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np.mean(np.square(np.exp(7.9740400812881065/np.exp(abs(array_x-1.938722462940945)))), axis=1) |
3.9838076361288093-3.7757049312410014*np.prod(7.254035326513582+array_x, axis=1) |
np.mean(np.square(1.354314982072048-np.sqrt(abs(6.419802508239892))-np.square((np.array(range(1, array_x.shape[1]+1)))*array_x-6.991181596054297)), axis=1) |
np.mean(10*(array_x+np.square(9.208449998822957))-5.488431602019047, axis=1) |
np.mean(2.6418881202484243-10*(array_x)+3.0184429816177145-array_x/7.137889317071436*4.205327001948776, axis=1)+np.sin(2*np.pi*np.mean(6.38165934931209-10*(array_x)+5.254782063297692-array_x/8.074664707231092*3.120779100762832, axis=1)) |
np.mean(np.square(7.774225706786185)*np.cos(2*np.pi*array_x)*9.67056045100514+array_x-3.6730247941084757, axis=1) |
4.984951233845732-np.cos(2*np.pi*np.exp(np.mean(9.391660124169096+array_x, axis=1)))+10*(np.sin(2*np.pi*4.775791204590423-np.cos(2*np.pi*np.exp(np.mean(6.403133696280065+array_x, axis=1))))) |
np.square(-(np.mean(array_x, axis=1)))-8.156388529106191+10*(np.sin(2*np.pi*np.square(-(np.mean(array_x, axis=1)))-8.542667907012094)) |
np.prod(np.sqrt(abs(10*(np.sin(2*np.pi*array_x)))), axis=1)-8.415060191692028 |
np.mean(np.square((np.dot(array_x, np.array([[0.18303779642236373, 0.49109716379686696, 0.27377195061262616, 0.6385643412842882, 0.3427343060672088], [0.11606481505135702, 0.7203127651343487, 0.2082417237653036, 0.5165871306257208, 0.957172006264979], [0.8854593079076984, 0.9386334464734614, 0.39368401620006477, 0.6785869353929261, 0.29875229070787257], [0.45723686415584985, 0.4668517274879548, 0.3834172709461562, 0.6361975457437664, 0.988765546056337], [0.9546271229145287, 0.18836087039737848, 0.507158384574755, 0.6256917115729195, 0.3026090163884969]])))*(np.dot(array_x, np.array([[0.04276175932662085, 0.1864713547272291, 0.24855815689448946, 0.9019478699563248, 0.6516637315618915], [0.9104404858096039, 0.3769192725144316, 0.9618093726899359, 0.9654531558573668, 0.7292243367329367], [0.6770112043555291, 0.12317155678933145, 0.70392438226655, 0.22532987884347688, 0.8944236559955977], [0.5876056037395152, 0.9646162011060027, 0.6813746625397311, 0.25720707354552796, 0.7624717051539492], [0.6524031296010476, 0.18578415610857202, 0.9414843685874095, 0.30510936698558266, 0.772076544606671]]))))-abs(3.1982520417368003), axis=1) |
np.mean(abs(2.558095290872157+np.exp(2.149540600159751-array_x/4.119720671480654+array_x)), axis=1) |
np.mean(np.sqrt(abs(8.076356391122502))+(np.dot(array_x, np.array([[0.6467778801609368, 0.692131439960559, 0.08806870896605123, 0.6069091692721968, 0.8715792727579975], [0.7839133646115124, 0.8752611136561652, 0.9313765786682868, 0.10944314725521875, 0.8357419979648982], [0.41964988809409565, 0.33266747528850693, 0.23836385102911573, 0.5818724627258028, 0.6422403412958833], [0.9727677095828842, 0.6803172120908618, 0.2503741317954813, 0.2511613263613083, 0.7321923314050576], [0.18705083837113257, 0.9016293250703324, 0.7659309907032652, 0.4556360278926822, 0.3403472395324343]])))+9.500856710840264, axis=1)*np.prod(np.square(4.100163141986689+array_x-np.sqrt(abs(array_x))), axis=1) |
np.mean(3.399534355031098-10*(abs(array_x)), axis=1) |
np.mean(np.exp(7.145696701334598*np.sqrt(abs(np.log(abs(9.579472572332886-np.square(array_x*4.964425301970566)))))), axis=1) |
np.sum(np.sin(2*np.pi*np.square((np.dot(array_x, np.array([[0.09667539700156746, 0.6877171181975346, 0.6267677776197779, 0.7489219596847396, 0.16754372203937073], [0.04442010143288111, 0.2912036845107192, 0.4576057850987294, 0.3773954289286554, 0.5255005301145173], [0.3669825651265758, 0.5759753451453662, 0.40574980457765086, 0.8807165142612197, 0.23429730179275055], [0.93843739408213, 0.4928270034559451, 0.5035435099163269, 0.2503910245695312, 0.1684308039856942], [0.02617131000224482, 0.16415052646978634, 0.21639784879002144, 0.6383703981105517, 0.1843662451752619]])))*1.0561443762961327)-7.58598399775762), axis=1) |
