diff --git a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/AnomalyDetectorAsyncClient.java b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/AnomalyDetectorAsyncClient.java
index 40e17f4d59a9..99d05eef9069 100644
--- a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/AnomalyDetectorAsyncClient.java
+++ b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/AnomalyDetectorAsyncClient.java
@@ -13,7 +13,6 @@
import com.azure.ai.anomalydetector.models.UnivariateChangePointDetectionOptions;
import com.azure.ai.anomalydetector.models.UnivariateChangePointDetectionResult;
import com.azure.ai.anomalydetector.models.UnivariateDetectionOptions;
-import com.azure.ai.anomalydetector.models.UnivariateEntireDetectionResult;
import com.azure.ai.anomalydetector.models.UnivariateLastDetectionResult;
import com.azure.core.annotation.Generated;
import com.azure.core.annotation.ReturnType;
@@ -53,9 +52,9 @@ public final class AnomalyDetectorAsyncClient {
/**
* Detect anomalies for the entire series in batch.
*
- * This operation generates a model with an entire series, each point is detected with the same model. With this
- * method, points before and after a certain point are used to determine whether it is an anomaly. The entire
- * detection can give user an overall status of the time series.
+ *
This operation generates a model with an entire series. Each point is detected with the same model. With this
+ * method, points before and after a certain point are used to determine whether it's an anomaly. The entire
+ * detection can give the user an overall status of the time series.
*
*
Request Body Schema
*
@@ -112,7 +111,7 @@ public final class AnomalyDetectorAsyncClient {
* @throws ClientAuthenticationException thrown if the request is rejected by server on status code 401.
* @throws ResourceNotFoundException thrown if the request is rejected by server on status code 404.
* @throws ResourceModifiedException thrown if the request is rejected by server on status code 409.
- * @return the response of entire anomaly detection along with {@link Response} on successful completion of {@link
+ * @return response of the entire anomaly detection along with {@link Response} on successful completion of {@link
* Mono}.
*/
@Generated
@@ -125,7 +124,7 @@ public Mono> detectUnivariateEntireSeriesWithResponse(
/**
* Detect anomaly status of the latest point in time series.
*
- * This operation generates a model using the points that you sent into the API, and based on all data to
+ *
This operation generates a model by using the points that you sent in to the API and based on all data to
* determine whether the last point is anomalous.
*
*
Request Body Schema
@@ -170,7 +169,7 @@ public Mono> detectUnivariateEntireSeriesWithResponse(
* @throws ClientAuthenticationException thrown if the request is rejected by server on status code 401.
* @throws ResourceNotFoundException thrown if the request is rejected by server on status code 404.
* @throws ResourceModifiedException thrown if the request is rejected by server on status code 409.
- * @return the response of last anomaly detection along with {@link Response} on successful completion of {@link
+ * @return response of the last anomaly detection along with {@link Response} on successful completion of {@link
* Mono}.
*/
@Generated
@@ -183,7 +182,7 @@ public Mono> detectUnivariateLastPointWithResponse(
/**
* Detect change point for the entire series
*
- * Evaluate change point score of every series point.
+ *
Evaluate the change point score of every series point.
*
*
Request Body Schema
*
@@ -223,8 +222,7 @@ public Mono> detectUnivariateLastPointWithResponse(
* @throws ClientAuthenticationException thrown if the request is rejected by server on status code 401.
* @throws ResourceNotFoundException thrown if the request is rejected by server on status code 404.
* @throws ResourceModifiedException thrown if the request is rejected by server on status code 409.
- * @return the response of change point detection along with {@link Response} on successful completion of {@link
- * Mono}.
+ * @return response of change point detection along with {@link Response} on successful completion of {@link Mono}.
*/
@Generated
@ServiceMethod(returns = ReturnType.SINGLE)
@@ -236,8 +234,8 @@ public Mono> detectUnivariateChangePointWithResponse(
/**
* Get Multivariate Anomaly Detection Result
*
- * For asynchronous inference, get multivariate anomaly detection result based on resultId returned by the
- * BatchDetectAnomaly api.
+ *
For asynchronous inference, get a multivariate anomaly detection result based on the resultId value that the
+ * BatchDetectAnomaly API returns.
*
*
Response Body Schema
*
@@ -263,7 +261,7 @@ public Mono> detectUnivariateChangePointWithResponse(
* ]
* setupInfo (Required): {
* dataSource: String (Required)
- * topContributorCount: int (Required)
+ * topContributorCount: Integer (Optional)
* startTime: OffsetDateTime (Required)
* endTime: OffsetDateTime (Required)
* }
@@ -301,7 +299,7 @@ public Mono> detectUnivariateChangePointWithResponse(
* @throws ClientAuthenticationException thrown if the request is rejected by server on status code 401.
* @throws ResourceNotFoundException thrown if the request is rejected by server on status code 404.
* @throws ResourceModifiedException thrown if the request is rejected by server on status code 409.
- * @return detection results for the given resultId along with {@link Response} on successful completion of {@link
+ * @return detection results for the resultId value along with {@link Response} on successful completion of {@link
* Mono}.
*/
@Generated
@@ -315,10 +313,12 @@ public Mono> getMultivariateBatchDetectionResultWithRespons
* Train a Multivariate Anomaly Detection Model
*
* Create and train a multivariate anomaly detection model. The request must include a source parameter to
- * indicate an externally accessible Azure blob storage URI.There are two types of data input: An URI pointed to an
- * Azure blob storage folder which contains multiple CSV files, and each CSV file contains two columns, timestamp
- * and variable. Another type of input is an URI pointed to a CSV file in Azure blob storage, which contains all the
- * variables and a timestamp column.
+ * indicate an Azure Blob Storage URI that's accessible to the service. There are two types of data input. The Blob
+ * Storage URI can point to an Azure Blob Storage folder that contains multiple CSV files, where each CSV file has
+ * two columns, time stamp and variable. Or the Blob Storage URI can point to a single blob that contains a CSV file
+ * that has all the variables and a time stamp column. The model object will be created and returned in the
+ * response, but the training process happens asynchronously. To check the training status, call
+ * GetMultivariateModel with the modelId value and check the status field in the modelInfo object.
*
*
Request Body Schema
*
@@ -450,8 +450,8 @@ public Mono> trainMultivariateModelWithResponse(
*
* Query Parameters
* | Name | Type | Required | Description |
|---|
- * | skip | Integer | No | Skip indicates how many models will be skipped. |
- * | top | Integer | No | Top indicates how many models will be fetched. |
+ * | skip | Integer | No | The number of result items to skip. |
+ * | top | Integer | No | The number of result items to return. |
*
*
* You can add these to a request with {@link RequestOptions#addQueryParam}
@@ -527,7 +527,7 @@ public PagedFlux listMultivariateModels(RequestOptions requestOption
/**
* Delete Multivariate Model
*
- * Delete an existing multivariate model according to the modelId.
+ *
Delete an existing multivariate model according to the modelId value.
*
* @param modelId Model identifier.
* @param requestOptions The options to configure the HTTP request before HTTP client sends it.
@@ -546,7 +546,8 @@ public Mono> deleteMultivariateModelWithResponse(String modelId,
/**
* Get Multivariate Model
*
- * Get detailed information of multivariate model, including the training status and variables used in the model.
+ *
Get detailed information about the multivariate model, including the training status and variables used in the
+ * model.
*
*
Response Body Schema
*
@@ -609,8 +610,8 @@ public Mono> deleteMultivariateModelWithResponse(String modelId,
* @throws ClientAuthenticationException thrown if the request is rejected by server on status code 401.
* @throws ResourceNotFoundException thrown if the request is rejected by server on status code 404.
* @throws ResourceModifiedException thrown if the request is rejected by server on status code 409.
- * @return detailed information of multivariate model, including the training status and variables used in the model
- * along with {@link Response} on successful completion of {@link Mono}.
+ * @return detailed information about the multivariate model, including the training status and variables used in
+ * the model along with {@link Response} on successful completion of {@link Mono}.
*/
@Generated
@ServiceMethod(returns = ReturnType.SINGLE)
@@ -621,18 +622,18 @@ public Mono> getMultivariateModelWithResponse(String modelI
/**
* Detect Multivariate Anomaly
*
- * Submit multivariate anomaly detection task with the modelId of trained model and inference data, the input
- * schema should be the same with the training request. The request will complete asynchronously and return a
- * resultId to query the detection result.The request should be a source link to indicate an externally accessible
- * Azure storage Uri, either pointed to an Azure blob storage folder, or pointed to a CSV file in Azure blob
- * storage.
+ *
Submit a multivariate anomaly detection task with the modelId value of a trained model and inference data. The
+ * input schema should be the same with the training request. The request will finish asynchronously and return a
+ * resultId value to query the detection result. The request should be a source link to indicate an externally
+ * accessible Azure Storage URI that either points to an Azure Blob Storage folder or points to a CSV file in Azure
+ * Blob Storage.
*
*
Request Body Schema
*
*
{@code
* {
* dataSource: String (Required)
- * topContributorCount: int (Required)
+ * topContributorCount: Integer (Optional)
* startTime: OffsetDateTime (Required)
* endTime: OffsetDateTime (Required)
* }
@@ -662,7 +663,7 @@ public Mono> getMultivariateModelWithResponse(String modelI
* ]
* setupInfo (Required): {
* dataSource: String (Required)
- * topContributorCount: int (Required)
+ * topContributorCount: Integer (Optional)
* startTime: OffsetDateTime (Required)
* endTime: OffsetDateTime (Required)
* }
@@ -701,7 +702,7 @@ public Mono> getMultivariateModelWithResponse(String modelI
* @throws ClientAuthenticationException thrown if the request is rejected by server on status code 401.
* @throws ResourceNotFoundException thrown if the request is rejected by server on status code 404.
* @throws ResourceModifiedException thrown if the request is rejected by server on status code 409.
- * @return detection results for the given resultId along with {@link Response} on successful completion of {@link
+ * @return detection results for the resultId value along with {@link Response} on successful completion of {@link
* Mono}.
*/
@Generated
@@ -714,8 +715,8 @@ public Mono> detectMultivariateBatchAnomalyWithResponse(
/**
* Detect anomalies in the last point of the request body
*
- * Submit multivariate anomaly detection task with the modelId of trained model and inference data, and the
- * inference data should be put into request body in a JSON format. The request will complete synchronously and
+ *
Submit a multivariate anomaly detection task with the modelId value of a trained model and inference data. The
+ * inference data should be put into the request body in JSON format. The request will finish synchronously and
* return the detection immediately in the response body.
*
*
Request Body Schema
@@ -733,7 +734,7 @@ public Mono> detectMultivariateBatchAnomalyWithResponse(
* ]
* }
* ]
- * topContributorCount: int (Required)
+ * topContributorCount: Integer (Optional)
* }
* }
*
@@ -781,13 +782,13 @@ public Mono> detectMultivariateBatchAnomalyWithResponse(
* }
*
* @param modelId Model identifier.
- * @param options Request of last detection.
+ * @param options Request of the last detection.
* @param requestOptions The options to configure the HTTP request before HTTP client sends it.
* @throws HttpResponseException thrown if the request is rejected by server.
* @throws ClientAuthenticationException thrown if the request is rejected by server on status code 401.
* @throws ResourceNotFoundException thrown if the request is rejected by server on status code 404.
* @throws ResourceModifiedException thrown if the request is rejected by server on status code 409.
- * @return results of last detection along with {@link Response} on successful completion of {@link Mono}.
+ * @return results of the last detection along with {@link Response} on successful completion of {@link Mono}.
*/
@Generated
@ServiceMethod(returns = ReturnType.SINGLE)
@@ -796,36 +797,10 @@ public Mono> detectMultivariateLastAnomalyWithResponse(
return this.serviceClient.detectMultivariateLastAnomalyWithResponseAsync(modelId, options, requestOptions);
}
- /**
- * Detect anomalies for the entire series in batch.
- *
- * This operation generates a model with an entire series, each point is detected with the same model. With this
- * method, points before and after a certain point are used to determine whether it is an anomaly. The entire
- * detection can give user an overall status of the time series.
- *
- * @param options Method of univariate anomaly detection.
- * @throws IllegalArgumentException thrown if parameters fail the validation.
- * @throws HttpResponseException thrown if the request is rejected by server.
- * @throws ClientAuthenticationException thrown if the request is rejected by server on status code 401.
- * @throws ResourceNotFoundException thrown if the request is rejected by server on status code 404.
- * @throws ResourceModifiedException thrown if the request is rejected by server on status code 409.
- * @throws RuntimeException all other wrapped checked exceptions if the request fails to be sent.
- * @return the response of entire anomaly detection on successful completion of {@link Mono}.
- */
- @Generated
- @ServiceMethod(returns = ReturnType.SINGLE)
- public Mono detectUnivariateEntireSeries(UnivariateDetectionOptions options) {
- // Generated convenience method for detectUnivariateEntireSeriesWithResponse
- RequestOptions requestOptions = new RequestOptions();
- return detectUnivariateEntireSeriesWithResponse(BinaryData.fromObject(options), requestOptions)
- .flatMap(FluxUtil::toMono)
- .map(protocolMethodData -> protocolMethodData.toObject(UnivariateEntireDetectionResult.class));
- }
-
/**
* Detect anomaly status of the latest point in time series.
*
- * This operation generates a model using the points that you sent into the API, and based on all data to
+ *
This operation generates a model by using the points that you sent in to the API and based on all data to
* determine whether the last point is anomalous.
*
* @param options Method of univariate anomaly detection.
@@ -835,7 +810,7 @@ public Mono detectUnivariateEntireSeries(Univar
* @throws ResourceNotFoundException thrown if the request is rejected by server on status code 404.
* @throws ResourceModifiedException thrown if the request is rejected by server on status code 409.
* @throws RuntimeException all other wrapped checked exceptions if the request fails to be sent.
- * @return the response of last anomaly detection on successful completion of {@link Mono}.
+ * @return response of the last anomaly detection on successful completion of {@link Mono}.
*/
@Generated
@ServiceMethod(returns = ReturnType.SINGLE)
@@ -850,7 +825,7 @@ public Mono detectUnivariateLastPoint(UnivariateD
/**
* Detect change point for the entire series
*
- * Evaluate change point score of every series point.
+ *
Evaluate the change point score of every series point.
*
* @param options Method of univariate anomaly detection.
* @throws IllegalArgumentException thrown if parameters fail the validation.
@@ -859,7 +834,7 @@ public Mono detectUnivariateLastPoint(UnivariateD
* @throws ResourceNotFoundException thrown if the request is rejected by server on status code 404.
* @throws ResourceModifiedException thrown if the request is rejected by server on status code 409.
* @throws RuntimeException all other wrapped checked exceptions if the request fails to be sent.
- * @return the response of change point detection on successful completion of {@link Mono}.
+ * @return response of change point detection on successful completion of {@link Mono}.
*/
@Generated
@ServiceMethod(returns = ReturnType.SINGLE)
@@ -875,8 +850,8 @@ public Mono detectUnivariateChangePoint(
/**
* Get Multivariate Anomaly Detection Result
*
- * For asynchronous inference, get multivariate anomaly detection result based on resultId returned by the
- * BatchDetectAnomaly api.
+ *
For asynchronous inference, get a multivariate anomaly detection result based on the resultId value that the
+ * BatchDetectAnomaly API returns.
*
* @param resultId ID of a batch detection result.
* @throws IllegalArgumentException thrown if parameters fail the validation.
@@ -885,7 +860,7 @@ public Mono detectUnivariateChangePoint(
* @throws ResourceNotFoundException thrown if the request is rejected by server on status code 404.
* @throws ResourceModifiedException thrown if the request is rejected by server on status code 409.
* @throws RuntimeException all other wrapped checked exceptions if the request fails to be sent.
- * @return detection results for the given resultId on successful completion of {@link Mono}.
+ * @return detection results for the resultId value on successful completion of {@link Mono}.
*/
@Generated
@ServiceMethod(returns = ReturnType.SINGLE)
@@ -901,10 +876,12 @@ public Mono getMultivariateBatchDetectionResult(Str
* Train a Multivariate Anomaly Detection Model
*
* Create and train a multivariate anomaly detection model. The request must include a source parameter to
- * indicate an externally accessible Azure blob storage URI.There are two types of data input: An URI pointed to an
- * Azure blob storage folder which contains multiple CSV files, and each CSV file contains two columns, timestamp
- * and variable. Another type of input is an URI pointed to a CSV file in Azure blob storage, which contains all the
- * variables and a timestamp column.
