Datasets:

BasStein

/

250000-randomfunctions-10d

like 0

np.mean(np.square(3.1463829843389526+array_x*9.02507205021823*np.cos(2*np.pi*2.5157476225317446)-array_x+abs(array_x)/7.105110683155421), axis=1)

2.426966865186618+np.mean(array_x*9.097505898483607, axis=1)*8.257268779018007

np.exp(np.exp(np.square(1.1222881379523146)-np.cos(2*np.pi*np.exp(np.mean(8.85658465113318+array_x+array_x, axis=1)))))-np.exp(1.9217373471665975)

np.mean(10*(np.sqrt(abs(np.cos(2*np.pi*np.sqrt(abs(-(array_x-2.8792412187167695/(np.array(range(1, array_x.shape[1]+1)))))))))+5.927127609496439), axis=1)+np.sin(2*np.pi*np.mean(10*(np.sqrt(abs(np.cos(2*np.pi*np.sqrt(abs(-(array_x-2.6881970976933975/(np.array(range(1, array_x.shape[1]+1)))))))))+7.453210372837137), axis=1))

np.mean(4.274613065994284-abs(6.103412773131492)*(np.array(range(1, array_x.shape[1]+1)))*array_x, axis=1)

np.mean(np.square(np.round(array_x))-2.3352736017702926-np.exp(array_x*array_x+5.990651637023697), axis=1)

9.69065400888379/10*(np.sqrt(abs(np.sum(array_x, axis=1))))*array_x[:,0]*1.9638814035104084+7.91638768193161+6.774704567339992

np.mean(10*(np.square(np.cos(2*np.pi*np.exp(5.845336388367229/np.log(abs(array_x)))+abs(4.277668139421596/9.874411683099163+np.exp(array_x)-array_x*9.64988378660054)))), axis=1)

5.221826767713958+np.square(np.sum(abs(array_x-array_x)-3.8265012279727073*(np.dot(array_x, np.array([[0.6886090319180326, 0.20258227909881243, 0.9140346634599844, 0.0270667144076171, 0.9656068700980701, 0.7631402820633215, 0.5446279466489832, 0.9455466677462732, 0.41206586861501004, 0.6773181411069665], [0.6915904890609971, 0.45723097606244867, 0.3749684645070296, 0.637244786208226, 0.3217497037465029, 0.7942967005357872, 0.5104539157512615, 0.3536435385444975, 0.7364533457804628, 0.19807693018569905], [0.5553218054103625, 0.9596733732811366, 0.8158047055449344, 0.8281322333311607, 0.5674207514286461, 0.8886054618221635, 0.7018028246825004, 0.40729847511098194, 0.40467990387143393, 0.2938249393633865], [0.3007359658428518, 0.8003404025203116, 0.1861566233495504, 0.7178206832667468, 0.5651569717984365, 0.30488704824017765, 0.9292253733248125, 0.9328827703060417, 0.5928964667558083, 0.5551538626422161], [0.6212516067013004, 0.5833120909860744, 0.59981664853077, 0.55951427804895, 0.4137557579316453, 0.3427123472129887, 0.601013868111512, 0.010977483527603749, 0.23538702219907048, 0.775358056851779], [0.8865355209039372, 0.10580482382188461, 0.6265090827605378, 0.6225944180022979, 0.9988047805345686, 0.8727543295572396, 0.8371511927872175, 0.8272514822654464, 0.8098870268726377, 0.442102428163219], [0.24496048375189594, 0.570492606353162, 0.5854132399839126, 0.42055582691205695, 0.7807321754452237, 0.7684383392881227, 0.26818527697010763, 0.2127189115399727, 0.8677427869138066, 0.7735006526846299], [0.44714444972357503, 0.11092182116462301, 0.3893343306342376, 0.6427099355448334, 0.7303301296801374, 0.04496054671825522, 0.45546728199774844, 0.008492839467625357, 0.5285366394886744, 0.20910567395145807], [0.7407757053218617, 0.7751626201343471, 0.6196153327683787, 0.8471718324783247, 0.9444640211389881, 0.05172405142420966, 0.18312175730638058, 0.8381759599098909, 0.7093404446103488, 0.07509649087134229], [0.7602747204145885, 0.11254700172069831, 0.340599170261783, 0.22757864721058219, 0.21631966445404216, 0.11705615061056274, 0.997598291270648, 0.360330956926334, 0.8764639521699495, 0.16438460570747304]]))), axis=1))

np.exp(np.sin(2*np.pi*7.7001283595749435))+array_x[:,0]-np.square(array_x[:,0])-np.sum((np.array(range(1, array_x.shape[1]+1)))*array_x, axis=1)

np.log(abs(np.square(np.sin(2*np.pi*2.469778968886566))))+np.sum(np.exp(array_x*5.828510904219655)+6.191022207818045, axis=1)

np.sum(np.sqrt(abs(np.cos(2*np.pi*np.round(6.9392743235006))-np.exp(array_x+5.885225484175292))), axis=1)

np.mean(5.70140800045323*(np.array(range(1, array_x.shape[1]+1)))*array_x+(np.array(range(1, array_x.shape[1]+1)))*array_x-9.29729760025134*1.287151672594005-(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(1/(2.142491813526761/(np.dot(array_x, np.array([[0.5217618920197972, 0.1757380435479049, 0.17158109475848438, 0.767340748032312, 0.26690182227939274, 0.6058673352024321, 0.6017508006862679, 0.5011342097933889, 0.6313488210192848, 0.48721781886606164], [0.15914382126645543, 0.3246456359625244, 0.7740743365550271, 0.09832674006507547, 0.975319793950396, 0.40926252046393174, 0.654185079404239, 0.9035464640670676, 0.1727034998886784, 0.8112307591969874], [0.7941263424056159, 0.015567982684834503, 0.6474164024354575, 0.0797756751087233, 0.7013557495119633, 0.27003862935637357, 0.669456443620698, 0.5174757549629563, 0.40370197985274403, 0.43341224943001255], [0.476341524519845, 0.018222396489901094, 0.40457599407753286, 0.1144500122671146, 0.47100224578100036, 0.700994485023045, 0.7008466519249367, 0.9216646387932249, 0.7338203628302798, 0.9234367525325223], [0.700317273040997, 0.2325056715772763, 0.4820943140437368, 0.6713785035243153, 0.4371110673979759, 0.21165650735196462, 0.9348436168926345, 0.682725277834693, 0.21788159674690188, 0.8156178079194972], [0.8368743157457378, 0.8188797897231727, 0.6240055754224954, 0.726515114549245, 0.42250645880670645, 0.909922240652054, 0.5168104974178293, 0.9924469729304349, 0.24736134528466303, 0.09963536250075944], [0.0020217535933447772, 0.21089762382029087, 0.40342511839912665, 0.3577873783903064, 0.9776672952507743, 0.935084806180865, 0.9320333463805816, 0.8471477114327078, 0.0045766329079359735, 0.7652402440057946], [0.006203651584976044, 0.8893121209264481, 0.5950896956399582, 0.3399516287925166, 0.9593301465652767, 0.25949060799977086, 0.10580961038596925, 0.9019326328698187, 0.06685469314064818, 0.06606792306635068], [0.007455645019498025, 0.8049734338021532, 0.2693571291306045, 0.40483744386238474, 0.4635393179971249, 0.7144625008447811, 0.6179909593968096, 0.6289763166468378, 0.6217975035015662, 0.04371277737121704], [0.3884461677688933, 0.5091190533308825, 0.7834712544853777, 0.5146578806799529, 0.7884262461746265, 0.32406464126688683, 0.6774942663838862, 0.22720495157189802, 0.21952347269292394, 0.00022078500003253065]])))+(np.dot(array_x, np.array([[0.9439408805349695, 0.592904549282618, 0.7590627251009027, 0.9779660071659768, 0.28943792927677603, 0.7366596639505724, 0.8226601910847484, 0.4564105443457196, 0.5710738508643723, 0.413888322374505], [0.19097126667623698, 0.1291221918139237, 0.5849486172927277, 0.9870993020681657, 0.35112209121177684, 0.7623770604465824, 0.00789144631576455, 0.25787857683683124, 0.050593429273947055, 0.03604360003859053], [0.13801208540768484, 0.9068270324159795, 0.7515231000475321, 0.795605845737506, 0.2874626920396237, 0.776532829211636, 0.7906519967377702, 0.8885990167568328, 0.8147543735820865, 0.5692656282792893], [0.7296153049899337, 0.6307570882206799, 0.874221032624965, 0.06667464068459295, 0.22848058544383665, 0.1730329260957254, 0.4676325338788536, 0.5265154872895677, 0.05701670115433477, 0.2439882504713352], [0.4463182504017713, 0.033721795480978534, 0.450705274049809, 0.9685263353545184, 0.8594071448130549, 0.46337048774517475, 0.760891622318368, 0.2361874784614637, 0.05846541678172423, 0.07650047710208063], [0.21085326244941394, 0.7327217364478997, 0.02560964210629435, 0.1837324700626506, 0.1560792398549542, 0.7337946588638613, 0.4018990478408696, 0.46021062112268585, 0.9192211402212476, 0.7085362445953173], [0.8391669201643146, 0.1149054279047762, 0.3103354014744679, 0.42897975851073555, 0.4888690873578564, 0.3550169587210871, 0.20542064100970825, 0.33082309555768097, 0.2254097552596397, 0.4763842972578516], [0.7238673540366553, 0.10647629401745262, 0.33554269369957, 0.9381692116361324, 0.8272116106935173, 0.5868151077760887, 0.04074964006828863, 0.8404877241814318, 0.10864409884183612, 0.12484863950181546], [0.9983295278646035, 0.28671541461433137, 0.5356263364316691, 0.6461237809781144, 0.6060255639059337, 0.09448591749679391, 0.8751986977338527, 0.45947582927421005, 0.7100054730464997, 0.005689670711933603], [0.520951767181994, 0.2645089365637002, 0.6406658236968601, 0.10725554970935869, 0.9283097333571212, 0.9341105824024806, 0.4466404162292207, 0.9609579359035262, 0.26519216150533953, 0.8931829396782519]]))))))/np.sqrt(abs(2.478720490408687))*np.exp((np.dot(array_x, np.array([[0.030741031493828563, 0.030753315213578714, 0.09853791219067698, 0.7074415462521163, 0.54899826124328, 0.5012964194717666, 0.2467249388001349, 0.24715989794858828, 0.33737152178505225, 0.0989161905200574], [0.4953322147182224, 0.9263532838390738, 0.780200067772377, 0.1565036022927685, 0.8904945925457117, 0.9122651941121012, 0.5732438664088582, 0.3536496297986318, 0.6155276449074365, 0.7798217011079505], [0.797464198510047, 0.8102606551317291, 0.01335320701931908, 0.47196294482389967, 0.5379582735227899, 0.4948594926974421, 0.021589222382324746, 0.7689672168824344, 0.15139793022543901, 0.18632437496015675], [0.19959543918207345, 0.5342225737605031, 0.5932291113229473, 0.0043522596969265415, 0.057838970740498374, 0.29136458265703313, 0.6725346292648073, 0.36028530451633, 0.7129667613730353, 0.3909504695453354], [0.9844206587927896, 0.42510218279516176, 0.5919815358033786, 0.9262534136177496, 0.36150672037994824, 0.03562764992911116, 0.735108202292193, 0.3992368769192892, 0.9014750070914375, 0.33762738857077823], [0.8753663442743933, 0.09170081338159097, 0.9643697109505371, 0.5187993782413195, 0.8145950081311215, 0.3928960152430274, 0.266932942501379, 0.17547321460602794, 0.13232027953741088, 0.5309854192283151], [0.5085004355402236, 0.5157356858911544, 0.5601584263310567, 0.5216448496798902, 0.9609262774833837, 0.8884491763402429, 0.882725903727834, 0.7874227528564537, 0.24351869302517126, 0.8211092293132662], [0.232777708817276, 0.48900855922227726, 0.9615399175208232, 0.7256233357841141, 0.37603535805032673, 0.12250363149374777, 0.7965063675102714, 0.2645403936153048, 0.9308860570117298, 0.10404292854185182], [0.04726864037163725, 0.7255635486154932, 0.6081206224214546, 0.766858973378113, 0.10038285163027438, 0.08416452088850868, 0.16071359658536544, 0.20766682443953477, 0.4474281734169341, 0.5183600364079425], [0.012471319986151364, 0.8622676768249078, 0.2333888381396061, 0.3722622255455321, 0.5770614727993815, 0.8079024504154384, 0.7149503557034275, 0.4281396414116323, 0.6660353630318078, 0.24959588715796688]]))))+6.252024116668566, axis=1)

np.mean(np.square(9.702723892969708+np.exp(np.sin(2*np.pi*3.4867720806889366+np.sqrt(abs(array_x))))), axis=1)+np.sin(2*np.pi*np.mean(np.square(4.924391010441351+np.exp(np.sin(2*np.pi*2.690440201100959+np.sqrt(abs(array_x))))), axis=1))

np.mean(8.286531423513654*-(array_x)*np.square(3.541426628716958)+9.17888053371662-np.round(array_x), axis=1)

np.mean(10*((np.dot(array_x, np.array([[0.48226822998379537, 0.08145690221843438, 0.0892389963643827, 0.08044654593786849, 0.06734273390517509, 0.7108448988229167, 0.1693861826887576, 0.6805258330718919, 0.9144171268022849, 0.9444450040500605], [0.3239261367153268, 0.469164739034193, 0.015151398312947761, 0.7885619175819321, 0.8787412689671972, 0.6913301581880168, 0.5392554373241809, 0.03234094712789126, 0.3510048008725756, 0.9260193826709046], [0.9963540621908344, 0.6006440026042791, 0.5379311723089251, 0.22044561628040327, 0.11758200876116587, 0.20704898797376514, 0.26975840330820966, 0.5298738498513806, 0.969961293559667, 0.6809392573334411], [0.08923046962899073, 0.4521424350840165, 0.5297045913631945, 0.8170157930655385, 0.8677767833485815, 0.4641351157189022, 0.5710127005442591, 0.00946995361727343, 0.5293218339928791, 0.3534557144855077], [0.16324319208594062, 0.6110743888823046, 0.023313408491680065, 0.8014228510851682, 0.20740674673357762, 0.8449044652839363, 0.6106104961347335, 0.43073648336861203, 0.013590833493867471, 0.9672613253787484], [0.6382147366303874, 0.9472932819579751, 0.3200013257680646, 0.09822828420684038, 0.07279861556595235, 0.6534979853072539, 0.7025530442070624, 0.32418745524373305, 0.8088663822259592, 0.1437539946293691], [0.08718219318378173, 0.41114502659127494, 0.17219470120277103, 0.35177603172416405, 0.28691505818254726, 0.6290732066245621, 0.08798976143094028, 0.8721741298577373, 0.21507328652771218, 0.9070064402444693], [0.4292899713968348, 0.016186029954050163, 0.1782025265592525, 0.8003478469218754, 0.20156779373743272, 0.18105288368429884, 0.8346915540903916, 0.18993024512457213, 0.42312969020156666, 0.039308999919438925], [0.5555272560275027, 0.08251528879005177, 0.6224346641354205, 0.7720296359396235, 0.33203001223308626, 0.43916184256521706, 0.923650341261591, 0.5279169786350254, 0.235010956567817, 0.4520874971697292], [0.4953656477488596, 0.2158613388332815, 0.8089007571175387, 0.673934662171565, 0.24172850338756624, 0.7324527496925619, 0.2027305459239641, 0.5407166355376741, 0.869385282299829, 0.8482977249896982]])))-3.036346773709602*9.508018863210678)*6.7575332569944955, axis=1)

np.mean(np.sqrt(abs(np.sqrt(abs(5.687294212326434+np.square(array_x)))/np.cos(2*np.pi*array_x+array_x*3.714402030393965)*8.800919083285105)), axis=1)+np.sin(2*np.pi*np.mean(np.sqrt(abs(np.sqrt(abs(8.888825565801103+np.square(array_x)))/np.cos(2*np.pi*array_x+array_x*5.320331784850131)*8.55495377233244)), axis=1))

np.cos(2*np.pi*abs(np.sum(np.sqrt(abs(array_x)), axis=1)/1.8928846514518698+3.257607756871566))+np.sin(2*np.pi*np.cos(2*np.pi*abs(np.sum(np.sqrt(abs(array_x)), axis=1)/9.29957021550139+8.852386501433626)))

