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abs(9.867989701595373)-10*(np.sum((np.dot(array_x, np.array([[0.9738129903273189, 0.42942528409195746, 0.5891285174615292, 0.23119648897538847, 0.703966507819479, 0.05510636491896803, 0.9436004946552442, 0.5059018370291817, 0.5955497432209135, 0.19601967179937074], [0.6137714310870725, 0.8237820579017341, 0.8646913509456955, 0.9381005896220166, 0.6623398158640006, 0.8451694580712752, 0.10992620274762244, 0.8165580150240557, 0.12696019220070853, 0.3812494400930846], [0.6982653171933108, 0.23709549502188343, 0.7944898498841949, 0.7315632703418633, 0.9953410355012491, 0.17196607910319495, 0.23412039777547156, 0.3001383832252843, 0.9226716987860671, 0.38266750448267506], [0.5223146554493346, 0.08432940700521951, 0.10987003556293584, 0.48758637296876595, 0.24424547761792648, 0.5093731852311566, 0.8620851908857552, 0.9429880775233968, 0.37398926962441925, 0.6589092489648359], [0.28113549935531634, 0.10635259991952983, 0.47756407616010643, 0.03755799925074732, 0.9174603914087407, 0.7412582377156508, 0.8309287088937107, 0.49755587630950315, 0.33407086815311204, 0.5699223411075163], [0.6119387481466949, 0.2828874409367602, 0.5220892470039679, 0.4967382446840236, 0.20381197204913448, 0.08471748076055996, 0.19930598633141283, 0.7265721163092111, 0.9092892170028014, 0.7234720547828206], [0.5874517957312295, 0.5741060845975853, 0.6168189524770592, 0.24682577984645315, 0.6508336832608047, 0.2086156227875453, 0.7964902336350475, 0.9579424162689982, 0.04466647079365238, 0.929866580012641], [0.9494156261919434, 0.5923228669537599, 0.08029289514482263, 0.0321427992591905, 0.9462538796981235, 0.24511560006963373, 0.07406589420501786, 0.6887971655227507, 0.06133756348795205, 0.11792265219993159], [0.8877332576350334, 0.7576680270915596, 0.5854150930925747, 0.825931361084987, 0.6384894346427444, 0.9776674142217412, 0.418850325208316, 0.5175270033539102, 0.919879844016784, 0.12188908975762147], [0.23900147686401874, 0.2743283285589243, 0.7706577554647501, 0.3233214523383835, 0.45618534235835295, 0.21029071176830316, 0.057171708279316946, 0.8859431053055233, 0.4418099796204735, 0.4112594097382217]]))), axis=1)+5.50150473067921) |
np.mean(np.sin(2*np.pi*array_x*5.026578699922408)-np.square(np.cumsum((np.dot(array_x, np.array([[0.7934865476705909, 0.8454314167264364, 0.05598785782481874, 0.8163591358760929, 0.8878922371667436, 0.6775303941245603, 0.14766911563130625, 0.42298540405864915, 0.5841892951457069, 0.44845208864302366], [0.7516664114846131, 0.7501453433482994, 0.4174510858897168, 0.5013346944131309, 0.2325961996166226, 0.5201019252077327, 0.6197969102756195, 0.7742371861579472, 0.44925164963334363, 0.8000838545122106], [0.38722664190737477, 0.39323054448032924, 0.7914992286911089, 0.9151866034172765, 0.3506371398557977, 0.6366377865403912, 0.8167172703255394, 0.04101432624745571, 0.03545793601140712, 0.6410648499237749], [0.6978877438679219, 0.1347373717364161, 0.6557952800392884, 0.9623480802960834, 0.5145112627276035, 0.2771573379237594, 0.31427304813689594, 0.8288948708211485, 0.8284310441594999, 0.8301860613881986], [0.33195429926285147, 0.15740340320686463, 0.17611365548529279, 0.6020262204902149, 0.5787900051881553, 0.5549519531321624, 0.6226363376955207, 0.7066193104785755, 0.2649776104784576, 0.8089627446937859], [0.9482439079990502, 0.010995106660468434, 0.30331451909654605, 0.5667703763829315, 0.9301982831586405, 0.8600831996216862, 0.5233270563480952, 0.5304915748218797, 0.06630905101153939, 0.44691712038625164], [0.5385720856768562, 0.7211362542117763, 0.3563473676965796, 0.1560826357391264, 0.27920839892401794, 0.6329121292019062, 0.33838208431170747, 0.6568558409955851, 0.7896055089531224, 0.6394424335177431], [0.7925712031130933, 0.49809905513729036, 0.9618598822400337, 0.29357579588434357, 0.6619585531927393, 0.8437086856673904, 0.34915599777507367, 0.05191881226091688, 0.35728837410559344, 0.8359088696789594], [0.31347043851133105, 0.42591551585659404, 0.07326211040457054, 0.890809709312115, 0.12838016294223276, 0.038888172582324576, 0.26667547221235666, 0.011401240980860927, 0.4608983092913198, 0.5718022195404331], [0.4971180469780768, 0.8418639575794812, 0.5619726871457574, 0.3886535423848926, 0.02916279227663321, 0.06804109366840694, 0.7925505636046911, 0.9179671486772866, 0.8423891325622099, 0.4412909022801852]]))), axis=1))+np.exp(array_x*np.log(abs(np.cos(2*np.pi*8.252028152854404)))), axis=1) |
np.mean(np.exp(8.003591805293187-array_x+np.sin(2*np.pi*10*(array_x)))-1/(2.386844576038399), axis=1) |
np.mean(3.7461073235646913-array_x*8.456142348433922+np.square(array_x)*np.sqrt(abs(np.exp(8.717931340380094))), axis=1) |
np.mean(3.94602362809485+array_x+np.square((np.dot(array_x, np.array([[0.4814180555220783, 0.9880841444887601, 0.2847542214438371, 0.6463034086365284, 0.5169758910738644, 0.5479237226706908, 0.36946896701327625, 0.12290511534425452, 0.09533011078756004, 0.45092331334989344], [0.4715908602474167, 0.8893914284047943, 0.20516193246260506, 0.9569966377686628, 0.180688242523098, 0.6642325234878708, 0.41195772981733747, 0.8200602962596703, 0.8131145568887302, 0.9291912818990703], [0.9847029421894256, 0.6464149957751184, 0.9341535421470013, 0.9148266544400955, 0.2920626042068858, 0.03828106807117071, 0.11095176995276534, 0.494938417897376, 0.12870863594217907, 0.23960336537570248], [0.45441460665711164, 0.111972297537555, 0.718134109137613, 0.6210983810262448, 0.7855413280433546, 0.8883504226776582, 0.17567756889627473, 0.9974466749814147, 0.5028850184514578, 0.8900806027895206], [0.4485786411896401, 0.43820173248660854, 0.8401945147169064, 0.20620034947194366, 0.31229153948704647, 0.8653002149921243, 0.6442098598983943, 0.4634295894727566, 0.24049076384439927, 0.6555525283307999], [0.1406613130451746, 0.9095203722782559, 0.8358848422149188, 0.08393727962727604, 0.682743682774612, 0.20674316927734682, 0.5199745265191675, 0.5468399105916034, 0.4755451935757169, 0.226808577234062], [0.8405083743077265, 0.426453887878551, 0.0345349113201322, 0.602611669587539, 0.04698638333189664, 0.7720973296889376, 0.8372975058518118, 0.02698189090425518, 0.20368891248738252, 0.39682545786363643], [0.9734812633725117, 0.8672034912494238, 0.3021854001258454, 0.9609418639670773, 0.34241850491071635, 0.5264534160628253, 0.4689232402212017, 0.3704783399929642, 0.21847475913183478, 0.3889661561986738], [0.6667766716580548, 0.5777668669015658, 0.06403910283545888, 0.35107772568064854, 0.5418242153434264, 0.5603957329283258, 0.15870497246828597, 0.07477774388088476, 0.6041615418617166, 0.2649817041534476], [0.3570271032495209, 0.5676989005432218, 0.5584325071375894, 0.20670207584819822, 0.17173325494593672, 0.3903055241756238, 0.12256582551901396, 0.724555372771165, 0.9018307235917759, 0.055219004548174966]]))))-np.square(np.round(np.sin(2*np.pi*2.171331291683129-array_x*9.538301197557292))), axis=1) |
np.mean(array_x+2.282575802071901+np.sqrt(abs(9.207076715837996)), axis=1)+10*(np.sin(2*np.pi*np.mean(array_x+1.6947446063005271+np.sqrt(abs(7.382416467068944)), axis=1))) |
np.mean(10*(10*((np.dot(array_x, np.array([[0.5548575839916529, 0.52377438490806, 0.7374503968678965, 0.901312520649895, 0.0994518185190797, 0.2231915560752017, 0.5008523383631841, 0.7852190758159087, 0.6200319761696723, 0.0359898483390958], [0.8831873487784543, 0.9454150600646922, 0.9900172365018217, 0.41941462876098856, 0.14144286522106475, 0.2992785413228256, 0.67269833120446, 0.3807445731530903, 0.9440329523566937, 0.15558350173806046], [0.44497976502934955, 0.7703274947221209, 0.7810813058087418, 0.9093426814988984, 0.7364367608276473, 0.6804861343873978, 0.7871754085470136, 0.8668414225629311, 0.03339211974912215, 0.21589754739058642], [0.8602231337254335, 0.9392371339687089, 0.3301111134603729, 0.6632780444364005, 0.859914019383765, 0.25531417643097787, 0.1467518987403793, 0.6432433826765004, 0.41976968159454886, 0.9837123440401813], [0.7893752076158914, 0.7006099185449115, 0.5474952381966505, 0.04855050298326624, 0.7469029748589691, 0.04822944421899922, 0.7300565304117538, 0.828514771046033, 0.3239364785411598, 0.4126429402720664], [0.9739396642131382, 0.2023001311635516, 0.03248961159347774, 0.1949238382106312, 0.28175507265303756, 0.9490770015971705, 0.9299822644792836, 0.39316047494756556, 0.10457478575533163, 0.84951524268318], [0.5847600096517119, 0.9960929816339372, 0.3253541376076482, 0.13964766696521003, 0.9612004572368255, 0.2577150886801275, 0.6967450330455682, 0.5178771684129198, 0.7783926987360914, 0.896469726384669], [0.45025492277007795, 0.8422813794670667, 0.5383098168986008, 0.5341045845091746, 0.33647049318470046, 0.40542772046455167, 0.7098436175529641, 0.7469093569823897, 0.09935298696534445, 0.14396244209600806], [0.044511835032522984, 0.82488904307504, 0.8894962330962435, 0.481680169670447, 0.6651106436690329, 0.1677938452243819, 0.38795247549542433, 0.4348547967619535, 0.5145247998656513, 0.7575572890581195], [0.6488508352324514, 0.473588527085649, 0.8719238674389065, 0.76600783888674, 0.39919384040461137, 0.3287793297739048, 0.3304040895300312, 0.9991954044625311, 0.3157393505696855, 0.43822913417312837]])))-9.915872807148503)/2.9074527686489224-(np.array(range(1, array_x.shape[1]+1)))), axis=1) |
np.mean(3.421428211231462*(np.array(range(1, array_x.shape[1]+1)))*array_x+3.940122187697715+(np.dot(array_x, np.array([[0.6421352517841615, 0.29358697981558324, 0.9324425028438579, 0.8364431038855928, 0.8375991894578172, 0.9617177981490332, 0.6657330488842724, 0.27930126385710863, 0.09905027372116693, 0.3024006226722663], [0.9455682282662691, 0.8589198959992816, 0.6530777120025322, 0.5405728651113112, 0.697817557328639, 0.10626650279669214, 0.609125510278299, 0.9391523760220695, 0.8803521766849932, 0.160351741963216], [0.1262286796430684, 0.11502567047402223, 0.9782695465852244, 0.6173545337353069, 0.5952933363266002, 0.6267455630970512, 0.3960148890202626, 0.8304277865606168, 0.8837545270504972, 0.4138705694011441], [0.036015003188039296, 0.32042085566621714, 0.741007239693413, 0.8281012090600737, 0.42546997756343796, 0.6539776244653264, 0.6772627934738628, 0.4768452248168581, 0.8932650848619057, 0.9331995067151512], [0.13674530054123946, 0.010804245431510373, 0.7439941332411648, 0.40344515485222343, 0.6692632626272385, 0.2062784215517831, 0.36833420243080417, 0.6261186404692606, 0.3214595467904722, 0.9674970952056784], [0.1300914714626462, 0.10184627181910133, 0.5493109943574402, 0.18098814000611962, 0.7650466008148404, 0.8992554124296142, 0.2089210285464297, 0.6717428376661121, 0.673282030815401, 0.3810919321873809], [0.8346805485479272, 0.4419871937239861, 0.2620981842327157, 0.2861863015620315, 0.170527613264164, 0.5902945896512221, 0.018519653218476373, 0.2151983020280157, 0.4608672302212117, 0.2725129398136382], [0.9521222043360479, 0.5894603930372236, 0.8076673389832892, 0.3869316276286181, 0.1364456632356863, 0.8879327364140002, 0.45329723953427903, 0.85650298275751, 0.9629295701754724, 0.6132762405046885], [0.862461450950084, 0.6423064292296669, 0.20009384893811555, 0.4311135668185707, 0.321470526998461, 0.6167138627302519, 0.9595953094980486, 0.18357713054786517, 0.9148404860574336, 0.741784671063787], [0.49607209114558326, 0.6269070690346826, 0.9493303986166438, 0.3329726118323748, 0.1613124892004646, 0.4554651334211125, 0.7718161061197071, 0.5048254271861602, 0.3449469614206172, 0.3263289597353858]])))+2.0849893983909, axis=1) |
