diff --git a/rgbAI/cost.txt b/rgbAI/cost.txt new file mode 100644 index 0000000..c0150ce --- /dev/null +++ b/rgbAI/cost.txt @@ -0,0 +1,100000 @@ +Cost: 0.27058570813026556 | [0.8835998 0.86530647 0.44381226] [0.80731057 0.70953723 0.6190742 ] +Cost: 0.1962753649306547 | [0.97514794 0.37501698 0.67340399] [0.75847994 0.64630063 0.55056429] +Cost: 0.08754776889513714 | [0.6726639 0.14174187 0.29955485] [0.71502189 0.59276247 0.49585999] +Cost: 0.14339123453363736 | [0.81154562 0.58415379 0.84672989] [0.71689646 0.59607404 0.49742211] +Cost: 0.1435248092577325 | [0.88582309 0.56439647 0.21664987] [0.66186582 0.53246063 0.43516916] +Cost: 0.06997885477704893 | [0.23395566 0.87122513 0.80590879] [0.64242286 0.5117464 0.41315079] +Cost: 0.05867758740590185 | [0.17397269 0.82426464 0.81623863] [0.61020998 0.47725297 0.3800845 ] +Cost: 0.1252822653858554 | [0.65292881 0.08099647 0.95909296] [0.58875722 0.45404289 0.35909921] +Cost: 0.18440550712599513 | [0.54211707 0.05178995 0.11268754] [0.5812053 0.44566849 0.35302779] +Cost: 0.02884726167821099 | [0.18871805 0.72346032 0.68695023] [0.61793037 0.48515224 0.38813471] +Cost: 0.03255302454273159 | [0.66077853 0.40080093 0.52146019] [0.60592342 0.47206038 0.37673167] +Cost: 0.18465166655264983 | [0.98419467 0.55542922 0.16669188] [0.59778456 0.463715 0.36974726] +Cost: 0.059806829977141346 | [0.16919055 0.87992163 0.54639673] [0.58295715 0.44902782 0.35469936] +Cost: 0.08719219643164555 | [0.77120585 0.3822669 0.9840601 ] [0.57574733 0.44139636 0.34776128] +Cost: 0.04271017102943673 | [0.43122864 0.93575735 0.60123827] [0.54675438 0.41308433 0.3215915 ] +Cost: 0.13048959779626282 | [0.09437938 0.42196921 0.23055262] [0.5281764 0.39393061 0.30564594] +Cost: 0.12524000869706375 | [0.82425969 0.96678957 0.90774905] [0.56743586 0.43386087 0.34079439] +Cost: 0.07632164032244482 | [0.43852721 0.71289256 0.24147193] [0.51058022 0.37783942 0.29147071] +Cost: 0.04172001329711495 | [0.27180371 0.60688435 0.987271 ] [0.52053127 0.38749991 0.29909196] +Cost: 0.0948945415632712 | [0.82049316 0.7039891 0.29847938] [0.51501674 0.38215318 0.29569278] +Cost: 0.018136448237183574 | [0.45310187 0.67670932 0.48907801] [0.50472056 0.37245948 0.2868398 ] +Cost: 0.16843084528532656 | [0.90653532 0.0547497 0.62087156] [0.50786138 0.37466368 0.28969228] +Cost: 0.05260044554653751 | [0.88282479 0.63827861 0.73745602] [0.51274559 0.38021309 0.29390422] +Cost: 0.016880534992108013 | [0.62073765 0.59000033 0.53735333] [0.48669666 0.35572047 0.27250661] +Cost: 0.08119443571802624 | [0.88750435 0.33586416 0.63609747] [0.48607115 0.35491455 0.27221364] +Cost: 0.2021829334850036 | [0.14450814 0.47294827 0.06977705] [0.48241472 0.35116919 0.26959629] +Cost: 0.06965682090782443 | [0.73578045 0.96456507 0.57593267] [0.52464381 0.39186124 0.305282 ] +Cost: 0.03811417333315034 | [0.32386758 0.48134806 0.47978439] [0.49529125 0.36336808 0.28037 ] +Cost: 0.025568504858836664 | [0.53190643 0.87980326 0.62482807] [0.51409964 0.38172652 0.29648819] +Cost: 0.04341326674348733 | [0.32349481 0.34854736 0.58647636] [0.49831466 0.36602733 0.28306166] +Cost: 0.1971200317263565 | [0.87610323 0.81164882 0.06856887] [0.51964315 0.38662402 0.30259667] +Cost: 0.051945002631709904 | [0.8272229 0.59430501 0.91089307] [0.50945816 0.37720833 0.29323771] +Cost: 0.03064843692559946 | [0.34874705 0.58367602 0.48288168] [0.48399345 0.35316947 