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The following commit(s) were added to refs/heads/main by this push:
new 5daaf40a fix the udf doc of anomaly detect function (#236)
5daaf40a is described below
commit 5daaf40ad439fc67f3882c8ae375c40851206515
Author: wanghui42 <[email protected]>
AuthorDate: Thu Oct 24 11:09:56 2024 +0800
fix the udf doc of anomaly detect function (#236)
* fix anomaly-fuction doc
* fix udf data3
---
src/UserGuide/Master/Reference/UDF-Libraries.md | 64 +++------------------
src/UserGuide/V1.2.x/Reference/UDF-Libraries.md | 65 +++-------------------
src/UserGuide/latest/Reference/UDF-Libraries.md | 64 +++------------------
src/zh/UserGuide/Master/Reference/UDF-Libraries.md | 65 +++-------------------
src/zh/UserGuide/V1.2.x/Reference/UDF-Libraries.md | 65 +++-------------------
src/zh/UserGuide/latest/Reference/UDF-Libraries.md | 64 +++------------------
6 files changed, 42 insertions(+), 345 deletions(-)
diff --git a/src/UserGuide/Master/Reference/UDF-Libraries.md
b/src/UserGuide/Master/Reference/UDF-Libraries.md
index 79599e24..e3364d51 100644
--- a/src/UserGuide/Master/Reference/UDF-Libraries.md
+++ b/src/UserGuide/Master/Reference/UDF-Libraries.md
@@ -520,59 +520,6 @@ Output series:
+-----------------------------+----------------------------------------+
```
-### Accuracy
-
-#### Usage
-
-This function is used to calculate the Accuracy of time series based on master
data.
-
-**Name**: Accuracy
-
-**Input Series:** Support multiple input series. The types are are in INT32 /
INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-+ `omega`: The window size. It is a non-negative integer whose unit is
millisecond. By default, it will be estimated according to the distances of two
tuples with various time differences.
-+ `eta`: The distance threshold. It is a positive number. By default, it will
be estimated according to the distance distribution of tuples in windows.
-+ `k`: The number of neighbors in master data. It is a positive integer. By
default, it will be estimated according to the tuple dis- tance of the k-th
nearest neighbor in the master data.
-
-**Output Series**: Output a single value. The type is DOUBLE. The range is
[0,1].
-
-#### Examples
-
-Input series:
-
-```
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-|
Time|root.test.t1|root.test.t2|root.test.t3|root.test.m1|root.test.m2|root.test.m3|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-|2021-07-01T12:00:01.000+08:00| 1704| 1154.55| 0.195|
1704| 1154.55| 0.195|
-|2021-07-01T12:00:02.000+08:00| 1702| 1152.30| 0.193|
1702| 1152.30| 0.193|
-|2021-07-01T12:00:03.000+08:00| 1702| 1148.65| 0.192|
1702| 1148.65| 0.192|
-|2021-07-01T12:00:04.000+08:00| 1701| 1145.20| 0.194|
1701| 1145.20| 0.194|
-|2021-07-01T12:00:07.000+08:00| 1703| 1150.55| 0.195|
1703| 1150.55| 0.195|
-|2021-07-01T12:00:08.000+08:00| 1694| 1151.55| 0.193|
1704| 1151.55| 0.193|
-|2021-07-01T12:01:09.000+08:00| 1705| 1153.55| 0.194|
1705| 1153.55| 0.194|
-|2021-07-01T12:01:10.000+08:00| 1706| 1152.30| 0.190|
1706| 1152.30| 0.190|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-```
-
-SQL for query:
-
-```sql
-select Accuracy(t1,t2,t3,m1,m2,m3) from root.test
-```
-
-Output series:
-
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------+
-|
Time|Accuracy(root.test.t1,root.test.t2,root.test.t3,root.test.m1,root.test.m2,root.test.m3)|
-+-----------------------------+---------------------------------------------------------------------------------------+
-|2021-07-01T12:00:01.000+08:00|
0.875|
-+-----------------------------+---------------------------------------------------------------------------------------+
-```
<!--
@@ -2890,6 +2837,7 @@ Output series:
|Time
|range(root.test.d1.s1,"lower_bound"="101.0","upper_bound"="125.0")|
+-----------------------------+------------------------------------------------------------------+
|2020-01-01T00:00:02.000+08:00|
100.0|
+|2020-01-01T00:00:08.000+08:00|
126.0|
|2020-01-01T00:00:28.000+08:00|
126.0|
+-----------------------------+------------------------------------------------------------------+
```
@@ -3075,8 +3023,9 @@ This function is used to train the VAR model based on
master data. The model is
**Installation**
- Install IoTDB from branch `research/master-detector`.