+ * indicate an Azure Blob Storage URI that's accessible to the service. There are two types of data input. The Blob
+ * Storage URI can point to an Azure Blob Storage folder that contains multiple CSV files, where each CSV file has
+ * two columns, time stamp and variable. Or the Blob Storage URI can point to a single blob that contains a CSV file
+ * that has all the variables and a time stamp column. The model object will be created and returned in the
+ * response, but the training process happens asynchronously. To check the training status, call
+ * GetMultivariateModel with the modelId value and check the status field in the modelInfo object.
*
* @param modelInfo Model information.
* @throws IllegalArgumentException thrown if parameters fail the validation.
@@ -1020,7 +997,7 @@ public PagedFlux listMultivariateModels() {
/**
* Delete Multivariate Model
*
- * Delete an existing multivariate model according to the modelId.
+ *
Delete an existing multivariate model according to the modelId value.
*
* @param modelId Model identifier.
* @throws IllegalArgumentException thrown if parameters fail the validation.
@@ -1042,7 +1019,8 @@ public Mono deleteMultivariateModel(String modelId) {
/**
* Get Multivariate Model
*
- * Get detailed information of multivariate model, including the training status and variables used in the model.
+ *
Get detailed information about the multivariate model, including the training status and variables used in the
+ * model.
*
* @param modelId Model identifier.
* @throws IllegalArgumentException thrown if parameters fail the validation.
@@ -1051,8 +1029,8 @@ public Mono deleteMultivariateModel(String modelId) {
* @throws ResourceNotFoundException thrown if the request is rejected by server on status code 404.
* @throws ResourceModifiedException thrown if the request is rejected by server on status code 409.
* @throws RuntimeException all other wrapped checked exceptions if the request fails to be sent.
- * @return detailed information of multivariate model, including the training status and variables used in the model
- * on successful completion of {@link Mono}.
+ * @return detailed information about the multivariate model, including the training status and variables used in
+ * the model on successful completion of {@link Mono}.
*/
@Generated
@ServiceMethod(returns = ReturnType.SINGLE)
@@ -1067,11 +1045,11 @@ public Mono getMultivariateModel(String modelId) {
/**
* Detect Multivariate Anomaly
*
- * Submit multivariate anomaly detection task with the modelId of trained model and inference data, the input
- * schema should be the same with the training request. The request will complete asynchronously and return a
- * resultId to query the detection result.The request should be a source link to indicate an externally accessible
- * Azure storage Uri, either pointed to an Azure blob storage folder, or pointed to a CSV file in Azure blob
- * storage.
+ *
Submit a multivariate anomaly detection task with the modelId value of a trained model and inference data. The
+ * input schema should be the same with the training request. The request will finish asynchronously and return a
+ * resultId value to query the detection result. The request should be a source link to indicate an externally
+ * accessible Azure Storage URI that either points to an Azure Blob Storage folder or points to a CSV file in Azure
+ * Blob Storage.
*
* @param modelId Model identifier.
* @param options Request of multivariate anomaly detection.
@@ -1081,7 +1059,7 @@ public Mono getMultivariateModel(String modelId) {
* @throws ResourceNotFoundException thrown if the request is rejected by server on status code 404.
* @throws ResourceModifiedException thrown if the request is rejected by server on status code 409.
* @throws RuntimeException all other wrapped checked exceptions if the request fails to be sent.
- * @return detection results for the given resultId on successful completion of {@link Mono}.
+ * @return detection results for the resultId value on successful completion of {@link Mono}.
*/
@Generated
@ServiceMethod(returns = ReturnType.SINGLE)
@@ -1097,19 +1075,19 @@ public Mono detectMultivariateBatchAnomaly(
/**
* Detect anomalies in the last point of the request body
*
- * Submit multivariate anomaly detection task with the modelId of trained model and inference data, and the
- * inference data should be put into request body in a JSON format. The request will complete synchronously and
+ *
Submit a multivariate anomaly detection task with the modelId value of a trained model and inference data. The
+ * inference data should be put into the request body in JSON format. The request will finish synchronously and
* return the detection immediately in the response body.
*
* @param modelId Model identifier.
- * @param options Request of last detection.
+ * @param options Request of the last detection.
* @throws IllegalArgumentException thrown if parameters fail the validation.
* @throws HttpResponseException thrown if the request is rejected by server.
* @throws ClientAuthenticationException thrown if the request is rejected by server on status code 401.
* @throws ResourceNotFoundException thrown if the request is rejected by server on status code 404.
* @throws ResourceModifiedException thrown if the request is rejected by server on status code 409.
* @throws RuntimeException all other wrapped checked exceptions if the request fails to be sent.
- * @return results of last detection on successful completion of {@link Mono}.
+ * @return results of the last detection on successful completion of {@link Mono}.
*/
@Generated
@ServiceMethod(returns = ReturnType.SINGLE)
diff --git a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/AnomalyDetectorClient.java b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/AnomalyDetectorClient.java
index 3b37c4eeb688..774f7de75873 100644
--- a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/AnomalyDetectorClient.java
+++ b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/AnomalyDetectorClient.java
@@ -12,7 +12,6 @@
import com.azure.ai.anomalydetector.models.UnivariateChangePointDetectionOptions;
import com.azure.ai.anomalydetector.models.UnivariateChangePointDetectionResult;
import com.azure.ai.anomalydetector.models.UnivariateDetectionOptions;
-import com.azure.ai.anomalydetector.models.UnivariateEntireDetectionResult;
import com.azure.ai.anomalydetector.models.UnivariateLastDetectionResult;
import com.azure.core.annotation.Generated;
import com.azure.core.annotation.ReturnType;
@@ -46,9 +45,9 @@ public final class AnomalyDetectorClient {
/**
* Detect anomalies for the entire series in batch.
*
- *
This operation generates a model with an entire series, each point is detected with the same model. With this
- * method, points before and after a certain point are used to determine whether it is an anomaly. The entire
- * detection can give user an overall status of the time series.
+ *
This operation generates a model with an entire series. Each point is detected with the same model. With this
+ * method, points before and after a certain point are used to determine whether it's an anomaly. The entire
+ * detection can give the user an overall status of the time series.
*
*
Request Body Schema
*
@@ -105,7 +104,7 @@ public final class AnomalyDetectorClient {
* @throws ClientAuthenticationException thrown if the request is rejected by server on status code 401.
* @throws ResourceNotFoundException thrown if the request is rejected by server on status code 404.
* @throws ResourceModifiedException thrown if the request is rejected by server on status code 409.
- * @return the response of entire anomaly detection along with {@link Response}.
+ * @return response of the entire anomaly detection along with {@link Response}.
*/
@Generated
@ServiceMethod(returns = ReturnType.SINGLE)
@@ -117,7 +116,7 @@ public Response detectUnivariateEntireSeriesWithResponse(
/**
* Detect anomaly status of the latest point in time series.
*
- * This operation generates a model using the points that you sent into the API, and based on all data to
+ *
This operation generates a model by using the points that you sent in to the API and based on all data to
* determine whether the last point is anomalous.
*
*
Request Body Schema
@@ -162,7 +161,7 @@ public Response detectUnivariateEntireSeriesWithResponse(
* @throws ClientAuthenticationException thrown if the request is rejected by server on status code 401.
* @throws ResourceNotFoundException thrown if the request is rejected by server on status code 404.
* @throws ResourceModifiedException thrown if the request is rejected by server on status code 409.
- * @return the response of last anomaly detection along with {@link Response}.
+ * @return response of the last anomaly detection along with {@link Response}.
*/
@Generated
@ServiceMethod(returns = ReturnType.SINGLE)
@@ -174,7 +173,7 @@ public Response detectUnivariateLastPointWithResponse(
/**
* Detect change point for the entire series
*
- * Evaluate change point score of every series point.
+ *
Evaluate the change point score of every series point.
*
*
Request Body Schema
*
@@ -214,7 +213,7 @@ public Response detectUnivariateLastPointWithResponse(
* @throws ClientAuthenticationException thrown if the request is rejected by server on status code 401.
* @throws ResourceNotFoundException thrown if the request is rejected by server on status code 404.
* @throws ResourceModifiedException thrown if the request is rejected by server on status code 409.
- * @return the response of change point detection along with {@link Response}.
+ * @return response of change point detection along with {@link Response}.
*/
@Generated
@ServiceMethod(returns = ReturnType.SINGLE)
@@ -226,8 +225,8 @@ public Response detectUnivariateChangePointWithResponse(
/**
* Get Multivariate Anomaly Detection Result
*
- * For asynchronous inference, get multivariate anomaly detection result based on resultId returned by the
- * BatchDetectAnomaly api.
+ *
For asynchronous inference, get a multivariate anomaly detection result based on the resultId value that the
+ * BatchDetectAnomaly API returns.
*
*
Response Body Schema
*
@@ -253,7 +252,7 @@ public Response detectUnivariateChangePointWithResponse(
* ]
* setupInfo (Required): {
* dataSource: String (Required)
- * topContributorCount: int (Required)
+ * topContributorCount: Integer (Optional)
* startTime: OffsetDateTime (Required)
* endTime: OffsetDateTime (Required)
* }
@@ -291,7 +290,7 @@ public Response detectUnivariateChangePointWithResponse(
* @throws ClientAuthenticationException thrown if the request is rejected by server on status code 401.
* @throws ResourceNotFoundException thrown if the request is rejected by server on status code 404.
* @throws ResourceModifiedException thrown if the request is rejected by server on status code 409.
- * @return detection results for the given resultId along with {@link Response}.
+ * @return detection results for the resultId value along with {@link Response}.
*/
@Generated
@ServiceMethod(returns = ReturnType.SINGLE)
@@ -304,10 +303,12 @@ public Response getMultivariateBatchDetectionResultWithResponse(
* Train a Multivariate Anomaly Detection Model
*
* Create and train a multivariate anomaly detection model. The request must include a source parameter to
- * indicate an externally accessible Azure blob storage URI.There are two types of data input: An URI pointed to an
- * Azure blob storage folder which contains multiple CSV files, and each CSV file contains two columns, timestamp
- * and variable. Another type of input is an URI pointed to a CSV file in Azure blob storage, which contains all the
- * variables and a timestamp column.
+ * indicate an Azure Blob Storage URI that's accessible to the service. There are two types of data input. The Blob
+ * Storage URI can point to an Azure Blob Storage folder that contains multiple CSV files, where each CSV file has
+ * two columns, time stamp and variable. Or the Blob Storage URI can point to a single blob that contains a CSV file
+ * that has all the variables and a time stamp column. The model object will be created and returned in the
+ * response, but the training process happens asynchronously. To check the training status, call
+ * GetMultivariateModel with the modelId value and check the status field in the modelInfo object.
*
*
Request Body Schema
*
@@ -439,8 +440,8 @@ public Response trainMultivariateModelWithResponse(
*
* Query Parameters
* | Name | Type | Required | Description |
|---|
- * | skip | Integer | No | Skip indicates how many models will be skipped. |
- * | top | Integer | No | Top indicates how many models will be fetched. |
+ * | skip | Integer | No | The number of result items to skip. |
+ * | top | Integer | No | The number of result items to return. |
*
*
* You can add these to a request with {@link RequestOptions#addQueryParam}
@@ -516,7 +517,7 @@ public PagedIterable listMultivariateModels(RequestOptions requestOp
/**
* Delete Multivariate Model
*
- * Delete an existing multivariate model according to the modelId.
+ *
Delete an existing multivariate model according to the modelId value.
*
* @param modelId Model identifier.
* @param requestOptions The options to configure the HTTP request before HTTP client sends it.
@@ -535,7 +536,8 @@ public Response deleteMultivariateModelWithResponse(String modelId, Reques
/**
* Get Multivariate Model
*
- * Get detailed information of multivariate model, including the training status and variables used in the model.
+ *
Get detailed information about the multivariate model, including the training status and variables used in the
+ * model.
*
*
Response Body Schema
*
@@ -598,8 +600,8 @@ public Response deleteMultivariateModelWithResponse(String modelId, Reques
* @throws ClientAuthenticationException thrown if the request is rejected by server on status code 401.
* @throws ResourceNotFoundException thrown if the request is rejected by server on status code 404.
* @throws ResourceModifiedException thrown if the request is rejected by server on status code 409.
- * @return detailed information of multivariate model, including the training status and variables used in the model
- * along with {@link Response}.
+ * @return detailed information about the multivariate model, including the training status and variables used in
+ * the model along with {@link Response}.
*/
@Generated
@ServiceMethod(returns = ReturnType.SINGLE)
@@ -610,18 +612,18 @@ public Response getMultivariateModelWithResponse(String modelId, Req
/**
* Detect Multivariate Anomaly
*
- * Submit multivariate anomaly detection task with the modelId of trained model and inference data, the input
- * schema should be the same with the training request. The request will complete asynchronously and return a
- * resultId to query the detection result.The request should be a source link to indicate an externally accessible
- * Azure storage Uri, either pointed to an Azure blob storage folder, or pointed to a CSV file in Azure blob
- * storage.
+ *
Submit a multivariate anomaly detection task with the modelId value of a trained model and inference data. The
+ * input schema should be the same with the training request. The request will finish asynchronously and return a
+ * resultId value to query the detection result. The request should be a source link to indicate an externally
+ * accessible Azure Storage URI that either points to an Azure Blob Storage folder or points to a CSV file in Azure
+ * Blob Storage.
*
*
Request Body Schema
*
*
{@code
* {
* dataSource: String (Required)
- * topContributorCount: int (Required)
+ * topContributorCount: Integer (Optional)
* startTime: OffsetDateTime (Required)
* endTime: OffsetDateTime (Required)
* }
@@ -651,7 +653,7 @@ public Response getMultivariateModelWithResponse(String modelId, Req
* ]
* setupInfo (Required): {
* dataSource: String (Required)
- * topContributorCount: int (Required)
+ * topContributorCount: Integer (Optional)
* startTime: OffsetDateTime (Required)
* endTime: OffsetDateTime (Required)
* }
@@ -690,7 +692,7 @@ public Response getMultivariateModelWithResponse(String modelId, Req
* @throws ClientAuthenticationException thrown if the request is rejected by server on status code 401.
* @throws ResourceNotFoundException thrown if the request is rejected by server on status code 404.
* @throws ResourceModifiedException thrown if the request is rejected by server on status code 409.
- * @return detection results for the given resultId along with {@link Response}.
+ * @return detection results for the resultId value along with {@link Response}.
*/
@Generated
@ServiceMethod(returns = ReturnType.SINGLE)
@@ -702,8 +704,8 @@ public Response detectMultivariateBatchAnomalyWithResponse(
/**
* Detect anomalies in the last point of the request body
*
- * Submit multivariate anomaly detection task with the modelId of trained model and inference data, and the
- * inference data should be put into request body in a JSON format. The request will complete synchronously and
+ *
Submit a multivariate anomaly detection task with the modelId value of a trained model and inference data. The
+ * inference data should be put into the request body in JSON format. The request will finish synchronously and
* return the detection immediately in the response body.
*
*
Request Body Schema
@@ -721,7 +723,7 @@ public Response detectMultivariateBatchAnomalyWithResponse(
* ]
* }
* ]
- * topContributorCount: int (Required)
+ * topContributorCount: Integer (Optional)
* }
* }
*
@@ -769,13 +771,13 @@ public Response detectMultivariateBatchAnomalyWithResponse(
* }
*
* @param modelId Model identifier.
- * @param options Request of last detection.
+ * @param options Request of the last detection.
* @param requestOptions The options to configure the HTTP request before HTTP client sends it.
* @throws HttpResponseException thrown if the request is rejected by server.
* @throws ClientAuthenticationException thrown if the request is rejected by server on status code 401.
* @throws ResourceNotFoundException thrown if the request is rejected by server on status code 404.
* @throws ResourceModifiedException thrown if the request is rejected by server on status code 409.
- * @return results of last detection along with {@link Response}.
+ * @return results of the last detection along with {@link Response}.
*/
@Generated
@ServiceMethod(returns = ReturnType.SINGLE)
@@ -784,36 +786,10 @@ public Response detectMultivariateLastAnomalyWithResponse(
return this.client.detectMultivariateLastAnomalyWithResponse(modelId, options, requestOptions).block();
}
- /**
- * Detect anomalies for the entire series in batch.
- *
- * This operation generates a model with an entire series, each point is detected with the same model. With this
- * method, points before and after a certain point are used to determine whether it is an anomaly. The entire
- * detection can give user an overall status of the time series.
- *
- * @param options Method of univariate anomaly detection.
- * @throws IllegalArgumentException thrown if parameters fail the validation.
- * @throws HttpResponseException thrown if the request is rejected by server.
- * @throws ClientAuthenticationException thrown if the request is rejected by server on status code 401.
- * @throws ResourceNotFoundException thrown if the request is rejected by server on status code 404.