np.round(np.mean(np.exp(np.sin(2*np.pi*abs((np.dot(array_x, np.array([[0.42175947975044714, 0.573629115621801, 0.8305480347703386, 0.19866495655220862, 0.7547337363349859], [0.5135595342090522, 0.17526004666987782, 0.5572328970077451, 0.9632247186505551, 0.8824490638945163], [0.13841861859410365, 0.7890677882342473, 0.5250751981541041, 0.41436323929205965, 0.47237681089859096], [0.08293350179333636, 0.2210624602673108, 0.673030148931884, 0.559000010552457, 0.5040966689766962], [0.4592060556202857, 0.01996205920258809, 0.08082707923508192, 0.4591748808685462, 0.5615613535968251]])))*array_x)+10*(3.24832315252496)))*np.exp(5.562841684837551)/np.cos(2*np.pi*(np.dot(array_x, np.array([[0.9843081560864015, 0.3300663882977397, 0.8733090706806939, 0.5012460772550644, 0.8717671114775207], [0.9198620842387161, 0.7859906075815166, 0.630065186195755, 0.0013930355950697582, 0.29493652929260805], [0.30571157520751313, 0.6489423679374139, 0.09644178009976478, 0.3126244307539079, 0.019954820942215568], [0.775163208729268, 0.7095025036451679, 0.5455750356699761, 0.9649017705479713, 0.4161658113575536], [0.5463468800121101, 0.2792515890539181, 0.6530635783761012, 0.3091179458880218, 0.8442020839158636]])))), axis=1)) |
np.sum(8.855394969828048+np.sqrt(abs(array_x))-abs(1.765233387212034)*np.exp(array_x), axis=1) |
np.sqrt(abs(8.786936665030069))*4.996975396557103+np.prod(np.round(1.7514410355985617)+array_x*(np.array(range(1, array_x.shape[1]+1))), axis=1)+np.sin(2*np.pi*np.sqrt(abs(5.295020466991936))*7.95342136887285+np.prod(np.round(9.905312268723222)+array_x*(np.array(range(1, array_x.shape[1]+1))), axis=1)) |
np.mean(np.cos(2*np.pi*1/(9.529775022105776))-np.cos(2*np.pi*np.sqrt(abs(array_x)))*6.410471531108172, axis=1) |
np.mean(np.round(np.log(abs(4.079367605684752))*array_x+1.2100563674470828), axis=1)+10*(np.sin(2*np.pi*np.mean(np.round(np.log(abs(5.087124684808443))*array_x+7.917548017405325), axis=1))) |
np.mean(array_x+np.round(array_x-9.352526912526145)+5.155506918877575*array_x-array_x-6.117532702026824+array_x/4.453172496899295, axis=1)+10*(np.sin(2*np.pi*np.mean(array_x+np.round(array_x-9.882924171785715)+6.740564915096175*array_x-array_x-1.516085970531122+array_x/9.663962004115335, axis=1))) |
np.mean(np.square(1.8758470864123664)+np.square(5.085373923946636*array_x+1.693394620835885), axis=1) |
np.mean(np.cumsum(np.cos(2*np.pi*4.97281722795265+np.sqrt(abs(9.645843742088656))-(np.dot(array_x, np.array([[0.9214009278322888, 0.22368816011272263, 0.22193640418051874, 0.05862578985351663, 0.8044867254287773], [0.7598118078728862, 0.9962616046991011, 0.2510600661050707, 0.016939921492020282, 0.8440915599540011], [0.42276455675417957, 0.9243202543061304, 0.09453737613972024, 0.6579149782174213, 0.4306378837491731], [0.05502067369892816, 0.96911906238261, 0.5523569091268821, 0.07298163913346822, 0.9358246385536751], [0.4532249451022431, 0.7683284815872286, 0.3900961510321945, 0.9701616194934588, 0.68573021771807]])))-np.square((np.array(range(1, array_x.shape[1]+1)))+abs(array_x+2.4987405739863218))), axis=1), axis=1) |
np.mean(np.square(3.915785937540143+np.sin(2*np.pi*5.151008808560249*(np.dot(array_x, np.array([[0.47776836445723747, 0.9348972626902365, 0.7112579217144172, 0.1067069645070603, 0.3722626357755049], [0.664810205156584, 0.7162934463006609, 0.3061785778194318, 0.8480259073509927, 0.888491911719011], [0.7382477722638847, 0.7848300345458192, 0.8001120523177324, 0.7787095775778152, 0.6964444935299221], [0.21096534026512237, 0.3301996361986407, 0.2546764562376844, 0.6122474031945364, 0.8378455319022816], [0.8025149433575104, 0.03600277372062477, 0.32609537702659375, 0.07885079104033033, 0.7411701177683636]]))))/np.cos(2*np.pi*np.sqrt(abs(3.375912955998108)))), axis=1) |
np.mean(np.exp(np.exp(1.9333216469377394)+np.square(3.9145384931893057/6.153275049185085+array_x))+np.exp(array_x/7.4850717813203795+np.sin(2*np.pi*7.6028313462565125)*7.352319553313778), axis=1) |
np.mean(array_x+6.546224054835346-np.square(np.square(3.052616104831985+np.sqrt(abs(8.124347987339231))-array_x)), axis=1) |
np.mean(np.square(np.exp(9.702933632061633-np.sqrt(abs((np.array(range(1, array_x.shape[1]+1)))*array_x+9.55924832435455)))), axis=1) |
np.mean(-(10*(5.239289397854746+array_x)-3.712021107957319-array_x+array_x+np.square(4.103734171369133)+2.9751968595346963), axis=1) |
np.square(6.774705047274218*np.sin(2*np.pi*np.sum(array_x, axis=1)))+6.202066913676118 |
np.amax(2.4450302260331185-4.693736901772662*(np.dot(array_x, np.array([[0.6621503164538081, 0.9182308403086891, 0.6248126084091693, 0.9870218069385938, 0.14409551088662742], [0.14960403367825714, 0.3999668902548824, 0.888471090728876, 0.7711455221042621, 0.852534258217807], [0.8072321410093598, 0.8326927362064563, 0.39777541100819747, 0.6589332874455873, 0.7134130569749412], [0.05204644564073868, 0.7174454575983006, 0.23887388516911434, 0.6678844661790835, 0.23776060376384855], [0.8992845697904425, 0.825124509541866, 0.9204482144207168, 0.3183526143156692, 0.4993113613259157]]))), axis=1) |
