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np.mean(np.square(array_x-7.7546716716726465+np.sin(2*np.pi*np.sin(2*np.pi*4.222874019902619))), axis=1) |
np.mean(np.sqrt(abs(6.828544149162526*array_x-np.exp(array_x+6.324034107482113+array_x)/8.259600838583147+array_x*np.cos(2*np.pi*1.1726120400260207)+np.sin(2*np.pi*np.sqrt(abs(4.323850908682166))))), axis=1) |
np.mean(10*(8.503174932553245*np.log(abs((np.array(range(1, array_x.shape[1]+1)))-np.log(abs(9.41928360172849))))-2.569094085341086-array_x-np.log(abs(np.square(3.83327272203209)))), axis=1) |
np.amax(np.sqrt(abs(6.288471067853009))+10*(abs(5.802688508602579)+array_x)-4.493112307087618*8.790591682374156-array_x+array_x, axis=1) |
np.mean(4.326798056344967+10*(3.5731380369295365+array_x)-array_x+np.exp(9.753978165666982), axis=1) |
np.sin(2*np.pi*np.sqrt(abs(np.square(np.log(abs(7.582915547320589*np.sum(8.102047540284495-array_x, axis=1))))*2.254055562178502)))+10*(np.sin(2*np.pi*np.sin(2*np.pi*np.sqrt(abs(np.square(np.log(abs(6.404632834947042*np.sum(8.816755522425824-array_x, axis=1))))*2.5255669128949236))))) |
np.mean(np.square(abs(np.sqrt(abs(array_x))-4.9097916228415235-np.log(abs(4.245126442331011)))), axis=1) |
np.log(abs(np.sum(np.sin(2*np.pi*array_x)+array_x+2.79700362944918*7.169583799884674-np.exp(3.1705861428632773*(np.dot(array_x, np.array([[0.24134266661428427, 0.0919726956920408, 0.7866202085337906, 0.1049953372892577, 0.10738956191437454], [0.08701136912331386, 0.9244328532749477, 0.6509793500493641, 0.9014948269447025, 0.29557665554507373], [0.23602490586620928, 0.5381728909480542, 0.9228629943701163, 0.5163641652751105, 0.6262769380097879], [0.08539007204624327, 0.05616750524542791, 0.01450699869363814, 0.9338314316068835, 0.8979709345191936], [0.2623525699201549, 0.8858428475168876, 0.043468635253460786, 0.5156865435589575, 0.23523770576741987]])))), axis=1))) |
np.amax(7.363858282271828-(np.dot(array_x, np.array([[0.2784914325165937, 0.05126004697307451, 0.6701646857144163, 0.49296312258982433, 0.8901181258315494], [0.8663292138481934, 0.8717944950703345, 0.06772527767959258, 0.05375445141268442, 0.14110879055222225], [0.6474505816034128, 0.2791784508941534, 0.2399292097561061, 0.044219014385065036, 0.8675739393511693], [0.28051876460368075, 0.6732576434373152, 0.7437303425504217, 0.8833487460789696, 0.9414850977559198], [0.6991756232214114, 0.09917122731802008, 0.24466785911748112, 0.6039047425667075, 0.6958505345367358]])))*6.057700458098082+array_x-np.cos(2*np.pi*-(9.06086843621865)), axis=1)+np.sin(2*np.pi*np.amax(1.2981360759351177-(np.dot(array_x, np.array([[0.808979437350905, 0.4180005759593348, 0.082373730737889, 0.5379259073490631, 0.45213015889585273], [0.9423873230168253, 0.7801636129554436, 0.5989034594506853, 0.7437972750362655, 0.7115228467954223], [0.6656805722108041, 0.36147340136149364, 0.7169053447636591, 0.2938119408707799, 0.8522846172369182], [0.3162606197113216, 0.22953259530572556, 0.2050701998366803, 0.7047698329050132, 0.6192724149435884], [0.3120590259391115, 0.1971005053667465, 0.5421474912380261, 0.4008009930128217, 0.002305511282153394]])))*1.2390660662703343+array_x-np.cos(2*np.pi*-(6.549217011098518)), axis=1)) |
np.mean(np.sqrt(abs(np.exp(array_x+9.148719861709697)*array_x+7.665858865603781)), axis=1) |
np.round(np.mean(1.5136525065968078-np.exp(array_x+6.485970031093813)+np.sqrt(abs(5.404200205157409)), axis=1)-np.amax(np.log(abs(-(1.7869096101208415+array_x))), axis=1)) |
5.549452022449702*np.prod(array_x-9.954226608478674, axis=1)-3.709312761219852 |
np.mean(np.cos(2*np.pi*8.461605079398966)-np.cos(2*np.pi*1.748076782280393*(np.dot(array_x, np.array([[0.42075052641249033, 0.33591363013593245, 0.6563086708610453, 0.21372668507089154, 0.27550690846584236], [0.016417010418802502, 0.12984484656579587, 0.9978246770099285, 0.39528325889781046, 0.707769710022383], [0.02649440792215796, 0.5620534714422476, 0.7799920578822571, 0.357719341066692, 0.3186042706176029], [0.6391637855212041, 0.8243781430484899, 0.5100919102688284, 0.5092356721626975, 0.5186623079461616], [0.900673115951883, 0.7251259154074651, 0.4623387084738001, 0.2896847825389517, 0.3237998262812646]])))*array_x), axis=1)+10*(np.sin(2*np.pi*np.mean(np.cos(2*np.pi*6.550438033674576)-np.cos(2*np.pi*2.445854905374582*(np.dot(array_x, np.array([[0.396281763159812, 0.6010360725788937, 0.3880648555341899, 0.5876874718215489, 0.4974374286068659], [0.7052861493525938, 0.09246043792694092, 0.8772955681299187, 0.5687372982538237, 0.5388728219985028], [0.22810870186277865, 0.8028674134501953, 0.9664048242530398, 0.8374370652970132, 0.033127947005082814], [0.6719908776758025, 0.9717678825020171, 0.5149089569973619, 0.22725613162955682, 0.2852814856721573], [0.3584638625852882, 0.06574359449700673, 0.49854043558755845, 0.10469108550915296, 0.5997415744026428]])))*array_x), axis=1))) |