- * @throws ResourceModifiedException thrown if the request is rejected by server on status code 409.
- * @throws RuntimeException all other wrapped checked exceptions if the request fails to be sent.
- * @return the response of entire anomaly detection.
- */
- @Generated
- @ServiceMethod(returns = ReturnType.SINGLE)
- public UnivariateEntireDetectionResult detectUnivariateEntireSeries(UnivariateDetectionOptions options) {
- // Generated convenience method for detectUnivariateEntireSeriesWithResponse
- RequestOptions requestOptions = new RequestOptions();
- return detectUnivariateEntireSeriesWithResponse(BinaryData.fromObject(options), requestOptions)
- .getValue()
- .toObject(UnivariateEntireDetectionResult.class);
- }
-
/**
* Detect anomaly status of the latest point in time series.
*
- *
This operation generates a model using the points that you sent into the API, and based on all data to
+ *
This operation generates a model by using the points that you sent in to the API and based on all data to
* determine whether the last point is anomalous.
*
* @param options Method of univariate anomaly detection.
@@ -823,7 +799,7 @@ public UnivariateEntireDetectionResult detectUnivariateEntireSeries(UnivariateDe
* @throws ResourceNotFoundException thrown if the request is rejected by server on status code 404.
* @throws ResourceModifiedException thrown if the request is rejected by server on status code 409.
* @throws RuntimeException all other wrapped checked exceptions if the request fails to be sent.
- * @return the response of last anomaly detection.
+ * @return response of the last anomaly detection.
*/
@Generated
@ServiceMethod(returns = ReturnType.SINGLE)
@@ -838,7 +814,7 @@ public UnivariateLastDetectionResult detectUnivariateLastPoint(UnivariateDetecti
/**
* Detect change point for the entire series
*
- *
Evaluate change point score of every series point.
+ *
Evaluate the change point score of every series point.
*
* @param options Method of univariate anomaly detection.
* @throws IllegalArgumentException thrown if parameters fail the validation.
@@ -847,7 +823,7 @@ public UnivariateLastDetectionResult detectUnivariateLastPoint(UnivariateDetecti
* @throws ResourceNotFoundException thrown if the request is rejected by server on status code 404.
* @throws ResourceModifiedException thrown if the request is rejected by server on status code 409.
* @throws RuntimeException all other wrapped checked exceptions if the request fails to be sent.
- * @return the response of change point detection.
+ * @return response of change point detection.
*/
@Generated
@ServiceMethod(returns = ReturnType.SINGLE)
@@ -863,8 +839,8 @@ public UnivariateChangePointDetectionResult detectUnivariateChangePoint(
/**
* Get Multivariate Anomaly Detection Result
*
- *
For asynchronous inference, get multivariate anomaly detection result based on resultId returned by the
- * BatchDetectAnomaly api.
+ *
For asynchronous inference, get a multivariate anomaly detection result based on the resultId value that the
+ * BatchDetectAnomaly API returns.
*
* @param resultId ID of a batch detection result.
* @throws IllegalArgumentException thrown if parameters fail the validation.
@@ -873,7 +849,7 @@ public UnivariateChangePointDetectionResult detectUnivariateChangePoint(
* @throws ResourceNotFoundException thrown if the request is rejected by server on status code 404.
* @throws ResourceModifiedException thrown if the request is rejected by server on status code 409.
* @throws RuntimeException all other wrapped checked exceptions if the request fails to be sent.
- * @return detection results for the given resultId.
+ * @return detection results for the resultId value.
*/
@Generated
@ServiceMethod(returns = ReturnType.SINGLE)
@@ -889,10 +865,12 @@ public MultivariateDetectionResult getMultivariateBatchDetectionResult(String re
* Train a Multivariate Anomaly Detection Model
*
*
Create and train a multivariate anomaly detection model. The request must include a source parameter to
- * indicate an externally accessible Azure blob storage URI.There are two types of data input: An URI pointed to an
- * Azure blob storage folder which contains multiple CSV files, and each CSV file contains two columns, timestamp
- * and variable. Another type of input is an URI pointed to a CSV file in Azure blob storage, which contains all the
- * variables and a timestamp column.
+ * indicate an Azure Blob Storage URI that's accessible to the service. There are two types of data input. The Blob
+ * Storage URI can point to an Azure Blob Storage folder that contains multiple CSV files, where each CSV file has
+ * two columns, time stamp and variable. Or the Blob Storage URI can point to a single blob that contains a CSV file
+ * that has all the variables and a time stamp column. The model object will be created and returned in the
+ * response, but the training process happens asynchronously. To check the training status, call
+ * GetMultivariateModel with the modelId value and check the status field in the modelInfo object.
*
* @param modelInfo Model information.
* @throws IllegalArgumentException thrown if parameters fail the validation.
@@ -950,13 +928,14 @@ PagedIterable listMultivariateModels(Integer skip, Intege
@ServiceMethod(returns = ReturnType.COLLECTION)
public PagedIterable listMultivariateModels() {
// Generated convenience method for listMultivariateModels
+ RequestOptions requestOptions = new RequestOptions();
return new PagedIterable<>(client.listMultivariateModels());
}
/**
* Delete Multivariate Model
*
- * Delete an existing multivariate model according to the modelId.
+ *
Delete an existing multivariate model according to the modelId value.
*
* @param modelId Model identifier.
* @throws IllegalArgumentException thrown if parameters fail the validation.
@@ -977,7 +956,8 @@ public void deleteMultivariateModel(String modelId) {
/**
* Get Multivariate Model
*
- *
Get detailed information of multivariate model, including the training status and variables used in the model.
+ *
Get detailed information about the multivariate model, including the training status and variables used in the
+ * model.
*
* @param modelId Model identifier.
* @throws IllegalArgumentException thrown if parameters fail the validation.
@@ -986,8 +966,8 @@ public void deleteMultivariateModel(String modelId) {
* @throws ResourceNotFoundException thrown if the request is rejected by server on status code 404.
* @throws ResourceModifiedException thrown if the request is rejected by server on status code 409.
* @throws RuntimeException all other wrapped checked exceptions if the request fails to be sent.
- * @return detailed information of multivariate model, including the training status and variables used in the
- * model.
+ * @return detailed information about the multivariate model, including the training status and variables used in
+ * the model.
*/
@Generated
@ServiceMethod(returns = ReturnType.SINGLE)
@@ -1002,11 +982,11 @@ public AnomalyDetectionModel getMultivariateModel(String modelId) {
/**
* Detect Multivariate Anomaly
*
- *
Submit multivariate anomaly detection task with the modelId of trained model and inference data, the input
- * schema should be the same with the training request. The request will complete asynchronously and return a
- * resultId to query the detection result.The request should be a source link to indicate an externally accessible
- * Azure storage Uri, either pointed to an Azure blob storage folder, or pointed to a CSV file in Azure blob
- * storage.
+ *
Submit a multivariate anomaly detection task with the modelId value of a trained model and inference data. The
+ * input schema should be the same with the training request. The request will finish asynchronously and return a
+ * resultId value to query the detection result. The request should be a source link to indicate an externally
+ * accessible Azure Storage URI that either points to an Azure Blob Storage folder or points to a CSV file in Azure
+ * Blob Storage.
*
* @param modelId Model identifier.
* @param options Request of multivariate anomaly detection.
@@ -1016,7 +996,7 @@ public AnomalyDetectionModel getMultivariateModel(String modelId) {
* @throws ResourceNotFoundException thrown if the request is rejected by server on status code 404.
* @throws ResourceModifiedException thrown if the request is rejected by server on status code 409.
* @throws RuntimeException all other wrapped checked exceptions if the request fails to be sent.
- * @return detection results for the given resultId.
+ * @return detection results for the resultId value.
*/
@Generated
@ServiceMethod(returns = ReturnType.SINGLE)
@@ -1032,19 +1012,19 @@ public MultivariateDetectionResult detectMultivariateBatchAnomaly(
/**
* Detect anomalies in the last point of the request body
*
- *
Submit multivariate anomaly detection task with the modelId of trained model and inference data, and the
- * inference data should be put into request body in a JSON format. The request will complete synchronously and
+ *
Submit a multivariate anomaly detection task with the modelId value of a trained model and inference data. The
+ * inference data should be put into the request body in JSON format. The request will finish synchronously and
* return the detection immediately in the response body.
*
* @param modelId Model identifier.
- * @param options Request of last detection.
+ * @param options Request of the last detection.
* @throws IllegalArgumentException thrown if parameters fail the validation.
* @throws HttpResponseException thrown if the request is rejected by server.
* @throws ClientAuthenticationException thrown if the request is rejected by server on status code 401.
* @throws ResourceNotFoundException thrown if the request is rejected by server on status code 404.
* @throws ResourceModifiedException thrown if the request is rejected by server on status code 409.
* @throws RuntimeException all other wrapped checked exceptions if the request fails to be sent.
- * @return results of last detection.
+ * @return results of the last detection.
*/
@Generated
@ServiceMethod(returns = ReturnType.SINGLE)
diff --git a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/implementation/AnomalyDetectorClientImpl.java b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/implementation/AnomalyDetectorClientImpl.java
index c691ab0a7989..10df3cec6861 100644
--- a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/implementation/AnomalyDetectorClientImpl.java
+++ b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/implementation/AnomalyDetectorClientImpl.java
@@ -50,13 +50,13 @@ public final class AnomalyDetectorClientImpl {
private final AnomalyDetectorClientService service;
/**
- * Supported Cognitive Services endpoints (protocol and hostname, for example:
+ * Supported Azure Cognitive Services endpoints (protocol and host name, such as
* https://westus2.api.cognitive.microsoft.com).
*/
private final String endpoint;
/**
- * Gets Supported Cognitive Services endpoints (protocol and hostname, for example:
+ * Gets Supported Azure Cognitive Services endpoints (protocol and host name, such as
* https://westus2.api.cognitive.microsoft.com).
*
* @return the endpoint value.
@@ -104,7 +104,7 @@ public SerializerAdapter getSerializerAdapter() {
/**
* Initializes an instance of AnomalyDetectorClient client.
*
- * @param endpoint Supported Cognitive Services endpoints (protocol and hostname, for example:
+ * @param endpoint Supported Azure Cognitive Services endpoints (protocol and host name, such as
* https://westus2.api.cognitive.microsoft.com).
* @param serviceVersion Service version.
*/
@@ -122,7 +122,7 @@ public AnomalyDetectorClientImpl(String endpoint, AnomalyDetectorServiceVersion
* Initializes an instance of AnomalyDetectorClient client.
*
* @param httpPipeline The HTTP pipeline to send requests through.
- * @param endpoint Supported Cognitive Services endpoints (protocol and hostname, for example:
+ * @param endpoint Supported Azure Cognitive Services endpoints (protocol and host name, such as
* https://westus2.api.cognitive.microsoft.com).
* @param serviceVersion Service version.
*/
@@ -136,7 +136,7 @@ public AnomalyDetectorClientImpl(
*
* @param httpPipeline The HTTP pipeline to send requests through.
* @param serializerAdapter The serializer to serialize an object into a string.
- * @param endpoint Supported Cognitive Services endpoints (protocol and hostname, for example:
+ * @param endpoint Supported Azure Cognitive Services endpoints (protocol and host name, such as
* https://westus2.api.cognitive.microsoft.com).
* @param serviceVersion Service version.
*/
@@ -385,9 +385,9 @@ Mono> listMultivariateModelsNext(
/**
* Detect anomalies for the entire series in batch.
*
- * This operation generates a model with an entire series, each point is detected with the same model. With this
- * method, points before and after a certain point are used to determine whether it is an anomaly. The entire
- * detection can give user an overall status of the time series.
+ *
This operation generates a model with an entire series. Each point is detected with the same model. With this
+ * method, points before and after a certain point are used to determine whether it's an anomaly. The entire
+ * detection can give the user an overall status of the time series.
*
*
Request Body Schema
*
@@ -444,7 +444,7 @@ Mono> listMultivariateModelsNext(
* @throws ClientAuthenticationException thrown if the request is rejected by server on status code 401.
* @throws ResourceNotFoundException thrown if the request is rejected by server on status code 404.
* @throws ResourceModifiedException thrown if the request is rejected by server on status code 409.
- * @return the response of entire anomaly detection along with {@link Response} on successful completion of {@link
+ * @return response of the entire anomaly detection along with {@link Response} on successful completion of {@link
* Mono}.
*/
@ServiceMethod(returns = ReturnType.SINGLE)
@@ -465,9 +465,9 @@ public Mono> detectUnivariateEntireSeriesWithResponseAsync(
/**
* Detect anomalies for the entire series in batch.
*
- * This operation generates a model with an entire series, each point is detected with the same model. With this
- * method, points before and after a certain point are used to determine whether it is an anomaly. The entire
- * detection can give user an overall status of the time series.
+ *
This operation generates a model with an entire series. Each point is detected with the same model. With this
+ * method, points before and after a certain point are used to determine whether it's an anomaly. The entire
+ * detection can give the user an overall status of the time series.
*
*
Request Body Schema
*
@@ -524,7 +524,7 @@ public Mono> detectUnivariateEntireSeriesWithResponseAsync(
* @throws ClientAuthenticationException thrown if the request is rejected by server on status code 401.
* @throws ResourceNotFoundException thrown if the request is rejected by server on status code 404.
* @throws ResourceModifiedException thrown if the request is rejected by server on status code 409.
- * @return the response of entire anomaly detection along with {@link Response}.
+ * @return response of the entire anomaly detection along with {@link Response}.
*/
@ServiceMethod(returns = ReturnType.SINGLE)
public Response detectUnivariateEntireSeriesWithResponse(
@@ -535,7 +535,7 @@ public Response detectUnivariateEntireSeriesWithResponse(
/**
* Detect anomaly status of the latest point in time series.
*
- * This operation generates a model using the points that you sent into the API, and based on all data to
+ *
This operation generates a model by using the points that you sent in to the API and based on all data to
* determine whether the last point is anomalous.
*
*
Request Body Schema
@@ -580,7 +580,7 @@ public Response detectUnivariateEntireSeriesWithResponse(
* @throws ClientAuthenticationException thrown if the request is rejected by server on status code 401.
* @throws ResourceNotFoundException thrown if the request is rejected by server on status code 404.
* @throws ResourceModifiedException thrown if the request is rejected by server on status code 409.
- * @return the response of last anomaly detection along with {@link Response} on successful completion of {@link
+ * @return response of the last anomaly detection along with {@link Response} on successful completion of {@link
* Mono}.
*/
@ServiceMethod(returns = ReturnType.SINGLE)
@@ -601,7 +601,7 @@ public Mono> detectUnivariateLastPointWithResponseAsync(
/**
* Detect anomaly status of the latest point in time series.
*
- * This operation generates a model using the points that you sent into the API, and based on all data to
+ *
This operation generates a model by using the points that you sent in to the API and based on all data to
* determine whether the last point is anomalous.
*
*
Request Body Schema
@@ -646,7 +646,7 @@ public Mono> detectUnivariateLastPointWithResponseAsync(
* @throws ClientAuthenticationException thrown if the request is rejected by server on status code 401.
* @throws ResourceNotFoundException thrown if the request is rejected by server on status code 404.
* @throws ResourceModifiedException thrown if the request is rejected by server on status code 409.
- * @return the response of last anomaly detection along with {@link Response}.
+ * @return response of the last anomaly detection along with {@link Response}.
*/
@ServiceMethod(returns = ReturnType.SINGLE)
public Response detectUnivariateLastPointWithResponse(
@@ -657,7 +657,7 @@ public Response detectUnivariateLastPointWithResponse(
/**
* Detect change point for the entire series
*
- * Evaluate change point score of every series point.
+ *
Evaluate the change point score of every series point.
*
*
Request Body Schema
*
@@ -697,8 +697,7 @@ public Response detectUnivariateLastPointWithResponse(
* @throws ClientAuthenticationException thrown if the request is rejected by server on status code 401.
* @throws ResourceNotFoundException thrown if the request is rejected by server on status code 404.
* @throws ResourceModifiedException thrown if the request is rejected by server on status code 409.
- * @return the response of change point detection along with {@link Response} on successful completion of {@link
- * Mono}.
+ * @return response of change point detection along with {@link Response} on successful completion of {@link Mono}.
*/
@ServiceMethod(returns = ReturnType.SINGLE)
public Mono> detectUnivariateChangePointWithResponseAsync(
@@ -718,7 +717,7 @@ public Mono> detectUnivariateChangePointWithResponseAsync(
/**
* Detect change point for the entire series
*
- * Evaluate change point score of every series point.
+ *
Evaluate the change point score of every series point.