-- Run `mvn clean package -am -Dmaven.test.skip=true`.
-- Copy
`./distribution/target/apache-iotdb-1.2.0-SNAPSHOT-library-udf-bin/apache-iotdb-1.2.0-SNAPSHOT-library-udf-bin/ext/udf/library-udf.jar`
to `./ext/udf/`.
+- Run `mvn spotless:apply`.
+- Run `mvn clean package -pl library-udf -DskipTests -am -P
get-jar-with-dependencies`.
+- Copy
`./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar` to
`./ext/udf/`.
- Start IoTDB server and run `create function MasterTrain as
'org.apache.iotdb.library.anomaly.UDTFMasterTrain'` in client.
#### Examples
@@ -3161,8 +3110,9 @@ This function is used to detect time series and repair
errors based on master da
**Installation**
- Install IoTDB from branch `research/master-detector`.
-- Run `mvn clean package -am -Dmaven.test.skip=true`.
-- Copy
`./distribution/target/apache-iotdb-1.2.0-SNAPSHOT-library-udf-bin/apache-iotdb-1.2.0-SNAPSHOT-library-udf-bin/ext/udf/library-udf.jar`
to `./ext/udf/`.
+- Run `mvn spotless:apply`.
+- Run `mvn clean package -pl library-udf -DskipTests -am -P
get-jar-with-dependencies`.
+- Copy
`./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar` to
`./ext/udf/`.
- Start IoTDB server and run `create function MasterDetect as
'org.apache.iotdb.library.anomaly.UDTFMasterDetect'` in client.
#### Examples
diff --git a/src/UserGuide/V1.2.x/Reference/UDF-Libraries.md
b/src/UserGuide/V1.2.x/Reference/UDF-Libraries.md
index 9eadabad..af4c7a25 100644
--- a/src/UserGuide/V1.2.x/Reference/UDF-Libraries.md
+++ b/src/UserGuide/V1.2.x/Reference/UDF-Libraries.md
@@ -520,60 +520,6 @@ Output series:
+-----------------------------+----------------------------------------+
```
-### Accuracy
-
-#### Usage
-
-This function is used to calculate the Accuracy of time series based on master
data.
-
-**Name**: Accuracy
-
-**Input Series:** Support multiple input series. The types are are in INT32 /
INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-+ `omega`: The window size. It is a non-negative integer whose unit is
millisecond. By default, it will be estimated according to the distances of two
tuples with various time differences.
-+ `eta`: The distance threshold. It is a positive number. By default, it will
be estimated according to the distance distribution of tuples in windows.
-+ `k`: The number of neighbors in master data. It is a positive integer. By
default, it will be estimated according to the tuple dis- tance of the k-th
nearest neighbor in the master data.
-
-**Output Series**: Output a single value. The type is DOUBLE. The range is
[0,1].
