Analyse

analyse.py

@@ -0,0 +1,9 @@
+import numpy as np
+
+list = [(200, [0.1, 0.10000000000000002, 0.09999999999999999, 0.1, 0.1, 0.1, 0.10000000000000002, 0.1, 0.10000000000000002, 0.09999999999999999]), (400, [0.10000000000000002, 0.09999999999999999, 0.1, 0.09999999999999999, 0.09999999999999998, 0.09999999999999999, 0.09999999999999999, 0.1, 0.1, 0.09999999999999999]), (600, [0.1, 0.1, 0.1, 0.09999999999999998, 0.09999999999999995, 0.10000000000000002, 0.1, 0.1, 0.1, 0.1]), (800, [0.09999999999999999, 0.10000000000000005, 0.10000000000000002, 0.09999999999999996, 0.1, 0.09999999999999999, 0.09999999999999999, 0.10000000000000002, 0.09999999999999999, 0.09999999999999999]), (1000, [0.1, 0.1, 0.09999999999999999, 0.1, 0.09999999999999998, 0.1, 0.1, 0.10000000000000005, 0.09999999999999999, 0.09999999999999999]), (1200, [0.10001718073289731, 0.100046130747437, 0.10000019344537885, 0.09999372226152767, 0.10006947400012749, 0.09999734536079026, 0.09996393803617871, 0.09998337523822401, 0.09961029019047209, 0.10031834998696662]), (1400, [0.09999496000801135, 0.10000236809135707, 0.10001321417276228, 0.10000305771721614, 0.10009645378014684, 0.0999886358406896, 0.09996985450430804, 0.10003305551980499, 0.099801133763366, 0.10009726660233767]), (1600, [0.09997582116760996, 0.10004114828803204, 0.09998489555523622, 0.09998938956731082, 0.10010703781938693, 0.09996732005992273, 0.09997082490300534, 0.10010925359685777, 0.09963429902932187, 0.1002200100133164]), (1800, [0.0999984522524924, 0.10002781061410274, 0.09999894897746879, 0.09997689586290998, 0.10008664742781251, 0.09998077192027596, 0.09998435911575997, 0.10003305201180963, 0.09977337914943848, 0.10013968266792964]), (2000, [0.09999844326539561, 0.10000470875288017, 0.10007621419983795, 0.09996782410128978, 0.10005538608903415, 0.0999807709311695, 0.09997767055930254, 0.0999624959428718, 0.09989399888218059, 0.10008248727603804]), (2200, [0.09992166352502187, 0.10003642540992692, 0.10016930167761876, 0.09986505053748654, 0.10019250530408615, 0.09993634690270345, 0.09984909540905078, 0.10013172787946982, 0.09953139213362526, 0.10036649122101046]), (2400, [0.09994423142662559, 0.10008699933434502, 0.10015968644343695, 0.0996681328213284, 0.1002933964365728, 0.09994859606023412, 0.099837844676045, 0.10007780748359585, 0.09873684985425149, 0.10124645546356503]), (2600, [0.09997134620622677, 0.10061161238730036, 0.10007879470188026, 0.09802474492756584, 0.10022439596710013, 0.09997103672641712, 0.09997144020409791, 0.09938517697621303, 0.09266897609233359, 0.10909247581086501]), (2800, [0.09999716704468094, 0.10261231713609646, 0.10007574270368291, 0.0912069128217618, 0.10045553880611709, 0.09999917041705464, 0.09997037118860325, 0.09349601857550789, 0.061425832338642426, 0.15076092896785243]), (3000, [0.09997611721090646, 0.10062447350412349, 0.10005343673937021, 0.04439825964448067, 0.10007137750858104, 0.09999732286058226, 0.09990011313022397, 0.06116169706941134, 0.05724635130690302, 0.23657085102541778]), (3200, [0.0958468972002909, 0.09999929374149093, 0.09939948557970989, 0.05004881991189837, 0.09984355863039598, 0.0988633453568703, 0.09355448201027032, 0.06180501383075292, 0.057976321878851, 0.24266278185946968]), (3400, [0.05564996976519352, 0.06720165209038609, 0.06731096413626474, 0.055672239403668486, 0.05988095287657014, 0.07046112230276026, 0.05241712758466425, 0.07351601195465221, 0.08330076095394137, 0.41458919893189927]), (3600, [0.06062034595482224, 0.062321246168922, 0.06390729204791616, 0.06491016469031625, 0.06896805734353781, 0.06555634254377253, 0.05832325770116953, 