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FindAnomalies[{example1,example2,…}]

给出相对于其他样例被认为是异常样例的 examplei 列表.

FindAnomalies[examples,prop]

给出指定的与异常样例计算相关的属性.

FindAnomalies[examples,{prop1,prop2,…}]

给出属性 propi.

FindAnomalies[fun,data]

用给定的 AnomalyDetectorFunction[…] 或 LearnedDistribution[…] 查找 data 中的异常样例.

FindAnomalies[fun,data,props]

给出与异常样例计算相关的属性.

更多信息和选项
Details and Options Details and Options
范例  
基本范例  
范围  
选项  
AcceptanceThreshold  
Method  
PerformanceGoal  
TimeGoal  
TrainingProgressReporting  
应用  
参见
相关指南
相关链接
历史
按以下格式引用：

FindAnomalies

FindAnomalies[{example1,example2,…}]

给出相对于其他样例被认为是异常样例的 examplei 列表.

FindAnomalies[examples,prop]

给出指定的与异常样例计算相关的属性.

FindAnomalies[examples,{prop1,prop2,…}]

给出属性 propi.

FindAnomalies[fun,data]

用给定的 AnomalyDetectorFunction[…] 或 LearnedDistribution[…] 查找 data 中的异常样例.

FindAnomalies[fun,data,props]

给出与异常样例计算相关的属性.

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  (3)

查找数值型数据集内的异常样例：

Wolfram Language code: FindAnomalies[{1.2, 2.5, 3.2, 100, 4.6, 5, 5.1}]

查找包含数字和名义变量的数据集中的异常样例：

Wolfram Language code: FindAnomalies[{{"A", 1}, {"A", 1.1}, {"B", 2}, {"B", 2.5}, {"C", 3}, {"C", 4}, {"C", 4.5}, {"C", 20}}]

根据给定的分布产生 100 种颜色：

Wolfram Language code: samples = RandomVariate[LearnDistribution[{RGBColor[0.5172966964096541, 0.4435322033449375, 1.], RGBColor[0.3984626930847484, 0.5592892024442906, 1.], RGBColor[0.6149389612362844, 0.5648721294502163, 1.], RGBColor[0.4129156497559272, 0.9146065592632544, 1.], RGBColor[0.7907065846445507, 0.41054133291260947, 1.], RGBColor[0.4878854162550912, 0.9281119680196579, 1.], RGBColor[0.9884362181280959, 0.49025178842859785, 1.], RGBColor[0.633242503827218, 0.9880985331612835, 1.], RGBColor[0.9215182482568276, 0.8103084921468551, 1.], RGBColor[0.667469513641223, 0.46420827644204676, 1.]}], 100]

添加分布之外的颜色并尝试检测它们：

Wolfram Language code: FindAnomalies[Join[{RGBColor[1, 0, 0], RGBColor[0, 0, 1], RGBColor[0, 1, 0]}, samples]]

范围  (4)

从表格数据中发现异常：

Wolfram Language code: FindAnomalies[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"]]}}]]]]]

在颜色上训练分布：

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]}]

用训练过的分布查找异常样例：

Wolfram Language code: ad = FindAnomalies[ld, {RGBColor[1, 0, 0], RGBColor[0, 1, 0], RGBColor[0.5977361225294263, 0.6855863458138487, 0.9985978114138284], RGBColor[0.6556661278732271, 0.8158702771438023, 0.993322923260324], RGBColor[0.6798268948534387, 0.9206455250817887, 0.9960592452202999], RGBColor[0.35817470590500133, 1.0319804703876905, 0.9962531895534059], RGBColor[0.4243082504253557, 0.8629909259478613, 0.9893894707275183]}]

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 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}}]

用检测器函数查找异常样例、它们的位置和非异常样例：

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

获取图像的训练和测试数据集的随机样本：

Wolfram Language code: SeedRandom[123]; cifarsample = RandomSample[ResourceData["CIFAR-10"], 5000][[All, 1]]; {test, train} = TakeDrop[cifarsample, 100]; RandomSample[train, 10]

添加异常样例，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]

在图像上训练分布：

Wolfram Language code: ldi = LearnDistribution[traincorup, PerformanceGoal -> "Quality"]

用训练过的分布在测试集中查找异常样例：

Wolfram Language code: FindAnomalies[ldi, testcorup]

选项  (5)

AcceptanceThreshold  (1)

用异常样例创建并可视化随机三维向量：

Wolfram Language code: ndata = RandomReal[1, {1000, 3}]; adata = RandomReal[{-2, -0.5}, {5, 3}]; data = Join[ndata, adata];
Wolfram Language code: ListPointPlot3D[{ndata, adata}, ...]

找出异常样例并可视化：

Wolfram Language code: ListPointPlot3D[{ndata, FindAnomalies[data]}, BoxRatios -> {1, 1, 1}, ...]

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

Wolfram Language code: ListPointPlot3D[{ndata, FindAnomalies[data, AcceptanceThreshold -> 0.2]}, ...]

