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np.sqrt(abs(4.328308117281928))*np.sum(np.exp((np.dot(array_x, np.array([[0.5338366876252406, 0.6627716859183357, 0.2130373785023275, 0.42333864436883817, 0.1295386735681766, 0.8180825280528937, 0.29750413810860177, 0.2005755125692491, 0.8512502959623649, 0.05174465043459309], [0.23977755924919553, 0.9043828750927999, 0.9932618026207881, 0.020889591472617708, 0.4210139990870869, 0.4807699651405265, 0.6317664656257843, 0.32069624569677113, 0.712885611328229, 0.8813164050422784], [0.1847876224138747, 0.5143248065649939, 0.26441381581901213, 0.38779740069484114, 0.3384282583782091, 0.44222900853804215, 0.10697629071750459, 0.420621564362068, 0.7766481475953058, 0.7514292705172673], [0.729247852921924, 0.561662263558959, 0.8041411495844637, 0.5298644670712093, 0.4009620502543211, 0.726170188034227, 0.7642546849886157, 0.9495136381334881, 0.14689839637697166, 0.1080810221290115], [0.7026994793262319, 0.5920860109145041, 0.4572864772728711, 0.557473823542299, 0.5158148323319015, 0.9502889095371894, 0.38883610214329767, 0.4449165423716056, 0.7761350187738902, 0.6788445609893842], [0.7673937108669708, 0.8279415247588894, 0.9379215410972069, 0.024330807287076928, 0.7637551319376135, 0.307193779835662, 0.223260299421439, 0.1010709644127884, 0.04237312096343193, 0.286101625562445], [0.9674757352495731, 0.2916587037034878, 0.21365837796750675, 0.667388288016584, 0.16360229714256946, 0.12742832174398333, 0.8414216379933537, 0.7547846860537774, 0.623751246974505, 0.7779741767600591], [0.36505584786149003, 0.5214464657596863, 0.6136540729316343, 0.7905354654096468, 0.44751972162108633, 0.32958081060047006, 0.753802822997716, 0.9053457868939716, 0.6728228954406967, 0.6121338675280766], [0.29109753978638375, 0.3921891112861715, 0.3360763977807658, 0.6542951753898087, 0.655968472119111, 0.3100310433264195, 0.5541580316483747, 0.057830330476369984, 0.8162884606595077, 0.45986873794088956], [0.6277892075255054, 0.741886241096802, 0.8005761225118109, 0.5582750630084227, 0.14085141433042847, 0.5224413228244896, 0.5852268908666516, 0.8373469777226865, 0.5418144618725409, 0.15218286516463908]])))+6.0750357822190235), axis=1) |
np.mean(7.704555805341012-array_x*np.exp(8.248408012219025), axis=1) |
np.mean((np.dot(array_x, np.array([[0.07057009191297936, 0.791078922335439, 0.5254716944507044, 0.8980603981949344, 0.8649581250111047, 0.9305626846750811, 0.9653200857416827, 0.4316841125685198, 0.6653903396748263, 0.2661195056887038], [0.009744767101932172, 0.9730121451600207, 0.07759343206624214, 0.3075605269320658, 0.9734070438929939, 0.881270239830334, 0.583953048412753, 0.10321501140488187, 0.552374065593914, 0.6559232274255051], [0.16967411754539774, 0.15861320721741434, 0.3311994076910739, 0.3275688792706385, 0.6667131676130111, 0.7191290055455763, 0.5403245707471044, 0.28112815569022975, 0.5983570846272283, 0.9079294797884643], [0.9078620223958985, 0.18915259567861353, 0.951599790989887, 0.6158004097517278, 0.7444788653338288, 0.6263377512026702, 0.7771917586650274, 0.7290797239323981, 0.41820752337710854, 0.9584459594897093], [0.35633206504918224, 0.8414894778205423, 0.9133375648020495, 0.11143626260287809, 0.962308950563573, 0.47131299362748, 0.5798422146189991, 0.8755352832209943, 0.41433407406324874, 0.33633033153119385], [0.15989603632983884, 0.24746604186567578, 0.009109677677361594, 0.16116553128172195, 0.1685049029476542, 0.149721659060723, 0.9635585487470817, 0.7644362563398419, 0.709228192151021, 0.6365164129382563], [0.4123545894518347, 0.021541292968843107, 0.07206721066048993, 0.4847710817385402, 0.46861967877625055, 0.43613081908732454, 0.630603200806728, 0.6582255495704042, 0.23766821718813747, 0.5786862961536682], [0.7159787175238442, 0.7289208775670482, 0.4246986136351075, 0.9184268924876033, 0.2824990279137489, 0.3082047431277857, 0.5545981850499906, 0.5502078022414373, 0.42014430189543106, 0.26004784306728135], [0.6791608689938228, 0.7580376917726046, 0.2976889416651133, 0.045960174303345314, 0.6863839530632325, 0.7099003640771955, 0.6232987337224496, 0.23032690897549557, 0.5242279432534698, 0.6679330391587107], [0.339339455862941, 0.7928655355764477, 0.838991733819608, 0.9839973930839306, 0.7897473442105021, 0.30561962143455734, 0.6676575603059014, 0.12418200038441785, 0.7682326576838866, 0.6884403771651364]])))+6.678897845029356/3.4414416790623283+10*(array_x*3.2677180157043364-7.543193916132193), axis=1) |
-(np.sum(5.281815283694648+array_x/5.974043115666987-8.670843370179433*array_x, axis=1)+7.979212696869776) |
np.mean(array_x/2.8592333259708336*9.13546785777157-np.log(abs(7.151892970670247))+np.square((np.dot(array_x, np.array([[0.7554409758538367, 0.583318182127684, 0.5788765024191485, 0.09585268614884601, 0.6624906905397918, 0.003603558860090117, 0.10125222519338728, 0.44424291699606244, 0.863906524521454, 0.3585559190053421], [0.014250570365725856, 0.5114073616727577, 0.5588337597142491, 0.9084036391184483, 0.8160275605768726, 0.6282331606033564, 0.13417218375297413, 0.034413849710172695, 0.39712661879491906, 0.8071974278096914], [0.5193680503832555, 0.4126686455030951, 0.6995883530850217, 0.42908058822019635, 0.7718449021551177, 0.5712226360547579, 0.22518946934440986, 0.03304639966491074, 0.6845824044225497, 0.3247495041798163], [0.0898109044404789, 0.07522517410343854, 0.7245998444889101, 0.13287570299860052, 0.8718111761601884, 0.11200203865452796, 0.6283780052289839, 0.32809868971589984, 0.34175745614333064, 0.06066033859890152], [0.340629933716285, 0.30416359862615505, 0.11227095802806342, 0.6647425894145644, 0.10489963712882311, 0.7171203886513323, 0.047505377202354704, 0.4290225882213391, 0.7932352789655907, 0.6779906896611486], [0.5734209449276039, 0.3871589580795237, 0.18430935977910956, 0.718024307590858, 0.4776949926309505, 0.8141046073454814, 0.462266545051789, 0.8355247392313273, 0.6760980436995965, 0.8599877204800327], [0.7341447608835686, 0.6160903047779016, 0.5964388707029706, 0.7188824822305238, 0.41762926045179327, 0.7803118379524574, 0.6826984549536168, 0.646181081095082, 0.411383702876822, 0.14806609037877383], [0.8221494356630039, 0.7026731462067828, 0.1405499163976106, 0.37086684451747676, 0.6558770920315188, 0.1025759747986551, 0.04950725443925963, 0.6328086584031624, 0.5422555806378798, 0.013654907628946722], [0.8983863591784703, 0.04542395061072391, 0.5684856836973614, 0.8345869450686396, 0.8838131854551906, 0.9955277200395133, 0.4742022654194996, 0.8549656085055776, 0.978504182057019, 0.902070684149093], [0.869931551589503, 0.18690204523190168, 0.06820571168201339, 0.048746474814831076, 0.4645870737872255, 0.8359691782943904, 0.6378153819363347, 0.6866417463704685, 0.42500697670548526, 0.2668880000030218]])))-9.551962129426743), axis=1)+np.sin(2*np.pi*np.mean(array_x/6.13865220408956*8.033816480532924-np.log(abs(3.836265165703079))+np.square((np.dot(array_x, np.array([[0.7361711595672726, 0.7037709446521949, 0.4567875949589886, 0.5918323478777604, 0.15847383786124247, 0.5223040144241321, 0.08582557082872888, 0.46552449189452494, 0.6426254606259046, 0.8116421869783664], [0.21398380066580458, 0.7427011096780788, 0.032302031952909704, 0.7009137388116712, 0.21146809324476312, 0.24703554678027517, 0.8789536224310176, 0.2810183530464979, 0.20503180238790641, 0.22068006685666375], [0.13613592844385625, 0.9380120138494222, 0.29220201282758784, 0.005068665952987161, 0.8421209622704301, 0.03176787150274096, 0.6832735727349067, 0.08437402448896125, 0.3816331353582454, 0.5731596843232419], [0.15173992143486525, 0.3826037446882483, 0.41741542175759305, 0.3809949248971968, 0.4926031516908478, 0.17435971917242332, 0.8405426329124552, 0.9755708546479731, 0.3934953417551502, 0.8016516165082906], [0.11787968158478823, 0.887929465334886, 0.4186901459748972, 0.32208937718601005, 0.7125532774824004, 0.9064654983424525, 0.08777240932149111, 0.8701378469276804, 0.08529060734711136, 0.6415914990321268], [0.4403080681773416, 0.9874888883122361, 0.19925870124696476, 0.7981629586077493, 0.26512577311381, 0.3497219497997863, 0.39770117473357514, 0.5543717788845095, 0.35358858892147416, 0.5307477020379207], [0.7981859689640385, 0.9630542372754931, 0.17776348521362095, 0.3859912139469528, 0.9985735882577826, 0.9826676024585277, 0.06509839095193037, 0.7261947101015872, 0.9008165322978643, 0.48889091001818774], [0.6356410597142483, 0.24365990432365414, 0.10156631601993926, 0.8264460739958046, 0.7785114664456172, 0.6815983656413463, 0.09712793973532186, 0.836277760053923, 0.11601275189388838, 0.22986889886062378], [0.8194133094552621, 0.537103754111571, 0.8735376948864203, 0.7820243521162894, 0.9332253614315565, 0.9973937605973321, 0.2238088928564259, 0.2774755399404558, 0.2880043842402299, 0.13778654050422845], [0.8813431627329866, 0.8928549378898284, 0.9162288279460836, 0.3118590342884513, 0.5995052486373535, 0.1639801172209493, 0.0938334081417529, 0.9720005981323037, 0.5797430785372865, 0.9864575138719849]])))-3.4197615649605093), axis=1)) |
np.mean(-(np.square(array_x+np.log(abs(array_x-4.0038072482734375-3.224584823649921)))), axis=1)+10*(np.sin(2*np.pi*np.mean(-(np.square(array_x+np.log(abs(array_x-5.9509224433400645-4.3040978228330085)))), axis=1))) |
np.mean(10*(np.cos(2*np.pi*2.75316709151616-array_x)*6.116002115599436), axis=1) |
np.mean(abs(np.square(array_x/5.1073604389859355+array_x-2.778987296908644))+4.367228284963423+np.exp(6.437565838192173)*array_x, axis=1) |
