7573191992005702, 0.11876651533716842, 0.283085141087177, 0.030597740868683054, 0.7420547794640009, 0.7887387660142177, 0.6916886625785598, 0.4707296467327672], [0.859889822542278, 0.19006870480096694, 0.11321301267317196, 0.6267061778704514, 0.34336315057916134, 0.3637357798285591, 0.7322473558879776, 0.48763062499750176, 0.9942534472176907, 0.600961353428547], [0.663902932421041, 0.5704015335171343, 0.5422133660237052, 0.23324994811274213, 0.5627807490611193, 0.20940167460358194, 0.03837087368427794, 0.01847340976911882, 0.9701958030502491, 0.8436093420645121], [0.3541345744771417, 0.7871839930594517, 0.3037939452179337, 0.7108173400055989, 0.603382518513453, 0.6778490724175928, 0.551234955994789, 0.10722864533773624, 0.24242178934937486, 0.7609614382792758], [0.6869850598383682, 0.6505766398589229, 0.9426818133424284, 0.46216515462460384, 0.0344761724173287, 0.18450481836876953, 0.6433211447988838, 0.2680882388950949, 0.7483144192503597, 0.1024918917998403], [0.5719708598198164, 0.04987990791722918, 0.35119292334872776, 0.15139150656261602, 0.735475085783812, 0.38984192989686417, 0.6590592670479685, 0.8188423970886365, 0.32625713650826027, 0.19323080855178454], [0.6394319390688373, 0.10063529270012384, 0.12632203053977686, 0.6013209939790957, 0.7469064832305469, 0.9780559771235111, 0.2802179671296482, 0.5050029130250513, 0.9704411644941255, 0.535868004051068], [0.16226630725387436, 0.4583542284320197, 0.12181623017469467, 0.8878060587833579, 0.4955603318428534, 0.26728836643936915, 0.061980579216674214, 0.5556960370010844, 0.40560672958686894, 0.5197856531897306], [0.414777138272452, 0.4523097826861976, 0.9661065848682958, 0.19153290094189668, 0.1642986784941307, 0.3691411209873632, 0.043959447000256224, 0.8469419079533665, 0.3508641836885693, 0.9538149623367698], [0.10646246312457341, 0.5783698589871756, 0.11834101279700293, 0.8054968732960586, 0.47876796425228507, 0.7381131209365258, 0.48978437552142595, 0.5076128200928165, 0.19076671923569488, 0.19018444515799882]]))))+(np.dot(array_x, np.array([[0.42722764652254497, 0.3930602036545878, 0.6872421854333998, 0.07660492796029716, 0.4866087879780734, 0.7344147225191487, 0.3540199457898616, 0.2970771189197178, 0.8716097716591465, 0.536641254494241], [0.05905600983082193, 0.5768803291092897, 0.38912521236435915, 0.9303109019600134, 0.9679056400222784, 0.7572919758864304, 0.07932668395638709, 0.5659183991352698, 0.20407085533417357, 0.9668462914982277], [0.8747257871472267, 0.9384979843352161, 0.36565343328192623, 0.24493391468777503, 0.5464618158338405, 0.2566995236166544, 0.39386421099376356, 0.9778168306592029, 0.7971389876323678, 0.4401001241939012], [0.022600638961867037, 0.8342903987447459, 0.7918221147835988, 0.3878194488809533, 0.5514099743486822, 0.02902189667762478, 0.3962363267670681, 0.11010109106483934, 0.2685377252249841, 0.47923773900978717], [0.16773832841535896, 0.41402215270419396, 0.43512058361272876, 0.20801119276259905, 0.6388021663200221, 0.6077543326963027, 0.6946277126809375, 0.8380196941128457, 0.38296666349725805, 0.30622701293807775], [0.3804901433060516, 0.5161010552625378, 0.8883243145182363, 0.24373496019978602, 0.042187760798096274, 0.6606569369836387, 0.7110911102533458, 0.391455923972597, 0.05788539721105401, 0.5840917252249248], [0.24893539112677288, 0.3751356347796251, 0.3864388621716489, 0.6228538546072279, 0.061307982187008725, 0.9994701044801564, 0.9001373786259764, 0.9048385076412875, 0.13973057447927784, 0.5962895467802021], [0.18358424664167372, 0.3287924362985051, 0.10420934136900994, 0.0016847748605187673, 0.3669851027987744, 0.8047242680915836, 0.28054679427716644, 0.428180750179178, 0.3554091555304911, 0.22678253556877093], [0.12637433204972526, 0.8189461291436473, 0.7687018141767625, 0.08808792056004555, 0.2553394608795426, 0.10548250881373178, 0.8084397423567443, 0.7280366879205323, 0.3035151363904016, 0.8567736367349462], [0.7242762539593464, 0.053948438121649844, 0.5116285654094329, 0.9850654951735996, 0.23068192104722107, 0.5267003411601474, 0.5845324090586644, 0.4436541839740241, 0.6835331373751106, 0.7405145281910592]])))+5.662462378424418+2.851878494310456), axis=1) |
np.round(np.mean(abs(np.exp(6.508153849768758+array_x+9.167638694504449)+10*(array_x)-np.sin(2*np.pi*7.994616519900923/3.7246244346382724-9.596974314865058*(np.dot(array_x, np.array([[0.33776684371571286, 0.20836272185977966, 0.2705901938946983, 0.7381610303165596, 0.8301640763500825, 0.1872766420636156, 0.9354943713535185, 0.024028641806243756, 0.9815317315176085, 0.3589080667752378], [0.5989798486674827, 0.39783234644812693, 0.3216290107873443, 0.8482306448688057, 0.4340593895902598, 0.7185382417221244, 0.6636441575420883, 0.9189543176340033, 0.8084811546547191, 0.21648674463419937], [0.8181951322995834, 0.3832314789417468, 0.6763096034785762, 0.8542071352150546, 0.9911371885333681, 0.6853011184295678, 0.6987936403045826, 0.062343670381618255, 0.4949719229230115, 0.43921663133974576], [0.5570535565210688, 0.4242361149478361, 0.8739526656059646, 0.9412184509447942, 0.291646938023264, 0.6372002932226126, 0.8116321958711876, 0.5143311400445653, 0.848208213850392, 0.09178909499159615], [0.5048924924230401, 0.06266138253347353, 0.795985546271669, 0.40262684037360397, 0.33749127830441883, 0.7537852503140855, 0.6690608061832429, 0.9594169235453214, 0.5966818790151951, 