-
-#### Examples
-
-Input series:
-
-```
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-|
Time|root.test.t1|root.test.t2|root.test.t3|root.test.m1|root.test.m2|root.test.m3|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-|2021-07-01T12:00:01.000+08:00| 1704| 1154.55| 0.195|
1704| 1154.55| 0.195|
-|2021-07-01T12:00:02.000+08:00| 1702| 1152.30| 0.193|
1702| 1152.30| 0.193|
-|2021-07-01T12:00:03.000+08:00| 1702| 1148.65| 0.192|
1702| 1148.65| 0.192|
-|2021-07-01T12:00:04.000+08:00| 1701| 1145.20| 0.194|
1701| 1145.20| 0.194|
-|2021-07-01T12:00:07.000+08:00| 1703| 1150.55| 0.195|
1703| 1150.55| 0.195|
-|2021-07-01T12:00:08.000+08:00| 1694| 1151.55| 0.193|
1704| 1151.55| 0.193|
-|2021-07-01T12:01:09.000+08:00| 1705| 1153.55| 0.194|
1705| 1153.55| 0.194|
-|2021-07-01T12:01:10.000+08:00| 1706| 1152.30| 0.190|
1706| 1152.30| 0.190|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-```
-
-SQL for query:
-
-```sql
-select Accuracy(t1,t2,t3,m1,m2,m3) from root.test
-```
-
-Output series:
-
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------+
-|
Time|Accuracy(root.test.t1,root.test.t2,root.test.t3,root.test.m1,root.test.m2,root.test.m3)|
-+-----------------------------+---------------------------------------------------------------------------------------+
-|2021-07-01T12:00:01.000+08:00|
0.875|
-+-----------------------------+---------------------------------------------------------------------------------------+
-```
-
<!--
Licensed to the Apache Software Foundation (ASF) under one
@@ -2890,6 +2836,7 @@ Output series:
|Time
|range(root.test.d1.s1,"lower_bound"="101.0","upper_bound"="125.0")|
+-----------------------------+------------------------------------------------------------------+
|2020-01-01T00:00:02.000+08:00|
100.0|
+|2020-01-01T00:00:08.000+08:00|
126.0|
|2020-01-01T00:00:28.000+08:00|
126.0|
+-----------------------------+------------------------------------------------------------------+
```
@@ -3075,8 +3022,9 @@ This function is used to train the VAR model based on
master data. The model is
**Installation**
- Install IoTDB from branch `research/master-detector`.
-- Run `mvn clean package -am -Dmaven.test.skip=true`.
-- Copy
`./distribution/target/apache-iotdb-1.2.0-SNAPSHOT-library-udf-bin/apache-iotdb-1.2.0-SNAPSHOT-library-udf-bin/ext/udf/library-udf.jar`
to `./ext/udf/`.
+- Run `mvn spotless:apply`.
+- Run `mvn clean package -pl library-udf -DskipTests -am -P
get-jar-with-dependencies`.
+- Copy
`./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar` to
`./ext/udf/`.
- Start IoTDB server and run `create function MasterTrain as
'org.apache.iotdb.library.anomaly.UDTFMasterTrain'` in client.
#### Examples
@@ -3161,8 +3109,9 @@ This function is used to detect time series and repair
errors based on master da
**Installation**
- Install IoTDB from branch `research/master-detector`.
-- Run `mvn clean package -am -Dmaven.test.skip=true`.
-- Copy
`./distribution/target/apache-iotdb-1.2.0-SNAPSHOT-library-udf-bin/apache-iotdb-1.2.0-SNAPSHOT-library-udf-bin/ext/udf/library-udf.jar`
to `./ext/udf/`.
+- Run `mvn spotless:apply`.
+- Run `mvn clean package -pl library-udf -DskipTests -am -P
get-jar-with-dependencies`.
+- Copy
`./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar` to
`./ext/udf/`.
- Start IoTDB server and run `create function MasterDetect as
'org.apache.iotdb.library.anomaly.UDTFMasterDetect'` in client.
#### Examples
diff --git a/src/UserGuide/latest/Reference/UDF-Libraries.md
b/src/UserGuide/latest/Reference/UDF-Libraries.md
index 26efdb6f..0295dfe7 100644
--- a/src/UserGuide/latest/Reference/UDF-Libraries.md
+++ b/src/UserGuide/latest/Reference/UDF-Libraries.md
@@ -520,59 +520,6 @@ Output series:
+-----------------------------+----------------------------------------+
```
-### Accuracy
-
-#### Usage
-
-This function is used to calculate the Accuracy of time series based on master
data.
-
-**Name**: Accuracy
-
-**Input Series:** Support multiple input series. The types are are in INT32 /
INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-+ `omega`: The window size. It is a non-negative integer whose unit is
millisecond. By default, it will be estimated according to the distances of two
tuples with various time differences.
-+ `eta`: The distance threshold. It is a positive number. By default, it will
be estimated according to the distance distribution of tuples in windows.
-+ `k`: The number of neighbors in master data. It is a positive integer. By
default, it will be estimated according to the tuple dis- tance of the k-th
nearest neighbor in the master data.
-
-**Output Series**: Output a single value. The type is DOUBLE. The range is
[0,1].