np.amax(np.log(abs(np.log(abs(6.955548522197434))))-(np.array(range(1, array_x.shape[1]+1)))*array_x*4.868026570141465, axis=1) |
np.mean(array_x+7.254433090188734/2.0898300204742286+np.square(7.4393755955050365*array_x+7.209051928862127), axis=1) |
np.mean(np.log(abs(np.log(abs(8.392663563534203))))/np.cos(2*np.pi*3.646031829531287-np.cos(2*np.pi*array_x)), axis=1)+np.sin(2*np.pi*np.mean(np.log(abs(np.log(abs(8.61760985169916))))/np.cos(2*np.pi*6.925378478726964-np.cos(2*np.pi*array_x)), axis=1)) |
np.mean(np.square(2.518267642441156+array_x+array_x+np.sqrt(abs(np.log(abs(3.106465257833036+7.906966535482056*array_x))+array_x))), axis=1) |
np.sum(np.cumsum(1/(np.log(abs(array_x+1.8726982787607858/3.88775145784742))), axis=1), axis=1)+4.716208840827383 |
np.square(np.sum(array_x*9.189364677548603, axis=1)-np.mean(4.439307968130196-(np.dot(array_x, np.array([[0.6655482065467965, 0.35017918039396, 0.8046196037810839, 0.2579988685943698, 0.9787829637646303, 0.47943818541219485, 0.4363709501179581, 0.1871647669779558, 0.8877518908400466, 0.4719456285177326], [0.42685504850924727, 0.38896937127780606, 0.5417138846734031, 0.9635828084864984, 0.4818391686572251, 0.8670749618404517, 0.7424000070351234, 0.332444063397748, 0.17701073494727493, 0.44322166709189437], [0.5072293508929104, 0.9799266719939509, 0.04013457166455148, 0.14071869461830167, 0.13045157805974672, 0.8107022135163419, 0.2329149895051652, 0.0031690079282188366, 0.41394086667172836, 0.32926626130904224], [0.09353307741263217, 0.4126215033568277, 0.878228801403081, 0.2911705980121235, 0.7888598075331488, 0.19826656911805496, 0.8405788893908339, 0.004536893167126466, 0.8424068074610582, 0.6077389768083566], [0.8383672921697523, 0.9657901325022897, 0.9684248035348794, 0.14323352823448543, 0.3443626232853896, 0.24345790993665262, 0.7873283441188849, 0.7765483104228343, 0.15965132596449494, 0.2972672951652022], [0.5672558543326818, 0.674865176447384, 0.35192372145023954, 0.3443259065400248, 0.5101857197940659, 0.9544939379747333, 0.5007311141527073, 0.6277547436951045, 0.7291349298276177, 0.2897309281390712], [0.5128028757507622, 0.28155790005376424, 0.505108517868503, 0.029308878137464967, 0.060156902100239606, 0.6917581733207021, 0.10987143330890026, 0.0861750400815785, 0.45401154761438733, 0.2699762955598125], [0.927974068668793, 0.984125487319549, 0.8756623842067627, 0.10385417451547974, 0.41750297577323825, 0.6052195169802123, 0.47368145455951927, 0.19162469004632787, 0.9722842659677435, 0.293829005803679], [0.1742340363603908, 0.19937961874012367, 0.8724277404054825, 0.503422648014934, 0.5095541005251154, 0.986760762015918, 0.44835400537937675, 0.6876996770176206, 0.8747922939421806, 0.4913156868196611], [0.7102058619544496, 0.3411352798532724, 0.7917080445117473, 0.06020985463626116, 0.08149857507164249, 0.8385616725154409, 0.37909503535100486, 0.19158937302037227, 0.46562066356161524, 0.23784987913295497]])))-6.118655638017713, axis=1)) |
np.mean(np.exp(np.exp(array_x))-array_x+4.639093051096539-10*(np.square(array_x))*np.log(abs(9.946793326029967)), axis=1)+np.sin(2*np.pi*np.mean(np.exp(np.exp(array_x))-array_x+3.341141306838897-10*(np.square(array_x))*np.log(abs(8.318122357053468)), axis=1)) |
5.867863721311784*10*(np.cos(2*np.pi*np.sum(-(array_x), axis=1)))+np.log(abs(3.46767065552402)) |
np.mean(np.sin(2*np.pi*array_x*3.5376566476014943+np.sqrt(abs((np.dot(array_x, np.array([[0.7748690639822678, 0.5137206268433305, 0.2467832717719447, 0.029886390640237903, 0.316765235662935, 0.4966953985891456, 0.05459617150665619, 0.7978942047102496, 0.16097248958325783, 0.9849674507430012], [0.6848131159030012, 0.979257411800915, 0.2456316623360989, 0.317094310882431, 0.711944318565219, 0.0143823700312522, 0.451735931375462, 0.3828905711517213, 0.40119597781088046, 0.9790073061675426], [0.5627405431091334, 0.6763427256006648, 0.7224842908373554, 0.10344876096187483, 0.7513973462645293, 0.7434138917791596, 0.5530266632072037, 0.1769009770015546, 0.29260413809588126, 0.6349693593076983], [0.29964255369208503, 0.41177996958726226, 0.35544676681093046, 0.2692113355913479, 0.1104874891099028, 0.42448032340363284, 0.9544545789185175, 0.7290268125972554, 0.7950995689681646, 0.6751448540018968], [0.3037146690118083, 0.8513507120591811, 0.3799911535913634, 0.8658873035430725, 0.8761947448376128, 0.43664883017255884, 0.9154453318472839, 0.2989307622459426, 0.1308149508660007, 0.2986384244972837], [0.18832130363561095, 0.265292356853697, 0.8893308980727307, 0.15042090225483795, 0.4190374396450035, 0.011014102355717759, 0.5626080210735637, 0.6310755202222952, 0.2665425760396095, 0.941912706255902], [0.18935671651721664, 0.4128018874138306, 0.13308951516240697, 0.1619232455638815, 0.6289685412893061, 0.9845318393927213, 0.48867299889379223, 0.7072283131747611, 0.6377676440291304, 0.8347593183929367], [0.37001435392317394, 0.44584076016688123, 0.13246353053546878, 0.48727209220078593, 0.762631655237003, 0.5987592135201747, 0.4733797640123846, 0.13307811922658563, 0.35328214413499837, 0.15844627822516333], [0.018684837015439548, 0.7115795227666829, 0.9803785470008709, 0.40624990331143795, 0.2620907056873534, 0.915221691636536, 0.6753433686797609, 0.7518863880490112, 0.9967488394548217, 0.5311432500378228], [0.9600717191920428, 0.40820279876706833, 0.22868575777817868, 0.5412407216937887, 0.6395244339273216, 0.11026618211423844, 0.7354846372983829, 0.5908558521264186, 0.7377222147612553, 0.7596816775631602]]))))))-np.cumsum(np.sqrt(abs(np.exp(array_x)))/np.sin(2*np.pi*np.square(np.log(abs(1.038243842599766)))), axis=1), axis=1) |
np.round(np.exp(np.sqrt(abs(np.mean(10*(array_x), axis=1)))+6.772052939705543)) |
np.mean(-(1/(6.221379973891464))-np.cumsum((np.dot(array_x, np.array([[0.6123878998969643, 0.202632300714232, 0.17172146369917873, 0.6996836665642368, 0.5407604494573978, 0.9845691371970241, 0.2132030464666259, 0.5681910675607912, 0.10480560868068323, 0.40434424711208117], [0.21213645978131046, 0.4059618563771994, 0.9672923620059417, 0.48364223951492236, 0.8880469659934935, 0.046813354407432284, 0.8169972464080758, 0.6000606600411353, 0.5288493759398397, 0.9445752073762947], [0.3181004759974717, 0.5077602391149774, 0.18438420556026158, 0.5691870959698082, 0.42687335959801986, 0.8850065020089877, 0.5849753737670847, 0.3319743763747983, 0.8080512224522922, 0.6100552721771252], [0.05582907763888667, 0.2916731492233122, 0.9699206335280856, 0.8866881304737221, 0.5739029706760377, 0.3027215180551248, 0.356453965029618, 0.5015707230376655, 0.6309248838458108, 0.04928098580875606], [0.0034398451515363915, 0.8639623444295891, 0.524770499446887, 0.17764848336130057, 0.7869952666109502, 0.32970128016840294, 0.10434926076385997, 0.5660076079357886, 0.1116333210099546, 0.9394533447314811], [0.9783443836192548, 0.1400430016485431, 0.6365128864117505, 0.2661086345283149, 0.06872227435672196, 0.6228504603313466, 0.29488843715539825, 0.6922365734531657, 0.5887100359797753, 0.956045127078926], [0.3062475634433883, 0.5818024538758444, 0.689513571837585, 0.17755739459477315, 0.20084951097381187, 0.7722914731365682, 0.18523950770897646, 0.6431003893636991, 0.16585171021272782, 0.4369963403067266], [0.5707386159244814, 0.3854288711298781, 0.8494947625690258, 0.21497964255852264, 0.07044859286781935, 0.6203030335062142, 0.7423473017590979, 0.0863993091363392, 0.9915447795537922, 0.48187913427821305], [0.4538697954528499, 0.6208141936941073, 0.1889173618894251, 0.4241310007167842, 0.5252207756574881, 0.3316111750271713, 0.8740792791915352, 0.4186610323634241, 0.10501665455939324, 0.12357913479169236], [0.004159594465795058, 0.45395062723563595, 0.1039975089892341, 0.9714186307242266, 0.6258779636540138, 0.14103836326905228, 0.5396247462437171, 0.4335009533562504, 0.6874486944010787, 0.21269300383159262]]))), axis=1)+(np.dot(array_x, np.array([[0.38726809924429095, 0.5993250539168714, 0.8016630593595195, 0.863882435093299, 0.19906446332227457, 0.16281021412213437, 0.6202629790535004, 0.46461735571717266, 0.45513109398222074, 0.6792134186864084], [0.45987188149920843, 0.9413593033073981, 0.6040096513434257, 0.37510962450348284, 0.3574723869722576, 0.4065673320310238, 0.6355921813810452, 0.5084170331870631, 0.6315602136591274, 0.06473782190713162], [0.12150529539397492, 0.8641945875250333, 0.7065533025007212, 0.5963153174339321, 0.6233927284615917, 0.5896676847332487, 0.45298977700509435, 0.09916321108762549, 0.5367544706563674, 0.220105268382344], [0.662042334416553, 0.3646873585840099, 0.12998207339855805, 0.049177747965434615, 0.46470143422950283, 0.5127421736244219, 0.5525283345647779, 0.6737283347049011, 0.7491338075649356, 0.049206654478581746], [0.9783952339689166, 0.0035454239921851594, 0.3583591341914292, 0.8463586688250594, 0.8306313712608134, 0.6465122890903975, 0.04319122078145554, 0.8138803088539556, 0.7381021539113394, 0.2563280365661217], [0.9450978755925726, 0.3409188148944604, 0.5553279225239923, 0.49057550684192, 0.3757272777615501, 0.07752766442984405, 0.12633179197266753, 0.5024222046320883, 0.4769487305653365, 0.3293101642993348], [0.05378797737723606, 0.29391494702054277, 0.25818228005810995, 0.7261805632796006, 0.726429202061888, 0.5938435508382214, 0.14825475164528445, 0.6953247753502992, 0.9124486682185258, 0.13311372412647726], [0.057807039923977954, 0.5176938201269815, 0.46984125690295586, 0.4917839981236096, 0.179010931660455, 0.9056545634188408, 0.8127330262957658, 0.9823105813264078, 0.2624417239006921, 0.4038551933671496], [0.6591175057920321, 0.9465079643691197, 0.6101062075968563, 0.42812052204510764, 0.7668527708655019, 0.16966263639857027, 0.9957505360500053, 0.6627380757889878, 0.529176002104142, 0.34289391695119453], [0.18816564605176445, 0.7865977547447187, 0.2470521812208466, 0.22175298195962967, 0.9298124527719736, 0.635905268041633, 0.8417732312308457, 0.31959344776803733, 0.7380807944067399, 0.231894453549638]])))+(np.array(range(1, array_x.shape[1]+1)))-np.square(2.076067887902579), axis=1) |