np.mean(8.23671516838861*array_x/9.382482558071032+np.sin(2*np.pi*np.sqrt(abs(3.3313100776665094))), axis=1)+10*(np.sin(2*np.pi*np.mean(2.9159070612567617*array_x/2.598930146713325+np.sin(2*np.pi*np.sqrt(abs(7.648354886839655))), axis=1))) |
np.round(np.square(np.sqrt(abs(2.7940973943704126))-np.square(9.906364095403408+np.sqrt(abs(10*(np.amax(array_x, axis=1))))))) |
np.mean(10*(7.392539329838651+np.round(np.round(1.1551349745844606-array_x))), axis=1) |
np.round(np.sum(10*(4.415220783209525-np.square(abs(array_x)+5.801615025065722)), axis=1)) |
np.mean(np.exp(9.546684629914859-array_x-np.sqrt(abs(array_x))-np.log(abs(np.sqrt(abs(9.502038747023388))))), axis=1) |
np.square(np.mean(array_x*np.round(array_x)+10*((np.array(range(1, array_x.shape[1]+1)))*(np.dot(array_x, np.array([[0.6261428916895103, 0.5897892948098956, 0.17531167250234936, 0.3513407025584445, 0.7147763633182806], [0.2981553834564067, 0.5557027898976313, 0.8434000007299551, 0.5222316492041491, 0.2531441035685865], [0.9142341177338159, 0.35124185873575886, 0.6917069629473261, 0.7284331665184022, 0.027915448345549376], [0.5553997201559391, 0.8032267874786274, 0.4439613038404303, 0.22100869528409428, 0.7946988399675897], [0.8303481930843332, 0.632145335920749, 0.6559958545073836, 0.6526244790529706, 0.31460599823485025]])))-1.4187439296384081), axis=1))+np.sin(2*np.pi*np.square(np.mean(array_x*np.round(array_x)+10*((np.array(range(1, array_x.shape[1]+1)))*(np.dot(array_x, np.array([[0.44119396746023065, 0.8967841761145069, 0.1966958946787346, 0.3285441011028457, 0.18547557731525255], [0.7097714171150676, 0.23232388587824915, 0.21538954764888607, 0.5565306424592703, 0.38499521776278867], [0.7617639390773986, 0.6830452030563456, 0.9084226193573003, 0.15040616717284416, 0.7933315383988863], [0.795576897730669, 0.7484437460279445, 0.6859718539209223, 0.1897553090493359, 0.6470342130705123], [0.22189224260385676, 0.13864164610082308, 0.5516030256584595, 0.1914626837792699, 0.960795598284937]])))-8.056127113030199), axis=1))) |
10*(np.sin(2*np.pi*-(np.sin(2*np.pi*4.577034064004474-np.mean(array_x-array_x-(np.array(range(1, array_x.shape[1]+1)))-array_x, axis=1)))))-np.sin(2*np.pi*8.818068573251223-np.mean(array_x-array_x-(np.array(range(1, array_x.shape[1]+1)))-array_x, axis=1)) |
np.mean(9.315639729211075+(np.dot(array_x, np.array([[0.3185865750663839, 0.9946977123315743, 0.7332208360180509, 0.8527589933967408, 0.4251519803493178], [0.9933766044094539, 0.7079559109420557, 0.9670842466288814, 0.9724031500724457, 0.2824062044388367], [0.9575443122642157, 0.3933576502779135, 0.5734199690768381, 0.1886125699276704, 0.1801725774258951], [0.647689739015007, 0.6305819440178244, 0.1886937347809421, 0.12821856851537938, 0.5229788133980816], [0.38935171410977965, 0.7980044619188219, 0.42844061905159025, 0.4195107958233891, 0.04201639544230362]])))+(np.dot(array_x, np.array([[0.16183713814050016, 0.28360788271469906, 0.9336786691674357, 0.8822282320257449, 0.702323468036815], [0.6657865805930144, 0.19948740473039206, 0.1458636495273652, 0.4890931174898072, 0.09254435516181936], [0.00970836509274109, 0.6352667346632064, 0.14318800424109412, 0.8916765294568018, 0.26064023635704303], [0.9731188608557085, 0.32403336355927403, 0.014917420639782852, 0.2859691933401456, 0.20375776446388105], [0.6291672560833961, 0.4892191068054712, 0.12676901143107633, 0.8580811098577317, 0.17674123912053352]])))/4.57925296201064-3.724765157413162*(np.dot(array_x, np.array([[0.7717323876569354, 0.9893188135924228, 0.24426262530720178, 0.3371882277657442, 0.8281809381169306], [0.5277598601576079, 