0.27220797] +Cost: 0.03239004673824654 | [0.73000295 0.4261062 0.76128499] [0.49786694 0.36601661 0.28379857] +Cost: 0.12813729125007364 | [0.67200411 0.74752135 0.13225039] [0.49157092 0.36026378 0.27950013] +Cost: 0.16682670523571275 | [0.07712705 0.12338114 0.48799647] [0.49507585 0.36248523 0.28149027] +Cost: 0.04102952484131014 | [0.25573684 0.79465516 0.88264052] [0.5389255 0.40552386 0.31948167] +Cost: 0.10270848012871385 | [0.19299085 0.72810927 0.22621312] [0.52743536 0.3938969 0.31002139] +Cost: 0.06923095858760826 | [0.477653 0.15252807 0.54343782] [0.54613734 0.41133166 0.32652925] +Cost: 0.0618496631560995 | [0.04609876 0.67594278 0.50591508] [0.56459107 0.43028321 0.34341103] +Cost: 0.2168300052620353 | [0.98166845 0.49005693 0.06539164] [0.57737342 0.44284278 0.35714861] +Cost: 0.13885125141729096 | [0.04259857 0.16358058 0.98132865] [0.5732507 0.43834034 0.35110933] +Cost: 0.14887686348171694 | [0.98202677 0.76765981 0.36744262] [0.5899279 0.45626626 0.36920296] +Cost: 0.15247169560361576 | [0.70470065 0.53324834 0.03758467] [0.56029488 0.42571344 0.34152217] +Cost: 0.16054490293290827 | [0.20673531 0.98867188 0.14886942] [0.56847621 0.43454708 0.34914396] +Cost: 0.03465511776478749 | [0.33928715 0.48526692 0.34605907] [0.57288123 0.43831905 0.35325529] +Cost: 0.14402355510734194 | [0.9194072 0.13060604 0.58998105] [0.58598258 0.45138548 0.36619263] +Cost: 0.10222425266064673 | [0.73143163 0.2410443 0.3053385 ] [0.58041598 0.4456442 0.36110531] +Cost: 0.12216543557563038 | [0.14581297 0.44180845 0.09677206] [0.58886609 0.45422789 0.36909999] +Cost: 0.019240459111976017 | [0.16685123 0.56356216 0.69769829] [0.61857371 0.48583322 0.39842152] +Cost: 0.08071456215379025 | [0.12233894 0.36499933 0.99288517] [0.61637783 0.4833151 0.39552988] +Cost: 0.09004999385047495 | [0.30930899 0.48700297 0.09326159] [0.61349434 0.47995875 0.39380651] +Cost: 0.08771380415878895 | [0.82973616 0.36434389 0.76420408] [0.63217801 0.50030335 0.41315527] +Cost: 0.2161361299501955 | [0.95856271 0.86941316 0.15278729] [0.6074008 0.47444767 0.3891151 ] +Cost: 0.06051954817533289 | [0.59469597 0.19314015 0.49194185] [0.58187675 0.44715127 0.36317731] +Cost: 0.08914793260545988 | [0.57522863 0.05834246 0.56441804] [0.5908196 0.45607663 0.37168391] +Cost: 0.1741281106067084 | [0.21355864 0.94007543 0.04930598] [0.60247087 0.46918374 0.3840847 ] +Cost: 0.0752772358971292 | [0.18893052 0.23057471 0.92416433] [0.60836974 0.47466791 0.38866566] +Cost: 0.053426445091809244 | [0.24619726 0.87063667 0.47398576] [0.61239868 0.47976194 0.39370762] +Cost: 0.17514770001296023 | [0.90743376 0.02615868 0.51659833] [0.60294963 0.46869142 0.38453071] +Cost: 0.06917341381957133 | [0.73167158 0.26865907 0.45157692] [0.60336596 0.4694482 0.38507596] +Cost: 0.056727719942328426 | [0.791288 0.47637495 0.72590837] [0.60248826 0.46910777 0.38432451] +Cost: 0.12906594420028847 | [0.13860612 0.98662993 0.98476835] [0.58011998 0.44691019 0.36182333] +Cost: 0.06985664210938282 | [0.43072657 0.989049 0.45988582] [0.55591676 0.42240711 0.33987582] +Cost: 0.10398521942935442 | [0.53271229 0.64658234 0.1214189 ] [0.54213723 0.40816334 0.32780336] +Cost: 0.0901620877387372 | [0.06038812 0.27027281 0.5207668 ] [0.55079464 0.41604624 0.33462449] +Cost: 0.13945324610887871 | [0.59470709 0.50640284 0.01564688] [0.57959222 0.44511864 0.36336253] +Cost: 0.12978612702344458 | [0.90335188 0.4153501 0.98381906] [0.59140055 0.45780385 0.37463385] +Cost: 0.030358843981483297 | [0.40937435 0.87340937 0.67020688] [0.5599956 0.42645511 0.34461283] +Cost: 0.14848932805686013 | [0.276788 0.48887774 0.03395117] [0.54478949 0.41036248 0.33084739] +Cost: 0.039347560688175624 | [0.15927795 0.64250297 0.84256198] [0.57159163 0.43771334 0.35536943] +Cost: 0.06254983190236457 | [0.65782841 0.93409797 0.6785685 ] [0.56775836 0.43437862 0.35281783] +Cost: 0.05819432003160704 | [0.54772478 0.31967731 0.37213418] [0.53894041 0.4047419 0.32599278] +Cost: 0.01068087283026646 | [0.44513799 0.69383979 0.52223467] [0.55189261 0.41811274 0.33812138] +Cost: 0.025595014810090216 | [0.72031913 0.57681717 0.73370346] [0.54802408 0.41435761 0.33484638] +Cost: 0.10954928860669051 | [0.00566772 0.41462819 0.39753875] [0.53215803 0.39814717 0.31963663] +Cost: 0.019435288259699625 | [0.21929225 0.61192906 0.74656212] [0.56174865 0.42781519 0.34715645] +Cost: 0.03334629468028575 | [0.50329422 0.77101578 0.89000555] [0.56135911 0.42788067 0.34726862] +Cost: 0.107188116499611 | [0.69434637 0.30472026 0.24473436] [0.5396887 0.40544764 0.32772699] +Cost: 0.09185521114590423 | [0.7143309 0.53361143 0.21089411] [0.55141079 0.41729434 0.33906506] +Cost: 0.021421891915879555 | [0.69831172 0.59340141 0.68668123] [0.55335846 0.41963981 0.34084554] +Cost: 0.09432545427025567 | [0.47709415 0.18863076 0.3302732 ] [0.53900565 0.40457312 0.32733218] +Cost: 0.17212914504041277 | [0.34746395 0.14453157 0.08425524] [0.56059204 0.42544993 0.34739818] +Cost: 0.05185247730855588 | [0.07030615 0.36561132 0.51042209] [0.59381227 0.45966115 0.3790732 ] +Cost: 0.034519391202132994 | [0.14316471 0.68540677 0.73061254] [0.61199023 0.47936161 0.39768573] +Cost: 0.1252098789466603 | [0.1323578 0.06504392 0.30448902] [0.60611537 0.47170235 0.39116031] +Cost: 0.013103174000437952 | [0.38542846 0.38900751 0.74185452] [0.63865194 0.50739175 0.42546715] +Cost: 0.05290696219973639 | [0.75002074 0.5559696 0.68145227] [0.63148288 0.50004946 0.41873623] +Cost: 0.030009402202299182 | [0.14018726 0.37563082 0.54006565] [0.60827744 0.47476658 0.3937735 ] +Cost: 0.11534199163126631 | [0.63460196 0.12998458 0.96036178] [0.62101649 0.48826156 0.40699251] +Cost: 0.0494180515407329 | [0.75629754 0.63208989 0.65014112] [0.60991998 0.47726017 0.39656485] +Cost: 0.015338937660339341 | [0.2634306 0.68631733 0.56280813] [0.58672231 0.45310503 0.37297296] +Cost: 0.08272237728347813 | [0.37595599 0.74782216 0.17305914] [0.58493263 0.45115945 0.37192026] +Cost: 0.20199783722785092 | [0.81795631 0.77739808 0.00142077] [0.58925797 0.45566311 0.37740894] +Cost: 0.06307348255814338 | [0.81023569 0.55934911 0.81883604] [0.58171899 0.44819979 0.36967123] +Cost: 0.14556263347210166 | [0.95297813 0.61727809 0.23510329] [0.55787405 0.42401 0.34774776] +Cost: 0.06391096650151952 | [0.68105521 0.84207006 0.93542246] [0.5468182 0.41385274 0.33717637] +Cost: 0.2078148364931706 | [0.79543562 0.22986533 0.07453065] [0.51848306 0.38501406 0.31180683] +Cost: 0.17849724628314878 | [0.81191603 0.58358944 0.02850042] [0.53467954 0.4011175 0.32719424] +Cost: 0.17147685199580553 | [0.08229046 0.00851712 0.51955452] [0.53747113 0.40280297 0.32778486] +Cost: 0.1431556051283642 | [0.90709527 0.12160077 0.71350607] [0.57222291 0.43790876 0.36191965] +Cost: 0.020037352251785063 | [0.27127202 0.52181615 