np.sqrt(abs(np.sum(np.square(5.989162553685775-(np.dot(array_x, np.array([[0.6643510202239158, 0.26837986720390505, 0.6787239659894322, 0.6992886292803048, 0.704577428358266], [0.156724770777391, 0.35736034616948453, 0.9777685557093275, 0.9497458183255705, 0.8072755793580393], [0.8811833232766867, 0.904037455477622, 0.9236384624413476, 0.37849810126777883, 0.8829963451323593], [0.8091667521808323, 0.7996728329757353, 0.2962615238341507, 0.8976332251498289, 0.6751294493152511], [0.31622937966712794, 0.7863215267800998, 0.29773951488069894, 0.9250289067558403, 0.4307067082316227]]))))*np.exp(array_x+array_x*4.168438605273981), axis=1)))+5.96442287537003 |
np.round(abs(np.prod(8.989978992289249+array_x, axis=1))) |
np.cos(2*np.pi*7.388048599779145)*np.mean(np.sin(2*np.pi*7.572823594535315+10*(array_x))*4.966201629182178, axis=1) |
np.mean(abs(np.exp(5.146730464828047+array_x/9.054125653551129*array_x)), axis=1) |
np.round(np.mean(array_x*np.square(4.6172053194248335)+np.log(abs(10*(np.sqrt(abs(1.159378462944503))))), axis=1)) |
np.mean(np.round(10*((np.dot(array_x, np.array([[0.21918419713517034, 0.8451193454544724, 0.5004140202605016, 0.4889097919337164, 0.7672037006494288], [0.5337486437570881, 0.3682707331052766, 0.8222923736159216, 0.4515419808212895, 0.77817124876698], [0.5126640300196078, 0.8067967674541972, 0.3651113009594308, 0.8057019883669337, 0.9052052493059848], [0.66180604666645, 0.9550974031758219, 0.11090484683918589, 0.5013065591029033, 0.35707329112745967], [0.8581460680382679, 0.8543490781144295, 0.8273702098157808, 0.1994187344193964, 0.485042712008391]])))-10*(5.830640488011614)))/np.cos(2*np.pi*np.square(3.1760486427890684)), axis=1) |
np.mean(array_x+10*(9.310889930317598)-abs(6.838726455433176*array_x-1.2110911922666063), axis=1)+10*(np.sin(2*np.pi*np.mean(array_x+10*(7.4666431803369)-abs(4.311247660600452*array_x-3.2551972888208267), axis=1))) |
np.mean(10*(np.sin(2*np.pi*array_x*np.sqrt(abs(array_x))))-6.490144729366831, axis=1)+np.sin(2*np.pi*np.mean(10*(np.sin(2*np.pi*array_x*np.sqrt(abs(array_x))))-5.152629650875122, axis=1)) |
np.mean(5.895580445005553*np.square(array_x+array_x-6.55944444963491), axis=1) |
np.mean(array_x*7.68655776870848*np.cos(2*np.pi*8.791223015316493)-np.square(7.5939865090662835*array_x+2.9630786387603787), axis=1) |
np.mean(np.sqrt(abs(10*(np.exp(np.sin(2*np.pi*np.exp(array_x*np.log(abs(1.3111277110752102)))-5.013111587532053/np.sin(2*np.pi*1.6737847646728228)))))), axis=1)+10*(np.sin(2*np.pi*np.mean(np.sqrt(abs(10*(np.exp(np.sin(2*np.pi*np.exp(array_x*np.log(abs(2.1134355891330667)))-1.548918819127528/np.sin(2*np.pi*6.630901170688288)))))), axis=1))) |
8.292971787875562/np.cos(2*np.pi*np.mean(np.exp(10*((np.array(range(1, array_x.shape[1]+1)))*array_x)), axis=1)) |
np.sum(np.log(abs(9.928088521337132-(np.dot(array_x, np.array([[0.900292583317898, 0.05246846531942073, 0.6493762111179792, 0.6113011657280265, 0.8932025901281206], [0.9268539424152373, 0.5267452324977915, 0.34738962465582424, 0.5951843563549855, 0.5494040934802508], [0.8373669560315911, 0.7205636839910311, 0.555365816612566, 0.4427619721833411, 0.3854272506212365], [0.1416527764955332, 0.9429004394016777, 0.34974521418571547, 0.8710799132146321, 0.5627653167548751], [0.4675097878644353, 0.4299687663528825, 0.989424541695955, 0.3854773597457981, 0.7453797149527868]]))))), axis=1)-np.square(10*(np.mean((np.dot(array_x, np.array([[0.2733813198195304, 0.67900510675257, 0.05971727992466702, 0.9092173491145674, 0.9989970428997474], [0.45754316001923745, 0.9749269694706857, 0.6647174064494484, 0.043692951499482824, 0.4526684075058167], [0.48206445847297874, 0.9319697028371527, 0.014430340527197383, 0.32058002913793593, 0.5233173365213153], [0.23005174166251763, 0.7082495323148544, 0.5066384549274865, 0.8250621942327724, 0.2471041496355172], [0.636774428126884, 0.37538867476738824, 0.5218211277426467, 0.5193859787481266, 0.7440335812760455]]))), axis=1)*2.6417597061287417/4.839403716672468)) |
np.mean(abs(array_x-array_x/np.log(abs(array_x))-4.63395337708684-np.cos(2*np.pi*np.round(4.441889518842377))/(np.array(range(1, array_x.shape[1]+1)))-5.638601309441341*4.101130909398432*array_x-8.257110368291006), axis=1) |
np.mean(1.8388151585056631/2.7325124863693366-np.round(np.exp((np.array(range(1, array_x.shape[1]+1)))*array_x)), axis=1) |