Method  (1)

获取图像数据集：

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

在数据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}]} data = Join[anomalies, data];

使用 "Multinormal" 方法找到数据集中的异常：

Wolfram Language code: FindAnomalies[data, Method -> "Multinormal"]

使用 "KernelDensityEstimation" 方法找到数据集的异常：

Wolfram Language code: FindAnomalies[data, Method -> "KernelDensityEstimation"]

PerformanceGoal  (1)

获取图像的数据集：

Wolfram Language code: data = RandomSample[ResourceData["CIFAR-100"][[All, 1]]]; RandomSample[data, 20]

通过指定 PerformanceGoal 找到数据集中 1% 的最不常见的范例：

Wolfram Language code: FindAnomalies[data, PerformanceGoal -> "Speed"]

使用默认的 PerformanceGoal 找到数据集中最不常见的范例：

Wolfram Language code: FindAnomalies[data]

TimeGoal  (1)

获取图像数据集并通过指定时间目标找到最不常见的范例:

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

通过指定不同的时间目标找到最不常见的范例:

Wolfram Language code: FindAnomalies[data, TimeGoal -> 50]

TrainingProgressReporting  (1)

获取图像数据集：

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

无图交互式显示培训过程：

Wolfram Language code: FindAnomalies[data, TrainingProgressReporting -> "SimplePanel"]

培训时周期打印培训进程：

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

显示简单的进程指示器：

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

应用  (5)

获取图像数据集：

Wolfram Language code: data = RandomSample[ResourceData["MNIST", "TrainingData"][[All, 1]], 30000]; RandomSample[data, 10]

查找数据集中0.1% 最不常见的示例：

Wolfram Language code: FindAnomalies[data, AcceptanceThreshold -> 0.0001]

获取图像数据集：

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]

给测试集添加异常范例：

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"]

找出测试集中的异常样例：

Wolfram Language code: FindAnomalies[ad, antest]

获取与木星的卫星的特征有关的数据集：

Wolfram Language code: data = Values@ResourceData["Sample Data: Solar System Planets and Moons"]["Jupiter", "Moons"]; SeedRandom[1234] RandomSample[data, 10]

查找数据集中的异常样例：

Wolfram Language code: FindAnomalies[data]

按更高的 RarerProbability 阈值查找异常样例：

Wolfram Language code: FindAnomalies[data, AcceptanceThreshold -> 0.01]

获取汽车数据集：

Wolfram Language code: data = ResourceData["Sample Tabular Data: Car Models"]

查找数据集中的异常范例：

Wolfram Language code: anomalies = FindAnomalies[data]

训练一个 DimensionReducerFunction，以可视化二维数据：

Wolfram Language code: reducer = DimensionReduction[data, 2, Method -> "TSNE"]

绘制原始数据集的化简嵌入图，突出显示异常点：

Wolfram Language code: ListPlot[{reducer[data], reducer[anomalies]}, PlotStyle -> {Gray, Red}, PlotTheme -> "Minimal"]

获取给定时间序列的事件值对:

Wolfram Language code: tsp = TemporalData[TimeSeries, {{{27.5, 27.684999465942383, 27.844999313354492, 27.860200881958008, 27.809999465942383, 27.315000534057617, 27.270000457763672, 27.5049991607666, 27.334999084472656, 27.239999771118164, 27.174999237060547, 27.25, ... 3732480000, 3732566400, 3732825600, 3732912000, 3732998400}}}, 1, {"Continuous", 1}, {"Discrete", 1}, 1, {ValueDimensions -> 1, DateFunction -> Automatic, ResamplingMethod -> {"Interpolation", InterpolationOrder -> 1}}}, True, 314.1]["Path"];

将时间 - 值对分区为连续窗口列表：

Wolfram Language code: pdata = Partition[tsp, 4, 3];

尝试找到异常事件并可视化：

Wolfram Language code: anomalies = FindAnomalies[pdata, AcceptanceThreshold -> 0.002]; p1 = ListLinePlot[tsp]; p2 = ListPlot[Flatten[anomalies, 1], Rule[...]]; Show[{p1, p2}]
Wolfram Research (2019)，FindAnomalies，Wolfram 语言函数，https://reference.wolfram.com/language/ref/FindAnomalies.html （更新于 2025 年）.

文本

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

CMS

Wolfram 语言. 2019. "FindAnomalies." 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/FindAnomalies.html.

APA

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

BibTeX

@misc{reference.wolfram_2026_findanomalies, author="Wolfram Research", title="{FindAnomalies}", year="2025", howpublished="\url{https://reference.wolfram.com/language/ref/FindAnomalies.html}", note=[Accessed: 14-October-2026]}

BibLaTeX

@online{reference.wolfram_2026_findanomalies, organization={Wolfram Research}, title={FindAnomalies}, year={2025}, url={https://reference.wolfram.com/language/ref/FindAnomalies.html}, note=[Accessed: 14-October-2026]}