0.9969808893723447], [0.8082068676589859, 0.23123307560939688, 0.8923078682992179, 0.1784677769102866, 0.6981304730298605, 0.1255280722977261, 0.6702839900527988, 0.5255499487560286, 0.09574421398720656, 0.11031053113726363], [0.6045999620995824, 0.27006184543347156, 0.521753107458671, 0.7228213538671789, 0.2008415899699877, 0.7128043937982098, 0.9628702927939913, 0.4221010640661126, 0.2914276563852246, 0.11540628327813962], [0.32508262549064204, 0.6356590286261051, 0.7553315367709906, 0.09870160915058335, 0.00107654143358471, 0.7302357074177274, 0.060011830422710966, 0.23886813211869595, 0.2717801143060661, 0.576090505022109], [0.5364793797030052, 0.2096806275351053, 0.4804037821110192, 0.3521297063969726, 0.3385636350653418, 0.9659938155107191, 0.9647160012549773, 0.2979408107115449, 0.088936724236645, 0.636643496291974], [0.00362337124981571, 0.3721330816867118, 0.128688722896652, 0.2815412704134195, 0.91252703345722, 0.01611352019548684, 0.6283915976501868, 0.7599409174016339, 0.8573734617185949, 0.3695092980757646]]))))), axis=1)) |
np.mean(np.square(4.797036253191868*(np.dot(array_x, np.array([[0.6684947185652269, 0.7016435122198299, 0.7163987132250913, 0.9396410887156118, 0.8871186011190704, 0.6084575747479819, 0.2713329604788228, 0.18048991792196578, 0.6050251228187346, 0.915265152040838], [0.4500373498690853, 0.634607216952727, 0.8262352939593532, 0.9109772575570958, 0.014288795021815681, 0.36995629442523714, 0.5250400454523976, 0.6654363607337612, 0.4384675707946607, 0.14104689411911975], [0.8561401772436618, 0.9858848158977417, 0.9455031393102087, 0.4528157511009592, 0.6890573790707069, 0.44887365216292985, 0.1800173019782254, 0.07913960020572319, 0.09062703620651136, 0.4958670983912057], [0.393502330787725, 0.7152442193145259, 0.9909735278899979, 0.5287660503519392, 0.556229068760816, 0.4645277061871107, 0.7522158460652035, 0.0283842918801831, 0.12190078089794376, 0.823680884602392], [0.63463602820839, 0.09416848205256323, 0.3506728946677602, 0.25439073535071843, 0.4821338499108442, 0.2596648726832368, 0.3677477359679402, 0.510265037137903, 0.3551163107494566, 0.5337816719113926], [0.5828824549687004, 0.5152749884165598, 0.2087095902098406, 0.9235922182054778, 0.12761485534435257, 0.8209342246944425, 0.008046590717943336, 0.3938020188266578, 0.47382595881046463, 0.9216705518550989], [0.12457961498145131, 0.3296204621114569, 0.8116173030534066, 0.566425438603339, 0.6448007143344318, 0.6236293049931935, 0.31072410009039075, 0.9568651137442469, 0.14886953257005264, 0.4822904353840466], [0.5792202538737389, 0.8706323324684448, 0.4522987033235726, 0.4669554743381835, 0.9836344307664182, 0.23475139019721758, 0.5848330607078975, 0.4204480383388026, 0.17475250441048296, 0.9522596808676634], [0.2878562595160604, 0.5192001219010064, 0.6929098495704649, 0.17580833268592844, 0.23121312812012917, 0.9140023363875514, 0.46084775842326275, 0.5066332952218016, 0.14563524840945863, 0.6376930802471773], [0.10503583150203466, 0.3161510260859898, 0.2006052220081821, 0.12250073171026488, 0.8446394426343272, 0.2135221704553345, 0.2964100038193326, 0.30841773335007516, 0.39333626674409394, 0.8171334659553792]])))/10*(9.98602739339038)-np.cos(2*np.pi*np.exp(array_x+array_x))), axis=1)+np.sin(2*np.pi*np.mean(np.square(1.5900638827978368*(np.dot(array_x, np.array([[0.9972577364678211, 0.6497532588582896, 0.18234459157392224, 0.5094342453266614, 0.3487706838096173, 0.7259334875792597, 0.947272188608103, 0.29272704613563183, 0.6472320101853088, 0.03770156311167139], [0.35663634397975585, 0.8699024428283912, 0.4024377713881321, 0.16867187585401555, 0.35257760792000514, 0.26625126501136087, 0.19426361836584016, 0.5153656431386003, 0.5371365008602565, 0.1963903269019207], [0.794822022742775, 0.5311013439284481, 0.9171829986829473, 0.2277652832358813, 0.7694399463545006, 0.09923835179994733, 0.7571303986382476, 0.5270022298407928, 0.5192302288928441, 0.3067324916279561], [0.5521758269940583, 0.2101461952869066, 0.6697511190454655, 0.978369924063486, 0.3797711529118962, 0.7992175571574006, 0.7326886986286326, 0.20874727124760328, 0.884379452778012, 0.8362556128802441], [0.008023983361637566, 0.6263613314053281, 0.9528991158460909, 0.8046305463286076, 0.6474626156655623, 0.30980238199987287, 0.004539922543150832, 0.02112063568740352, 0.48930282730839125, 0.17877554286435937], [0.16162447452664908, 0.2550581362521035, 0.8819574018264694, 0.8144346196975685, 0.21076258543947657, 0.638341098444, 0.5982135516576901, 0.05649043788166708, 0.4367368964938455, 0.5795558591545419], [0.49347427827975754, 0.6236816149322636, 0.7882266229877942, 0.7817414500720826, 0.14042765415466973, 0.3123888993440367, 0.536315167308764, 0.9383465920768392, 0.19869085437170997, 0.8256318874321628], [0.3762377187612781, 0.3817421554421838, 0.8670099085724118, 0.6132800014662627, 0.750853052596813, 0.573118074484144, 0.4449834137845128, 0.2882955521929237, 0.6431279403356992, 0.5608111695447525], [0.8516212533527326, 0.880432234693959, 0.539237935068353, 0.05241039155344285, 0.11024135740295571, 0.09443180687606356, 0.36015308876248464, 0.15496273847911446, 0.08803179226150182, 0.07588339767708696], [0.3421507621460357, 0.3002001306232974, 0.8375444600117268, 0.06556422804276796, 0.9864427699566923, 0.56744434110171, 0.37006603218594203, 0.2992147025776246, 0.9465402425689124, 0.5958144496896322]])))/10*(7.375459121662431)-np.cos(2*np.pi*np.exp(array_x+array_x))), axis=1)) |