*
*
Request Body Schema
*
@@ -758,7 +757,7 @@ public Mono> detectUnivariateChangePointWithResponseAsync(
* @throws ClientAuthenticationException thrown if the request is rejected by server on status code 401.
* @throws ResourceNotFoundException thrown if the request is rejected by server on status code 404.
* @throws ResourceModifiedException thrown if the request is rejected by server on status code 409.
- * @return the response of change point detection along with {@link Response}.
+ * @return response of change point detection along with {@link Response}.
*/
@ServiceMethod(returns = ReturnType.SINGLE)
public Response detectUnivariateChangePointWithResponse(
@@ -769,8 +768,8 @@ public Response detectUnivariateChangePointWithResponse(
/**
* Get Multivariate Anomaly Detection Result
*
- * For asynchronous inference, get multivariate anomaly detection result based on resultId returned by the
- * BatchDetectAnomaly api.
+ *
For asynchronous inference, get a multivariate anomaly detection result based on the resultId value that the
+ * BatchDetectAnomaly API returns.
*
*
Response Body Schema
*
@@ -796,7 +795,7 @@ public Response detectUnivariateChangePointWithResponse(
* ]
* setupInfo (Required): {
* dataSource: String (Required)
- * topContributorCount: int (Required)
+ * topContributorCount: Integer (Optional)
* startTime: OffsetDateTime (Required)
* endTime: OffsetDateTime (Required)
* }
@@ -834,7 +833,7 @@ public Response detectUnivariateChangePointWithResponse(
* @throws ClientAuthenticationException thrown if the request is rejected by server on status code 401.
* @throws ResourceNotFoundException thrown if the request is rejected by server on status code 404.
* @throws ResourceModifiedException thrown if the request is rejected by server on status code 409.
- * @return detection results for the given resultId along with {@link Response} on successful completion of {@link
+ * @return detection results for the resultId value along with {@link Response} on successful completion of {@link
* Mono}.
*/
@ServiceMethod(returns = ReturnType.SINGLE)
@@ -855,8 +854,8 @@ public Mono> getMultivariateBatchDetectionResultWithRespons
/**
* Get Multivariate Anomaly Detection Result
*
- * For asynchronous inference, get multivariate anomaly detection result based on resultId returned by the
- * BatchDetectAnomaly api.
+ *
For asynchronous inference, get a multivariate anomaly detection result based on the resultId value that the
+ * BatchDetectAnomaly API returns.
*
*
Response Body Schema
*
@@ -882,7 +881,7 @@ public Mono> getMultivariateBatchDetectionResultWithRespons
* ]
* setupInfo (Required): {
* dataSource: String (Required)
- * topContributorCount: int (Required)
+ * topContributorCount: Integer (Optional)
* startTime: OffsetDateTime (Required)
* endTime: OffsetDateTime (Required)
* }
@@ -920,7 +919,7 @@ public Mono> getMultivariateBatchDetectionResultWithRespons
* @throws ClientAuthenticationException thrown if the request is rejected by server on status code 401.
* @throws ResourceNotFoundException thrown if the request is rejected by server on status code 404.
* @throws ResourceModifiedException thrown if the request is rejected by server on status code 409.
- * @return detection results for the given resultId along with {@link Response}.
+ * @return detection results for the resultId value along with {@link Response}.
*/
@ServiceMethod(returns = ReturnType.SINGLE)
public Response getMultivariateBatchDetectionResultWithResponse(
@@ -932,10 +931,12 @@ public Response getMultivariateBatchDetectionResultWithResponse(
* Train a Multivariate Anomaly Detection Model
*
* Create and train a multivariate anomaly detection model. The request must include a source parameter to
- * indicate an externally accessible Azure blob storage URI.There are two types of data input: An URI pointed to an
- * Azure blob storage folder which contains multiple CSV files, and each CSV file contains two columns, timestamp
- * and variable. Another type of input is an URI pointed to a CSV file in Azure blob storage, which contains all the
- * variables and a timestamp column.
+ * indicate an Azure Blob Storage URI that's accessible to the service. There are two types of data input. The Blob
+ * Storage URI can point to an Azure Blob Storage folder that contains multiple CSV files, where each CSV file has
+ * two columns, time stamp and variable. Or the Blob Storage URI can point to a single blob that contains a CSV file
+ * that has all the variables and a time stamp column. The model object will be created and returned in the
+ * response, but the training process happens asynchronously. To check the training status, call
+ * GetMultivariateModel with the modelId value and check the status field in the modelInfo object.
*
*
Request Body Schema
*
@@ -1069,10 +1070,12 @@ public Mono> trainMultivariateModelWithResponseAsync(
* Train a Multivariate Anomaly Detection Model
*
* Create and train a multivariate anomaly detection model. The request must include a source parameter to
- * indicate an externally accessible Azure blob storage URI.There are two types of data input: An URI pointed to an
- * Azure blob storage folder which contains multiple CSV files, and each CSV file contains two columns, timestamp
- * and variable. Another type of input is an URI pointed to a CSV file in Azure blob storage, which contains all the
- * variables and a timestamp column.
+ * indicate an Azure Blob Storage URI that's accessible to the service. There are two types of data input. The Blob
+ * Storage URI can point to an Azure Blob Storage folder that contains multiple CSV files, where each CSV file has
+ * two columns, time stamp and variable. Or the Blob Storage URI can point to a single blob that contains a CSV file
+ * that has all the variables and a time stamp column. The model object will be created and returned in the
+ * response, but the training process happens asynchronously. To check the training status, call
+ * GetMultivariateModel with the modelId value and check the status field in the modelInfo object.
*
*
Request Body Schema
*
@@ -1203,8 +1206,8 @@ public Response trainMultivariateModelWithResponse(
*
* Query Parameters
* | Name | Type | Required | Description |
|---|
- * | skip | Integer | No | Skip indicates how many models will be skipped. |
- * | top | Integer | No | Top indicates how many models will be fetched. |
+ * | skip | Integer | No | The number of result items to skip. |
+ * | top | Integer | No | The number of result items to return. |
*
*
* You can add these to a request with {@link RequestOptions#addQueryParam}
@@ -1303,8 +1306,8 @@ private Mono> listMultivariateModelsSinglePageAsync(Re
*
* Query Parameters
* | Name | Type | Required | Description |
|---|
- * | skip | Integer | No | Skip indicates how many models will be skipped. |
- * | top | Integer | No | Top indicates how many models will be fetched. |
+ * | skip | Integer | No | The number of result items to skip. |
+ * | top | Integer | No | The number of result items to return. |
*
*
* You can add these to a request with {@link RequestOptions#addQueryParam}
@@ -1393,8 +1396,8 @@ public PagedFlux listMultivariateModelsAsync(RequestOptions requestO
*
* Query Parameters
* | Name | Type | Required | Description |
|---|
- * | skip | Integer | No | Skip indicates how many models will be skipped. |
- * | top | Integer | No | Top indicates how many models will be fetched. |
+ * | skip | Integer | No | The number of result items to skip. |
+ * | top | Integer | No | The number of result items to return. |
*
*
* You can add these to a request with {@link RequestOptions#addQueryParam}
@@ -1469,7 +1472,7 @@ public PagedIterable listMultivariateModels(RequestOptions requestOp
/**
* Delete Multivariate Model
*
- * Delete an existing multivariate model according to the modelId.
+ *
Delete an existing multivariate model according to the modelId value.
*
* @param modelId Model identifier.
* @param requestOptions The options to configure the HTTP request before HTTP client sends it.
@@ -1497,7 +1500,7 @@ public Mono> deleteMultivariateModelWithResponseAsync(
/**
* Delete Multivariate Model
*
- * Delete an existing multivariate model according to the modelId.
+ *
Delete an existing multivariate model according to the modelId value.
*
* @param modelId Model identifier.
* @param requestOptions The options to configure the HTTP request before HTTP client sends it.
@@ -1515,7 +1518,8 @@ public Response deleteMultivariateModelWithResponse(String modelId, Reques
/**
* Get Multivariate Model
*
- * Get detailed information of multivariate model, including the training status and variables used in the model.
+ *
Get detailed information about the multivariate model, including the training status and variables used in the
+ * model.
*
*
Response Body Schema
*
@@ -1578,8 +1582,8 @@ public Response deleteMultivariateModelWithResponse(String modelId, Reques
* @throws ClientAuthenticationException thrown if the request is rejected by server on status code 401.
* @throws ResourceNotFoundException thrown if the request is rejected by server on status code 404.
* @throws ResourceModifiedException thrown if the request is rejected by server on status code 409.
- * @return detailed information of multivariate model, including the training status and variables used in the model
- * along with {@link Response} on successful completion of {@link Mono}.
+ * @return detailed information about the multivariate model, including the training status and variables used in
+ * the model along with {@link Response} on successful completion of {@link Mono}.
*/
@ServiceMethod(returns = ReturnType.SINGLE)
public Mono> getMultivariateModelWithResponseAsync(
@@ -1599,7 +1603,8 @@ public Mono> getMultivariateModelWithResponseAsync(
/**
* Get Multivariate Model
*
- * Get detailed information of multivariate model, including the training status and variables used in the model.
+ *
Get detailed information about the multivariate model, including the training status and variables used in the
+ * model.
*
*
Response Body Schema
*
@@ -1662,8 +1667,8 @@ public Mono> getMultivariateModelWithResponseAsync(
* @throws ClientAuthenticationException thrown if the request is rejected by server on status code 401.
* @throws ResourceNotFoundException thrown if the request is rejected by server on status code 404.
* @throws ResourceModifiedException thrown if the request is rejected by server on status code 409.
- * @return detailed information of multivariate model, including the training status and variables used in the model
- * along with {@link Response}.
+ * @return detailed information about the multivariate model, including the training status and variables used in
+ * the model along with {@link Response}.
*/
@ServiceMethod(returns = ReturnType.SINGLE)
public Response getMultivariateModelWithResponse(String modelId, RequestOptions requestOptions) {
@@ -1673,18 +1678,18 @@ public Response getMultivariateModelWithResponse(String modelId, Req
/**
* Detect Multivariate Anomaly
*
- * Submit multivariate anomaly detection task with the modelId of trained model and inference data, the input
- * schema should be the same with the training request. The request will complete asynchronously and return a
- * resultId to query the detection result.The request should be a source link to indicate an externally accessible
- * Azure storage Uri, either pointed to an Azure blob storage folder, or pointed to a CSV file in Azure blob
- * storage.
+ *
Submit a multivariate anomaly detection task with the modelId value of a trained model and inference data. The
+ * input schema should be the same with the training request. The request will finish asynchronously and return a
+ * resultId value to query the detection result. The request should be a source link to indicate an externally
+ * accessible Azure Storage URI that either points to an Azure Blob Storage folder or points to a CSV file in Azure
+ * Blob Storage.
*
*
Request Body Schema
*
*
{@code
* {
* dataSource: String (Required)
- * topContributorCount: int (Required)
+ * topContributorCount: Integer (Optional)
* startTime: OffsetDateTime (Required)
* endTime: OffsetDateTime (Required)
* }
@@ -1714,7 +1719,7 @@ public Response getMultivariateModelWithResponse(String modelId, Req
* ]
* setupInfo (Required): {
* dataSource: String (Required)
- * topContributorCount: int (Required)
+ * topContributorCount: Integer (Optional)
* startTime: OffsetDateTime (Required)
* endTime: OffsetDateTime (Required)
* }
@@ -1753,7 +1758,7 @@ public Response getMultivariateModelWithResponse(String modelId, Req
* @throws ClientAuthenticationException thrown if the request is rejected by server on status code 401.
* @throws ResourceNotFoundException thrown if the request is rejected by server on status code 404.
* @throws ResourceModifiedException thrown if the request is rejected by server on status code 409.
- * @return detection results for the given resultId along with {@link Response} on successful completion of {@link
+ * @return detection results for the resultId value along with {@link Response} on successful completion of {@link
* Mono}.
*/
@ServiceMethod(returns = ReturnType.SINGLE)
@@ -1775,18 +1780,18 @@ public Mono> detectMultivariateBatchAnomalyWithResponseAsyn
/**
* Detect Multivariate Anomaly
*
- * Submit multivariate anomaly detection task with the modelId of trained model and inference data, the input
- * schema should be the same with the training request. The request will complete asynchronously and return a
- * resultId to query the detection result.The request should be a source link to indicate an externally accessible
- * Azure storage Uri, either pointed to an Azure blob storage folder, or pointed to a CSV file in Azure blob
- * storage.
+ *
Submit a multivariate anomaly detection task with the modelId value of a trained model and inference data. The
+ * input schema should be the same with the training request. The request will finish asynchronously and return a
+ * resultId value to query the detection result. The request should be a source link to indicate an externally
+ * accessible Azure Storage URI that either points to an Azure Blob Storage folder or points to a CSV file in Azure
+ * Blob Storage.
*
*
Request Body Schema
*
*
{@code
* {
* dataSource: String (Required)
- * topContributorCount: int (Required)
+ * topContributorCount: Integer (Optional)
* startTime: OffsetDateTime (Required)
* endTime: OffsetDateTime (Required)
* }
@@ -1816,7 +1821,7 @@ public Mono> detectMultivariateBatchAnomalyWithResponseAsyn
* ]
* setupInfo (Required): {
* dataSource: String (Required)
- * topContributorCount: int (Required)
+ * topContributorCount: Integer (Optional)
* startTime: OffsetDateTime (Required)
* endTime: OffsetDateTime (Required)
* }
@@ -1855,7 +1860,7 @@ public Mono> detectMultivariateBatchAnomalyWithResponseAsyn
* @throws ClientAuthenticationException thrown if the request is rejected by server on status code 401.
* @throws ResourceNotFoundException thrown if the request is rejected by server on status code 404.
* @throws ResourceModifiedException thrown if the request is rejected by server on status code 409.
- * @return detection results for the given resultId along with {@link Response}.
+ * @return detection results for the resultId value along with {@link Response}.
*/
@ServiceMethod(returns = ReturnType.SINGLE)
public Response detectMultivariateBatchAnomalyWithResponse(
@@ -1866,8 +1871,8 @@ public Response detectMultivariateBatchAnomalyWithResponse(
/**
* Detect anomalies in the last point of the request body
*
- * Submit multivariate anomaly detection task with the modelId of trained model and inference data, and the
- * inference data should be put into request body in a JSON format. The request will complete synchronously and
+ *
Submit a multivariate anomaly detection task with the modelId value of a trained model and inference data. The
+ * inference data should be put into the request body in JSON format. The request will finish synchronously and
* return the detection immediately in the response body.
*
*
Request Body Schema
@@ -1885,7 +1890,7 @@ public Response detectMultivariateBatchAnomalyWithResponse(
* ]
* }
* ]
- * topContributorCount: int (Required)
+ * topContributorCount: Integer (Optional)
* }
* }
*
@@ -1933,13 +1938,13 @@ public Response detectMultivariateBatchAnomalyWithResponse(
* }
*
* @param modelId Model identifier.
- * @param options Request of last detection.
+ * @param options Request of the last detection.
* @param requestOptions The options to configure the HTTP request before HTTP client sends it.
* @throws HttpResponseException thrown if the request is rejected by server.
* @throws ClientAuthenticationException thrown if the request is rejected by server on status code 401.
* @throws ResourceNotFoundException thrown if the request is rejected by server on status code 404.
* @throws ResourceModifiedException thrown if the request is rejected by server on status code 409.
- * @return results of last detection along with {@link Response} on successful completion of {@link Mono}.
+ * @return results of the last detection along with {@link Response} on successful completion of {@link Mono}.
*/
@ServiceMethod(returns = ReturnType.SINGLE)
public Mono> detectMultivariateLastAnomalyWithResponseAsync(
@@ -1960,8 +1965,8 @@ public Mono> detectMultivariateLastAnomalyWithResponseAsync
/**
* Detect anomalies in the last point of the request body
*
- * Submit multivariate anomaly detection task with the modelId of trained model and inference data, and the
- * inference data should be put into request body in a JSON format. The request will complete synchronously and
+ *
Submit a multivariate anomaly detection task with the modelId value of a trained model and inference data. The
+ * inference data should be put into the request body in JSON format. The request will finish synchronously and
* return the detection immediately in the response body.
*
*
Request Body Schema
@@ -1979,7 +1984,7 @@ public Mono> detectMultivariateLastAnomalyWithResponseAsync
* ]
* }
* ]
- * topContributorCount: int (Required)
+ * topContributorCount: Integer (Optional)
* }
* }
*
@@ -2027,13 +2032,13 @@ public Mono> detectMultivariateLastAnomalyWithResponseAsync
* }
*
* @param modelId Model identifier.
- * @param options Request of last detection.