np.exp(7.4537321356349056)*np.mean(6.698539700040803+array_x, axis=1) |
np.mean(-(np.exp(4.108021232331449*array_x-8.424446769546524+array_x+8.527385548428668)), axis=1) |
np.sum(3.4747924591108985*(np.array(range(1, array_x.shape[1]+1)))+np.log(abs(9.531732578477165-array_x*8.689034230491973))+4.943077736258767-np.square(7.9310725826441795*4.986816216793911+np.cos(2*np.pi*array_x)), axis=1)+np.sin(2*np.pi*np.sum(3.2930864502950783*(np.array(range(1, array_x.shape[1]+1)))+np.log(abs(9.323000284729748-array_x*4.5781199935952746))+6.4556495003262855-np.square(8.62278111733421*4.945577633227955+np.cos(2*np.pi*array_x)), axis=1)) |
np.mean(10*(np.square(np.square(10*(1.5550109932219356)))-array_x)+np.cos(2*np.pi*abs(array_x-(np.array(range(1, array_x.shape[1]+1)))))-array_x-7.879395167249155+6.641524608838021, axis=1)+np.sin(2*np.pi*np.mean(10*(np.square(np.square(10*(7.939835958692669)))-array_x)+np.cos(2*np.pi*abs(array_x-(np.array(range(1, array_x.shape[1]+1)))))-array_x-1.8822752102606404+5.036967050594318, axis=1)) |
np.mean(np.square(np.round(4.325579555993019))-array_x, axis=1)+10*(np.sin(2*np.pi*np.mean(np.square(np.round(9.104789332451018))-array_x, axis=1))) |
np.mean(np.cumsum(9.021249244884427+array_x+np.exp(array_x)-6.45379725475036*array_x+np.cos(2*np.pi*np.sqrt(abs(1.2984899382895843+array_x-2.336598256730006))), axis=1), axis=1) |
np.square(9.268534759848695-np.amax(np.sin(2*np.pi*array_x), axis=1)/np.sin(2*np.pi*-(1.7447577776615066))) |
np.square(np.sum(6.201770343074731+np.round(array_x), axis=1))+np.sum(np.sqrt(abs(np.round(2.7954469715603687-abs(array_x+1.4123435472513541)))), axis=1) |
np.round(np.mean(np.sin(2*np.pi*np.sqrt(abs(array_x))*1.0857874541883201)-9.053100960305745*array_x-np.square(1/(6.135330087776658-array_x-array_x-(np.array(range(1, array_x.shape[1]+1)))-np.sin(2*np.pi*5.332944600771044))), axis=1)) |
np.mean(np.exp(8.6958843084047-np.square((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.sqrt(abs(np.cos(2*np.pi*7.5265088356997625)*array_x))+abs(-(1.43335022038676*array_x)-np.square(np.square(array_x)+6.5454176061986455)), axis=1) |
np.mean(10*(10*(1.7566866081059147)+1.260115143100403/10*(array_x)+6.176964560263627*4.778973881295277), axis=1)+10*(np.sin(2*np.pi*np.mean(10*(10*(3.639278595317669)+5.901497722921347/10*(array_x)+6.131060071192414*1.0436634088081531), axis=1))) |
np.mean(abs(1.4334898188941219)-array_x-np.square((np.dot(array_x, np.array([[0.5359874710064565, 0.08454638544099902, 0.19185833646778638, 0.8539742887099496, 0.34864328592003346, 0.005093189877441562, 0.9330503754364324, 0.7267500281038739, 0.19062588267847258, 0.6414618304935299], [0.9111193352777898, 0.30284476580494823, 0.11591766316592644, 0.7185580055582573, 0.4299001721049399, 0.33174925441842407, 0.06733444263664723, 0.3785689326478273, 0.9276212669825112, 0.35384136097693053], [0.6228040047637101, 0.29931567310986573, 0.21126725817928826, 0.6144024436614308, 0.8663106601609919, 0.3075332520725005, 0.9766561852008142, 0.5354126756554928, 0.07639424473570056, 0.9456757324711026], [0.6643410542598581, 0.5858263711662914, 0.4947113111890141, 0.6224339991439977, 0.8912425238930552, 0.7749848193215726, 0.603129315658555, 0.8516140051960647, 0.4479663577992895, 0.5628646845609881], [0.3371480014515229, 0.05209218748910549, 0.3284113492905888, 0.7537979045571477, 0.7709733074520002, 0.26793975321632857, 0.4980194307554795, 0.956163281155152, 0.7848145581922267, 0.7694975452448329], [0.7397659850322131, 0.7187203988759516, 0.5358394472892636, 0.8754800985269011, 0.27925873433259063, 0.5312463804099202, 0.5486151631355654, 0.8808396191912541, 0.10783407178356708, 0.2708164702108101], [0.07467698753856045, 0.11055607877363782, 0.15672216865063104, 0.9934162129948179, 0.17162109682637283, 0.5637334357505968, 0.563629950125748, 0.9021766752735659, 0.7662017286003896, 0.048995159969424495], [0.7305110698753401, 0.11780661501479994, 0.7857871529306444, 0.1782312610662603, 0.7603544184007733, 0.8399722584009639, 0.8031926411121746, 0.2038973449948177, 0.3199564363060251, 0.9206382770122691], [0.5020034713628557, 0.9705620602762703, 0.07601889144733953, 0.9757656847427686, 0.1990848620427359, 0.6121810790019585, 0.596902016891418, 0.8771541971119146, 0.7194498843803496, 0.6394347323248439], [0.5010958162167845, 0.3417881103498147, 0.4705821993037633, 0.6358111081113417, 0.4556901207357016, 0.7024491327705411, 0.5764268956574675, 0.6702424584181506, 0.16612049312802868, 0.224850900737677]]))))+4.395692340515797, axis=1) |
np.square(2.4218052198149644-np.mean((np.array(range(1, array_x.shape[1]+1)))*array_x+5.9422873631188775+(np.array(range(1, array_x.shape[1]+1)))*array_x, axis=1)) |
np.mean(np.log(abs(np.exp(9.74549560713284)+(np.dot(array_x, np.array([[0.190802165862344, 0.0801557592178046, 0.903990171140451, 0.5732364803884131, 0.2819875639929086, 0.06994665957731827, 0.08016074659055572, 0.20557419428444246, 0.5312288843422445, 0.6155007105617301], [0.6789798669749307, 0.050086187643133884, 0.1738436594736087, 0.33497276527465036, 0.5102418351176818, 0.09368353856827005, 0.420858440422209, 0.18699955648620914, 0.8038242007599112, 0.2507369511016416], [0.4548628154929143, 0.267382193013337, 0.02411026393561011, 0.16767793485604976, 0.0908843477441742, 0.9043951527923607, 0.6473892917477172, 0.4209891060657799, 0.7594354235440932, 0.8308395767348147], [0.06276747936201665, 0.971593490479095, 0.22795680986837785, 0.21906510096772536, 0.13240895902889194, 0.9963297095054952, 0.13087534928601063, 0.021888403740031426, 0.05700245699043782, 0.020736407947853697], [0.8536694356519303, 0.8249659744944342, 0.03221061766998179, 0.7945643822219459, 0.059436651412852926, 0.7104274572875352, 0.3851833274413372, 0.18840243513639476, 0.5778856242904533, 0.45250115603739993], [0.52225249006662, 0.7335334498664612, 0.8044452883108223, 0.709966405115732, 0.24512347025445036, 0.20200857773255576, 0.8058144397744259, 0.6431012818227748, 0.7723996185833205, 0.4893111189200148], [0.3108187517264158, 0.26873864500055655, 0.19929673229305167, 0.5937292640960753, 0.11558078832536112, 0.3078705196509721, 0.45410238987109697, 0.2880875926042832, 0.8210259697705155, 0.8342240896819558], [0.8382228700174127, 0.9031655095870083, 0.40920969204640023, 0.12194528451939257, 0.4436181590781435, 0.1971375489904933, 0.5179908812189307, 0.38036301689259644, 0.3548328869861397, 0.9217852461081859], [0.430393641714931, 0.25221261046329835, 0.4696910427542835, 0.9215946801585708, 0.35538512662016597, 0.9954087110082354, 0.03477790668899561, 0.029413518200019806, 0.6715665556656591, 0.6754384157747126], [0.12849011843610703, 0.2305510787682421, 0.4421799208830748, 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[0.6669192364277707, 0.9259317355567998, 0.03455357610222387, 0.7488868295183706, 0.7653696588306423, 0.4175878379646871, 0.4371615224415005, 0.060121381087659964, 0.760213536715216, 0.6148874770036042], [0.910762631829579, 0.6509679828314421, 0.27890443750905003, 0.9339481582389576, 0.3940360685398274, 0.9820845700288915, 0.40788940611646396, 0.457918683762341, 0.5530518733159571, 0.7628413336834886], [0.34324817830584275, 0.589472058775047, 0.2686715177507367, 0.9269108309588135, 0.1779908329753821, 0.6524375947411204, 0.4225461460458395, 0.7384676783322619, 0.0942506344462608, 0.6302887083769712]])))/9.154295343712986-np.square(array_x)/4.235770241931599, axis=1))) |
np.sum(4.579051153924494+array_x/5.241236452250875+np.sin(2*np.pi*(np.dot(array_x, np.array([[0.3214106147801069, 0.38037804339955195, 0.6415392092075741, 0.6193537153172417, 0.3677970428326318, 0.28932931752490654, 0.3125003795943242, 0.7655009226221173, 0.4773039970558317, 0.16861692460606148], [0.41385672727056955, 0.8398320085410665, 0.1530608887565995, 0.8485252780591861, 0.1338978482804859, 0.5274429048792667, 0.18224502140400678, 0.9134275252840395, 0.4930042278014537, 0.6257723149871891], [0.457333259957454, 0.9150096325928033, 0.7752588077671778, 0.09285541865622626, 0.32652368119017017, 0.9086834912955769, 0.0801403646134935, 0.8078918462143638, 0.41610247002664724, 0.801109536242125], [0.3171132255489628, 0.683726271312356, 0.304803630304025, 0.07880123841747932, 0.0404899010202745, 0.06926975934713564, 0.12440128410694018, 0.6209044211855006, 0.7329885203171618, 0.7419036598758721], [0.8481271050714174, 0.6347022978405348, 0.21502596671924112, 0.9139414302486953, 0.9037358953803114, 0.7281074056118861, 0.6585450514044465, 0.9624888936819923, 0.3573452506834299, 0.2571067203625348], [0.7358170926912783, 0.9505626868883232, 0.9350706483417994, 0.9718773345921435, 0.8599086525923902, 0.8368825395200602, 0.10070032630510428, 0.05912723197988734, 0.34957170723986364, 0.11525716372445816], [0.21867533574962605, 0.3882187768054477, 0.22024597138797763, 0.023865324385450082, 0.43616563114914275, 0.4941911195749168, 0.3569015023572143, 0.5191089505302231, 0.5370376920368293, 0.15448324003650316], [0.3647133485454316, 0.4988631462859736, 0.27049349230478503, 0.6387269158245243, 0.7007309923783006, 0.8771215684119815, 0.8418525298858571, 0.3092507841454175, 0.5459045809545297, 0.9204558603872842], [0.5482085208066708, 0.4950418183352152, 0.547848405647482, 0.7968686362098163, 0.9356207419091148, 0.48231099228306284, 0.02553253165150693, 0.5551679280645581, 0.8911497168498156, 0.5529299335894384], [0.49326318193879826, 0.44420482432335207, 0.023596124829969423, 0.7331403598478221, 0.30206400398503086, 0.014871828627264994, 0.34702121835805677, 0.7294213376578129, 0.5757712379182122, 0.6457748062161051]])))), axis=1) |
np.mean(np.square(6.273682111215289)*array_x+7.378582098256933-5.060671930322098*np.sqrt(abs(array_x)), axis=1) |
np.mean(10*((np.array(range(1, array_x.shape[1]+1)))*array_x)+np.sqrt(abs(9.154420832626172))*5.276794012078074+np.square(np.square(np.cos(2*np.pi*(np.array(range(1, array_x.shape[1]+1)))*array_x-5.8741387427697696))+9.3402178797668*(np.array(range(1, array_x.shape[1]+1)))*array_x), axis=1) |
np.mean(np.square(5.285882024735121*np.sin(2*np.pi*array_x))-6.639721715475247, axis=1)+10*(np.sin(2*np.pi*np.mean(np.square(7.615052624668278*np.sin(2*np.pi*array_x))-8.930458021260165, axis=1))) |
np.mean(1/(np.sin(2*np.pi*4.3201501312257475-array_x*5.803689050241828)), axis=1)+np.sin(2*np.pi*np.mean(1/(np.sin(2*np.pi*1.2978066497350182-array_x*1.982271143102407)), axis=1)) |