0.13425014696157522, 0.9011185508418316, 0.7981390872937663, 0.40493168671610524], [0.30737488732141205, 0.06070333013504936, 0.4707845200228816, 0.47103454475438633, 0.9891697162657298], [0.9098563104043619, 0.6742795790344165, 0.4131594343656787, 0.3653919410289339, 0.8121202814544138], [0.21399023841431186, 0.3925582279004606, 0.4287775769979104, 0.9541878718232286, 0.3532043344727229]])))-np.square((np.dot(array_x, np.array([[0.5948886646080027, 0.09390189621511047, 0.5983681030097464, 0.5037804326997739, 0.9467375186827074], [0.7532501885965709, 0.2897625627772077, 0.45125477774162215, 0.29081082821780213, 0.8840066270547132], [0.6848755960850857, 0.02879924995871841, 0.9886975688640833, 0.8369674649299136, 0.732762976447227], [0.9692298796707963, 0.8748235014761031, 0.15064707349498208, 0.14155775060927434, 0.4135163559029481], [0.8332227660669228, 0.1694428983365468, 0.2595359852384388, 0.3899336545628599, 0.2863229751839015]])))+8.698898962769498-(np.dot(array_x, np.array([[0.04382035660813499, 0.4913821255657166, 0.2570578443480226, 0.11498754372301356, 0.6049016922770257], [0.4262048662298371, 0.24804361073135028, 0.4930480704701581, 0.04788236639661603, 0.3403209808271074], [0.3573707174475824, 0.1073688228398999, 0.5819405144605174, 0.7774351132760176, 0.19840830847079194], [0.9701929810494407, 0.06620484192898834, 0.11804020776771862, 0.834187584511276, 0.08603886862380594], [0.433414733920663, 0.42064837139658584, 0.46615357168741023, 0.4839803839967135, 0.6394526474716622]])))*8.111120460548298/5.597027181193819), axis=1) |
np.mean(1/(np.log(abs(np.cos(2*np.pi*(np.dot(array_x, np.array([[0.9399451247280549, 0.8824995986339726, 0.551101835143954, 0.29825028514021823, 0.8288727366456865], [0.7891906241739914, 0.7900775951594664, 0.6546344301177595, 0.7646753414906077, 0.5390771560766406], [0.4325210132698225, 0.173522434782439, 0.3282218045548818, 0.3547683762730989, 0.6464180552233761], [0.9156672475831596, 0.9362831273385771, 0.2870340737080628, 0.2703113349013633, 0.7528649088653157], [0.5587546641257566, 0.722194096314138, 0.5049278923371755, 0.7442624076789014, 0.3978845858950494]])))+3.359047080224874/3.3911780706476757)))), axis=1) |
np.mean(1/(np.log(abs(np.sin(2*np.pi*10*(4.926184634907601))-array_x))), axis=1) |
np.mean(9.364506522694215+np.square(np.sin(2*np.pi*array_x)+3.158234441431496), axis=1) |
np.mean(array_x+10*(array_x)+9.754221788217144+9.015539133963008, axis=1) |
np.sum(3.627068432797172+np.cos(2*np.pi*(np.dot(array_x, np.array([[0.4622077447813523, 0.39557828395912953, 0.11451604500126755, 0.9032812274766774, 0.9905324996115328], [0.21907247390925744, 0.9487373754581816, 0.2615265977697413, 0.47591386530903323, 0.16776847906975323], [0.541557843331943, 0.4156998610632475, 0.18148643009835708, 0.3202791034172504, 0.4872550208877282], [0.07064844627338629, 0.6164025777321871, 0.2278569192305524, 0.5487218636189698, 0.19141117319766443], [0.7984247365255434, 0.5100280061129431, 0.6624582900433632, 0.5412007950848748, 0.5874106092902477]])))), axis=1) |
np.mean(7.134553588975061-np.square(array_x*3.599483547751437-8.878606191280463), axis=1)+10*(np.sin(2*np.pi*np.mean(1.4603609863492557-np.square(array_x*8.223394443481016-1.2058566011820937), axis=1))) |
np.mean(np.square(array_x+10*(1.3005348392499911))-10*(np.sqrt(abs(array_x))/np.sin(2*np.pi*1.113061441461856+array_x))+array_x, axis=1) |
np.round(np.mean(abs(7.190163967964734+array_x*9.351435748018774)+np.log(abs(9.93925361967138))-(np.array(range(1, array_x.shape[1]+1))), axis=1)) |
np.amax(np.square((np.array(range(1, array_x.shape[1]+1)))*array_x-(np.array(range(1, array_x.shape[1]+1)))-5.192934229292316+(np.array(range(1, array_x.shape[1]+1)))+array_x*8.69019078784363), axis=1) |
np.mean(np.square(abs(np.round(1/((np.array(range(1, array_x.shape[1]+1)))-3.98016689118103/array_x)+4.47924015497554))), axis=1)+10*(np.sin(2*np.pi*np.mean(np.square(abs(np.round(1/((np.array(range(1, array_x.shape[1]+1)))-3.308865461157797/array_x)+1.7181490118823228))), axis=1))) |
