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AnomalyDetectorFunction[…]

represents a function generated by AnomalyDetection for detecting whether data is anomalous or not.

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AnomalyDetectorFunction

AnomalyDetectorFunction[…]

represents a function generated by AnomalyDetection for detecting whether data is anomalous or not.

Details and Options

Examples

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Basic Examples  (2)

Train a detector function on a numeric dataset:

Wolfram Language code: ad = AnomalyDetection[{1.2, 2.5, 3.2, 4.6, 5.6, 7, 8, 9, 8.3}]

Use the trained detector to find examples that are considered anomalous:

Wolfram Language code: ad[{5, 6, 11, 100}]

Find rarer probabilities of new examples:

Wolfram Language code: ad[{5, 6, 11, 100}, "RarerProbability"]

Train an AnomalyDetectorFunction on a two-dimensional array of pseudorandom real numbers:

Wolfram Language code: ad = AnomalyDetection[RandomReal[1, {20, 2}]]

Use the trained AnomalyDetectorFunction to find anomalies in new examples with FindAnomalies:

Wolfram Language code: FindAnomalies[ad, {{5, 0.6}, {0.3, 0.5}, {0.1, 0.2}}]

Find anomalies and their corresponding positions:

Wolfram Language code: FindAnomalies[ad, {{0.8, 0.7}, {5, 0.6}, {0.3, 0.5}, {0.1, 0.2}}, {"Anomalies", "AnomalyPositions"}]

Scope  (1)

Train an AnomalyDetectorFunction on a list of colors:

Wolfram Language code: ad = AnomalyDetection[{RGBColor[0.34423361016735393, 0.9949706023751553, 0.9913428099254031], RGBColor[0.974441953220345, 0.5882988960563109, 1.0068782254084538], RGBColor[0.6919394636833026, 0.9725016422639512, 1.0005784902145627], RGBColor[0.6609415139121996, 0.5317017233919189, 0.9984727629540214], RGBColor[1.030082196239495, 0.15562868152451315, 1.0135215761762417], RGBColor[0.7139636743692107, 0.5910083012886909, 0.9948556194109465], RGBColor[0.6236652587743242, 0.6870661704360941, 1.0022299478695442], RGBColor[0.39687500809516707, 0.5108643112778942, 0.9952467598808223], RGBColor[0.7443884965650738, 0.4349634688965386, 1.0003462995235857], RGBColor[0.6288110830431992, 0.5724921773022011, 1.0022472231639297], RGBColor[0.5632946778321253, 0.7631572865298704, 1.00798403643781], RGBColor[0.6773040387676239, 0.6976386941501098, 1.0022055898686073], RGBColor[0.8457795253477449, 0.7398849813050531, 1.005573228966982], RGBColor[1.0205520113446505, 0.690968271602499, 1.0035737791209818], RGBColor[0.7549242858326427, 0.6235127402960372, 0.9944743070141697], RGBColor[0.6834128429576036, 0.39958492739867546, 1.0026226416576371], RGBColor[0.5501400976778553, 0.7625174064725535, 0.9977464842503022], RGBColor[0.2615551876290124, 0.765008680557518, 0.9942818885148523], RGBColor[0.6189412061989856, 0.6852860381116407, 0.9997035911561294], RGBColor[0.7138254755324788, 0.6853604572170703, 1.0029509153000369], RGBColor[0.4043093843454192, 0.6125541754696873, 0.9999118021859527], RGBColor[0.8205010700454642, 0.7806771392383814, 1.00562852909626], RGBColor[0.812290231487381, 0.5331790321181455, 1.0009593659644767], RGBColor[0.8437915419001423, 0.24940229677216713, 1.006288621546154], RGBColor[0.7195507678442272, 0.8579676385844572, 1.0076774216805127], RGBColor[0.7235256726749637, 0.04660266162004301, 1.002355100767978], RGBColor[0.5049286226975376, 0.5589372943296006, 0.9995003905321003], RGBColor[0.7902098896284458, 0.6116626144639822, 0.9990427737864], RGBColor[0.6442706429715445, 0.9072560478512116, 1.000385767571579], RGBColor[0.567894775541235, 0.5752444998384344, 1.001663707952634], RGBColor[0.7090487279645562, 0.5421179945569079, 1.001712577867206], RGBColor[0.5354836215932339, 0.6106357574940795, 0.993204356795479], RGBColor[0.5030637654134358, 0.4669355559078886, 0.9963828836620333], RGBColor[0.7415981367902569, 0.277727705996566, 0.9989098811714942], RGBColor[0.8203464087906245, 1.0704830725870433, 0.9977753741130557], RGBColor[0.757424602054376, 0.8870888154019599, 1.0026308777744768], RGBColor[1.044681726621579, 0.43148260079970435, 1.0000554098379415], RGBColor[0.7123234424159927, 0.7660133438966698, 0.9939497163541979], RGBColor[0.8613007435666715, 0.6948619856745523, 1.001530354102207], RGBColor[0.41181251755343673, 0.9672315750613055, 0.9944842976740979], RGBColor[0.5453938214145515, 0.5240998539872904, 0.9988752883080638], RGBColor[0.41015258996245973, 0.6605139315627333, 0.9963386834508943], RGBColor[0.6381328222903362, 0.6263605273048086, 0.9986231684853986], RGBColor[0.7175060009642432, 0.4181868220413135, 0.9952981757098316], RGBColor[0.6561208785194351, 0.8052553301188847, 1.0044150292740501], RGBColor[0.6801324193509629, 0.6860611673374102, 1.0063709449623603], RGBColor[0.48439775318648953, 1.0445503544672912, 0.995164617560423], RGBColor[0.7504260932976325, 1.2526284300521322, 0.9974906868890978], RGBColor[0.758986473236708, 0.6980863906805845, 0.9905624502205769], RGBColor[0.68653014676303, 0.3987433510251484, 1.0101914043475726], RGBColor[0.7258934369166311, 0.839312286794006, 1.0017190709090071], RGBColor[0.6767174761974676, 0.5887816929279261, 0.9967239999218663], RGBColor[0.495023238613132, 0.7849304242049479, 1.0003358461174956], RGBColor[0.6211007158802914, 0.7092442266431815, 0.9969717261106543], RGBColor[0.47474787148341835, 0.8183897812391591, 0.9894645947886548], RGBColor[1.1455719220773122, 0.5517697342804933, 1.0097752283765262], RGBColor[0.7345871487081479, 0.6663797125171111, 0.9939592574893115], RGBColor[0.2759607351913042, 0.20891869281293557, 0.9987113275242301], RGBColor[0.5249977054607774, 0.8776246823905124, 0.9998731789999219], RGBColor[0.7234140693542443, 0.6648642463332401, 0.9925799746288244], RGBColor[0.4508787001289919, 0.8402095981476847, 0.9904182728404552], RGBColor[0.7240651441388569, 0.9354306986341794, 1.0003509407564144], RGBColor[0.49381361328171114, 0.8466232420511408, 0.9950118848626659], RGBColor[0.5732848248526256, 0.6045762183258268, 0.9987556290351441], RGBColor[0.4916601726869988, 0.46374455643463786, 1.0041987233871592], RGBColor[0.9058580136974359, 0.6931994481820869, 1.0015707887617427], RGBColor[0.8208441638435511, 0.3148647305027396, 1.000675398083302], RGBColor[0.8480087279269137, 0.48876093804105386, 1.0048303602991822], RGBColor[0.6235206218788477, 0.6964248242748691, 1.0022249395243656], RGBColor[0.5005490046162244, 0.7286296126977386, 0.9979758094120281], RGBColor[0.956882935321649, 0.6333171909243984, 1.0023047047916631], RGBColor[0.82864340742 ... 