np.mean(4.396236540563057/np.sqrt(abs(3.4559560399375404-np.log(abs((np.array(range(1, array_x.shape[1]+1)))*array_x))))-10*((np.dot(array_x, np.array([[0.6295073778794749, 0.43548010627795963, 0.2078594955615688, 0.1436795736055917, 0.6687449802362219, 0.3708431248290256, 0.9800202070489247, 0.3803608757500553, 0.317872684787472, 0.33901538454647084], [0.16138565501074098, 0.11515266500216659, 0.33209849818811665, 0.8393689306807621, 0.4712457657507797, 0.4713457453085047, 0.6611817110440928, 0.8509353535952502, 0.4483596004582123, 0.7234239633985362], [0.7869611484866016, 0.4824912852906481, 0.9498085401975443, 0.33823113069631217, 0.5380069829989227, 0.19050875624633767, 0.5692167444962798, 0.8910629511171068, 0.32882297374103475, 0.6438131066096645], [0.5637795048939395, 0.26346934837045977, 0.6566549342922032, 0.8495848703770555, 0.7390300842833665, 0.7263175505203577, 0.33019143968467946, 0.07212872312583674, 0.975544894277161, 0.7062526427870558], [0.14883078964158047, 0.5591131918956896, 0.7540174185928585, 0.47299828303739877, 0.503845683703982, 0.9320233198720732, 0.47185237867969587, 0.21693313377082324, 0.06463978497766931, 0.7711623953705572], [0.6967134257341452, 0.6959540138063619, 0.3860479876247179, 0.3373202481461022, 0.07619948748221272, 0.1939210207205988, 0.7442030845892129, 0.9191097318529, 0.9735857363066984, 0.16910397249272102], [0.7962839899406858, 0.5316986816639127, 0.7515597227918941, 0.009261236438359632, 0.7081426256947603, 0.3490471384656072, 0.7426563129714389, 0.1542851825146654, 0.09889137734798181, 0.2528452466211666], [0.09137415385912695, 0.7530831541370832, 0.4891082067698652, 0.2607742692984044, 0.06558919998148505, 0.6000743133309948, 0.521743226796831, 0.9158465294858196, 0.09030680162708393, 0.404893574210785], [0.9623489727710143, 0.2319160746831893, 0.09733153378938075, 0.5950395300531142, 0.5583670525262833, 0.9435957862882528, 0.49801801900713527, 0.07797410952687822, 0.2333066097934462, 0.9978210075093018], [0.9149938865707499, 0.16353250095809746, 0.7062610025529605, 0.1532725061718918, 0.5361829598219738, 0.36289311341352837, 0.9929394343112008, 0.5754023437079346, 0.4826172447723329, 0.10241605323778569]]))))-np.cos(2*np.pi*abs(10*(3.107822893034138)+np.sin(2*np.pi*(np.array(range(1, array_x.shape[1]+1)))))), axis=1) |
np.mean(10*(array_x+1.2165687841418826+(np.dot(array_x, np.array([[0.9702660685160196, 0.6020343482974296, 0.4185688306564598, 0.2631470580599955, 0.4139389762610236, 0.6322226928157751, 0.8402968957822088, 0.4449292388846219, 0.5975371261504107, 0.523749043222372], [0.0012505750904563406, 0.231608518072549, 0.9616120452272591, 0.7435312420329262, 0.4026877978324801, 0.42248713336918764, 0.5307538214581574, 0.08902696044545955, 0.9914624441570133, 0.8241526762112112], [0.5108946129915429, 0.047320190373187576, 0.29689796789969447, 0.6468925661224486, 0.4086993591045104, 0.6229815792631407, 0.5004619112700476, 0.22319855120640997, 0.9947685902004701, 0.7373936535127054], [0.16816425365649634, 0.26650833382357586, 0.919771245959917, 0.7482248896153438, 0.33391388504266906, 0.9078945972237721, 0.7105833284531805, 0.9446602523867504, 0.9181794546008843, 0.7054404710094486], [0.375158951738809, 0.43320897673787007, 0.25491798185735104, 0.05919730098466869, 0.7810095382684065, 0.2680446567875018, 0.2777838725368582, 0.4298671426273347, 0.0945532289456652, 0.7099828688664694], [0.8932629683721587, 0.8811359787050038, 0.4778269870522256, 0.960819091248159, 0.636286236460338, 0.22037185414852856, 0.5695949106981748, 0.9603896937105195, 0.29916537964793155, 0.5024038293112243], [0.4506192831595841, 0.772290423054917, 0.19296423894732295, 0.8626054986844873, 0.10887011766592025, 0.8812219737499477, 0.12370971745016268, 0.5043651666024805, 0.690128455198051, 0.1344953397351344], [0.10252818732501923, 0.2875985312597056, 0.8054077632371917, 0.0330532853709643, 0.26502870901071884, 0.6195223604376565, 0.4662636939625877, 0.982915368475139, 0.7114634023822907, 0.5562134557082203], [0.3513325398050844, 0.743062410939719, 0.5831139707628558, 0.603335806133669, 0.08677739143409136, 0.6990777226554902, 0.5264503493848652, 0.282400222318532, 0.611294822491379, 0.9787027452998844], [0.11460741046314316, 0.3802004705735108, 0.33017757074680243, 0.9175212704914549, 0.9232391823569909, 0.36834598425765763, 0.5112390966457132, 0.13868460027140694, 0.4237237016649167, 0.23877460126221584]])))-2.4988024325275324-np.log(abs(3.1829930353243814))), axis=1) |
10*(np.sin(2*np.pi*np.mean((np.dot(array_x, np.array([[0.15544895894609567, 0.05102604385587317, 0.5270799415941052, 0.10925179883099878, 0.4376474139835752, 0.8866994613398735, 0.7509797358880688, 0.4081244653144449, 0.29851880420062005, 0.4582910177195644], [0.121911612534084, 0.04861661836695763, 0.2809949770433281, 0.30212087839973467, 0.909053169390514, 0.059399225146573253, 0.4003985582274088, 0.29131319322997007, 0.6948918436828294, 0.1489452508525394], [0.9308395243967049, 0.729195469454588, 0.17968111783554275, 0.10118662274535539, 0.26571503069275293, 0.5452060032258985, 0.23043752170992493, 0.8194518715833301, 0.8640586760704322, 0.3254623804816641], [0.5084981069652882, 0.6089893480664081, 0.6976037017091201, 0.7269700222939444, 0.05671942724057355, 0.43742778630543544, 0.5886239314771878, 0.02440609824327844, 0.22358021107187687, 0.7452885691435127], [0.8332646205350995, 0.9900696776184272, 0.7719064048261918, 0.3396587339383752, 0.9444050887668256, 0.5156819676095332, 0.5934992804862853, 0.5510717699868256, 0.6823785711191833, 0.5468596690044973], [0.8451766817846058, 0.7263058310765786, 0.4792148894299034, 0.8228344819475942, 0.7938051857563423, 0.14630338902072937, 0.3909501241125827, 0.14108650464213068, 0.05493495891308009, 0.17709975872337058], [0.9256895055338198, 0.4723730919951362, 0.02457564905282883, 0.9900832314970353, 0.6272476964408322, 0.5405669104741586, 0.17645112702986443, 0.6106080404786517, 0.7841777407242757, 0.7771480155684091], [0.276058328578638, 0.1840822036485702, 0.2577937039902227, 0.8699305041312042, 0.3636369315822392, 0.6759867758482576, 0.6404099581231832, 0.9955811934133963, 0.6227499913107875, 0.17352222256250793], [0.06853523281952456, 0.01977943372875146, 0.49221369669001935, 0.2227967208134063, 0.0847287407674141, 0.6370504479557986, 0.0690716911656446, 0.9904354705232696, 0.8507242227721169, 0.3347345725069265], [0.04618929530854132, 0.1823366922953218, 0.9767251394360611, 0.7145344656901373, 0.4925187449411993, 0.1331575410085467, 0.4953905728248317, 0.05669233893470382, 0.5540141428228197, 0.15095400722263508]]))), axis=1)-6.389695573014566)) |
np.mean(10*((np.array(range(1, array_x.shape[1]+1)))+(np.array(range(1, array_x.shape[1]+1)))*array_x+(np.array(range(1, array_x.shape[1]+1)))*array_x)-np.log(abs(5.537615476839788))-10*(4.321116715601934+(np.array(range(1, array_x.shape[1]+1)))*array_x), axis=1) |
10*(np.mean(3.4024105116829375-np.exp(array_x), axis=1)) |
np.mean(np.sqrt(abs(2.8572214111009426+array_x)), axis=1)+10*(np.sin(2*np.pi*np.mean(np.sqrt(abs(2.074283952135774+array_x)), axis=1))) |
np.mean(abs(np.cumsum(array_x*7.822666932430584, axis=1)+np.sqrt(abs(np.sqrt(abs(7.8487356534096735))))), axis=1) |
np.mean(np.cos(2*np.pi*9.655102448878084)+np.square((np.array(range(1, array_x.shape[1]+1)))*array_x)/np.sin(2*np.pi*5.500649420880726), axis=1) |
np.mean((np.dot(array_x, np.array([[0.5225990528920869, 0.16549543926716737, 0.1843463150923793, 0.7077582365800883, 0.1540362104771298, 0.36087807935958405, 0.14534969777472528, 0.9807789772658445, 0.7394960675496534, 0.024084036417866383], [0.3074401961111185, 0.4398967509363252, 0.7386143103043157, 0.8451463755799082, 0.9606001060532974, 0.24264375821256712, 0.4982377101897423, 0.25054890693280796, 0.6095761038512924, 0.8727677844171724], [0.8609697197403512, 0.3656514044475342, 0.5560201748677578, 0.8440425479791228, 0.607126869527305, 0.9968195774255991, 0.6656990438941434, 0.6270774082330751, 0.04524016655266383, 0.024511295155192014], [0.1583863184557135, 0.6400771988062298, 0.37185264720297106, 0.6187391404959327, 0.08653434359584466, 0.29001098061904096, 0.6993507382867538, 0.3423941599264907, 0.37392827277112606, 0.815598156050413], [0.42296061427757936, 0.034415983699500496, 0.7084176432744251, 0.9017802960044009, 0.021432655746476126, 0.8283401119307777, 0.3289784595867211, 0.7267866334975687, 0.009032329359842217, 0.7924255359361153], [0.3440090523647995, 0.3563794450715224, 0.2643549636866409, 0.09152404464373554, 0.4183291876162155, 0.2966964818368498, 0.932411766438617, 0.9095245977260111, 0.7705632477933722, 0.3170980525692385], [0.5185274883028922, 0.3750798394004985, 0.7135165885025828, 0.41971890628458763, 0.8627647290952444, 0.8446031152665061, 0.417630982680424, 0.322682441414057, 0.4746794760520696, 0.5443321333557829], [0.034601185743841634, 0.054421343579468595, 0.03812666510407092, 0.6712457542405533, 0.3865428205846788, 0.06894581206165551, 0.742463828178313, 0.9417536986851038, 0.9650127937145183, 0.23234840898475384], [0.9531667687858681, 0.13063334178396258, 0.8170831430866657, 0.3973778411142672, 0.44779843558255117, 0.332824163536266, 0.6551360268239966, 0.7860519064808443, 0.9750198887333392, 0.5656718753872912], [0.11383348280359562, 0.5263474902109391, 0.2059249638112255, 0.5751979766702398, 0.41855688273462643, 0.148935731399644, 0.9883616495018899, 0.5546098548161784, 0.42975189409469594, 0.14352595663773693]])))+abs(5.0291564467194485*(np.array(range(1, array_x.shape[1]+1))))*np.sqrt(abs(8.188857157910249-(np.dot(array_x, np.array([[0.12221718315567442, 0.8167094357082996, 0.229368263875683, 0.2629979644649407, 0.5835672980895492, 0.6219241431070123, 0.4419518998939176, 0.12606057255021097, 0.15039022688661075, 0.4223285867492681], [0.5237566490581177, 0.7950389698734056, 0.5969626541381472, 0.9042344356218401, 0.4850150180659081, 0.5672605431870106, 0.8942527535900321, 0.44422619436529986, 0.12690758459638052, 0.42339090785476974], [0.07110909913338204, 0.629019334169731, 0.051592444057443676, 0.8869740486331326, 0.4860868632189963, 0.41883725598510746, 0.9616480094993624, 0.7149629644803187, 0.3416872149110741, 0.16890935623525927], [0.10653112403497678, 0.6592376219400385, 0.17183884694941975, 0.8406882279767319, 0.6027960222298031, 0.3983799839339517, 0.5258350196002127, 0.003641348098868513, 0.9168944236501353, 0.32342016900696513], [0.9173095803224172, 0.2947219879451136, 0.5063299448584498, 0.47037714709378786, 0.9078860346134653, 0.8834792964336802, 0.24085558316642475, 0.28927917241057854, 0.644710047721618, 0.30127873671193006], [0.2620407136855376, 0.32013554788498677, 0.7161727769692062, 0.6248782601360671, 0.3094261022837337, 0.06321074664218607, 0.16084792023791594, 0.9441136752407157, 0.9511262306072771, 0.386170930548974], [0.8170902030252416, 0.9114103835967882, 0.442201865249348, 0.7465288940775308, 0.6693439898831498, 0.6945858470569213, 0.7195411468913065, 0.5762218199490126, 0.7776373080886934, 0.4746031759202678], [0.8475967650390179, 0.3037425568772435, 0.08406125591646818, 0.6231460831132242, 0.9397655278735417, 0.5901841503168704, 0.5593545877037444, 0.5005481653765526, 0.3564391688412665, 0.35549658753803204], [0.18232869657794148, 0.15312576379067444, 0.3276340736466917, 0.4090204522020351, 0.16836973352918772, 0.4190102280895853, 0.08104347339768692, 0.5013553923679852, 0.2555668439944839, 0.2452851349993811], [0.4049395472019319, 0.6513932170831999, 0.7128254016427366, 0.23698531399542744, 0.06429319132239986, 0.8193933297772921, 0.7416205263490226, 0.5715985796391394, 0.13581427110158806, 0.06836443355548116]])))+8.620524427981376)), axis=1) |