np.mean(10*(np.round((np.array(range(1, array_x.shape[1]+1)))*1.6395332925426935+(np.dot(array_x, np.array([[0.8640498606384942, 0.6575827082969946, 0.7889021521417319, 0.14601004454302302, 0.829498214508505], [0.8520811994540151, 0.2977141431520204, 0.27776528289761926, 0.0709268068202158, 0.8345920756244685], [0.9098056918846859, 0.2591669202263205, 0.15058144535492213, 0.08744398937458908, 0.8843062714581107], [0.4963511467133145, 0.7742389553460408, 0.6520802303369919, 0.21527996158114637, 0.8830330265104901], [0.06010525823466273, 0.28679789979870807, 0.854728033836701, 0.5708859220163952, 0.8706522619669391]])))-array_x-array_x)), axis=1) |
np.round(np.mean(np.square(abs(array_x+4.158551811383472*np.square(np.cos(2*np.pi*7.95303826276141)))), axis=1)) |
np.round(np.mean(3.487164919846429*np.cumsum(np.cumsum(array_x, axis=1), axis=1)-np.sqrt(abs(array_x*np.square(7.6716307075305235)-5.665313999593017-3.604023388610861)), axis=1)) |
np.mean(10*(2.2919116530985773+array_x)+6.339116221575631*array_x-np.sqrt(abs((np.array(range(1, array_x.shape[1]+1)))/1.6267639030383778)), axis=1) |
10*(np.amax(abs(np.square(10*((np.dot(array_x, np.array([[0.8838871596290947, 0.22939064878142212, 0.007446080975656688, 0.2737875025449642, 0.2072884243955967], [0.8833553718419366, 0.046411947600351056, 0.9803491923830795, 0.6233505653661096, 0.71526115852252], [0.966814023765422, 0.5201439411548866, 0.3324665284761513, 0.100459550993786, 0.9022244006032671], [0.3219225360327006, 0.46365817676058485, 0.9647306956205173, 0.7428994876747597, 0.07150073479494456], [0.008966500359334106, 0.6507473263131546, 0.8553217548385474, 0.624211248614124, 0.104465431799038]])))))), axis=1)-4.912584485737401) |
np.mean(3.9059626012893887/(np.array(range(1, array_x.shape[1]+1)))*array_x-np.cumsum(10*(np.square((np.dot(array_x, np.array([[0.32992526348228146, 0.46477133036586527, 0.8174727394550797, 0.44675988090940155, 0.30797954685794116], [0.3318091002172979, 0.41537301202769916, 0.6887449571955157, 0.3981707949658615, 0.28499496770927857], [0.6849388832494271, 0.3478140266762809, 0.21752790446555303, 0.49191778436292555, 0.49934323456373597], [0.19384940651431903, 0.9130023776882842, 0.0762979743478851, 0.28514704811441216, 0.040482330299434044], [0.7951402451150146, 0.6377367266966741, 0.7789596456815279, 0.3713698356581119, 0.7405550182753458]]))))), axis=1)+5.226989013063948, axis=1) |
np.mean(np.cos(2*np.pi*np.log(abs(2.628278073567376-5.5764868592460815*array_x))-array_x*2.6504891121593195), axis=1)+10*(np.sin(2*np.pi*np.mean(np.cos(2*np.pi*np.log(abs(8.100202062552693-1.8593086316239287*array_x))-array_x*3.2816750648071036), axis=1))) |
np.mean(np.square(3.1463690758306386)*-(np.cos(2*np.pi*array_x))+np.round(array_x+3.0263616872535826), axis=1) |
np.mean((np.dot(array_x, np.array([[0.3761813573051117, 0.5882588249342362, 0.5920488590680292, 0.46547225451392704, 0.8795923292993262], [0.4671500034294701, 0.6328900064075821, 0.7305439009639603, 0.3344062165434305, 0.9547387529146685], [0.9513848216592089, 0.34859782478221535, 0.6185959092861374, 0.018156714135195484, 0.7081056150533175], [0.43227024894117494, 0.7438086119706514, 0.44088835838441376, 0.7032845663533591, 0.5685144611239817], [0.6690169233343068, 0.9063321857651171, 0.10701600278345424, 0.04566972573047334, 0.2123996142820772]])))*(np.array(range(1, array_x.shape[1]+1)))-4.94027012631652*7.553497366351875+array_x*1.8802843503274898*8.751645500773318+1.6798485295655892, axis=1) |
np.round(np.round(np.sum(7.2413254514355865-2.7650244843563194*array_x-np.log(abs(np.sin(2*np.pi*2.22649091553783))), axis=1))) |
np.mean(np.exp(3.9154122753638623)/np.cos(2*np.pi*(np.dot(array_x, np.array([[0.6399775090207663, 0.6827199311314089, 0.6432257415670876, 0.5631872281299269, 0.5179113812280082], [0.5449806807895504, 0.9661745886032674, 0.686300205388183, 0.12222329122121522, 0.5599205372919227], [0.935872268776102, 0.7053287096492851, 0.90085320592956, 0.5431153589542703, 0.21056735473493682], [0.19273234698530828, 0.7616523664586603, 0.43280645203115076, 0.5932840562892456, 0.8014689348137536], [0.6341680266054474, 0.2450669955057464, 0.9874977396615617, 0.5655837013606465, 0.6004972162753414]])))*3.1878683134242394)+np.sqrt(abs(9.110512316815086)), axis=1) |
np.mean(10*(2.233701253622405-array_x-np.round(9.188608535172332)), axis=1) |