0.46715286] [0.56591697 0.43185341 0.35548276] +Cost: 0.06293652018886438 | [0.53353525 0.97290881 0.5628104 ] [0.5747902 0.44166987 0.36508084] +Cost: 0.15387297622474663 | [0.39175497 0.05805945 0.22283287] [0.55506266 0.42010131 0.34534034] +Cost: 0.13680709626556 | [0.77479825 0.97397404 0.30628031] [0.58363162 0.45055582 0.37477813] +Cost: 0.031677992540857146 | [0.13650644 0.55426687 0.71140931] [0.56249361 0.42865799 0.35294476] +Cost: 0.0036751107179847993 | [0.37950034 0.51920694 0.71955291] [0.56808598 0.43429393 0.35865175] +Cost: 0.05577347448127105 | [0.205577 0.82019326 0.86969329] [0.56582412 0.43256967 0.35652314] +Cost: 0.12571719042399931 | [0.90040066 0.9864544 0.59965627] [0.55506865 0.42213004 0.34782679] +Cost: 0.07964340965405022 | [0.26275528 0.18029226 0.58977663] [0.52359693 0.39001798 0.31720962] +Cost: 0.0139724699748671 | [0.56298129 0.68878242 0.51987825] [0.54485253 0.41153674 0.33790592] +Cost: 0.06858904310679975 | [0.3737052 0.99653892 0.47755301] [0.53881507 0.40605274 0.33261976] +Cost: 0.10791216799812797 | [0.77775199 0.12511535 0.6886892 ] [0.53031224 0.39670365 0.32457196] +Cost: 0.09594346215976757 | [0.52376437 0.61732619 0.13911863] [0.53306576 0.3997275 0.32767419] +Cost: 0.10782882935618103 | [0.91384043 0.88049294 0.84300241] [0.54162926 0.4090188 0.33648798] +Cost: 0.08063182673244017 | [0.58883268 0.3351299 0.30664956] [0.50961189 0.37706208 0.30686539] +Cost: 0.21128178261009958 | [0.96623853 0.12586279 0.28121465] [0.52268086 0.38925404 0.31917117] +Cost: 0.165027024764456 | [0.01670774 0.16761727 0.36378414] [0.52922126 0.39518927 0.32354595] +Cost: 0.12717058309493415 | [0.9693259 0.84753745 0.49701272] [0.56307564 0.42997786 0.35791627] +Cost: 0.15725562906889137 | [0.29047323 0.11728063 0.20603306] [0.53801359 0.40365461 0.33261341] +Cost: 0.062282175523644144 | [0.70142583 0.47369579 0.96429318] [0.56729321 0.43388688 0.36143008] +Cost: 0.092364762296568 | [0.156203 0.14911914 0.69513028] [0.55006755 0.4157672 0.34351427] +Cost: 0.10768102951916743 | [0.34006974 0.72746775 0.09958765] [0.5695335 0.43583394 0.36356593] +Cost: 0.0563748904416986 | [0.10393137 0.59991007 0.37129383] [0.5782445 0.44451021 0.37153048] +Cost: 0.1217406626956365 | [0.07983049 0.5988411 0.11295274] [0.59039227 0.45671426 0.38367606] +Cost: 0.08316628586068897 | [0.00679806 0.83937639 0.55339493] [0.60883651 0.47656889 0.40255215] +Cost: 0.05316370129085146 | [0.56499654 0.22061874 0.79032815] [0.60819039 0.47520433 0.40195754] +Cost: 0.03752120691344769 | [0.62785436 0.62287889 0.37803574] [0.60426518 0.47154404 0.39887583] +Cost: 0.14281876598299384 | [0.81138184 0.84551404 0.20060194] [0.5972618 0.46456405 0.39280459] +Cost: 0.08453803316005405 | [0.59256133 0.08845099 0.71722638] [0.58330382 0.44925205 0.37746825] +Cost: 0.13892261555629326 | [0.8657746 0.32002469 0.21720608] [0.58722859 0.45345555 0.3824881 ] +Cost: 0.12154165587327537 | [0.27496469 0.02747056 0.33978061] [0.5886227 0.45413375 0.38222214] +Cost: 0.1952029780262646 | [0.98381564 0.1958187 0.94483566] [0.61279863 0.48035778 0.4084329 ] +Cost: 0.05898002279169678 | [0.20466645 0.19129479 0.4952783 ] [0.59313579 0.4591745 0.38688953] +Cost: 0.14947274691035875 | [0.97390025 0.84221002 0.51938214] [0.6096387 0.47775658 0.40630832] +Cost: 0.08564015662611156 | [0.5434329 0.21412146 0.97992705] [0.58165454 0.44800572 0.37660563] +Cost: 