np.mean(np.exp(1.739139778050827-(np.dot(array_x, np.array([[0.07472558158268272, 0.4534589877033035, 0.4302735465402223, 0.9646230778023539, 0.7956880314085638, 0.6503563022334762, 0.7208773064225457, 0.11371170140474884, 0.690904900928742, 0.616281693895862], [0.9086676405092183, 0.6700512770016577, 0.3308237016144784, 0.35040935945062834, 0.30722964003105857, 0.049443402540967796, 0.02875552079267618, 0.8055102955018189, 0.44779605467824146, 0.11961333165744481], [0.15805556701493495, 0.06809288249449974, 0.22453009992213435, 0.5949585650867227, 0.24627608021783354, 0.28372501948626383, 0.6583628333424704, 0.894877519928226, 0.32663802989492985, 0.8707417409078898], [0.7597578921711017, 0.7976558118348266, 0.5222729401781704, 0.679509254194878, 0.710898652033962, 0.008708935128543205, 0.5132754575755651, 0.32217138036082504, 0.5371152570526695, 0.300921406475411], [0.507936871853384, 0.18594912303436628, 0.09412325150969225, 0.9241138883265372, 0.9136154275748901, 0.724809041747461, 0.23395884246527643, 0.5469972914560168, 0.8323436509738338, 0.089341340916118], [0.053664965180831636, 0.4628428933173292, 0.6582663954258371, 0.5419794051608117, 0.9982576315866303, 0.43504141422607245, 0.27590760400147674, 0.9384813667780892, 0.5851023520565843, 0.49010501460989164], [0.15733092996476972, 0.0033508202873102633, 0.9029569638292646, 0.5332547447791862, 0.516924735191725, 0.14479383759073738, 0.8024972373001932, 0.9485034935798938, 0.4944894629295771, 0.31875012425097593], [0.8568876808095237, 0.484641770011351, 0.17622883095574016, 0.3437336101081745, 0.4474858270197928, 0.4380660957260559, 0.9458600304735937, 0.014835680795681427, 0.6788393490053215, 0.5381320718144703], [0.685423270797059, 0.5380376160871069, 0.8724670769364524, 0.03286666405215888, 0.27876780401045254, 0.021316012524223327, 0.6618236842449153, 0.057974646687055875, 0.16162021976954055, 0.4637144203805422], [0.682642506341546, 0.005210922049486477, 0.7219466716336373, 0.7602122211632618, 0.38691639780052345, 0.06825537498544965, 0.7804587377149327, 0.3764164183354889, 0.03715107139724083, 0.5174881330245368]])))+4.38177924977456)+array_x-8.58853349535909+np.sin(2*np.pi*6.445389236222417)/np.log(abs(np.sqrt(abs(2.2576693181670526)))), axis=1) |
np.prod(np.exp(np.cos(2*np.pi*7.918207662905214-array_x-array_x/5.512854719749167*(np.dot(array_x, np.array([[0.5124814284462009, 0.6927868800224656, 0.7847918855465283, 0.49491804347029167, 0.5165948597300459, 0.3551597534568337, 0.5066851591844325, 0.7950901392779852, 0.39525006705573085, 0.5823000648432926], [0.2573412739632521, 0.9953419167915587, 0.6597702690612786, 0.3528248975340087, 0.05654498598403446, 0.02990071960044749, 0.8003308035099334, 0.14096719607018215, 0.07987389656123522, 0.5232338847410094], [0.49025979104220296, 0.6272793800595196, 0.13011408906872035, 0.35483442240885, 0.36242099940948425, 0.5797362601903298, 0.2736098082390874, 0.3278119217781953, 0.2889125007935185, 0.08302692709201143], [0.6767629791591848, 0.2238001946130066, 0.8455890034877904, 0.10494061237892627, 0.25441581940572644, 0.49889427959509514, 0.4016219281952257, 0.6206548886064979, 0.4987517085466391, 0.44710152653007995], [0.9390162289311913, 0.6582251566925744, 0.37770782430888905, 0.274481911323629, 0.539616336226828, 0.7520203733494157, 0.6770888751963556, 0.2877669973672815, 0.17514005802705135, 0.5980441741219603], [0.17655278222052906, 0.22406081231604047, 0.3974414453072185, 0.07699446242213925, 0.8966077598600968, 0.6185397406710278, 0.36593887882611575, 0.2006308217622772, 0.8061399162738616, 0.2627712560120501], [0.13816056357316886, 0.5551311445226378, 0.6394910249779011, 0.6874430084266835, 0.9426480340293245, 0.5406539790742574, 0.770245448957841, 0.4845842360625261, 0.9823435530278615, 0.7687157430433836], [0.7587200359449516, 0.47063127722780385, 0.47199630131219206, 0.9070348487671955, 0.6453030502582557, 0.3840909919084974, 0.895922596252584, 0.19334378798544427, 0.6445775360807557, 0.9155070963082472], [0.003474061385546068, 0.23644391413302213, 0.497174867066444, 0.4621835712175527, 0.1171571876164551, 0.5375370094174016, 0.22943238674481625, 0.14916525582748552, 0.03887193180245607, 0.3843085450011049], [0.021591300847711548, 0.6660400749058364, 0.47843361943349016, 0.6740202279532792, 0.6766675785976597, 0.6949226627508556, 0.7694477053614912, 0.36615031915958396, 0.9775555300673284, 0.07549240923723943]])))/5.72037969471123)), axis=1) |
np.mean(10*(2.6575222261680262*np.sqrt(abs(5.464264802262105+array_x*(np.array(range(1, array_x.shape[1]+1)))-6.946776583541924+array_x))), axis=1) |
np.mean(np.cumsum(-(array_x), axis=1)*4.2001768367968655+(np.array(range(1, array_x.shape[1]+1)))*3.0198456736599457*np.square(1.6776060612990573), axis=1) |
np.sum(8.142481949140759+array_x*(np.array(range(1, array_x.shape[1]+1)))-array_x*np.cos(2*np.pi*7.388288566361757)*array_x, axis=1)-np.cos(2*np.pi*np.prod(np.sqrt(abs(array_x)), axis=1)-9.621584472448836+1/(np.sqrt(abs(4.584666495123312)))) |
np.mean((np.dot(array_x, np.array([[0.20716696199011497, 0.33345225448643745, 0.334359412620744, 0.5322947315874131, 0.8079068921378237, 0.3135074780394834, 0.5289950022467187, 0.11797725160296657, 0.41351125788017673, 0.9816034359003665], [0.34961098174428074, 0.8705568176167476, 0.5011836548039524, 0.28462050722933674, 0.25644007446549255, 0.09570734000416037, 0.01178085203159418, 0.6816083970453894, 0.4715919405131199, 0.7871397759101876], [0.3009697280771181, 0.48596444961939267, 0.6205706441272143, 0.8181248933166005, 0.2590540256587479, 0.3307892881633192, 0.6330513290538997, 0.22528772644924477, 0.31784287015634416, 0.6703337424293675], [0.13593888802940945, 0.9559713384636752, 0.19484822993507467, 0.9900098093035128, 0.9248842051577191, 0.17530090227456385, 0.12300620363545045, 0.5013807919460176, 0.724840657192432, 0.14570654690822382], [0.3802909307783532, 0.5809066676578489, 0.6640929555587358, 0.002269555847673632, 0.2637561038806725, 0.9978452204841313, 0.2756691858203194, 0.3981097189111148, 0.7907899047241026, 0.7659929360029121], [0.90022336941929, 0.32180110108092264, 0.6405377875423921, 0.6965754139450951, 0.10208926042013589, 0.7398879019867142, 0.4348792017012135, 0.2539271675672793, 0.7447138227509962, 0.7225665847679412], [0.6300135856367309, 0.43982795734296243, 0.06989287975858693, 0.4237730500226329, 0.5538331474827162, 0.0069620651652539944, 0.3691308682157921, 0.8329136097882814, 0.013812338846639127, 0.9923442108201377], [0.3984362395648685, 0.5850942487855575, 0.8223901827027557, 0.2397350692581075, 0.1512928315549522, 0.5596221011420351, 0.6436523230175283, 0.5747375428962895, 0.9187267792862027, 0.6942243116245225], [0.28820931730801147, 0.12606873793161277, 0.2356244072508341, 0.8171853345601112, 0.0956457176667036, 0.34769052037938075, 0.7051323007067828, 0.9797829523762233, 0.42874912320227565, 0.8874495629368876], [0.4920181045508717, 0.3370730394434108, 0.06732440313805044, 0.39798718115829557, 0.4463813390822374, 0.254050038067002, 0.6481268237577109, 0.5058222106998108, 0.8161806551349092, 0.09088664434023641]])))-5.595752080350763*np.square(np.exp(1.5049545457302969-array_x))-np.cos(2*np.pi*5.211771622842184*array_x), axis=1) |
np.mean(10*(np.sqrt(abs(3.2235574057631324*np.log(abs(np.sqrt(abs(np.round(np.exp(2.2055125804893922+array_x))))))))), axis=1)+10*(np.sin(2*np.pi*np.mean(10*(np.sqrt(abs(9.896755357446622*np.log(abs(np.sqrt(abs(np.round(np.exp(3.443463828310302+array_x))))))))), axis=1))) |
1/(np.mean(1/(np.exp(4.6431641757929905*(np.array(range(1, array_x.shape[1]+1)))*array_x)), axis=1))-9.647446495897697 |
np.mean(2.339468157377323+(np.dot(array_x, np.array([[0.6093686932742145, 0.33883635567943915, 0.9057234792748358, 0.4195174971751702, 0.9873370866479872, 0.6959398642269168, 0.8440577680324951, 0.7556129253629608, 0.5888609260195405, 0.040809140999891635], [0.6978985557739972, 0.9146265923595601, 0.7182329692118206, 0.39499046917780456, 0.741717174311988, 0.7137502524792803, 0.5722267759420905, 0.4961367938179959, 0.24154036083901131, 0.3180226759069905], [0.37375612203052366, 0.9339264799561182, 0.4678557060296432, 0.06553996736111689, 0.9309619845148326, 0.6883833502607387, 0.27712709968113614, 0.7555914361781394, 0.38022165392925045, 0.46197768815948403], [0.04446520473171034, 0.5531213658959728, 0.07166029932134599, 0.6965620221259639, 0.6343894035495524, 0.0560017871474674, 0.6047303391264341, 0.4419168048896128, 0.4746430942240115, 0.05307328044027093], [0.48095350706137474, 0.18783853333295475, 0.4153625567942453, 0.1593080828726401, 0.1737774292800709, 0.43319343682023637, 0.567873221196442, 0.6011008869694369, 0.5182619800183051, 0.7468009287009861], [0.41900321411011576, 0.04141137379514881, 0.6543499259984782, 0.8420586408823785, 0.49719479511031206, 0.3226637218978563, 0.15248805633620344, 0.830045259794055, 0.5222864419769114, 0.591399569480776], [0.519969333364766, 0.2719433807246944, 0.9471698989676507, 0.9895381656834578, 0.011135411290385533, 0.7431440660991873, 0.2544385635065415, 0.4827560791884383, 0.7188369922965274, 0.0051880809342294], [0.019734603210205925, 0.4498014019694738, 0.6441570903397015, 0.2688451686104558, 0.05409398948785116, 0.65782352237163, 0.0278697124321744, 0.3373150369985407, 0.6885480723268578, 0.8461386095863378], [0.8577224117960972, 0.04694462675024369, 0.23743324216042794, 0.24721261270065265, 0.23450984698745858, 0.40116345619771887, 0.4830571189385995, 0.09414229860179157, 0.5765329656603352, 0.15050483736608733], [0.17508933570877505, 0.11953402290587967, 0.4118824579214826, 0.13538497968709584, 0.7934697521707924, 0.3079968055706047, 0.8823262452485923, 0.8232272294995286, 0.7168069502776978, 0.9328288471990153]])))+3.818984286501465*(np.array(range(1, array_x.shape[1]+1)))*array_x+np.sqrt(abs(2.6173033143848694)), axis=1) |
np.mean(2.6980624465877905+np.sqrt(abs(10*(array_x)))*np.square(np.log(abs(9.768942944354023))), axis=1) |
np.cos(2*np.pi*np.sum(np.log(abs(10*(8.102868008930221-abs(array_x)+3.6054712898136416*array_x))), axis=1))+10*(np.sin(2*np.pi*np.cos(2*np.pi*np.sum(np.log(abs(10*(2.7694710401863647-abs(array_x)+9.38534756450678*array_x))), axis=1)))) |
np.mean(5.654703672110485*array_x-np.sin(2*np.pi*3.3758166789069333), axis=1)+10*(np.sin(2*np.pi*np.mean(2.9655385223475506*array_x-np.sin(2*np.pi*2.192529117951427), axis=1))) |
np.mean(abs(1/(np.cos(2*np.pi*10*(array_x)-np.log(abs(6.631225775529737))-9.72415805005935/10*(np.exp(array_x))))), axis=1) |