6.3711555179450245+np.prod((np.dot(array_x, np.array([[0.10012587938078288, 0.7521673999277233, 0.027226869394157527, 0.6594610399190485, 0.81965665721641, 0.878033632738354, 0.6982272750363631, 0.7366570444756916, 0.4655679844640451, 0.12674584115574516], [0.371688288147883, 0.7654432346194736, 0.6584350290266654, 0.05040207075038594, 0.05572127757033951, 0.021400086196925017, 0.7129975383924096, 0.42036474886488584, 0.08035799732650428, 0.2923206393139218], [0.5113568414987977, 0.3561700236695602, 0.45078249042496243, 0.43343193789939116, 0.7648837793478886, 0.6121648355251345, 0.9323670345833109, 0.652117674661962, 0.36472508011355387, 0.538180859971747], [0.2681457384517195, 0.848031707142049, 0.31380142510012976, 0.33542002301840923, 0.840891182623125, 0.9794795543149394, 0.7703269209705594, 0.2205077774883195, 0.806348153861641, 0.8637579303568426], [0.39632600026688414, 0.837464189019416, 0.8393780025476363, 0.20490754504063713, 0.8271727783438492, 0.5235480471382097, 0.1034888931153064, 0.6922788945052681, 0.7962809364195929, 0.4146731344596083], [0.5773843345220079, 0.5620007942443761, 0.4758603821036761, 0.8300053389850177, 0.3381194795998417, 0.9040757877559088, 0.8070515974696343, 0.9178983021419576, 0.40787609075457754, 0.9808776865104404], [0.9058860103907446, 0.5378761928638289, 0.44557295757522464, 0.6549959002292768, 0.09065583640096242, 0.1778692409287317, 0.41084254064532544, 0.14689490293763774, 0.38940287902058646, 0.8217319635216193], [0.15271280744010962, 0.9298648502512311, 0.509049120846931, 0.6202164939145683, 0.38530067247473754, 0.5776268250476972, 0.04032373306751946, 0.3520175471309547, 0.8260120680473689, 0.4811881972231482], [0.006898374823866793, 0.2827848389886619, 0.3044083593203123, 0.08207209982164998, 0.6038895568545211, 0.5054448752060049, 0.9633180038023711, 0.8604639020093514, 0.014735720648237693, 0.0904815365175966], [0.6482266155959928, 0.7153286088386352, 0.9709571711697801, 0.8433894699713362, 0.709613792166255, 0.6289689938974471, 0.253562807035282, 0.6054568524212178, 0.3228000931909146, 0.09546149233501999]]))), axis=1)

np.square(np.sum(np.sqrt(abs(array_x-np.sqrt(abs(np.sin(2*np.pi*10*(array_x))))+abs(np.square(1.4292919042081427)))), axis=1))+10*(np.sin(2*np.pi*np.square(np.sum(np.sqrt(abs(array_x-np.sqrt(abs(np.sin(2*np.pi*10*(array_x))))+abs(np.square(4.750896287047958)))), axis=1))))

np.mean(np.square(5.9795817301929715+array_x), axis=1)

np.mean(10*(5.185652099380967/np.exp(5.125530131072834*array_x)), axis=1)

np.mean(5.421635928917845+abs(np.sqrt(abs(8.988246751879997))+np.exp(array_x*7.299220970332186)), axis=1)

np.mean(np.square(6.200697951867536-array_x*8.715747014826999/np.exp(array_x))+9.678630312271029, axis=1)

np.mean(np.square(5.813829466989095/np.cos(2*np.pi*array_x+array_x))+np.cos(2*np.pi*6.222639370666241), axis=1)

1/(7.438739742828756)-np.sum(np.sqrt(abs(array_x*8.974940626751982)), axis=1)*7.331371934660825

np.mean(np.cos(2*np.pi*3.995077210868816)-6.602150626408941/np.log(abs(array_x))/10*(1.7909360610185576), axis=1)

np.mean(1.1132758810654182+np.exp(np.sqrt(abs(6.5673162672573*(np.dot(array_x, np.array([[0.6699401075022017, 0.32579682337283, 0.4916406564651935, 0.6315247909782771, 0.3978394699379839, 0.02553105809034062, 0.5033429909658637, 0.496668223670942, 0.5446083574049211, 0.6444482887525693], [0.5076031664059258, 0.8320828228315642, 0.12309842448876551, 0.31640972151741353, 0.40199262671986025, 0.9889449897960952, 0.46299816871529254, 0.35747848434082385, 0.73418145314931, 0.4309079454760091], [0.06285432492778809, 0.10640319952888544, 0.5511134348980294, 0.29318869551070625, 0.6251682635238204, 0.7631887819564964, 0.5144694763980564, 0.9210427966002407, 0.1799847464274007, 0.5831175704717521], [0.13919782343839537, 0.5807881350556605, 0.29047858971692186, 0.49784755555714744, 0.8842292951789905, 0.2084380315399459, 0.5248014860488076, 0.07923669959628954, 0.714314929519018, 0.3736323017058323], [0.5701212106700685, 0.6800766104001794, 0.9351953639583013, 0.6528205251530194, 0.48510644415874815, 0.6367798779825024, 0.12051549413593154, 0.2551491613712973, 0.32496279042603715, 0.8343161360490547], [0.4374539813432421, 0.4161752181332269, 0.3975169065711639, 0.6359420110664169, 0.8566340539423455, 0.6690401473451929, 0.9413307443147013, 0.35259827752813, 0.5024240562625085, 0.196221114413493], [0.3759987148500684, 0.4400772876343949, 0.522413763264857, 0.5917683978115513, 0.3424345432003626, 0.36212080468375274, 0.9643371094862454, 0.5426399558092851, 0.22740000677348282, 0.26221974123103897], [0.2156261248200093, 0.30523777366620786, 0.36676081692153406, 0.3825215930421214, 0.03964858445088759, 0.8853176533718785, 0.6146475647674231, 0.4224385963895063, 0.6864826999439394, 0.6911707087004437], [0.11957550838725195, 0.3866020801150051, 0.09866597763743823, 0.81102852410668, 0.9853610675321032, 0.4237935144607199, 0.6262201189781077, 0.08268639948187329, 0.5634006699208641, 0.32723660426775225], [0.5365506766366381, 0.6229096435721871, 0.35081377853936624, 0.12143485579113433, 0.9835990105081326, 0.6856549888712917, 0.7689944106103282, 0.9152442358060998, 0.9993467118112189, 0.5171886711897852]])))))), axis=1)

np.sin(2*np.pi*np.sin(2*np.pi*np.square(np.sum(array_x, axis=1))))-np.sin(2*np.pi*abs(10*(np.exp(6.911781455608153))))+10*(np.sin(2*np.pi*np.sin(2*np.pi*np.sin(2*np.pi*np.square(np.sum(array_x, axis=1))))-np.sin(2*np.pi*abs(10*(np.exp(6.901503396032742))))))

np.exp(np.mean(np.square(array_x+6.104563458220331-7.418515821800837/np.sqrt(abs(6.40444286613803))), axis=1))+np.sin(2*np.pi*np.exp(np.mean(np.square(array_x+9.319023363545607-8.295196012976913/np.sqrt(abs(8.748484008041846))), axis=1)))

np.mean(np.exp(2.80719952716351)*array_x+8.405765550313365, axis=1)

np.mean(np.square(5.332909856225367-np.square(6.176950984350713)*array_x/np.square(3.046334342728052/array_x)), axis=1)

np.mean((np.dot(array_x, np.array([[0.3294685155970454, 0.971461758410953, 0.3149051874889418, 0.7219391176143299, 0.07839005433616808, 0.09545329307862649, 0.07607136859004637, 0.13412693648905916, 0.2556626815530648, 0.8451044403629476], [0.4491936493417439, 0.2840574037297393, 0.9703480004018014, 0.9689139337203502, 0.6106439839553973, 0.3209902729148455, 0.44879847299829556, 0.3685215106291243, 0.24074221828824272, 0.08816185972516033], [0.1277322500028948, 0.6756118235141134, 0.3075083702024345, 0.6486381655761969, 0.09244677318192296, 0.1317092042939263, 0.8584820135100371, 0.5245807522540282, 0.47184419822248036, 0.5437297875157057], [0.9147899979338634, 0.01558943823433756, 0.061120396480985884, 0.8407898359200119, 0.46371345621041815, 0.33856468692320496, 0.4574922985673826, 0.7022381475859386, 0.25165538600363646, 0.7474586906538118], [0.040616434721379546, 0.7071173840746559, 0.24728417793480728, 0.3689740270168921, 0.9341959581064004, 0.9530361190369283, 0.3014964851657399, 0.6579437499023834, 0.9968917388668623, 0.35392541892904894], [0.4132369950336422, 0.5001772916399515, 0.3239872899058881, 0.7089401768976055, 0.8341614528048165, 0.2104147571810353, 0.5849400689786715, 0.8008676154297659, 0.5810024405085551, 0.19071461071192675], [0.2645539060577139, 0.286368478448225, 0.2417258946829126, 0.8933235698587878, 0.29768686534897504, 0.9701605152587186, 0.292062407446232, 0.5836592856975233, 0.8305412389276021, 0.10039034527101298], [0.8808399918234728, 0.14124291916770493, 0.2902082269030436, 0.6338165807428889, 0.3969235751143446, 0.767658295448887, 0.7085805356877551, 0.17525404363153263, 0.3332916839471838, 0.718884574989559], [0.38640890883061674, 0.20350102858710528, 0.577024402414406, 0.7304836597513732, 0.6797382731485024, 0.5312576801785639, 0.3995966648816158, 0.9300058542614731, 0.41942196183448754, 0.812844060510699], [0.2325651589313531, 0.482644731021475, 0.09415910292185703, 0.3235285919285713, 0.04570392556881775, 0.5633833416923409, 0.04853124337626924, 0.42021152415924523, 0.07923341046985044, 0.8932395745221083]])))+1.5911824502339789-np.square(np.square((np.dot(array_x, np.array([[0.9709102626434057, 0.5355599452839734, 0.4862817354935781, 0.6989581469491202, 0.837813542529497, 0.7876497762047677, 0.08874463219266904, 0.7876059930369766, 0.9217746905401714, 0.1958192095236908], [0.5040602581498036, 0.9972396455491447, 0.4541697228441365, 0.9797831587409125, 0.8554691109022762, 0.7683669143369486, 0.16756838776295413, 0.9808513124694226, 0.40585681583930544, 0.4495354884264533], [0.7594898988904591, 0.8980562612885793, 0.5604359695405248, 0.20095138711648297, 0.08314952393667063, 0.23567289040880046, 0.26357805983500027, 0.9445424383599589, 0.09012533641377363, 0.06010331106649225], [0.5775128339120879, 0.5413033179374721, 0.38926843605333283, 0.013308789951675393, 0.13071558772520742, 0.8479325583587877, 0.5902412061535667, 0.5828357508643803, 0.46548069607979203, 0.048662649099139976], [0.09607319627299948, 0.9348257733899102, 0.702002038596266, 0.9235199474466206, 0.11388135826769963, 0.944925943432838, 0.8545258350712285, 0.7572940349154169, 0.9908894083790437, 0.447084232825694], [0.7558099165984058, 0.8615941359826483, 0.5043967111142553, 0.31804535342651086, 0.8352142595614475, 0.7099685519593022, 0.7550653162751438, 0.3299278180440849, 0.8145941393579755, 0.19912278208642054], [0.20432884421084674, 0.9137284623797484, 0.2843654964048994, 0.13653081611060136, 0.979714737809698, 0.8369617152262288, 0.15167727662968067, 0.33800807816747924, 0.02956271339760108, 0.26156952815505075], [0.39674733149753094, 0.5460889202907246, 0.9339458956279784, 0.892097365831599, 0.8048722377378167, 0.5883211993018247, 0.19257085320337164, 0.31345467549548, 0.7358679524912731, 0.7484357495961163], [0.8589897946829045, 0.0854246265624633, 0.590789727180551, 0.008600806923362736, 0.7314056232524552, 0.7374615064415468, 0.7411584717341047, 0.6360492830897276, 0.47601487721572167, 0.12077004246299239], [0.5800171163810166, 0.8817318593298455, 0.7520370700860174, 0.8385109272762367, 0.359545057747256, 0.03845723706777937, 0.3935084187642367, 0.1449611416121731, 0.47598670704308066, 0.12944900458270958]])))))+3.6891767826765047+np.sqrt(abs(np.exp((np.dot(array_x, np.array([[0.3703481586866778, 0.30822819241119426, 0.050818055342879975, 0.8562459598302626, 0.5223343690591951, 0.3676442890169983, 0.0140306925780328, 0.11297916834840471, 0.8418439765545912, 0.7293911937255046], [0.5673769454383252, 0.8511975048893368, 0.44984947074787385, 0.3716154908506425, 0.8676257531184534, 0.8897154989175609, 0.6972863680800797, 0.7880606151611125, 0.06341654291169241, 0.44594134307606936], [0.5623138430790172, 0.8728806578200929, 0.9293122038713884, 0.14979598321712928, 0.5434532713561061, 0.8719912409289583, 0.968072922012259, 0.953826134749629, 0.1450906076270132, 0.08579494773973462], [0.027266150071396877, 0.8609730623833424, 0.8081770026353783, 0.7224769546548362, 0.8139847131951237, 0.3613474898302791, 0.34830797084255805, 0.1677923074090032, 0.4434704659262665, 0.6025986504744453], [0.9435845346205001, 0.033375541475824444, 0.5802992286549015, 0.0423249165974644, 0.027990695100938434, 0.5823475864390522, 0.7991615415573786, 0.8125863696380055, 0.27707212553033933, 0.12596313421227578], [0.683382913638207, 0.8204767694925066, 0.3688737655373041, 0.2914584457251784, 0.2155713587929835, 0.39101867794907474, 0.3043195777035538, 0.2633608385853998, 0.9241082300833511, 0.308813570031097], [0.2706117387171688, 0.4155897199351437, 0.007188089119245178, 0.7205097819229525, 0.6117086079729179, 0.8627699468974737, 0.23552612136767548, 0.36231092769015727, 0.8054329213477569, 0.4976163738517744], [0.4628086241224788, 0.03885783157999634, 0.07053100012220437, 0.6725683133185199, 0.14193718405990197, 0.0477669696355395, 0.3811716717244623, 0.13075150129655466, 0.59951266797657, 0.9601094086815583], [0.6991252898564384, 0.08558038889517872, 0.10644655533812697, 0.4491214352909926, 0.5942301134685702, 0.14252950504715, 0.029012452701359215, 0.32803331533846747, 0.36811296472708743, 0.6874236232799753], [0.8884272176493698, 0.9728582997036921, 0.11207489050112296, 0.053653534054576735, 0.6847564543270732, 0.7864312722345388, 0.9615896085984551, 0.43749120268382147, 0.1797937322492933, 0.9289288525941063]])))+4.130479035326192))), axis=1)