+ * @param options Request of the last detection.
* @param requestOptions The options to configure the HTTP request before HTTP client sends it.
* @throws HttpResponseException thrown if the request is rejected by server.
* @throws ClientAuthenticationException thrown if the request is rejected by server on status code 401.
* @throws ResourceNotFoundException thrown if the request is rejected by server on status code 404.
* @throws ResourceModifiedException thrown if the request is rejected by server on status code 409.
- * @return results of last detection along with {@link Response}.
+ * @return results of the last detection along with {@link Response}.
*/
@ServiceMethod(returns = ReturnType.SINGLE)
public Response detectMultivariateLastAnomalyWithResponse(
diff --git a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/implementation/package-info.java b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/implementation/package-info.java
index 7460f62ca500..1675f0d766a8 100644
--- a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/implementation/package-info.java
+++ b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/implementation/package-info.java
@@ -4,16 +4,16 @@
/**
* Package containing the implementations for AnomalyDetector. The Anomaly Detector API detects anomalies automatically
- * in time series data. It supports two kinds of mode, one is for stateless using, another is for stateful using. In
- * stateless mode, there are three functionalities. Entire Detect is for detecting the whole series with model trained
- * by the time series, Last Detect is detecting last point with model trained by points before. ChangePoint Detect is
- * for detecting trend changes in time series. In stateful mode, user can store time series, the stored time series will
- * be used for detection anomalies. Under this mode, user can still use the above three functionalities by only giving a
- * time range without preparing time series in client side. Besides the above three functionalities, stateful model also
- * provide group based detection and labeling service. By leveraging labeling service user can provide labels for each
- * detection result, these labels will be used for retuning or regenerating detection models. Inconsistency detection is
- * a kind of group based detection, this detection will find inconsistency ones in a set of time series. By using
- * anomaly detector service, business customers can discover incidents and establish a logic flow for root cause
- * analysis.
+ * in time series data. It supports both a stateless detection mode and a stateful detection mode. In stateless mode,
+ * there are three functionalities. Entire Detect is for detecting the whole series, with the model trained by the time
+ * series. Last Detect is for detecting the last point, with the model trained by points before. ChangePoint Detect is
+ * for detecting trend changes in the time series. In stateful mode, the user can store time series. The stored time
+ * series will be used for detection anomalies. In this mode, the user can still use the preceding three functionalities
+ * by only giving a time range without preparing time series on the client side. Besides the preceding three
+ * functionalities, the stateful model provides group-based detection and labeling services. By using the labeling
+ * service, the user can provide labels for each detection result. These labels will be used for retuning or
+ * regenerating detection models. Inconsistency detection is a kind of group-based detection that finds inconsistencies
+ * in a set of time series. By using the anomaly detector service, business customers can discover incidents and
+ * establish a logic flow for root cause analysis.
*/
package com.azure.ai.anomalydetector.implementation;
diff --git a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/AlignMode.java b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/AlignMode.java
index 0f3275035816..051002ad0526 100644
--- a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/AlignMode.java
+++ b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/AlignMode.java
@@ -4,48 +4,43 @@
package com.azure.ai.anomalydetector.models;
+import com.azure.core.util.ExpandableStringEnum;
import com.fasterxml.jackson.annotation.JsonCreator;
-import com.fasterxml.jackson.annotation.JsonValue;
+import java.util.Collection;
/** Defines values for AlignMode. */
-public enum AlignMode {
- /** Enum value Inner. */
- INNER("Inner"),
+public final class AlignMode extends ExpandableStringEnum {
+ /** Static value Inner for AlignMode. */
+ public static final AlignMode INNER = fromString("Inner");
- /** Enum value Outer. */
- OUTER("Outer");
+ /** Static value Outer for AlignMode. */
+ public static final AlignMode OUTER = fromString("Outer");
- /** The actual serialized value for a AlignMode instance. */
- private final String value;
-
- AlignMode(String value) {
- this.value = value;
- }
+ /**
+ * Creates a new instance of AlignMode value.
+ *
+ * @deprecated Use the {@link #fromString(String)} factory method.
+ */
+ @Deprecated
+ public AlignMode() {}
/**
- * Parses a serialized value to a AlignMode instance.
+ * Creates or finds a AlignMode from its string representation.
*
- * @param value the serialized value to parse.
- * @return the parsed AlignMode object, or null if unable to parse.
+ * @param name a name to look for.
+ * @return the corresponding AlignMode.
*/
@JsonCreator
- public static AlignMode fromString(String value) {
- if (value == null) {
- return null;
- }
- AlignMode[] items = AlignMode.values();
- for (AlignMode item : items) {
- if (item.toString().equalsIgnoreCase(value)) {
- return item;
- }
- }
- return null;
+ public static AlignMode fromString(String name) {
+ return fromString(name, AlignMode.class);
}
- /** {@inheritDoc} */
- @JsonValue
- @Override
- public String toString() {
- return this.value;
+ /**
+ * Gets known AlignMode values.
+ *
+ * @return known AlignMode values.
+ */
+ public static Collection values() {
+ return values(AlignMode.class);
}
}
diff --git a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/AlignPolicy.java b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/AlignPolicy.java
index a6cbbab5fa48..ca93481db33a 100644
--- a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/AlignPolicy.java
+++ b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/AlignPolicy.java
@@ -7,25 +7,24 @@
import com.azure.core.annotation.Fluent;
import com.fasterxml.jackson.annotation.JsonProperty;
-/** An optional field, indicating the manner to align multiple variables. */
+/** Manner of aligning multiple variables. */
@Fluent
public final class AlignPolicy {
/*
- * An optional field, indicating how to align different variables to the same
- * time-range. Either Inner or Outer.
+ * Field that indicates how to align different variables to the same
+ * time range.
*/
@JsonProperty(value = "alignMode")
private AlignMode alignMode;
/*
- * An optional field, indicating how missing values will be filled. One of
- * Previous, Subsequent, Linear, Zero, Fixed.
+ * Field that indicates how missing values will be filled.
*/
@JsonProperty(value = "fillNAMethod")
private FillNAMethod fillNAMethod;
/*
- * An optional field. Required when fillNAMethod is Fixed.
+ * Field that's required when fillNAMethod is Fixed.
*/
@JsonProperty(value = "paddingValue")
private Double paddingValue;
@@ -34,8 +33,7 @@ public final class AlignPolicy {
public AlignPolicy() {}
/**
- * Get the alignMode property: An optional field, indicating how to align different variables to the same
- * time-range. Either Inner or Outer.
+ * Get the alignMode property: Field that indicates how to align different variables to the same time range.
*
* @return the alignMode value.
*/
@@ -44,8 +42,7 @@ public AlignMode getAlignMode() {
}
/**
- * Set the alignMode property: An optional field, indicating how to align different variables to the same
- * time-range. Either Inner or Outer.
+ * Set the alignMode property: Field that indicates how to align different variables to the same time range.
*
* @param alignMode the alignMode value to set.
* @return the AlignPolicy object itself.
@@ -56,8 +53,7 @@ public AlignPolicy setAlignMode(AlignMode alignMode) {
}
/**
- * Get the fillNAMethod property: An optional field, indicating how missing values will be filled. One of Previous,
- * Subsequent, Linear, Zero, Fixed.
+ * Get the fillNAMethod property: Field that indicates how missing values will be filled.
*
* @return the fillNAMethod value.
*/
@@ -66,8 +62,7 @@ public FillNAMethod getFillNAMethod() {
}
/**
- * Set the fillNAMethod property: An optional field, indicating how missing values will be filled. One of Previous,
- * Subsequent, Linear, Zero, Fixed.
+ * Set the fillNAMethod property: Field that indicates how missing values will be filled.
*
* @param fillNAMethod the fillNAMethod value to set.
* @return the AlignPolicy object itself.
@@ -78,7 +73,7 @@ public AlignPolicy setFillNAMethod(FillNAMethod fillNAMethod) {
}
/**
- * Get the paddingValue property: An optional field. Required when fillNAMethod is Fixed.
+ * Get the paddingValue property: Field that's required when fillNAMethod is Fixed.
*
* @return the paddingValue value.
*/
@@ -87,7 +82,7 @@ public Double getPaddingValue() {
}
/**
- * Set the paddingValue property: An optional field. Required when fillNAMethod is Fixed.
+ * Set the paddingValue property: Field that's required when fillNAMethod is Fixed.
*
* @param paddingValue the paddingValue value to set.
* @return the AlignPolicy object itself.
diff --git a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/AnomalyDetectionModel.java b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/AnomalyDetectionModel.java
index c488f04619ca..1492090de057 100644
--- a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/AnomalyDetectionModel.java
+++ b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/AnomalyDetectionModel.java
@@ -31,7 +31,7 @@ public final class AnomalyDetectionModel {
private OffsetDateTime lastUpdatedTime;
/*
- * Training result of a model including its status, errors and diagnostics
+ * Training result of a model, including its status, errors, and diagnostics
* information.
*/
@JsonProperty(value = "modelInfo")
@@ -79,7 +79,8 @@ public OffsetDateTime getLastUpdatedTime() {
}
/**
- * Get the modelInfo property: Training result of a model including its status, errors and diagnostics information.
+ * Get the modelInfo property: Training result of a model, including its status, errors, and diagnostics
+ * information.
*
* @return the modelInfo value.
*/
diff --git a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/AnomalyInterpretation.java b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/AnomalyInterpretation.java
index e2a9fce56f69..21c270efa87c 100644
--- a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/AnomalyInterpretation.java
+++ b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/AnomalyInterpretation.java
@@ -7,7 +7,7 @@
import com.azure.core.annotation.Immutable;
import com.fasterxml.jackson.annotation.JsonProperty;
-/** Interpretation of the anomalous timestamp. */
+/** Interpretation of the anomalous time stamp. */
@Immutable
public final class AnomalyInterpretation {
/*
@@ -17,14 +17,14 @@ public final class AnomalyInterpretation {
private String variable;
/*
- * This score shows the percentage contributing to the anomalous timestamp. A
+ * This score shows the percentage that contributes to the anomalous time stamp. It's a
* number between 0 and 1.
*/
@JsonProperty(value = "contributionScore")
private Double contributionScore;
/*
- * Correlation changes among the anomalous variables
+ * Correlation changes among the anomalous variables.
*/
@JsonProperty(value = "correlationChanges")
private CorrelationChanges correlationChanges;
@@ -42,8 +42,8 @@ public String getVariable() {
}
/**
- * Get the contributionScore property: This score shows the percentage contributing to the anomalous timestamp. A
- * number between 0 and 1.
+ * Get the contributionScore property: This score shows the percentage that contributes to the anomalous time stamp.
+ * It's a number between 0 and 1.
*
* @return the contributionScore value.
*/
diff --git a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/AnomalyState.java b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/AnomalyState.java
index 78caad875798..eea2d3dc582b 100644
--- a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/AnomalyState.java
+++ b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/AnomalyState.java
@@ -14,19 +14,19 @@
@Immutable
public final class AnomalyState {
/*
- * The timestamp for this anomaly.
+ * Time stamp for this anomaly.
*/
@JsonProperty(value = "timestamp", required = true)
private OffsetDateTime timestamp;
/*
- * The detailed value of this anomalous timestamp.
+ * Detailed value of this anomalous time stamp.
*/
@JsonProperty(value = "value")
private AnomalyValue value;
/*
- * Error message for the current timestamp.
+ * Error message for the current time stamp.
*/
@JsonProperty(value = "errors")
private List errors;
@@ -42,7 +42,7 @@ private AnomalyState(@JsonProperty(value = "timestamp", required = true) OffsetD
}
/**
- * Get the timestamp property: The timestamp for this anomaly.
+ * Get the timestamp property: Time stamp for this anomaly.
*
* @return the timestamp value.
*/
@@ -51,7 +51,7 @@ public OffsetDateTime getTimestamp() {
}
/**
- * Get the value property: The detailed value of this anomalous timestamp.
+ * Get the value property: Detailed value of this anomalous time stamp.
*
* @return the value value.
*/
@@ -60,7 +60,7 @@ public AnomalyValue getValue() {
}
/**
- * Get the errors property: Error message for the current timestamp.
+ * Get the errors property: Error message for the current time stamp.
*
* @return the errors value.
*/
diff --git a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/AnomalyValue.java b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/AnomalyValue.java
index 61a03c2f7636..63207f283d5b 100644
--- a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/AnomalyValue.java
+++ b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/AnomalyValue.java
@@ -9,11 +9,11 @@
import com.fasterxml.jackson.annotation.JsonProperty;
import java.util.List;
-/** Detailed information of the anomalous timestamp. */
+/** Detailed information of the anomalous time stamp. */
@Immutable
public final class AnomalyValue {
/*
- * True if an anomaly is detected at the current timestamp.
+ * True if an anomaly is detected at the current time stamp.
*/
@JsonProperty(value = "isAnomaly", required = true)
private boolean isAnomaly;
@@ -26,14 +26,13 @@ public final class AnomalyValue {
private double severity;
/*
- * Raw anomaly score of severity, will help indicate the degree of abnormality as
- * well.
+ * Raw anomaly score of severity, to help indicate the degree of abnormality.
*/
@JsonProperty(value = "score", required = true)
private double score;
/*
- * Interpretation of this anomalous timestamp.
+ * Interpretation of this anomalous time stamp.
*/
@JsonProperty(value = "interpretation")
private List interpretation;
@@ -56,7 +55,7 @@ private AnomalyValue(
}
/**
- * Get the isAnomaly property: True if an anomaly is detected at the current timestamp.
+ * Get the isAnomaly property: True if an anomaly is detected at the current time stamp.
*
* @return the isAnomaly value.
*/
@@ -75,7 +74,7 @@ public double getSeverity() {
}
/**
- * Get the score property: Raw anomaly score of severity, will help indicate the degree of abnormality as well.
+ * Get the score property: Raw anomaly score of severity, to help indicate the degree of abnormality.
*
* @return the score value.
*/
@@ -84,7 +83,7 @@ public double getScore() {
}
/**
- * Get the interpretation property: Interpretation of this anomalous timestamp.
+ * Get the interpretation property: Interpretation of this anomalous time stamp.
*
* @return the interpretation value.
*/
diff --git a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/CorrelationChanges.java b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/CorrelationChanges.java
index 941641457de2..27ecb58f0a42 100644
--- a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/CorrelationChanges.java
+++ b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/CorrelationChanges.java
@@ -12,7 +12,7 @@
@Immutable
public final class CorrelationChanges {
/*
- * The correlated variables that have correlation changes under an anomaly.
+ * Correlated variables that have correlation changes under an anomaly.
*/
@JsonProperty(value = "changedVariables")
private List changedVariables;
@@ -21,7 +21,7 @@ public final class CorrelationChanges {
private CorrelationChanges() {}
/**
- * Get the changedVariables property: The correlated variables that have correlation changes under an anomaly.
+ * Get the changedVariables property: Correlated variables that have correlation changes under an anomaly.
*
* @return the changedVariables value.
*/
diff --git a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/DataSchema.java b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/DataSchema.java
index b996f1595862..45353984bbc7 100644
--- a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/DataSchema.java
+++ b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/DataSchema.java
@@ -8,18 +8,18 @@
import com.fasterxml.jackson.annotation.JsonCreator;
import java.util.Collection;
-/** Data schema of input data source: OneTable or MultiTable. The default DataSchema is OneTable. */
+/** Data schema of the input data source. The default is OneTable. */
public final class DataSchema extends ExpandableStringEnum {
/**
- * OneTable means that your input data are all in one CSV file, which contains one 'timestamp' column and several
- * variable columns. The default DataSchema is OneTable.
+ * OneTable means that your input data is in one CSV file, which contains one time stamp column and several variable
+ * columns. The default DataSchema value is OneTable.
*/
public static final DataSchema ONE_TABLE = fromString("OneTable");
/**
- * MultiTable means that your input data are separated in multiple CSV files, in each file containing one
- * 'timestamp' column and one 'variable' column, and the CSV file name should indicate the name of the variable. The
- * default DataSchema is OneTable.
+ * MultiTable means that your input data is separated in multiple CSV files. Each file contains one time stamp
+ * column and one variable column, and the CSV file name should indicate the name of the variable. The default
+ * DataSchema value is OneTable.
*/
public static final DataSchema MULTI_TABLE = fromString("MultiTable");
diff --git a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/DiagnosticsInfo.java b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/DiagnosticsInfo.java
index 7746d0fbcaa4..ac55865677a5 100644
--- a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/DiagnosticsInfo.java
+++ b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/DiagnosticsInfo.java
@@ -8,7 +8,7 @@
import com.fasterxml.jackson.annotation.JsonProperty;
import java.util.List;
-/** Diagnostics information to help inspect the states of model or variable. */
+/** Diagnostics information to help inspect the states of a model or variable. */
@Fluent
public final class DiagnosticsInfo {
/*
@@ -18,7 +18,7 @@ public final class DiagnosticsInfo {
private ModelState modelState;
/*
- * Variable Status.