np.mean(np.sin(2*np.pi*4.7941848018936835)+(np.dot(array_x, np.array([[0.08327786681245342, 0.7379244682757915, 0.43630812199801705, 0.7988668170481281, 0.3675773246321037, 0.0716571263757726, 0.7041104131292436, 0.09915644165692128, 0.32030078444242216, 0.9439501908113187], [0.8110853211196052, 0.1293790384604524, 0.39756538186484247, 0.9212842094115651, 0.9210700648006193, 0.6052166475300161, 0.41216208774916474, 0.9883766806539196, 0.896892681734948, 0.7746574723373172], [0.2254816183912981, 0.6999234395069419, 0.7664074415115707, 0.41451833960913087, 0.7017471140191814, 0.027006269795149307, 0.7569001132636687, 0.7490217363404732, 0.7598802702629242, 0.6076824877712301], [0.12028181601108723, 0.9152138698505039, 0.6304993446866864, 0.5238237081731094, 0.6134410018946003, 0.8837042295790649, 0.1563451345718735, 0.4213204684622648, 0.6648549967124338, 0.4838025415986368], [0.0324025095777537, 0.4638687152712483, 0.13222385068306897, 0.9216191263013891, 0.918958564897309, 0.8305696839693382, 0.7390680110225922, 0.47607840979370386, 0.8123736284887365, 0.9213148298261847], [0.16137122055340636, 0.06808435023505, 0.48280276145643286, 0.6137275811526444, 0.8308941539382924, 0.47759698625181435, 0.6863751942539436, 0.9074979325585644, 0.40798236718875724, 0.339777761728389], [0.12326406162360526, 0.7879738547886185, 0.8435821920115966, 0.4931021627969313, 0.21676829193959568, 0.9883200290479921, 0.0803328226810619, 0.727741654169888, 0.9953810909877138, 0.007635336766353751], [0.35134119648885276, 0.5604648814781981, 0.2734354520590717, 0.060604193246020066, 0.9607445258860848, 0.45100092746438225, 0.8893479093441196, 0.6674478326576558, 0.9232683366918, 0.1402599160896536], [0.40825716929117717, 0.9618855916998793, 0.9763724950681258, 0.498250213142563, 0.22237908898812608, 0.11839658919620966, 0.9812528498551838, 0.05708675360027915, 0.6445519982313046, 0.2386508336391], [0.03816435211442626, 0.9216316395724062, 0.32357153607908373, 0.13700736009345882, 0.6854597143854805, 0.3542617647218608, 0.20621944281042248, 0.4009285302601697, 0.6913070633539063, 0.5626710254431051]])))+np.sqrt(abs(1.7471352845706216))+1/(3.489373170323885)+np.exp((np.array(range(1, array_x.shape[1]+1)))*array_x/2.8954243741403323), axis=1) |
np.mean(np.round((np.dot(array_x, np.array([[0.07128059005962573, 0.04647094612184921, 0.6630451260330058, 0.30135179868762985, 0.044949868532666915, 0.9402064738332501, 0.5770042932419257, 0.4368123821223814, 0.5981898058718415, 0.17860845102561018], [0.8537349668181513, 0.24155945000186219, 0.1671811151060818, 0.19453161913977157, 0.8655150079921725, 0.852103428101799, 0.8333522360841563, 0.6315912257466907, 0.1786230105012152, 0.18441018678701016], [0.9751921111156786, 0.8628511959929038, 0.7171819111967574, 0.08445198776104812, 0.620098795575976, 0.713419422740644, 0.7424588996046841, 0.31104216624567427, 0.952602645050372, 0.9720256358091757], [0.35250166973377706, 0.9709731366874258, 0.3116790308361671, 0.2634155722729271, 0.9611072307853139, 0.9366382340269485, 0.5997801876576576, 0.9936517114169127, 0.6710586234693271, 0.7356458651803606], [0.24465972632931776, 0.3187253601984904, 0.0745347517016074, 0.4803927837573284, 0.045513079579933335, 0.5435872975453395, 0.4864037946051011, 0.7320209588163016, 0.7838481485011459, 0.7253803391935383], [0.6239266549381745, 0.713266194785367, 0.6369662478621577, 0.5402918233305206, 0.8769308029556393, 0.27287368544040946, 0.19026596077703462, 0.7071344795254534, 0.5393782424470976, 0.5302967474250755], [0.2024854101975595, 0.8319307758453908, 0.5140838115660135, 0.9994079532561054, 0.8014092399044614, 0.10047082354450043, 0.31148005296514725, 0.8631108803845062, 0.37595275558953967, 0.19653858656173084], [0.31584764907740703, 0.8037807385158959, 0.6852490147316574, 0.3207809452601851, 0.730855066198496, 0.604608434186714, 0.5080498044308959, 0.6715215653962646, 0.9937651911946719, 0.34621884915024415], [0.98766148050031, 0.9369827881013254, 0.6727939433721887, 0.9697691305463095, 0.4332237089615516, 0.9866305078863509, 0.19493709655887592, 0.7348587242900284, 0.5350243038074033, 0.2319790049261793], [0.9006833627318487, 0.7051830352397167, 0.892095335168469, 0.5839303869310081, 0.34403786281135373, 0.41345081541196593, 0.7375032218883762, 0.5865669462864069, 0.1251877686026719, 0.07767561617912666]])))+3.536745285644188+abs(2.330923039797506)+array_x*3.320358196597498-array_x-array_x), axis=1)+10*(np.sin(2*np.pi*np.mean(np.round((np.dot(array_x, np.array([[0.930858764650698, 0.5743792507842757, 0.673647597445874, 0.76753405092388, 0.5500587025615524, 0.28842508274224343, 0.7689119225052763, 0.16342204181490882, 0.19748618632342607, 0.40675058484554094], [0.8304369327882756, 0.03784183032760269, 0.45735069552090224, 0.062333107352689376, 0.2880200416231572, 0.5030684395239635, 0.8960359663443457, 0.7248193737509736, 0.938378798375843, 0.30353284612975995], [0.5570233757426776, 0.012342958138530258, 0.40813005275569847, 0.4774558997943045, 0.2562562813491607, 0.7372134002699775, 0.8667466805540683, 0.92886501776737, 0.6164133407426237, 0.2635983308337889], [0.24529605431037727, 0.24765066332610775, 0.532910417617114, 0.2584574212441002, 0.668054884375697, 0.6415980048617699, 0.9350356951822125, 0.9832401585426087, 0.36120222878234, 0.5232508061379262], [0.018324223628668035, 0.9195141126949665, 0.3773530558920135, 0.8449771331702155, 0.6204660989335987, 0.8256241662615807, 0.7342002774834395, 0.8775758082399276, 0.37400943456625324, 0.7518593749297674], [0.849258849305816, 0.2957133911372307, 0.5031204525920956, 0.5559202451981882, 0.8994091411909355, 0.608138591916295, 0.8289657721384115, 0.5426141079581435, 0.5480084717988838, 0.6686638185501781], [0.14150895986605783, 0.8115641839443208, 0.22938900392263395, 0.2881162711681763, 0.023578203852988477, 0.38909465482264727, 0.32733197076488885, 0.8387975159580406, 0.1149207511851984, 0.5835400252197901], [0.11463446012441447, 0.13410724906844917, 0.48134547590095655, 0.2967548436600892, 0.9364664347433378, 0.5448265444130306, 0.04142982347474178, 0.4058693753129182, 0.7812201723763975, 0.3293852168447522], [0.7502176069443124, 0.1153323607302319, 0.10995348264545501, 0.2823055868272395, 0.3900146526303918, 0.3549917775408272, 0.03037623811870116, 0.8455150513353168, 0.962927545343716, 0.6942714341479863], [0.3641885038876421, 0.4703242387179648, 0.961007312838381, 0.16804378649867735, 0.9056952339311729, 0.8876994654168818, 0.8066614323408611, 0.6425928319260035, 0.5157751751207105, 0.24974665162463294]])))+9.32671461603402+abs(7.42638240936119)+array_x*5.491589128929579-array_x-array_x), axis=1))) |
np.mean(array_x-np.square(6.0547906302849706)/2.9316402311869885*array_x+2.976153724097742, axis=1) |
np.mean(np.round(array_x-5.564142251976682*np.sin(2*np.pi*7.844352339639679)+np.square(array_x)+8.490672080652324/2.3558756058250387-array_x-np.sin(2*np.pi*1.0759740506872166)), axis=1)+10*(np.sin(2*np.pi*np.mean(np.round(array_x-2.9829564505997466*np.sin(2*np.pi*7.073934417367377)+np.square(array_x)+4.431696012605556/2.394365363082613-array_x-np.sin(2*np.pi*3.735976928948907)), axis=1))) |
np.mean(10*(np.sqrt(abs(4.066360055839255))+np.cos(2*np.pi*array_x))*np.sqrt(abs(5.124503806450604)), axis=1) |
np.mean(6.482375134381739+np.sqrt(abs(np.cos(2*np.pi*-(array_x/5.758457075574735*10*(8.066209724593001))))), axis=1)+10*(np.sin(2*np.pi*np.mean(5.731153629674182+np.sqrt(abs(np.cos(2*np.pi*-(array_x/1.683813430640463*10*(5.608339697456142))))), axis=1))) |
np.mean(np.round(5.5647591562445715*3.730352294129017-array_x*5.189567682188176-10*((np.dot(array_x, np.array([[0.7859503582221701, 0.4628009090972417, 0.9101938155785205, 0.06105617749464809, 0.6784382251325551, 0.39653662962057024, 0.3607972451190964, 0.13184598764660527, 0.8446107716128007, 0.5074878252499625], [0.06491165222907125, 0.04769613454494204, 0.28110988056133357, 0.7262328845538909, 0.14777991277931413, 0.5663601482631062, 0.2177036147865239, 0.6941297507796089, 0.3908318546663373, 0.35497902767071376], [0.23861847039665973, 0.2729174644615736, 0.5373759574458412, 0.2412540844535268, 0.6709011966853813, 0.1814016723041021, 0.09799314147503091, 0.9133707925018484, 0.9773924440816256, 0.03771773604945283], [0.5066453928902512, 0.9881773043013525, 0.8047063982739289, 0.8224320210687918, 0.8194421103372851, 0.5234138291597096, 0.42074979319204553, 0.4852304428026025, 0.7325049701864332, 0.24770452664955966], [0.4721445666672991, 0.436455352652209, 0.19267307490316943, 0.49115247377206417, 0.2887969276457196, 0.30556674767089154, 0.6662398846814523, 0.6543593691100119, 0.006570813659091068, 0.5029101849724898], [0.7506255594428834, 0.33414535153270664, 0.5674702175744443, 0.3081722740600864, 0.018650487983285613, 0.7454082218787356, 0.8123487997100636, 0.8773420401803909, 0.39586173774962874, 0.2624523656057851], [0.19458846597953072, 0.7283149309653195, 0.9280176407393483, 0.9855922747619977, 0.9866732858281068, 0.29680176916851286, 0.3518429423008562, 0.9848946885695374, 0.8817586071693001, 0.7775105148578921], [0.6331891463639315, 0.5773648421783657, 0.26128736824006116, 0.05754535962775531, 0.5775017476841942, 0.9152121546988125, 0.6658461414372854, 0.8616165589765654, 0.4424081381705205, 0.9642451476737156], [0.0481546606982044, 0.8006849409260398, 0.6833173525559052, 0.728353755534457, 0.16307768006436785, 0.7777549170603054, 0.4119344639111294, 0.2871718760457441, 0.447833232565383, 0.8861437561118053], [0.14651762649888667, 0.22142671706657835, 0.6413931326234268, 0.9527580447662196, 0.512108041189787, 0.6231802626892841, 0.1509746537251465, 0.740158961678735, 0.7091008893317218, 0.21556151355887798]])))+9.30565687501298)), axis=1)+10*(np.sin(2*np.pi*np.mean(np.round(6.249488665843511*3.8881220816414492-array_x*9.46884921521127-10*((np.dot(array_x, np.array([[0.09197398813288571, 0.20104761257652137, 0.3281779458437454, 0.6432643236495621, 0.917585849828164, 0.24699437162954518, 0.9067248113829464, 0.4051799054480629, 0.3493070306397237, 0.27262298388255224], [0.8026649520967262, 0.044380319323954254, 0.7086231590196888, 0.4203741154661371, 0.5732830749613518, 0.39583396816288474, 0.9536537174041794, 0.858091258332974, 0.6099861082774377, 0.14565035913778313], [0.7898764200434558, 0.46584569834863754, 0.9171187219616808, 0.12125078073766016, 0.3225229664888525, 0.3771492168073436, 0.15819467635422102, 0.5252054914354423, 0.19362066771961772, 0.27847126979137504], [0.8845849819919105, 0.7975475985081677, 0.6160840172679455, 0.10511766043757753, 0.9351573104316929, 0.01503779872334865, 0.9611215837951522, 0.8897912633427312, 0.6506805549592124, 0.8371248624847181], [0.9893160541971964, 0.43680938658587176, 0.9527272594854046, 0.3288402897364713, 0.06670732699699877, 0.608028062049531, 0.6719276245631844, 0.881187126769929, 0.41850046138528463, 0.007053947061370414], [0.5863943967292925, 0.5849086749778513, 0.0814250534424148, 0.43731513239690956, 0.9170054845354639, 0.973729745390794, 0.9382648644648542, 0.4006477585936412, 0.2606985489474445, 0.9722866543073836], [0.982982132822245, 0.9092678092150992, 0.38329282414502375, 0.4160371559875544, 0.9385001925470076, 0.33234966629969875, 0.4731536569190897, 0.46382532815584365, 0.7725590833012047, 0.386601730408893], [0.3309218029125125, 0.6156619597611608, 0.9987526518914291, 0.045257242213843196, 0.831098906622035, 0.8961796488143916, 0.7312394016347356, 0.2750808244294404, 0.7507635911307486, 0.8934235913285754], [0.5045880699361048, 0.004774182679180394, 0.24563688344671297, 0.07669451152583406, 0.5708819252506587, 0.6267292266132827, 0.27822192294657144, 0.25464343447175053, 0.29235267870598747, 0.4456112171664318], [0.16338272073254956, 0.25106063297587355, 0.31976020370502733, 0.5737766910236773, 0.6152836899098735, 0.6453619974814189, 0.05894265398980836, 0.4433993330250142, 0.872385318383054, 0.6407268569826872]])))+3.4842362719061093)), axis=1))) |