np.sum(np.sqrt(abs(np.exp(3.9600818366302892+array_x+abs(np.cos(2*np.pi*np.sin(2*np.pi*(np.dot(array_x, np.array([[0.2243333061074062, 0.9897300397040678, 0.6551501028207233, 0.43211126770313235, 0.9938238673430985, 0.017916855392260622, 0.6538474820091514, 0.8665422926078014, 0.936283752731514, 0.1032475683985653], [0.7164298074972401, 0.9078970966934147, 0.7123855995714677, 0.9304732062703188, 0.3869764029698929, 0.3294776794695492, 0.6277957269956945, 0.3667155923718963, 0.9380073981288719, 0.900046097177319], [0.1411493583964184, 0.6436789175555799, 0.1002004013502592, 0.0008518323920185811, 0.6273936213994408, 0.146482820062671, 0.9341565359244752, 0.555288317735257, 0.9949991362296526, 0.5305621393929737], [0.7154109213309802, 0.14638250651589424, 0.570121045469054, 0.32796525007633714, 0.44277397048224065, 0.8809293139636211, 0.33792924661593016, 0.7842635549548381, 0.5255281684624172, 0.13743063026960123], [0.28994223857204326, 0.8063572780868027, 0.47159712576932855, 0.30320834925593376, 0.3233496503077212, 0.7293070517997661, 0.5247070859692561, 0.5873434296583407, 0.18641842334885728, 0.9111982615845131], [0.11857019407127478, 0.7594881135550435, 0.49770062070309573, 0.2775522247983051, 0.9727867664818671, 0.5332782369652256, 0.17087642015233484, 0.5161195112595116, 0.779662493010618, 0.3119338737872903], [0.7643839855972672, 0.9040550199781682, 0.8996368880262907, 0.6969489343283232, 0.9285907910357352, 0.6970584407608637, 0.5317914605587649, 0.35931982722821965, 0.9613717026227673, 0.46146909133897884], [0.5965379308390919, 0.4670694024041169, 0.16092534980379758, 0.20423695345869508, 0.8777906740230141, 0.30709874011224336, 0.5442205263287258, 0.4148996079154227, 0.3297310533802279, 0.05502559686730524], [0.2404761427001122, 0.7748145572654239, 0.0516275898270947, 0.27795511423412556, 0.7000858694143268, 0.2063972238414109, 0.3436356404764901, 0.5720524962814908, 0.03209751962033158, 0.3449926629167972], [0.8294083821151835, 0.6327650112836972, 0.8133660635174629, 0.11118195317653279, 0.9911585803187329, 0.9745780203088839, 0.7611289852788113, 0.7729820598982973, 0.11474765374080953, 0.3031066911375949]]))))-np.round(np.sqrt(abs((np.array(range(1, array_x.shape[1]+1)))))))/2.9169783004315484)))), axis=1)

np.prod(np.sqrt(abs(-(np.cos(2*np.pi*array_x))))-4.649700144211689+(np.dot(array_x, np.array([[0.27678270669472027, 0.7582899111678291, 0.141176319744707, 0.0013375936819297607, 0.8240076741437785, 0.9672497905330094, 0.7461018933629551, 0.2742031329150747, 0.9232146032726051, 0.1254321780294002], [0.5314311470990271, 0.10573567245495552, 0.6202837317084487, 0.29046444249195447, 0.6899978622299802, 0.8708044388146262, 0.06637380654566538, 0.07345524276116333, 0.7275877375936711, 0.3263655124363316], [0.2110986028399897, 0.8401969817798834, 0.6759830363878737, 0.6913794784676297, 0.5442311432236818, 0.034242508777822644, 0.6435176842032043, 0.9374556026227666, 0.11446587164528332, 0.25283098844347196], [0.752281475005309, 0.9363944770426047, 0.5158352542422028, 0.8616786953706272, 0.01574634797281782, 0.3533006125167497, 0.6407214578107722, 0.38438296917150905, 0.05713766423032274, 0.0999334599444972], [0.5145023382167403, 0.34034839928652794, 0.6662392336648743, 0.7974927070302537, 0.8361536677020371, 0.6315859272422822, 0.24922321676082215, 0.4525468897117445, 0.4165791580705901, 0.20773836403256174], [0.1848793436935695, 0.37880749646343803, 0.26116982197081084, 0.37988191730864784, 0.7167508893855782, 0.21851526862152248, 0.7759409061617957, 0.7595560460111227, 0.6150311338341422, 0.9037962750595097], [0.8196440056856956, 0.5132949839858908, 0.6961210790256068, 0.7889031679369091, 0.5457380565418125, 0.9405194820358015, 0.7005632459435435, 0.519423800692051, 0.032811272997031904, 0.9244406058908958], [0.41822439328830174, 0.7378579351008329, 0.803521442029253, 0.0043650167458709754, 0.8584501885377093, 0.042925864950208004, 0.12457118215638696, 0.6400013056057363, 0.7040320073795368, 0.06658287557665854], [0.31533161603578397, 0.4714315413964235, 0.8996860916089408, 0.8794774627314595, 0.23848475717768058, 0.26734730020954567, 0.0026758887090599925, 0.8580696002671426, 0.527339415024421, 0.8227979326431852], [0.7734170331094279, 0.6925943197018141, 0.30812482811210473, 0.4102509421871867, 0.3733699568592449, 0.5767194348051968, 0.8091346079123326, 0.4486792820432888, 0.6343701787154881, 0.9266350090165872]])))+array_x*np.square(2.78947039003378)/np.round(9.136738048074132), axis=1)+np.sin(2*np.pi*np.prod(np.sqrt(abs(-(np.cos(2*np.pi*array_x))))-1.8500230610173056+(np.dot(array_x, np.array([[0.08044117676198925, 0.6571951136089594, 0.969639939514276, 0.27836028237227894, 0.19652582110055594, 0.045959947216884345, 0.9146563291275833, 0.897088806230156, 0.4464333392185085, 0.9795652669841818], [0.6707599717994328, 0.9782024951050181, 0.6515169263956401, 0.6230521190808815, 0.9879984171566766, 0.67093178042836, 0.08061334650777874, 0.6064057548599118, 0.366266507496281, 0.7359417841877153], [0.3160289094118862, 0.7176511938845553, 0.527894797566214, 0.7832149526286061, 0.6055645026959742, 0.41960779771425327, 0.06668828910188984, 0.9574029461468954, 0.6219289235934643, 0.34118907080158334], [0.2920235960404788, 0.8305995516946099, 0.6882387098347472, 0.535039719289964, 0.3772043282721821, 0.7838507003609173, 0.5480373801638178, 0.06599071308676618, 0.6989859926593712, 0.7018537662240164], [0.5036531155388487, 0.07533456616865197, 0.8814182141424629, 0.7514295409637647, 0.9873420256914235, 0.38785585968864744, 0.6141863260018013, 0.2457254387591491, 0.1435410708589856, 0.1140599480868435], [0.3130065087944087, 0.7347125280092489, 0.5755783258968145, 0.6413250499158238, 0.7199012236775957, 0.8456058714775885, 0.7243431162876294, 0.5358267181899063, 0.0857659763010532, 0.49825629275311334], [0.3162800147469824, 0.4848202131233501, 0.4120122340853294, 0.3821364023215983, 0.48667136572654557, 0.6167748331009925, 0.20519732910410116, 0.6350733091251897, 0.5391339747889299, 0.7877479125736045], [0.005368196047339402, 0.5788078382312845, 0.8041359629070822, 0.3625637813427034, 5.433349894456985e-05, 0.38084888909911396, 0.3208756616260938, 0.8394075326177793, 0.35285406193951585, 0.48457093516813776], [0.5890074015624325, 0.8210471939811884, 0.49105286747234456, 0.5905803123751326, 0.48056344369057724, 0.4556098869340307, 0.4217154360323009, 0.7083441126898268, 0.6343039593982249, 0.5445198561246036], [0.8193455909100427, 0.6716922714041148, 0.9153514371361755, 0.3373150768221481, 0.201866954345839, 0.9228420573977071, 0.23905034320921137, 0.7047332090496378, 0.2284107044292777, 0.43348066222235737]])))+array_x*np.square(2.737722043742089)/np.round(9.575068509370833), axis=1))

np.mean(5.114241011911388-np.square(np.square(np.square(7.390035197915318))-np.square(array_x/4.6360705647680955)), axis=1)

np.mean(np.sqrt(abs(9.477142965508492-array_x))+10*(9.732100487605692*array_x-1.094015362443154), axis=1)

np.mean(10*(10*(np.log(abs(np.sqrt(abs(array_x))-3.0461090683304217))))*np.sqrt(abs(4.788469594344571))-np.sqrt(abs(np.log(abs((np.array(range(1, array_x.shape[1]+1))))))), axis=1)

np.mean(np.sqrt(abs(2.5867671101109337))+array_x+np.cumsum(np.square((np.dot(array_x, np.array([[0.6820764638741956, 0.7871747905030699, 0.21665860139585735, 0.03691275193539201, 0.8433627188978571, 0.11864273709530948, 0.1162557827487003, 0.10695274615295058, 0.9130928317581363, 0.23996706342148266], [0.28558474646112175, 0.08571152004272442, 0.7459480519841937, 0.4559026880318644, 0.451399012472241, 0.9978993940697832, 0.2265795182438003, 0.5319375199087857, 0.45144435363338753, 0.427394500057776], [0.16800366176831294, 0.10853966535440274, 0.24596718307068333, 0.6129165512684358, 0.09741866794801601, 0.44589176970425026, 0.2623966324141094, 0.03328908226228178, 0.7173736995752077, 0.7845579646677915], [0.8041939747483171, 0.60259541149074, 0.08509988644419142, 0.5538779074678586, 0.6397613891265317, 0.8239172657964391, 0.35862297383212405, 0.373750106573682, 0.1806699491766317, 0.3885188421961888], [0.9603121602526843, 0.6075406915407982, 0.23754855847142464, 0.432570716181787, 0.11789382702775275, 0.4760583685296629, 0.6584671615845458, 0.19898199986493847, 0.13796779545806792, 0.791415998885943], [0.9575793334012827, 0.25402678671950685, 0.1535183892818961, 0.28284069119647837, 0.5574602058818766, 0.4969442123897455, 0.8078934511904958, 0.933611492436927, 0.9358216841988573, 0.8497300163585746], [0.548080261547315, 0.9599573065731826, 0.3986679506074011, 0.9172066345953773, 0.19886408625856944, 0.6826327265500336, 0.345552220746411, 0.17295140146988486, 0.19359290381681027, 0.7871003429617656], [0.375998568614182, 0.47435309155837424, 0.5326756314982655, 0.30841710911064546, 0.8448670297536305, 0.23303888491954683, 0.4543231503713794, 0.41832778852236774, 0.18766807403939278, 0.9868861680274224], [0.7386415928785108, 0.31569163098406705, 0.4905457081242651, 0.41784848009249587, 0.37478117937932176, 0.4173914087083548, 0.09880713080307169, 0.8683644065009553, 0.06881889206142733, 0.32453671119685745], [0.12657054074048024, 0.8357651640277579, 0.9022315532287886, 0.9088079823006304, 0.8714521346165742, 0.7936469570818749, 0.07895578481671406, 0.430318545912887, 0.47454460930532083, 0.29780371088123647]])))), axis=1)*1.7980627676666323, axis=1)+np.sin(2*np.pi*np.mean(np.sqrt(abs(9.008142124199486))+array_x+np.cumsum(np.square((np.dot(array_x, np.array([[0.981208568299973, 0.6281446321067907, 0.8746759727536393, 0.8452387492127228, 0.7327451578947494, 0.29226814102130283, 0.6585165352053843, 0.014218891367391517, 0.7071462824707196, 0.12488256850594426], [0.27157169271048964, 0.13928896320535533, 0.1276685288665187, 0.7213166573771557, 0.04291936529128426, 0.08531074019780205, 0.18596471774739354, 0.2067111089878686, 0.8911930961043644, 0.4856031823016731], [0.10550791652544245, 0.07137820275612938, 0.9569872420071975, 0.5524748399994197, 0.3205528281762423, 0.40164777901515036, 0.055556844316128506, 0.08715394959692424, 0.8400172714427511, 0.9862880315413016], [0.20568330653881917, 0.6119669664649716, 0.5248229390251873, 0.35912140649985624, 0.5381441815108254, 0.11178247384680229, 0.5446655749505488, 0.14964731050284197, 0.381481766970203, 0.5998960458456086], [0.7411377194040956, 0.2599183156599125, 0.7119049022690458, 0.9611769451017859, 0.25888551050566644, 0.7209917802016906, 0.7507739255206776, 0.06960858080353904, 0.4585456031497538, 0.19755921248464103], [0.5992990906333303, 0.010396031276230011, 0.08887220954034292, 0.04652688372730995, 0.15761185496410224, 0.39426761630400664, 0.3269510598594709, 0.6877681847094413, 0.20135540547919872, 0.006886229099238972], [0.7172117999145319, 0.6666822842182415, 0.7151959808076239, 0.28355690660146227, 0.5275313940828678, 0.8206497240806884, 0.0812949861180653, 0.974755252794041, 0.8009390804853479, 0.33883055254411265], [0.4941147568949419, 0.45165069544607817, 0.545994014055313, 0.5640539885568072, 0.49910142392789014, 0.8847969881776474, 0.9650281148495362, 0.6572481842855316, 0.5440662282181721, 0.41557025780343304], [0.6660263388406298, 0.5867626261534898, 0.9050980004366543, 0.19071006922091638, 0.1710777125507318, 0.6972774614579355, 0.49055894269614997, 0.23772483909859443, 0.3277051230150252, 0.3857839242726675], [0.4903998944915984, 0.704369323706335, 0.850946958423539, 0.994813601310553, 0.21410679455219006, 0.27839547893887484, 0.7384641457074372, 0.8340516743551999, 0.8139040973789471, 0.8706051899977205]])))), axis=1)*9.22177799569721, axis=1))

np.mean(np.square(np.round(-(2.94898301000931)/np.round(9.132182267106348/array_x-array_x))-np.sqrt(abs(6.367329751778295*array_x-array_x-np.sin(2*np.pi*7.402367393924337)))), axis=1)+10*(np.sin(2*np.pi*np.mean(np.square(np.round(-(2.5975418965724453)/np.round(4.534816994431347/array_x-array_x))-np.sqrt(abs(1.246060516561327*array_x-array_x-np.sin(2*np.pi*3.3709130870595696)))), axis=1)))

