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AnomalyDetection[{example1,example2,…}]

根据所给样例产生 AnomalyDetectorFunction[…].

AnomalyDetection[LearnedDistribution[…]]

基于给定分布生成异常检测器.

AnomalyDetection[True{example11,example12,…},False{example21,…}]

可用于表明应将哪些样例视为异常样例.

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按以下格式引用：

AnomalyDetection

AnomalyDetection[{example1,example2,…}]

根据所给样例产生 AnomalyDetectorFunction[…].

AnomalyDetection[LearnedDistribution[…]]

基于给定分布生成异常检测器.

AnomalyDetection[True{example11,example12,…},False{example21,…}]

可用于表明应将哪些样例视为异常样例.

更多信息和选项

范例

打开所有单元 关闭所有单元

基本范例  (2)

在数值型数据集上训练检测器函数：

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

用训练过的检测器找出异常样例：

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

在颜色列表上训练 AnomalyDetectorFunction：

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 ... 345947, 1.0050529949272649], 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]}]

用训练过的检测器找出颜色列表中的异常样例：

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]}]

范围  (8)

通过用 True 标注异常样例，用 False 标注其他样例来训练 AnomalyDetectorFunction：

Wolfram Language code: ad = AnomalyDetection[<|True -> {-100, 200}, False -> {3.3, 4, 5.2, 6, 7, 8, 9, 10, 12}|> ]

以明确列表的形式指定异常点：

Wolfram Language code: AnomalyDetection[{-100, 200, 3.3, 4, 5.2, 6, 7, 8, 9, 10, 12} -> {True, True, False, False, False, False, False, False, False, False, False} ]

用规则列表指定异常情况：

Wolfram Language code: AnomalyDetection[{-100 -> True, 200 -> True, 3.3 -> False, 4 -> False, 5.2 -> False, 6 -> False, 7 -> False, 8 -> False, 9 -> False, 10 -> False, 12 -> False} ]

仅指定异常示例的位置：

Wolfram Language code: AnomalyDetection[{-100, 200, 3.3, 4, 5.2, 6, 7, 8, 9, 10, 12} -> {1, 2} ]

通过指定没有样例为异常样例来训练 AnomalyDetectorFunction：

Wolfram Language code: ad = AnomalyDetection[{3.3, 4, 5.2, 6, 7, 8, 9, 10, 12} -> None]

使用训练好的 AnomalyDetectorFunction 来查找异常样例：

Wolfram Language code: ad[{150, 1, 2, 12, -50}]

在表格数据上训练 AnomalyDetectorFunction：

Wolfram Language code: ad = AnomalyDetection[Tabular[Association["RawSchema" -> Association["ColumnProperties" -> Association["Value" -> Association["ElementType" -> "NumberExpression"]], "KeyColumns" -> None, "Backend" -> "WolframKernel"], "Options" -> {}, "BackendData" -> Association["ColumnData" -> DataStructure["ColumnTable", {{TabularColumn[Association["Data" -> {{-0.7381498640269486, -0.3288040202205629, -0.5152387024806573, -0.6675243566986415, -0.17429727352303326, 0.4959818162388929, 100}, {}, None}, "ElementType" -> "NumberExpression"]]}}]]]] -> {7}]

在新表格上应用检测器：

Wolfram Language code: ad[Tabular[Association["RawSchema" -> Association["ColumnProperties" -> Association["Value" -> Association["ElementType" -> "Integer64"]], "KeyColumns" -> None, "Backend" -> "WolframKernel"], "Options" -> {}, "BackendData" -> Association["ColumnData" -> DataStructure["ColumnTable", {{TabularColumn[Association["Data" -> {{0, 1, 1000}, {}, None}, "ElementType" -> "Integer64"]]}}]]]]]

在伪随机实数组成的二维数组上训练 AnomalyDetectorFunction：

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

用训练过的 AnomalyDetectorFunction 和 FindAnomalies 在新样例中查找异常样例:

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

用训练过的 AnomalyDetectorFunction 查找异常样例及其位置：

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

训练 LearnedDistribution 有关颜色：

Wolfram Language code: ld = LearnDistribution[{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[0.8286434074 ... 345947, 1.0050529949272649], 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]}]

根据训练好的分布生成 AnomalyDetectorFunction：

Wolfram Language code: ad = AnomalyDetection[ld]

使用检测器函数查找超出分布范围的颜色：

Wolfram Language code: ad[{RGBColor[1, 0, 0], RGBColor[0, 1, 0], RGBColor[0.5977361225294263, 0.6855863458138487, 0.9985978114138284], RGBColor[0.6556661278732271, 0.8158702771438023, 0.993322923260324]}]

选项  (12)

AcceptanceThreshold  (1)

用异常样例创建并可视化随机三维向量：

Wolfram Language code: ntrain = RandomReal[1, {1000, 3}]; atrain = RandomReal[{-2, -0.5}, {5, 3}]; train = Join[ntrain, atrain];
Wolfram Language code: ListPointPlot3D[{ntrain, atrain}, ...]

在训练集上训练异常样例检测函数：

Wolfram Language code: ad = AnomalyDetection[train]

用异常样例检测函数查找并可视化测试集中的异常样例：

Wolfram Language code: ntest = RandomReal[1, {1000, 3}]; atest = RandomReal[{-2, -0.5}, {5, 3}]; test = Join[ntest, atest];
Wolfram Language code: ListPointPlot3D[{ntest, FindAnomalies[ad, test]}, Sequence[...]]

通过指定 AcceptanceThreshold 改变异常样例检测伪正类率：

Wolfram Language code: ListPointPlot3D[{ntest, FindAnomalies[ad, test, AcceptanceThreshold -> 0.2]}, Sequence[...]]

FeatureExtractor  (1)

使用图像特征 FeatureExtractor 在猫图像上训练异常检测器：

Wolfram Language code: SeedRandom[1234]; cats = RandomSample[Keys@Select[ ResourceData["CIFAR-10", "TrainingData"], MatchQ[_ -> "cat"]], 500]; ad = AnomalyDetection[cats, FeatureExtractor -> "ImageFeatures"]

在猫和飞机的图片上使用异常检测器：

Wolfram Language code: ad[{[image], [image], [image], [image]}]

这相当于手动训练您自己的 FeatureExtractorFunction，并将其用作预处理器：

Wolfram Language code: fe = FeatureExtraction[cats, "ImageFeatures"]; AnomalyDetection[cats, FeatureExtractor -> fe]@{[image], [image], [image], [image]}

FeatureNames  (1)

训练一个异常检测器并为每一列指定名称：

Wolfram Language code: ad = AnomalyDetection[{{0., "a"}, {2., "b"}, {10., "b"}, {0., "a"}, {7., "a"}, {0., "a"}}, FeatureNames -> {"number", "letter"}, PerformanceGoal -> "Quality"]

使用在具有相同键的关联上训练好的函数：

Wolfram Language code: ad[{<|"number" -> 1, "letter" -> "a"|>, <|"number" -> -5, "letter" -> "a"|>, <|"number" -> 5, "letter" -> "zz"|>}]

FeatureTypes  (1)

在整数上训练异常检测器，但将它们当作类别而非数字处理：

Wolfram Language code: ad = AnomalyDetection[RandomInteger[3, 20], FeatureTypes -> {"Nominal"}]

此时机器数将被识别为异常值：

Wolfram Language code: ad[{1, 1.1, 2}]

Method  (1)

获取训练和测试用图像数据集：

Wolfram Language code: train = RandomSample[ResourceData["CIFAR-10", "TrainingData"][[All, 1]], 10000]; test = RandomSample[ResourceData["CIFAR-10", "TestData"][[All, 1]], 100]; RandomSample[train, 10]