np.mean(np.square(np.sqrt(abs(array_x*5.969085648771104))+np.square(7.000150326066179+np.square(8.315904926477526*array_x))), axis=1) |
np.mean(-(np.exp(3.223595217639738*array_x)*1.0229740804686676), axis=1) |
np.mean(np.square(7.561865595657976*np.cos(2*np.pi*np.exp(array_x)))*np.exp(array_x+8.73220148034806), axis=1) |
np.mean(abs(np.square(-(1.8937947369864492+array_x+1.982948836491746*array_x+np.square(9.677499193762644)))), axis=1) |
np.mean(np.sqrt(abs(np.cos(2*np.pi*4.939332761953841)))/np.square(10*(array_x)-6.702686343091951), axis=1) |
np.mean(3.2216381959173916+np.square(np.square(array_x-6.998006144169176)), axis=1) |
np.sum(np.round(array_x+3.180023086582305)*1.024361973098677-array_x*array_x+2.751590975051189, axis=1)+np.sin(2*np.pi*np.sum(np.round(array_x+4.592282095405933)*1.4981435477403355-array_x*array_x+4.1685251435068675, axis=1)) |
np.mean(1.2581065274929153*6.939622418961715+array_x*np.exp(6.63684078668539)-np.square(10*(array_x*6.02433599177737)), axis=1) |
np.mean(np.square(1.4650587393819583-array_x+8.885816734982384*np.exp(5.246098508644465)), axis=1) |
np.mean(np.sqrt(abs(3.538056502078731))-8.465717295475454*(np.dot(array_x, np.array([[0.1549489444541805, 0.750874959354451, 0.6335387722124436, 0.29463256637832547, 0.2900202194341771, 0.24681776354078722, 0.3058915432582915, 0.7905563176696115, 0.424383263889189, 0.8786537530049863], [0.7354230312044028, 0.9738373515660748, 0.6504198990450704, 0.4527219615015937, 0.12056275024307561, 0.5224353044546063, 0.6609218318089107, 0.4457681259297731, 0.07071782020771644, 0.5732402853148442], [0.04696022434262459, 0.36044693748053824, 0.22609526950744796, 0.1115007463854073, 0.48182113127046877, 0.4135644989183199, 0.2703193582357907, 0.9349919210844353, 0.24001374438266343, 0.8058266925843312], [0.684767153505401, 0.5304089520880817, 0.0787228149469803, 0.3686658082892791, 0.15733250699255474, 0.2349518470065275, 0.7153498811176402, 0.7960296797760608, 0.6088838765122732, 0.3771435456939247], [0.6177515062884866, 0.15147790932369587, 0.02452481883734825, 0.17816677770548106, 0.9045211300853483, 0.0008963944922391054, 0.15107117536464254, 0.484542560790611, 0.5089224287278213, 0.23366888444244294], [0.3381787108925559, 0.8644262357072909, 0.3920951765219284, 0.07883797350263066, 0.897296005858857, 0.18412633811890167, 0.585832224444148, 0.5399498608133749, 0.02841808360152187, 0.07825069907525295], [0.5880084673187552, 0.40030750604467213, 0.8620501872463497, 0.6482661410187547, 0.13652587086144807, 0.8083734125096242, 0.5944401569054737, 0.18234518351376716, 0.5247935427113858, 0.6742228906286803], [0.3772070394445537, 0.26556006749872174, 0.3792928434689563, 0.5343791699199046, 0.5437558987295545, 0.47689457767943577, 0.02036518138116239, 0.7106454092932547, 0.07336662617703416, 0.07694165954264331], [0.7082459592719119, 0.7377963890701802, 0.45100529858412786, 0.7563748578068448, 0.8484304729178137, 0.28085413577693685, 0.7625131939625078, 0.3665447958073408, 0.5208410249435167, 0.8659512263410384], [0.2191944101762603, 0.3697485543443758, 0.8967971806415175, 0.5842274201743514, 0.5781914327946702, 0.9646327272653885, 0.2859048114984333, 0.832155836479558, 0.38306340044063125, 0.608021348553162]])))*(np.dot(array_x, np.array([[0.3987610766769546, 0.05030891123639536, 0.7286101837385892, 0.7713515664446992, 0.964074360002846, 0.8910129107660919, 0.8817208599245564, 0.9731868806582479, 0.41901846410057353, 0.9428768732922211], [0.4158600612000489, 0.3268973393274496, 0.23536352053084864, 0.9837711175759654, 0.237360578139031, 0.31275934976484776, 0.2050420729906931, 0.762565906693542, 0.8976994688549917, 0.3211921607105841], [0.5010588984284586, 0.47160469215600953, 0.8030717061171261, 0.47769991423774116, 0.2500693863028036, 0.8776644476465746, 0.7519064127082443, 0.7016724188966088, 0.7944272310857012, 0.34169658467373787], [0.9939242762362195, 0.30418737335889834, 0.2859652007423735, 0.9060537025420431, 0.5927557128998117, 0.7968570840988072, 0.9061361873992942, 0.23639396617541875, 0.20328819495034955, 0.19503523675328005], [0.19092134475111722, 0.5726109324333818, 0.4125742018355456, 0.5945968906646697, 0.8589025414081874, 0.018940140109768078, 0.7202178789664483, 0.34940499516058476, 0.007511690012560801, 0.1385759809951137], [0.24142572956092723, 0.5903386956665129, 0.2623758306094335, 0.5031050490755777, 0.1449466408712351, 0.7720620781462135, 0.8322596128129806, 0.20618671746562878, 0.6862063648911247, 0.6749734515425628], [0.3842938796620783, 0.9211806076982937, 0.5242949426416145, 0.6879029254353966, 0.024267004425981842, 0.7955335695666276, 0.334531486742089, 0.10831611611611269, 0.4963641889054673, 0.01486669757836856], [0.30748066138081454, 0.3153439575510837, 0.22149321529555965, 0.8305509912890001, 0.5373599482657907, 0.5559121335845644, 0.8225095650628717, 0.931751976349046, 0.5122230915420144, 0.9643012383415608], [0.416451920389749, 0.45349882409671016, 0.5203472937549576, 0.5663360864746604, 0.45782001450767407, 0.707935010315899, 0.0372453405089922, 0.9011864121809227, 0.6550554270753056, 0.02416244347562324], [0.6904424644799515, 0.839577831754332, 0.7637481035514674, 0.5773868135921323, 0.06485469261407961, 0.15915779811548914, 0.5533095586335026, 0.203917906607459, 0.37791302303676677, 0.5096499467823521]]))), axis=1) |
-(np.square(1.3824443497290144)+np.sum(10*(np.sqrt(abs(array_x/4.179117197539365))-np.square(np.cos(2*np.pi*5.8610287440596025))), axis=1)) |
np.mean(np.exp(3.436585355195931*np.sin(2*np.pi*4.286266030179861)*array_x), axis=1) |
np.mean(np.exp(np.exp(np.sqrt(abs(np.cos(2*np.pi*array_x+9.006412064194548))))), axis=1) |
np.mean(np.cos(2*np.pi*array_x+6.362801807511944/np.square(7.337658828211436))*np.sqrt(abs(1.0112463977023536*array_x+3.330070274348356*np.square(9.891986572894364)*(np.array(range(1, array_x.shape[1]+1))))), axis=1) |
np.mean(np.square(np.square(np.round(np.log(abs(np.cos(2*np.pi*(np.dot(array_x, np.array([[0.5433201078798596, 0.5201597659954167, 0.8261788992915111, 0.9177375746543758, 0.7832043732699911, 0.563713536381887, 0.22317783692598225, 0.7496533926544902, 0.08518986287031904, 0.9918997201966605], [0.4139455756827366, 0.9823702682122445, 0.4332043990236637, 0.5362337885653068, 0.0996761880162369, 0.3526286832334671, 0.21213234510015766, 0.26433999691922183, 0.25324822837345573, 0.3180064878012322], [0.6620706494953437, 0.1799953843348865, 0.9014233665213225, 0.7612841639545017, 0.579101167570062, 0.46648521614989447, 0.43337289225437026, 0.09027939391469952, 0.6856202522872713, 0.2171794102530492], [0.6603036590207322, 0.40855896809706216, 0.8637790498753143, 0.5443137001560671, 0.43891240968076917, 0.13783405324031361, 0.09444296420964038, 0.6871457322474568, 0.47658645854569537, 0.7521111439685145], [0.599973133242005, 0.24367825034157742, 0.2960260463195753, 0.8737722693622363, 0.811708796718174, 0.5078930337584007, 0.37118218980229845, 0.3916905955912138, 0.33380968819993373, 0.5252448321847111], [0.7905088568547279, 0.3660796041109319, 0.020872006638596563, 0.3934048353180546, 0.6271934823053107, 0.4987477392619608, 0.8028201295437376, 0.387118927354141, 0.779695313699654, 0.44694986347565113], [0.7238669454353868, 0.5309936294858587, 0.20934181336266444, 0.14892268658512553, 0.15996481284573738, 0.3955153718559462, 0.2802735049156372, 0.34084102629617674, 0.7160615406036613, 0.801874016590158], [0.34847755147053416, 0.2130230219778122, 0.3077116866352477, 0.5565788876593062, 0.8355080191229168, 0.15893291647127628, 0.20401009884135768, 0.039681876257263715, 0.3193928025204036, 0.4781164141203511], [0.28323303825626767, 0.9278232365451755, 0.26885837377548294, 0.3918648854369483, 0.671431087047693, 0.7720215024464537, 0.23578455918319874, 0.5462796762358935, 0.23430335223206944, 0.5330867009924799], [0.28488887602472435, 0.9972254777067289, 0.2371674151404174, 0.38187187140045487, 0.02615129151495854, 0.9803479391687522, 0.7657970194296889, 0.4230784231472682, 0.28123248464859485, 0.5119802975489127]]))))-(np.array(range(1, array_x.shape[1]+1)))/4.262944954023569)))/abs(array_x-3.1652042172424+np.square(array_x)))), axis=1) |
np.mean(10*(array_x)-3.9394557752483648+np.square(5.636919475174157/(np.array(range(1, array_x.shape[1]+1)))+array_x+np.sin(2*np.pi*10*(6.630198859796448))), axis=1)+np.sin(2*np.pi*np.mean(10*(array_x)-5.264280346422+np.square(1.6083971379263597/(np.array(range(1, array_x.shape[1]+1)))+array_x+np.sin(2*np.pi*10*(4.838683019660723))), axis=1)) |
np.mean(array_x*3.5763545033912885-7.174472927331552+array_x*2.6883841507626633--(array_x)*7.381454122147144-6.711370936451321, axis=1) |
np.mean(np.exp(3.3077149548289153-10*(array_x)), axis=1) |
np.mean(np.log(abs(np.square(np.round(np.exp(np.square(3.04211108886008+array_x)))))), axis=1) |
np.mean(10*(np.sin(2*np.pi*np.sqrt(abs(8.930523043164108-(np.array(range(1, array_x.shape[1]+1)))*array_x+(np.array(range(1, array_x.shape[1]+1)))*array_x+(np.array(range(1, array_x.shape[1]+1)))*array_x)))), axis=1) |