np.mean(np.sqrt(abs(9.400580332101477))+np.square(array_x)+np.sin(2*np.pi*array_x+5.6298174605370725/1.3193465757777911)+np.exp((np.dot(array_x, np.array([[0.32993892699283944, 0.6438394908522161, 0.5239672198718104, 0.7326712580836803, 0.051278112018229294, 0.8407964401984571, 0.12076248566201209, 0.3681663900577774, 0.7243118951247534, 0.8414992653517176], [0.7310466368109371, 0.2927365847373997, 0.27821761907914877, 0.3420953819442041, 0.1256995247542365, 0.06380962170144999, 0.24563324087842275, 0.7872482108139629, 0.4219744216331618, 0.7245337913293887], [0.3655356793525616, 0.6824079116064347, 0.6004708404414277, 0.4782433144514334, 0.24137712097454345, 0.5690235166745174, 0.3501981826170433, 0.5472774060627575, 0.3179129272769229, 0.48961761877005905], [0.2823025976095589, 0.3021387932166443, 0.7969047498810657, 0.30699537697536483, 0.41385070761943765, 0.5093350286790748, 0.36282006172501147, 0.45897330973269024, 0.8087833400377255, 0.0632121571180575], [0.7185160620878489, 0.44148069296247994, 0.9465818071520402, 0.7016842589550691, 0.038853272090831226, 0.4044973665366576, 0.5748659185800202, 0.9497369624819854, 0.14964225300079192, 0.5953643850121872], [0.08959663735338641, 0.7977200166674173, 0.5553507570236809, 0.6140467730129676, 0.5444371041692012, 0.07330369858006913, 0.6271308747799367, 0.28899351022131614, 0.9114766763457021, 0.7343584551997884], [0.38231828481276653, 0.31130235699383346, 0.17008467393255355, 0.484095242765333, 0.17195764259294744, 0.5656202463541627, 0.45483135277539277, 0.7495289930089315, 0.8833132049565827, 0.010206028903482656], [0.5603551502074972, 0.1311792720952074, 0.41016470909625724, 0.8808241812091914, 0.09112218747013634, 0.10639835362838068, 0.3363792058511844, 0.9836587313927229, 0.6423508994973297, 0.45845262186773006], [0.5915306855695225, 0.4348557822434703, 0.3228663363702039, 0.5072187105114467, 0.0019926461419602637, 0.6533546085616732, 0.7513743711451828, 0.8260661151666178, 0.2516998033520532, 0.6108613768749702], [0.7810513602869715, 0.23526976392063137, 0.28200436582266875, 0.6691233995808175, 0.41191194785941365, 0.6694754022452646, 0.9241768656914643, 0.8672184126857141, 0.5972539771425595, 0.349827498828327]])))+2.68441452456687), axis=1)+np.sin(2*np.pi*np.mean(np.sqrt(abs(2.1085577657270047))+np.square(array_x)+np.sin(2*np.pi*array_x+6.380099994599371/4.240748508570874)+np.exp((np.dot(array_x, np.array([[0.6368359020199666, 0.5554660641397361, 0.3046424495156932, 0.7106482248847079, 0.19203930944074243, 0.8698373831097718, 0.32920371375841173, 0.8671412215526988, 0.7405706095950163, 0.2960538975782374], [0.4147459513977534, 0.16001305872070515, 0.1721297896809384, 0.4406598494533227, 0.07312064361998849, 0.6723334383794067, 0.589852622248165, 0.7801226770368201, 0.09050910869925721, 0.33107540513704037], [0.886049494654634, 0.12344943683671317, 0.07041742797381556, 0.4991307174011078, 0.5671990479214106, 0.9269131607627855, 0.6504574953724075, 0.2393938074804508, 0.20544406612041977, 0.6266322989142412], [0.00012158172298315506, 0.8231497285557406, 0.35004970538452296, 0.5943652609739155, 0.23050556871184313, 0.025587927882928407, 0.6153239046613209, 0.5048847947149291, 0.7308645380137215, 0.1267430595820498], [0.4931406114695014, 0.5839207175253908, 0.8520370082550687, 0.4446208772003142, 0.0163330727415556, 0.793809565654366, 0.5175089534626742, 0.41590561202054466, 0.9643297985962771, 0.6075573639853383], [0.7947931698157756, 0.39363103027030066, 0.18009308712818584, 0.7480166883537777, 0.26873505628431693, 0.6610608605187701, 0.7733519302528886, 0.9067282003317073, 0.5476725488481541, 0.4718647294365822], [0.001323100404482025, 0.7314211022436077, 0.5178419215863542, 0.44401304460356084, 0.997819034281925, 0.6965779607604088, 0.9759316292898803, 0.9450179238573132, 0.12155999545371776, 0.7260378921467516], [0.5187669385356394, 0.3589971751498582, 0.3743671835034402, 0.44754846281330507, 0.8947098898956252, 0.7952503481744871, 0.5197595091487733, 0.253084811135882, 0.7880046920617727, 0.9196111881626485], [0.3694843023316715, 0.29949417440556836, 0.5417981879681013, 0.7405382198845605, 0.8193664395437401, 0.6512156471054592, 0.4508360690145232, 0.7416547558951715, 0.5272596188711399, 0.43266263826660445], [0.8810431635946494, 0.49993303193169747, 0.47702172030172607, 0.6517832060490208, 0.5192569463042067, 0.7903458495167747, 0.31888249587255435, 0.3610636272018788, 0.04408919348114282, 0.34255657585167587]])))+4.743201648521604), axis=1))

np.round(np.exp(6.956151978570819+np.mean(array_x, axis=1)/np.sin(2*np.pi*6.787238206074649)))

np.mean(6.0131376721573755*np.sqrt(abs(1.3633039183512463-(np.array(range(1, array_x.shape[1]+1)))*5.494232441055151+array_x)), axis=1)+10*(np.sin(2*np.pi*np.mean(7.198751189806094*np.sqrt(abs(6.134688006371514-(np.array(range(1, array_x.shape[1]+1)))*3.3882993072840413+array_x)), axis=1)))

np.mean(np.round(5.690819733556772+(np.dot(array_x, np.array([[0.9334307796765036, 0.5355108115473447, 0.40559481599039027, 0.1129551669365173, 0.6329605172967809, 0.9100118231788797, 0.9722329638466675, 0.10009214411373168, 0.6427662711899443, 0.7249821774752164], [0.8440148674844503, 0.04447416846675156, 0.34448615072002053, 0.5949155887839462, 0.3277364433023354, 0.34518516648405884, 0.22378795934535034, 0.4346345021553909, 0.03864707710097148, 0.6910520793464086], [0.1516529166285211, 0.7814718386602971, 0.2640227499733617, 0.02468059891839569, 0.8091825751900006, 0.2652258828683469, 0.464966818280211, 0.19753972068470171, 0.8368763355721026, 0.4793566237022877], [0.21947157356026714, 0.21539833112661544, 0.4174249888154272, 0.40797861522809753, 0.8379350747471431, 0.06117663063737755, 0.3584451987983689, 0.31833662876227187, 0.4744000650317364, 0.7663716688746807], [0.890830779883643, 0.4084216881610273, 0.6230887975557351, 0.8254680452863821, 0.7625425940748406, 0.26000337254915384, 0.7971627556834324, 0.8062851846682572, 0.5641374719394281, 0.6520723704617123], [0.41291154206844083, 0.07298317901455464, 0.7895197953243119, 0.8795940801169729, 0.656392890581531, 0.2630621529420937, 0.21003480867384117, 0.04062159524087183, 0.7030942799890297, 0.9540741924911862], [0.1134263882121419, 0.025358770654341334, 0.4157452682957373, 0.0674611895591356, 0.995804577973286, 0.6454445538624125, 0.2851667755787478, 0.220568446795399, 0.8618881458771066, 0.14711843868757168], [0.7909553002270516, 0.83184664302887, 0.7297766457238978, 0.11535808552651683, 0.9302198065424769, 0.3735652412598146, 0.9842990482315863, 0.5890535474540424, 0.244058548053655, 0.2706122189968281], [0.5766928163795625, 0.38118353974449215, 0.34353207566452637, 0.0889228115099927, 0.051882462962557496, 0.40909795609848265, 0.7055488287422876, 0.04541104535424001, 0.826046324579194, 0.45588706404600254], [0.2953165174747976, 0.2600691953254465, 0.7460793542359991, 0.44090358573614763, 0.8351923536568483, 0.7435232742595388, 0.13835109676602908, 0.9489240979267239, 0.015471086979946036, 0.11818023753569096]])))*7.181398891779544-7.138013291064128-np.square(array_x)), axis=1)

np.mean(3.457075557168121-10*(array_x-9.801149477725591)*np.round(1.8091790876465506)+array_x+5.800668504829495, axis=1)

np.mean(np.square(np.square(5.59817743460495-array_x)-np.exp(1.285049593864)), axis=1)+np.sin(2*np.pi*np.mean(np.square(np.square(6.044397460709988-array_x)-np.exp(8.72029789744788)), axis=1))

np.mean(np.square(np.sqrt(abs(np.cos(2*np.pi*np.sqrt(abs(8.936467766402576)))))/2.3592310078371046-array_x-array_x-8.68615275707563), axis=1)

-(np.square(np.sum(array_x*np.round(7.692018430892573)+np.exp(3.1388187709359787), axis=1)+4.358745095634875))

np.mean(1.4841476418892365-np.square(9.583454218766779)-10*(array_x*(np.array(range(1, array_x.shape[1]+1)))), axis=1)+10*(np.sin(2*np.pi*np.mean(2.154682841302271-np.square(5.910930642835374)-10*(array_x*(np.array(range(1, array_x.shape[1]+1)))), axis=1)))

np.sum(np.round(np.cos(2*np.pi*7.107064502734768+np.log(abs(6.567907907762184))-array_x*8.49279800751105)+1.9267292152845545*array_x-6.330694089772276), axis=1)

np.mean(np.sqrt(abs(np.exp((np.dot(array_x, np.array([[0.15507295074638117, 0.5454299836398526, 0.6489565268522094, 0.9295707579313657, 0.9919754513161443, 0.5001350194254012, 0.7076839130974492, 0.46799267847432413, 0.7700942955779191, 0.26444889502700186], [0.2693728110154151, 0.14412546008245386, 0.621742560473048, 0.8917852880992734, 0.8653128166246176, 0.12708709101588012, 0.2561681117268033, 0.0019165150663462693, 0.1489071986789442, 0.31800600576978566], [0.8266654190456914, 0.8337581223378607, 0.5511515636044876, 0.027665668163316393, 0.004625326046464373, 0.8567026918515493, 0.9059145610971683, 0.5634509061939348, 0.38331465032144096, 0.13580289180179606], [0.6786142932226648, 0.7233181316268565, 0.83847261870269, 0.8642138936573432, 0.0904141336746459, 0.1979938882476182, 0.6858912419665657, 0.39898996913962914, 0.3653304529718878, 0.747221693549736], [0.24159986837606306, 0.4951028480425158, 0.0647091759400128, 0.8122051767071792, 0.5388589675086974, 0.9256990428147169, 0.36846247038377533, 0.11911537382880433, 0.5459555457274811, 0.978121205823], [0.06737700798861235, 0.8646097180202326, 0.5126322907523368, 0.7613423059570167, 0.11704337025822042, 0.31704241904745467, 0.4289299273149465, 0.605359118422098, 0.1245811777018565, 0.27812288222182424], [0.8285579191401352, 0.2829888199380137, 0.32392136133802585, 0.6923702319519658, 0.38209363333497703, 0.4080680393318571, 0.4426106130356642, 0.8298915667893418, 0.4947558419528455, 0.19042345999476817], [0.3931128065996753, 0.6873272165383013, 0.873131919209915, 0.36849133598093553, 0.7225013560012041, 0.3767590468134455, 0.31075344498784363, 0.6520131251309441, 0.9209550805611232, 0.3293102645737228], [0.09148121772697271, 0.7043068895428465, 0.7945645258451357, 0.8327682966518742, 0.6388601908507664, 0.1614356152976325, 0.19975381193852093, 0.8751882879225692, 0.4180378210239519, 0.2595649537284428], [0.443795156779655, 0.9338731020997731, 0.9329780041787779, 0.09000506390866214, 0.9165624547561169, 0.7574106759664708, 0.19066625975420803, 0.46522238451849474, 0.24609650835473762, 0.6230059780280134]]))))*(np.dot(array_x, np.array([[0.1870862217352377, 0.43198470603991235, 0.8036495883332333, 0.03127550079683916, 0.1751147153085192, 0.5935633672213617, 0.7305291541924772, 0.6656227090626834, 0.1783099091950312, 0.3753238555317234], [0.6074056821810353, 0.6063472757642996, 0.6130241899463053, 0.11214699621017765, 0.15422381525493112, 0.2575170048592227, 0.22120782008681505, 0.2410077889075296, 0.10272671232710184, 0.09155862595823838], [0.49600060850464855, 0.437363749255932, 0.9805979738809102, 0.8516057116955411, 0.9497694095791048, 0.8297060402125018, 0.5262468377437842, 0.5079953818632374, 0.28386060954278736, 0.09478312179793236], [0.520667372346167, 0.19231516475658939, 0.6064324032801464, 0.1201511423220113, 0.9899134286997593, 0.9866039510688143, 0.13236232918815594, 0.4204454592780382, 0.1275995581362016, 0.20087703711836347], [0.37451676580245263, 0.47491833011184614, 0.3411160292596024, 0.59238281250784, 0.7575035327205066, 0.3561742999515092, 0.8517980542871872, 0.8393405133736584, 0.48976753988099675, 0.08889285657914103], [0.07224526277521093, 0.050564016829912406, 0.8315715693191438, 0.832068945918995, 0.6261645295912401, 0.9892165998280003, 0.29649142431192266, 0.7062768098629074, 0.6425308390420196, 0.022834850584623423], [0.27029968492435363, 0.8649980086587832, 0.12992907486854044, 0.044830286523252716, 0.8375660491792193, 0.5715724647685277, 0.8704353620021814, 0.281312289337007, 0.19348634415947585, 0.5064862833108907], [0.7418710722568173, 0.3539583431383806, 0.6646185101937788, 0.7888372619924161, 0.6072281093971932, 0.5345082178395557, 0.4242255957414369, 0.060621340046099204, 0.1056067331651811, 0.5687683162126621], [0.6479162841776216, 0.21506695318723257, 0.7497877864899263, 0.5179650471047466, 0.5830992339429928, 0.31078692582973044, 0.83287808846683, 0.07032071439340737, 0.588554268852823, 0.18278535946864627], [0.846703511418438, 0.414141861917362, 0.4800988540162886, 0.8353828881056337, 0.9927833748979178, 0.8006859518106682, 0.09596112183997774, 0.7977921292120012, 0.009840405088323512, 0.8336780256023493]])))))+6.05702801593537, axis=1)

-(np.sum(np.cumsum(np.sqrt(abs(np.square(8.10657965495555)+(np.array(range(1, array_x.shape[1]+1)))*array_x)), axis=1), axis=1))*-(7.990539866558294-array_x[:,0]*np.sqrt(abs(3.2092073729764357)))