0.10716082913754567 | [0.45766897 0.00674138 0.7576686 ] [0.5763691 0.44204598 0.37073907] +Cost: 0.11498025431014318 | [0.84877443 0.42779448 0.99434016] [0.5862335 0.45316771 0.38187635] +Cost: 0.09328634903440274 | [0.03591292 0.30105829 0.42301975] [0.56425984 0.42997163 0.35868612] +Cost: 0.10215492179125955 | [0.67486443 0.15480588 0.32781663] [0.58713652 0.45309609 0.38221889] +Cost: 0.08740653131975168 | [0.44722556 0.86826232 0.99554683] [0.59606259 0.46384195 0.39181925] +Cost: 0.07546635535841896 | [0.00556431 0.55392638 0.41656634] [0.57125082 0.43745411 0.36608753] +Cost: 0.14151197228056042 | [0.36672578 0.20499591 0.06613952] [0.58671662 0.45242602 0.38158467] +Cost: 0.027047599412100026 | [0.29684501 0.65365422 0.8296813 ] [0.60956085 0.47742102 0.40527022] +Cost: 0.10469341224653435 | [0.13904083 0.33645405 0.1520004 ] [0.59910986 0.46539402 0.39385095] +Cost: 0.015860273865862022 | [0.22011276 0.36673702 0.54135635] [0.62144679 0.48923427 0.41701218] +Cost: 0.1679860607495768 | [0.89707859 0.92694871 0.35289587] [0.62770047 0.49694002 0.42600809] +Cost: 0.20317565211057773 | [0.9156443 0.0898523 0.98357586] [0.60309683 0.47010289 0.3995627 ] +Cost: 0.07714899417918837 | [0.73694661 0.40835408 0.28767678] [0.58904856 0.45557585 0.38573622] +Cost: 0.043346431916872936 | [0.14971847 0.30004622 0.56685366] [0.58943984 0.45570575 0.38473348] +Cost: 0.15058218038692314 | [0.96428262 0.17617949 0.53082356] [0.60250501 0.46936453 0.39946787] +Cost: 0.08108507412862319 | [0.16652818 0.95323564 0.49292226] [0.59627259 0.4638554 0.39294271] +Cost: 0.10843360749825283 | [0.04588046 0.90248609 0.86428492] [0.5905399 0.45807012 0.3869319 ] +Cost: 0.029389373830988868 | [0.36959592 0.41360191 0.3650366 ] [0.58097762 0.44726003 0.3773479 ] +Cost: 0.17505113454630813 | [0.76430229 0.77829344 0.02528334] [0.59015303 0.45716627 0.38813882] +Cost: 0.03115129207349822 | [0.46359809 0.27063752 0.50002723] [0.58529099 0.45157786 0.38207689] +Cost: 0.08884685589307635 | [0.13291259 0.16699347 0.83763979] [0.59235938 0.45868284 0.38807429] +Cost: 0.03328799741366384 | [0.65397223 0.71038569 0.56112787] [0.60250252 0.47018395 0.40041047] +Cost: 0.07430251563518758 | [0.26970619 0.11027295 0.50041581] [0.58843163 0.45438232 0.38444239] +Cost: 0.04390087795892633 | [0.42796331 0.2152892 0.77971204] 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[0.00085853 0.75197471 0.37374822] [0.55974613 0.43056942 0.39877233] +Cost: 0.04132257645289538 | [0.70897304 0.76799426 0.49634696] [0.56156133 0.43237828 0.40055248] +Cost: 0.07894089986519388 | [0.89499442 0.47209248 0.45809784] [0.55992009 0.43074343 0.39895241] +Cost: 0.056040849725146454 | [0.83023652 0.63484478 0.71390056] [0.55891072 0.42973989 0.39797344] +Cost: 0.07718428225882427 | [0.01926025 0.51366348 0.823631 ] [0.55646228 0.42730823 0.39558838] +Cost: 0.13928519804488224 | [0.48706935 0.21096008 0.06907019] [0.55736078 0.42819705 0.39644503] +Cost: 0.09891344277114615 | [0.82436141 0.194097 0.50918695] [0.56066837 0.43148785 0.39967751] +Cost: 0.030508285305205888 | [0.13909011 0.59013471 0.63196693] [0.56084775 0.43166649 0.39984936] +Cost: 0.05444291813543006 | [0.54969791 0.33134029 0.90778392] [0.56170547 0.43252171 0.40069118] +Cost: 0.0503768310928071 | [0.56507172 0.63098883 0.96256351] [0.56084736 0.43166698 0.39985769] +Cost: 0.012047486277568624 | [0.36473237 