-
-#### Examples
-
-Input series:
-
-```
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-|
Time|root.test.t1|root.test.t2|root.test.t3|root.test.m1|root.test.m2|root.test.m3|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-|2021-07-01T12:00:01.000+08:00| 1704| 1154.55| 0.195|
1704| 1154.55| 0.195|
-|2021-07-01T12:00:02.000+08:00| 1702| 1152.30| 0.193|
1702| 1152.30| 0.193|
-|2021-07-01T12:00:03.000+08:00| 1702| 1148.65| 0.192|
1702| 1148.65| 0.192|
-|2021-07-01T12:00:04.000+08:00| 1701| 1145.20| 0.194|
1701| 1145.20| 0.194|
-|2021-07-01T12:00:07.000+08:00| 1703| 1150.55| 0.195|
1703| 1150.55| 0.195|
-|2021-07-01T12:00:08.000+08:00| 1694| 1151.55| 0.193|
1704| 1151.55| 0.193|
-|2021-07-01T12:01:09.000+08:00| 1705| 1153.55| 0.194|
1705| 1153.55| 0.194|
-|2021-07-01T12:01:10.000+08:00| 1706| 1152.30| 0.190|
1706| 1152.30| 0.190|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-```
-
-SQL for query:
-
-```sql
-select Accuracy(t1,t2,t3,m1,m2,m3) from root.test
-```
-
-Output series:
-
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------+
-|
Time|Accuracy(root.test.t1,root.test.t2,root.test.t3,root.test.m1,root.test.m2,root.test.m3)|
-+-----------------------------+---------------------------------------------------------------------------------------+
-|2021-07-01T12:00:01.000+08:00|
0.875|
-+-----------------------------+---------------------------------------------------------------------------------------+
-```
<!--
@@ -2890,6 +2837,7 @@ Output series:
|Time
|range(root.test.d1.s1,"lower_bound"="101.0","upper_bound"="125.0")|
+-----------------------------+------------------------------------------------------------------+
|2020-01-01T00:00:02.000+08:00|
100.0|
+|2020-01-01T00:00:08.000+08:00|
126.0|
|2020-01-01T00:00:28.000+08:00|
126.0|
+-----------------------------+------------------------------------------------------------------+
```
@@ -3075,8 +3023,9 @@ This function is used to train the VAR model based on
master data. The model is
**Installation**
- Install IoTDB from branch `research/master-detector`.
-- Run `mvn clean package -am -Dmaven.test.skip=true`.
-- Copy
`./distribution/target/apache-iotdb-1.2.0-SNAPSHOT-library-udf-bin/apache-iotdb-1.2.0-SNAPSHOT-library-udf-bin/ext/udf/library-udf.jar`
to `./ext/udf/`.
+- Run `mvn spotless:apply`.
+- Run `mvn clean package -pl library-udf -DskipTests -am -P
get-jar-with-dependencies`.
+- Copy
`./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar` to
`./ext/udf/`.
- Start IoTDB server and run `create function MasterTrain as
'org.apache.iotdb.library.anomaly.UDTFMasterTrain'` in client.
#### Examples
@@ -3161,8 +3110,9 @@ This function is used to detect time series and repair
errors based on master da
**Installation**
- Install IoTDB from branch `research/master-detector`.
-- Run `mvn clean package -am -Dmaven.test.skip=true`.
-- Copy
`./distribution/target/apache-iotdb-1.2.0-SNAPSHOT-library-udf-bin/apache-iotdb-1.2.0-SNAPSHOT-library-udf-bin/ext/udf/library-udf.jar`
to `./ext/udf/`.
+- Run `mvn spotless:apply`.
+- Run `mvn clean package -pl library-udf -DskipTests -am -P
get-jar-with-dependencies`.
+- Copy
`./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar` to
`./ext/udf/`.
- Start IoTDB server and run `create function MasterDetect as
'org.apache.iotdb.library.anomaly.UDTFMasterDetect'` in client.