np.mean(np.cumsum(-(np.cos(2*np.pi*abs(2.3222029516548615-array_x))), axis=1), axis=1) |
np.mean(10*(np.square((np.array(range(1, array_x.shape[1]+1)))*array_x/np.cos(2*np.pi*abs(2.8841849259923755))-1/(6.002535229498618))), axis=1) |
np.mean(np.cumsum(np.cumsum(np.log(abs(7.941989776828143))*-(array_x)*9.97543997550975, axis=1), axis=1)+9.292438506254653-(np.dot(array_x, np.array([[0.4346978142221811, 0.2449610516746622, 0.13097316050380214, 0.7337484945119316, 0.5445065536498723], [0.6079418005521743, 0.18825035224306863, 0.5257568450861676, 0.8373871175741603, 0.16597124404067776], [0.31695849570571355, 0.5978169293823521, 0.22770916684956732, 0.21047497940831827, 0.30523360986000725], [0.41298728286185393, 0.7527725032488249, 0.28236301174349043, 0.8378790375233598, 0.4007223520723372], [0.9143268516148937, 0.4166509047594501, 0.7796245930940835, 0.2799336935416298, 0.32522472427791316]])))+np.sqrt(abs(array_x+array_x+2.5753631358386095-array_x/1.5142375285204315)), axis=1) |
np.mean(10*(9.359171920294596+array_x*4.49464509594461), axis=1)+np.sin(2*np.pi*np.mean(10*(8.597276518556455+array_x*6.76673837841466), axis=1)) |
np.mean(np.sqrt(abs(np.sin(2*np.pi*np.cos(2*np.pi*np.square(np.sin(2*np.pi*array_x-8.868308535924271))))))*array_x*10*(1.1520201246616768)-2.7269405339720123, axis=1) |
np.mean(6.031150970695459-np.sqrt(abs(np.sin(2*np.pi*array_x*array_x)))*9.891932731327703, axis=1) |
2.1478299329049633*np.prod(7.443938365462718/(np.array(range(1, array_x.shape[1]+1)))-4.586442817894893+array_x, axis=1)+5.4847552322542175+10*(np.sin(2*np.pi*8.861906989999337*np.prod(8.840128443465714/(np.array(range(1, array_x.shape[1]+1)))-2.322689628411708+array_x, axis=1)+6.930758240732698)) |
np.mean(np.exp(1.8027687663835303)*(np.dot(array_x, np.array([[0.03675808878134068, 0.8133128044587964, 0.3538617519854613, 0.9002602877990727, 0.5253051614610109], [0.5122928654121464, 0.8381210496459927, 0.09506890916892319, 0.9580896373829128, 0.6664299910006306], [0.3425010651371767, 0.8489754942992384, 0.4092508294215337, 0.41210367867164555, 0.596434441391915], [0.35362634150438754, 0.767949016428595, 0.6887759857609266, 0.9131372439000338, 0.4395614939509218], [0.6675078069912906, 0.49499382447127627, 0.0995098179885453, 0.04476772631843673, 0.6758030906421743]])))+array_x-1.846418995760223, axis=1) |
np.mean(np.log(abs(7.342864251786139))+array_x/(np.array(range(1, array_x.shape[1]+1)))+np.square(5.233965197111668*array_x), axis=1) |
np.sum(np.sqrt(abs(np.exp(np.square(3.791579111283985-array_x*9.177156150126285+array_x*np.sqrt(abs(5.99127814423425)))))), axis=1) |
np.mean(np.square(np.exp(5.829260632964805+(np.dot(array_x, np.array([[0.7754128561250914, 0.778261844401359, 0.9891445349161558, 0.16976176807564125, 0.5974901707904517], [0.28786518470728495, 0.10245674477187727, 0.5692108754443799, 0.45754934464391395, 0.9038604204994157], [0.1276792092571668, 0.6473922372977587, 0.16354953784901394, 0.10382233310571476, 0.7968398930646252], [0.6284526275708018, 0.7625388026666445, 0.352253435570658, 0.4671777179467582, 0.7606234634223935], [0.696367044278937, 0.9535799516177419, 0.11599587768403374, 0.8265123727664013, 0.9054396446075015]])))-array_x)-1.3914406433549178), axis=1) |
np.sum(np.square(abs(np.sin(2*np.pi*np.square(array_x*array_x+3.2094556954348588)))-3.680609950297839), axis=1) |
np.exp(np.square(array_x[:,0]-np.prod(array_x/4.758100638460025, axis=1)*np.sin(2*np.pi*5.35206793108687)))+10*(np.mean(np.round(array_x+array_x), axis=1)) |
np.mean(6.469490072272215*np.sqrt(abs(10*(np.exp(array_x-9.02277185447791/9.82193082355811)))), axis=1) |
np.mean(2.7518053154569433-np.cos(2*np.pi*np.sqrt(abs(np.exp(3.0680833639250698))))+10*((np.dot(array_x, np.array([[0.7664159978402474, 0.935784660372194, 0.4486011491580254, 0.6236472862519562, 0.7718566560551849], [0.37714093127215786, 0.8088251019168102, 0.7734081289238527, 0.8519920241970269, 0.8843501226113757], [0.5039230724802768, 0.6115181817870401, 0.7585647463576662, 0.8552995036436751, 0.1913965600503128], [0.9568170551418484, 0.7610689597980428, 0.4387473039672478, 0.7746025794500937, 0.8719229827869409], [0.3904544052701082, 0.9937165636054566, 0.5025148670697707, 0.41445343204607443, 0.23084659215023084]])))), axis=1) |
np.square(np.sqrt(abs(4.11783561466898))+np.sum(np.exp(array_x+np.sqrt(abs(array_x*5.949937901385359))), axis=1))+np.sin(2*np.pi*np.square(np.sqrt(abs(3.589701101754713))+np.sum(np.exp(array_x+np.sqrt(abs(array_x*2.4752385892476805))), axis=1))) |