np.mean(10*(np.cumsum(np.sqrt(abs(array_x-8.220633445712057)), axis=1)-3.6302008718281007), axis=1) |
np.mean(np.cos(2*np.pi*np.sqrt(abs(6.7692897976795505)))+np.cumsum(7.370194735096317-array_x-array_x*3.793517761836166, axis=1), axis=1) |
np.mean(10*(abs(2.5122670521733563+3.083503204024334*array_x)+7.578334476742864-(np.dot(array_x, np.array([[0.7826292051728263, 0.000682618690613146, 0.7125078746294637, 0.7165981585860346, 0.7922855393396135, 0.4356840739583624, 0.5579784180042046, 0.9127226094290791, 0.24342761841918947, 0.21948089045476482], [0.5442755735111242, 0.8947035912972114, 0.8923129256520735, 0.6323900529620488, 0.5515097488233555, 0.9647182922305564, 0.8777404367892271, 0.27277150261634653, 0.2928614726130272, 0.9760614424335594], [0.1650846021312169, 0.016687364443860098, 0.19230881580596804, 0.24688042492914963, 0.10950350583876911, 0.26578425238513137, 0.6506537673092623, 0.004949054445122836, 0.7458757206421504, 0.6740745144924734], [0.2615158178621121, 0.2643366404516426, 0.25816576811975545, 0.8208607992417924, 0.8983840599790099, 0.5543030078857516, 0.9371558940428154, 0.6477859461959808, 0.6045395288282983, 0.08415061733380891], [0.5185314280595759, 0.8691001795248406, 0.7345410869610625, 0.5266161357096572, 0.8467252006059943, 0.9376923460225111, 0.018348871581320148, 0.5537118362793546, 0.0133412115617918, 0.07853613407594773], [0.13295492604750936, 0.8318639717055797, 0.5028422047553193, 0.683349945749687, 0.6909449959073078, 0.967514405046596, 0.03947897143058787, 0.10205900460942008, 0.6602090425151252, 0.14902515291526242], [0.18782368638949354, 0.1959174727330394, 0.8791075473052413, 0.9913491026196645, 0.19585041998070574, 0.7489193762539069, 0.5694519983368184, 0.030790996005922056, 0.9611606484820687, 0.9259978262896907], [0.4502107867113022, 0.8363434242378586, 0.003895373755117282, 0.05308383315078524, 0.5825433491028025, 0.6373799911376846, 0.2620348593962689, 0.23981131460432104, 0.40211655482555664, 0.4232602776755926], [0.4722734264036351, 0.20099170529905008, 0.3673727381610795, 0.3334412233373596, 0.5345008926144624, 0.14341978792517374, 0.7116335295032403, 0.3838697642111769, 0.7202407920542759, 0.8079995387508451], [0.17931526195867142, 0.5784090524940427, 0.9702688359054684, 0.024700524547816127, 0.25078457215291017, 0.7496355200611385, 0.815487423416566, 0.480837525499502, 0.5147483209886807, 0.5060814735601715]])))*np.cos(2*np.pi*array_x)*1.8701869409379288), axis=1) |
np.mean(3.8333090148145184/(np.array(range(1, array_x.shape[1]+1)))*array_x/(np.array(range(1, array_x.shape[1]+1)))*array_x+6.3612976049496925*(np.array(range(1, array_x.shape[1]+1)))*array_x-4.117318497364144+np.sin(2*np.pi*(np.array(range(1, array_x.shape[1]+1)))-5.7805756975695575), axis=1) |
np.mean(-(np.exp(4.673390624417374-np.exp((np.array(range(1, array_x.shape[1]+1)))*array_x)/(np.array(range(1, array_x.shape[1]+1)))*array_x)*np.exp(np.sqrt(abs(np.log(abs(5.399761966004078)))))), axis=1) |
np.mean(4.295252013651474/np.sin(2*np.pi*np.square(array_x)+np.cos(2*np.pi*np.sin(2*np.pi*np.sqrt(abs(8.374019924208119))))), axis=1) |
np.mean(10*(array_x+np.exp((np.dot(array_x, np.array([[0.11493990084678918, 0.49113656030261155, 0.9712380856834208, 0.796948397857094, 0.006216375947465824, 0.9864481094978256, 0.3242778853047994, 0.5809556533378325, 0.8651871190066214, 0.9795597193619499], [0.2561708429502999, 0.21906995303126608, 0.6860891893732407, 0.5706668646636197, 0.1384975843244599, 0.47902430948635477, 0.8058287010718143, 0.06418850042422974, 0.49745938175044124, 0.7408793384898983], [0.15108937864833105, 0.4781855319957008, 0.4984730964024474, 0.3078585872360625, 0.32932093206932145, 0.7138859964326432, 0.37246388214741266, 0.6070290023583448, 0.3106428936596536, 0.5588770537083259], [0.0696801996202957, 0.17273408323574413, 0.04680829826386468, 0.41743783291578596, 0.44539354982967616, 0.004987186472316774, 0.45195716061465097, 0.32694763252014336, 0.9893122067046398, 0.3196129596128635], [0.35938632595380693, 0.6250035357719277, 0.6663117408997917, 0.2482746493703304, 0.45611979948176506, 0.2401389860402614, 0.49586620891224653, 0.4575334910185864, 0.7218197320553149, 0.6158425578983633], [0.1164918583008856, 0.37265469831137277, 0.25471268310427087, 0.42265059376537206, 0.8812267925093044, 0.06532408129811917, 0.9415137970122404, 0.6058555740040178, 0.3874663115913246, 0.19391157905408485], [0.14641587731484584, 0.8828580722030277, 0.22044318912060923, 0.28505856698601495, 0.26977801820246017, 0.7752800062168512, 0.07298375523063583, 0.8772410349479398, 0.4464300195988068, 0.9798418391081016], [0.27186655897560486, 0.9296534687159962, 0.8293741427917848, 0.3653152956322695, 0.7051244202466931, 0.5643196106805031, 0.7150733249671936, 0.452629857459121, 0.6682885052931243, 0.4082832039091778], [0.9495925229089168, 0.6380764517734778, 0.7562451217984774, 0.09731010926281425, 0.11141465042088483, 0.2550213286241416, 0.40301185140618034, 0.311089666610349, 0.5164494761366782, 0.6819265698600062], [0.04567250822275537, 0.7209279125742611, 0.427169959706741, 0.354728322774141, 0.4512735424210199, 0.832698660783039, 0.2495005800540585, 0.7925495546205967, 0.7640158184876373, 0.6256034815233816]])))))+np.round(array_x+4.472991398476238)-np.log(abs(6.3482273986924))-array_x/np.sin(2*np.pi*7.148558710044138), axis=1) |