#### Examples
diff --git a/src/zh/UserGuide/Master/Reference/UDF-Libraries.md
b/src/zh/UserGuide/Master/Reference/UDF-Libraries.md
index 6aff1087..cbd82993 100644
--- a/src/zh/UserGuide/Master/Reference/UDF-Libraries.md
+++ b/src/zh/UserGuide/Master/Reference/UDF-Libraries.md
@@ -524,60 +524,6 @@ select validity(s1,"window"="15") from root.test.d1 where
time <= 2020-01-01 00:
+-----------------------------+----------------------------------------+
```
-### Accuracy
-
-#### 函数简介
-
-本函数基于主数据计算原始时间序列的准确性。
-
-**函数名**：Accuracy
-
-**输入序列：** 支持多个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数：**
-
-- `omega`：算法窗口大小，非负整数（单位为毫秒）， 在缺省情况下，算法根据不同时间差下的两个元组距离自动估计该参数。
-- `eta`：算法距离阈值，正数， 在缺省情况下，算法根据窗口中元组的距离分布自动估计该参数。
-- `k`：主数据中的近邻数量，正整数， 在缺省情况下，算法根据主数据中的k个近邻的元组距离自动估计该参数。
-
-**输出序列**：输出单个值，类型为DOUBLE，值的范围为[0,1]。
-
-#### 使用示例
-
-输入序列：
-
-```
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-|
Time|root.test.t1|root.test.t2|root.test.t3|root.test.m1|root.test.m2|root.test.m3|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-|2021-07-01T12:00:01.000+08:00| 1704| 1154.55| 0.195|
1704| 1154.55| 0.195|
-|2021-07-01T12:00:02.000+08:00| 1702| 1152.30| 0.193|
1702| 1152.30| 0.193|
-|2021-07-01T12:00:03.000+08:00| 1702| 1148.65| 0.192|
1702| 1148.65| 0.192|
-|2021-07-01T12:00:04.000+08:00| 1701| 1145.20| 0.194|
1701| 1145.20| 0.194|
-|2021-07-01T12:00:07.000+08:00| 1703| 1150.55| 0.195|
1703| 1150.55| 0.195|
-|2021-07-01T12:00:08.000+08:00| 1694| 1151.55| 0.193|
1704| 1151.55| 0.193|
-|2021-07-01T12:01:09.000+08:00| 1705| 1153.55| 0.194|
1705| 1153.55| 0.194|
-|2021-07-01T12:01:10.000+08:00| 1706| 1152.30| 0.190|
1706| 1152.30| 0.190|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-```
-
-用于查询的 SQL 语句：
-
-```sql
-select Accuracy(t1,t2,t3,m1,m2,m3) from root.test
-```
-
-输出序列：
-
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------+
-|
Time|Accuracy(root.test.t1,root.test.t2,root.test.t3,root.test.m1,root.test.m2,root.test.m3)|
-+-----------------------------+---------------------------------------------------------------------------------------+
-|2021-07-01T12:00:01.000+08:00|
0.875|
-+-----------------------------+---------------------------------------------------------------------------------------+
-```
-
<!--
Licensed to the Apache Software Foundation (ASF) under one
@@ -2893,6 +2839,7 @@ select
range(s1,"lower_bound"="101.0","upper_bound"="125.0") from root.test.d1 w
|Time
|range(root.test.d1.s1,"lower_bound"="101.0","upper_bound"="125.0")|
+-----------------------------+------------------------------------------------------------------+
|2020-01-01T00:00:02.000+08:00|
100.0|
+|2020-01-01T00:00:08.000+08:00|
126.0|
|2020-01-01T00:00:28.000+08:00|
126.0|
+-----------------------------+------------------------------------------------------------------+
```
@@ -3079,8 +3026,9 @@ select outlier(s1,"r"="5.0","k"="4","w"="10","s"="5")
from root.test
**安装方式：**
- 从IoTDB代码仓库下载`research/master-detector`分支代码到本地
-- 在根目录运行 `mvn clean package -am -Dmaven.test.skip=true` 编译项目
-- 将
`./distribution/target/apache-iotdb-1.2.0-SNAPSHOT-library-udf-bin/apache-iotdb-1.2.0-SNAPSHOT-library-udf-bin/ext/udf/library-udf.jar`复制到IoTDB服务器的`./ext/udf/`
路径下。