np.mean(-(np.square(array_x-7.987909127297972/np.sqrt(abs(5.495052292106194-10*(array_x))))), axis=1) |
np.mean(7.648821738905389/9.987751642662644+np.round(array_x)+8.455270260318052*array_x, axis=1) |
np.round(np.mean(np.square(10*(6.225542500412133+array_x/6.210098027244594))-(np.dot(array_x, np.array([[0.8475524243232931, 0.6428620792142867, 0.27359120505893475, 0.33140801164607414, 0.8271552470935374], [0.28007856596456915, 0.04026766712395047, 0.647796138597049, 0.34302195045147255, 0.38937493310636284], [0.37298884094023776, 0.15385334279706653, 0.12414048015510104, 0.46825414827722145, 0.397872112888328], [0.5662221769492783, 0.7409049388020621, 0.35428937002733696, 0.8402154508867182, 0.711288814418899], [0.00034281119735946053, 0.027789836813988722, 0.823692466492446, 0.5584660915707168, 0.6592893500792069]])))/10*(6.006870086561344)-3.0738474810053713, axis=1)) |
np.mean(array_x-4.027427850142979-3.8048959513379765*2.96349445465904, axis=1)+10*(np.sin(2*np.pi*np.mean(array_x-6.935221657424711-6.5773054879544794*2.16720374505497, axis=1))) |
np.mean(10*(np.sin(2*np.pi*np.cos(2*np.pi*(np.dot(array_x, np.array([[0.19458386186759413, 0.04886684462336188, 0.2337638342070979, 0.25727942355665745, 0.2904268482434288], [0.02941695830953206, 0.36392128839306404, 0.368277236205646, 0.3371899472602917, 0.15931740390700566], [0.46880721845656703, 0.5169321180469097, 0.07870368820509255, 0.5664878556845409, 0.677637632303429], [0.9991260363320434, 0.39575557626904445, 0.7522965891115103, 0.841564268870429, 0.3198459071733458], [0.4596654747444904, 0.2751295083298382, 0.28907599449071886, 0.16699844094017202, 0.6464388629608918]])))+3.7993168480811645))*7.784377462224158+(np.dot(array_x, np.array([[0.32232028632039655, 0.98266208902744, 0.914278234027178, 0.44389646898143453, 0.8603506483384128], [0.8681474465814143, 0.2859996089736343, 0.48459024158804687, 0.4705585276581987, 0.09495005543257673], [0.42357685281845214, 0.1611261970546355, 0.6496836425015733, 0.41463474798989164, 0.17928902643944777], [0.29215754459256094, 0.3728740078840409, 0.9884136820219626, 0.03580931668830167, 0.6253606787103029], [0.9340026243213317, 0.8827415901838886, 0.9106422043989673, 0.21286291015925496, 0.6086197466058884]])))), axis=1) |
np.mean(9.931471761030577-5.751012483532996*(np.dot(array_x, np.array([[0.8772159804901862, 0.15709238092409394, 0.27451080171628683, 0.5207788546242651, 0.502320409205976], [0.7766955087720205, 0.9456233731007027, 0.46314071196529893, 0.8576929202564711, 0.9984106605018812], [0.09925881590476071, 0.4062466228102416, 0.2052246536571315, 0.9014250992376863, 0.08747512554079018], [0.6753273497352613, 0.16414113673283626, 0.7544288366852153, 0.1850267284574013, 0.15056894030343304], [0.0035012339751825383, 0.9588095038112304, 0.8740344448956043, 0.08546574217377922, 0.3048903254839198]])))+array_x, axis=1) |
np.mean(6.560532951917587+array_x-np.cos(2*np.pi*1/(9.98236861523626))*1.8648153317503873, axis=1)+10*(np.sin(2*np.pi*np.mean(3.4159864693094644+array_x-np.cos(2*np.pi*1/(3.3993731590352376))*1.5861085962582888, axis=1))) |
np.mean(np.cumsum(abs(np.square(np.square(array_x+9.759493485364496)+np.cos(2*np.pi*np.sqrt(abs((np.dot(array_x, np.array([[0.5961956017969198, 0.7378369780866574, 0.560494250074655, 0.5433824227360827, 0.1345737469110293], [0.8502325178094854, 0.840454211857861, 0.44081350536103325, 0.5023749071339639, 0.5991492741066613], [0.3649961865507557, 0.055888350931433406, 0.6174450198411197, 0.32948695333854106, 0.6967299351607295], [0.004259597557135786, 0.6161132273253316, 0.6444501860992282, 0.044375812685432, 0.7343574959420569], [0.20527689597666454, 0.7601849191779485, 0.16286392337770794, 0.7661287282544912, 0.9411932481875157]])))+7.342739860261619+np.square(np.round(6.315752178339082))))))), axis=1), axis=1)+np.sin(2*np.pi*np.mean(np.cumsum(abs(np.square(np.square(array_x+5.445788646914588)+np.cos(2*np.pi*np.sqrt(abs((np.dot(array_x, np.array([[0.7647952217602045, 0.9886522330660847, 0.5199876262895952, 0.2322834716803095, 0.16199242513752876], [0.026352921496958004, 0.6822339131180822, 0.23732209557851547, 0.2393907709352694, 0.6238409354031218], [0.7133011445175371, 0.3369755186331208, 0.9394730955602046, 0.16051709723754903, 0.13047015120909056], [0.49770861951162626, 0.18435019146067455, 0.7085632616512142, 0.46753705623475705, 0.09719009637832854], [0.9835801715333419, 0.5993225176990601, 0.8118688764813194, 0.1411700509868311, 0.5325032818764434]])))+6.704752086855182+np.square(np.round(4.029627811511951))))))), axis=1), axis=1)) |