np.log(abs(np.square(np.cos(2*np.pi*np.amax(array_x-1.1351153710813207, axis=1))/7.211432572962719)))+np.sin(2*np.pi*np.log(abs(np.square(np.cos(2*np.pi*np.amax(array_x-7.505253523225706, axis=1))/8.266616347665472)))) |
np.mean(np.round(8.786340220817355)+array_x+np.cos(2*np.pi*np.log(abs(7.001532804166605)))+8.366479403823615+10*(array_x)+np.sqrt(abs(3.8322531368560804*array_x))+np.square((np.array(range(1, array_x.shape[1]+1)))), axis=1) |
np.mean(np.sqrt(abs(-(np.sin(2*np.pi*array_x+7.563612881424126*array_x-6.00587796226171)))), axis=1)+10*(np.sin(2*np.pi*np.mean(np.sqrt(abs(-(np.sin(2*np.pi*array_x+2.3035492093386054*array_x-6.166906737924068)))), axis=1))) |
np.mean(10*(np.cos(2*np.pi*np.sqrt(abs(array_x))+np.square(4.0957624493716684)+4.481748549862278/abs(np.exp(-(9.919498526453632)*array_x)))), axis=1) |
np.mean(np.square(np.sqrt(abs((np.dot(array_x, np.array([[0.49050388230429, 0.3765022504251283, 0.31387506492040695, 0.2637861460007632, 0.5804522799203669, 0.37921825439350176, 0.7687186782551558, 0.625436583194287, 0.8059953331930274, 0.6821888075500617], [0.5313581631813966, 0.9238159587962883, 0.7883807731910674, 0.9867484199879126, 0.5707960811000203, 0.07604603049460668, 0.8077663123976634, 0.1236566182272264, 0.009408746862667683, 0.33044044731241684], [0.8783275847883364, 0.5502162999475857, 0.5492901431531435, 0.40912797720356964, 0.5228972137782261, 0.6047988210131326, 0.04272217630840469, 0.769899429872405, 0.6473314763810597, 0.8579857364224428], [0.7205409559939677, 0.8437519877527445, 0.3730351724646018, 0.21832960490825604, 0.7376164131268179, 0.16229464597080434, 0.3856471832523506, 0.13904011890866108, 0.43959193676378683, 0.33367163454999116], [0.8597656634185, 0.7372281064262107, 0.9817437301206938, 0.791723809727734, 0.49755452154648416, 0.09950439961330582, 0.5625772764834815, 0.4163700500385772, 0.6998777213897134, 0.4624380980974103], [0.36248227315271675, 0.6702652844267316, 0.46237966630375027, 0.6000090302890246, 0.9846190214320639, 0.5631143548431471, 0.7829481378293792, 0.355024115482496, 0.19897091041164205, 0.8002146996113843], [0.36178468075562487, 0.8587114235816239, 0.41903096855204425, 0.4328792598626876, 0.3004252328525282, 0.20138044656702792, 0.510111682606743, 0.6186964708688807, 0.22843924227616885, 0.19797563635244797], [0.5526235273132081, 0.8531239638969215, 0.8139148531411479, 0.4902379042134548, 0.3735902627151727, 0.5589527053126179, 0.7571138591533076, 0.6385228295185031, 0.5800385038606146, 0.4424746066886537], [0.4208357675220762, 0.08945303480942246, 0.7501361203092999, 0.14339649637519392, 0.05467711374331086, 0.7566198785089393, 0.09061588408737331, 0.9390723494748122, 0.7369855800127593, 0.6563609893873785], [0.35724474006204243, 0.9840611504723287, 0.7663753962858907, 0.08053794310479556, 0.09279236988656214, 0.9097252128159268, 0.5686961117474004, 0.24615665802779851, 0.007904225921686603, 0.6541305530785472]])))))+array_x+(np.array(range(1, array_x.shape[1]+1)))+5.4287046168878454-np.square(2.964458293856639)), axis=1) |
np.mean(np.cumsum(np.square(9.793529586290532-array_x), axis=1)*np.sqrt(abs(-(2.92964186710402*array_x+abs(5.301815637480209))-10*(8.68783177172533))), axis=1) |
np.mean(np.sqrt(abs(array_x-4.386441382521642/3.135370271431648))*10*(np.square(8.284821802826844)), axis=1) |
np.mean(2.5403238943948914+array_x, axis=1)+10*(np.sin(2*np.pi*np.mean(2.8450760647233695+array_x, axis=1))) |
np.mean(8.419955722604973-np.square((np.array(range(1, array_x.shape[1]+1)))*array_x)-9.630515922613613, axis=1) |
np.sum(2.0216949017574146/2.9706968735815855+array_x-3.375463773062386*array_x, axis=1)-8.963964225258291 |
np.log(abs(9.581753952304476))*np.sum(array_x+(np.array(range(1, array_x.shape[1]+1)))+9.736875785549591+np.sin(2*np.pi*array_x+np.log(abs(5.731583998983476))), axis=1) |
np.square(np.round(np.sum(array_x, axis=1))-4.305421146409313/1.5095489393491786*10*(np.sum(1/(2.95101470989745-array_x), axis=1))) |
np.square(10*(np.sin(2*np.pi*np.mean(np.round(array_x)+3.6636508503351726, axis=1)))) |
np.square(np.sum(-(4.607162827580461)-np.sin(2*np.pi*2.7649924086011275-array_x/4.586721417535534), axis=1)/np.sqrt(abs(3.7262353739849945))+np.mean(array_x, axis=1))+np.sin(2*np.pi*np.square(np.sum(-(6.109914583922181)-np.sin(2*np.pi*9.656961185252415-array_x/7.519306360125619), axis=1)/np.sqrt(abs(8.049223571438812))+np.mean(array_x, axis=1))) |
np.amax(np.round(8.11370228772812+array_x)-np.exp(array_x+2.535011677909208)+4.3536567727844675, axis=1) |