+- 在根目录运行 `mvn spotless:apply`
+- 在根目录运行 `mvn clean package -pl library-udf -DskipTests -am -P
get-jar-with-dependencies` 编译项目
+- 将
`./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar`复制到IoTDB服务器的`./ext/udf/`
路径下。
- 启动 IoTDB服务器，在客户端中执行 `create function MasterTrain as
org.apache.iotdb.library.anomaly.UDTFMasterTrain'`。
#### 使用示例
@@ -3165,8 +3113,9 @@ select MasterTrain(lo,la,m_lo,m_la,'p'='3','eta'='1.0')
from root.test
**安装方式：**
- 从IoTDB代码仓库下载`research/master-detector`分支代码到本地
-- 在根目录运行 `mvn clean package -am -Dmaven.test.skip=true` 编译项目
-- 将
`./distribution/target/apache-iotdb-1.2.0-SNAPSHOT-library-udf-bin/apache-iotdb-1.2.0-SNAPSHOT-library-udf-bin/ext/udf/library-udf.jar`复制到IoTDB服务器的`./ext/udf/`
路径下。
+- 在根目录运行 `mvn spotless:apply`
+- 在根目录运行 `mvn clean package -pl library-udf -DskipTests -am -P
get-jar-with-dependencies` 编译项目
+- 将
`./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar`复制到IoTDB服务器的`./ext/udf/`
路径下。
- 启动 IoTDB服务器，在客户端中执行 `create function MasterDetect as
'org.apache.iotdb.library.anomaly.UDTFMasterDetect'`。
**输出序列：** 输出单个序列，类型与输入数据中对应列的类型相同，序列为输入列修复后的结果。
diff --git a/src/zh/UserGuide/V1.2.x/Reference/UDF-Libraries.md
b/src/zh/UserGuide/V1.2.x/Reference/UDF-Libraries.md
index 2f115960..005865d5 100644
--- a/src/zh/UserGuide/V1.2.x/Reference/UDF-Libraries.md
+++ b/src/zh/UserGuide/V1.2.x/Reference/UDF-Libraries.md
@@ -524,60 +524,6 @@ select validity(s1,"window"="15") from root.test.d1 where
time <= 2020-01-01 00:
+-----------------------------+----------------------------------------+
```
-### Accuracy
-
-#### 函数简介
-
-本函数基于主数据计算原始时间序列的准确性。
-
-**函数名**：Accuracy
-
-**输入序列：** 支持多个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数：**
-
-- `omega`：算法窗口大小，非负整数（单位为毫秒）， 在缺省情况下，算法根据不同时间差下的两个元组距离自动估计该参数。
-- `eta`：算法距离阈值，正数， 在缺省情况下，算法根据窗口中元组的距离分布自动估计该参数。
-- `k`：主数据中的近邻数量，正整数， 在缺省情况下，算法根据主数据中的k个近邻的元组距离自动估计该参数。
-
-**输出序列**：输出单个值，类型为DOUBLE，值的范围为[0,1]。
-
-#### 使用示例
-
-输入序列：
-
-```
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-|
Time|root.test.t1|root.test.t2|root.test.t3|root.test.m1|root.test.m2|root.test.m3|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-|2021-07-01T12:00:01.000+08:00| 1704| 1154.55| 0.195|
1704| 1154.55| 0.195|
-|2021-07-01T12:00:02.000+08:00| 1702| 1152.30| 0.193|
1702| 1152.30| 0.193|
-|2021-07-01T12:00:03.000+08:00| 1702| 1148.65| 0.192|
1702| 1148.65| 0.192|
-|2021-07-01T12:00:04.000+08:00| 1701| 1145.20| 0.194|
1701| 1145.20| 0.194|
-|2021-07-01T12:00:07.000+08:00| 1703| 1150.55| 0.195|
1703| 1150.55| 0.195|
-|2021-07-01T12:00:08.000+08:00| 1694| 1151.55| 0.193|
1704| 1151.55| 0.193|
-|2021-07-01T12:01:09.000+08:00| 1705| 1153.55| 0.194|
1705| 1153.55| 0.194|
-|2021-07-01T12:01:10.000+08:00| 1706| 1152.30| 0.190|
1706| 1152.30| 0.190|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-```
-
-用于查询的 SQL 语句：
-
-```sql
-select Accuracy(t1,t2,t3,m1,m2,m3) from root.test
-```
-
-输出序列：
-
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------+
-|
Time|Accuracy(root.test.t1,root.test.t2,root.test.t3,root.test.m1,root.test.m2,root.test.m3)|
-+-----------------------------+---------------------------------------------------------------------------------------+
-|2021-07-01T12:00:01.000+08:00|