+ * Variable status.
*/
@JsonProperty(value = "variableStates")
private List variableStates;
@@ -47,7 +47,7 @@ public DiagnosticsInfo setModelState(ModelState modelState) {
}
/**
- * Get the variableStates property: Variable Status.
+ * Get the variableStates property: Variable status.
*
* @return the variableStates value.
*/
@@ -56,7 +56,7 @@ public List getVariableStates() {
}
/**
- * Set the variableStates property: Variable Status.
+ * Set the variableStates property: Variable status.
*
* @param variableStates the variableStates value to set.
* @return the DiagnosticsInfo object itself.
diff --git a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/ErrorResponse.java b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/ErrorResponse.java
index f3b26161746d..0ef23ff8fc20 100644
--- a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/ErrorResponse.java
+++ b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/ErrorResponse.java
@@ -8,17 +8,17 @@
import com.fasterxml.jackson.annotation.JsonCreator;
import com.fasterxml.jackson.annotation.JsonProperty;
-/** ErrorResponse contains code and message that shows the error information. */
+/** Error information that the API returned. */
@Immutable
public final class ErrorResponse {
/*
- * The error code.
+ * Error code.
*/
@JsonProperty(value = "code", required = true)
private String code;
/*
- * The message explaining the error reported by the service.
+ * Message that explains the error that the service reported.
*/
@JsonProperty(value = "message", required = true)
private String message;
@@ -38,7 +38,7 @@ public ErrorResponse(
}
/**
- * Get the code property: The error code.
+ * Get the code property: Error code.
*
* @return the code value.
*/
@@ -47,7 +47,7 @@ public String getCode() {
}
/**
- * Get the message property: The message explaining the error reported by the service.
+ * Get the message property: Message that explains the error that the service reported.
*
* @return the message value.
*/
diff --git a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/FillNAMethod.java b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/FillNAMethod.java
index 136f6221d31d..8d4ad0f31edf 100644
--- a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/FillNAMethod.java
+++ b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/FillNAMethod.java
@@ -8,9 +8,7 @@
import com.fasterxml.jackson.annotation.JsonCreator;
import java.util.Collection;
-/**
- * An optional field, indicating how missing values will be filled. One of Previous, Subsequent, Linear, Zero, Fixed.
- */
+/** Field that indicates how missing values will be filled. */
public final class FillNAMethod extends ExpandableStringEnum {
/** Static value Previous for FillNAMethod. */
public static final FillNAMethod PREVIOUS = fromString("Previous");
diff --git a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/ModelInfo.java b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/ModelInfo.java
index 0155a4c77ad0..9d5a64b92647 100644
--- a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/ModelInfo.java
+++ b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/ModelInfo.java
@@ -10,74 +10,74 @@
import java.time.OffsetDateTime;
import java.util.List;
-/** Training result of a model including its status, errors and diagnostics information. */
+/** Training result of a model, including its status, errors, and diagnostics information. */
@Fluent
public final class ModelInfo {
/*
- * Source link to the input data to indicate an accessible Azure storage Uri,
- * either pointed to an Azure blob storage folder, or pointed to a CSV file in
- * Azure blob storage based on you data schema selection.
+ * Source link to the input data to indicate an accessible Azure Storage URI.
+ * It either points to an Azure Blob Storage folder or points to a CSV file in
+ * Azure Blob Storage, based on your data schema selection.
*/
@JsonProperty(value = "dataSource", required = true)
private String dataSource;
/*
- * Data schema of input data source: OneTable or MultiTable. The default
- * DataSchema is OneTable.
+ * Data schema of the input data source. The default
+ * is OneTable.
*/
@JsonProperty(value = "dataSchema")
private DataSchema dataSchema;
/*
- * A required field, indicating the start time of training data, which should be
- * date-time of ISO 8601 format.
+ * Start date/time of training data, which should be
+ * in ISO 8601 format.
*/
@JsonProperty(value = "startTime", required = true)
private OffsetDateTime startTime;
/*
- * A required field, indicating the end time of training data, which should be
- * date-time of ISO 8601 format.
+ * End date/time of training data, which should be
+ * in ISO 8601 format.
*/
@JsonProperty(value = "endTime", required = true)
private OffsetDateTime endTime;
/*
- * An optional field. The display name of the model whose maximum length is 24
+ * Display name of the model. Maximum length is 24
* characters.
*/
@JsonProperty(value = "displayName")
private String displayName;
/*
- * An optional field, indicating how many previous timestamps will be used to
- * detect whether the timestamp is anomaly or not.
+ * Number of previous time stamps that will be used to
+ * detect whether the time stamp is an anomaly or not.
*/
@JsonProperty(value = "slidingWindow")
private Integer slidingWindow;
/*
- * An optional field, indicating the manner to align multiple variables.
+ * Manner of aligning multiple variables.
*/
@JsonProperty(value = "alignPolicy")
private AlignPolicy alignPolicy;
/*
- * Model status. One of CREATED, RUNNING, READY, and FAILED.
+ * Model status.
*/
- @JsonProperty(value = "status")
+ @JsonProperty(value = "status", access = JsonProperty.Access.WRITE_ONLY)
private ModelStatus status;
/*
- * Error messages when failed to create a model.
+ * Error messages after failure to create a model.
*/
@JsonProperty(value = "errors", access = JsonProperty.Access.WRITE_ONLY)
private List errors;
/*
- * Diagnostics information to help inspect the states of model or variable.
+ * Diagnostics information to help inspect the states of a model or variable.
*/
- @JsonProperty(value = "diagnosticsInfo")
+ @JsonProperty(value = "diagnosticsInfo", access = JsonProperty.Access.WRITE_ONLY)
private DiagnosticsInfo diagnosticsInfo;
/**
@@ -98,8 +98,8 @@ public ModelInfo(
}
/**
- * Get the dataSource property: Source link to the input data to indicate an accessible Azure storage Uri, either
- * pointed to an Azure blob storage folder, or pointed to a CSV file in Azure blob storage based on you data schema
+ * Get the dataSource property: Source link to the input data to indicate an accessible Azure Storage URI. It either
+ * points to an Azure Blob Storage folder or points to a CSV file in Azure Blob Storage, based on your data schema
* selection.
*
* @return the dataSource value.
@@ -109,8 +109,7 @@ public String getDataSource() {
}
/**
- * Get the dataSchema property: Data schema of input data source: OneTable or MultiTable. The default DataSchema is
- * OneTable.
+ * Get the dataSchema property: Data schema of the input data source. The default is OneTable.
*
* @return the dataSchema value.
*/
@@ -119,8 +118,7 @@ public DataSchema getDataSchema() {
}
/**
- * Set the dataSchema property: Data schema of input data source: OneTable or MultiTable. The default DataSchema is
- * OneTable.
+ * Set the dataSchema property: Data schema of the input data source. The default is OneTable.
*
* @param dataSchema the dataSchema value to set.
* @return the ModelInfo object itself.
@@ -131,8 +129,7 @@ public ModelInfo setDataSchema(DataSchema dataSchema) {
}
/**
- * Get the startTime property: A required field, indicating the start time of training data, which should be
- * date-time of ISO 8601 format.
+ * Get the startTime property: Start date/time of training data, which should be in ISO 8601 format.
*
* @return the startTime value.
*/
@@ -141,8 +138,7 @@ public OffsetDateTime getStartTime() {
}
/**
- * Get the endTime property: A required field, indicating the end time of training data, which should be date-time
- * of ISO 8601 format.
+ * Get the endTime property: End date/time of training data, which should be in ISO 8601 format.
*
* @return the endTime value.
*/
@@ -151,8 +147,7 @@ public OffsetDateTime getEndTime() {
}
/**
- * Get the displayName property: An optional field. The display name of the model whose maximum length is 24
- * characters.
+ * Get the displayName property: Display name of the model. Maximum length is 24 characters.
*
* @return the displayName value.
*/
@@ -161,8 +156,7 @@ public String getDisplayName() {
}
/**
- * Set the displayName property: An optional field. The display name of the model whose maximum length is 24
- * characters.
+ * Set the displayName property: Display name of the model. Maximum length is 24 characters.
*
* @param displayName the displayName value to set.
* @return the ModelInfo object itself.
@@ -173,8 +167,8 @@ public ModelInfo setDisplayName(String displayName) {
}
/**
- * Get the slidingWindow property: An optional field, indicating how many previous timestamps will be used to detect
- * whether the timestamp is anomaly or not.
+ * Get the slidingWindow property: Number of previous time stamps that will be used to detect whether the time stamp
+ * is an anomaly or not.
*
* @return the slidingWindow value.
*/
@@ -183,8 +177,8 @@ public Integer getSlidingWindow() {
}
/**
- * Set the slidingWindow property: An optional field, indicating how many previous timestamps will be used to detect
- * whether the timestamp is anomaly or not.
+ * Set the slidingWindow property: Number of previous time stamps that will be used to detect whether the time stamp
+ * is an anomaly or not.
*
* @param slidingWindow the slidingWindow value to set.
* @return the ModelInfo object itself.
@@ -195,7 +189,7 @@ public ModelInfo setSlidingWindow(Integer slidingWindow) {
}
/**
- * Get the alignPolicy property: An optional field, indicating the manner to align multiple variables.
+ * Get the alignPolicy property: Manner of aligning multiple variables.
*
* @return the alignPolicy value.
*/
@@ -204,7 +198,7 @@ public AlignPolicy getAlignPolicy() {
}
/**
- * Set the alignPolicy property: An optional field, indicating the manner to align multiple variables.
+ * Set the alignPolicy property: Manner of aligning multiple variables.
*
* @param alignPolicy the alignPolicy value to set.
* @return the ModelInfo object itself.
@@ -215,7 +209,7 @@ public ModelInfo setAlignPolicy(AlignPolicy alignPolicy) {
}
/**
- * Get the status property: Model status. One of CREATED, RUNNING, READY, and FAILED.
+ * Get the status property: Model status.
*
* @return the status value.
*/
@@ -224,18 +218,7 @@ public ModelStatus getStatus() {
}
/**
- * Set the status property: Model status. One of CREATED, RUNNING, READY, and FAILED.
- *
- * @param status the status value to set.
- * @return the ModelInfo object itself.
- */
- public ModelInfo setStatus(ModelStatus status) {
- this.status = status;
- return this;
- }
-
- /**
- * Get the errors property: Error messages when failed to create a model.
+ * Get the errors property: Error messages after failure to create a model.
*
* @return the errors value.
*/
@@ -244,22 +227,11 @@ public List getErrors() {
}
/**
- * Get the diagnosticsInfo property: Diagnostics information to help inspect the states of model or variable.
+ * Get the diagnosticsInfo property: Diagnostics information to help inspect the states of a model or variable.
*
* @return the diagnosticsInfo value.
*/
public DiagnosticsInfo getDiagnosticsInfo() {
return this.diagnosticsInfo;
}
-
- /**
- * Set the diagnosticsInfo property: Diagnostics information to help inspect the states of model or variable.
- *
- * @param diagnosticsInfo the diagnosticsInfo value to set.
- * @return the ModelInfo object itself.
- */
- public ModelInfo setDiagnosticsInfo(DiagnosticsInfo diagnosticsInfo) {
- this.diagnosticsInfo = diagnosticsInfo;
- return this;
- }
}
diff --git a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/ModelState.java b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/ModelState.java
index fcf914eb63b8..d5e4a3f7692e 100644
--- a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/ModelState.java
+++ b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/ModelState.java
@@ -12,7 +12,7 @@
@Fluent
public final class ModelState {
/*
- * This indicates the number of passes of the entire training dataset the
+ * Number of passes of the entire training dataset that the
* algorithm has completed.
*/
@JsonProperty(value = "epochIds")
@@ -42,8 +42,7 @@ public final class ModelState {
public ModelState() {}
/**
- * Get the epochIds property: This indicates the number of passes of the entire training dataset the algorithm has
- * completed.
+ * Get the epochIds property: Number of passes of the entire training dataset that the algorithm has completed.
*
* @return the epochIds value.
*/
@@ -52,8 +51,7 @@ public List getEpochIds() {
}
/**
- * Set the epochIds property: This indicates the number of passes of the entire training dataset the algorithm has
- * completed.
+ * Set the epochIds property: Number of passes of the entire training dataset that the algorithm has completed.
*
* @param epochIds the epochIds value to set.
* @return the ModelState object itself.
diff --git a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/ModelStatus.java b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/ModelStatus.java
index 2ff7519fd5d9..96d1db73adc1 100644
--- a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/ModelStatus.java
+++ b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/ModelStatus.java
@@ -4,54 +4,49 @@
package com.azure.ai.anomalydetector.models;
+import com.azure.core.util.ExpandableStringEnum;
import com.fasterxml.jackson.annotation.JsonCreator;
-import com.fasterxml.jackson.annotation.JsonValue;
+import java.util.Collection;
/** Defines values for ModelStatus. */
-public enum ModelStatus {
- /** Enum value CREATED. */
- CREATED("CREATED"),
+public final class ModelStatus extends ExpandableStringEnum {
+ /** The model has been created. Training has been scheduled but not yet started. */
+ public static final ModelStatus CREATED = fromString("CREATED");
- /** Enum value RUNNING. */
- RUNNING("RUNNING"),
+ /** The model is being trained. */
+ public static final ModelStatus RUNNING = fromString("RUNNING");
- /** Enum value READY. */
- READY("READY"),
+ /** The model has been trained and is ready to be used for anomaly detection. */
+ public static final ModelStatus READY = fromString("READY");
- /** Enum value FAILED. */
- FAILED("FAILED");
+ /** The model training failed. */
+ public static final ModelStatus FAILED = fromString("FAILED");
- /** The actual serialized value for a ModelStatus instance. */
- private final String value;
-
- ModelStatus(String value) {
- this.value = value;
- }
+ /**
+ * Creates a new instance of ModelStatus value.
+ *
+ * @deprecated Use the {@link #fromString(String)} factory method.
+ */
+ @Deprecated
+ public ModelStatus() {}
/**
- * Parses a serialized value to a ModelStatus instance.
+ * Creates or finds a ModelStatus from its string representation.
*
- * @param value the serialized value to parse.
- * @return the parsed ModelStatus object, or null if unable to parse.
+ * @param name a name to look for.
+ * @return the corresponding ModelStatus.
*/
@JsonCreator
- public static ModelStatus fromString(String value) {
- if (value == null) {
- return null;
- }
- ModelStatus[] items = ModelStatus.values();
- for (ModelStatus item : items) {
- if (item.toString().equalsIgnoreCase(value)) {
- return item;
- }
- }
- return null;
+ public static ModelStatus fromString(String name) {
+ return fromString(name, ModelStatus.class);
}
- /** {@inheritDoc} */
- @JsonValue
- @Override
- public String toString() {
- return this.value;
+ /**
+ * Gets known ModelStatus values.
+ *
+ * @return known ModelStatus values.
+ */
+ public static Collection values() {
+ return values(ModelStatus.class);
}
}
diff --git a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/MultivariateBatchDetectionOptions.java b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/MultivariateBatchDetectionOptions.java
index 040382c0f001..917fe9ebee8f 100644
--- a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/MultivariateBatchDetectionOptions.java
+++ b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/MultivariateBatchDetectionOptions.java
@@ -4,44 +4,44 @@
package com.azure.ai.anomalydetector.models;
-import com.azure.core.annotation.Immutable;
+import com.azure.core.annotation.Fluent;
import com.fasterxml.jackson.annotation.JsonCreator;
import com.fasterxml.jackson.annotation.JsonProperty;
import java.time.OffsetDateTime;
/**
- * Detection request for batch inference. This is an asynchronous inference which will need another API to get detection
+ * Detection request for batch inference. This is an asynchronous inference that will need another API to get detection
* results.
*/
-@Immutable
+@Fluent
public final class MultivariateBatchDetectionOptions {
/*
- * Source link to the input data to indicate an accessible Azure storage Uri,
- * either pointed to an Azure blob storage folder, or pointed to a CSV file in
- * Azure blob storage based on you data schema selection. The data schema should
- * be exactly the same with those used in the training phase.
+ * Source link to the input data to indicate an accessible Azure Storage URI.
+ * It either points to an Azure Blob Storage folder or points to a CSV file in
+ * Azure Blob Storage, based on your data schema selection. The data schema should
+ * be exactly the same as those used in the training phase. The input data must
+ * contain at least slidingWindow entries preceding the start time of the data
+ * to be detected.