np.mean(abs(np.square(2.5469488494083015))-(np.dot(array_x, np.array([[0.9194576177114565, 0.024211011963617635, 0.9311162068813718, 0.07680619579844095, 0.10202415088382788, 0.12065360049390539, 0.557596879180528, 0.6714781634722032, 0.12346881384952424, 0.08017500087577478], [0.9856969217759328, 0.02154500954104266, 0.8116723083578681, 0.47198230683631215, 0.5548541439991396, 0.3416706005269524, 0.17247673028708743, 0.6919520035344447, 0.650646714710517, 0.8075477895497694], [0.7976071905049885, 0.09318092179178594, 0.0198310227060855, 0.33732622045131255, 0.09198673449837813, 0.10606069697812404, 0.8963837913237517, 0.3219184716957322, 0.250503289255831, 0.5654416010770338], [0.4215539660677493, 0.586343561392657, 0.7171043594771199, 0.841247635118919, 0.5347176816632483, 0.7039204501622611, 0.45475615988429086, 0.7935882237262019, 0.9773589502949408, 0.056913765841656305], [0.2806321924908156, 0.51106320806689, 0.4483018622861983, 0.25741653074235127, 0.6391961975193646, 0.16295723894722525, 0.10752737877475338, 0.7968178460719393, 0.03321923307524277, 0.027760813053759992], [0.24987152739492902, 0.41081948286077397, 0.6998001445696134, 0.6335972685548412, 0.8653721586120277, 0.3506842058293309, 0.05083980175482139, 0.45174169068562053, 0.47523027162336096, 0.8751208225112803], [0.5972016014905395, 0.40720429782129997, 0.0435106241952703, 0.21622460910992436, 0.552771832966047, 0.9586511124080094, 0.6982898539469342, 0.5530180555628941, 0.8364512129570479, 0.5614077344850318], [0.8851415448880372, 0.7105046218971347, 0.09836919789817955, 0.4702494574095245, 0.20103134404464418, 0.3805905302236485, 0.916973748301763, 0.3575789942311959, 0.7727117118533546, 0.022217199067348647], [0.5695230787684211, 0.9923438982292085, 0.19862024556313262, 0.6279812810875258, 0.5333040395143898, 0.9991500852701511, 0.17933966579016847, 0.32079589785832974, 0.024207739611277757, 0.8328018803746657], [0.9857538977365975, 0.02522185477011407, 0.5378186957544648, 0.23751741297329332, 0.2569262357746992, 0.900846476881712, 0.9385086708370058, 0.35530326058023654, 0.11987137501528156, 0.6886594616365062]])))*6.255873641781183-1.0258372234350694*(np.array(range(1, array_x.shape[1]+1)))+array_x*6.356957877845832+8.79482638063953/(np.array(range(1, array_x.shape[1]+1))), axis=1) |
np.mean(array_x-1.5847664629912281+np.round((np.array(range(1, array_x.shape[1]+1))))*np.square(9.418809286142606), axis=1)+10*(np.sin(2*np.pi*np.mean(array_x-3.174383582138503+np.round((np.array(range(1, array_x.shape[1]+1))))*np.square(6.6950281276473635), axis=1))) |
np.mean(np.sqrt(abs(np.log(abs(7.136385945051506+array_x))))-np.sin(2*np.pi*array_x)*5.818112571877611, axis=1) |
np.mean(abs(10*(np.square(np.square(array_x+5.987486305205546)))), axis=1) |
np.mean(abs(10*(array_x*3.2053110107012377))+array_x-np.round(6.122582666565821), axis=1) |
np.mean(10*(abs(10*(-(3.322094363375073*2.936487273638197+array_x)))), axis=1)+10*(np.sin(2*np.pi*np.mean(10*(abs(10*(-(9.608049692012333*7.090058936640288+array_x)))), axis=1))) |
np.mean(np.square(np.round(1.689487097520253))*10*(np.sqrt(abs(6.945762818082062*2.713487873913725-(np.array(range(1, array_x.shape[1]+1)))*array_x))), axis=1) |
np.mean(10*(abs((np.dot(array_x, np.array([[0.5710022918666355, 0.4436550739048363, 0.24565138824428423, 0.34611083326350245, 0.7630204496430496, 0.6872428634883925, 0.30678798995753265, 0.1374496826264625, 0.4121094480413364, 0.02421250210247239], [0.24291437422269868, 0.685512223263861, 0.26648897102366764, 0.8048373155792519, 0.452762278658812, 0.8040602373066831, 0.13975786511647548, 0.8652238498792301, 0.13653251702551594, 0.1519818195329694], [0.3723667398291822, 0.33785925130977856, 0.9820127305192282, 0.9558080757552037, 0.9920612277308912, 0.9404279260207408, 0.7918645142250353, 0.8249271611767814, 0.13414750850959034, 0.46225135232616055], [0.3162259402991585, 0.7983295030436041, 0.2915591910439729, 0.11795262431109266, 0.9777475306950439, 0.9116073845395899, 0.996897172553597, 0.530598838097077, 0.47287630592253205, 0.1542992593118624], [0.29815431529276215, 0.8478024784820715, 0.8275259188047256, 0.06888236018835314, 0.2786829144319505, 0.31972389778421173, 0.5074041728140495, 0.7034980952652169, 0.39591988013249857, 0.50736123572551], [0.6275451918933663, 0.5475388801353414, 0.32598005291456733, 0.20183725911907469, 0.313452908798748, 0.29788487191408175, 0.2045069331889222, 0.2631462332430834, 0.7953588645976007, 0.17577348548177718], [0.37792146867146703, 0.8204859303597792, 0.9893276864949617, 0.2810271399770228, 0.6167190945613349, 0.3238694999841327, 0.336014083896933, 0.02716236138822714, 0.6354181620548696, 0.6036949427055813], [0.3706193436171149, 0.6235798273580297, 0.13781676332483261, 0.9833990451382251, 0.1362048398676684, 0.8352297614236898, 0.5158186413322624, 0.5493010365548131, 0.28698686668326834, 0.5506702286886687], [0.2822954155260904, 0.29656501192364004, 0.1876765899552576, 0.02397587714337901, 0.6460293687711851, 0.9377485489799566, 0.9425260899370909, 0.6573201370305398, 0.31607477354444735, 0.6483933635458494], [0.9373419861969383, 0.5984549920292823, 0.6383047040354848, 0.5912323929688345, 0.3673912819220746, 0.9474400436990289, 0.9400912264636027, 0.5679717196235569, 0.3725217130729589, 0.1201173598772195]])))+array_x+5.675422115474213-5.8515878272262905)-np.sqrt(abs(array_x-3.726195945049447))), axis=1) |
np.round(np.sum(array_x*array_x, axis=1)-3.3792647410396244/np.mean(array_x-2.6550299594740903, axis=1)) |
np.sum(np.cos(2*np.pi*7.2999444843438575+np.sin(2*np.pi*array_x+2.6783293682176326)), axis=1)+np.sin(2*np.pi*np.sum(np.cos(2*np.pi*1.9498761070776394+np.sin(2*np.pi*array_x+1.7956977956094158)), axis=1)) |
np.sum(array_x+5.292653197080279+array_x+1.4861638310960479, axis=1) |
np.mean(np.square(10*(array_x)+np.cos(2*np.pi*8.830023098006718))-8.752129453989351+array_x, axis=1) |
np.sum(1/(np.cos(2*np.pi*10*(np.exp(array_x)+1.6248586839938897))), axis=1) |
np.mean(np.exp(3.5306762019565854*array_x+array_x+8.059169020874837/6.85547427860209), axis=1) |