np.mean(np.square(np.round((np.array(range(1, array_x.shape[1]+1)))/1.197973926357577+3.0105935082403867-(np.dot(array_x, np.array([[0.9223635833915965, 0.6827023799769216, 0.4934376724544808, 0.872086840322816, 0.144969176684825], [0.6487911163366047, 0.5789347437363124, 0.04019158781145904, 0.010822660225167424, 0.8086857091916988], [0.6906053001313929, 0.6343257142558351, 0.29290920192763803, 0.04434722683905534, 0.20975938918418513], [0.6950012591220177, 0.1036741436030324, 0.6705755570890726, 0.3896875051416968, 0.8896194552483278], [0.962283164407874, 0.8577404482858405, 0.8428555288993946, 0.457160895767286, 0.5853408138575691]])))-(np.dot(array_x, np.array([[0.44689132709428525, 0.8950416121591763, 0.004561311217682995, 0.17061922298988674, 0.9863042742761019], [0.08898917220721547, 0.5255197901510028, 0.5335998580844068, 0.053811063462992026, 0.5071248804142527], [0.8115651638439315, 0.844880256322693, 0.4149414471854055, 0.6665756765829762, 0.9092422833699977], [0.6084663882704217, 0.8164496985116233, 0.5413052510961841, 0.5924765287483189, 0.40363626000032704], [0.7786380500820597, 0.5923773305008504, 0.134728294519187, 0.28377678123382133, 0.9671718223839736]])))*3.0346763243492654)), axis=1) |
np.mean(9.594901271942053-(np.dot(array_x, np.array([[0.9167188938692505, 0.4217021857547768, 0.733095461133513, 0.8912862423188193, 0.7377111776672732], [0.9418446536756178, 0.6412686965856101, 0.39122752647241676, 0.47681373820929307, 0.2229207179277829], [0.15677058197181093, 0.9670859120599455, 0.21322092620753397, 0.7712961574040991, 0.0856125043488356], [0.08695578995360675, 0.6557333205787199, 0.8813921373106425, 0.5694316783851651, 0.03914224396584931], [0.8278458492415393, 0.3348673122026886, 0.2253865398006517, 0.8857719521013472, 0.4663614016779515]])))*7.7209649839640075+10*(np.exp(array_x-1.4012976854821648))/np.log(abs(array_x/(np.array(range(1, array_x.shape[1]+1))))), axis=1) |
np.round(np.mean(np.exp(8.291248184931263+array_x)+2.0441979408688806, axis=1)) |
np.mean((np.dot(array_x, np.array([[0.6192045471178225, 0.6473145422765881, 0.7005430659149309, 0.5070024249896787, 0.6075441868575713], [0.7903133850426775, 0.47185353916961503, 0.988456962764948, 0.24808235126984968, 0.8816581700927616], [0.9862342241284279, 0.8243525890775584, 0.39760078387427167, 0.6690684223042924, 0.22603005236658102], [0.6040823541881578, 0.14287557544739682, 0.5686497272253627, 0.5106604292814018, 0.12523390993610228], [0.08952886623809242, 0.9088402995806522, 0.5924006553891445, 0.17538908119146146, 0.8989850943007248]])))+np.cumsum(np.sin(2*np.pi*8.531250010882701*(np.dot(array_x, np.array([[0.6086149567723789, 0.7281546449867389, 0.7335713425496155, 0.26656062198560904, 0.5179272335486909], [0.7560224265489297, 0.09469659625286808, 0.20598734263877438, 0.6490946054822442, 0.47404269916901254], [0.3581119936181344, 0.4164000582181693, 0.4806176269601651, 0.4845734975551039, 0.4777049678928852], [0.8045795281487086, 0.671113036294012, 0.8355832080263155, 0.8449483628867871, 0.2687652301732686], [0.18077696742314087, 0.7311780684856473, 0.7327733007071853, 0.7995709026397445, 0.5407086872120079]])))-3.0396784197770446), axis=1)-1/(9.82228543487084), axis=1) |
np.mean(1.7302813122183052+np.sqrt(abs(array_x))*np.exp(2.480187253636832)*np.square(array_x*np.cos(2*np.pi*array_x)+array_x), axis=1)+np.sin(2*np.pi*np.mean(3.1916482160729953+np.sqrt(abs(array_x))*np.exp(5.7492370397037185)*np.square(array_x*np.cos(2*np.pi*array_x)+array_x), axis=1)) |
np.mean(10*(np.round(1.0509073657302226-array_x+6.207181105044841))*9.172953220468903+array_x-7.866446353818618/5.8191278835167655, axis=1) |
np.sum(np.cos(2*np.pi*np.exp(array_x+np.cos(2*np.pi*np.exp(3.0782616648903445)))), axis=1) |
np.sum(array_x-2.1216739352056555+array_x-np.sin(2*np.pi*4.640218780800926), axis=1) |
np.mean(2.244012150936618-7.823220363944352*array_x*array_x-np.cos(2*np.pi*8.900568905939204/np.cumsum(np.exp(array_x)/9.023621336087759, axis=1))-np.log(abs(array_x-np.sqrt(abs(6.7813357314545755))-4.05926841375714)), axis=1) |