np.mean(np.square((np.dot(array_x, np.array([[0.03127119827625047, 0.5813375591313356, 0.15325964134046632, 0.005788377240331433, 0.7447513569991809, 0.73159594862537, 0.2210851896826822, 0.41757533380235834, 0.4435649046940713, 0.669678922118754], [0.34510481929625636, 0.6112967330001691, 0.9288695825078377, 0.10411647122569534, 0.24051987184028667, 0.4330127802615674, 0.14370214594778452, 0.8307593268200065, 0.2344682169469654, 0.5272417874883915], [0.8916717976069595, 0.8656209227144057, 0.9605059510280557, 0.10564564814318, 0.8190473742758844, 0.43359988086142354, 0.7195250692975944, 0.356266988053455, 0.1234240970489815, 0.9428835314195523], [0.07617146029647703, 0.9778500583357261, 0.23035409556423359, 0.48606069165131627, 0.8052790543036935, 0.5203193678241437, 0.044481708201488446, 0.16367158240615032, 0.6090066760189743, 0.12810354730345663], [0.12029728609613088, 0.44787585610154124, 0.4053544395040961, 0.28230982789720593, 0.18818347497098686, 0.799272793090803, 0.05195836096536055, 0.4808392264153539, 0.5915921400254475, 0.604759676988465], [0.20540380358028498, 0.1728410325526749, 0.018278605167245665, 0.6227595979942119, 0.4964561582781971, 0.126491365427432, 0.6979643787303803, 0.7879241899285001, 0.14646682065720373, 0.3206056571186724], [0.8543202053784839, 0.686445243985304, 0.20177832881079993, 0.7290940981551762, 0.1849211813439301, 0.6498005956556343, 0.9033116395843286, 0.1703196410419624, 0.8748995116587552, 0.07309500329505403], [0.9452959155560692, 0.9757535495090963, 0.29825295957243925, 0.8773926750148922, 0.8504540189567634, 0.5606974060485693, 0.00928302274485715, 0.5114312211881193, 0.5527078167455001, 0.6404573949686839], [0.13196386573210028, 0.26921128289525276, 0.7861107285994509, 0.9134060648136484, 0.8604297156650608, 0.9683799966291665, 0.5720196042325364, 0.7783404753854367, 0.8383793412090885, 0.8056117912035723], [0.9195473982599927, 0.37428455886277334, 0.9845203334515932, 0.2740102236258364, 0.8559246166187133, 0.13744549431669628, 0.1422074389541813, 0.11603133341642113, 0.4540581087523935, 0.34655482295824536]])))+4.7403941733624775-np.sqrt(abs(np.sqrt(abs(array_x+7.351258400769676))))-np.exp((np.dot(array_x, np.array([[0.30731779525837555, 0.889464562644192, 0.44505949727673166, 0.11758550064502638, 0.08946968242292697, 0.45826262477340063, 0.971473091337853, 0.589147020504465, 0.5150553851153249, 0.9745174986001649], [0.36855743309804434, 0.7523198901694595, 0.9537559852105475, 0.7492544880894272, 0.34062604040410904, 0.6867426383519122, 0.02327489656283177, 0.18206856791360837, 0.023705523540676676, 0.2734302103038242], [0.6530433163064374, 0.9296143997918959, 0.3248843847804236, 0.3891136533602574, 0.35693493391228237, 0.8551273009815661, 0.8797631074211443, 0.966123042920422, 0.7211074252119603, 0.11280985100851193], [0.5118363862162187, 0.444621666553479, 0.7519167272330204, 0.6698515558180445, 0.18420885008988386, 0.28882986126061727, 0.05578269821582138, 0.23290007744279984, 0.9336024334375101, 0.6386781118749593], [0.3407166075703896, 0.05026468049245014, 0.24909498664997154, 0.7794755528646492, 0.03798537015183845, 0.5189488143885054, 0.6908708172045982, 0.019678758305782473, 0.287273218865142, 0.8903676577589027], [0.11923156993919626, 0.5227073303129383, 0.8106279988094941, 0.58541889868417, 0.008023799894460004, 0.5825467676138186, 0.9447685219350456, 0.4608265651349984, 0.7999180330919111, 0.44275161784063777], [0.847707702077753, 0.07572390615840785, 0.005673810929357259, 0.7371001090085951, 0.6962768403415049, 0.15237846748654083, 0.8565566003732932, 0.356530697435571, 0.9661581078659796, 0.4108527236739822], [0.07662224367864634, 0.8606357418381804, 0.7039250968285445, 0.09950592715378659, 0.18358171967794212, 0.5607211648848317, 0.737188704731859, 0.16036493065101054, 0.3088484892108204, 0.7134472355195602], [0.8430391129036047, 0.6624727605865134, 0.8647541542692699, 0.26161190492513964, 0.8897784374263119, 0.7051387155327989, 0.511532922259637, 0.6319206896267116, 0.6079320681889897, 0.17464027508544322], [0.2504165340970601, 0.5701631294528535, 0.7375511070493334, 0.7550043746217013, 0.8261595787179391, 0.41406015326494816, 0.40804307955903085, 0.10784230486837743, 0.5627016177011707, 0.07277466398664001]])))-3.0722881074983945)+np.square((np.dot(array_x, np.array([[0.14528264332913854, 0.8599233460090968, 0.7322964011187217, 0.6485555627527437, 0.23101200353254747, 0.4661612714074892, 0.5563378615939134, 0.2016000123348214, 0.5747036347555231, 0.4217295806806658], [0.11369244147481783, 0.9427652522103391, 0.26940578814233007, 0.3459906198759255, 0.49766283959843816, 0.3216381976940633, 0.7783153790306334, 0.3336973539186189, 0.05116642530719784, 0.3916448032948543], [0.8584963990993372, 0.09183481294763263, 0.5193332241600476, 0.9647015362732682, 0.9074694864127004, 0.43642693834308255, 0.2666831600080286, 0.2024412817197504, 0.8895055624681906, 0.012009983002474467], [0.3974481328005013, 0.840259412418341, 0.9802181386106863, 0.447436372982596, 0.3447475551145156, 0.7235420007911318, 0.5493451431762656, 0.1312875229937852, 0.9313353851259453, 0.5230609871947779], [0.6055417607153875, 0.4186361263300271, 0.6821388337705029, 0.22507448299104849, 0.313849897776317, 0.8153011853394679, 0.34914491588348284, 0.8212621710513045, 0.23535275660725985, 0.07594671009024079], [0.1977450954739547, 0.10055069431573216, 0.3440001072721828, 0.7091729267242805, 0.8923171553397597, 0.8264948577203557, 0.4047211499913467, 0.9776904265372719, 0.20794059105066287, 0.8579132580995209], [0.6005701405278383, 0.4186299411209855, 0.38207808862925574, 0.6332734263459298, 0.3777564061986911, 0.40299400480085823, 0.21651629827292984, 0.4158549211624364, 0.5452625578579641, 0.7348416932747052], [0.9000147657972605, 0.0455243693848012, 0.16777404740871715, 0.6776341684520356, 0.05224172832876861, 0.06503953447928734, 0.3784143067078308, 0.6313709351822774, 0.5238957312695361, 0.7149522025949131], [0.7543062078938517, 0.8277776529908855, 0.9330674594212105, 0.12044373748249293, 0.704563385927395, 0.08274752280112263, 0.31495498794764687, 0.6263221839295052, 0.6023032329425103, 0.5161244075230994], [0.8464136030274575, 0.031599300359859805, 0.9521259868729101, 0.8689722097507491, 0.19862286182535593, 0.0640153846302467, 0.7298204474760238, 0.8460744570854583, 0.9464108937319532, 0.7355882733170346]])))-3.013111695235132)), axis=1)

np.sum(1.192643386123151/1/(np.round(1.5804572351238448)-array_x)+array_x+np.sqrt(abs(8.010730069507506)), axis=1)

np.sum(np.round(2.511603436309203-array_x), axis=1)+np.sqrt(abs(np.mean(8.048634251007076+array_x, axis=1)))-np.sum(np.square(np.round(array_x)+3.972651319937788), axis=1)

np.mean(5.864563757224907*6.093628237051435+array_x-np.sqrt(abs((np.dot(array_x, np.array([[0.1554944281003232, 0.33254747112722305, 0.64763406071053, 0.6233868487367245, 0.7309064440873922, 0.3653710050380585, 0.5687316804612137, 0.2081521117112053, 0.49708936235990486, 0.7249847181637268], [0.13497116029824663, 0.817379047544881, 0.9465349483582941, 0.4411542866566468, 0.4108116053570505, 0.8678490594877785, 0.5366900558744047, 0.40273374044306276, 0.9528482679352509, 0.3988566866342792], [0.501066751237991, 0.5781028365028535, 0.16696244719106024, 0.9199977906983666, 0.8773868277776419, 0.6606698184927069, 0.29271990350965926, 0.8586466841797147, 0.9923541716253098, 0.9631954007185294], [0.4324947894082469, 0.007227074339385409, 0.5778594918540505, 0.005023073945660261, 0.9445824817708085, 0.7666931173677358, 0.11543624158623955, 0.9914638598168369, 0.29928444116309627, 0.36881420158922806], [0.03751822741742017, 0.6529845337611415, 0.9715835167107574, 0.6048673474917469, 0.6275546494176187, 0.07015215289910481, 0.14318362278803676, 0.21209985187293212, 0.2928125682234144, 0.5031241883719405], [0.7704935288390949, 0.7756528601356627, 0.7324061417394853, 0.04534745829716036, 0.7551575185220032, 0.11749629209138845, 0.5523678611769941, 0.0814818941282558, 0.8831919527116711, 0.8044060813352344], [0.23819217353101163, 0.4976693746021159, 0.31952980922740126, 0.763336428556839, 0.9509440717006193, 0.5899897156757874, 0.6811083859528411, 0.6230687226026563, 0.3119534892675786, 0.4999399715218704], [0.26437499022997824, 0.6798608252358925, 0.20844166883920567, 0.23433523684694213, 0.4597109854928798, 0.024156205599571545, 0.6836705087455962, 0.9249874832829377, 0.9562554957381975, 0.8638333643590602], [0.06998247613848352, 0.4856243836348736, 0.5022127917549715, 0.7300552364979213, 0.11709048131630317, 0.9121063483514155, 0.4469591965853644, 0.55852279542923, 0.8575693575064324, 0.8960528259927585], [0.4386234538926451, 0.0364497236175817, 0.18922256321385722, 0.09529661555493663, 0.8227691671361685, 0.3993803214382219, 0.047932913138633015, 0.13835588410755673, 0.41743170871633917, 0.40402751620499056]])))))*3.9794351686568*4.623268579758056+(np.array(range(1, array_x.shape[1]+1)))+7.800318037805612+np.sqrt(abs(7.590589369282316))+np.cos(2*np.pi*array_x+7.69805266829157), axis=1)

np.mean(-(3.3555547277573528)-6.641178580578314*-(np.square(array_x-9.160639808223152)), axis=1)+10*(np.sin(2*np.pi*np.mean(-(1.4809026631843412)-4.104126004554869*-(np.square(array_x-1.842634237511862)), axis=1)))

np.mean(1.6384881363421053*np.cumsum(array_x, axis=1)+7.2382706373547645, axis=1)+np.sin(2*np.pi*np.mean(7.806077422664239*np.cumsum(array_x, axis=1)+8.157535470312126, axis=1))

np.mean(array_x-np.sqrt(abs(5.41400250641541))-4.4648757717096705+np.sin(2*np.pi*9.860036785695819-array_x-9.949454311639835), axis=1)+10*(np.sin(2*np.pi*np.mean(array_x-np.sqrt(abs(3.31872624111273))-1.274869678328189+np.sin(2*np.pi*9.21245302443178-array_x-5.816499485326937), axis=1)))

np.mean(10*(10*(-(2.282086595333708))*10*(4.3902182293478695)+array_x), axis=1)+10*(np.sin(2*np.pi*np.mean(10*(10*(-(8.1914154436717))*10*(6.425526173814535)+array_x), axis=1)))

np.mean(1.1984062770654131*(np.dot(array_x, np.array([[0.20031026061183965, 0.5993047327159651, 0.019914744401493878, 0.851407634035875, 0.9246404787872824, 0.2302660024357156, 0.43310266567191824, 0.7501930017193686, 0.11034426261185204, 0.5752569842171321], [0.20984405442486997, 0.8617586467224372, 0.5996911121524977, 0.20280057446494482, 0.1498386425411561, 0.09962108871822328, 0.8668406500587068, 0.324091602396108, 0.21815932474571942, 0.7987138150086281], [0.1085271641710055, 0.5040067525200699, 0.13653466364765066, 0.6208190768142321, 0.2523062696100288, 0.12868183591117766, 0.6405318127370289, 0.2086658050332849, 0.13331844542735982, 0.3887689793952537], [0.5936652189635883, 0.7573408983898875, 0.8396418406939279, 0.38902722784587773, 0.09929464535322374, 0.42227711928499523, 0.3798349365276201, 0.23992306803428654, 0.1272393330792958, 0.7806734018540079], [0.8059023559755547, 0.8884269842716319, 0.9833343597200935, 0.013713937293835077, 0.4939394859916201, 0.5091825067447509, 0.5622966877284904, 0.6587264919952392, 0.4772483327771916, 0.050500079578901014], [0.09571059974099783, 0.13855529213983075, 0.6713042973870788, 0.10707627699382827, 0.9964486035269406, 0.9140342842002298, 0.849227217651992, 0.5966699836179339, 0.7691407396577852, 0.985489094902299], [0.9302731258289555, 0.023572113690132235, 0.21148143171136335, 0.5709165942219148, 0.7642881686592304, 0.9559079224241405, 0.9895654448378126, 0.39951833375269163, 0.6918049296339184, 0.5753441225559207], [0.008085826219953018, 0.19008278549734314, 0.08100745317927882, 0.3557354796774437, 0.13980924932505734, 0.9370028212334767, 0.3353961501423419, 0.5443679588230883, 0.988523779486666, 0.036564768196762465], [0.1536893155661172, 0.09855430941939247, 0.8672694024433762, 0.11564322468705945, 0.22666206307085446, 0.9279239883821085, 0.3935590417225424, 0.3319955299271754, 0.6871384617020649, 0.891082729837711], [0.4623717208525644, 0.26035188817643595, 0.9954910068694274, 0.16256917799195714, 0.15251891315482946, 0.23439002683251797, 0.9522518240155033, 0.23043459424694868, 0.827720818451876, 0.1452124723945466]])))-10*((np.dot(array_x, np.array([[0.5180565996238785, 0.34229975634944587, 0.5499616671492031, 0.956453523912943, 0.25601568349700865, 0.08362503026900248, 0.8951472879023586, 0.029899016001768364, 0.6742036015267839, 0.8143189920881786], [0.312248025655393, 0.5594880112482451, 0.28892244706162673, 0.32941810527727566, 0.8525291189855473, 0.4642101922650763, 0.5457245149919311, 0.9114697855913636, 0.556150632410148, 0.3471334226798166], [0.3259155177810509, 0.6672870432396717, 0.21489449136668604, 0.5863163395630899, 0.3443694878068503, 0.10084579866678223, 0.7760729347638077, 0.5333201982677576, 0.4259665764400621, 0.9099923762026915], [0.2773295253639413, 0.10344962484180487, 0.17924509058920923, 0.6655952961258429, 0.22539158325648856, 0.7905936735081258, 0.22746455186885495, 0.5448903037772156, 0.20582001741878497, 0.9765906699126947], [0.9088996663140128, 0.07192668898508248, 0.6212299490659907, 0.9127801188425926, 0.827808753791355, 0.4162085674868897, 0.26363150202655405, 0.2751550152624972, 0.10590238381450812, 0.8891609442052404], [0.6417233915145552, 0.46278698885439884, 0.2930899281197228, 0.6414461069233407, 0.017248072057483355, 0.9109223003537905, 0.6008369278855592, 0.08983478041542758, 0.44074945920830944, 0.2289905346831118], [0.9622165173649402, 0.24232918804108805, 0.6460453702075748, 0.15510429528333203, 0.2842235373029579, 0.08984340628003384, 0.046871520527489, 0.5826908366928857, 0.6922133063404542, 0.8973129109428377], [0.8387194959491576, 0.719703738854317, 0.6985108665959241, 0.5148937491916105, 0.31614139996232016, 0.782900124801101, 0.8536965770951417, 0.7318608967832801, 0.43886889628586967, 0.12318640833370043], [0.5649669215753996, 0.5493259718595289, 0.6665044355767735, 0.5366475531546167, 0.06235785385446213, 0.18189198523077255, 0.607057508020926, 0.970986874794938, 0.14842666307995933, 0.8845518082031696], [0.43193210494393985, 0.7625419133685486, 0.45555852859067736, 0.5723885265025755, 0.17808793571442383, 0.9727951057867888, 0.6987573761192162, 0.794883010976367, 0.0019576082032917164, 0.5657564187314651]]))))-np.exp(8.803498110490597)*-(3.708826206213708)-(np.dot(array_x, np.array([[0.07984559821781945, 0.16535964588189112, 0.3179593405367208, 0.9999015536806273, 0.48786005333895244, 0.6160852869816105, 0.4368333006202654, 0.7912402722613611, 0.8150768685713689, 0.8825445774546438], [0.2170340472471609, 0.9054434148823174, 0.9001624233584443, 0.40665949172781524, 0.7930476045124025, 0.4169625580221339, 0.4270362962094201, 0.5324423728021066, 0.6605748975262116, 0.9652862694948078], [0.9189848473013205, 0.6075623246566936, 0.5460536471402386, 0.0584496203162711, 0.15720118396997695, 0.2555765066580451, 0.9646199262994747, 0.5335971143127631, 0.3648638945189767, 0.5589419276595883], [0.786147391389914, 0.6480990147849595, 0.3824417905690388, 0.9502689894216482, 0.37029629864522484, 0.722643580344005, 0.7159805493017732, 0.6216755964814246, 0.832898123341979, 0.19130520041639953], [0.10929379849599341, 0.06580605077601209, 0.5448390918579901, 0.3082372661610797, 0.8954620860733037, 0.937396896171976, 0.9961251819604051, 0.10414703804249004, 0.8933158416016062, 0.512135708906325], [0.748644495200139, 0.5017635153823725, 0.09042539925210069, 0.8083077471797911, 0.017237726851651813, 0.2706444647815526, 0.29623844926673704, 0.944151182494251, 0.28272999583645575, 0.5961940938922807], [0.39661566070622123, 0.9128925468535393, 0.2962085311630739, 0.7676001455267559, 0.9333689148328855, 0.17071718787927537, 0.1106114369197414, 0.8363601350291138, 0.2166289227333863, 0.13706509856334592], [0.1901456279005207, 0.5291213502636539, 0.8383561113470254, 0.7401778865746347, 0.847661895951058, 0.08929881493055125, 0.348194017125816, 0.6730950399226674, 0.6974596125477518, 0.7910491791052747], [0.6695519559552164, 0.3711074955621303, 0.5118806188021823, 0.4840478566166162, 0.1584573199636795, 0.5829942850747467, 0.7920205434918822, 0.917069433118963, 0.7603598098154194, 0.1934320879245851], [0.5876500776112558, 0.8431029329344962, 0.06360201329495319, 0.6648633509323797, 0.9890342678421767, 0.5608671941160122, 0.601057516018183, 0.5006955847396907, 0.9238801725484664, 0.5149643296591293]])))-np.cos(2*np.pi*np.log(abs(8.732159349191985))), axis=1)