np.mean(10*(np.square(np.square(np.log(abs(np.round((np.dot(array_x, np.array([[0.30871883223168683, 0.6303706487185916, 0.5042196084885932, 0.6858528592777716, 0.0988321540364735, 0.7705163583345218, 0.12266385415216763, 0.9683837459365215, 0.9640809128202733, 0.33833815431438463], [0.0840652614465397, 0.2539737764493184, 0.22444940798057833, 0.37903138156978555, 0.8175915545478116, 0.30449154151139146, 0.5570800377089634, 0.8044600761071529, 0.8274573448941466, 0.7733825789793317], [0.6531777165386148, 0.25409252385960557, 0.049615452885240496, 0.615272974340506, 0.22098344148521665, 0.8237533585636517, 0.03139737382291219, 0.13199370299171087, 0.9923195364761932, 0.05062310221362465], [0.1519547452405876, 0.329194235491598, 0.17590523749976705, 0.8329864782757576, 0.09876812953934777, 0.06456839630243905, 0.4483218488122478, 0.3080865004518777, 0.2813129371231947, 0.42213418014212434], [0.8763461931742036, 0.3937376271052565, 0.6160290663198631, 0.2520671552909973, 0.014210788867947577, 0.6269154777314289, 0.7576921504333949, 0.750865479429018, 0.49817352015800553, 0.13556049742219467], [0.25885438751482737, 0.5926545260188435, 0.8661642812177907, 0.18534750197240057, 0.22393230392994778, 0.2301196045942817, 0.822387523918369, 0.9538112246713396, 0.43573436207830485, 0.5337145903571119], [0.16440974797404928, 0.5755799549205034, 0.2098680124966935, 0.06683548258659633, 0.06781807046202226, 0.7361641195429167, 0.11363296457941441, 0.3844709067729414, 0.6294753886527974, 0.9125109319411363], [0.8074263664078964, 0.002362863501731316, 0.5952459818420988, 0.7066322184519683, 0.1456142309413282, 0.15014202453941716, 0.9464286021042178, 0.355232952646805, 0.5397800901399612, 0.809652061919616], [0.11899761532266495, 0.8242118274702247, 0.605245463814546, 0.34032366681841064, 0.1352862635489227, 0.06379524738807651, 0.12131306631982042, 0.43488971888977246, 0.007242916617289263, 0.31463262311212037], [0.10890109798508485, 0.9281850008769531, 0.4114553424616775, 0.5649158538754357, 0.5356200904117439, 0.8140144170891476, 0.1725678455730415, 0.18792341732206608, 0.9874958352872601, 0.03582561750319613]])))+8.018056362487297)))))), axis=1) |
np.mean(np.round(array_x-np.sqrt(abs(2.2757459475801416))+4.534015352836845-(np.dot(array_x, np.array([[0.8233827839179383, 0.9500746436014696, 0.18852073802862312, 0.14722988479027754, 0.24272597283050001, 0.3732302427215296, 0.09246758655431964, 0.707168326769211, 0.11442191604731955, 0.7303212488653111], [0.41274005321261575, 0.38522265485344, 0.5615010556559124, 0.5507956731542613, 0.04257444574453262, 0.3912608182808599, 0.9471376405318469, 0.6923260923512427, 0.4380510653923879, 0.5224100686046064], [0.5420122010289417, 0.6469428180458887, 0.8062506699924595, 0.36182493436671215, 0.25939585089308026, 0.5025059548530691, 0.9685442009104438, 0.17715057437840787, 0.03378735290382617, 0.5670368440766866], [0.9457545024445383, 0.8321316301892789, 0.29036268941353016, 0.12771556489267188, 0.62493991192747, 0.9615723119518188, 0.7356817883085744, 0.5430502664364608, 0.2619989968930547, 0.3528904596070268], [0.6806524080623562, 0.2521698159571406, 0.41879684173955456, 0.012376707509016094, 0.16982153603576366, 0.47015960069073304, 0.8309623217907273, 0.36923749952290275, 0.3184758340191237, 0.8813940898700706], [0.038887206927923024, 0.8423932784238791, 0.4899744458719404, 0.46688076321317284, 0.7876153995818801, 0.26477369745951995, 0.13000212063018524, 0.2028251245688245, 0.07988112628329191, 0.6730078674865005], [0.9311564361363307, 0.06402989781436763, 0.6731723229671298, 0.9265506645125191, 0.6895615002332344, 0.47275071115851464, 0.1305045220326756, 0.5862209108301536, 0.18132008783252274, 0.4555728725549937], [0.32549006949283943, 0.267899385447668, 0.36397332054670584, 0.7909100744531592, 0.9785380498180216, 0.646562106533744, 0.14667344915848668, 0.7501171415557475, 0.5722768888833275, 0.9169835637707971], [0.3887224392549544, 0.729674185115214, 0.11671170851589852, 0.30615560847570145, 0.6661865885725206, 0.9448962544302618, 0.7580790671363167, 0.2786903545270396, 0.8515858298558743, 0.33010281677386144], [0.2798877078741866, 0.7298505089001333, 0.1519868256106689, 0.47034862680720124, 0.597146443465972, 0.1290536069052155, 0.9150590962505318, 0.518891346009153, 0.3176125603305231, 0.34916589416563604]])))+np.square(4.950045719069417*np.log(abs(array_x+3.8004012135896126)))), axis=1) |
np.sqrt(abs(np.square(4.201543657386712)-np.sum(np.sin(2*np.pi*np.sqrt(abs(array_x))), axis=1)))+10*(np.sin(2*np.pi*np.sqrt(abs(np.square(4.8084262051823785)-np.sum(np.sin(2*np.pi*np.sqrt(abs(array_x))), axis=1))))) |
np.square(9.77964135491322+np.sum(array_x, axis=1)+8.995096137311517) |
6.332614159646576*np.sum(8.98244762444632-10*(array_x), axis=1) |
np.mean(np.square(np.square(9.310376429222218)-np.sqrt(abs(array_x))*np.sin(2*np.pi*abs(2.6856076283322974))), axis=1) |
np.mean(np.square(3.43939294662359+(np.array(range(1, array_x.shape[1]+1)))*array_x+7.158870354454463)/np.sqrt(abs(9.581884097807485)), axis=1) |
np.sum(array_x-7.769048752778098, axis=1)+np.sum(array_x-4.590819495221984+2.502851264042603, axis=1) |
np.mean(7.717558742375509-array_x-1.1636258810411888, axis=1)+10*(np.sin(2*np.pi*np.mean(5.497760989925807-array_x-4.557181490180911, axis=1))) |
np.mean(np.cos(2*np.pi*np.exp(3.570012150060993+(np.dot(array_x, np.array([[0.8893808988828431, 0.49326643287389205, 0.40470878732283955, 0.42193091972401164, 0.22088454497291732, 0.9727310203621788, 0.686829594392422, 0.32853110172977895, 0.8593427222358917, 0.8727405305255117], [0.5173983953045584, 0.3791490295356017, 0.8678550755208855, 0.4121922732311494, 0.7938001818596799, 0.5069025232967013, 0.38879381096690724, 0.9143854583653835, 0.32478686135179746, 0.04749927747766447], [0.3463995428294414, 0.03518764517781936, 0.5952726131024864, 0.9429263511347871, 0.5053993028585886, 0.49751595902938284, 0.4754064464429988, 0.6103587789009081, 0.7937420057560297, 0.8428388175069602], [0.8698378398493072, 0.14486410924077187, 0.5710164193438385, 0.6052790943272405, 0.8270679958664513, 0.6531527270348523, 0.9041292099586722, 0.5911468839586352, 0.15838668524855803, 0.05007618989835283], [0.812347144952365, 0.17373210561702812, 0.7213058850092631, 0.4945848793184373, 0.272254444254844, 0.7195348432584858, 0.1904140471606932, 0.295191153735452, 0.05480165769004708, 0.9368510852344982], [0.7788325793569454, 0.6904288512740047, 0.06755904102482468, 0.30340784648302455, 0.5251602705724113, 0.2534072041151746, 0.3055679056354472, 0.8315401356364062, 0.2551646660540512, 0.6113269703337524], [0.5418525239351324, 0.643398948223707, 0.5565311839960815, 0.6236826110115953, 0.3882513756486675, 0.7490407919742308, 0.924460544880235, 0.9912271832524429, 0.13366708708655672, 0.9399893149819907], [0.4489318312138474, 0.7809994345616215, 0.651048485409044, 0.48733763352628057, 0.4880766294971286, 0.7001182003314884, 0.9861830402574253, 0.3775888577491625, 0.46316159779988997, 0.776057536903448], [0.7191602122341344, 0.5266029190174336, 0.8829455019803529, 0.7567458375633652, 0.017444520644544714, 0.8798683980109886, 0.3178115261837522, 0.1685437811510554, 0.6714559795703235, 0.018369710170290543], [0.7525390385209485, 0.6772231755711696, 0.7920827884300983, 0.5747358820580473, 0.022134669550344155, 0.36781398900812035, 0.5909301766280886, 0.9908616647706383, 0.15708687226372953, 0.4976324121672441]])))-array_x*1.3828293520803152*np.sqrt(abs(9.023351148667492)))), axis=1)+10*(np.sin(2*np.pi*np.mean(np.cos(2*np.pi*np.exp(2.424646754477274+(np.dot(array_x, np.array([[0.6037859424011256, 0.8856302108192036, 0.005984104416364988, 0.4416639887417868, 0.5968397363377521, 0.40750176111455083, 0.29199868977459664, 0.04591220544020913, 0.7524986011387711, 0.7367685581256784], [0.29520686041465294, 0.43409117344504333, 0.4781980845650937, 0.8601415141499574, 0.6141288172105582, 0.7538806164508725, 0.5093367828081655, 0.9591202489355413, 0.36457935366545446, 0.7798103711638733], [0.09821329151519509, 0.7070935438852975, 0.05261280165381954, 0.7442804929774307, 0.1678525538863268, 0.7900959310533712, 0.8914876557023417, 0.17661728013748412, 0.14627922937063842, 0.78595437638631], [0.23759710519462796, 0.8586095133757272, 0.4726290793996536, 0.9341647637973739, 0.5319303550528584, 0.557022217992106, 0.9560836472822877, 0.3677743776544641, 0.42512647979109586, 0.47375879443670743], [0.7480630517622904, 0.1991660251931654, 0.9524795678945288, 0.3465170589178763, 0.12276390239001711, 0.40992915963949095, 0.8124329223435651, 0.608084205053146, 0.04989281975842963, 0.9803757266918711], [0.2693002119853012, 0.5136233518105733, 0.6936501775175115, 0.6615866429895985, 0.3586445312996912, 0.3784859530223016, 0.4509426508771873, 0.1799567240812543, 0.7684212530833306, 0.3914969436849274], [0.2543966929041941, 0.9047516938762241, 0.06824987262465676, 0.05868417202001652, 0.4214076860319894, 0.040849582741031676, 0.3944121419910809, 0.44297330268048085, 0.43373433535307526, 0.8325647792652101], [0.07763262298974327, 0.46472331368104514, 0.666313770182331, 0.5288168432346401, 0.39659417954141163, 0.8626835061333445, 0.6928708092008452, 0.12581666615218545, 0.4297253975654245, 0.13872184962225853], [0.2916601399507788, 0.3035006836811325, 0.5777005351743567, 0.0010652247192499686, 0.9348754642980595, 0.3136154590474267, 0.24293229108829362, 0.8627734719280954, 0.6917661759167271, 0.6114954189619755], [0.2843092676308321, 0.8508254329459245, 0.9866343595601463, 0.7634980504336623, 0.4549971226915448, 0.45809795266035225, 0.9855739739537187, 0.7071342296887363, 0.5587451552277657, 0.9731713460586235]])))-array_x*8.177512377316656*np.sqrt(abs(6.893920718401658)))), axis=1))) |
np.mean(np.cos(2*np.pi*np.sqrt(abs(3.6638343203172044+array_x*np.log(abs(6.608400420791842)))))*7.444644933196022+np.square(array_x), axis=1) |
np.round(np.square(np.sum(np.log(abs(array_x+np.log(abs(3.8030974292948665))))+2.6869520794380652, axis=1))) |