在测试集中添加“分布之外”的样例：

Wolfram Language code: anomalies = {RandomImage[1, {32, 32}, ColorSpace -> "RGB"], RandomImage[1, {32, 32}]} antest = Join[anomalies, test];

用 "Multinormal" 方法训练异常检测器：

Wolfram Language code: ad = AnomalyDetection[train, Method -> "Multinormal"]

在测试集中查找异常样例：

Wolfram Language code: FindAnomalies[ad, antest]

用 "KernelDensityEstimation" 方法训练异常检测器，并尝试找出异常样例：

Wolfram Language code: ad = AnomalyDetection[train, Method -> "KernelDensityEstimation"] FindAnomalies[ad, antest]

PerformanceGoal  (1)

加载 Fisher 鸢尾花数据集及其数值属性：

Wolfram Language code: data = ResourceData["Sample Data: Fisher's Irises"][[All, {"PetalLength", "SepalWidth", "SepalLength"}]];

通过指定 PerformanceGoal 训练异常检测函数：

Wolfram Language code: ad = AnomalyDetection[data, PerformanceGoal -> "Quality", TrainingProgressReporting -> None]
Wolfram Language code: adsp = AnomalyDetection[data, PerformanceGoal -> "Speed", TrainingProgressReporting -> None]

比较不同性能目标情况下异常检测函数的训练时间：

Wolfram Language code: Information[#, "TrainingTime"]& /@ {ad, adsp}

RandomSeeding  (1)

指定训练期间使用的随机种子以获得可重复的结果：

Wolfram Language code: an = AnomalyDetection[{1.2, 2.5, 3.2, 4.6, 5.6, 7, 8, 9, 8.3}, RandomSeeding -> 1234]

在一定的值范围内测试训练好的异常检测器：

Wolfram Language code: testVals = Table[i, {i, -5, 0, 0.25}]; an[testVals]

检查在使用相同的 RandomSeeding 训练一个新的检测器后，你是否得到相同的结果：

Wolfram Language code: % === AnomalyDetection[{1.2, 2.5, 3.2, 4.6, 5.6, 7, 8, 9, 8.3}, RandomSeeding -> 1234]@testVals

TargetDevice  (1)

使用 GPU 训练异常检测器：

Wolfram Language code: AnomalyDetection[RandomReal[10, 10 ^ 5], TargetDevice -> "GPU", Method -> "KernelDensityEstimation"]

TimeGoal  (1)

获取图像数据集，并通过指定时间目标训练异常检测函数：

Wolfram Language code: data = RandomSample[ResourceData["CIFAR-10", "TrainingData"][[All, 1]], 1000]; ad = AnomalyDetection[data, TimeGoal -> 10]

获取异常检测函数的训练时间：

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

TrainingProgressReporting  (1)

获取图像数据集：

Wolfram Language code: data = RandomSample[ResourceData["CIFAR-10", "TrainingData"][[All, 1]], 5000]; RandomSample[data, 10]

在没有图表的情况下交互式地显示训练进程：

Wolfram Language code: AnomalyDetection[data, TrainingProgressReporting -> "SimplePanel"];
Wolfram Language code: [image]

在训练期间定期显示训练进程：

Wolfram Language code: AnomalyDetection[data, TrainingProgressReporting -> "Print"]

显示简单的进程指示：

Wolfram Language code: AnomalyDetection[data, TrainingProgressReporting -> "ProgressIndicator"]

ValidationSet  (1)

在训练期间指定一个测试数据集以验证异常检测器：

Wolfram Language code: train = Keys@ExampleData[{"MachineLearning", "FisherIris"}, "TrainingData"]; test = Keys@ExampleData[{"MachineLearning", "FisherIris"}, "TestData"]; an = AnomalyDetection[train, ValidationSet -> test]