np.sum(np.round(6.211732935066296*(np.dot(array_x, np.array([[0.459453479527273, 0.05244247420829362, 0.5060969610207687, 0.1427008855203712, 0.8829887905147205, 0.12097164033953889, 0.86267517039494, 0.3016859804999683, 0.8948187428814491, 0.3369087486080167], [0.2996388193890719, 0.9045763717692458, 0.311018734849287, 0.5020000112606617, 0.9803167980269142, 0.7531413486954727, 0.5630040443377395, 0.6526367050027024, 0.7179963637277967, 0.10403495582019096], [0.6465936175773003, 0.3618797891013671, 0.5120673426049419, 0.3979919180004493, 0.8182733949654772, 0.8322062686407381, 0.680545177123236, 0.47145766173689974, 0.6322288791172158, 0.5919293646475651], [0.17682578531954252, 0.5716979552433823, 0.26833431154059695, 0.5491890313927655, 0.6098714644045998, 0.026231426285672987, 0.553227953142616, 0.6338947002421143, 0.5709039348592289, 0.7523833777038751], [0.3003619463143983, 0.9822760312080507, 0.26090697511009253, 0.46421029012420245, 0.17377702076231583, 0.8278164225927322, 0.46947192197925536, 0.5330525677347522, 0.3545670568497561, 0.08559309295627415], [0.5768023214455547, 0.28827066364990916, 0.07165833259087251, 0.3263890370523421, 0.2631970630919852, 0.7945236849057169, 0.9457695738778236, 0.05261270373113436, 0.7335819109239053, 0.12941127573400812], [0.541378468003183, 0.2524888217148644, 0.22412992783444485, 0.21773573157595072, 0.2784271717337674, 0.49646159588440897, 0.5664350396506119, 0.4700390737743524, 0.9632187410555281, 0.10380146272606061], [0.6402784532788799, 0.33588598142453596, 0.29210388352049754, 0.2653625306994385, 0.7521570239135357, 0.7004393548620453, 0.9347461341773583, 0.8473105711170976, 0.1676109826502996, 0.43336302037575747], [0.5534710951523455, 0.040418582188423424, 0.8529030421195656, 0.6638031195781967, 0.5985065895262831, 0.580827639862734, 0.09529566297663983, 0.7686795311043801, 0.46028951532444473, 0.9222661217052817], [0.7279997371692101, 0.6122158918326139, 0.1328668048960513, 0.653984668649321, 0.20697251326809607, 0.8264982049542416, 0.41714740472535117, 0.7563581472347264, 0.7523917121293975, 0.5699420893904954]])))+(np.dot(array_x, np.array([[0.4727144197386547, 0.4816227722338521, 0.3758347818215674, 0.8134759997978552, 0.7197556381908723, 0.4984783152517548, 0.7867353203900944, 0.5936828105291557, 0.8013617731940221, 0.30136326658992296], [0.29085942225898054, 0.3497990153077253, 0.01986830566555209, 0.5882450587497402, 0.763436052235809, 0.06876456914335471, 0.6868035432671233, 0.09486162462368675, 0.9423829931731714, 0.5133939665336824], [0.1685124182145583, 0.409731277242704, 0.4040339617051055, 0.2910828668149089, 0.6216281126782379, 0.36089745015230734, 0.01190793974781712, 0.8716222900805314, 0.3104274878545814, 0.6351278131104792], [0.7687080028401717, 0.4164610404664778, 0.4235542028569883, 0.3672496346327937, 0.8661747871110821, 0.48180618881758186, 0.8399204516516119, 0.1760366013262824, 0.5075647901110546, 0.1711583470363659], [0.6981582571476186, 0.35135351335166465, 0.24484023533264732, 0.9355829239264725, 0.10123743820520636, 0.20025445553296595, 0.16825609126300478, 0.8780369663079518, 0.9541274947849804, 0.05526187934174431], [0.9971950736855467, 0.9511050094396342, 0.46504144757276955, 0.5573008560990514, 0.773876396051109, 0.3901825692756987, 0.656618979232995, 0.4132144703675128, 0.23527283359437956, 0.009219328058432508], [0.24357285727037237, 0.3263103931203023, 0.6210129019253384, 0.13414732012373765, 0.36311872457792993, 0.9686055846431321, 0.513160408979858, 0.8009529228737816, 0.6612537532304159, 0.8030224925958105], [0.03190738453694375, 0.9752380359977044, 0.5024878066741947, 0.7097467706221045, 0.16455008924892023, 0.22544862759798734, 0.7081615422129494, 0.4960704440454803, 0.8284459500265421, 0.5954885700898337], [0.31325515618009914, 0.14378192923636635, 0.45553179008830424, 0.25379508695199116, 0.5365561240995629, 0.9759160193078124, 0.3709464680742359, 0.7050049012332411, 0.07587635545028804, 0.3298893486760822], [0.5646236823553299, 0.4995445142039684, 0.15410239605743403, 0.5351400545700958, 0.31933206292418514, 0.035652295577359716, 0.8258318828859309, 0.6779146941580176, 0.7791322996938508, 0.5603730110333145]])))+3.9666857350502545), axis=1)+4.553982841081217 |
1/(np.exp(np.amax(array_x/5.463551798092553-7.467108661312048, axis=1))) |
np.mean(np.sqrt(abs(7.792333862267724*np.round(array_x+7.380781587912294)))+7.21338706008351, axis=1)+10*(np.sin(2*np.pi*np.mean(np.sqrt(abs(3.4589367546466687*np.round(array_x+4.744839657833)))+1.7826537601190675, axis=1))) |
np.mean(-((np.dot(array_x, np.array([[0.890286934128763, 0.3445762124821924, 0.7810436820630237, 0.6645482882517969, 0.3203438755142155, 0.567708762456739, 0.7951991859538756, 0.9313337986977539, 0.3092700856745162, 0.23672290540039276], [0.8737953586738312, 0.09549698820751373, 0.2654934826522254, 0.2257024856390768, 0.9936683472271666, 0.6086631458313257, 0.4884223634626782, 0.7206542282736046, 0.3728883860136234, 0.9505986261018781], [0.7914689571711334, 0.8991334454942472, 0.492774541210681, 0.22700699049904904, 0.6836204838795172, 0.10961833592817494, 0.4326274861910302, 0.23067521204302277, 0.8012597043450068, 0.048542175553749956], [0.6632336337069471, 0.04589421103968905, 0.30417472624485387, 0.448529242273337, 0.9303715740167708, 0.6422961176048235, 0.07884061166321366, 0.8094725431444207, 0.9554815105824187, 0.31340925454628243], [0.3135789361813196, 0.37672545520837886, 0.9810389131692218, 0.2365223163167035, 0.43009111542745093, 0.6155641513669758, 0.8938530421521129, 0.11230403875628503, 0.5861043221409491, 0.7370232641501689], [0.11604295850891422, 0.011295827167671635, 0.42315130666777767, 0.2990866496634361, 0.30832824403687487, 0.4071560306725047, 0.7734422412415674, 0.6852152069220557, 0.2374277924908328, 0.4820141738734468], [0.04221600347442922, 0.04562544976745986, 0.31487680567641885, 0.023774295987810556, 0.447465911682025, 0.045689435460458006, 0.40233867862442374, 0.8083272510115451, 0.686137531669031, 0.07365303669903733], [0.5593121671701574, 0.724726070063373, 0.043995002477626, 0.6946166209422316, 0.6158124719610814, 0.043841642649057655, 0.04285295998129146, 0.34329029246168175, 0.8756989612841833, 0.9993195436258033], [0.21822781391066737, 0.33015614819959915, 0.3438108763324179, 0.8695813870080149, 0.9017176690659556, 0.85786037594276, 0.6369687816584945, 0.9945685783375819, 0.897224294784075, 0.8063278302509971], [0.5983271440770408, 0.2358853002038246, 0.49094928383509673, 0.49902415426410607, 0.4999958755393257, 0.6022907672804547, 0.968606100382344, 0.8934701873620087, 0.18107568865424817, 0.0385145170177279]])))/3.7384984178475267*(np.array(range(1, array_x.shape[1]+1)))-4.863371875919519+np.square(2.7572250255442547+array_x)), axis=1) |
np.mean(10*(array_x)*5.546736356631995-6.100820914766162+1/(-(7.820549690224169/array_x)), axis=1) |
10*(np.prod(1.7703468789796726*array_x, axis=1))-abs(4.0162796581322375) |
np.mean(3.9977544322515364*array_x*np.sqrt(abs(2.1477332374870226))-np.exp(array_x*3.072216077412243), axis=1) |
np.exp(np.sin(2*np.pi*np.mean(np.sin(2*np.pi*array_x), axis=1)+8.600551627324316)+np.log(abs(7.888595303727899))) |
np.mean(np.exp(np.sqrt(abs(np.cumsum(7.875533448090665+array_x, axis=1)))), axis=1) |
np.mean(5.621869218290528/1.0081466787207578-array_x, axis=1)+10*(np.sin(2*np.pi*np.mean(1.2659160562387908/2.9371027433728902-array_x, axis=1))) |
np.mean(np.log(abs(4.766243769430527))*np.log(abs(6.331833564975988-array_x-4.6410151555478505-2.856516367980424)), axis=1)+10*(np.sin(2*np.pi*np.mean(np.log(abs(2.8026658437394834))*np.log(abs(9.142200895253932-array_x-3.5306419751867373-2.6171038819025587)), axis=1))) |
np.mean(np.exp(abs(np.sqrt(abs(array_x))+3.06720132936909))*np.sin(2*np.pi*np.square(array_x))+np.exp(np.sin(2*np.pi*2.973334830602364)), axis=1) |
np.sum(np.cumsum(np.square(7.132645899623906-array_x-7.921846370571173), axis=1), axis=1) |
np.mean(array_x-7.945240722604151+np.sqrt(abs(7.327524856773666))/10*(abs(np.exp((np.dot(array_x, np.array([[0.859432528318537, 0.38531980808876487, 0.5456156621184767, 0.03972953690402625, 0.9894056222602111, 0.13560321263149488, 0.4989312504853157, 0.024555198843751458, 0.15316312064782367, 0.9307010563706752], [0.9015497082442999, 0.24838199246922277, 0.9005790699320707, 0.11237929677816016, 0.44451016171391144, 0.030025019229471428, 0.2988477858067037, 0.6386737429119692, 0.8952714961026705, 0.7447573651906589], [0.018388269988745654, 0.08253128881929961, 0.24202480646031332, 0.9305611671651268, 0.14652507539848947, 0.9066912151648824, 0.9082724421064996, 0.029078942408935382, 0.5418038904042854, 0.7470414071403064], [0.10790696145806689, 0.9837291540332717, 0.20449544093234706, 0.3819229710020291, 0.3400869270413135, 0.9192900029455496, 0.5765558941355535, 0.051763353262306744, 0.15900463902135142, 0.2952193025914618], [0.6568126704648873, 0.12626823873463966, 0.7140618361878176, 0.3520426818067851, 0.9651523668527465, 0.31067425304886975, 0.41564027378716595, 0.7692671247639246, 0.7586594896636547, 0.1556273121437186], [0.8618232904982481, 0.8778616390774332, 0.07791273396239928, 0.24501989225048937, 0.12838134666156698, 0.30529171197208305, 0.5377981471435351, 0.125717131359544, 0.10126264597415968, 0.41327758702039397], [0.6280271712736145, 0.5769062999995034, 0.26760681552538323, 0.3977902721817701, 0.9481467680977399, 0.31240757254660656, 0.9181198322456307, 0.9509608706425386, 0.8879849896055892, 0.9826857371151803], [0.8050032810775194, 0.08139302312967123, 0.2756514837030798, 0.20389019648975826, 0.09588198843098839, 0.34230979028977404, 0.7666377346568227, 0.9359543512326258, 0.9660164475763889, 0.44496982162780074], [0.26770403594781855, 0.9067753523309515, 0.5212650016474645, 0.07685421362224143, 0.9955301735488108, 0.7140390585076707, 0.5597761115682884, 0.8998489485298103, 0.8891919101936491, 0.7882335541928348], [0.8563888640163148, 0.6862775834215442, 0.28604014544051437, 0.17578846398826453, 0.5985037304389942, 0.2445922782194283, 0.528718180979005, 0.2822002433408921, 0.9744928756262542, 0.23994914594093586]])))))), axis=1) |
2.7493719027034333*np.cos(2*np.pi*-(10*(np.sum(array_x/9.860283842926167, axis=1))))+np.sin(2*np.pi*7.858941999788533*np.cos(2*np.pi*-(10*(np.sum(array_x/7.876210891319934, axis=1))))) |
np.mean(np.square(3.851058864698252-np.round(9.946850448386563*array_x)), axis=1) |
np.exp(np.cos(2*np.pi*np.round(np.sum(array_x, axis=1))*3.5285156694775064))*np.mean(np.log(abs(3.4155618794045695-array_x)), axis=1)-4.323330126512706+np.sin(2*np.pi*np.exp(np.cos(2*np.pi*np.round(np.sum(array_x, axis=1))*6.27283529657561))*np.mean(np.log(abs(5.316299504372365-array_x)), axis=1)-1.1124312325330008) |