np.cos(2*np.pi*np.sum(array_x+1.979105172830772-np.cos(2*np.pi*np.log(abs(2.5326013913487038-array_x))), axis=1))+10*(np.sin(2*np.pi*np.cos(2*np.pi*np.sum(array_x+6.460702027308126-np.cos(2*np.pi*np.log(abs(5.059027010561431-array_x))), axis=1))))

np.round(4.224806159853409*np.sum(np.cumsum(abs(abs(5.723481395239631)-array_x), axis=1), axis=1))

np.mean(10*(np.sqrt(abs(9.25594180115817/9.776471124091058-array_x-(np.dot(array_x, np.array([[0.18457234932631517, 0.9407019583418617, 0.18574503313747492, 0.6232271564353599, 0.14750652850690038, 0.6575988765797229, 0.8320571790970905, 0.08094026288385103, 0.5371596692576645, 0.624722148355833], [0.23451896569829211, 0.6680998839117556, 0.27519812859193715, 0.4857435905130293, 0.5459460057516732, 0.04414157378987016, 0.05748130290973241, 0.7741230430429525, 0.15362887335176134, 0.38260352502787887], [0.8468920028974612, 0.2812690273052034, 0.29731208483778604, 0.33961003741153994, 0.8561360545882835, 0.6742129612533314, 0.9261172980991441, 0.8860091929213858, 0.9477629046026554, 0.19891319529987395], [0.07470310275844605, 0.05918928471289475, 0.8338527739785992, 0.3256681015706878, 0.6695754371030871, 0.9464732504602598, 0.2290688372393922, 0.47502050413225994, 0.044371086669129256, 0.8763873090992688], [0.3146555405917243, 0.8044993716392682, 0.5725895214738621, 0.08172390286232667, 0.56097499361949, 0.6098394743103487, 0.4149980135448016, 0.16335614448837021, 0.9388757504505807, 0.09177734324595499], [0.43074716016817816, 0.9796719303096244, 0.34586821480610686, 0.41057270666535695, 0.7760388798507225, 0.45062955031312135, 0.28432636268187994, 0.46859007350712045, 0.12193691347357216, 0.028577098394066636], [0.7945883498094944, 0.16328963651313488, 0.347343107012659, 0.0791134394523163, 0.2544993248874433, 0.7322675624352674, 0.3682563041818503, 0.6522282638713278, 0.2783733461236896, 0.28322887050598067], [0.4470633393661425, 0.890628372636595, 0.24008352861887094, 0.01146475890506038, 0.8733502367554713, 0.7089614325438531, 0.9756742234388823, 0.9812960725575811, 0.6782336496080129, 0.9140846344537389], [0.1715780108486331, 0.8217238932295421, 0.8934056077995458, 0.14521294286300412, 0.5147013729810211, 0.0997309284257456, 0.1729099877073249, 0.48887636745655416, 0.6951683024457354, 0.35579426921015866], [0.8763752363020131, 0.960199179023929, 0.9007703200910365, 0.9816701665840455, 0.400647281951948, 0.17693901033393122, 0.26448023863388115, 0.8716234495443447, 0.264231812389663, 0.3463787575531456]])))-9.846072378819802-4.97594141433838)))/4.816978842393113+array_x, axis=1)+10*(np.sin(2*np.pi*np.mean(10*(np.sqrt(abs(1.625139466711863/5.00103462409767-array_x-(np.dot(array_x, np.array([[0.8593885834960644, 0.9131602230829391, 0.6612482062898953, 0.6713839191705419, 0.94152887726587, 0.38648693238542675, 0.9502003961510191, 0.8723700821177269, 0.3633327166374434, 0.13533924407081832], [0.8430243090983042, 0.11548049407138328, 0.34697217474345676, 0.3996163672674683, 0.7016461943372622, 0.002884030937994164, 0.7031912739491438, 0.1371518596797312, 0.4970153044274914, 0.2858986594768167], [0.2990888539696215, 0.44253911039247584, 0.09812799893417246, 0.02672722104898595, 0.5058948105614064, 0.24521153785712502, 0.08225602019778722, 0.526616792297542, 0.6549639977909826, 0.23493108035331167], [0.5114655297622677, 0.4522806567207198, 0.5301084932750095, 0.19483227752795096, 0.931583117537917, 0.4474511401977137, 0.7520372658099932, 0.9017372146276542, 0.2371708675828995, 0.4882287774419013], [0.7287927145583167, 0.7720082799566412, 0.041346348441413716, 0.1989558242164957, 0.7313564738725982, 0.05494004593128199, 0.461904765558226, 0.4772712810313826, 0.22891178490171593, 0.1218427048212899], [0.91969976435412, 0.5002182202126167, 0.2678367109347132, 0.5788809629799002, 0.026125018219389506, 0.7667137225705081, 0.7870463050091588, 0.4970420856067068, 0.8666612238717604, 0.2034428869599454], [0.5711208487303377, 0.8618235608718015, 0.17720744261118315, 0.1759339082104927, 0.9957457487740261, 0.07559407709772437, 0.43019826778168335, 0.6280961079183919, 0.5733764682054981, 0.13257398786357366], [0.6799093007847603, 0.7456761339812631, 0.7352239974159609, 0.28017252632004785, 0.053681970200121776, 0.8620680705084398, 0.5538538836543386, 0.5577958157454468, 0.7347818090299498, 0.5626936580578188], [0.7405957709457717, 0.8513275217253896, 0.9412637070152146, 0.1294681304873052, 0.20732494622985087, 0.8649865653329007, 0.3697498550163667, 0.452151470203737, 0.74507788490061, 0.9946252339987224], [0.2343269098041083, 0.473016734817612, 0.5156686449586122, 0.6357389642809922, 0.5701641154340482, 0.48423398881732593, 0.3603732080540918, 0.6396715091427153, 0.6510433789319816, 0.041827325965934525]])))-6.451405915036811-3.3734165202130035)))/2.8780385270200344+array_x, axis=1)))

1/(np.sin(2*np.pi*np.sqrt(abs(np.sqrt(abs(np.sin(2*np.pi*np.square(np.sqrt(abs(9.769351904030266))*np.amax(np.cos(2*np.pi*(np.dot(array_x, np.array([[0.6504726956306688, 0.7100300732270197, 0.9551804293333112, 0.7124060137953613, 0.2352359132740599, 0.7973995333462911, 0.6223064223951386, 0.8378660373834215, 0.970707812002609, 0.8927882427529541], [0.34745991756658257, 0.025234982864745348, 0.31707350261764644, 0.5763417352581146, 0.3413074115807625, 0.22421673680206167, 0.7459717559185559, 0.8262686084384513, 0.618931558226162, 0.294771851960968], [0.8518297149488308, 0.937464080972859, 0.20842908221164957, 0.3356545847431256, 0.07974337642266305, 0.4115628164382634, 0.01902640073487749, 0.963280052489188, 0.17621338473556059, 0.8744572372698389], [0.7421897130013211, 0.7212803077759682, 0.6442228945990622, 0.055824443675167035, 0.5607328708130905, 0.965907171174079, 0.7301330310282288, 0.16179987400761897, 0.6390800975282508, 0.4956862451055285], [0.014993855081555929, 0.05541415790984794, 0.45382859408412046, 0.7167480583498135, 0.6340332503342824, 0.35656856230024325, 0.7783281422411911, 0.5089355099047841, 0.2953182301813526, 0.6063527983288458], [0.042233840283134816, 0.7862437579908644, 0.42006788224452873, 0.09684474599919846, 0.15576855291484815, 0.9535903964761211, 0.23384931165595213, 0.5604907409840328, 0.7953784262521534, 0.9642088977849606], [0.1587169080846197, 0.7572664749710778, 0.06760652147761881, 0.6218563124255431, 0.7363978794490454, 0.8285093213674831, 0.8306395963633291, 0.37681842053614456, 0.7391773342007378, 0.7638870952491721], [0.9591363060695849, 0.1038825683854051, 0.7565558040341862, 0.44110070618630026, 0.33317979813730925, 0.4637361777911183, 0.7344673929809845, 0.5822773320120989, 0.2162054435944376, 0.8236825033932366], [0.34523585187472183, 0.5573168948564142, 0.9816409363621376, 0.7905393760830101, 0.35617281789666433, 0.2296986292289337, 0.8047748171188334, 0.7793601900612278, 0.8827274451945404, 0.47776312146818767], [0.03529901979321892, 0.352723492399997, 0.5690646767471051, 0.3161245979579681, 0.8458016663137335, 0.6149547481184351, 0.06844177642600857, 0.6642353583492032, 0.37369443309270645, 0.3818615986553433]])))), axis=1)+6.325617716828093))))))))

np.mean(np.exp(np.sqrt(abs(array_x))+(np.dot(array_x, np.array([[0.8161377053795585, 0.850812117872705, 0.32915605494376776, 0.7160613985557073, 0.24357836353409434, 0.92994979178156, 0.05031513143276334, 0.021564067103118756, 0.9810681513080913, 0.43380619150147937], [0.056733329763376306, 0.9514089548709185, 0.38908298159205057, 0.0437487510964365, 0.9128324141602705, 0.49330589001926173, 0.5887902452329082, 0.6122898756354638, 0.3053465103324644, 0.46641266929693936], [0.20815884237306337, 0.6859017628251789, 0.660281439016287, 0.35592264859662526, 0.9326907989577534, 0.261839283858765, 0.2485101735157721, 0.38343538568493396, 0.568788513073521, 0.575256493798193], [0.07902863628367529, 0.04943246197586382, 0.037478820518740186, 0.5067406081384704, 0.9302589474973391, 0.967080026257159, 0.8221840147482322, 0.017931246269304313, 0.9389621773939236, 0.5718096342910353], [0.9445755523456382, 0.46685093818705514, 0.3643449207770587, 0.2464748892477704, 0.3097915705126101, 0.45937848595813546, 0.3666170464863343, 0.25492956438319914, 0.9617925747062293, 0.01142668107760303], [0.41585556437980387, 0.6002645258699383, 0.547305370541297, 0.46371591199209616, 0.6210173181277794, 0.6680744325154222, 0.8380100661678016, 0.9659199733869983, 0.21382800952341574, 0.8735013127955591], [0.9545806630378654, 0.001879867861220963, 0.3603267957953037, 0.13264871609839457, 0.9463208121034861, 0.3346539349375215, 0.29954419501054974, 0.294335731166831, 0.7793373267227922, 0.7005594085665536], [0.7411025880226552, 0.8421049195936396, 0.16562685921331766, 0.5005767256741522, 0.3046283830455342, 0.3335475621391788, 0.6662494209343423, 0.03840336205652717, 0.4618326133628129, 0.6035668904100384], [0.9971222666360092, 0.15920019876290636, 0.11282600865636794, 0.8719947327184739, 0.9881332299586782, 0.9883686050787895, 0.38306245642738224, 0.9818378046784495, 0.5825219701768113, 0.4289340121018932], [0.04092022377648519, 0.7377983971258883, 0.32411452949619135, 0.25282090899853216, 0.2538932886070522, 0.8858889899820013, 0.10318730009041621, 0.49397931437182396, 0.9900894631110687, 0.49129655274959094]])))*np.round(1/(3.1784215260033024))), axis=1)+10*(np.sin(2*np.pi*np.mean(np.exp(np.sqrt(abs(array_x))+(np.dot(array_x, np.array([[0.8120557603477421, 0.6512241158370928, 0.6309663917545046, 0.5860319638629302, 0.07255896213549884, 0.24928780995989208, 0.9204175082577272, 0.2205190561445095, 0.5183528352940665, 0.64277988277168], [0.03836591601122108, 0.6480861204926666, 0.9569514784903216, 0.8173443269026883, 0.8980288710547163, 0.8333830509758156, 0.2284644315928609, 0.07124741725798112, 0.8953411896911911, 0.5535517375203769], [0.20007057379690374, 0.6568185419364557, 0.5087970788210485, 0.10349259708364955, 0.08078014175148163, 0.055369457759980256, 0.7881797191862854, 0.6614860884237949, 0.35925095585098676, 0.7571834552547441], [0.12664620495168966, 0.25399171084106087, 0.5706394828901981, 0.3477019406885776, 0.22478459510845505, 0.2158345326142831, 0.6245413447280626, 0.06018846613173923, 0.7792854867886381, 0.9600487345755301], [0.49505835201391957, 0.4639351901746597, 0.731522519543124, 0.3078950571358683, 0.8754037188223491, 0.5144120949443427, 0.855262370373275, 0.5299462040508742, 0.11250686499442442, 0.049943917552524586], [0.13266890346196614, 0.41429516263041255, 0.49495563343669746, 0.5479046331942096, 0.0009523619550706464, 0.20876563661404368, 0.6342595028817561, 0.6818288185574487, 0.9488563269167065, 0.20044944563065925], [0.171933097656789, 0.10684623009091532, 0.2164519463627791, 0.3551257620410754, 0.662094824886126, 0.5262695842425746, 0.5479194762473699, 0.976423554784645, 0.8682126144079259, 0.16971557474675125], [0.05323201568829006, 0.9337270440189985, 0.45361889767735164, 0.8343458695507788, 0.05785892079623145, 0.004666326344248128, 0.4118723389203566, 0.1475136637660599, 0.2882065354071893, 0.2067964166388433], [0.11922606227574706, 0.09738166524960168, 0.7250135843376918, 0.9925744343576348, 0.6752745902332871, 0.21970595369078927, 0.5680247510205106, 0.90396523009632, 0.7951850702100783, 0.8408441026563256], [0.5141031368822689, 0.7439823126959489, 0.29224890496064015, 0.8302410572778937, 0.3640116427125656, 0.3257900161851761, 0.9342999320897172, 0.935698562418927, 0.6898514374299143, 0.28628053002511034]])))*np.round(1/(9.818003625778486))), axis=1)))