np.sum(1/(5.560311140673569-(np.dot(array_x, np.array([[0.6690939383782655, 0.5827548435449025, 0.20314945864902867, 0.32403821589800463, 0.48656336200094497], [0.5336412889339597, 0.26249814949156514, 0.20674132541274826, 0.547247683661193, 0.44684931741240663], [0.5754048232404055, 0.5874372162410478, 0.1766581213796946, 0.6516597741392905, 0.5026010399441642], [0.622763017523042, 0.9401995184473159, 0.6502352620375133, 0.8657751828301151, 0.48108409131491037], [0.4878608494486292, 0.5493579865640762, 0.056120935151220186, 0.37216408883287266, 0.6272773668600727]]))))/np.cos(2*np.pi*np.exp(5.33649971367258-(np.dot(array_x, np.array([[0.3350904442117837, 0.9160184773768872, 0.5538418360753011, 0.6958139044123026, 0.5747141076758252], [0.5551080143043955, 0.621188186064325, 0.3997604814474338, 0.780323240924514, 0.16844583627180498], [0.6754363021433571, 0.4514226412458664, 0.16953689196144317, 0.7216591268494825, 0.1738891845539543], [0.09803941890129075, 0.34754767068068704, 0.5550863714803064, 0.41683513349931745, 0.5192532075678991], [0.8408269872841355, 0.6324868070445939, 0.07454801609824724, 0.33230089287462417, 0.3858419196475026]]))))+np.sqrt(abs((np.dot(array_x, np.array([[0.7089987620393113, 0.10063024394194842, 0.2716359164710438, 0.41826806642513326, 0.7677088423962534], [0.6532440224014683, 0.7728662791042404, 0.4632585415232475, 0.630663398736651, 0.23979758712071741], [0.23398672235821727, 0.7403407619075008, 0.6835782156431226, 0.9618657153016459, 0.19624856957511672], [0.0506974126594405, 0.0359167686123657, 0.18017181867427146, 0.909365558796758, 0.8843613180844205], [0.8157841446275054, 0.7147658572128874, 0.7004172653409848, 0.9853006047866173, 0.8224589035448941]])))))), axis=1) |
np.mean(2.4308311456339107-np.exp(6.98937831412419*array_x)+2.8497250280492024-8.595153963103801*array_x, axis=1) |
np.mean(np.cumsum(np.square(np.square(4.201416302098653)+(np.dot(array_x, np.array([[0.8538787917298933, 0.07914453225798479, 0.5980873949346427, 0.8679322513410971, 0.21675636918461316], [0.9687826913342732, 0.43026756762348795, 0.1235175056247596, 0.9752480507827611, 0.15332910909078679], [0.5782161060057373, 0.6600921281491154, 0.049680712489621026, 0.7031694900033237, 0.8053379173135162], [0.6855561063863813, 0.5087558217652816, 0.4824133647662173, 0.3659353180523083, 0.16986485876282376], [0.45959611404964185, 0.06986500387433436, 0.07757160854903511, 0.10118085480591088, 0.010775818129618142]])))+np.exp(10*(5.426008566299582-(np.dot(array_x, np.array([[0.6115506255836387, 0.28979541334812753, 0.11553174427304225, 0.18496392275164275, 0.5870750051066684], [0.5034217206783917, 0.17339814267285492, 0.7828062550259017, 0.7364067344761632, 0.4148419341223393], [0.9134990516569367, 0.7672936407536451, 0.13573576858808067, 0.20503978450638194, 0.44364884250231873], [0.1862340464496205, 0.1049883125839638, 0.6883566458753215, 0.22052222657355158, 0.05644351250111823], [0.38218441353909594, 0.416457730332758, 0.5037677592390906, 0.43583955007858843, 0.34655465183001477]]))))/8.89848823311724+array_x+array_x-array_x)), axis=1), axis=1) |
np.log(abs(np.log(abs(2.2728910467282457))))+np.sum(np.round(np.log(abs(np.sqrt(abs(7.563600378187936))))*array_x), axis=1)*1.4207217565093875 |
np.round(np.mean(6.219537714694562/np.square(4.167226950183028/5.20230992194665+array_x)+np.cos(2*np.pi*array_x*8.080537361932357), axis=1)) |
np.mean(np.square(np.log(abs(1/(4.872188170742138-np.cumsum(10*(array_x), axis=1))))*abs(3.7970736900512283)), axis=1) |
1/(np.cos(2*np.pi*np.mean((np.array(range(1, array_x.shape[1]+1)))-2.6604957222460968-array_x, axis=1)))-np.cos(2*np.pi*7.2863201200893615) |
np.mean(np.square(array_x+5.503169520324185)*9.20779892035338-(np.array(range(1, array_x.shape[1]+1))), axis=1) |
np.mean(np.cumsum(array_x+2.524500035589246-np.log(abs(8.661022750790274)), axis=1)-7.6434005952160335, axis=1)+10*(np.sin(2*np.pi*np.mean(np.cumsum(array_x+9.495319795895579-np.log(abs(4.139988922077519)), axis=1)-7.463304742579512, axis=1))) |
np.mean(8.551096447810625*np.sqrt(abs(np.cos(2*np.pi*array_x)*(np.array(range(1, array_x.shape[1]+1)))+3.407909931356025)), axis=1) |
np.mean(np.exp(9.125529388238078+array_x/2.3586922414409353*np.sin(2*np.pi*array_x)), axis=1)+10*(np.sin(2*np.pi*np.mean(np.exp(2.844869418844487+array_x/4.04393572252909*np.sin(2*np.pi*array_x)), axis=1))) |
np.mean(4.752977355674098/np.log(abs(np.sqrt(abs(array_x))))+np.round(6.422843037619)+array_x*5.134517355135725*array_x+np.cos(2*np.pi*3.6246510944473003), axis=1) |
np.square(1.523957342566563+np.prod(np.log(abs((np.array(range(1, array_x.shape[1]+1)))+np.square(array_x))), axis=1)) |