0.074596658662051, 0.05724270254206452, 0.42355393234542793]), (3800, [0.07500992845714745, 0.08999854476418553, 0.08844588013883696, 0.05893277082599126, 0.08968357602971802, 0.08925849841712277, 0.08520428645096846, 0.05181996552474388, 0.05374827613553176, 0.3178982732557539]), (4000, [0.06250690291334796, 0.07489829509042013, 0.05584183674982107, 0.08241569971328723, 0.08283230606123541, 0.05456177828985966, 0.0650114380397333, 0.16756090822731645, 0.07963883741735944, 0.27473199749761934]), (4200, [0.04298157924869793, 0.06689294536749979, 0.04203566811359606, 0.0675495473618533, 0.0685715984253935, 0.05411667880274544, 0.05876152936907554, 0.23407059473956787, 0.07935537571084786, 0.2856644828607229]), (4400, [0.06598583898606994, 0.05094229765528725, 0.06421627052976588, 0.057197285227729135, 0.05558503963595605, 0.026678688494799135, 0.05295584646913402, 0.24808718738334043, 0.14304068919310176, 0.23531085642481692]), (4600, [0.05893920217385777, 0.05429844142804856, 0.03889648083476091, 0.06957713522896666, 0.05290101874275479, 0.053059111910343584, 0.05749870559980891, 0.0757150445755826, 0.057428005429863135, 0.481686854076013]), (4800, [0.08002126978465882, 0.06398681718208363, 0.043676647948239514, 0.09735407349949937, 0.07093311071443296, 0.05940362088006421, 0.0482000618847631, 0.18385673422204332, 0.11339272767220206, 0.23917493621201333]), (5000, [0.07318321396408553, 0.06500627294616422, 0.07539143581787856, 0.07543230587507904, 0.061515576170242524, 0.054186207840444846, 0.048778829145770884, 0.07655402689746557, 0.0782656317019289, 0.39168649964093993]), (5200, [0.0847654036471858, 0.07582645102206961, 0.041848508719589415, 0.06924750733472541, 0.06977328937428709, 0.07976728910743433, 0.07987130743738621, 0.07913979220215853, 0.09407038356583126, 0.3256900675893323]), (5400, [0.05828792013076649, 0.05203638229261256, 0.05938588885963525, 0.04779755455018419, 0.0524801896636337, 0.06421688671760531, 0.06884484886571912, 0.07711949918714166, 0.06441408102353989, 0.4554167487091615]), (5600, [0.05960026767954622, 0.06380407442085952, 0.0552964136654334, 0.05926405938684311, 0.051709943799974824, 0.04533119500381867, 0.07183842821894551, 0.1041021089378145, 0.08549191397005229, 0.403561594916712]), (5800, [0.07842938649978591, 0.07516513710972342, 0.07616006681644262, 0.06445917596365236, 0.07512983014790156, 0.07173770527066921, 0.08158906313347901, 0.06412959790256255, 0.06987950895361505, 0.34332052820216846]), (6000, [0.059313494799141595, 0.04898034802361691, 0.06272813928295683, 0.055431044275035465, 0.04275870340426561, 0.057686757908680594, 0.034620527014186844, 0.23806755497918042, 0.17283436679043135, 0.22757906352250462]), (6200, [0.0883771613825968, 0.0553085020814563, 0.09076660616104855, 0.05645809736792619, 0.03662863161190862, 0.08818325669847517, 0.08620712166128111, 0.0942836244305322, 0.15870665864229455, 0.2450803399624813]), (6400, [0.04527576001462252, 0.05459282169791537, 0.04948001977191986, 0.08283249165277419, 0.06419092283229468, 0.05618933298074733, 0.04548788825131475, 0.17378589768993774, 0.08097010206362953, 0.34719476304484437]), (6600, [0.07962858923173946, 0.0901218519206459, 0.08338769489782555, 0.06064879299133287, 0.09061576715688886, 0.08832667237163633, 0.08819942133761934, 0.11804961326158721, 0.06578901164343877, 0.23523258518728643]), (6800, [0.054607984546722624, 0.02757345041800017, 0.05010840188219484, 0.07487725633760237, 0.0386372832654602, 0.05168527791690654, 0.06510523092345576, 0.13153488616861705, 0.1464246761510397, 0.35944555239000087]), (7000, [0.06952743937438588, 0.03877464809928161, 