np.mean(np.exp(np.sqrt(abs(np.square(3.8628834450362675)*np.cos(2*np.pi*(np.dot(array_x, np.array([[0.8747991865260297, 0.5672012177003638, 0.8268421359805131, 0.4442716754834596, 0.41159365502715684, 0.34851517085580563, 0.6854324278850591, 0.6503277555841164, 0.4016236202916472, 0.6867529755489511], [0.9553026350964784, 0.16616461800554838, 0.6122131834770669, 0.3145222500478897, 0.9958719284349241, 0.4193901878594104, 0.022772685578370422, 0.27264034746569665, 0.4336568453409637, 0.9695934747314736], [0.36326069465600686, 0.2958659897360304, 0.3431007388653101, 0.21032890033309615, 0.556560558111154, 0.04137691131277477, 0.6919289492763679, 0.4585491008290582, 0.19360173286617655, 0.38727820687478354], [0.6515451782535471, 0.45406021516782546, 0.24098422163336053, 0.12789183634940937, 0.8822097754347623, 0.17511411645844854, 0.47184814845062995, 0.8520989323663821, 0.8600788489356275, 0.46398379585842164], [0.6076807534046447, 0.46668589788897763, 0.07808544301006426, 0.7954786161738292, 0.7476797704910235, 0.8373588732807496, 0.39509971668383015, 0.6924309596960377, 0.8969561196832391, 0.34335872422913405], [0.5708024238779521, 0.7945438082800098, 0.7122131011086796, 0.8475983324986095, 0.7166801037878541, 0.7367504564419695, 0.6393048550820809, 0.16777172292094933, 0.21345209627191442, 0.695812245941317], [0.2291477502515309, 0.034407878370943834, 0.3865857431377725, 0.13974525831780704, 0.20768822117696284, 0.2772502621301626, 0.9939118286301585, 0.4419343074803792, 0.4998214381292214, 0.33671777842008], [0.05270296184559442, 0.9678244540482691, 0.02153854434090141, 0.8505728457306585, 0.658071903564695, 0.7873522361668758, 0.45877184153231, 0.3329831485498326, 0.8749722427822167, 0.4215522397436169], [0.6324585641413896, 0.9459972315704814, 0.002754846927803589, 0.7965697638378437, 0.5750174989403212, 0.2204498940278281, 0.8006270673726155, 0.0380993888886485, 0.31925848271174817, 0.6501203568849713], [0.39096180386445056, 0.7054093439124508, 0.6473455685187354, 0.322633730772367, 0.1395195513990769, 0.14482654796279515, 0.18560056277913528, 0.5503642680989307, 0.2620468575834791, 0.28628152033048493]]))))))), axis=1)

np.round(np.exp(8.092864748559865-np.mean(array_x, axis=1))/1.4490820111906664)

np.sum(5.215112148031096+10*(array_x)-np.cos(2*np.pi*np.cos(2*np.pi*7.890658720472535)), axis=1)

np.mean(np.exp(3.7573554389842845+(np.array(range(1, array_x.shape[1]+1)))*array_x/np.log(abs(9.572071816475594))), axis=1)

np.mean(np.cos(2*np.pi*array_x+2.668057534301792+2.4612008386361377), axis=1)+10*(np.sin(2*np.pi*np.mean(np.cos(2*np.pi*array_x+3.24479381801762+2.0041886147672723), axis=1)))

np.mean(3.142869275303095/abs((np.array(range(1, array_x.shape[1]+1)))*array_x-6.104704891803227)/np.cos(2*np.pi*2.84394725303666-(np.array(range(1, array_x.shape[1]+1))))/4.025994075986878+(np.array(range(1, array_x.shape[1]+1)))/np.cos(2*np.pi*(np.array(range(1, array_x.shape[1]+1))))*(np.dot(array_x, np.array([[0.5116842807794665, 0.1610790434122995, 0.40783590511919365, 0.6437579318894339, 0.8589204981951966, 0.5053500900720638, 0.3762214926890318, 0.7755627307897832, 0.8546268668581656, 0.7678435775781985], [0.9308730305158764, 0.305750653073675, 0.11901280918737622, 0.12389220410851498, 0.7782228235949202, 0.576026945135301, 0.36251442571534764, 0.7684903526077346, 0.32314054647755897, 0.5366027214741489], [0.7771532467336879, 0.2150230568513085, 0.10242848084006406, 0.6678631807173537, 0.05774119101811115, 0.8571723097203315, 0.02000093263054059, 0.9400375764052092, 0.9571494053144091, 0.40886306865851996], [0.0505026626919447, 0.45170824215306615, 0.008150374825590112, 0.030981778345105737, 0.13122075815302536, 0.40803844342624984, 0.5878224288877979, 0.8490618109330258, 0.3810197722807135, 0.17446182092920648], [0.16526352655837484, 0.1782260118180593, 0.10122243166798295, 0.28389799322945886, 0.2620283608772569, 0.008870604002848781, 0.09356041403075743, 0.9516203148700397, 0.37272782516732217, 0.1367230064260767], [0.4404921207298931, 0.4611939014489458, 0.6044917665885655, 0.7806011304399009, 0.29884696920514664, 0.9906698618512594, 0.6950879472869563, 0.5197225552325533, 0.014585243029543116, 0.2530033002768981], [0.9654158122213212, 0.9805396649028906, 0.6529891384077515, 0.6580848767209063, 0.5586233415810968, 0.03340856031377437, 0.2840491509758898, 0.11617765650117928, 0.4943785477799262, 0.08610385360044082], [0.8216938788822711, 0.315574073200217, 0.2516823168652871, 0.4163554772145539, 0.7989423046661221, 0.8930297871480805, 0.7314976376135592, 0.9930730703474571, 0.8805249590931016, 0.027667896941488523], [0.30193054063962965, 0.6528490250760832, 0.7755033285524584, 0.23291562568324276, 0.5551684854303002, 0.033442601411063366, 0.3364650807255293, 0.741766705360226, 0.7326636708012533, 0.6379990689461648], [0.12628896566647563, 0.0038810477300125212, 0.8684860519025585, 0.30019274780710403, 0.7548127183991021, 0.2502944170675132, 0.7028102325812157, 0.6230412234436474, 0.17569732156804385, 0.4693360926885527]])))+4.7408828128189855, axis=1)

np.mean(np.exp(array_x-array_x+7.293898326914272+array_x)*9.583683153773423, axis=1)

np.sum(8.664546270770206*6.690424926715542*array_x+2.2910858661851314, axis=1)-np.exp(np.mean(array_x, axis=1)/6.956420261810369)

np.round(np.mean(np.square(array_x-8.24543960426239-6.149327392548406), axis=1))

np.mean(8.779334010274933-np.square(np.cos(2*np.pi*(np.dot(array_x, np.array([[0.34575583287351686, 0.5282945668338027, 0.7257953787262954, 0.3320377175369279, 0.40164880697493033, 0.755925868957343, 0.3779975059405004, 0.011063260625448268, 0.8181172443158322, 0.14682510338451338], [0.9542398538164321, 0.862379182585286, 0.526486808356814, 0.7360315071082099, 0.6017403975094611, 0.16029957146878604, 0.5419508482145685, 0.22955502800422412, 0.5871644258636992, 0.18780569983948858], [0.7819596787115418, 0.7412804313884497, 0.1300054917461151, 0.7576077435311203, 0.9949032225420003, 0.8918020627352993, 0.7116922213508723, 0.83740098125362, 0.864779917979687, 0.42345209345196566], [0.4813591822007437, 0.3741147212582685, 0.9161697510939528, 0.5582051384310941, 0.892868446103414, 0.22390431800968102, 0.24822000925057786, 0.8407503703837338, 0.9235718147402228, 0.34919775895421756], [0.08496850114083443, 0.7640728467530011, 0.10220031485970049, 0.5404317035018987, 0.4628126514663762, 0.8767461318038937, 0.8209942941287248, 0.8314132514949206, 0.2860541237238913, 0.07420210633488378], [0.78762001739762, 0.18041565173833973, 0.7067610607612941, 0.36704103314038505, 0.9737482017901155, 0.41283333660935584, 0.8273647794128808, 0.7052869671106626, 0.9673322100009759, 0.28084900070755514], [0.8097003351701534, 0.013187462349890211, 0.863368806200548, 0.46983501637261027, 0.7357853398853438, 0.45121504995957307, 0.6197971848470145, 0.3548303823463772, 0.7306686012322321, 0.8879411292358481], [0.8613433420642652, 0.5024943508549211, 0.07130029195013898, 0.9664780038353904, 0.5996242620529737, 0.2813541019044693, 0.8986295199418604, 0.8679296407160911, 0.4372216867579328, 0.3603194979202554], [0.37602759670732966, 0.7054738601747095, 0.0030519832463878904, 0.8957689012460136, 0.9587463814955907, 0.5297399752449312, 0.24196787793648689, 0.9526372954430814, 0.29255929743352627, 0.6693433922195381], [0.7397668988973166, 0.09248904251700407, 0.5901519203458717, 0.9385259132038454, 0.4823711626502435, 0.3359772183660453, 0.12629050049419843, 0.7167375960916755, 0.8806869569465743, 0.7795303530967989]])))/7.932875189791489))/np.exp((np.dot(array_x, np.array([[0.6980251875630906, 0.7097720186701496, 0.7857026673716803, 0.40152674619703776, 0.7866328689679205, 0.6527065008922898, 0.07528266390490101, 0.9932523208244607, 0.8178771490858637, 0.01759168712606718], [0.5589019453029213, 0.6805363276778518, 0.918355595621099, 0.31536642830722517, 0.3995271046084422, 0.7551173101734466, 0.04111075017864263, 0.15822027312520648, 0.34440005473340696, 0.013872080249694108], [0.8005283004046567, 0.6540789006381502, 0.6284251370576228, 0.9732835408184983, 0.8196973482425988, 0.8573266085004575, 0.6282683439460591, 0.9235248143414859, 0.4425967919761823, 0.44358818809013223], [0.2190610500508593, 0.13493706676923556, 0.8600083991930896, 0.8152296456233684, 0.5993690813722676, 0.3302429315909893, 0.8994253338005449, 0.9922240619545616, 0.840342524430736, 0.5592875763463248], [0.34034323212561746, 0.906230996411695, 0.3723898033053179, 0.7760363529185437, 0.7136089146647907, 0.030075154285395422, 0.11301178616607055, 0.30429985421543737, 0.20369792726466973, 0.39554833472808126], [0.9388948982432754, 0.15442935650435108, 0.40144535069665765, 0.006147682005038346, 0.7838697975562227, 0.2615526518833744, 0.5461313999966357, 0.5619883718614339, 0.8640848991823747, 0.01926466667072746], [0.01717847191992594, 0.15125809336096785, 0.48548052871116043, 0.7239254506017783, 0.3601222558052857, 0.6916639346983252, 0.6786933828302243, 0.23805205295251064, 0.19559678849085815, 0.4983571296946504], [0.6235227389868649, 0.3476379915935979, 0.4840559965349912, 0.6297367002093395, 0.3549047748729536, 0.08087382678292543, 0.5980854802273616, 0.5237262215206412, 0.2330841973519221, 0.8786732007848912], [0.853264814322269, 0.4214306272472702, 0.5717096866874944, 0.8017714099663985, 0.3435644132951, 0.017024912866165698, 0.5738690158508059, 0.17339654781387193, 0.7686993182961304, 0.06085923009280558], [0.8898811059915295, 0.19603405250096229, 0.813883364070198, 0.01712156482692473, 0.09116477120282818, 0.7232396758030774, 0.7174922355714499, 0.5208919110573809, 0.5219396435999356, 0.2839493294785055]])))/np.exp((np.dot(array_x, np.array([[0.1564776124141084, 0.2433137012373977, 0.7699317806907405, 0.39375863574309344, 0.4467780743715234, 0.7402585932774607, 0.875179416231105, 0.21044722520912484, 0.5369186293678456, 0.6153469851736871], [0.890047730376747, 0.8650929052330908, 0.3756593657316156, 0.3372582295701445, 0.6657455486881774, 0.8567993704751665, 0.22496832177456172, 0.9291036440450267, 0.6731945896690792, 0.662859221822993], [0.1425515095124581, 0.8968344123777818, 0.8755509820990598, 0.24860530617215804, 0.5503880100540354, 0.1278917161558938, 0.17272720788303308, 0.5745682938233172, 0.6333872492993592, 0.603419129700574], [0.7016308705358779, 0.637165487632974, 0.9521776972806102, 0.025920912407469077, 0.6106533517252053, 0.5408098885134899, 0.4924920722926872, 0.2807079324345769, 0.06507889795329025, 0.49272019492097396], [0.992082263254792, 0.5100305605045395, 0.30506145071388957, 0.1227349180386571, 0.9594692295741908, 0.6649229511841687, 0.21569542431876754, 0.6088929901382749, 0.6123656055027269, 0.004782676241517625], [0.3844241539186415, 0.621278003471201, 0.633510325055405, 0.9877642487697654, 0.5717155052554678, 0.5636077473343905, 0.6472949830757553, 0.8351723382821019, 0.15105273897326088, 0.4192990005545376], [0.4291308706162632, 0.4577413408538752, 0.5026896863540438, 0.1561660447656642, 0.08404286917344772, 0.8573415623770362, 0.8904219119672917, 0.9462216933934685, 0.05772145972537801, 0.4218434345818801], [0.6859825372066914, 0.18823232391148603, 0.6700719050176783, 0.544467148088147, 0.20155036291664774, 0.3175567729534722, 0.22706194805198832, 0.5629097230924388, 0.45760125829183107, 0.7329914357768875], [0.5607894745818454, 0.9572224708382497, 0.6034039988469446, 0.7686575568292527, 0.1665166428544259, 0.638490435489679, 0.8383775143446558, 0.6614960113404835, 0.2843515060386578, 0.83121865788842], [0.7986200979067651, 0.34223542396802964, 0.8492001329055123, 0.4374341207015816, 0.9374544405150306, 0.6163794445982178, 0.6765399512943903, 0.9666126636322507, 0.5503548284957652, 0.25874424018128395]]))))-4.987942969106829), axis=1)

np.mean(np.round(3.4363250054846914*array_x-1.714217139171236-array_x)*8.58678990730836+np.sin(2*np.pi*4.983985747382487*(np.array(range(1, array_x.shape[1]+1)))), axis=1)+np.sin(2*np.pi*np.mean(np.round(6.378816954251706*array_x-3.073127651750109-array_x)*8.425848110307763+np.sin(2*np.pi*3.9095223496074176*(np.array(range(1, array_x.shape[1]+1)))), axis=1))

np.sum(7.296903999979695-array_x, axis=1)+10*(-(array_x[:,0]/6.4101637399323295))+10*(np.sin(2*np.pi*np.sum(1.6564474900735693-array_x, axis=1)+10*(-(array_x[:,0]/3.9839378511378247))))

np.log(abs(-(np.mean((np.array(range(1, array_x.shape[1]+1)))*array_x-(np.array(range(1, array_x.shape[1]+1))), axis=1))-3.286699701469876))