9272649], RGBColor[0.8627518311306448, 0.0794264949802388, 1.0002723544629901], RGBColor[0.6914563210496609, 0.9597900303860196, 1.0027003789563933], RGBColor[0.47151759679847793, 0.6703810557168081, 0.9936579983346822], RGBColor[0.28644198136082055, 0.8084736876073915, 0.994779633532951], RGBColor[1.247808497378954, 0.20814656783371066, 1.005363642300721], RGBColor[0.8910488167137816, 0.29066194421125463, 1.0071152523979605], RGBColor[0.4108215503422852, 0.6763153894909187, 1.0022850061898758], RGBColor[0.6032059481765558, 0.851239218329519, 1.0032178496930786], RGBColor[0.5624507815658685, 0.5929363506963986, 1.0021290242411665], RGBColor[0.3905583278155051, 0.5915402428465296, 0.9925865664945029], RGBColor[0.2951000915320075, 0.9352243432386041, 0.9955043799002488], RGBColor[0.8891141436712875, 0.6447199381306787, 1.0068159967304242], RGBColor[0.28950245718518364, 0.5700444001184012, 0.999261094342013], RGBColor[0.44758759117758345, 0.5686921520482265, 0.9958695857484522], RGBColor[0.7591965541685731, 0.8568508974291336, 1.0028092759699814], RGBColor[0.6236344925090667, 0.4824053762946353, 1.0002773461009513], RGBColor[0.7408545254024963, 0.4528899451570373, 1.0019655567961037], RGBColor[0.7830147685728901, 0.9200780202949879, 0.9998502105724043], RGBColor[0.37916507157886625, 0.7498847335479669, 0.9923470080089104], RGBColor[0.7872647806451795, 0.38484116213136405, 0.9981402312625489], RGBColor[0.7316779335859452, 0.6261357916619933, 0.9959633386629378], RGBColor[0.564873609889761, 0.5380472260782597, 1.0039101521302316], RGBColor[0.8463209258052469, 0.2960669667189687, 0.9979044114987232], RGBColor[0.6665145129050796, 0.7172754114784181, 0.9955123897280956], RGBColor[0.7763623320333368, 0.5625495927644992, 1.0019204423891395], RGBColor[0.6352480964210127, 0.32527126925838057, 1.0035008637074296], RGBColor[0.9020136283955144, 0.6090213717980114, 1.0009101044133322], RGBColor[0.35179616385345197, 0.8873458187461462, 0.9943398446119561], RGBColor[0.6892481471309895, 0.7744224337307276, 0.9968957963300527], RGBColor[0.881777607028809, 0.28361895706387896, 1.0018137618822816], RGBColor[1.157450430970441, 0.1612042870171504, 1.0073340454635225], RGBColor[0.58833725060706, 0.8449924253878058, 0.9983715783218108], RGBColor[0.6191109730835055, 0.6775103986947937, 0.9949238484663862], RGBColor[0.7686934120958153, 0.7217689419082524, 0.996245549712363], RGBColor[1.0707153454415779, 0.38934552676857115, 1.0061105031044426], RGBColor[0.4257513272330986, 1.0566489722356325, 0.9924773502391904], RGBColor[0.7242224636275749, 0.6570073428675383, 0.9986005675093127], RGBColor[0.7438174356063888, 0.617198235612156, 0.9995822700402942], RGBColor[0.8630763450328225, 0.19018510514491999, 1.0072262681987556], RGBColor[0.5498309264384517, 0.5759523884599071, 0.9984433057481402], RGBColor[0.5580772280281927, 0.7827387076975386, 0.9921445191296031], RGBColor[0.5388185556361564, 0.6315463683002376, 0.9945446192822313], RGBColor[0.9092345217986617, 0.8178777007876094, 1.0082161475701457], RGBColor[0.6798268948534387, 0.9206455250817887, 0.9960592452202999], RGBColor[0.35817470590500133, 1.0319804703876905, 0.9962531895534059], RGBColor[0.4243082504253557, 0.8629909259478613, 0.9893894707275183], RGBColor[0.6893625455483994, 0.7282867546233841, 0.9995391420394324], RGBColor[0.8492549370744441, 0.4293920924003687, 1.001183247096189], RGBColor[0.9228050292581644, 0.5846748916660687, 1.0073887470371234], RGBColor[0.7710987660489688, 0.5139598451199441, 1.0045375564342613], RGBColor[0.26446220648498037, 1.3857675896295196, 0.9847082217180225], RGBColor[0.5977361225294263, 0.6855863458138487, 0.9985978114138284], RGBColor[0.6556661278732271, 0.8158702771438023, 0.993322923260324], RGBColor[0.5515829348142409, 0.6299540702384048, 0.9961627823888869], RGBColor[0.9113690850189474, 0.7229386146592239, 1.009840553585347], RGBColor[0.8105097994612612, 0.365932931712455, 1.0043040292006629], RGBColor[0.6802336267354607, 0.638936773009825, 1.0004422669075017], RGBColor[0.6466466017372586, 0.34063068295261795, 1.001895695310875], RGBColor[0.34202256370318235, 0.26768956801319244, 1.0015339776108454], RGBColor[0.5849023017175482, 0.5707330850816604, 0.9965948391799168], RGBColor[0.5923501432235162, 0.780365029033844, 0.9923633927991188], RGBColor[0.6020893847553299, 0.8955500346685753, 0.9900602565416496], RGBColor[0.4014655775892002, 0.7530375151576032, 1.0023764444423535], RGBColor[0.6948987347321366, 0.3798050222617043, 1.010216846343959], RGBColor[0.7053547826953562, 1.10911275911845, 1.0009824591202139], RGBColor[0.6532425293189801, 0.8514070086152319, 0.9999438607498327], RGBColor[0.7628789845612727, 0.5511297681637599, 0.9928137690353301], RGBColor[0.7779887292228563, 0.5377213936445591, 1.0007558812680644], RGBColor[0.47108617035959904, 1.2887392515479092, 0.9910866448506571], RGBColor[0.6030534147511111, 0.8340466905076884, 0.9881370811088525], RGBColor[0.38958968973612385, 0.9823046094943719, 0.9881878339326584], RGBColor[1, 0, 0]}]