0.875|
-+-----------------------------+---------------------------------------------------------------------------------------+
-```
-
<!--
Licensed to the Apache Software Foundation (ASF) under one
@@ -2893,6 +2839,7 @@ select
range(s1,"lower_bound"="101.0","upper_bound"="125.0") from root.test.d1 w
|Time
|range(root.test.d1.s1,"lower_bound"="101.0","upper_bound"="125.0")|
+-----------------------------+------------------------------------------------------------------+
|2020-01-01T00:00:02.000+08:00|
100.0|
+|2020-01-01T00:00:08.000+08:00|
126.0|
|2020-01-01T00:00:28.000+08:00|
126.0|
+-----------------------------+------------------------------------------------------------------+
```
@@ -3079,8 +3026,9 @@ select outlier(s1,"r"="5.0","k"="4","w"="10","s"="5")
from root.test
**安装方式：**
- 从IoTDB代码仓库下载`research/master-detector`分支代码到本地
-- 在根目录运行 `mvn clean package -am -Dmaven.test.skip=true` 编译项目
-- 将
`./distribution/target/apache-iotdb-1.2.0-SNAPSHOT-library-udf-bin/apache-iotdb-1.2.0-SNAPSHOT-library-udf-bin/ext/udf/library-udf.jar`复制到IoTDB服务器的`./ext/udf/`
路径下。
+- 在根目录运行 `mvn spotless:apply`
+- 在根目录运行 `mvn clean package -pl library-udf -DskipTests -am -P
get-jar-with-dependencies` 编译项目
+- 将
`./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar`复制到IoTDB服务器的`./ext/udf/`
路径下。
- 启动 IoTDB服务器，在客户端中执行 `create function MasterTrain as
org.apache.iotdb.library.anomaly.UDTFMasterTrain'`。
#### 使用示例
@@ -3165,8 +3113,9 @@ select MasterTrain(lo,la,m_lo,m_la,'p'='3','eta'='1.0')
from root.test
**安装方式：**
- 从IoTDB代码仓库下载`research/master-detector`分支代码到本地
-- 在根目录运行 `mvn clean package -am -Dmaven.test.skip=true` 编译项目
-- 将
`./distribution/target/apache-iotdb-1.2.0-SNAPSHOT-library-udf-bin/apache-iotdb-1.2.0-SNAPSHOT-library-udf-bin/ext/udf/library-udf.jar`复制到IoTDB服务器的`./ext/udf/`
路径下。
+- 在根目录运行 `mvn spotless:apply`
+- 在根目录运行 `mvn clean package -pl library-udf -DskipTests -am -P
get-jar-with-dependencies` 编译项目
+- 将
`./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar`复制到IoTDB服务器的`./ext/udf/`
路径下。
- 启动 IoTDB服务器，在客户端中执行 `create function MasterDetect as
'org.apache.iotdb.library.anomaly.UDTFMasterDetect'`。
**输出序列：** 输出单个序列，类型与输入数据中对应列的类型相同，序列为输入列修复后的结果。
diff --git a/src/zh/UserGuide/latest/Reference/UDF-Libraries.md
b/src/zh/UserGuide/latest/Reference/UDF-Libraries.md
index 6aff1087..e587777e 100644
--- a/src/zh/UserGuide/latest/Reference/UDF-Libraries.md
+++ b/src/zh/UserGuide/latest/Reference/UDF-Libraries.md
@@ -524,59 +524,6 @@ select validity(s1,"window"="15") from root.test.d1 where
time <= 2020-01-01 00:
+-----------------------------+----------------------------------------+
```
-### Accuracy
-
-#### 函数简介
-
-本函数基于主数据计算原始时间序列的准确性。
-
-**函数名**：Accuracy
-
-**输入序列：** 支持多个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数：**
-
-- `omega`：算法窗口大小，非负整数（单位为毫秒）， 在缺省情况下，算法根据不同时间差下的两个元组距离自动估计该参数。
-- `eta`：算法距离阈值，正数， 在缺省情况下，算法根据窗口中元组的距离分布自动估计该参数。
-- `k`：主数据中的近邻数量，正整数， 在缺省情况下，算法根据主数据中的k个近邻的元组距离自动估计该参数。
-
-**输出序列**：输出单个值，类型为DOUBLE，值的范围为[0,1]。
-
-#### 使用示例
-
-输入序列：
-
-```
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-|
Time|root.test.t1|root.test.t2|root.test.t3|root.test.m1|root.test.m2|root.test.m3|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-|2021-07-01T12:00:01.000+08:00| 1704| 1154.55| 0.195|