np.mean(np.square(7.947113697044649+np.square(np.sqrt(abs(np.sqrt(abs(1.5224043612357705/4.984392475412456-array_x*2.2175634491612977))))-array_x+3.7220108781274694)), axis=1) |
np.mean(np.sqrt(abs(np.round(np.exp(7.424717137281184+np.sqrt(abs(array_x))+2.043537004451336)))), axis=1)+np.sin(2*np.pi*np.mean(np.sqrt(abs(np.round(np.exp(1.1585542140354952+np.sqrt(abs(array_x))+3.7027214080568034)))), axis=1)) |
np.sum(1.6159087353386246-array_x, axis=1)*np.sin(2*np.pi*1.1787728110645792-np.sum(array_x, axis=1))*np.cos(2*np.pi*8.122745021900984) |
np.mean(np.round(9.072263718619089)*array_x+np.exp(array_x)*9.54113290000443*array_x+1.8192870669928416, axis=1) |
np.mean(8.038078640956886-np.cos(2*np.pi*array_x)*4.215125435613881+4.99015301675646-np.exp(9.230887102482614)*-(array_x), axis=1) |
np.mean(8.8105308613138-np.square(array_x)/2.177701358486038/np.cos(2*np.pi*np.cumsum(array_x, axis=1)*np.exp(2.588247491040421)), axis=1) |
np.round(np.sum(5.642898512067092-array_x-array_x/1.0170487032260196, axis=1)) |
np.mean(np.log(abs(np.log(abs(np.cumsum((np.dot(array_x, np.array([[0.5677535526732872, 0.29396674024623126, 0.5732508208589221, 0.10751262184834709, 0.1674383670962618], [0.48172903356869523, 0.8068375877658526, 0.6093228657996684, 0.8832923343468733, 0.884851782869885], [0.1657155422661174, 0.3055453262515888, 0.08826941022764168, 0.13083125977990007, 0.8230947909655842], [0.0013570989106158615, 0.5385164493945587, 0.5020958881370539, 0.19157450475868032, 0.857100458993712], [0.919082885173251, 0.8181642045730357, 0.2410873852757115, 0.921235534209436, 0.9849443531775844]])))-1/((np.array(range(1, array_x.shape[1]+1)))*array_x-9.4095165239347), axis=1)))))+3.9805571610627863-10*((np.array(range(1, array_x.shape[1]+1)))*(np.array(range(1, array_x.shape[1]+1)))*array_x)*9.904172787359636, axis=1) |
np.exp(np.sum(np.sqrt(abs((np.array(range(1, array_x.shape[1]+1)))*array_x-9.41746134863759)), axis=1)) |
np.mean(np.square(np.square(np.exp((np.array(range(1, array_x.shape[1]+1)))*array_x-(np.array(range(1, array_x.shape[1]+1)))*array_x))+6.210944093935991/(np.array(range(1, array_x.shape[1]+1)))*array_x), axis=1) |
np.mean(1.757806989559183-array_x+np.log(abs(9.446787239384829))-np.exp(10*(array_x)), axis=1) |
np.mean(array_x/np.cos(2*np.pi*8.18381174087536)/8.76790730375561+np.sin(2*np.pi*abs(3.6492607223800926)), axis=1)+10*(np.sin(2*np.pi*np.mean(array_x/np.cos(2*np.pi*9.828770416321174)/2.3712094516844524+np.sin(2*np.pi*abs(2.9231338122015083)), axis=1))) |
np.mean(np.square(7.418573478000032-array_x/1.2241298411021306)/np.cos(2*np.pi*-((np.dot(array_x, np.array([[0.4242108691392744, 0.8556758343417726, 0.6110006893450389, 0.8254182417390081, 0.5482904719764553], [0.8053707695387347, 0.6498334644284391, 0.09501465331958336, 0.42777167514254877, 0.4028283057761708], [0.527101486954538, 0.7581250280223387, 0.8223641941515044, 0.7078705372814275, 0.8280460289632607], [0.6225442453141379, 0.24601317431062286, 0.4793326887200162, 0.25546398606628185, 0.9761215660838726], [0.5860155943280938, 0.5278929107303267, 0.030259429032863383, 0.1978419461402673, 0.6282299303765942]]))))-6.75146823374325), axis=1)+np.sin(2*np.pi*np.mean(np.square(8.896909742622967-array_x/5.693598925209665)/np.cos(2*np.pi*-((np.dot(array_x, np.array([[0.5070855333865861, 0.8197737829864037, 0.5976879904704655, 0.1609886788233177, 0.582671697188051], [0.07781184783190964, 0.21639728265729152, 0.5190798674631488, 0.2092589765188132, 0.20465565665778263], [0.10531907084375369, 0.5911146947073683, 0.9463819980624468, 0.1652330368418855, 0.4749208515773413], [0.45227041984987215, 0.9389973231464692, 0.5828829702368672, 0.6508948262649865, 0.24444217908347754], [0.7916033417797762, 0.4174096198263768, 0.615056139412141, 0.07102376242653174, 0.021127161428868058]]))))-7.798282500829377), axis=1)) |
np.mean(np.cos(2*np.pi*array_x)+array_x*-(np.round(5.793947136204816*array_x)/4.117725179401345-array_x*np.square(7.077691335167092)), axis=1) |