np.mean(np.square(np.exp(4.881367641994468)*10*(array_x))/np.cos(2*np.pi*2.3331546762705786)*8.237312930940899+(np.array(range(1, array_x.shape[1]+1)))/np.exp(6.39218185041996+array_x), axis=1) |
np.mean(6.356991697677955*10*(9.643512131864243*(np.dot(array_x, np.array([[0.5352570732798739, 0.9040442474735421, 0.5023965695769852, 0.10087001306374044, 0.527581978320121, 0.7112289340539139, 0.3129542767585728, 0.05032535136481031, 0.12328205786375424, 0.7796907504448145], [0.948021874097845, 0.7181860323169413, 0.7538935813301847, 0.10964897382863892, 0.6381499303712981, 0.28945066775618533, 0.3617020044974597, 0.0031597762876556246, 0.043524261098607187, 0.7941940946989586], [0.777468274557954, 0.5641193766607804, 0.3873677430415171, 0.34514564337222053, 0.410041021199809, 0.06705749087187196, 0.9735684694802187, 0.06405921839829876, 0.10844152906132454, 0.47859636044082277], [0.8714837790626163, 0.9803097341454196, 0.6778073830962883, 0.8771606623363065, 0.26757640899732316, 0.6158697437415162, 0.44980163635766424, 0.6176157605971222, 0.8028146306772315, 0.03813337320825916], [0.2964156740539099, 0.33406013977648186, 0.509074887527785, 0.7450476605857299, 0.5930746377220666, 0.3436011826039316, 0.3026601257541983, 0.2796797563240523, 0.9210704793226087, 0.8753211340246627], [0.8413542940804266, 0.8704044254611449, 0.8213548025612305, 0.9783079744288519, 0.7985596879771727, 0.6771491260431791, 0.43506256009627353, 0.9475319951759603, 0.6088837609797048, 0.9697980201812456], [0.0758816026938357, 0.04949169034143941, 0.7952277108018683, 0.21919860991458306, 0.33333956808074605, 0.22411006143882772, 0.04640910732296433, 0.734534861920738, 0.19232566343214763, 0.844824610876259], [0.8329867716217766, 0.8012317442537876, 0.6536706803201808, 0.3472458126607775, 0.4697823110843554, 0.10031206541180371, 0.19865379082352808, 0.6084390123392771, 0.2528985600340594, 0.35457257281915366], [0.5036794335864389, 0.21011773077028095, 0.873807836659892, 0.024744942815296822, 0.6617556239601329, 0.9340397600647735, 0.4481543126590827, 0.6521773367486362, 0.17761123174482696, 0.3090670827911812], [0.9810522070198507, 0.6965136049137644, 0.9740994929630857, 0.46277195689114803, 0.3750735712012314, 0.8390648187918709, 0.5618661869375406, 0.5187339082223473, 0.7699893651433475, 0.6614981926793557]])))+7.230232019292361-7.696786697216938)-abs(np.sqrt(abs(4.095612762308832))*array_x)*5.298519109114108, axis=1) |
np.square(np.sum(array_x-2.192532275843944/8.682347900247175, axis=1))+np.sin(2*np.pi*np.square(np.sum(array_x-2.9110372693999156/4.40928320714233, axis=1))) |
np.mean(np.sin(2*np.pi*8.887526918192757)+array_x, axis=1)+10*(np.sin(2*np.pi*np.mean(np.sin(2*np.pi*1.0272298484005935)+array_x, axis=1))) |
np.mean(np.cumsum(-(np.exp(np.cos(2*np.pi*np.square(np.cos(2*np.pi*1.3570667330200235)+np.cos(2*np.pi*np.square(array_x*4.519504801136036)))))), axis=1), axis=1) |
np.mean(np.sqrt(abs(np.exp(8.280848085980615+(np.dot(array_x, np.array([[0.27790510552778824, 0.6405057401420329, 0.9223803304900563, 0.11052356757986526, 0.43674987018058975, 0.03320431635297072, 0.3181267020277795, 0.3490152267584469, 0.4280563925328702, 0.5796161329895063], [0.3934892354641859, 0.03802001969977176, 0.880878589551709, 0.09706341937477914, 0.030200139038872642, 0.10423085916008268, 0.2340256976018691, 0.18343276716113122, 0.04115720331053896, 0.5548483248219412], [0.777672582790115, 0.5445975920246284, 0.5191804606478614, 0.011967366312290562, 0.6086220576810156, 0.6543749641614145, 0.062340286875422035, 0.9518761065478285, 0.7228626280803423, 0.33657388348384554], [0.842557180124113, 0.9193697611309015, 0.36301227327061525, 0.43568868238175706, 0.6218360851935422, 0.1351069605628804, 0.37134266995366216, 0.3479633127745744, 0.9249006459282988, 0.5202284383742276], [0.6950979630199943, 0.15916712309116887, 0.7259667822128533, 0.7993699649219358, 0.11315091228059859, 0.05155462036127678, 0.09911425814912245, 0.04251241215806312, 0.18236795888546065, 0.3036023976997274], [0.723577987065083, 0.6594921266444915, 0.6896619614003866, 0.01605189980333077, 0.316122655585546, 0.22979048890334808, 0.8385616301782014, 0.1275467595511075, 0.7350803063526961, 0.7683134358289073], [0.9426221146616093, 0.8231801791421625, 0.9221077550525304, 0.3465680192792968, 0.7641931544042492, 0.6420068694679009, 0.36321950513947343, 0.0004753641061820968, 0.4914717384368905, 0.29291159852910076], [0.7055672286584503, 0.47304380922109124, 0.30345925585425426, 0.5399147463426629, 0.7978280256606347, 0.19389426366579043, 0.7362573310668806, 0.1466446668847906, 0.6971392210485529, 0.767020703714366], [0.7685908952141204, 0.13294507541748457, 0.6832506551803994, 0.5924724709645958, 0.34678376213621953, 0.37585245967277514, 0.358478245153504, 0.21153970210426132, 0.742462754151702, 0.8424845219487859], [0.748590001567863, 0.7264494922853328, 0.17484402593906512, 0.6677105308557357, 0.7101802678812867, 0.597658878265357, 0.7203519284085231, 0.5185490340213033, 0.8318490290928445, 0.2482668801637169]]))))+np.round(2.523577128032826)/np.log(abs(array_x))/-(8.18031002802605))), axis=1)+np.sin(2*np.pi*np.mean(np.sqrt(abs(np.exp(8.656135428070737+(np.dot(array_x, np.array([[0.6100340759292006, 0.8220955444465219, 0.9324299857889702, 0.34866949660146085, 0.6531993500569026, 0.5141506912206555, 0.8789644196147842, 0.4669389097535721, 0.4079272226944367, 0.6963571852685608], [0.24132366557495621, 0.09444903141221872, 0.0672975321122754, 0.15412694923578996, 0.20174698663944146, 0.66345517695654, 0.8631809336549494, 0.8843559419900406, 0.7696213459847118, 0.5529506001180187], [0.22597276612007255, 0.5825911298200959, 0.43194171406617665, 0.28146488929935853, 0.46179853208722677, 0.5809062093382407, 0.4081381203262783, 0.29220540014206486, 0.46286498171373036, 0.6078359193549528], [0.22723278239678035, 0.08346538028915318, 0.7970636644507441, 0.8669260136237739, 0.238479608593125, 0.9551541530481799, 0.09610503772355694, 0.7118644396285482, 0.288175566742934, 0.14249178982319188], [0.848801641266338, 0.7054940687487512, 0.11068660669411179, 0.49998618072121037, 0.992685124182706, 0.8839226572381134, 0.5546612258966881, 0.6161261851874188, 0.7556149013259245, 0.7012149411709444], [0.5200590346253663, 0.6540361203851862, 0.7441539864902889, 0.6836303154148885, 0.5902016887887915, 0.221433194447354, 0.49900885138184337, 0.3303708315712788, 0.2952333048483411, 0.27863938073684946], [0.42828161931746167, 0.03758613649276055, 0.018139782966491325, 0.10514195592011266, 0.3803095378425426, 0.6126826744821181, 0.2169144427399049, 0.139277670353113, 0.5448247939168391, 0.1204448124432208], [0.607137594916318, 0.3173347146925245, 0.3225554240844687, 0.3024438609838187, 0.40099970524431117, 0.5258109535829255, 0.037146167777532546, 0.41290529625743533, 0.9225866535348765, 0.7123062671191812], [0.9658458091658922, 0.08744277757222063, 0.11469828101049817, 0.561011423303486, 0.5196128505095472, 0.442718097603367, 0.8823866305336436, 0.7171211279498085, 0.3164564705093935, 0.16615897731709528], [0.6015765754754177, 0.33202125906556224, 0.5092770810686116, 0.6934358733535684, 0.77517337548954, 0.08654051103961946, 0.9881981836210342, 0.5089974487891855, 0.2838588324424498, 0.6759110153007271]]))))+np.round(8.386735727830668)/np.log(abs(array_x))/-(6.822216737012575))), axis=1)) |
np.sum(np.exp(array_x), axis=1)+2.666106089849422+10*(np.sin(2*np.pi*np.sum(np.exp(array_x), axis=1)+2.538172660991358)) |
np.mean(4.764049447831823/np.cumsum(3.1659174751740395+array_x-array_x*5.272684674921402, axis=1), axis=1) |
np.mean(1/(2.3729402985556907*np.log(abs(np.log(abs(np.round(np.round((np.dot(array_x, np.array([[0.6689411502888364, 0.09680916824276575, 0.12743964938892094, 0.2804346221208279, 0.9965552006715868, 0.9374870260628414, 0.7950467223303174, 0.3743969017828709, 0.057235723694121665, 0.21643376537846626], [0.4134735883864872, 0.4469166486464079, 0.8212423484923181, 0.846765599105058, 0.7618629164544471, 0.34993508700777287, 0.9552578546939874, 0.463986901483181, 0.032594858621214584, 0.14152680340220003], [0.2197740573429604, 0.3401626584852785, 0.6592800410613923, 0.03359874002157659, 0.08631155937709523, 0.20611324789034924, 0.977206058729936, 0.1718072849658323, 0.5665074833310105, 0.7888043039243843], [0.9633666069065356, 0.7419450484570719, 0.22465675005242858, 0.3328619544745177, 0.5209667095465548, 0.027361434124912987, 0.9251420621656922, 0.3657657288789671, 0.13108642172947738, 0.1788092456471636], [0.21569452724937055, 0.07717430621536425, 0.3841756672810266, 0.035389057946876856, 0.258367763426799, 0.13022561611272254, 0.3600844554558893, 0.9167188938692505, 0.4217021857547768, 0.733095461133513], [0.8912862423188193, 0.7377111776672732, 0.9418446536756178, 0.6412686965856101, 0.39122752647241676, 0.47681373820929307, 0.2229207179277829, 0.15677058197181093, 0.9670859120599455, 0.21322092620753397], [0.7712961574040991, 0.0856125043488356, 0.08695578995360675, 0.6557333205787199, 0.8813921373106425, 0.5694316783851651, 0.03914224396584931, 0.8278458492415393, 0.3348673122026886, 0.2253865398006517], [0.8857719521013472, 0.4663614016779515, 0.005560133356030006, 0.7785551207865291, 0.39475253661717213, 0.5958767605021753, 0.19689731329825155, 0.9535459722708088, 0.09340011661842296, 0.8869524460885047], [0.5030638108392051, 0.47170388713506084, 0.6977973984248629, 0.6414655155647702, 0.9360575611911712, 0.5980733289565819, 0.10737459963963125, 0.8640133872969467, 0.8445081685667432, 0.09846553788682888], [0.44843368318737353, 0.34197203909457474, 0.7237076246651353, 0.38021887802555643, 0.019389394937285864, 0.1476344342239817, 0.9283860349569686, 0.6821843050202492, 0.9283108680536218, 0.6041136881674776]])))))-(np.dot(array_x, np.array([[0.7999613428361457, 0.1337856742875858, 0.4842957091124379, 0.48790712125574587, 0.9778934183737735, 0.6899887955121147, 0.42147860153048733, 0.8210574000110501, 0.7051481195549566, 0.5256320991751747], [0.5039450043383015, 0.9543772394287865, 0.6275747512260373, 0.9859182189624225, 0.7840406015557017, 0.5596741031494556, 0.6018970348719925, 0.9105748323259025, 0.8142218566078742, 0.8973916239760886], [0.3478298829642641, 0.34154151921812803, 0.3736371024904179, 0.008889548012284254, 0.1032504847149397, 0.92766535724583, 0.5578146099985599, 0.36778337187558374, 0.994574436467503, 0.2306086384444228], [0.7984564971938408, 0.4054658217648771, 0.9607913633970697, 0.8265127619760122, 0.5769031315320686, 0.11961321760102839, 0.575427662670737, 