np.round(np.mean(10*(np.square(10*(2.3990531662634043)/np.exp(array_x))*np.sin(2*np.pi*array_x)-1.8322437047895228), axis=1)) |
-(np.sum(7.669089118058216-np.exp((np.array(range(1, array_x.shape[1]+1)))*array_x), axis=1)) |
np.mean(1/(4.062539412626426*(np.dot(array_x, np.array([[0.965958210226976, 0.8868142969103756, 0.19716633098685976, 0.6563946293631278, 0.5429784818487603], [0.2611743771874665, 0.3140267183535137, 0.190858378207034, 0.32952073559700223, 0.16027711366166286], [0.41096733733013113, 0.3765327252485259, 0.2176284369644147, 0.15814674032386355, 0.2675462259453707], [0.8822355967205621, 0.013570133853174626, 0.2742468488857023, 0.9775002534598071, 0.750041457130702], [0.910323729693407, 0.8042274352345281, 0.9046010432179572, 0.4836037028043453, 0.6205862631910675]])))-5.8028446670210965-7.914518537145759*np.square((np.array(range(1, array_x.shape[1]+1)))*array_x)), axis=1) |
np.mean(np.square(np.round(np.square(array_x-8.726570790379691))*6.123851979577541+(np.dot(array_x, np.array([[0.3509770613971044, 0.16305063935648345, 0.7424919264505055, 0.2787419955437913, 0.20782663983105476], [0.131340914484519, 0.9257761301785311, 0.6664754923941163, 0.37543868391995405, 0.6827944820534744], [0.5658935655783287, 0.06594782090868212, 0.6930488878810545, 0.8591732170797586, 0.6858219860999089], [0.13020443601956688, 0.20073683031712541, 0.037045729342096045, 0.3508936703821036, 0.9873925563442882], [0.982150120111993, 0.03089216739910694, 0.45089510456180637, 0.37051269854430946, 0.9686372273762928]])))), axis=1) |
np.mean(9.472602677251318/np.cos(2*np.pi*np.sqrt(abs(array_x+8.061414703270959/1.028490650225831+array_x))), axis=1) |
np.mean(np.cos(2*np.pi*np.cos(2*np.pi*abs(array_x*np.sqrt(abs(3.340994055263105))))-np.square(np.cos(2*np.pi*array_x)))+10*(2.5455983008967316*array_x/np.log(abs(4.359453115373201))), axis=1) |
np.mean(1/(7.190173087327261-np.cos(2*np.pi*np.exp((np.dot(array_x, np.array([[0.10729525443208898, 0.531426233066079, 0.1504394174508078, 0.0569390671869503, 0.8319796440917469], [0.6097183963574114, 0.7156751013406475, 0.3937121389483226, 0.45147992310566054, 0.1080763709060929], [0.6977719815893532, 0.13927226166842643, 0.44084538075398116, 0.48463556406572594, 0.35060079576345093], [0.9839925064012893, 0.47625985193485487, 0.6786634882803727, 0.9952188410669183, 0.7060331561748764], [0.4920534671580019, 0.95607035610266, 0.02691976795239448, 0.8932187099265942, 0.396442850695099]]))))-4.853824471269457)-np.sqrt(abs(9.86513633370603))-np.cos(2*np.pi*np.cos(2*np.pi*6.7270963406646365))*6.054218531840835-array_x+(np.dot(array_x, np.array([[0.5301300299842615, 0.8536422818969451, 0.6252489304067378, 0.3290799832914627, 0.544774554852001], [0.4079183173477985, 0.8473665482581751, 0.9636877707265888, 0.18500971523012189, 0.42300778346080226], [0.4174730538097129, 0.3145653521566497, 0.4581914075055623, 0.10236881637198048, 0.6960991220442326], [0.7952931533659363, 0.4802074103527647, 0.7520401488825126, 0.7494812954343091, 0.5010091734421158], [0.647019623460572, 0.171799271693985, 0.6299123144673857, 0.3931536031597196, 0.3832651937529]])))), axis=1) |
np.sum(np.exp(np.round((np.dot(array_x, np.array([[0.7517073720441193, 0.32228774558043594, 0.4421990845602921, 0.9059452340599189, 0.4106952894284003], [0.6877508426013125, 0.042644571843516776, 0.9532694230730365, 0.17103929653911487, 0.6561205318756752], [0.77239747106178, 0.2212946303545128, 0.06987856072385246, 0.26610656643355235, 0.6489991791400082], [0.6712781256811459, 0.22363353780466044, 0.049385436607749855, 0.103942188418092, 0.1535931080586378], [0.263250677331037, 0.8891243819021925, 0.364955633383543, 0.8161167185788376, 0.7537327289682405]]))))/np.sqrt(abs((np.dot(array_x, np.array([[0.5047377999546815, 0.7245860247690519, 0.2448638744617987, 0.8022570628086491, 0.7681662040214849], [0.7520315884188101, 0.565342878980655, 0.6894959739736544, 0.2273420372035584, 0.6389116669534662], [0.082278424710718, 0.6315945329421824, 0.8756385021881787, 0.6603604034462074, 0.9990997607163218], [0.20352264804617437, 0.43649792286083666, 0.36506566292810294, 0.6876015319058105, 0.2197347082957738], [0.6129459237980112, 0.3858808145815392, 0.5695176595827696, 0.45454708036062863, 0.1011174539340215]])))-6.661442802474824))), axis=1) |
np.sum(np.square(6.128755780297647*array_x-3.874332649410964-array_x)/np.sqrt(abs(1.271983741057631)), axis=1) |