Attempt to find outliers in a list of colors using the trained anomaly detector:

Wolfram Language code: ad[{RGBColor[1, 0, 0], RGBColor[0, 0, 1], RGBColor[0, 1, 0], RGBColor[0.6489069067250937, 0.5755756573803703, 1.003672353132791], RGBColor[0.7057443474984795, 0.8378739475634039, 0.994666604840032], RGBColor[0.8514932374154848, 0.6577551436509919, 1.0040492460246024], RGBColor[0.7659083378262406, 0.3515834404371405, 1.0034001669125583], RGBColor[0.6507402669657356, 0.755721701773461, 1.0021331605458152], RGBColor[0.7423441703196316, 0.7817619172473703, 1.0057619503122168], RGBColor[0.6266268813681602, 0.6297653726914617, 0.9997898817416094], RGBColor[0.7569527399301882, 0.6816489606911452, 0.9947672162483416], RGBColor[0.6554483765238713, 0.40057368425245776, 1.004481079970983], RGBColor[0.4768853043963855, 0.5927113842554544, 0.9989095211404951], RGBColor[0.7092377211322144, 0.9854091931950826, 1.0036363899562788], RGBColor[0.6695176003467702, 0.5477660604245632, 0.9988405619087125], RGBColor[0.3929643278490828, 0.6511568040964537, 0.9912589973644573], RGBColor[0.770688916213984, 0.8810088927042221, 1.0054877076630708], RGBColor[0.7649991624963735, 0.44806190154436465, 1.000062718345653], RGBColor[0.6498745893581738, 0.6377533151410597, 0.9942373350867331], RGBColor[0.684671816883397, 0.6450952259271742, 0.9996307705130395], RGBColor[0.8217993634264111, 0.5548900766689218, 1.0027475551791536], RGBColor[0.5011391747697476, 0.9551810292863081, 1.0012714675653138], RGBColor[0.7115065630502229, 0.5749466239018227, 0.9981767404305243], RGBColor[0.5126966915311191, 0.5878379904677047, 1.0042594359046022], RGBColor[0.7046967873331059, 0.5293353682933218, 0.9999334590140067], RGBColor[0.4926331760416396, 0.7530695229780565, 1.0001829034222909], RGBColor[0.8555058768935649, 0.5640049330472888, 1.0069230058887315], RGBColor[0.4910760147881812, 0.5804793980307775, 1.0058540115102932], RGBColor[0.5454883994263123, 0.6650926223195235, 1.0053286971713007], RGBColor[0.8532886060980814, 0.8021650005975375, 0.9953950569168204], RGBColor[0.7070746291172416, 0.7765272968960811, 0.9985966093273773], RGBColor[0.7015106973990245, 0.8211871735201386, 0.9963153787588797], RGBColor[0.44569478954203, 0.7419787584255451, 0.9898981710789684], RGBColor[0.7411728831330745, 0.7847098598680122, 0.9928200648120465], RGBColor[0.6764805255850147, 0.7432648958454903, 1.001277370463186], RGBColor[0.7045562925666643, 0.9483563196436429, 0.9958421147410664], RGBColor[0.8147038799415312, 0.6344891142586896, 1.0038814001490544], RGBColor[0.6813372654360339, 0.8198472586918514, 0.9963925120493498], RGBColor[0.6708813434747505, 0.33411265499158055, 0.9994792518245261], RGBColor[0.5455185452371998, 0.8472373302374782, 0.9952777829391884], RGBColor[0.8368811132992047, 0.9231368436930435, 1.0003466469891626]}]