*/
@JsonProperty(value = "dataSource", required = true)
private String dataSource;
/*
- * An optional field, which is used to specify the number of top contributed
- * variables for one anomalous timestamp in the response. The default number is
- * 10.
+ * Number of top contributed variables for one anomalous time stamp in the response.
*/
- @JsonProperty(value = "topContributorCount", required = true)
- private int topContributorCount;
+ @JsonProperty(value = "topContributorCount")
+ private Integer topContributorCount;
/*
- * A required field, indicating the start time of data for detection, which should
- * be date-time of ISO 8601 format.
+ * Start date/time of data for detection, which should
+ * be in ISO 8601 format.
*/
@JsonProperty(value = "startTime", required = true)
private OffsetDateTime startTime;
/*
- * A required field, indicating the end time of data for detection, which should
- * be date-time of ISO 8601 format.
+ * End date/time of data for detection, which should
+ * be in ISO 8601 format.
*/
@JsonProperty(value = "endTime", required = true)
private OffsetDateTime endTime;
@@ -50,26 +50,24 @@ public final class MultivariateBatchDetectionOptions {
* Creates an instance of MultivariateBatchDetectionOptions class.
*
* @param dataSource the dataSource value to set.
- * @param topContributorCount the topContributorCount value to set.
* @param startTime the startTime value to set.
* @param endTime the endTime value to set.
*/
@JsonCreator
public MultivariateBatchDetectionOptions(
@JsonProperty(value = "dataSource", required = true) String dataSource,
- @JsonProperty(value = "topContributorCount", required = true) int topContributorCount,
@JsonProperty(value = "startTime", required = true) OffsetDateTime startTime,
@JsonProperty(value = "endTime", required = true) OffsetDateTime endTime) {
this.dataSource = dataSource;
- this.topContributorCount = topContributorCount;
this.startTime = startTime;
this.endTime = endTime;
}
/**
- * Get the dataSource property: Source link to the input data to indicate an accessible Azure storage Uri, either
- * pointed to an Azure blob storage folder, or pointed to a CSV file in Azure blob storage based on you data schema
- * selection. The data schema should be exactly the same with those used in the training phase.
+ * Get the dataSource property: Source link to the input data to indicate an accessible Azure Storage URI. It either
+ * points to an Azure Blob Storage folder or points to a CSV file in Azure Blob Storage, based on your data schema
+ * selection. The data schema should be exactly the same as those used in the training phase. The input data must
+ * contain at least slidingWindow entries preceding the start time of the data to be detected.
*
* @return the dataSource value.
*/
@@ -78,18 +76,29 @@ public String getDataSource() {
}
/**
- * Get the topContributorCount property: An optional field, which is used to specify the number of top contributed
- * variables for one anomalous timestamp in the response. The default number is 10.
+ * Get the topContributorCount property: Number of top contributed variables for one anomalous time stamp in the
+ * response.
*
* @return the topContributorCount value.
*/
- public int getTopContributorCount() {
+ public Integer getTopContributorCount() {
return this.topContributorCount;
}
/**
- * Get the startTime property: A required field, indicating the start time of data for detection, which should be
- * date-time of ISO 8601 format.
+ * Set the topContributorCount property: Number of top contributed variables for one anomalous time stamp in the
+ * response.
+ *
+ * @param topContributorCount the topContributorCount value to set.
+ * @return the MultivariateBatchDetectionOptions object itself.
+ */
+ public MultivariateBatchDetectionOptions setTopContributorCount(Integer topContributorCount) {
+ this.topContributorCount = topContributorCount;
+ return this;
+ }
+
+ /**
+ * Get the startTime property: Start date/time of data for detection, which should be in ISO 8601 format.
*
* @return the startTime value.
*/
@@ -98,8 +107,7 @@ public OffsetDateTime getStartTime() {
}
/**
- * Get the endTime property: A required field, indicating the end time of data for detection, which should be
- * date-time of ISO 8601 format.
+ * Get the endTime property: End date/time of data for detection, which should be in ISO 8601 format.
*
* @return the endTime value.
*/
diff --git a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/MultivariateBatchDetectionResultSummary.java b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/MultivariateBatchDetectionResultSummary.java
index 10b62cc16da5..717da8a4f12f 100644
--- a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/MultivariateBatchDetectionResultSummary.java
+++ b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/MultivariateBatchDetectionResultSummary.java
@@ -13,25 +13,25 @@
@Immutable
public final class MultivariateBatchDetectionResultSummary {
/*
- * Status of detection results. One of CREATED, RUNNING, READY, and FAILED.
+ * Status of detection results.
*/
@JsonProperty(value = "status", required = true)
private MultivariateBatchDetectionStatus status;
/*
- * Error message when detection is failed.
+ * Error message when detection fails.
*/
@JsonProperty(value = "errors")
private List errors;
/*
- * Variable Status.
+ * Variable status.
*/
@JsonProperty(value = "variableStates")
private List variableStates;
/*
- * Detection request for batch inference. This is an asynchronous inference which
+ * Detection request for batch inference. This is an asynchronous inference that
* will need another API to get detection results.
*/
@JsonProperty(value = "setupInfo", required = true)
@@ -52,7 +52,7 @@ private MultivariateBatchDetectionResultSummary(
}
/**
- * Get the status property: Status of detection results. One of CREATED, RUNNING, READY, and FAILED.
+ * Get the status property: Status of detection results.
*
* @return the status value.
*/
@@ -61,7 +61,7 @@ public MultivariateBatchDetectionStatus getStatus() {
}
/**
- * Get the errors property: Error message when detection is failed.
+ * Get the errors property: Error message when detection fails.
*
* @return the errors value.
*/
@@ -70,7 +70,7 @@ public List getErrors() {
}
/**
- * Get the variableStates property: Variable Status.
+ * Get the variableStates property: Variable status.
*
* @return the variableStates value.
*/
@@ -79,7 +79,7 @@ public List getVariableStates() {
}
/**
- * Get the setupInfo property: Detection request for batch inference. This is an asynchronous inference which will
+ * Get the setupInfo property: Detection request for batch inference. This is an asynchronous inference that will
* need another API to get detection results.
*
* @return the setupInfo value.
diff --git a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/MultivariateBatchDetectionStatus.java b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/MultivariateBatchDetectionStatus.java
index c3f17996f22c..bd51e097ee1d 100644
--- a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/MultivariateBatchDetectionStatus.java
+++ b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/MultivariateBatchDetectionStatus.java
@@ -4,54 +4,49 @@
package com.azure.ai.anomalydetector.models;
+import com.azure.core.util.ExpandableStringEnum;
import com.fasterxml.jackson.annotation.JsonCreator;
-import com.fasterxml.jackson.annotation.JsonValue;
+import java.util.Collection;
/** Defines values for MultivariateBatchDetectionStatus. */
-public enum MultivariateBatchDetectionStatus {
- /** Enum value CREATED. */
- CREATED("CREATED"),
+public final class MultivariateBatchDetectionStatus extends ExpandableStringEnum {
+ /** Static value CREATED for MultivariateBatchDetectionStatus. */
+ public static final MultivariateBatchDetectionStatus CREATED = fromString("CREATED");
- /** Enum value RUNNING. */
- RUNNING("RUNNING"),
+ /** Static value RUNNING for MultivariateBatchDetectionStatus. */
+ public static final MultivariateBatchDetectionStatus RUNNING = fromString("RUNNING");
- /** Enum value READY. */
- READY("READY"),
+ /** Static value READY for MultivariateBatchDetectionStatus. */
+ public static final MultivariateBatchDetectionStatus READY = fromString("READY");
- /** Enum value FAILED. */
- FAILED("FAILED");
+ /** Static value FAILED for MultivariateBatchDetectionStatus. */
+ public static final MultivariateBatchDetectionStatus FAILED = fromString("FAILED");
- /** The actual serialized value for a MultivariateBatchDetectionStatus instance. */
- private final String value;
-
- MultivariateBatchDetectionStatus(String value) {
- this.value = value;
- }
+ /**
+ * Creates a new instance of MultivariateBatchDetectionStatus value.
+ *
+ * @deprecated Use the {@link #fromString(String)} factory method.
+ */
+ @Deprecated
+ public MultivariateBatchDetectionStatus() {}
/**
- * Parses a serialized value to a MultivariateBatchDetectionStatus instance.
+ * Creates or finds a MultivariateBatchDetectionStatus from its string representation.
*
- * @param value the serialized value to parse.
- * @return the parsed MultivariateBatchDetectionStatus object, or null if unable to parse.
+ * @param name a name to look for.
+ * @return the corresponding MultivariateBatchDetectionStatus.
*/
@JsonCreator
- public static MultivariateBatchDetectionStatus fromString(String value) {
- if (value == null) {
- return null;
- }
- MultivariateBatchDetectionStatus[] items = MultivariateBatchDetectionStatus.values();
- for (MultivariateBatchDetectionStatus item : items) {
- if (item.toString().equalsIgnoreCase(value)) {
- return item;
- }
- }
- return null;
+ public static MultivariateBatchDetectionStatus fromString(String name) {
+ return fromString(name, MultivariateBatchDetectionStatus.class);
}
- /** {@inheritDoc} */
- @JsonValue
- @Override
- public String toString() {
- return this.value;
+ /**
+ * Gets known MultivariateBatchDetectionStatus values.
+ *
+ * @return known MultivariateBatchDetectionStatus values.
+ */
+ public static Collection values() {
+ return values(MultivariateBatchDetectionStatus.class);
}
}
diff --git a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/MultivariateDetectionResult.java b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/MultivariateDetectionResult.java
index aa2938743296..d6f61ab1b59c 100644
--- a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/MultivariateDetectionResult.java
+++ b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/MultivariateDetectionResult.java
@@ -9,11 +9,11 @@
import com.fasterxml.jackson.annotation.JsonProperty;
import java.util.List;
-/** Detection results for the given resultId. */
+/** Detection results for the resultId value. */
@Immutable
public final class MultivariateDetectionResult {
/*
- * Result identifier, which is used to fetch the results of an inference call.
+ * Result identifier that's used to fetch the results of an inference call.
*/
@JsonProperty(value = "resultId", required = true, access = JsonProperty.Access.WRITE_ONLY)
private String resultId;
@@ -25,7 +25,7 @@ public final class MultivariateDetectionResult {
private MultivariateBatchDetectionResultSummary summary;
/*
- * Detection result for each timestamp.
+ * Detection result for each time stamp.
*/
@JsonProperty(value = "results", required = true)
private List results;
@@ -45,7 +45,7 @@ private MultivariateDetectionResult(
}
/**
- * Get the resultId property: Result identifier, which is used to fetch the results of an inference call.
+ * Get the resultId property: Result identifier that's used to fetch the results of an inference call.
*
* @return the resultId value.
*/
@@ -63,7 +63,7 @@ public MultivariateBatchDetectionResultSummary getSummary() {
}
/**
- * Get the results property: Detection result for each timestamp.
+ * Get the results property: Detection result for each time stamp.
*
* @return the results value.
*/
diff --git a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/MultivariateLastDetectionOptions.java b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/MultivariateLastDetectionOptions.java
index faf9d874b8fb..2b2af98b6412 100644
--- a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/MultivariateLastDetectionOptions.java
+++ b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/MultivariateLastDetectionOptions.java
@@ -4,45 +4,42 @@
package com.azure.ai.anomalydetector.models;
-import com.azure.core.annotation.Immutable;
+import com.azure.core.annotation.Fluent;
import com.fasterxml.jackson.annotation.JsonCreator;
import com.fasterxml.jackson.annotation.JsonProperty;
import java.util.List;
-/** Request of last detection. */
-@Immutable
+/** Request of the last detection. */
+@Fluent
public final class MultivariateLastDetectionOptions {
/*
- * This contains the inference data, including the name, timestamps(ISO 8601) and
+ * Contains the inference data, including the name, time stamps (ISO 8601), and
* values of variables.
*/
@JsonProperty(value = "variables", required = true)
private List variables;
/*
- * An optional field, which is used to specify the number of top contributed
- * variables for one anomalous timestamp in the response. The default number is
+ * Number of top contributed
+ * variables for one anomalous time stamp in the response. The default is
* 10.
*/
- @JsonProperty(value = "topContributorCount", required = true)
- private int topContributorCount;
+ @JsonProperty(value = "topContributorCount")
+ private Integer topContributorCount;
/**
* Creates an instance of MultivariateLastDetectionOptions class.
*
* @param variables the variables value to set.
- * @param topContributorCount the topContributorCount value to set.
*/
@JsonCreator
public MultivariateLastDetectionOptions(
- @JsonProperty(value = "variables", required = true) List variables,
- @JsonProperty(value = "topContributorCount", required = true) int topContributorCount) {
+ @JsonProperty(value = "variables", required = true) List variables) {
this.variables = variables;
- this.topContributorCount = topContributorCount;
}
/**
- * Get the variables property: This contains the inference data, including the name, timestamps(ISO 8601) and values
+ * Get the variables property: Contains the inference data, including the name, time stamps (ISO 8601), and values
* of variables.
*
* @return the variables value.
@@ -52,12 +49,24 @@ public List getVariables() {
}
/**
- * Get the topContributorCount property: An optional field, which is used to specify the number of top contributed
- * variables for one anomalous timestamp in the response. The default number is 10.
+ * Get the topContributorCount property: Number of top contributed variables for one anomalous time stamp in the
+ * response. The default is 10.
*
* @return the topContributorCount value.
*/
- public int getTopContributorCount() {
+ public Integer getTopContributorCount() {
return this.topContributorCount;
}
+
+ /**
+ * Set the topContributorCount property: Number of top contributed variables for one anomalous time stamp in the
+ * response. The default is 10.
+ *
+ * @param topContributorCount the topContributorCount value to set.
+ * @return the MultivariateLastDetectionOptions object itself.
+ */
+ public MultivariateLastDetectionOptions setTopContributorCount(Integer topContributorCount) {
+ this.topContributorCount = topContributorCount;
+ return this;
+ }
}
diff --git a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/MultivariateLastDetectionResult.java b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/MultivariateLastDetectionResult.java
index 356567313ae6..6f7ba6056e5a 100644
--- a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/MultivariateLastDetectionResult.java
+++ b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/MultivariateLastDetectionResult.java
@@ -8,11 +8,11 @@
import com.fasterxml.jackson.annotation.JsonProperty;
import java.util.List;
-/** Results of last detection. */
+/** Results of the last detection. */
@Immutable
public final class MultivariateLastDetectionResult {
/*
- * Variable Status.
+ * Variable status.
*/
@JsonProperty(value = "variableStates")
private List variableStates;
@@ -27,7 +27,7 @@ public final class MultivariateLastDetectionResult {
private MultivariateLastDetectionResult() {}
/**
- * Get the variableStates property: Variable Status.
+ * Get the variableStates property: Variable status.
*
* @return the variableStates value.
*/
diff --git a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/TimeGranularity.java b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/TimeGranularity.java
index fea416ba0eca..e817d059182f 100644
--- a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/TimeGranularity.java
+++ b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/TimeGranularity.java
@@ -4,69 +4,64 @@
package com.azure.ai.anomalydetector.models;
+import com.azure.core.util.ExpandableStringEnum;
import com.fasterxml.jackson.annotation.JsonCreator;
-import com.fasterxml.jackson.annotation.JsonValue;
+import java.util.Collection;
/** Defines values for TimeGranularity. */
-public enum TimeGranularity {
- /** Enum value yearly. */
- YEARLY("yearly"),
+public final class TimeGranularity extends ExpandableStringEnum {
+ /** Static value yearly for TimeGranularity. */
+ public static final TimeGranularity YEARLY = fromString("yearly");
- /** Enum value monthly. */
- MONTHLY("monthly"),
+ /** Static value monthly for TimeGranularity. */
+ public static final TimeGranularity MONTHLY = fromString("monthly");
- /** Enum value weekly. */
- WEEKLY("weekly"),
+ /** Static value weekly for TimeGranularity. */
+ public static final TimeGranularity WEEKLY = fromString("weekly");
- /** Enum value daily. */
- DAILY("daily"),
+ /** Static value daily for TimeGranularity. */
+ public static final TimeGranularity DAILY = fromString("daily");
- /** Enum value hourly. */
- HOURLY("hourly"),
+ /** Static value hourly for TimeGranularity. */
+ public static final TimeGranularity HOURLY = fromString("hourly");
- /** Enum value minutely. */
- PER_MINUTE("minutely"),
+ /** Static value minutely for TimeGranularity. */
+ public static final TimeGranularity PER_MINUTE = fromString("minutely");
- /** Enum value secondly. */
- PER_SECOND("secondly"),
+ /** Static value secondly for TimeGranularity. */
+ public static final TimeGranularity PER_SECOND = fromString("secondly");
- /** Enum value microsecond. */
- MICROSECOND("microsecond"),
+ /** Static value microsecond for TimeGranularity. */
+ public static final TimeGranularity MICROSECOND = fromString("microsecond");
- /** Enum value none. */
- NONE("none");
+ /** Static value none for TimeGranularity. */
+ public static final TimeGranularity NONE = fromString("none");
- /** The actual serialized value for a TimeGranularity instance. */
- private final String value;
-
- TimeGranularity(String value) {
- this.value = value;
- }
+ /**
+ * Creates a new instance of TimeGranularity value.