np.mean(np.cos(2*np.pi*np.sqrt(abs(np.exp(array_x+4.849184870982852)))*np.sqrt(abs(10*(np.sin(2*np.pi*np.sin(2*np.pi*6.304597389252782)))))), axis=1)+10*(np.sin(2*np.pi*np.mean(np.cos(2*np.pi*np.sqrt(abs(np.exp(array_x+4.9399428440520445)))*np.sqrt(abs(10*(np.sin(2*np.pi*np.sin(2*np.pi*3.1101858567784566)))))), axis=1))) |
np.prod(2.1798768355050977-array_x, axis=1) |
np.mean(1.1688228088127781-array_x+abs(array_x-4.78242901893908*array_x)*np.square(7.666340543661974), axis=1) |
np.sum(np.sin(2*np.pi*-(array_x+3.69326558319797*5.118941190440596)), axis=1) |
np.mean(8.364975389240909+6.7784861042611695*np.square((np.dot(array_x, np.array([[0.8654168374989128, 0.37640713592603936, 0.11838800882563993, 0.8319034454407671, 0.284179036666089, 0.5293995461563248, 0.5215950839163354, 0.650253555919908, 0.9786146856087169, 0.053368580797843346], [0.5857183138753426, 0.2297778668062902, 0.8117589999368924, 0.185534225638871, 0.26360824881047695, 0.1344643976181873, 0.035628955912190174, 0.7586590053588861, 0.14588237045818364, 0.1785034440828397], [0.1830429863624473, 0.7332491843745035, 0.8066281530720479, 0.2705268569709707, 0.8339300987331495, 0.014621038990353052, 0.2748748999674694, 0.23626683639521262, 0.11560441807218458, 0.3027051165695188], [0.858955721463184, 0.7604766550630266, 0.2561851249949153, 0.22748962751532864, 0.6941349395521371, 0.9106616923261585, 0.6045534606777917, 0.11199170227497535, 0.291715315604967, 0.8444721626236898], [0.8962356803596542, 0.1636641739037351, 0.19241626290673186, 0.7891234925319965, 0.7705239319429373, 0.4712205967540153, 0.8289788561468094, 0.3440291646470972, 0.9710252817723547, 0.13321765846715283], [0.7143776343085102, 0.28117135418338757, 0.8279307813452502, 0.03756912718148386, 0.7836207949398944, 0.4037576885575904, 0.35600114277593686, 0.46037735124318535, 0.9083135650273285, 0.06053080170667702], [0.2426081281457192, 0.7615762843457969, 0.47109940280194196, 0.22727246930921263, 0.2652881576626954, 0.35891510702532725, 0.3856068075476977, 0.010399455577866257, 0.04202871558520305, 0.45109333729456413], [0.8735517429462344, 0.610513986543366, 0.4161003902777828, 0.44726025249790213, 0.85005682016396, 0.5361899170737727, 0.8133768724561049, 0.9828085520462424, 0.8189382859962606, 0.2454993002998611], [0.18831601745912518, 0.36513355449323215, 0.7059979209962445, 0.7501285800110578, 0.622523274644942, 0.05944218336368157, 0.7726214442758599, 0.17384222833900476, 0.7861780753744632, 0.8106426963368921], [0.31170055724231915, 0.3352224894854786, 0.03814440315277212, 0.2839819117555745, 0.6751938924694932, 0.5542775923114649, 0.3555457026412676, 0.37076292404517597, 0.4714035091251738, 0.40576878315060916]]))))/np.exp(array_x), axis=1)+np.sin(2*np.pi*np.mean(6.7388782060884855+1.884127416281265*np.square((np.dot(array_x, np.array([[0.7335413491274806, 0.9580299276300231, 0.5413806677387859, 0.8292303849784377, 0.6454375837764952, 0.002459736436579485, 0.4864021448815137, 0.22963526629662256, 0.43726730240521394, 0.3780568115433137], [0.7193065542606495, 0.25951628521019354, 0.016493487915677396, 0.3394340099921064, 0.7382663427135373, 0.9604087700925322, 0.06843101050627731, 0.5772359440763408, 0.5604876706400691, 0.4197325911321358], [0.3058863304075632, 0.9958527769846086, 0.6101594065490493, 0.10771901989827759, 0.4434843869260676, 0.14404045528556708, 0.05341393897757918, 0.40211773276044716, 0.6990957096954634, 0.10830080760722827], [0.8429918959362122, 0.1774674249501701, 0.6052532678083785, 0.07164549111055951, 0.48100426368493454, 0.7191775320022759, 0.5322331294546013, 0.060820045953088764, 0.31338926701979875, 0.17025497116172028], [0.23063626084443056, 0.21986653181825755, 0.03442610503092891, 0.8818403910783774, 0.3452982409429656, 0.31593352730192525, 0.4942473422353163, 0.6991863588108673, 0.34148141653296105, 0.46886276031099017], [0.27875670291031607, 0.6283729994401235, 0.5748624380540016, 0.5657585830087398, 0.09698655422741498, 0.0877108900913447, 0.9394274981586704, 0.4105925091211947, 0.8684328236623176, 0.8601798322980432], [0.20137172640352863, 0.5061292430756542, 0.06994175592654217, 0.035723333320044515, 0.4769435757639324, 0.7898100050934446, 0.42745730726776965, 0.35960345471392285, 0.32461090330600895, 0.10048555329216458], [0.7570685144181384, 0.8792355371049524, 0.8520004589459809, 0.45167315022049437, 0.7095527622726011, 0.21933246688366115, 0.18470088467325707, 0.7021780241563759, 0.8205573582091683, 0.1724470950291036], [0.5251892136542146, 0.7409865640094568, 0.003338549813068359, 0.28746789006703777, 0.7689812553063999, 0.5016992896458149, 0.4317122734347022, 0.3524078657699149, 0.47698083234268684, 0.8292975652461405], [0.2131366022823724, 0.066811495117031, 0.9580875514961704, 0.518047545290663, 0.30678504178456845, 0.1495989589211696, 0.7553594253075027, 0.924822720162201, 0.593149302452285, 0.7266943530361256]]))))/np.exp(array_x), axis=1)) |
10*(np.sum(np.square((np.array(range(1, array_x.shape[1]+1)))*array_x)+8.21540597658448, axis=1))+np.sin(2*np.pi*10*(np.sum(np.square((np.array(range(1, array_x.shape[1]+1)))*array_x)+7.2308486436933315, axis=1))) |
np.amax(np.square(5.169055575654182+(np.array(range(1, array_x.shape[1]+1)))*array_x/1.9362695689932203)*np.sqrt(abs(np.round(7.262581990891915))), axis=1) |
np.mean(9.209979194857079*array_x-5.386790512633223/10*(8.497052900943306)/3.6093183992387905-array_x-5.610793938867431, axis=1)+np.sin(2*np.pi*np.mean(8.7445777749012*array_x-9.670435666980236/10*(5.180712467160819)/2.836416448324341-array_x-4.534415182392367, axis=1)) |