Obtain information on the trained anomaly detector:

Wolfram Language code: Information[ad]

Obtain information on training time:

Wolfram Language code: Information[ad, "TrainingTime"]

Find the training method:

Wolfram Language code: Information[ad, "Method"]

Options  (1)

AcceptanceThreshold  (1)

Create a dataset sampled from two different distributions:

Wolfram Language code: data = Join[RandomVariate[NormalDistribution[-1, 0.5], 70], RandomVariate[ChiDistribution[6], 2]]

Train an anomaly detector function:

Wolfram Language code: ad = AnomalyDetection[data]

Find anomalous/non-anomalous examples by specifying the AcceptanceThreshold:

Wolfram Language code: ad[data, AcceptanceThreshold -> .00001]
Wolfram Research (2019), AnomalyDetectorFunction, Wolfram Language function, https://reference.wolfram.com/language/ref/AnomalyDetectorFunction.html.

Text

Wolfram Research (2019), AnomalyDetectorFunction, Wolfram Language function, https://reference.wolfram.com/language/ref/AnomalyDetectorFunction.html.

CMS

Wolfram Language. 2019. "AnomalyDetectorFunction." Wolfram Language & System Documentation Center. Wolfram Research. https://reference.wolfram.com/language/ref/AnomalyDetectorFunction.html.

APA

Wolfram Language. (2019). AnomalyDetectorFunction. Wolfram Language & System Documentation Center. Retrieved from https://reference.wolfram.com/language/ref/AnomalyDetectorFunction.html

BibTeX

@misc{reference.wolfram_2026_anomalydetectorfunction, author="Wolfram Research", title="{AnomalyDetectorFunction}", year="2019", howpublished="\url{https://reference.wolfram.com/language/ref/AnomalyDetectorFunction.html}", note=[Accessed: 13-June-2026]}

BibLaTeX

@online{reference.wolfram_2026_anomalydetectorfunction, organization={Wolfram Research}, title={AnomalyDetectorFunction}, year={2019}, url={https://reference.wolfram.com/language/ref/AnomalyDetectorFunction.html}, note=[Accessed: 13-June-2026]}