np.mean(np.sqrt(abs(9.575886353833122-np.square(7.708620787311827)+array_x))*abs((np.dot(array_x, np.array([[0.7891944386710088, 0.5392633056457544, 0.4629387142421376, 0.38216050438762295, 0.5927527685361395, 0.9697546902491349, 0.011239348319740938, 0.19495665026456477, 0.4499262881694214, 0.41528397533351014], [0.6968482968572384, 0.993260519229192, 0.5730354790287426, 0.4552609068945226, 0.05568744801392567, 0.2585640148833964, 0.48270785282414297, 0.09757781533467424, 0.24811112146482972, 0.17468800585572686], [0.7987456762152972, 0.8812726097480105, 0.645737344839329, 0.057665521848648815, 0.06593244149617561, 0.27332748937274076, 0.25477796953501153, 0.033038719111755266, 0.5935498836863393, 0.4141861286278653], [0.8897280759081319, 0.6749236480826171, 0.9980304168479794, 0.47686921762416523, 0.042668666703207125, 0.4656324271517488, 0.13897716525386428, 0.0028483429143369987, 0.969608423313879, 0.902183615356162], [0.030865140697850957, 0.8869099901503478, 0.9828343397860638, 0.07087842392946542, 0.19042483197161986, 0.004025995859882614, 0.6641182662138856, 0.29290634374238156, 0.00923827511574471, 0.5358654808473423], [0.3001066254719149, 0.746087675086069, 0.6882386322774676, 0.24587805424857512, 0.3250438273316113, 0.9259626791003424, 0.32519122969459124, 0.8509299228930033, 0.5620419908635105, 0.34733078930296823], [0.7720642088835314, 0.8607188105118552, 0.03229152531004409, 0.01826949356101315, 0.35420851186861324, 0.1350368020300895, 0.7617745867420156, 0.7834768099529689, 0.4082242914587224, 0.34250348547118814], [0.6816273669763735, 0.6342529600422203, 0.5099148710616553, 0.30715150556629967, 0.7054692885880508, 0.19005427424087817, 0.7654713985685044, 0.441370173523137, 0.19402310353378593, 0.1691430366662382], [0.9755900259371407, 0.6895452657216236, 0.08481113836839715, 0.07495470764397505, 0.7946844161508673, 0.2910432064232479, 0.1872222908073934, 0.18218641962339344, 0.6423631937037388, 0.5833270459602424], [0.7782824582566895, 0.49391460868616, 0.5703937433782525, 0.2216316715906983, 0.9049255227182663, 0.20661539627773984, 0.4262177298493267, 0.12383779971942732, 0.9502830479473235, 0.7547590359204506]])))+9.994264063643788-array_x), axis=1)+10*(np.sin(2*np.pi*np.mean(np.sqrt(abs(9.009937523757152-np.square(8.548350794482221)+array_x))*abs((np.dot(array_x, np.array([[0.77855430855258, 0.4699753656293246, 0.9236611324116604, 0.9520650018770419, 0.6332305174499403, 0.45618840633554925, 0.7424335552411643, 0.06692731268164753, 0.12876120408838398, 0.8561376880813221], [0.23126762606416829, 0.6103166534451032, 0.34510988525091435, 0.910193621357657, 0.3865248618805679, 0.03253662798450785, 0.6104144633270556, 0.46743956292437294, 0.089592932682658, 0.5075228997539742], [0.6178107683515633, 0.9481534994499117, 0.5892573032478617, 0.11908523637042934, 0.8610447411614126, 0.7121607993808814, 0.10398588860301039, 0.6983376622471396, 0.4837267154001985, 0.5755263730312542], [0.8137946416609109, 0.16400087368255678, 0.27634295013060706, 0.9774545391456548, 0.21518681834266973, 0.9149469964656799, 0.23787112904499708, 0.6208129842039318, 0.07672888656615429, 0.12849962789869607], [0.806986708987886, 0.5769784293474509, 0.6497756526018548, 0.33494603021064406, 0.6468608228488935, 0.05435282299302324, 0.13889796074363592, 0.521095813114375, 0.47973059710081534, 0.41366275260190344], [0.17199773596923917, 0.3923331391233068, 0.17192311385534964, 0.5432956942571703, 0.7461925705555587, 0.6701669591569013, 0.7806951792214679, 0.5788440669493086, 0.6297316624292374, 0.10327695314311447], [0.2257900260117729, 0.34706081126434607, 0.48310081113453796, 0.3070429036084188, 0.6835624570005852, 0.4844608005343637, 0.8271439264801292, 0.27741394044664647, 0.7979841555675368, 0.11623763565888034], [0.5388327992641818, 0.5102680586302869, 0.21539778619390448, 0.7573450596068135, 0.442959425814772, 0.44022333887577925, 0.1100067015536641, 0.3206560683580223, 0.8803897833350932, 0.251815637557649], [0.8326721028924046, 0.4964058721124158, 0.06545710869311083, 0.9806613656207792, 0.33370845682522243, 0.3179479132728631, 0.8395459331432812, 0.16026688525770827, 0.3310674562608492, 0.1343146299889698], [0.015000298185595895, 0.2345493762459222, 0.49899714445117693, 0.16935347248046329, 0.3311670249772497, 0.053073973865039425, 0.6785546014538143, 0.21985959526656085, 0.1173687289654749, 0.9294780332596988]])))+4.948636673977532-array_x), axis=1))) |
np.mean(6.227220309755519*10*(abs((np.dot(array_x, np.array([[0.10982721793922001, 0.6049528364931737, 0.1299112073987656, 0.13291548406646914, 0.9937239042005945, 0.5666386497511475, 0.5733678627423904, 0.5436504423316721, 0.13464809392722887, 0.858678723492164], [0.6260504156832758, 0.7824864279378827, 0.2493700544856431, 0.894469007313111, 0.18524693513010737, 0.9771846589651225, 0.09372576614364436, 0.47983234690782417, 0.37664157212477456, 0.8979790530240135], [0.0446642590837123, 0.5952296905430423, 0.38528529539283907, 0.8651435182239312, 0.07728770464079815, 0.7560370430654509, 0.9378919558012342, 0.7843961671689031, 0.7742125963435869, 0.404952186743343], [0.06796643610372977, 0.782606668338607, 0.3281168925722936, 0.9028770163696016, 0.17652222087129632, 0.4212983801310275, 0.31097445293716175, 0.643393104792555, 0.15069278416434673, 0.6280681330255814], [0.6464653163949423, 0.15129466382075574, 0.013745285886446856, 0.3703956466810646, 0.08637990146653218, 0.6751916510024923, 0.4399647032018291, 0.20240516119983043, 0.43831451649855124, 0.20534069544618727], [0.31898918709031, 0.8404639058965706, 0.49073672342444696, 0.049722428672127506, 0.7681020941764697, 0.9818534762544542, 0.5609899912210283, 0.1543501974502408, 0.3197741257935499, 0.6284831267321951], [0.8968417464447601, 0.33706858841755105, 0.837879575094983, 0.05639954362446775, 0.808336613961972, 0.15687546337232083, 0.23437775111890224, 0.9302625131153083, 0.8234274394270907, 0.717626405266013], [0.7800541468064404, 0.1491758955299054, 0.6743602567182101, 0.5150189168580744, 0.4745702735902001, 0.6261878352226651, 0.15861366728783677, 0.6410128965175002, 0.3067196179071263, 0.4661300625988407], [0.01923494796776659, 0.5452656041616462, 0.05635705973424221, 0.7576145996879912, 0.619258211077722, 0.8198968096437421, 0.1722178262182117, 0.9877503073782175, 0.12335434584502813, 0.06177583607904957], [0.3503445885279083, 0.08807224181816808, 0.5557986917348371, 0.47112696939903154, 0.20136513005637013, 0.6803186828163912, 0.9790381198922857, 0.9537482273529195, 0.6798835725706044, 0.42841711355691703]]))))*np.square((np.dot(array_x, np.array([[0.9177312449373222, 0.28365485416102443, 0.8148385500388867, 0.1739189229569802, 0.9784550771434255, 0.035423728100895335, 0.7659231270009376, 0.47574384391806934, 0.15744610041607465, 0.2714430879403136], [0.5515031106592386, 0.10996603688911266, 0.33226694066036344, 0.4904863943821358, 0.3216417390763252, 0.3943394370972313, 0.7586705728531108, 0.5921402694055058, 0.5349355407423791, 0.11609869048040866], [0.6259772136210849, 0.5806395415793228, 0.8549537504883064, 0.392286770689928, 0.9535986027347322, 0.8651923077031953, 0.7495860323940152, 0.3243109008121402, 0.561045671322231, 0.20548712828550952], [0.1737344612517916, 0.43833163690914545, 0.004566716425350559, 0.21119806024877863, 0.3253857778411059, 0.14238147800998713, 0.39438333254476776, 0.9278732917252809, 0.43680845145212044, 0.9344537718146115], [0.7208598716722582, 0.11097190535459855, 0.8582897885076184, 0.9724212947403112, 0.6569392074414654, 0.4869543878638648, 0.8155593530919956, 0.7268699061503355, 0.9551171440825801, 0.8890953132482337], [0.18105164611195879, 0.882522173193669, 0.5551882495216807, 0.22881357032227756, 0.30014211364725585, 0.7906969935099908, 0.9356381736274162, 0.4454467791273896, 0.8660859188566445, 0.8794912157179356], [0.25125657551484026, 0.24669963501977532, 0.8841248060325952, 0.23468836872457655, 0.5799000252538273, 0.3803119034850828, 0.4909912625359366, 0.2290945317016494, 0.23617166000182277, 0.09476793968711827], [0.7838803090842954, 0.4617497098520684, 0.7755819382102092, 0.33023124473662446, 0.976348023981085, 0.6262216128716441, 0.6244103324237023, 0.8674403794817351, 0.10254987073657762, 0.28112380917580004], [0.4718678299608621, 0.013080918666040997, 0.8798778442805144, 0.9238097868443583, 0.34184482657340065, 0.03814021481180074, 0.5318734381910021, 0.23815581488446025, 0.21501912493065078, 0.3366487207014057], [0.9532011408999288, 0.23467662654209231, 0.6050179943843229, 0.532963080026749, 0.938075848032347, 0.30062135161867287, 0.029021871817557554, 0.6293646865982497, 0.7926599271832763, 0.8673146209195224]]))))+1.0705370585869673), axis=1) |
np.mean(np.exp(7.924452772987308-np.log(abs(5.089866025954226))-array_x), axis=1) |
np.sum(np.sin(2*np.pi*array_x-7.7336158539736015*np.sqrt(abs(9.25110903496585)))+np.sqrt(abs(array_x-3.1097555294646284-np.sin(2*np.pi*np.round(9.813632607573139)))), axis=1) |
np.sqrt(abs(np.sum(np.cos(2*np.pi*array_x), axis=1)))*9.964743223862616 |
np.mean(np.round(1.890266325625861+1.5200848312537478+9.372615309096238*(np.dot(array_x, np.array([[0.04223828130179641, 0.8786551250543463, 0.35983982021858885, 0.7583177639043401, 0.780193313038678, 0.19721883300350962, 0.7376031859698313, 0.25947218720662246, 0.15645635047505035, 0.7846394324215769], [0.42873099180409846, 0.8011291178956673, 0.9110085416182069, 0.801789962375867, 0.4138984726619488, 0.9547034696895851, 0.19450380453884075, 0.875038988272966, 0.9513252179789989, 0.8823604853734209], [0.044121909556481986, 0.20013201364899735, 0.37134683498420684, 0.21756068070765422, 0.4774218449915969, 0.7105216278867525, 0.10340843266855348, 0.19270493812347378, 0.41694034745008746, 0.7795582957583558], [0.9770802183079881, 0.4675308852628425, 0.28867905885299516, 0.9384874066841435, 0.5285039758683177, 0.3838364974028279, 0.6895321626340625, 0.8424434904769372, 0.3839762640507679, 0.4958662348855145], [0.6458273696302065, 0.2151699004572606, 0.15316507787384936, 0.3852264323617882, 0.6952017848531096, 0.36662519268053806, 0.7495234067052118, 0.7679739708768402, 0.5929439189615903, 0.10540042845166109], [0.23173379857960286, 0.7155107628810403, 0.280950714531233, 0.7892447288629669, 0.46914135808232016, 0.1410273656950407, 0.5294061392328968, 0.08725143003420088, 0.3336417571851459, 0.15991088436993983], [0.366897124127297, 0.11195604374056789, 0.7845263850667593, 0.8893519669220186, 0.8195194632591091, 0.6061641420402869, 0.6907050227218681, 0.9821686568896888, 0.23852907420850689, 0.7940011803134968], [0.8890860402344408, 0.09104005244218982, 0.04420988002472637, 0.7191889290605588, 0.9209674270473576, 0.6281008059109779, 0.6206728026044941, 0.44395998990645424, 0.5226898570457524, 0.5037547666789647], [0.741112906859753, 0.8821193249185686, 0.1946858404395414, 0.2025731951211449, 0.4144779069168216, 0.41060449407649835, 0.5072976233528421, 0.5086047564764126, 0.673805650398401, 0.5074809080316044], [0.6964428092990843, 0.42136004002261784, 0.7527128955285122, 0.689926895931052, 0.5537729523717558, 0.1823493266627101, 0.7502481203718158, 0.24768809868369257, 0.8393738951133795, 0.16201889536236846]])))+4.540613130179028-3.945252339322542*array_x), axis=1) |
np.round(np.mean(1/(10*(np.log(abs(3.995085179562057))+array_x/3.830400730591396))+np.exp(abs(abs(array_x)+abs(8.737303057386779))), axis=1)) |
np.mean(array_x-4.222801647251034/4.225762112107614+5.463862690886257*(np.array(range(1, array_x.shape[1]+1)))*9.850397791043456*np.sqrt(abs(np.sin(2*np.pi*np.sin(2*np.pi*array_x*(np.array(range(1, array_x.shape[1]+1))))))), axis=1) |
np.mean(10*(array_x*7.235616948800457+10*(np.log(abs(np.sin(2*np.pi*8.646572368415612))))), axis=1) |