ValidationSet 影响用于检测异常的已训练分布：

Wolfram Language code: Information[an, "MethodOption"]
Wolfram Language code: Information[AnomalyDetection[train], "MethodOption"]

Weights  (1)

为样本指定权重，使训练的检测器对某些特定值更敏感：

Wolfram Language code: AnomalyDetection[{1.2, 2.5, 3.2, 4.6, 5.6, 7, 8, 9, 8.3}, Weights -> {1, 1, 2, 3, 3, 2, 1, 1, 1}]

应用  (3)

获取费雪鸢尾花卉数据集（Fisher's Iris dataset）：

Wolfram Language code: data = ResourceData["Sample Tabular Data: Fisher Iris"]

训练异常检测器，假定没有超出分布范围的范例：

Wolfram Language code: detector = AnomalyDetection[data -> None]

将检测器用于新的、无标记的局部测量：

Wolfram Language code: detector[<|"SepalLength" -> Quantity[18.2, "Centimeters"], "SepalWidth" -> Quantity[4.5, "Centimeters"]|>]

获取训练和测试用图像数据集：

Wolfram Language code: train = RandomSample[ResourceData["MNIST", "TrainingData"][[All, 1]], 30000]; test = RandomSample[ResourceData["MNIST", "TestData"][[All, 1]], 40];
Wolfram Language code: RandomSample[train, 10]

在测试集中添加异常样例：

Wolfram Language code: antest = Join[{[image], [image]}, test]

在训练集上训练异常样例检测器：

Wolfram Language code: ad = AnomalyDetection[train, Method -> "Multinormal"]

在测试集中查找异常样例：

Wolfram Language code: FindAnomalies[ad, antest]

获取训练和测试图像数据集的随机样本：

Wolfram Language code: SeedRandom[123]; cifarsample = RandomSample[ResourceData["CIFAR-10"], 1000][[All, 1]]; {test, train} = TakeDrop[cifarsample, 100]; RandomSample[train, 10]

添加异常样例以破坏数据集：

Wolfram Language code: traincorup = Flatten[Join[Table[RandomImage[1, {12, 12}], 4], train ], 1]; testcorup = Flatten[Join[Table[RandomImage[1, {12, 12}], 4], test], 1]

通过指定训练集中已知异常样例的位置训练一个 "supervised" 异常检测器：

Wolfram Language code: ad = AnomalyDetection[traincorup -> {1, 2, 3, 4}, PerformanceGoal -> "Quality"]

在测试集上使用训练好的异常检测器：

Wolfram Language code: ad[testcorup]
Wolfram Research (2019)，AnomalyDetection，Wolfram 语言函数，https://reference.wolfram.com/language/ref/AnomalyDetection.html （更新于 2025 年）.

文本

Wolfram Research (2019)，AnomalyDetection，Wolfram 语言函数，https://reference.wolfram.com/language/ref/AnomalyDetection.html （更新于 2025 年）.

CMS

Wolfram 语言. 2019. "AnomalyDetection." Wolfram 语言与系统参考资料中心. Wolfram Research. 最新版本 2025. https://reference.wolfram.com/language/ref/AnomalyDetection.html.

APA

Wolfram 语言. (2019). AnomalyDetection. Wolfram 语言与系统参考资料中心. 追溯自 https://reference.wolfram.com/language/ref/AnomalyDetection.html 年

BibTeX

@misc{reference.wolfram_2026_anomalydetection, author="Wolfram Research", title="{AnomalyDetection}", year="2025", howpublished="\url{https://reference.wolfram.com/language/ref/AnomalyDetection.html}", note=[Accessed: 10-August-2026]}

BibLaTeX

@online{reference.wolfram_2026_anomalydetection, organization={Wolfram Research}, title={AnomalyDetection}, year={2025}, url={https://reference.wolfram.com/language/ref/AnomalyDetection.html}, note=[Accessed: 10-August-2026]}