np.mean(np.square(3.110447493053255)*np.square(np.square(9.620496008745343+(np.dot(array_x, np.array([[0.6415309684199829, 0.4634133104726832, 0.5925186045604626, 0.8446289378163183, 0.3279540568244528], [0.6444528881995658, 0.09953485970140896, 0.9944042010479635, 0.10097751679787947, 0.38296330799543743], [0.2183043604727417, 0.9027377736127986, 0.43142633985842105, 0.26953084700938634, 0.6463882509538754], [0.9762218255560359, 0.08224876590519359, 0.38048183855118156, 0.4787404015907477, 0.2877251673046144], [0.9581902111639747, 0.8019575774447777, 0.3792198989011948, 0.33478679850110815, 0.11129150417197742]])))+array_x)), axis=1) |
np.mean(array_x*3.8381571631348383+4.405387886120244+5.304392738148023*np.square(np.sqrt(abs(6.569161798262362))-array_x), axis=1) |
abs(np.exp(np.mean(8.372063554482459/np.sin(2*np.pi*2.2628624434021165-array_x), axis=1))) |
np.mean(4.66097539914569+np.exp(np.round(3.235610150798605)*array_x)-array_x/np.sqrt(abs(4.026866185800462/np.sqrt(abs(np.exp(8.604989910265598)))-array_x)), axis=1) |
np.mean(np.square(8.581365886340528)*np.cumsum(np.exp(np.round(2.5851706584674927)*np.square(array_x)), axis=1), axis=1) |
np.mean(10*(array_x+1.0584285828177964*7.96815450048121), axis=1) |
np.mean(-(np.sqrt(abs(np.sqrt(abs(4.97869773168761*(np.array(range(1, array_x.shape[1]+1)))*array_x+3.0901644394836127)))))--((np.array(range(1, array_x.shape[1]+1)))*array_x+9.383425524670322)*4.053481628390417, axis=1) |
np.cos(2*np.pi*np.mean(np.exp(9.621791066536062/1.6528195022707695+(np.dot(array_x, np.array([[0.9685282510852243, 0.4166516663790628, 0.766949063884088, 0.856710526809584, 0.12974489459522154], [0.6355643053057288, 0.49409229458665904, 0.04993936083813222, 0.18095318748757627, 0.6030316790797559], [0.9564079083869267, 0.27426916735806617, 0.1454757281881448, 0.8314984107607146, 0.5690978679238863], [0.5958894898324759, 0.11236628839803164, 0.03420330793255977, 0.5036071272020574, 0.8106502554914028], [0.26369018707801306, 0.2535981878659981, 0.8308673410758183, 0.7767081989846779, 0.10690187985978594]])))/np.sqrt(abs(np.log(abs(array_x-np.square(3.9449129200315727)))))-7.234501348377926), axis=1))+10*(np.sin(2*np.pi*np.cos(2*np.pi*np.mean(np.exp(3.3566052841434497/2.59231603526231+(np.dot(array_x, np.array([[0.6257655953009603, 0.0850516766502345, 0.4776926779626506, 0.7064266362821676, 0.5537984997304063], [0.3570400416832685, 0.10788908217313153, 0.5381777774576514, 0.7999175098623691, 0.7924260482275769], [0.012808419445395569, 0.11828885198628014, 0.5290140288628608, 0.3322083602200756, 0.15226755135237935], [0.09802576233422466, 0.010624255533623117, 0.8034715516409024, 0.44176877983043283, 0.8898679407886156], [0.7182469788663484, 0.9928642025045169, 0.775651269261137, 0.1636979299499265, 0.7425519135358741]])))/np.sqrt(abs(np.log(abs(array_x-np.square(6.580725814723349)))))-2.801611367499103), axis=1)))) |
np.sum(np.cos(2*np.pi*array_x)-2.048599118134528, axis=1)/8.930621156700873/np.mean(np.square(1.4035297778707467-array_x), axis=1)+9.665625008380816-3.3597095944211066*np.sum(array_x, axis=1) |
np.mean(np.square(1.2302415079946059)/np.sqrt(abs(np.cumsum(array_x, axis=1)-1.6943481257259245+array_x)), axis=1) |
np.sum(abs(np.square(9.66288187406298+array_x+9.535986784660317))*array_x*9.957639524681667-abs(8.388905162246887), axis=1) |
np.round(np.sum(np.square(2.427537385907274*np.exp(3.162728388815852)+(np.dot(array_x, np.array([[0.1264225791388982, 0.6874052640071178, 0.7498650050028609, 0.059154712230848006, 0.45931679593132546], [0.4133046388109056, 0.7224055358079227, 0.5736027185589776, 0.39570615593947345, 0.6089981539455911], [0.5948058212726457, 0.24875377431111578, 0.4797449588384216, 0.030987227723843436, 0.5819453947232939], [0.11457390311592097, 0.6203899211770268, 0.20970785003290537, 0.42957158985918753, 0.3759574763408794], [0.8354557403905393, 0.9239751401223866, 0.7759988297660187, 0.5424279233779414, 0.41635043528387095]])))*2.315074821593299+array_x), axis=1)) |
np.mean(np.exp(2.6400619381962565)-np.square(6.903278740620278/(np.array(range(1, array_x.shape[1]+1)))*array_x+7.4197358221394545)+np.sin(2*np.pi*(np.array(range(1, array_x.shape[1]+1)))*array_x+6.830978373641212)+5.219498100199334*np.exp(1.138546388494228)-np.sqrt(abs(4.342329680810927)), axis=1) |
np.cos(2*np.pi*np.exp(10*(np.cos(2*np.pi*np.sum(9.174779775807249*array_x, axis=1)))))*4.1891292149932875 |