1/(np.sin(2*np.pi*np.sqrt(abs(np.round(np.mean(np.exp(array_x), axis=1)+4.947843729340073)))))

np.mean(8.998305645990609+array_x*2.929187514114352+array_x+np.exp(abs(1.9802659036146901*array_x)), axis=1)+10*(np.sin(2*np.pi*np.mean(5.463868980160113+array_x*7.446238988169062+array_x+np.exp(abs(3.2822137969689047*array_x)), axis=1)))

np.mean(np.cos(2*np.pi*1/(3.90517132838069-array_x)+array_x)+np.cos(2*np.pi*np.sin(2*np.pi*array_x))-9.164529253861646, axis=1)+10*(np.sin(2*np.pi*np.mean(np.cos(2*np.pi*1/(6.339361714153654-array_x)+array_x)+np.cos(2*np.pi*np.sin(2*np.pi*array_x))-7.027374154093375, axis=1)))

np.mean(np.exp(2.6029098903336276/7.04950835431291*(np.dot(array_x, np.array([[0.8096623617161246, 0.02577319054351246, 0.04478178969094171, 0.2012376110249684, 0.2787851625929044, 0.14171406954934085, 0.262967458596439, 0.963808346286154, 0.37202531438142217, 0.0966939867962141], [0.36531107281108344, 0.2900780017971054, 0.5608304194943793, 0.3604604792260213, 0.04335888280361333, 0.32366885448837124, 0.8930566318592978, 0.567544620058043, 0.1492926434377212, 0.9724501890659982], [0.46821309339249484, 0.4088916619320808, 0.3024762442707374, 0.4826722128181723, 0.7425223352345355, 0.7385055996423872, 0.13609203158020478, 0.9320455428230056, 0.8952395225121885, 0.2486944517606644], [0.5406910446016694, 0.707845692533245, 0.8202185328102178, 0.2620609859731, 0.9494533902721404, 0.265055118643458, 0.9558537401219142, 0.5364596386745337, 0.6158369603638079, 0.26545519959001584], [0.41338595120470134, 0.05052659447934649, 0.860350411123232, 0.2987285372893783, 0.7908869300555081, 0.1554098788961763, 0.620353284687072, 0.3263537919778269, 0.8724429947623816, 0.7515805846180391], [0.9022474192904315, 0.08240952514895006, 0.1894309766002894, 0.5535119692851458, 0.812534206050733, 0.8037175111533655, 0.0022040650119496963, 0.3115366613309579, 0.030477826258905605, 0.1419653964714751], [0.002504829931356367, 0.24143398360411417, 0.6810576792825027, 0.3033262106612796, 0.9241726956225126, 0.5004489619226419, 0.07940539143924596, 0.05215325138506732, 0.18288732293327103, 0.3078780737938357], [0.9532171219522312, 0.40806734435023806, 0.09782968592798358, 0.17831574190669341, 0.35437956161329365, 0.4363988678832108, 0.10987006339177408, 0.6382173692390871, 0.619192647188286, 0.05800615164595069], [0.8843880921719625, 0.9081725470416087, 0.7892441834269157, 0.3050275021399208, 0.4340382205802906, 0.3542830526797822, 0.266572870914638, 0.3725245321374505, 0.13926190522281712, 0.6854066623862419], [0.33210671152748217, 0.6991737618302811, 0.9730139800675753, 0.08040979284813976, 0.1576922913319252, 0.20664183637560396, 0.851628921620077, 0.4440881871541116, 0.5361668927698043, 0.2939173805348885]])))+7.973267787464829)+7.247742050173272, axis=1)

np.mean(np.square(np.square(np.square(8.594272897436136+np.square(array_x/np.round(6.5496038541415))))), axis=1)

np.mean(np.exp(6.274601350698781)-np.square((np.array(range(1, array_x.shape[1]+1)))*array_x)-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(10*(9.166498022802818)+np.sin(2*np.pi*array_x)-array_x, axis=1)+10*(np.sin(2*np.pi*np.mean(10*(7.7716333771637585)+np.sin(2*np.pi*array_x)-array_x, axis=1)))

np.mean(np.square(array_x/(np.array(range(1, array_x.shape[1]+1))))*np.log(abs(6.735390946707712))+6.983780350113721+np.square(array_x)*9.944270845487203, axis=1)+np.sin(2*np.pi*np.mean(np.square(array_x/(np.array(range(1, array_x.shape[1]+1))))*np.log(abs(5.386758168975769))+4.156629554865095+np.square(array_x)*7.174526243647072, axis=1))

np.mean(np.cos(2*np.pi*array_x-9.249977156040277+5.3399069680142315)-10*(array_x)-(np.dot(array_x, np.array([[0.0884761642950963, 0.6522927277604187, 0.0978622043321522, 0.10466844950845933, 0.8258682294623317, 0.6128225715121628, 0.7951468772906481, 0.1730904942219017, 0.44717240491359334, 0.4715952927248125], [0.2207796974062326, 0.8134648458980878, 0.9626498539223987, 0.34200607644281067, 0.7363328296108698, 0.6186926556381256, 0.760427284409203, 0.963440576753452, 0.5521347454165396, 0.4871114228695804], [0.9242636101392938, 0.8515329013365913, 0.5803178096498139, 0.16489297752311383, 0.7242333809984818, 0.6484437475179831, 0.9797612410074232, 0.7755260311808683, 0.5169662656948882, 0.9200400589867938], [0.45096733404577094, 0.6170009771825367, 0.9611522330793907, 0.8360449610870504, 0.5922279297614421, 0.5547665392909128, 0.3559253353458365, 0.7469601436765491, 0.20606064930059664, 0.6401218093724111], [0.4876271624422226, 0.4602458098914324, 0.698834343266085, 0.9743354070349208, 0.4607240629223198, 0.12930215605778506, 0.18794841167370002, 0.3239235442843571, 0.8705319236368086, 0.48367160418272004], [0.13323562210077933, 0.5840346618927892, 0.8781687226246498, 0.04921388999483867, 0.6422786780808535, 0.15764023496727142, 0.6966846811616076, 0.0017623338202154004, 0.520377674142523, 0.6879930668901674], [0.14971150962911306, 0.1749058298009991, 0.0918932920184139, 0.8045822562368342, 0.5564223122499812, 0.056710592471319266, 0.7270784114673617, 0.875386344637574, 0.12316152555124404, 0.4527402879456145], [0.18171193380370165, 0.40312210752540945, 0.4731971313768958, 0.9280306925685806, 0.45490214060909073, 0.49670831081148825, 0.4224036305043549, 0.7556557334455477, 0.30954176403695155, 0.09369156462467598], [0.2986217764499084, 0.9416663176979448, 0.721133809161212, 0.009548733539812337, 0.7241205525232445, 0.26709171995360526, 0.5688578702788325, 0.45627250805710984, 0.5369878109221276, 0.5736466179163443], [0.21426128445795, 0.849548277264055, 0.12370901129993528, 0.12926034712702617, 0.9420438414998247, 0.1374386710716584, 0.7060558696433122, 0.4349450545228084, 0.33509289490723815, 0.8267602126051387]])))*8.542407829725516+np.log(abs(1.717513739523954)), axis=1)

np.square(3.8953935620836857)-np.mean(array_x, axis=1)*4.060221820709245+np.sqrt(abs(np.mean(np.sqrt(abs(array_x)), axis=1)))+10*(np.sin(2*np.pi*np.square(3.0241588735474183)-np.mean(array_x, axis=1)*6.17235720561955+np.sqrt(abs(np.mean(np.sqrt(abs(array_x)), axis=1)))))

np.mean(np.cos(2*np.pi*array_x/np.sqrt(abs(8.964961020645852))-array_x-3.483492406675533*4.8038030318173055+9.344274740317289), axis=1)+10*(np.sin(2*np.pi*np.mean(np.cos(2*np.pi*array_x/np.sqrt(abs(2.2209449865670585))-array_x-4.650308489537077*1.5672168363966725+2.112771359863313), axis=1)))

np.mean(np.square(np.square(6.552896866039232-(np.dot(array_x, np.array([[0.43806345929459245, 0.11632472051339604, 0.7829087762469934, 0.6585625122339211, 0.37720427654186783, 0.6152354423386323, 0.5113523731916769, 0.5039543583616675, 0.40802391755559253, 0.07972722559711265], [0.48774342551981276, 0.5513571570105055, 0.8147459250330882, 0.18182233981720597, 0.8185184643195969, 0.8105234137995038, 0.25643634327856324, 0.5825202547521596, 0.02925794546166338, 0.5197575460768046], [0.2976420547646649, 0.1594072346721792, 0.4776639005459723, 0.3332334030712256, 0.5761365596403576, 0.8332251146494316, 0.949167945402101, 0.8955030310290657, 0.10706459650430045, 0.7066923145181736], [0.9071820224814529, 0.9372852776467935, 0.6663686660584066, 0.14961025269279038, 0.4572219459480119, 0.6949037946117685, 0.9568402716858497, 0.26359331839084854, 0.879479849814567, 0.9159866938761811], [0.2557154599729936, 0.9834279818483741, 0.9272317195642681, 0.9661915053333033, 0.5133335999511494, 0.8721445030851976, 0.28952697935684757, 0.022008946693439224, 0.1112453747445028, 0.8417461039803605], [0.12604011007668336, 0.4684681182257572, 0.13946301678782158, 0.8364633841397833, 0.2690003925665392, 0.6643250440356928, 0.65943287908045, 0.24405747460024463, 0.714903382176894, 0.958989161170341], [0.9331554877838889, 0.06348920978451433, 0.4459646334035653, 0.12724916740040415, 0.530724815734981, 0.5413923211741696, 0.19113492977269775, 0.3674292390266891, 0.2668634529160542, 0.1996498459587338], [0.293435161420608, 0.9359782517584255, 0.38927504213793906, 0.7958427837470675, 0.5781696685791559, 0.18895039837013128, 0.6556501673380424, 0.8125977037441976, 0.03285406422664794, 0.2305281927627547], [0.6053451599717663, 0.8298585749618552, 0.3232323257429669, 0.44409648932604484, 0.8131711430406859, 0.7319371013340228, 0.4387764536961385, 0.059227840496730466, 0.9289237397958554, 0.5265209802973776], [0.6299677824547562, 0.14599245826648555, 0.30341184054236825, 0.04006449613317331, 0.6534365510611153, 0.21693386399823078, 0.5387892115232524, 0.7547329974438909, 0.42569508210750007, 0.4328290061182748]])))-1.1632498164028817*np.cumsum(np.sqrt(abs(array_x/3.00106083958805)), axis=1))), axis=1)

np.mean(9.09089166507283/np.sqrt(abs((np.array(range(1, array_x.shape[1]+1)))*array_x-8.36908670558677)), axis=1)

np.mean(np.exp(np.sqrt(abs(6.292593031220324+10*(7.015100047757414)-array_x)))+np.log(abs(6.46109457753748))+4.672882208766953, axis=1)

np.mean(1.948051544942104*1.078221924377877-10*(abs((np.dot(array_x, np.array([[0.40731580617189, 0.43510094184746284, 0.8638505050521219, 0.5602791988671901, 0.06254149965462719, 0.3431577505365968, 0.8778516333565511, 0.5560039754720817, 0.2803954338209168, 0.025176722744314706], [0.9236485304390947, 0.6679937755203951, 0.7560021422820273, 0.9343428888671681, 0.8219791122855814, 0.30140518863594545, 0.39443895118510597, 0.2923550837380896, 0.4040160993494317, 0.6421699172096988], [0.4951692237365759, 0.2555167625100685, 0.2605802082230283, 0.5371598101172522, 0.9519565602302394, 0.8933122817690874, 0.6280708156694034, 0.4839503768400535, 0.6697197666805007, 0.581277361997161], [0.6170822172493907, 0.7754206850883779, 0.7444877736096231, 0.3704932789152059, 0.7115550518824377, 0.3510116054879626, 0.7197672838949007, 0.9459942371687621, 0.7325753096294572, 0.002592015965949601], [0.7792865082256147, 0.9193090368653232, 0.9220781505017396, 0.24659153134102196, 0.9488311094735334, 0.33111952386488164, 0.13709473242096504, 0.4132971112040005, 0.4161402746390056, 0.6308676037884613], [0.886706108830392, 0.9186342158710484, 0.6175258158296038, 0.6694689628613335, 0.4487296379643523, 0.4984141799840893, 0.6457325809143984, 0.27327238449704094, 0.9002673971185495, 0.9803397237742072], [0.41677421713569907, 0.7513593194670903, 0.311789432948231, 0.3961129943687154, 0.019305268170246137, 0.5771517110713621, 0.2958670808297508, 0.5716492337341753, 0.04564353401879184, 0.3418344855139721], [0.746305810407003, 0.6411433979096164, 0.658693844371135, 0.19728341467966415, 0.20405562646649233, 0.8127115228227875, 0.3089587942307426, 0.2639384097431521, 0.2868270823302639, 0.7439741385388939], [0.5004083591835644, 0.681784307762251, 0.42329591595894533, 0.7417167134348235, 0.9620385510154577, 0.4655271765268374, 0.2746535623647759, 0.8383367462843371, 0.12167950476863276, 0.35021437051789994], [0.6963840635712535, 0.11364876037378857, 0.3301444713714835, 0.18147795763409558, 0.041388785920466686, 0.14042023486401423, 0.7365770684744269, 0.27583896031834043, 0.9266627458145773, 0.968259983361336]])))))*np.cos(2*np.pi*array_x), axis=1)+np.sin(2*np.pi*np.mean(6.266988977720885*3.29208688036359-10*(abs((np.dot(array_x, np.array([[0.6332657124725988, 0.10394489949056074, 0.3776118266256908, 0.4253182454555099, 0.36540882723074297, 0.16790523974274607, 0.4096860800659282, 0.678358063335136, 0.06300777850475436, 0.2727124456581741], [0.5244519255642174, 0.5871681860349858, 0.745891232939575, 0.5853534716956798, 0.9336177094193575, 0.24383408071335289, 0.49354034025293636, 0.507749344440624, 0.8441430858380167, 0.9270595243582811], [0.7956318192606707, 0.12457028758967648, 0.8837789482176297, 0.5215832162590858, 0.48908705362321026, 0.21056911232350561, 0.8654618330475464, 0.854364726647836, 0.6021083298238749, 0.2655272172090156], [0.9076651474086667, 0.0173164399712199, 0.6675144300980854, 0.28256727296483, 0.33096369494553524, 0.9199428247676319, 0.6182892991618046, 0.6570803312314745, 0.12054928284143873, 0.90528139084055], [0.1477293729504432, 0.9001531023407054, 0.9997867204502593, 0.11142992355972581, 0.285340441189053, 0.726198503734996, 0.9925944047422871, 0.06406126981267002, 0.6549929919801434, 0.9266386823855896], [0.6942960225777898, 0.9160793688154064, 0.3158421909482958, 0.8231826906782989, 0.0965808417781645, 0.8658696977038289, 0.10901729140142824, 0.700224299680175, 0.542875121945967, 0.35616339496364213], [0.8101422065110844, 0.5443982161856162, 0.8965627346862898, 0.7814378351850185, 0.05611073912161013, 0.030690063640580578, 0.5377398867265192, 0.2641131602316511, 0.45375444930778985, 0.9856871913070556], [0.6914226671776394, 0.8042772225693487, 0.5525931320035455, 0.37038391779695923, 0.07665987624051684, 0.2083795597262349, 0.015869935015395242, 0.1438850116386694, 0.36411480915706596, 0.5731503942362111], [0.6350108296480591, 0.9321442745064452, 0.23877167473740468, 0.7860887843245651, 0.35085290456168583, 0.10117822294627954, 0.877016153100125, 0.312830228879744, 0.7242184445576054, 0.6155148146892118], [0.1220843988184247, 0.07942381418382538, 0.3821740149520316, 0.9810503090625067, 0.23245622073682837, 0.9576362930000318, 0.18261372995092628, 0.23580739087084712, 0.577795734723604, 0.8787621728548476]])))))*np.cos(2*np.pi*array_x), axis=1))

np.mean(np.sqrt(abs(10*(np.square(np.cos(2*np.pi*array_x)-np.sqrt(abs(array_x))*5.185436906538628*9.01645373012597)))), axis=1)+np.sin(2*np.pi*np.mean(np.sqrt(abs(10*(np.square(np.cos(2*np.pi*array_x)-np.sqrt(abs(array_x))*5.927356494970719*3.980670496438494)))), axis=1))

np.mean(np.sqrt(abs(np.exp((np.array(range(1, array_x.shape[1]+1)))*array_x)))+2.24412605593651*9.394598174142992, axis=1)

np.square(np.log(abs(np.sin(2*np.pi*np.sum(array_x+1.2013583309439637+3.20457176486513, axis=1))*np.round(np.log(abs(10*(np.exp(3.549130255915572))))))))

abs(np.square(np.sum(np.exp((np.array(range(1, array_x.shape[1]+1)))-np.log(abs((np.array(range(1, array_x.shape[1]+1)))))-np.sqrt(abs(array_x))), axis=1)))

np.mean(1/(np.log(abs(np.sqrt(abs(np.round(abs(2.999559080573472))))))+np.exp(array_x-7.925095118227035+array_x)-array_x), axis=1)+10*(np.sin(2*np.pi*np.mean(1/(np.log(abs(np.sqrt(abs(np.round(abs(7.262931338205673))))))+np.exp(array_x-4.3878677527600605+array_x)-array_x), axis=1)))