1704| 1154.55| 0.195|
-|2021-07-01T12:00:02.000+08:00| 1702| 1152.30| 0.193|
1702| 1152.30| 0.193|
-|2021-07-01T12:00:03.000+08:00| 1702| 1148.65| 0.192|
1702| 1148.65| 0.192|
-|2021-07-01T12:00:04.000+08:00| 1701| 1145.20| 0.194|
1701| 1145.20| 0.194|
-|2021-07-01T12:00:07.000+08:00| 1703| 1150.55| 0.195|
1703| 1150.55| 0.195|
-|2021-07-01T12:00:08.000+08:00| 1694| 1151.55| 0.193|
1704| 1151.55| 0.193|
-|2021-07-01T12:01:09.000+08:00| 1705| 1153.55| 0.194|
1705| 1153.55| 0.194|
-|2021-07-01T12:01:10.000+08:00| 1706| 1152.30| 0.190|
1706| 1152.30| 0.190|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-```
-
-用于查询的 SQL 语句：
-
-```sql
-select Accuracy(t1,t2,t3,m1,m2,m3) from root.test
-```
-
-输出序列：
-
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------+
-|
Time|Accuracy(root.test.t1,root.test.t2,root.test.t3,root.test.m1,root.test.m2,root.test.m3)|
-+-----------------------------+---------------------------------------------------------------------------------------+
-|2021-07-01T12:00:01.000+08:00|
0.875|
-+-----------------------------+---------------------------------------------------------------------------------------+
-```
<!--
@@ -2893,6 +2840,7 @@ select
range(s1,"lower_bound"="101.0","upper_bound"="125.0") from root.test.d1 w
|Time
|range(root.test.d1.s1,"lower_bound"="101.0","upper_bound"="125.0")|
+-----------------------------+------------------------------------------------------------------+
|2020-01-01T00:00:02.000+08:00|
100.0|
+|2020-01-01T00:00:08.000+08:00|
126.0|
|2020-01-01T00:00:28.000+08:00|
126.0|
+-----------------------------+------------------------------------------------------------------+
```
@@ -3079,8 +3027,9 @@ select outlier(s1,"r"="5.0","k"="4","w"="10","s"="5")
from root.test
**安装方式：**
- 从IoTDB代码仓库下载`research/master-detector`分支代码到本地
-- 在根目录运行 `mvn clean package -am -Dmaven.test.skip=true` 编译项目
-- 将
`./distribution/target/apache-iotdb-1.2.0-SNAPSHOT-library-udf-bin/apache-iotdb-1.2.0-SNAPSHOT-library-udf-bin/ext/udf/library-udf.jar`复制到IoTDB服务器的`./ext/udf/`
路径下。
+- 在根目录运行 `mvn spotless:apply`
+- 在根目录运行 `mvn clean package -pl library-udf -DskipTests -am -P
get-jar-with-dependencies` 编译项目
+- 将
`./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar`复制到IoTDB服务器的`./ext/udf/`
路径下。
- 启动 IoTDB服务器，在客户端中执行 `create function MasterTrain as
org.apache.iotdb.library.anomaly.UDTFMasterTrain'`。
#### 使用示例
@@ -3165,8 +3114,9 @@ select MasterTrain(lo,la,m_lo,m_la,'p'='3','eta'='1.0')
from root.test
**安装方式：**
- 从IoTDB代码仓库下载`research/master-detector`分支代码到本地
-- 在根目录运行 `mvn clean package -am -Dmaven.test.skip=true` 编译项目
-- 将
`./distribution/target/apache-iotdb-1.2.0-SNAPSHOT-library-udf-bin/apache-iotdb-1.2.0-SNAPSHOT-library-udf-bin/ext/udf/library-udf.jar`复制到IoTDB服务器的`./ext/udf/`
路径下。
+- 在根目录运行 `mvn spotless:apply`
+- 在根目录运行 `mvn clean package -pl library-udf -DskipTests -am -P
get-jar-with-dependencies` 编译项目
+- 将
`./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar`复制到IoTDB服务器的`./ext/udf/`
路径下。
- 启动 IoTDB服务器，在客户端中执行 `create function MasterDetect as
'org.apache.iotdb.library.anomaly.UDTFMasterDetect'`。
**输出序列：** 输出单个序列，类型与输入数据中对应列的类型相同，序列为输入列修复后的结果。