np.mean(7.570054310996329/np.sqrt(abs(5.4979830570491055-array_x-(np.array(range(1, array_x.shape[1]+1)))))*1.4382493704431738+array_x-array_x/4.092920721624201, axis=1) |
np.mean(10*(array_x)+9.059965183164694+(np.dot(array_x, np.array([[0.9194730841381841, 0.49106341203366355, 0.11974109274690792, 0.5597163549741736, 0.9788225880202158], [0.30541395402964977, 0.18620582247941808, 0.5611772816427791, 0.8360711080584767, 0.3118111547663155], [0.5892419269023716, 0.24936677307903288, 0.23703824971929077, 0.48795889642969836, 0.7762454934853957], [0.17962249027253674, 0.673092475029374, 0.8367158266968633, 0.8675800291689331, 0.8397825410776676], [0.8917532842246931, 0.48465693039605573, 0.3419791996906876, 0.31664883094182117, 0.6269639024498437]])))/abs(np.square(7.895685237526709))-np.sin(2*np.pi*(np.dot(array_x, np.array([[0.9113764150409903, 0.018071723911954662, 0.1500851241604083, 0.483285411638248, 0.6241674969392392], [0.7782287930995319, 0.6177702193067793, 0.02755419809668125, 0.5210905316966504, 0.42614190832966037], [0.6877256624705271, 0.429158069583485, 0.9230807003448916, 0.5314150647612067, 0.7504715494199234], [0.7617021522857993, 0.5874344964789097, 0.6831233286082349, 0.349931607967632, 0.6874566228841062], [0.17317066833348693, 0.9946753625273429, 0.2430707241935277, 0.763439926406484, 0.6141614922534021]]))))/np.exp(3.716109400147697), axis=1) |
np.mean(np.exp(np.round(3.572508342017684)*np.square(array_x)+np.cos(2*np.pi*4.233615750839476)), axis=1)+np.sin(2*np.pi*np.mean(np.exp(np.round(5.2920315836611485)*np.square(array_x)+np.cos(2*np.pi*7.711213615127622)), axis=1)) |
np.mean(abs(np.round(np.sqrt(abs(np.round(np.exp(7.9245118473251495)))))*abs(7.825593782314737-array_x)*np.sqrt(abs(3.448924297437488))), axis=1) |
8.442531276161274+np.sum(array_x, axis=1)*9.51293660854769+np.sin(2*np.pi*3.1127273945096516+np.sum(array_x, axis=1)*2.84255978975541) |
np.mean(np.square(array_x)+np.square(np.square(8.94184848563855+array_x))*np.sin(2*np.pi*np.log(abs(1.712791323519684))), axis=1) |
np.mean(np.sqrt(abs(np.round(-(np.round(8.301054243583664))-(np.dot(array_x, np.array([[0.24363342027774315, 0.07997321329034957, 0.38047480508908327, 0.6504562272416565, 0.5895802454080371], [0.3039115785402142, 0.6827997520685406, 0.7592580821250702, 0.11105136487920408, 0.5104413842926864], [0.0020595422705131927, 0.5210658287396842, 0.3063399753500673, 0.8091196148412657, 0.0562737044288788], [0.4433451887664186, 0.3037557503418449, 0.22461207890157064, 0.6160862813044171, 0.016532669901491492], [0.020765757869269486, 0.48913932266478466, 0.6490932389293107, 0.31237221771334456, 0.1206065241981551]])))/np.sqrt(abs(5.784670407792973/array_x))))), axis=1)+10*(np.sin(2*np.pi*np.mean(np.sqrt(abs(np.round(-(np.round(5.787053223122651))-(np.dot(array_x, np.array([[0.06352212828800441, 0.9503898149216411, 0.1569925133069996, 0.4178167280088435, 0.7086242866816403], [0.25800335790474993, 0.33800001848477934, 0.21533439784355257, 0.525365588589259, 0.6206759579124373], [0.9853692363154863, 0.8323287393356102, 0.9508996197793511, 0.044521179814030254, 0.9002786449545692], [0.6615335414769531, 0.2674988145438333, 0.9815661210308498, 0.38486806478085256, 0.08371993273772205], [0.8053910251015403, 0.43720826189092543, 0.11266468010833408, 0.30647621381735657, 0.7785302670430853]])))/np.sqrt(abs(1.162209743655372/array_x))))), axis=1))) |
np.mean(np.sin(2*np.pi*(np.array(range(1, array_x.shape[1]+1)))-6.128200995823453+np.square(5.115805302087537))-array_x*-(np.square(8.00372737597162)), axis=1) |
np.mean(np.exp(5.292417079609382-array_x), axis=1) |
np.sum(np.square(np.round(1.8822830608382306-array_x*np.round(4.0719993835373005)/2.652108115543809)), axis=1) |
np.mean(10*(np.sin(2*np.pi*4.736331063351699)/6.660858261393837*array_x+5.020657802872856-array_x), axis=1)+10*(np.sin(2*np.pi*np.mean(10*(np.sin(2*np.pi*3.2996082491451473)/1.0452172964607112*array_x+6.140869510275017-array_x), axis=1))) |
np.square(np.square(np.amax(10*(1/(np.exp(array_x*2.5700192443718297))), axis=1))) |
np.exp(np.mean(np.square(np.cos(2*np.pi*array_x+(np.array(range(1, array_x.shape[1]+1)))-9.226785738822517+8.508716145952238)*4.154712844073498), axis=1)) |