0.31897118325628504, 0.5998606868875151, 0.026023019672557934], [0.564200184927468, 0.4747268274186315, 0.8736977225587378, 0.39232034678758465, 0.38427568293474856, 0.7052891539354905, 0.30391362614621475, 0.06275354572452618, 0.3055332768217086, 0.9499823869639337], [0.2619832887114074, 0.15822163799429623, 0.10813861440600492, 0.6462309365504654, 0.16182131768484298, 0.886382686362846, 0.08861919410756236, 0.13198333498400938, 0.30295200008091794, 0.3367110364568746], [0.28371850861839154, 0.714344533521976, 0.41939889395465013, 0.5439576792975442, 0.31148593665570956, 0.8004566154137961, 0.7713384873608711, 0.9084077881244782, 0.43183991285679935, 0.2229224469538934], [0.37238182363433614, 0.4550902307258441, 0.7261814456250365, 0.2142018914375442, 0.5458090201933723, 0.3639635079158412, 0.7851428087999576, 0.4348092211725213, 0.18238881702279408, 0.03840628296048465], [0.6749069889523497, 0.8193885033218806, 0.540872346179036, 0.8668227303301853, 0.15320026346254034, 0.13608980054028497, 0.5558996532254163, 0.2822863609131351, 0.054375247865662946, 0.7547775206352083], [0.24918802083587477, 0.6201050760363057, 0.8860657523895996, 0.17969180488384584, 0.867481504548506, 0.9941290665071835, 0.287618578426, 0.8349620365151331, 0.5581849082759183, 0.5256209092112487]])))+4.039114430666155-(np.dot(array_x, np.array([[0.5256928709316128, 0.4961862076261986, 0.987794223312895, 0.849609563324679, 0.9916096581999932, 0.054931013975950016, 0.3644440821946352, 0.689235676844162, 0.8663861713525018, 0.330482893080829], [0.017105934783776866, 0.9759649862492165, 0.8681290577342907, 0.989222936344968, 0.9272126680648833, 0.3102170834344583, 0.3056005226165174, 0.49155987151758496, 0.3589657932008019, 0.9532575798729178], [0.7286780682120163, 0.23367753276581582, 0.31437111945267293, 0.4524242532122432, 0.42928924685898995, 0.03533727598017011, 0.595279287644971, 0.8741653689992791, 0.5602381539921791, 0.9193443941381161], [0.7213465581048625, 0.6192129912661056, 0.5840909873350788, 0.540468498176661, 0.18827932898759736, 0.23177915054970633, 0.6282274157762772, 0.20020949431290413, 0.9232326219674828, 0.6431417689285607], [0.4631570510829809, 0.7701953864530204, 0.032555042825421876, 0.8198484603529376, 0.8736959692920816, 0.726612668039831, 0.8406750189590447, 0.8306777917904268, 0.7682710956004428, 0.44522704797769186], [0.3408729203365469, 0.6963077401266352, 0.2526370580253152, 0.18128447649587232, 0.5027347282076354, 0.17605190614262145, 0.00857542615202056, 0.587169903175679, 0.14690236053025785, 0.4672967018433176], [0.16407502133370366, 0.6898154722892482, 0.45649050523422907, 0.970630916296342, 0.0673404833240564, 0.7105007364852208, 0.7062785097816763, 0.9784867155895278, 0.6425497601849354, 0.13875943588305506], [0.42621764310649, 0.6039101284276884, 0.9294585373068921, 0.10230079362022082, 0.32405280521744473, 0.720857953910232, 0.11005924279906676, 0.7949222711939926, 0.8322755714953454, 0.33989948642382917], [0.14822295557528953, 0.33259491276223396, 0.39010089173593676, 0.15247262245024906, 0.7915274067827294, 0.08108973814778875, 0.6241583778513878, 0.37192354238195013, 0.388643914012189, 0.526884258505927], [0.867850474245949, 0.38891630196028637, 0.1712876663265952, 0.41686585774686136, 0.15136666239264196, 0.24418653623730358, 0.9761760846287987, 0.11451723137396519, 0.9101090921685243, 0.01740167303518536]])))))))), axis=1) |
np.mean(10*(np.cos(2*np.pi*np.exp(np.cos(2*np.pi*6.1735151910505115*array_x)+9.290997418811791))*5.84806806809226), axis=1) |
np.mean(np.square(np.log(abs(np.cumsum(np.sin(2*np.pi*np.log(abs(10*(np.cos(2*np.pi*np.sqrt(abs(array_x))-(np.array(range(1, array_x.shape[1]+1)))))))-np.square(7.894538075386865)), axis=1)))), axis=1) |
np.mean(4.132538800366639*9.481523811422607-array_x+9.968512351112825, axis=1)+10*(np.sin(2*np.pi*np.mean(4.651408474434497*4.042603906762679-array_x+5.060938774020768, axis=1))) |
np.mean(abs(1/(np.cos(2*np.pi*array_x*6.250863599107664-np.square(array_x))))*np.sqrt(abs(1/(1.9911862544789114-array_x))), axis=1)+np.sin(2*np.pi*np.mean(abs(1/(np.cos(2*np.pi*array_x*4.564597850707582-np.square(array_x))))*np.sqrt(abs(1/(4.069738318797697-array_x))), axis=1)) |
np.square(np.square(np.sum(array_x*8.472562671345898, axis=1)-np.round(np.sqrt(abs(4.50511946152515))))) |
np.mean(7.487468947169322+array_x-np.square(7.081398616967784)*np.square(10*(array_x/1.0977294548724688)), axis=1)+10*(np.sin(2*np.pi*np.mean(4.657542020257617+array_x-np.square(7.4251185198252)*np.square(10*(array_x/7.804726643183751)), axis=1))) |
np.mean(np.sin(2*np.pi*array_x-array_x/6.990761586495998-array_x*6.7827393042123285-np.sqrt(abs(np.sin(2*np.pi*10*(np.sqrt(abs(1.1614165513881582))))))), axis=1)+10*(np.sin(2*np.pi*np.mean(np.sin(2*np.pi*array_x-array_x/6.195591769060145-array_x*5.2605703972402225-np.sqrt(abs(np.sin(2*np.pi*10*(np.sqrt(abs(9.291609980758036))))))), axis=1))) |
np.mean(2.3278272461739955+(np.dot(array_x, np.array([[0.8486579698100278, 0.30604909061065144, 0.9180784561038526, 0.2889321693157446, 0.8543868503384741, 0.5086059041181921, 0.7871339677220205, 0.23960165671407008, 0.5841717338546489, 0.44158287738789825], [0.9669402428195473, 0.27306757589256836, 0.5644021349158151, 0.0634764220513867, 0.4252991541054669, 0.11097803460976519, 0.7427829278058019, 0.3323588832206047, 0.9040098296433727, 0.9121557335672434], [0.03958180296933622, 0.851190509098651, 0.8919117918642897, 0.6918972173927123, 0.9618454731061509, 0.9951204402706146, 0.5986446401599883, 0.8684172800864858, 0.9130259237627016, 0.6736419394056438], [0.7623806812058811, 0.28946118979508806, 0.21163555271022993, 0.7346745143392492, 0.9421535924227782, 0.5186305645395085, 0.16784766291274533, 0.18748415057406564, 0.6335913977155759, 0.18917222991901583], [0.39374731958981746, 0.5462071002064482, 0.606970360868403, 0.5709950818330989, 0.19721925022111675, 0.9824229320626843, 0.8933465021939877, 0.012874105387953194, 0.9062439674890587, 0.22851936742976897], [0.9887605782517996, 0.4589938197009219, 0.2800532066877989, 0.3634546809591408, 0.4872342800859315, 0.8939338309847833, 0.8107546853334457, 0.04784406428271637, 0.6622686139668016, 0.7336171811559064], [0.8714777701063701, 0.45687823268359473, 0.13129903056953762, 0.5218724517400912, 0.9703992025943521, 0.3765235125742682, 0.41055432535815006, 0.7995238160172068, 0.315826999333808, 0.4026535088721458], [0.4108587018758757, 0.800590257696929, 0.7086082351326665, 0.26715516277932194, 0.05426685349797533, 0.12681938848480134, 0.26045234761283564, 0.8473023415446334, 0.7589525855675685, 0.997463504694634], [0.4356835879158084, 0.31799664120451177, 0.2967930660842888, 0.6668246830634117, 0.22515817341091704, 0.48987780647846313, 0.9964207379723422, 0.09847526759212877, 0.599290407045101, 0.9953717617661529], [0.18366302676719304, 0.6434119973319897, 0.2619266539978016, 0.90732665189029, 0.9461072291385819, 0.28388517612011244, 0.6577146556407512, 0.3280081964303002, 2.921719411141943e-05, 0.42228506366862395]])))-6.602134401178226+abs((np.array(range(1, array_x.shape[1]+1)))*array_x/np.square(8.843929973255552)-(np.array(range(1, array_x.shape[1]+1)))*array_x), axis=1) |
np.log(abs(9.402005338716775))+np.sum(5.832169476199701*np.sqrt(abs(array_x)), axis=1) |
np.mean(np.exp(np.round(array_x))+8.369524562998066-np.round(np.square(array_x)), axis=1)+10*(np.sin(2*np.pi*np.mean(np.exp(np.round(array_x))+2.379944512731142-np.round(np.square(array_x)), axis=1))) |
np.mean(np.exp(np.round(3.9446149092420093+np.sin(2*np.pi*-(np.sin(2*np.pi*array_x*3.4087572652734237))))), axis=1)+np.sin(2*np.pi*np.mean(np.exp(np.round(4.5697860830134385+np.sin(2*np.pi*-(np.sin(2*np.pi*array_x*1.4153505050104038))))), axis=1)) |
np.mean(np.square(np.sin(2*np.pi*6.651277689725052))/np.sin(2*np.pi*np.cos(2*np.pi*array_x+8.892496417024788)), axis=1) |
np.sum(np.sin(2*np.pi*10*((np.array(range(1, array_x.shape[1]+1)))+9.338189675705783+np.log(abs(np.cos(2*np.pi*array_x))))), axis=1) |
np.mean(np.square((np.array(range(1, array_x.shape[1]+1)))-7.731543566803893-8.059328302420372+array_x*array_x*array_x-9.134443612181823), axis=1)+np.sin(2*np.pi*np.mean(np.square((np.array(range(1, array_x.shape[1]+1)))-4.389028912513435-8.32293291339872+array_x*array_x*array_x-9.133220270282049), axis=1)) |
np.round(np.mean(np.exp(abs(7.726256626980471*array_x))+1/(np.exp(3.4442975284336543)), axis=1)) |
np.square(3.7241123773888503+np.mean(np.sin(2*np.pi*(np.dot(array_x, np.array([[0.4836037028043453, 0.6205862631910675, 0.3509770613971044, 0.16305063935648345, 0.7424919264505055, 0.2787419955437913, 0.20782663983105476, 0.131340914484519, 0.9257761301785311, 0.6664754923941163], [0.37543868391995405, 0.6827944820534744, 0.5658935655783287, 0.06594782090868212, 0.6930488878810545, 0.8591732170797586, 0.6858219860999089, 0.13020443601956688, 0.20073683031712541, 0.037045729342096045], [0.3508936703821036, 0.9873925563442882, 0.982150120111993, 0.03089216739910694, 0.45089510456180637, 0.37051269854430946, 0.9686372273762928, 0.2520496729057502, 0.4227304688174901, 0.39575217083361414], [0.6186492452881072, 0.685116165650962, 0.44564207943571643, 0.6216223141053691, 0.2567598252437515, 0.16480246489506623, 0.944702306013744, 0.440846958660322, 0.12043428611156426, 0.15595580531392517], [0.6028254100383857, 0.41068997991272715, 0.5258039429230545, 0.9471691393239565, 0.8362982187193572, 0.783541867706154, 0.7831406193126834, 0.825395724812209, 0.9435511949734255, 0.8713540743164472], [0.3344192832161713, 0.444706608622516, 0.37961448881840465, 0.7462757822002213, 0.5179041781328925, 0.01119266091529747, 0.27797650204884705, 0.27437662689010633, 0.3593659834295435, 0.9427981402733131], [0.9078035827211336, 0.005976063032855916, 0.05957577207739362, 0.11745683565336862, 0.591776557522358, 0.3219724767199489, 0.8363552538829773, 0.0786738666456751, 0.17261994063168218, 0.5322847361178203], [0.26156543637981544, 0.5953531297347574, 0.029730110329610482, 0.2997289012824268, 0.15062641456944947, 0.9882498195876083, 0.5165689098072908, 0.8391532344482608, 0.335556613949305, 0.9366195680920596], [0.9389532823686364, 0.28635914795846074, 0.12728050236893818, 0.5924938515076007, 0.8841277345775415, 0.8446800623141986, 0.043522850041990635, 0.8759880022918779, 0.010547138677142853, 0.19705425520567177], [0.3045977312998043, 0.301791428220734, 0.5423280768831065, 0.2963868791907893, 0.45020727829559404, 0.08850909337686785, 0.5814418286932646, 0.7844983942238358, 0.3769328515235024, 0.4978426099219565]])))), axis=1)) |