np.mean(10*(8.50144511752467)-np.exp(8.4898071069135)*array_x, axis=1) |
np.mean(np.exp(9.979037053358812/3.2027226412551464-array_x), axis=1) |
8.933568965886952+np.round(np.sum(array_x/6.332740708032955, axis=1))*np.exp(abs(7.546632229019025))+np.sin(2*np.pi*5.081012505298095+np.round(np.sum(array_x/5.506113017386547, axis=1))*np.exp(abs(4.583425314190979))) |
10*(np.sum(np.log(abs(np.sqrt(abs(np.exp(7.190720815026782)/np.exp(np.sin(2*np.pi*9.97820936608137)-array_x))))), axis=1)) |
10*(np.exp(7.2443105880866145))*np.log(abs(np.sum(abs(5.200481501661925)-np.square(array_x)*1.744291493948137, axis=1)))+10*(np.sin(2*np.pi*10*(np.exp(6.265024293018385))*np.log(abs(np.sum(abs(1.4593866659907824)-np.square(array_x)*1.3841421425302847, axis=1))))) |
np.mean(1.5119972449444181/np.cos(2*np.pi*array_x+np.sin(2*np.pi*5.79091499402257)*8.88685984693651), axis=1) |
np.round(np.mean(abs(2.239937857650351+np.sqrt(abs(4.558939495709891))+array_x*7.771510148205536*(np.dot(array_x, np.array([[0.7805033578740339, 0.7203411973684285, 0.661875203873323, 0.21029797838034658, 0.28697453722235977, 0.8889697740658404, 0.12287341048971945, 0.04372924364383057, 0.6481726639667407, 0.8993847474336295], [0.40066853715290196, 0.2109318065663356, 0.9658044302296145, 0.7436149634991044, 0.5569450763493354, 0.9904877620561657, 0.8874448932663405, 0.429995411227719, 0.08571887511229126, 0.9102421893166786], [0.8301125330869817, 0.6776971696145915, 0.5804375393743689, 0.24659856713147732, 0.5214702664866686, 0.5981820089140439, 0.8045260084920266, 0.2762530498954118, 0.4053371141787546, 0.749808980968245], [0.5509661425115604, 0.8537900633510798, 0.8822551985726088, 0.039182955403518016, 0.2698621973191905, 0.8282404443625484, 0.9372188184517765, 0.9351744610303625, 0.6420002176500773, 0.5330885771026271], [0.7134901985164566, 0.9973577696286214, 0.4383551845132385, 0.43203975466377964, 0.8079568997446358, 0.5250920209412625, 0.4771983595393786, 0.12366899371604356, 0.039784859268012895, 0.7304852963200383], [0.25191313182053765, 0.06728291862917946, 0.10854067721020122, 0.36324132575840573, 0.14669543505368388, 0.3706766554462084, 0.7622951767403323, 0.2657683848861341, 0.21913018351871072, 0.5429551973030993], [0.2927447052146759, 0.36784815331702914, 0.16094141816023766, 0.0923938862877074, 0.2865902289118135, 0.22331685456152572, 0.44276951148937493, 0.6906830966731219, 0.753597587109691, 0.12155623010563188], [0.7061864006994908, 0.47064208154522713, 0.38941467065775814, 0.1521800971147359, 0.07119648730713779, 0.14519297304155787, 0.23319723032387962, 0.05882990852206604, 0.6991154901227542, 0.24867522852362012], [0.9660723820335689, 0.784536915745995, 0.6030415505672053, 0.6414116513260814, 0.5834460782326504, 0.2387066422608135, 0.007073020132025043, 0.7177222511755639, 0.36305154161967756, 0.4499255868087155], [0.48076537945394493, 0.0418896341886793, 0.04421465284105475, 0.9817330534602828, 0.13025593875023478, 0.48991266695910696, 0.2546977231801979, 0.4164228629688759, 0.11114073399227686, 0.5685748393179676]])))), axis=1)) |
np.mean(np.square(np.exp(6.027468677751818)-8.503707840972059+array_x+1.5024573696450325/abs(4.439748542890056)*4.505801064498144), axis=1) |
np.mean(6.0579656086243014+abs(np.square(array_x))/np.log(abs(np.cos(2*np.pi*1/(9.572537613722805)))), axis=1)+10*(np.sin(2*np.pi*np.mean(5.118870620243407+abs(np.square(array_x))/np.log(abs(np.cos(2*np.pi*1/(3.2555138662483873)))), axis=1))) |
np.mean(1/(-(1.7240480272056973)-array_x+1.7909043248643273/(np.array(range(1, array_x.shape[1]+1)))+array_x*5.899847378812356), axis=1) |
np.mean(np.sin(2*np.pi*abs(np.sqrt(abs(np.log(abs(2.3479402339816975))))/9.653507891629523+np.sqrt(abs(array_x*4.803161964930741)))), axis=1)+10*(np.sin(2*np.pi*np.mean(np.sin(2*np.pi*abs(np.sqrt(abs(np.log(abs(8.944302222586202))))/6.662135787106611+np.sqrt(abs(array_x*1.4809904951150896)))), axis=1))) |
np.mean(np.exp((np.array(range(1, array_x.shape[1]+1)))*array_x/8.174980386613427)/9.388636340217115-5.493879500471583-np.exp((np.array(range(1, array_x.shape[1]+1)))*array_x/(np.array(range(1, array_x.shape[1]+1)))*array_x+3.596395182956466), axis=1) |
np.mean(5.473984967938962-(np.dot(array_x, np.array([[0.09979034161141764, 0.5797560172553434, 0.10051748824199924, 0.07713929110059126, 0.4210621948701173, 0.6442361663545622, 0.9526369069348802, 0.15977328381153277, 0.862001313975678, 0.8223419968418345], [0.34765764039975644, 0.4749076897421862, 0.12423872751276754, 0.9748735350372003, 0.5783704393730074, 0.30332951694314914, 0.5326826429524479, 0.8427099129176043, 0.2430351457197426, 0.4361603978997386], [0.6488096114707829, 0.6464089834010176, 0.05045954744019099, 0.503416795289099, 0.3932793687552054, 0.7534496333125363, 0.5851990707629792, 0.6399781541414208, 0.8143384856385577, 0.22928913588262156], [0.5617741192107005, 0.9916103831087424, 0.7481211706308151, 0.45019701026061343, 0.894403732491634, 0.8953236350320671, 0.18135879698156165, 0.5983113713961498, 0.924319541105434, 0.9024977586565412], [0.30774241591044627, 0.6833471615078381, 0.4065276222308265, 0.029469430337841995, 0.07793021826336444, 0.8022262676413148, 0.4016450844467664, 0.19441823697728045, 0.5900573998989576, 0.6950195747151662], [0.9448007947599651, 