np.mean(np.round(5.540376460226116-array_x)-abs(abs(6.919251264842075*array_x+7.369676030115839)+5.18624376509987), axis=1)+np.sin(2*np.pi*np.mean(np.round(3.4814705055535047-array_x)-abs(abs(8.627363203825304*array_x+1.857348319634835)+7.801628734367801), axis=1)) |
np.mean(6.072478303539915-abs(array_x)*array_x*3.485135903209474-np.exp(np.round(np.round(array_x))), axis=1)+np.sin(2*np.pi*np.mean(5.451827051111274-abs(array_x)*array_x*3.1122385609184757-np.exp(np.round(np.round(array_x))), axis=1)) |
np.mean(np.log(abs(np.square(array_x-2.155857204082317)))+np.square((np.array(range(1, array_x.shape[1]+1)))*array_x+5.565360410969204)*9.175171057467875, axis=1)+np.sin(2*np.pi*np.mean(np.log(abs(np.square(array_x-4.480043011133545)))+np.square((np.array(range(1, array_x.shape[1]+1)))*array_x+7.684446266521801)*7.378534323741583, axis=1)) |
np.mean(np.cos(2*np.pi*3.2318321820889233+(np.array(range(1, array_x.shape[1]+1)))*array_x+2.274423350188751)*np.square(5.7099066827665546-(np.array(range(1, array_x.shape[1]+1)))*array_x)+7.273149713893242, axis=1) |
np.mean((np.array(range(1, array_x.shape[1]+1)))+8.945353592905823*array_x-6.364859448161158-np.exp(array_x/3.7738505958139665), axis=1) |
np.mean(np.cos(2*np.pi*array_x*4.605607857003811/(np.array(range(1, array_x.shape[1]+1))))/7.2328752947739705, axis=1)+10*(np.sin(2*np.pi*np.mean(np.cos(2*np.pi*array_x*3.111041842479701/(np.array(range(1, array_x.shape[1]+1))))/3.4737362433355883, axis=1))) |
np.mean(np.square(8.702476434369963-array_x)+np.cumsum(abs(2.9569898223999704)-array_x, axis=1)-8.411439003644912, axis=1) |
np.mean(np.cos(2*np.pi*1/(1.641452043888521))/abs(5.074237763966349)-array_x-4.929356399081703-array_x, axis=1)+10*(np.sin(2*np.pi*np.mean(np.cos(2*np.pi*1/(8.837384506778928))/abs(1.0456486118544457)-array_x-2.5617049462011137-array_x, axis=1))) |
np.mean(7.479012999885276+np.square(1.610652169801784+array_x), axis=1)+10*(np.sin(2*np.pi*np.mean(9.947949162022434+np.square(1.300011973770438+array_x), axis=1))) |
np.sin(2*np.pi*-(6.040064348498455+10*(np.mean(np.sqrt(abs(array_x)), axis=1))))-5.215177758870333+10*(np.mean(np.sqrt(abs(array_x)), axis=1)) |
np.mean(np.square(abs(8.369183705533947+array_x-abs(8.052854857047345*array_x))), axis=1) |
np.square(np.mean(np.square(1/(np.square(5.973524405972076))-array_x), axis=1))/np.mean(8.098981902209822-np.exp((np.dot(array_x, np.array([[0.8146778557216454, 0.10153057051359726, 0.34240300087462283, 0.16283596901233544, 0.6343595070070094], [0.5256466647543783, 0.028776673366146643, 0.5263759207937325, 0.7614406270368095, 0.4626885023804628], [0.7179876061226259, 0.3344834554770594, 0.12578253778935022, 0.9921493751904672, 0.34895761056445795], [0.7814836489703221, 0.8797276370677758, 0.5606112845965832, 0.7542979512574486, 0.27049649020366184], [0.2230083766127382, 0.4704269700689293, 0.9941895238484539, 0.5475673322020614, 0.06326763919711598]])))), axis=1)+10*(np.sin(2*np.pi*np.square(np.mean(np.square(1/(np.square(1.3642992513787455))-array_x), axis=1))/np.mean(3.894011721824984-np.exp((np.dot(array_x, np.array([[0.7305140735224243, 0.7861747999417665, 0.17743124358932028, 0.3141530899583024, 0.14138715181002826], [0.8806664256214919, 0.09067130377308963, 0.05583556417222224, 0.047736039210670156, 0.00308212443908229], [0.7441592356458007, 0.323179822496486, 0.23404970992100682, 0.9544848405926998, 0.5042451091725073], [0.8193431816659072, 0.7673828565397232, 0.9802114957778375, 0.03817323273017259, 0.01764366021752728], [0.8289607271097584, 0.7588097244775482, 0.9684061325810002, 0.3691255714564844, 0.44859249534900747]])))), axis=1))) |
np.mean(9.28761770770218*np.sqrt(abs(9.428718561849228-array_x*10*(4.511234449418335)*array_x)), axis=1)+np.sin(2*np.pi*np.mean(8.957968540388629*np.sqrt(abs(8.224910496410605-array_x*10*(4.032949442116697)*array_x)), axis=1)) |
np.round(3.167177072658176+np.sqrt(abs(np.exp(10*(np.amax(np.exp(array_x), axis=1)))))) |
np.mean(np.sin(2*np.pi*np.sin(2*np.pi*8.986327623328108-np.exp(5.051335197991318)*array_x)), axis=1)+10*(np.sin(2*np.pi*np.mean(np.sin(2*np.pi*np.sin(2*np.pi*9.369724712713971-np.exp(7.178283525080119)*array_x)), axis=1))) |
np.prod(np.log(abs(array_x*6.878906403089156+np.sin(2*np.pi*np.cumsum(1.5585919636746792-array_x/6.6735945612945855, axis=1)))), axis=1) |