np.mean(np.cos(2*np.pi*np.square(array_x)/8.40551043614257)+10*(array_x+array_x), axis=1) |
np.mean(np.exp(np.sqrt(abs(4.908254146535835)))/6.984296681446705-np.exp((np.dot(array_x, np.array([[0.30130297665669203, 0.505196565515563, 0.2039618149558008, 0.6829031929545204, 0.8119011840762117, 0.42012913021569775, 0.8392000691040992, 0.4093030780967172, 0.7889319747522168, 0.30448134294753193], [0.6163861751363622, 0.9854904506688441, 0.6280239808229069, 0.9679934115828925, 0.8831992921509594, 0.43882458222676224, 0.47071225996532895, 0.8740116101377815, 0.4382715103694145, 0.9104739342671271], [0.21729765674038215, 0.8248266157802788, 0.740073721546153, 0.2846570922896805, 0.15709976439361906, 0.6706436625183304, 0.35385346105905857, 0.5133604858379799, 0.4983944799222594, 0.18693381859581548], [0.2761854684570877, 0.7374599940629859, 0.40605181208329244, 0.7990052842198141, 0.7675927492343474, 0.6060083088457006, 0.6403938744042587, 0.21406218777083597, 0.8751861543058127, 0.7864059190182222], [0.5322611143432676, 0.277506880142244, 0.34347279340329906, 0.5523515087687929, 0.19257278524949106, 0.1750649414811548, 0.2323203582086436, 0.09295014316376249, 0.5029711693052598, 0.7309346044931893], [0.45747480921244865, 0.8201161161371268, 0.7513887786565409, 0.5869329863720134, 0.8718337240025946, 0.3542978685118139, 0.07690287107985716, 0.08044577602972547, 0.32217373209178835, 0.21355886423021353], [0.4194051790120813, 0.9881008322828004, 0.6128229527725138, 0.5225831060074628, 0.30459618626172436, 0.8588792418121344, 0.8046360437693296, 0.2780681290322674, 0.32035534081941464, 0.8181454128272859], [0.9446249141702753, 0.1948659606637646, 0.4568290995164108, 0.3238637662972196, 0.5414202212313867, 0.6236517724500326, 0.04778818968345322, 0.8182956800031063, 0.8448563044412077, 0.8393769275293033], [0.11191265403708506, 0.9194400449865147, 0.0011117336363820618, 0.49627565300139276, 0.6645807062501535, 0.5024044900618547, 0.5799654600685602, 0.47012148226830097, 0.9169882002711567, 0.46612030513576186], [0.4496585446862277, 0.2065482894896531, 0.28942927726848877, 0.6081671823305277, 0.0030791443923060546, 0.9194201731730005, 0.6874306772585778, 0.030758804161977293, 0.1441506689698605, 0.8772206875238677]])))-1.3416997229071885), axis=1) |
-(np.sum(np.sin(2*np.pi*np.sin(2*np.pi*2.411988644089204-array_x+array_x/3.666638988669929+np.square(3.111325152970833))), axis=1)) |
np.mean(np.square(np.sqrt(abs(np.cos(2*np.pi*np.round(np.square(array_x)+array_x-8.301349137726572))))-1.8460807453219135-np.square(np.square(8.94639342049675*array_x/3.208198247995834))), axis=1)+np.sin(2*np.pi*np.mean(np.square(np.sqrt(abs(np.cos(2*np.pi*np.round(np.square(array_x)+array_x-2.626237431674598))))-7.751116766410829-np.square(np.square(4.134424594940485*array_x/3.186893414455774))), axis=1)) |
np.mean(np.square(np.sin(2*np.pi*np.sin(2*np.pi*6.318466431014534))+6.899825969589896*array_x), axis=1) |
np.square(9.809434068322577-np.log(abs(array_x[:,0]+6.033793836179213))*2.1666492553534287)/np.sum(np.cos(2*np.pi*array_x)+5.70672513827372-10*(array_x), axis=1) |
np.mean(1/(np.sin(2*np.pi*np.exp(6.076136234073206+(np.dot(array_x, np.array([[0.05806800020218683, 0.004186162592929921, 0.917448180086638, 0.6707251865479018, 0.7633401775865185, 0.5933716745412142, 0.15703131565385597, 0.9621875561220893, 0.8398281558926085, 0.3692605226679294], [0.24463173710468078, 0.16875126971303933, 0.7985197125088501, 0.18709500208341456, 0.16911943806526053, 0.9463815939793458, 0.22656590226136875, 0.548177036206247, 0.5521553618658951, 0.6740948337374201], [0.7865962793200515, 0.41198542898972923, 0.5250532225225685, 0.0331528893994949, 0.29876063074546844, 0.333026185011986, 0.5978460122306094, 0.02289330972674264, 0.08546390182188501, 0.5879141145969669], [0.6320489819575958, 0.4183901884509661, 0.02113453241512564, 0.8427662511326454, 0.33643559705388504, 0.8239930370263941, 0.480274228574879, 0.32895074498406396, 0.7007948506837818, 0.7490819097780774], [0.45226975759891674, 0.2436278642013645, 0.13154740870078618, 0.3542470174484351, 0.028575329698953822, 0.8666020328858489, 0.9711339481005264, 0.327528656291061, 0.22332326368757105, 0.16994291229568737], [0.20699183358284845, 0.09270126319056016, 0.2611452748140801, 0.38039827393729486, 0.9526699272152098, 0.5490791398745828, 0.46218639509880155, 0.5193039388785037, 0.8953640023823266, 0.7477560286130491], [0.6724859365111951, 0.23350654850307584, 0.8334989280089995, 0.5122095219154419, 0.7367659860069722, 0.2756546093226271, 0.862466748590246, 0.953855091051135, 0.28549045179164234, 0.6744819382020104], [0.1273435159435985, 0.3807724119268513, 0.5824224085713623, 0.9309016989552984, 0.948930841614721, 0.6234017250294559, 0.4336326690109543, 0.44222428113988754, 0.5473986077519757, 0.9365109836565894], [0.9230809140157364, 0.9575418740701713, 0.4491011285516664, 0.38671224474860777, 0.7146260944674406, 0.9286797803425139, 0.23093658811453954, 0.8427991226170097, 0.853148388475094, 0.03556727615088051], [0.1525686742864668, 0.345202762452015, 0.40280212857389175, 0.7831024951884551, 0.6906691204140873, 0.04661226538612684, 0.5237866073156647, 0.05189169489706602, 0.8574121222586079, 0.8100851722759391]])))-6.236694515851816+np.square(10*((np.dot(array_x, np.array([[0.2579959068351392, 0.7897862800676578, 0.7044705568040013, 0.23973932556621602, 0.2674594078164183, 0.6274390667930613, 0.4406611964243654, 0.6426570409632154, 0.8824307184344768, 0.4443387390816106], [0.6488319522334339, 0.07903457501484457, 0.8682302813139108, 0.02632654199187401, 0.934621770289589, 0.9590527261978412, 0.029765882976704572, 0.6764448170903937, 0.441628013278617, 0.7115150397965542], [0.9678865000786985, 0.7872570752588449, 0.5456401377060848, 0.6241128380206523, 0.2852998442366744, 0.5100966108437942, 0.8965827971102446, 0.4233035212902765, 0.6504269212805122, 0.22777217594896249], [0.6910651774276614, 0.5292893129047851, 0.6539707288999593, 0.8240454027977412, 0.40836632327876643, 0.9502149085610433, 0.012098102923438048, 0.08680510958046528, 0.48348595011772977, 0.02905859629463803], [0.5766966110789944, 0.2898954655260253, 0.2503717511043435, 0.3935321088211583, 0.1984893163528838, 0.9839762718068674, 0.4554046741040686, 0.7467483885604302, 0.8663166573580169, 0.27946924908038406], [0.2609553444680882, 0.16087739305484716, 0.5287414641960004, 0.10594885246490893, 0.9845990809241598, 0.570814081712654, 0.4237456059498045, 0.0976175779728129, 0.07355693033914967, 0.7922942867287347], [0.8332726906906912, 0.12335905592987395, 0.4093781011403711, 0.43045670074050724, 0.5797342395241868, 0.5760789445921208, 0.48512722072205916, 0.4640151581035553, 0.5899471847640515, 0.7479107117772532], [0.1174504250623547, 0.04422178592349146, 0.38542089832113857, 0.9359070542406149, 0.058649816767232466, 0.4121546690750877, 0.7779288418453741, 0.8580189377314612, 0.13416499703330487, 0.6716734567347125], [0.8647677933155797, 0.40021299097651797, 0.04201665835752777, 0.5868375538206606, 0.9419814886374388, 0.001946821400958143, 0.630314297225446, 0.018222517120743742, 0.04201129113875035, 0.027857153136386703], [0.5093645384573163, 0.1634120594304873, 0.1210385300671788, 0.09302935922973787, 0.8936395094924288, 0.7836198015504705, 0.9101657310737973, 0.23795354313247474, 0.6353814389051614, 0.3772261989917258]]))))/5.818834901438888)))), axis=1) |
4.832143631552968-np.sum(np.cos(2*np.pi*4.056147185615428*array_x*np.round(array_x)), axis=1)+10*(np.sin(2*np.pi*1.6260806892875819-np.sum(np.cos(2*np.pi*5.4895034198788695*array_x*np.round(array_x)), axis=1))) |
np.sqrt(abs(np.exp(10*(np.mean(1.02385016066199*array_x, axis=1))))) |
np.mean(np.square(np.square(array_x-8.812309406864692))/7.821670276485165, axis=1) |
np.mean(np.square(10*(np.log(abs(np.log(abs(9.098375337950834)))))-array_x+(np.dot(array_x, np.array([[0.9240935309363429, 0.9672698321163407, 0.5366333759363944, 0.16974944686782578, 0.9609873428549492, 0.008524422344460603, 0.2762288236274546, 0.6707773701831001, 0.6922110999322844, 0.014250035334587019], [0.7535082338392984, 0.16021263636861893, 0.745868787858766, 0.582338932518647, 0.6162398004741433, 0.26802966404833284, 0.2008603319760408, 0.162761130408422, 0.22379943052994056, 0.22808368595155304], [0.23429718932178456, 0.6692577638138991, 0.694272546964395, 0.49993741584526086, 0.31087930985871237, 0.5035211927012749, 0.7188994028088636, 0.2852238690484692, 0.19164372116614725, 0.8429364771713513], [0.33272068982419967, 0.8709126643803434, 0.8318666694472502, 0.2616517374765207, 0.2699235899820793, 0.7244383172346472, 0.02325835290195477, 0.5415330399263006, 0.791666439702984, 0.7521726133132135], [0.33332339377268894, 0.694530597424338, 0.38933069422060107, 0.8468540418527779, 0.9111990939995894, 0.5432767006640085, 0.339793182816877, 0.9812164724111824, 0.2614470827100207, 0.03543748708647321], [0.1791111521694445, 0.4402497944635769, 0.34955935273592753, 0.16347662379474537, 0.8893922367543498, 0.5124100524874586, 0.6742236189653861, 0.46055998458222325, 0.31327170390245396, 0.7484672009406583], [0.7262765711061402, 0.7542046759788229, 0.6600357560535345, 0.7789468272612713, 0.4759538108410527, 0.9221538507883937, 0.2741931191607805, 0.6076359791995645, 0.6650860882330857, 0.08986805520841701], [0.2544135159307698, 0.7580303111847256, 0.2205206420514242, 0.7095100649344533, 0.7981336899241414, 0.3411225346176786, 0.7070835445157072, 0.6908664605073785, 0.4111362406661673, 0.9028139948850751], [0.5219531755327821, 0.648386182865325, 0.65516541132852, 0.09557882712729504, 0.45346429068010286, 0.5609001949112444, 0.551997957317157, 0.2310049306724118, 0.4487543458748332, 0.3053231503452103], [0.9860528821981325, 0.07496518804324903, 0.41162650737936557, 0.5042019535153152, 0.3603524292544845, 0.013791909398227165, 0.1248735355869357, 0.05578385044874834, 0.34843451104606715, 0.978460781381189]])))-(np.array(range(1, array_x.shape[1]+1)))-1.2001400666174658+array_x+1.7181772500658652), axis=1) |
np.mean(np.cos(2*np.pi*9.865099981811904+np.exp(array_x+9.763520980952524-np.square(array_x-array_x))), axis=1)+10*(np.sin(2*np.pi*np.mean(np.cos(2*np.pi*2.0629545499388806+np.exp(array_x+5.339992507156371-np.square(array_x-array_x))), axis=1))) |
np.sum(np.square(np.sqrt(abs(array_x))/7.884059454590088)*np.exp(6.798196772431227)-1.0597018676511252/5.287154966338916+array_x, axis=1)+np.sin(2*np.pi*np.sum(np.square(np.sqrt(abs(array_x))/6.980404194962418)*np.exp(1.5115542072918018)-5.259790609538362/1.9353492462568376+array_x, axis=1)) |
np.mean(10*(1.899082223084711)/7.278416792515889-9.992908131304336*np.exp(array_x), axis=1) |