0.51321206 0.43832744] [0.55847181 0.42930264 0.39753755] +Cost: 0.11763652927218808 | [0.45865496 0.93634458 0.13440787] [0.55952901 0.43035416 0.39856862] +Cost: 0.030663064771486493 | [0.31580968 0.64927184 0.33649386] [0.55966982 0.43049377 0.3987019 ] +Cost: 0.08323754000108326 | [0.11638941 0.29686212 0.86806306] [0.56075173 0.4315705 0.39975392] +Cost: 0.024520439180311975 | [0.49589411 0.47597509 0.35031326] [0.56194279 0.43275764 0.40091677] +Cost: 0.05707731159198515 | [0.76204365 0.81480964 0.6599513 ] [0.56294649 0.43376023 0.40190631] +Cost: 0.12879414872599645 | [0.77557212 0.24252968 0.19203207] [0.56020983 0.43103059 0.39922592] +Cost: 0.07557788455269919 | [0.70374622 0.96102153 0.56174286] [0.56169056 0.43250731 0.40068022] +Cost: 0.12845799040275838 | [0.05045674 0.8974682 0.24737357] [0.55899774 0.42982536 0.39804626] +Cost: 0.08118263021377635 | [0.86656107 0.35538702 0.72559 ] [0.56053146 0.43135212 0.39954857] +Cost: 0.13051952465296426 | [0.0914675 0.24991864 0.19366336] [0.55895194 0.42977602 0.39797836] +Cost: 0.11658565737983416 | [0.46606145 0.02888488 0.35214695] [0.5631731 0.43398327 0.40210585] +Cost: 0.19554268909603323 | [9.06935506e-01 6.60728333e-01 5.94800864e-04] [0.56610307 0.43691584 0.40499105] +Cost: 0.02034945559341661 | [0.40943028 0.58012022 0.84033858] [0.56604242 0.43685552 0.40493393] +Cost: 0.17212693204139948 | [9.49108071e-05 7.87694585e-01 6.96551402e-02] [0.56490797 0.43571813 0.4038042 ] +Cost: 0.10740097382557579 | [0.76120287 0.10710659 0.50640637] [0.56771981 0.438536 0.40657563] +Cost: 0.0394939466537507 | [0.60056731 0.6645051 0.87399404] [0.5684666 0.43928674 0.4073183 ] +Cost: 0.05768263854053731 | [0.18042803 0.37655473 0.32309501] [0.56605913 0.43687 0.40493289] +Cost: 0.09944129496434384 | [0.28813141 0.93831572 0.95978836] [0.56880844 0.43963014 0.40765496] +Cost: 0.08227254381503273 | [0.73897155 0.66783139 0.21716486] [0.56622183 0.43703494 0.40510814] +Cost: 0.025915248679304965 | [0.43638647 0.28743102 0.55450373] [0.56592213 0.43673403 0.40480849] +Cost: 0.16837713879101082 | [0.01445292 0.58943215 0.02037416] [0.56700774 0.43781946 0.40585448] +Cost: 0.0648479314691769 | [0.00727157 0.62116621 0.47849419] [0.57074168 0.44157243 0.40954671] +Cost: 0.027849415767431232 | [0.35151086 0.4699125 0.85375901] [0.57248272 0.44332735 0.41127674] +Cost: 0.1327086070606329 | [0.17217231 0.96231034 0.17872827] [0.57192241 0.44276218 0.41071776] +Cost: 0.021464111318858416 | [0.327097 0.73934857 0.4439003 ] [0.57279241 0.44363954 0.41158134] +Cost: 0.032677037901221805 | [0.59915348 0.52611963 0.84817632] [0.57288605 0.44373444 0.4116789 ] +Cost: 0.07512152230442813 | [0.4521126 0.50257683 0.11918179] [0.57111714 0.44195008 0.40991729] +Cost: 0.06171566251121469 | [0.17171631 0.84515056 0.77914389] [0.57300117 0.44385054 0.41179113] +Cost: 0.1028612316300503 | [0.20180462 0.70778 0.10482257] [0.57190963 0.4427484 0.41069869] +Cost: 0.040873300998404145 | [0.32828111 0.80271584 0.35967515] [0.57399993 0.4448583 0.41277716] +Cost: 0.127815272839814 | [0.46649804 0.33239355 0.01072692] [0.57411731 0.44497445 0.41287807] +Cost: 0.06107550678318319 | [0.27626826 0.82534507 0.87919297] [0.57704542 0.44793963 0.41580723] +Cost: 0.16466423696020713 | [0.07070528 0.95607292 0.13120883] [0.57517431 0.4460446 0.41393631] +Cost: 0.2502415430604115 | [0.95686765 0.03577642 0.138407 ] [0.57661235 0.44749898 0.41536502] +Cost: 0.1709377243904766 | [0.95657535 0.57495904 0.10739718] [0.57824567 0.44915597 0.41699992] +Cost: 0.14792420174840926 | [0.41167245 0.1959122 0.02020041] [0.57775218 0.44865171 0.41648267] +Cost: 0.18786855788441215 | [0.95759057 0.5010695 0.05934669] [0.58136899 0.45232963 0.42011837] +Cost: 0.023794476829781083 | [0.20176818 0.42718865 0.48073505] [0.58136188 0.45232159 0.42010309] +Cost: 0.08678595078482448 | [0.88120888 0.43750262 0.76025834] [0.58296695 0.45395821 0.42172491] +Cost: 0.15779372109845438 | [0.02407775 0.08437343 0.2615558 ] [0.58065147 0.45159333 0.41935907] +Cost: 0.011923159270980768 | [0.49714462 0.44727269 0.43569258] [0.58525982 0.45629799 0.42402066] +Cost: 0.07927902088040457 | [0.69190112 0.19227845 0.76939383] [0.5857453 0.45679466 0.42451297] +Cost: 0.19621742210084644 | [0.07488724 0.10800145 0.04416237] [0.58509776 0.45612431 0.42380783] +Cost: 0.11524344699837429 | [0.34217078 0.84121772 0.07138321] [0.59021569 0.4613766 0.42902152] +Cost: 0.05224266055605877 | [0.49658017 0.67580731 0.2095642 ] [0.59110448 0.4622904 0.42992449] +Cost: 0.03555039928764354 | [0.38019936 0.32993232 0.8203673 ] [0.59150103 0.46269847 0.43032841] +Cost: 0.10434285506574252 | [0.05894253 0.14241037 0.755852 ] [0.59128938 0.46247902 0.43010083] +Cost: 0.028538379832978078 | [0.63462199 0.71879133 0.56863006] [0.5933615 0.46461516 0.43222297] +Cost: 0.14872770869839338 | [0.51415419 0.17615022 0.01768902] [0.59157317 0.46276959 0.43038169] +Cost: 0.05120336009514249 | [0.37558943 0.15674184 0.46456144] [0.59467906 0.46597232 0.43354885] +Cost: 0.0913318991721009 | [0.45397675 0.99718338 0.32976191] [0.59658009 0.46793898 0.43550229] +Cost: 0.18012114210363742 | [0.76716087 0.85829509 0.01497238] [0.59530593 0.4666215 0.43420055] +Cost: 0.03970061622397563 | [0.57705021 0.31459966 0.36285889] [0.59464947 0.4659427 0.43352528] +Cost: 0.11961948729986764 | [0.02805403 0.28623337 0.17042033] [0.59551568 0.46683343 0.43438011] +Cost: 0.09894209942799222 | [0.02309637 0.14680385 0.47157502] [0.59938733 0.47084335 0.43834684] +Cost: 0.06269255129182849 | [0.71825715 0.81234101 0.61351002] [0.60258837 0.47417472 0.44166737] +Cost: 0.04230942427355875 | [0.60425298 0.36112145 0.80029786] [0.59985984 0.4713376 0.43885954] +Cost: 0.07116319790078382 | [0.68661527 0.89091811 0.52020856] [0.59866086 0.47009411 0.43763339] +Cost: 0.08444858047622389 | [0.16211032 0.44457586 0.13236157] [0.59612757 0.46746802 0.43501788] +Cost: 0.01843960913884389 | [0.43022839 0.32992264 0.68290161] [0.59899464 0.47043935 0.43796817] +Cost: 0.0493039440895709 | [0.153876 0.6740595 0.81881129] [0.59906025 0.4705079 0.43803809] +Cost: 0.04810041131675815 | [0.60478527 0.77943769 0.76529311] [0.59826154 0.46968023 0.43722476] +Cost: 0.01856843182611312 | [0.31106247 0.52810154 0.78226124] [0.59553202 0.46685509 0.4344304 ] +Cost: 0.050806536613634885 | [0.42623684 0.87579862 0.37911273] [0.59490448 0.46620701 0.43379155] +Cost: 0.1997420897280088 | [0.05144891 0.93467759 0.00714371] [0.59403738 0.46531079 0.43289593] +Cost: 0.043274035138799266 | [0.67307978 0.7642338 0.50269916] [0.59588723 0.46722256 0.43479638] +Cost: 0.09533848834717647 | [0.69288722 0.28016478 0.93980234] [0.59402855 0.46530298 0.43290149] +Cost: 0.07170683324776621 | [0.62581336 0.2438486 0.85505256] [0.59231748 0.46353911 