0.05273461146579411, 0.05675044218037527, 0.05220292191624905, 0.05965981726816742, 0.0678716321187755, 0.15556900553607134, 0.14637881390011853, 0.30053066814078166]), (7200, [0.05572088733333337, 0.045357631818466394, 0.06717717077041527, 0.08325209277867725, 0.05627507509232342, 0.05625858951925809, 0.07013150358285859, 0.07055158618968152, 0.06828895479916688, 0.4269865081158197]), (7400, [0.058218349999845063, 0.05186579936997687, 0.04108908926894142, 0.05299853787908517, 0.06857357996127947, 0.05027034987907271, 0.06172725255257011, 0.16166179028082756, 0.12375858845790508, 0.3298366623504971]), (7600, [0.059377784512711695, 0.08035951136215166, 0.047836359825041364, 0.04621489578769872, 0.05346465630547238, 0.06327572499985087, 0.0470454509709292, 0.2099798619071375, 0.14543492129561245, 0.24701083303339474]), (7800, [0.06229628318452833, 0.07996962163192649, 0.05754230032971584, 0.06741394242859887, 0.04479751434318622, 0.06719442663122192, 0.07480561059950044, 0.20496994446619388, 0.12010902776829906, 0.22090132861682904]), (8000, [0.03993642116285256, 0.06686537228027697, 0.04471379507402443, 0.06527969819782152, 0.06927204552012647, 0.03560279089795909, 0.04431214891729506, 0.22140508431528105, 0.08054216687243906, 0.33207047676192414]), (8200, [0.031209103007474443, 0.05814960358205238, 0.03598682506047039, 0.08012252239287623, 0.04269280478754212, 0.039581674226586636, 0.0588330224811527, 0.19567109803784155, 0.11876559538754931, 0.3389877510364547]), (8400, [0.09983161819053057, 0.08578463362436764, 0.09985417625219789, 0.09697592210264226, 0.08938123556768263, 0.09986682397596228, 0.09983597578930488, 0.08948959323463668, 0.10831171423543258, 0.13066830702724339]), (8600, [0.08072151242204736, 0.06610597586728838, 0.09211793541796884, 0.07701987524658092, 0.062317641681151635, 0.05319890569345799, 0.04614437220635885, 0.0650111326313225, 0.07148958227100685, 0.3858730665628172]), (8800, [0.06789877235351353, 0.07161970575335451, 0.0637615216132196, 0.0838793261175131, 0.04199998356202257, 0.0424466956993199, 0.07089964867980142, 0.09999627561504612, 0.0666741011718437, 0.3908239694343661]), (9000, [0.08767129240109087, 0.07251707827025397, 0.08670157288455638, 0.08383411130579214, 0.07870704736766469, 0.08553381077995048, 0.09254856760017616, 0.08482795854065672, 0.08365884738350841, 0.24399971346635052]), (9200, [0.05925478839940192, 0.05395551530806333, 0.06025103213216561, 0.07153443965562842, 0.06023484310278689, 0.06695669026482118, 0.052955606065194824, 0.22471592359483356, 0.1454469106336669, 0.20469425084343787]), (9400, [0.08721918227054747, 0.06473132380325528, 0.08486839965086998, 0.09407616175742142, 0.041428472734535815, 0.08814499522076694, 0.08778221415405615, 0.10407045482781836, 0.14004651647947067, 0.20763227910125837]), (9600, [0.08105845182352978, 0.054622624230315706, 0.06670665799428234, 0.04711893322455495, 0.05569198397893214, 0.06979364900578465, 0.07618223380655559, 0.1355813067013079, 0.15057282871051897, 0.26267133052421876]), (9800, [0.04842860158379152, 0.03823878476687396, 0.04026421261873044, 0.033743225247951564, 0.038946758766686615, 0.026440431828922174, 0.059346230920981645, 0.23984901417071658, 0.14909904926940842, 0.32564369082593736]), (10000, [0.06392584570315127, 0.054426163465085844, 0.05127928797665267, 0.07603385943347883, 0.0468244365828735, 0.06935852421178502, 0.04931897111692838, 0.10687024483437939, 0.11854914524996987, 0.36341352142569616])]
+
+averages = []
+
+for agent in range(10):
+ average = np.round(np.mean([i[1][agent] for i in list]),3)
+ print(str(agent) + " " + str(average))