np.mean((np.dot(array_x, np.array([[0.43643562163167915, 0.47404505672446706, 0.4938724058203028, 0.311825405403963, 0.7272479573630249, 0.9226917577696593, 0.8492512538529652, 0.4049046540881239, 0.30195713214120046, 0.024530385250586595], [0.8104326174621412, 0.39522480302690555, 0.7187361296375301, 0.5882359588918437, 0.5040757300187777, 0.45844093806880204, 0.7774317445091816, 0.8672809234217909, 0.01314876538532006, 0.08544893372250295], [0.8215859920043419, 0.39139610514941514, 0.4124233035428154, 0.3813709231043205, 0.9727597628124044, 0.25300838669749726, 0.724238420600237, 0.5657765003780759, 0.018912879442318453, 0.5153895911998707], [0.05513912242336183, 0.4101315239334654, 0.7247003301890539, 0.2422000385737817, 0.7344953293020765, 0.6087872880083126, 0.015584773232967808, 0.0012387984402149055, 0.5367151818171384, 0.45934823296203453], [0.26057181613627844, 0.5973199104992885, 0.46683746814055127, 0.4233714508081474, 0.2149177730325874, 0.9797460731511115, 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[0.97135671007525, 0.8537518809919781, 0.9463457524138305, 0.5486483582782851, 0.7217819308239457, 0.6610185129833333, 0.8488032936205163, 0.8074045724768953, 0.8138733747052727, 0.84189928528881], [0.7766334684866172, 0.40708780577903303, 0.7410867877850917, 0.42701934722894497, 0.3669331309612345, 0.9717410719547284, 0.1491716918060163, 0.4450481554922948, 0.19366460671221997, 0.14124145447247516], [0.5205339969821132, 0.7518044255131403, 0.7578888999300446, 0.167385805120433, 0.9501882719023784, 0.00510481520908368, 0.9865587065065189, 0.995054937407823, 0.8638739964400628, 0.7445426459197693], [0.29954419807497035, 0.9609593275137134, 0.47361999347556283, 0.7367908402449118, 0.0738311427561329, 0.08941868132576802, 0.9028170570772889, 0.9320525944841037, 0.8292159038885462, 0.8757880829356709], [0.047746357717086685, 0.6503365751650486, 0.530987561133201, 0.07510479555011407, 0.4775996038915934, 0.5755931265537656, 0.7817945776803903, 0.49124695568854015, 0.1222815720601691, 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0.8194363296583409, 0.882870114065724, 0.5369377996086435, 0.7432093255278633, 0.5156525176448423, 0.12246101401958298, 0.7587726956211712, 0.8263815405172608], [0.21734045765624077, 0.13427464930898847, 0.2974797725527851, 0.7171542399513874, 0.7692496962325687, 0.6182946298228631, 0.7535396706429377, 0.2690357579836927, 0.10033549093291882, 0.5747933828891653]])))), axis=1) |
np.mean(9.729880417631572*(np.array(range(1, array_x.shape[1]+1)))*array_x/9.989678586436732-np.square((np.array(range(1, array_x.shape[1]+1)))*array_x)+np.sin(2*np.pi*7.774951631157369), axis=1) |
6.10051489813314-np.mean(1.7976698986648856+(np.dot(array_x, np.array([[0.23520084063443636, 0.2696607421901449, 0.7153472556732567, 0.9983397282433566, 0.3986771002556413, 0.6280992778348949, 0.7976538835787572, 0.34915043575931626, 0.5224827801583904, 0.7483180387465789], [0.8797777373589504, 0.4697608794968803, 0.7733022792208706, 0.3069071727451945, 0.8713685024554949, 0.7954065237854313, 0.4870238710515613, 0.08786449959736431, 0.8444819788435266, 0.15769177316492766], [0.11686372522947097, 0.6171959745189061, 0.5164342032282171, 0.35165936971804657, 0.5540535394829573, 0.6570703967377003, 0.5511839804367117, 0.19821069393503088, 0.96400244323636, 0.9138124509921483], [0.034275216448382384, 0.2094056600841936, 0.2444651955248366, 0.6850686818373435, 0.2660063167170771, 0.408582305609137, 0.16057730494972178, 0.4420663587256429, 0.14900606243923797, 0.542083522651625], [0.37484338509209914, 0.08244650997870395, 0.4486465363144435, 0.19924054885135634, 0.4776495913960802, 0.01929125220245731, 0.10420576710429719, 0.8927970074174377, 0.7126938029592512, 0.4499918650975563], [0.7003414425278736, 0.8203794378155703, 0.7473506632978315, 0.7339131090094804, 0.054374094062366485, 0.9096602075536644, 0.051132675263256044, 0.31884197817105564, 0.889283467350463, 0.9498700096752795], [0.64002939924545, 0.3339782883585608, 0.07509344086335823, 0.6529505883503087, 0.6975561819774647, 0.3357729035636542, 0.12238315826929314, 0.267676721758639, 0.009256568616862193, 0.4116216268024371], [0.5791996660163029, 0.41869245729175775, 0.20114060980964676, 0.2922841081489831, 0.20250879615829698, 0.6270040355976981, 0.2773143953156939, 0.8108783587700806, 0.9332329904049648, 0.30297993607806517], [0.6215654842020566, 0.5084461982057371, 0.3557096284064415, 0.11694260566876091, 0.4613547318219785, 0.12072657950595511, 0.26647299974363237, 0.3538161260819833, 0.040776805113933, 0.03587522295090417], [0.7394049809002414, 0.7376632331121593, 0.05807232736498258, 0.3149670090645572, 0.37215421913385427, 0.4482998248723399, 0.9210970484518162, 0.38392342423854453, 0.933386547075718, 0.20903608739066692]])))+array_x*6.1161784846226395-np.exp(-(7.453964952859651*array_x)), axis=1) |
np.square(3.600026576271677-np.mean(np.square(array_x+array_x-9.366650646301222), axis=1)) |
np.square(np.sum(np.round(array_x)+5.354673135706943, axis=1)*6.859565148114738)+np.sin(2*np.pi*np.square(np.sum(np.round(array_x)+8.605054162462608, axis=1)*2.2937271817633382)) |
np.mean(10*((np.array(range(1, array_x.shape[1]+1)))*array_x+(np.array(range(1, array_x.shape[1]+1)))*array_x+7.013035525585831/np.sqrt(abs(5.476934313881005-(np.array(range(1, array_x.shape[1]+1)))*array_x))), axis=1) |
3.2634943220313812+np.sum(array_x, axis=1)*4.446350218481791 |
np.sum(np.sqrt(abs(np.exp(np.cos(2*np.pi*(np.array(range(1, array_x.shape[1]+1))))*array_x+8.624971206435365+4.857285610273721+array_x*7.093123405309757*np.log(abs(2.4680312273776064))))), axis=1) |
np.amax(np.cumsum(np.square(3.018185947345633)-(np.dot(array_x, np.array([[0.2746889518483975, 0.9650092248638241, 0.045782698909652586, 0.14758904396463413, 0.45483570352695923, 0.12120145471378319, 0.8531377823264686, 0.41830512217117133, 0.1287418234474681, 0.3705401830479149], [0.24443763879589064, 0.8355036389711963, 0.4816595507681839, 0.24228565499745391, 0.7404729681517326, 0.28999323119039755, 0.6311634320845555, 0.7781325743158648, 0.794921059693807, 0.11389122772333293], [0.15441248789120932, 0.9233690347646176, 0.7066837830455136, 0.5597756215254859, 0.8083363734591924, 0.3484924987039747, 0.6651599348758037, 0.3065360255225422, 0.1623727402126176, 0.7082960905573399], [0.5492643018816012, 0.07921279085813515, 0.9193329556170141, 0.414923873006927, 0.4563872168584421, 0.3390832693146433, 0.43975255293958115, 0.8642336814400225, 0.38715225245362306, 0.1427334572760387], [0.8804929417002921, 0.10599067279436791, 0.43305250108953974, 0.8610114669361197, 0.9748467049106753, 0.04572797694460462, 0.15989989391835657, 0.6317069007335172, 0.5834818813759554, 0.5620819385729863], [0.9667542650760673, 0.971813574490022, 0.7852593964302685, 0.010418981786547432, 0.1677402678117096, 0.16399957850480418, 0.8375669964162972, 0.3468311442615397, 0.6517320859981449, 0.34190826832601984], [0.9004439813023265, 0.7213952763863006, 0.1930857716405685, 0.9528663817129585, 0.5550099676962867, 0.6400851746207376, 0.2747967097215205, 0.3957782171228428, 0.6495095185269333, 0.2572552625609028], [0.9631875998287999, 0.034368376855871796, 0.8049454470925064, 0.32184685297217375, 0.4375487377878158, 0.3883271408902774, 0.5375822272681917, 0.0216729247586962, 0.8185267547582875, 0.9302290346371568], [0.23027267424816256, 0.9381816306882965, 0.24518392204877815, 0.7489259685820118, 0.5606392293957999, 0.40489558774212697, 0.10418922565082134, 0.24403530296908993, 0.9035650988379631, 0.6508918077360738], [0.9912170260468677, 0.652779514274369, 0.3037276539927197, 0.16274574234450578, 0.7359096210057446, 0.5686628337085688, 0.4999387376224177, 0.8037104334535592, 0.7695972157120265, 0.6435366794648565]])))/4.836331153672788*np.square(1.3449815743948714), axis=1), axis=1) |
np.mean(np.square(array_x-8.202072851354991-4.705622449351951)-np.sqrt(abs(8.277130886507923))-array_x/np.log(abs(7.027243527971718))-(np.dot(array_x, np.array([[0.41786536668211804, 0.21488979528770114, 0.04746775455537988, 0.6673000152535642, 0.5875682204405881, 0.8760008034831743, 0.45430853146119576, 0.6766820768527992, 0.9871009583861428, 0.4778684219308805], [0.18389282248463623, 0.1159665279200357, 0.3474501847814634, 0.9677469920043301, 0.8558294120863791, 0.7660338047082285, 0.6680426160579946, 0.7567755353057729, 0.3405685733097078, 0.6617358665891878], [0.8761185393053271, 0.0905336123089081, 0.6177300888628119, 0.829682381705116, 0.7655810518472486, 0.4642278443657035, 0.9834298906399993, 0.4880656460379519, 0.6244223708601101, 0.15914976732547237], [0.4585892446146608, 0.8341355612774255, 0.3534236350453386, 0.723603868230851, 0.44590559253967055, 0.6425875218297717, 0.03457188442162229, 0.9602436904751056, 0.7103443751939102, 0.9246764202193996], [0.7063817079056248, 0.17801309295566026, 0.6468065366389207, 0.18397930190625522, 0.25553869917883487, 0.5307675000583778, 0.1335662300960595, 0.9819773135001177, 0.8391067840929282, 0.3144951306551793], [0.722052192985394, 0.6148339990968611, 0.8564406768088486, 0.8357046484554986, 0.6204695547045946, 0.8017038129428963, 0.2778344126926434, 0.19533834228596225, 0.2754065008707779, 0.08633977615438015], [0.6807223088519146, 0.6673179084895134, 0.6044794071165986, 0.8404560975026121, 0.5133553326092634, 0.5666404669216388, 0.262617911123205, 0.3620738796434849, 0.6688120744138177, 0.5306220686696391], [0.18745882016066018, 0.5240771414492452, 0.45701705556744465, 0.15695242963147082, 0.9981768458675992, 0.00047161839915565995, 0.6148471042040649, 0.6096404400142198, 0.6698321168316862, 0.3127906645618519], [0.7822433332337324, 0.11791490586261488, 0.02362844018170318, 0.8168446766991158, 0.28878090665746203, 0.6950345258810006, 0.9885210453463551, 0.3318889977524202, 0.4024731348423297, 0.9101897212523803], [0.9913245161590569, 0.8449153618228823, 0.349166148084866, 0.3732595106620903, 0.42727431186857845, 0.8604982166798494, 0.4309515207065675, 0.23437470683908057, 0.7706800385655828, 0.7880225326346452]])))/8.664609083658197-10*(np.cos(2*np.pi*1.7144568297234)), axis=1) |
np.mean(np.log(abs(8.400440728792143))+10*(np.cos(2*np.pi*array_x))*7.623711477795182, axis=1) |