0.43115975] +Cost: 0.0016861911447053686 | [0.4312125 0.49913528 0.62531896] [0.5913665 0.46256017 0.43019251] +Cost: 0.06996750231335373 | [0.77656359 0.30930399 0.7203156 ] [0.59106964 0.46225506 0.42989441] +Cost: 0.06276581659475002 | [0.30988292 0.64051428 0.98116849] [0.58982534 0.46097652 0.42863468] +Cost: 0.10371938022082043 | [0.0659712 0.94670279 0.73671205] [0.58802414 0.4591283 0.42681176] diff --git a/rgbAI/lib/func.py b/rgbAI/lib/func.py index 0729d45..0ffbfc0 100644 --- a/rgbAI/lib/func.py +++ b/rgbAI/lib/func.py @@ -6,7 +6,7 @@ class AIlib: return 1/(1 + np.exp(-x)) def correctFunc(inp:np.array): # generates the correct answer for the AI - return np.array( [inp[2], inp[1], inp[0]] ) # basically invert the rgb values + return np.asarray( [1.0 - inp[0], 1.0 - inp[1], 1.0 - inp[2]] ) # basically invert the rgb values def calcCost( predicted:np.array, correct:np.array ): # cost function, lower -> good, higher -> bad, bad bot, bad costSum = 0 @@ -87,15 +87,27 @@ class AIlib: obj.weights[i] -= obj.learningrate * gradient[i]["weight"] # mutate the weights obj.bias[i] -= obj.learningrate * gradient[i]["bias"] - def learn( inp:np.array, obj, theta:float ): + def learn( inputNum:int, targetCost:float, obj, curCost: float=None ): # Calculate the derivative for: # Cost in respect to weights # Cost in respect to biases # i.e. : W' = W - lr * gradient (respect to W in layer i) = W - lr*[ dC / dW[i] ... ] # So if we change all the weights with i.e. 0.01 = theta, then we can derive the gradient with math and stuff - maxLen = len(obj.bias) - grads, res, cost = AIlib.gradient( inp, obj, theta, maxLen - 1 ) - AIlib.mutateProps( obj, maxLen, grads ) # mutate the props for next round - print("Cost:", cost, "|", inp, res) + if( not curCost or curCost > targetCost ): # targetCost is the target for the cost function + inp = np.asarray(np.random.rand( 1, inputNum ))[0] + + maxLen = len(obj.bias) + grads, res, curCost = AIlib.gradient( inp, obj, theta, maxLen - 1 ) + AIlib.mutateProps( obj, maxLen, grads ) # mutate the props for next round + print("Cost:", curCost, "|", inp, res) + + return AIlib.learn( inputNum, targetCost, obj, theta, curCost ) + + else: + print("DONE\n") + print(obj.weights) + print(obj.bias) + return + diff --git a/rgbAI/main.py b/rgbAI/main.py index 35bb2b6..f58bc28 100755 --- a/rgbAI/main.py +++ b/rgbAI/main.py @@ -14,9 +14,7 @@ class rgb(object): self.bias = [ ai.genRandomMatrix(1, 4), ai.genRandomMatrix(1, 4), ai.genRandomMatrix(1, 3) ] # This doesn't look very good, but it works so... - self.generation = 0 - - self.learningrate = 0.1 # the learning rate of this ai + self.learningrate = 0.01 # the learning rate of this ai print( self.weights ) print( self.bias ) @@ -30,8 +28,8 @@ class rgb(object): # Cost needs to get to 0, we can figure out this with backpropagation return cost - def learn( self, inp:np.array, theta:float ): - ai.learn( inp, self, theta ) + def learn( self ): + ai.learn( 3, 0.001, self, 0.0001 ) def think( self, inp:np.array ): print("-----Gen " + str(self.generation) + "------") @@ -46,17 +44,8 @@ class rgb(object): print("\n----------------\n\n") return res - def train( self ): - for i in range(self.traintimes): - inpArr = np.asarray(np.random.rand( 1, 3 ))[0] - self.generation = i - self.learn( inpArr, 0.1 ) - def init(): bot = rgb() - - bot.traintimes = 10000 - bot.train() - + bot.learn() init()