0.37351947856504153, 0.993226005154831, 0.610620362366562, 0.5531484539986147, 0.17365886925799667, 0.012218382167363817, 0.1863374464911347, 0.8045064614761299, 0.7625486823697494], [0.8024422101529944, 0.17956928144272966, 0.8695735275145722, 0.6293585260790125, 0.780106786413233, 0.4391927410082397, 0.9843817540281952, 0.6789701088868341, 0.11734966613125253, 0.6394820068123958], [0.8056353460471439, 0.9068337185702408, 0.4042013717413008, 0.09031471507212507, 0.6775235256100782, 0.7805786968589981, 0.4505268922024318, 0.9032962231345428, 0.41525587385642604, 0.7355049776313115], [0.6105443724781062, 0.7667270797026918, 0.9259049970701291, 0.8257391949138254, 0.9637374938554205, 0.07853921062587033, 0.924301306770989, 0.7244170808970544, 0.37597160065938584, 0.052810596667017484], [0.5018618326714116, 0.31856338598582556, 0.7623064221904285, 0.7236449188205543, 0.7078225959404024, 0.10653840295415318, 0.044188058361821714, 0.4374490867873372, 0.2606015688333778, 0.22298569486189546]])))+np.sqrt(abs(array_x))/np.sin(2*np.pi*3.8217017220195455)*8.202464115114932-array_x, axis=1) |
np.mean(np.sin(2*np.pi*np.sin(2*np.pi*array_x-2.8837031780023494))/np.square(8.059074501097564)*10*(9.595906092410116)*5.60581874980373+array_x, axis=1) |
np.mean(np.cumsum(np.cos(2*np.pi*np.sqrt(abs(3.906025931366798/3.4463529238738575-array_x))), axis=1), axis=1) |
np.sin(2*np.pi*np.sum(np.sqrt(abs(array_x)), axis=1)*2.3004501682908938+7.067977045418631)*9.443873524547882 |
np.round(np.mean(np.exp(np.square(1.698270225913728)+array_x/6.848514520675744)+np.square(8.288800875586114*array_x+3.8797853181737096+np.round(np.log(abs(9.753887690072084)))/3.7436223092891314), axis=1)) |
np.mean(np.sqrt(abs(2.315399535732588-np.sqrt(abs(5.919632761302675*(np.array(range(1, array_x.shape[1]+1)))*array_x-(np.array(range(1, array_x.shape[1]+1)))/2.073174297327176))))/10*(7.189563099273774)-(np.array(range(1, array_x.shape[1]+1)))*array_x+(np.dot(array_x, np.array([[0.6396088745925101, 0.6335759693976436, 0.7124262837796079, 0.865856041733598, 0.9586188211542548, 0.8189515985759771, 0.4317122460704057, 0.37147430291367634, 0.7866220839662285, 0.9898737138384309], [0.2451077112714084, 0.036612436880269716, 0.3846154406295166, 0.4201293987141317, 0.9402123195902752, 0.16316378514834862, 0.8146683514818414, 0.49121224483977466, 0.7450661062496228, 0.33329433327674907], [0.9341037212289939, 0.13849684981368204, 0.45235661277063544, 0.04227159658564139, 0.05472154690606723, 0.786101953538027, 0.2175633868445659, 0.9391127109595618, 0.5585716017461951, 0.8764059538848675], [0.21549470900875778, 0.311092373544556, 0.901128885195797, 0.43084208108907185, 0.04844656143043813, 0.42342351299500625, 0.7773780250299311, 0.8983434003101122, 0.8346921065602143, 0.1442756094085924], [0.09798381014743662, 0.860671988724631, 0.33382502218775, 0.545315243948582, 0.5222769913960154, 0.30365704630251267, 0.10300849240854837, 0.9292433363246599, 0.443595932018524, 0.3633479743204393], [0.8401014076182993, 0.7693558683742231, 0.48436522369065416, 0.871089971549119, 0.4562942541588443, 0.44745191550768193, 0.23196886652555948, 0.3876781581549028, 0.9427327341436831, 0.2221414140352107], [0.7530457409095527, 0.9875925955342306, 0.907864399498585, 0.953867169747223, 0.6918850358966957, 0.39154459536738473, 0.19447621576044138, 0.5791598753928622, 0.10497811731040974, 0.5837064110568291], [0.352594842969557, 0.7385237244822985, 0.5179123887488829, 0.03968431757877555, 0.12122842116134291, 0.6802108777024192, 0.36957689070969024, 0.275763100775652, 0.10840536208196538, 0.4622663356996116], [0.6580173244364877, 0.9197366314694163, 0.11176098273537571, 0.4168441181607552, 0.6865219624290269, 0.19658816109669952, 0.20490726626203837, 0.24585165501697515, 0.27425935253228284, 0.4321471233026287], [0.745865100486707, 0.0986906488204986, 0.303686097840837, 0.25027392639291857, 0.6710855895078606, 0.6887689988126146, 0.8765806284515428, 0.532102870934569, 0.057210473643152016, 0.5383862800082542]])))*8.400490146142833, axis=1) |
np.sum(abs(4.226864472771421)*5.767081606188877+(np.array(range(1, array_x.shape[1]+1)))*array_x, axis=1) |
np.sin(2*np.pi*np.sqrt(abs(8.195980065465177)))-np.sum(array_x-5.274011707914216*array_x, axis=1) |
np.mean(10*(np.square(np.square(np.sqrt(abs(array_x*7.034013600033036))+array_x*4.819461951219864-6.48573836437569/np.cos(2*np.pi*np.square(np.square(4.733101628742444)))))), axis=1) |
np.square(9.719322248967902-np.sum(6.168363018111558+(np.array(range(1, array_x.shape[1]+1)))*array_x, axis=1)) |
np.exp(np.round(np.sin(2*np.pi*np.mean(array_x*7.702778614355566*3.2465609569894203, axis=1))))-7.941875041270849/np.mean(3.546945546207591/array_x, axis=1) |
np.mean(np.square(array_x)*7.82285340898685-np.sin(2*np.pi*7.125236267909494), axis=1)+10*(np.sin(2*np.pi*np.mean(np.square(array_x)*7.637267602500168-np.sin(2*np.pi*1.123972695186136), axis=1))) |
np.round(abs(np.square(6.412935746960324+np.sum(9.07730792592956-array_x, axis=1)))) |
np.mean(np.round(5.489213394066007-array_x-8.96674900023932-array_x*10*(np.exp(6.636850472980863)+array_x+array_x)), axis=1) |
np.square(-(np.amax(7.911642169303039-array_x+abs(8.320484582822369), axis=1)))+4.644102349050307+np.sin(2*np.pi*np.square(-(np.amax(3.9159584705897936-array_x+abs(6.167390373876687), axis=1)))+1.7333452384538408) |