np.mean(abs((np.dot(array_x, np.array([[0.5609564440084951, 0.31892453684123856, 0.1146230031605977, 0.8686073902579873, 0.5673186980358521, 0.1331712187048033, 0.04111769489933981, 0.5872177644967432, 0.6844246329503979, 0.8820954505090355], [0.7940951008447283, 0.135032394364081, 0.5165226380794159, 0.2524025263703026, 0.5100792872700526, 0.5702971388430164, 0.13896076724997275, 0.3694186964256768, 0.4587226326527246, 0.34487898327529265], [0.9788156687962655, 0.12090147194079814, 0.7711088299599742, 0.7614509198079072, 0.276968744254676, 0.8823687553594389, 0.22596829057224355, 0.44506501592917824, 0.8022770203092674, 0.4110066671740068], [0.7302934008696098, 0.8872487998401959, 0.6912105710191264, 0.7243332887325789, 0.7728464609822803, 0.5715636792423509, 0.8719823998568639, 0.15800549675579678, 0.18479858320585973, 0.6707731377609609], [0.39771938016581865, 0.49229156299500165, 0.5622613266547605, 0.15960782803979878, 0.6831576698444076, 0.6121145576504765, 0.2907451278690324, 0.820773174237246, 0.7759283121093079, 0.4356885149618692], [0.8949443356680361, 0.8597035595608455, 0.1297053134478976, 0.8317199101213605, 0.10820727564193588, 0.46084386137684663, 0.444100898513376, 0.799914277310859, 0.0008030301293315834, 0.46833311158873225], [0.7492660613970257, 0.5338064439012681, 0.7577194051363852, 0.8702295227234986, 0.6235352082390416, 0.7138095507705727, 0.7598771031239817, 0.2739103042383919, 0.25819043647663786, 0.1375518843677599], [0.020883871109293017, 0.003161446547294111, 0.3810032314961195, 0.08041232406189591, 0.08759131475998116, 0.45636188044896897, 0.9292240786714042, 0.5402681816615106, 0.9840255750835835, 0.40312022061998876], [0.6717359391014396, 0.1997223434255777, 0.06847142545571805, 0.6444196743492638, 0.16615300886926976, 0.9601599655809882, 0.43570512648259363, 0.9006161190667809, 0.780997201406491, 0.09003376636790938], [0.5262002826452427, 0.5461032144180864, 0.4612190504708512, 0.5351229919278263, 0.15131057313144214, 0.10887113272716376, 0.8212512828780276, 0.931498971076634, 0.13573501024858048, 0.9918850828095915]])))+(np.dot(array_x, np.array([[0.5153279431758245, 0.6776060623450325, 0.5960538976164498, 0.8275589130590307, 0.11271447532833301, 0.4262692007838398, 0.20111709044184067, 0.31633433502668074, 0.7232649648261416, 0.6532690507962227], [0.6579161748744943, 0.43431144525843834, 0.9877478396629845, 0.21663905150918317, 0.38879398332506765, 0.4632993997489956, 0.9107600123061621, 0.18088416706158605, 0.6037186740324759, 0.9402904494930452], [0.7581390104510302, 0.3548741776209403, 0.9532194740069907, 0.0692415905031285, 0.6850054112411303, 0.09232299152794643, 0.839336269302669, 0.762640805707316, 0.17660626975868488, 0.08397711114851414], [0.11498662799208492, 0.0706016285945319, 0.15388726439774536, 0.5262032320154287, 0.2202523249893069, 0.889941206121965, 0.18834991586054417, 0.6894923461778877, 0.5845065125383301, 0.593190576635225], [0.7411782736676144, 0.17876324067929328, 0.6634913168540406, 0.6832336984918579, 0.6978126375968133, 0.07102055741799107, 0.4790561579698379, 0.4362765944381727, 0.847126165427537, 0.608662513978452], [0.455609167750184, 0.9885394205050018, 0.2242872734504915, 0.5655256464925827, 0.8078511127975563, 0.9841008530008652, 0.984407367488798, 0.3234247068850229, 0.8248404772666407, 0.26503070554281705], [0.07249290024853094, 0.6718377737258993, 0.3549054775914863, 0.5386898632071545, 0.8706120185546616, 0.12178641821408898, 0.9405704395782108, 0.757950169437607, 0.09340157832146323, 0.9781232771582331], [0.47896347152080165, 0.7746187960124996, 0.9172343528909336, 0.46498243553012375, 0.7428501968968083, 0.018723220811021846, 0.49754420373353014, 0.5056243639486515, 0.9917204109835397, 0.9569711747178575], [0.6276614760873613, 0.7758503463145564, 0.295029515435451, 0.733186365929533, 0.2356983785684229, 0.6624587616833132, 0.666000503887827, 0.018632301236623405, 0.6889550120948981, 0.39446608471071354], [0.6921821703459403, 0.36503958513932677, 0.10704971588098078, 0.39944510646439246, 0.24141081184706503, 0.6968297922507375, 0.8477243008849016, 0.02064374231911359, 0.610080072792687, 0.6717393569029058]]))))-9.859131830365229+array_x+np.square(2.037675261800284/2.011304511096214+array_x+4.655113090454027-np.sqrt(abs(np.sin(2*np.pi*array_x)/1.3442139957663977))), axis=1) |
abs(-(7.357957169064166+np.exp(5.0943540794549635)*np.amax(array_x, axis=1)-1.295127224407536)) |
np.mean(np.sqrt(abs(np.cos(2*np.pi*array_x*4.678482152248802*4.428380640582509/np.exp(np.square(np.cos(2*np.pi*array_x)))))), axis=1)+10*(np.sin(2*np.pi*np.mean(np.sqrt(abs(np.cos(2*np.pi*array_x*4.935432647895374*3.4708485671735185/np.exp(np.square(np.cos(2*np.pi*array_x)))))), axis=1))) |
np.sum(7.49983180910556+(np.array(range(1, array_x.shape[1]+1)))*array_x, axis=1)+2.731276707153114/np.exp(1/(1.3117477478069768+array_x[:,0])/6.585887047055918) |
10*(array_x[:,0]/3.4996826556174048*6.045752199952459)-8.657628115176983*np.cos(2*np.pi*abs(array_x[:,0]*np.log(abs(3.44790010642551))+np.sum(array_x, axis=1)))+10*(np.sin(2*np.pi*10*(array_x[:,0]/5.397131204754896*6.0886308520066885)-9.507404462395387*np.cos(2*np.pi*abs(array_x[:,0]*np.log(abs(5.5906032144168565))+np.sum(array_x, axis=1))))) |
np.mean(np.cos(2*np.pi*np.sin(2*np.pi*(np.array(range(1, array_x.shape[1]+1)))*8.659984968861313/6.628833225007417))*np.sqrt(abs(9.246047679452706))-array_x/np.log(abs(array_x)), axis=1) |
np.mean(10*(array_x-6.863681309455733*3.330126188106817*np.round(8.317214538990385)), axis=1)+np.sin(2*np.pi*np.mean(10*(array_x-5.297721479920926*2.460607101258205*np.round(6.729569543779863)), axis=1)) |
np.mean(1/(np.sqrt(abs(5.325353125155098*np.cumsum(array_x, axis=1)))-np.log(abs(array_x+8.37746802015361))), axis=1) |
1/(10*(np.cos(2*np.pi*np.sqrt(abs(np.sin(2*np.pi*np.sum(np.sqrt(abs((np.array(range(1, array_x.shape[1]+1)))*array_x+np.square((np.array(range(1, array_x.shape[1]+1)))*array_x))), axis=1))))))) |
np.mean(2.0834539910544194+(np.dot(array_x, np.array([[0.7961828102874474, 0.713037447241454, 0.12435893051819347, 0.7432835223913923, 0.9997145754974119, 0.6398632114750042, 0.12981557355841045, 0.8029626859123244, 0.4757903074603451, 0.60242134715162], [0.8731658982932534, 0.6158654977833472, 0.641578394159465, 0.8500309277902918, 0.1436794013439433, 0.029060300766762914, 0.67756685793728, 0.20947287562008354, 0.9058902065072624, 0.23857979674169083], [0.38627798701467275, 0.3235422747804306, 0.729905500763745, 0.7210685198392678, 0.052057235409843194, 0.8807455596437891, 0.3979700631382558, 0.9769631850086512, 0.2354202296219996, 0.8556813335528223], [0.7859212549866215, 0.019365709936081554, 0.9721546611463592, 0.31342892142281065, 0.3902965388913281, 0.11355792794160047, 0.8573939467442183, 0.7415020778263526, 0.2656283916198009, 0.0769313562418944], [0.44806785278793027, 0.5996102980333888, 0.9571127313636287, 0.6941691699677134, 0.6417170945547558, 0.435891830123575, 0.09086497481101741, 0.1167488179548899, 0.24990548618792596, 0.7157264636578509], [0.6200844273431295, 0.3265851327602066, 0.969190222577186, 0.890663411583256, 0.6089750749313632, 0.9895382268915436, 0.6425857476055796, 0.7336005246787726, 0.4513749211945204, 0.7803408483939716], [0.3543330375257433, 0.26883611774029115, 0.7152871845711684, 0.05625067560443464, 0.7205990380711471, 0.6652559461088968, 0.7940899866999376, 0.8281770753990695, 0.45136288749279063, 0.08819431013661039], [0.9813313556868452, 0.781868374176944, 0.2332404697918422, 0.6186212941277554, 0.6773385012039495, 0.6639965612603781, 0.015727839052606996, 0.7246981962664876, 0.19538837221761696, 0.23666526283230083], [0.5779797673021918, 0.526055236410623, 0.7315605349698444, 0.13503178101308277, 0.18742957192833787, 0.021945623412107595, 0.025501675306205285, 0.07637262690495261, 0.7101577128763483, 0.9312015961668834], [0.35518412941762045, 0.3961589748058816, 0.05452870116825448, 0.08410529327767347, 0.580631134779524, 0.8514425953300206, 0.7609764395679289, 0.07382769402451117, 0.28144690837934727, 0.08777608673337911]])))*np.square(2.1798926182365634)-np.exp(1/(np.cos(2*np.pi*4.841998943767767)))-np.round(5.604754894668638/2.1462432171713117-(np.dot(array_x, np.array([[0.4459340914206522, 0.036871062308798, 0.12771882900288922, 0.45598226336537784, 0.7508658148867089, 0.6640137342089697, 0.583587052003868, 0.3770431612376224, 0.31489098300332563, 0.08783408655789282], [0.22435055964391248, 0.4903468598925623, 0.8882166229527028, 0.8567849051903252, 0.7402545286663259, 0.8132480206661128, 0.8079593062535569, 0.639233297803843, 0.5855434146170746, 0.4460022014332442], [0.0028903604303675, 0.8233836195647619, 0.31644867056223314, 0.9930592240876951, 0.826774386862976, 0.5816487590252134, 0.6281424733044916, 0.03423235375993472, 0.5747732603042219, 0.5204779712901829], [0.8524988783803168, 0.23967174873421615, 0.032009774100650534, 0.3456550206975332, 0.2665860203819611, 0.7065954429041785, 0.5554440909338942, 0.609760170007204, 0.402542550865584, 0.9508604995660089], [0.9361489720473497, 0.030128144858105443, 0.6834387436195998, 0.556873833351977, 0.9964022368665486, 0.6210433327885226, 0.12933645053984266, 0.9858497206140094, 0.4780401460531355, 0.04451324869728912], [0.9354205923421265, 0.3236760594377318, 0.1712414528385472, 0.6502230420561107, 0.4640882217153445, 0.4234916875145821, 0.17599422875827675, 0.2321472151977444, 0.8654682786079828, 0.09190150823929255], [0.6326002801336648, 0.05865931052183493, 0.4599734840942382, 0.25355092612369534, 0.5857470022930671, 0.00962458342372885, 0.48153207536104103, 0.8934769631095197, 0.19649846362185808, 0.395860813186826], [0.9397553336138966, 0.3172701553871693, 0.04630629427057076, 0.9855701824856887, 0.3030704425500613, 0.6889648516423796, 0.9243808074897423, 0.6908853630767372, 0.623165682934613, 0.19759959083657364], [0.5904126238736905, 0.3303151671748904, 0.37586033855916523, 0.6765619234633435, 0.9000339711078699, 0.18878112898673882, 0.17350683169413084, 0.9541859902059547, 0.8568082345876286, 0.6568641389315039], [0.3812244755615234, 0.7352530739966393, 0.017021324096704094, 0.3023153206253886, 0.19291540495099258, 0.6328783792639401, 0.1488489659594191, 0.9675783815733336, 0.26514819252067456, 0.38136265229672095]]))))+2.0909024329042625, axis=1) |
np.sum(8.707673853981053*10*(np.exp(8.288366941994902-4.248158221884032/array_x))-array_x+3.968726041705713, axis=1) |
np.sum(np.sin(2*np.pi*np.sqrt(abs(array_x-np.cos(2*np.pi*3.7527780608720103)))), axis=1) |
np.mean(np.square(7.419047317707719-array_x+(np.array(range(1, array_x.shape[1]+1)))-array_x/1.8786198348748262), axis=1) |
np.mean(np.square(3.082206283094938*np.cos(2*np.pi*7.383023735853577-array_x)*np.square(8.357077034102948)), axis=1) |
np.mean(np.sqrt(abs(np.exp(5.981678638196065-array_x+array_x-array_x-array_x))), axis=1) |
np.round(np.mean(np.square(1.814199764177228-8.435323627617036*array_x), axis=1)) |