+ *
+ * @deprecated Use the {@link #fromString(String)} factory method.
+ */
+ @Deprecated
+ public TimeGranularity() {}
/**
- * Parses a serialized value to a TimeGranularity instance.
+ * Creates or finds a TimeGranularity from its string representation.
*
- * @param value the serialized value to parse.
- * @return the parsed TimeGranularity object, or null if unable to parse.
+ * @param name a name to look for.
+ * @return the corresponding TimeGranularity.
*/
@JsonCreator
- public static TimeGranularity fromString(String value) {
- if (value == null) {
- return null;
- }
- TimeGranularity[] items = TimeGranularity.values();
- for (TimeGranularity item : items) {
- if (item.toString().equalsIgnoreCase(value)) {
- return item;
- }
- }
- return null;
+ public static TimeGranularity fromString(String name) {
+ return fromString(name, TimeGranularity.class);
}
- /** {@inheritDoc} */
- @JsonValue
- @Override
- public String toString() {
- return this.value;
+ /**
+ * Gets known TimeGranularity values.
+ *
+ * @return known TimeGranularity values.
+ */
+ public static Collection values() {
+ return values(TimeGranularity.class);
}
}
diff --git a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/TimeSeriesPoint.java b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/TimeSeriesPoint.java
index e4cf70e77a62..112217ab97ba 100644
--- a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/TimeSeriesPoint.java
+++ b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/TimeSeriesPoint.java
@@ -9,17 +9,17 @@
import com.fasterxml.jackson.annotation.JsonProperty;
import java.time.OffsetDateTime;
-/** The definition of input timeseries points. */
+/** Definition of input time series points. */
@Fluent
public final class TimeSeriesPoint {
/*
- * Optional argument, timestamp of a data point (ISO8601 format).
+ * Argument that indicates the time stamp of a data point (ISO8601 format).
*/
@JsonProperty(value = "timestamp")
private OffsetDateTime timestamp;
/*
- * The measurement of that point, should be float.
+ * Measurement of that point.
*/
@JsonProperty(value = "value", required = true)
private double value;
@@ -35,7 +35,7 @@ public TimeSeriesPoint(@JsonProperty(value = "value", required = true) double va
}
/**
- * Get the timestamp property: Optional argument, timestamp of a data point (ISO8601 format).
+ * Get the timestamp property: Argument that indicates the time stamp of a data point (ISO8601 format).
*
* @return the timestamp value.
*/
@@ -44,7 +44,7 @@ public OffsetDateTime getTimestamp() {
}
/**
- * Set the timestamp property: Optional argument, timestamp of a data point (ISO8601 format).
+ * Set the timestamp property: Argument that indicates the time stamp of a data point (ISO8601 format).
*
* @param timestamp the timestamp value to set.
* @return the TimeSeriesPoint object itself.
@@ -55,7 +55,7 @@ public TimeSeriesPoint setTimestamp(OffsetDateTime timestamp) {
}
/**
- * Get the value property: The measurement of that point, should be float.
+ * Get the value property: Measurement of that point.
*
* @return the value value.
*/
diff --git a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/UnivariateChangePointDetectionOptions.java b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/UnivariateChangePointDetectionOptions.java
index 1d3103748d65..9e764cb2181a 100644
--- a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/UnivariateChangePointDetectionOptions.java
+++ b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/UnivariateChangePointDetectionOptions.java
@@ -9,48 +9,47 @@
import com.fasterxml.jackson.annotation.JsonProperty;
import java.util.List;
-/** The request of change point detection. */
+/** Request of change point detection. */
@Fluent
public final class UnivariateChangePointDetectionOptions {
/*
- * Time series data points. Points should be sorted by timestamp in ascending
+ * Time series data points. Points should be sorted by time stamp in ascending
* order to match the change point detection result.
*/
@JsonProperty(value = "series", required = true)
private List series;
/*
- * Can only be one of yearly, monthly, weekly, daily, hourly, minutely or
- * secondly. Granularity is used for verify whether input series is valid.
+ * Granularity is used to verify whether the input series is valid.
*/
@JsonProperty(value = "granularity", required = true)
private TimeGranularity granularity;
/*
- * Custom Interval is used to set non-standard time interval, for example, if the
- * series is 5 minutes, request can be set as {"granularity":"minutely",
+ * A custom interval is used to set a nonstandard time interval. For example, if the
+ * series is 5 minutes, the request can be set as {"granularity":"minutely",
* "customInterval":5}.
*/
@JsonProperty(value = "customInterval")
private Integer customInterval;
/*
- * Optional argument, periodic value of a time series. If the value is null or
- * does not present, the API will determine the period automatically.
+ * Argument that indicates the periodic value of a time series. If the value is null or
+ * not present, the API will determine the period automatically.
*/
@JsonProperty(value = "period")
private Integer period;
/*
- * Optional argument, advanced model parameter, a default stableTrendWindow will
+ * Argument that indicates an advanced model parameter. A default stableTrendWindow value will
* be used in detection.
*/
@JsonProperty(value = "stableTrendWindow")
private Integer stableTrendWindow;
/*
- * Optional argument, advanced model parameter, between 0.0-1.0, the lower the
- * value is, the larger the trend error will be which means less change point will
+ * Argument that indicates an advanced model parameter between 0.0 and 1.0. The lower the
+ * value is, the larger the trend error is, which means less change point will
* be accepted.
*/
@JsonProperty(value = "threshold")
@@ -71,7 +70,7 @@ public UnivariateChangePointDetectionOptions(
}
/**
- * Get the series property: Time series data points. Points should be sorted by timestamp in ascending order to
+ * Get the series property: Time series data points. Points should be sorted by time stamp in ascending order to
* match the change point detection result.
*
* @return the series value.
@@ -81,8 +80,7 @@ public List getSeries() {
}
/**
- * Get the granularity property: Can only be one of yearly, monthly, weekly, daily, hourly, minutely or secondly.
- * Granularity is used for verify whether input series is valid.
+ * Get the granularity property: Granularity is used to verify whether the input series is valid.
*
* @return the granularity value.
*/
@@ -91,8 +89,8 @@ public TimeGranularity getGranularity() {
}
/**
- * Get the customInterval property: Custom Interval is used to set non-standard time interval, for example, if the
- * series is 5 minutes, request can be set as {"granularity":"minutely", "customInterval":5}.
+ * Get the customInterval property: A custom interval is used to set a nonstandard time interval. For example, if
+ * the series is 5 minutes, the request can be set as {"granularity":"minutely", "customInterval":5}.
*
* @return the customInterval value.
*/
@@ -101,8 +99,8 @@ public Integer getCustomInterval() {
}
/**
- * Set the customInterval property: Custom Interval is used to set non-standard time interval, for example, if the
- * series is 5 minutes, request can be set as {"granularity":"minutely", "customInterval":5}.
+ * Set the customInterval property: A custom interval is used to set a nonstandard time interval. For example, if
+ * the series is 5 minutes, the request can be set as {"granularity":"minutely", "customInterval":5}.
*
* @param customInterval the customInterval value to set.
* @return the UnivariateChangePointDetectionOptions object itself.
@@ -113,7 +111,7 @@ public UnivariateChangePointDetectionOptions setCustomInterval(Integer customInt
}
/**
- * Get the period property: Optional argument, periodic value of a time series. If the value is null or does not
+ * Get the period property: Argument that indicates the periodic value of a time series. If the value is null or not
* present, the API will determine the period automatically.
*
* @return the period value.
@@ -123,7 +121,7 @@ public Integer getPeriod() {
}
/**
- * Set the period property: Optional argument, periodic value of a time series. If the value is null or does not
+ * Set the period property: Argument that indicates the periodic value of a time series. If the value is null or not
* present, the API will determine the period automatically.
*
* @param period the period value to set.
@@ -135,8 +133,8 @@ public UnivariateChangePointDetectionOptions setPeriod(Integer period) {
}
/**
- * Get the stableTrendWindow property: Optional argument, advanced model parameter, a default stableTrendWindow will
- * be used in detection.
+ * Get the stableTrendWindow property: Argument that indicates an advanced model parameter. A default
+ * stableTrendWindow value will be used in detection.
*
* @return the stableTrendWindow value.
*/
@@ -145,8 +143,8 @@ public Integer getStableTrendWindow() {
}
/**
- * Set the stableTrendWindow property: Optional argument, advanced model parameter, a default stableTrendWindow will
- * be used in detection.
+ * Set the stableTrendWindow property: Argument that indicates an advanced model parameter. A default
+ * stableTrendWindow value will be used in detection.
*
* @param stableTrendWindow the stableTrendWindow value to set.
* @return the UnivariateChangePointDetectionOptions object itself.
@@ -157,8 +155,8 @@ public UnivariateChangePointDetectionOptions setStableTrendWindow(Integer stable
}
/**
- * Get the threshold property: Optional argument, advanced model parameter, between 0.0-1.0, the lower the value is,
- * the larger the trend error will be which means less change point will be accepted.
+ * Get the threshold property: Argument that indicates an advanced model parameter between 0.0 and 1.0. The lower
+ * the value is, the larger the trend error is, which means less change point will be accepted.
*
* @return the threshold value.
*/
@@ -167,8 +165,8 @@ public Double getThreshold() {
}
/**
- * Set the threshold property: Optional argument, advanced model parameter, between 0.0-1.0, the lower the value is,
- * the larger the trend error will be which means less change point will be accepted.
+ * Set the threshold property: Argument that indicates an advanced model parameter between 0.0 and 1.0. The lower
+ * the value is, the larger the trend error is, which means less change point will be accepted.
*
* @param threshold the threshold value to set.
* @return the UnivariateChangePointDetectionOptions object itself.
diff --git a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/UnivariateChangePointDetectionResult.java b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/UnivariateChangePointDetectionResult.java
index 0d8774223073..e588682fc7e8 100644
--- a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/UnivariateChangePointDetectionResult.java
+++ b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/UnivariateChangePointDetectionResult.java
@@ -8,26 +8,26 @@
import com.fasterxml.jackson.annotation.JsonProperty;
import java.util.List;
-/** The response of change point detection. */
+/** Response of change point detection. */
@Immutable
public final class UnivariateChangePointDetectionResult {
/*
- * Frequency extracted from the series, zero means no recurrent pattern has been
+ * Frequency extracted from the series. Zero means no recurrent pattern has been
* found.
*/
@JsonProperty(value = "period", access = JsonProperty.Access.WRITE_ONLY)
private Integer period;
/*
- * isChangePoint contains change point properties for each input point. True means
- * an anomaly either negative or positive has been detected. The index of the
+ * Change point properties for each input point. True means
+ * an anomaly (either negative or positive) has been detected. The index of the
* array is consistent with the input series.
*/
@JsonProperty(value = "isChangePoint")
private List isChangePoint;
/*
- * the change point confidence of each point
+ * Change point confidence of each point.
*/
@JsonProperty(value = "confidenceScores")
private List confidenceScores;
@@ -36,7 +36,7 @@ public final class UnivariateChangePointDetectionResult {
private UnivariateChangePointDetectionResult() {}
/**
- * Get the period property: Frequency extracted from the series, zero means no recurrent pattern has been found.
+ * Get the period property: Frequency extracted from the series. Zero means no recurrent pattern has been found.
*
* @return the period value.
*/
@@ -45,9 +45,8 @@ public Integer getPeriod() {
}
/**
- * Get the isChangePoint property: isChangePoint contains change point properties for each input point. True means
- * an anomaly either negative or positive has been detected. The index of the array is consistent with the input
- * series.
+ * Get the isChangePoint property: Change point properties for each input point. True means an anomaly (either
+ * negative or positive) has been detected. The index of the array is consistent with the input series.
*
* @return the isChangePoint value.
*/
@@ -56,7 +55,7 @@ public List getIsChangePoint() {
}
/**
- * Get the confidenceScores property: the change point confidence of each point.
+ * Get the confidenceScores property: Change point confidence of each point.
*
* @return the confidenceScores value.
*/
diff --git a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/UnivariateDetectionOptions.java b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/UnivariateDetectionOptions.java
index a84d8b12ae70..0a0e0eac4d56 100644
--- a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/UnivariateDetectionOptions.java
+++ b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/UnivariateDetectionOptions.java
@@ -9,65 +9,64 @@
import com.fasterxml.jackson.annotation.JsonProperty;
import java.util.List;
-/** The request of entire or last anomaly detection. */
+/** Request of the entire or last anomaly detection. */
@Fluent
public final class UnivariateDetectionOptions {
/*
- * Time series data points. Points should be sorted by timestamp in ascending
+ * Time series data points. Points should be sorted by time stamp in ascending
* order to match the anomaly detection result. If the data is not sorted
- * correctly or there is duplicated timestamp, the API will not work. In such
- * case, an error message will be returned.
+ * correctly or there's a duplicated time stamp, the API won't work. In such
+ * a case, an error message is returned.
*/
@JsonProperty(value = "series", required = true)
private List series;
/*
- * Optional argument, can be one of yearly, monthly, weekly, daily, hourly,
- * minutely, secondly, microsecond or none. If granularity is not present, it will
- * be none by default. If granularity is none, the timestamp property in time
+ * Argument that indicates time granularity. If granularity is not present, the value
+ * is none by default. If granularity is none, the time stamp property in the time
* series point can be absent.
*/
@JsonProperty(value = "granularity")
private TimeGranularity granularity;
/*
- * Custom Interval is used to set non-standard time interval, for example, if the
- * series is 5 minutes, request can be set as {"granularity":"minutely",
+ * A custom interval is used to set a nonstandard time interval. For example, if the
+ * series is 5 minutes, the request can be set as {"granularity":"minutely",
* "customInterval":5}.
*/
@JsonProperty(value = "customInterval")
private Integer customInterval;
/*
- * Optional argument, periodic value of a time series. If the value is null or
- * does not present, the API will determine the period automatically.
+ * Argument that indicates the periodic value of a time series. If the value is null or
+ * is not present, the API determines the period automatically.
*/
@JsonProperty(value = "period")
private Integer period;
/*
- * Optional argument, advanced model parameter, max anomaly ratio in a time series.
+ * Argument that indicates an advanced model parameter. It's the maximum anomaly ratio in a time series.
*/
@JsonProperty(value = "maxAnomalyRatio")
private Double maxAnomalyRatio;
/*
- * Optional argument, advanced model parameter, between 0-99, the lower the value
- * is, the larger the margin value will be which means less anomalies will be
+ * Argument that indicates an advanced model parameter between 0 and 99. The lower the value
+ * is, the larger the margin value is, which means fewer anomalies will be
* accepted.
*/
@JsonProperty(value = "sensitivity")
private Integer sensitivity;
/*
- * Used to specify how to deal with missing values in the input series, it's used
+ * Specifies how to deal with missing values in the input series. It's used
* when granularity is not "none".
*/
@JsonProperty(value = "imputeMode")
private ImputeMode imputeMode;
/*
- * Used to specify the value to fill, it's used when granularity is not "none"
+ * Specifies the value to fill. It's used when granularity is not "none"
* and imputeMode is "fixed".
*/
@JsonProperty(value = "imputeFixedValue")
@@ -84,9 +83,9 @@ public UnivariateDetectionOptions(@JsonProperty(value = "series", required = tru
}
/**
- * Get the series property: Time series data points. Points should be sorted by timestamp in ascending order to
- * match the anomaly detection result. If the data is not sorted correctly or there is duplicated timestamp, the API
- * will not work. In such case, an error message will be returned.
+ * Get the series property: Time series data points. Points should be sorted by time stamp in ascending order to
+ * match the anomaly detection result. If the data is not sorted correctly or there's a duplicated time stamp, the
+ * API won't work. In such a case, an error message is returned.
*
* @return the series value.
*/
@@ -95,9 +94,8 @@ public List getSeries() {
}
/**
- * Get the granularity property: Optional argument, can be one of yearly, monthly, weekly, daily, hourly, minutely,
- * secondly, microsecond or none. If granularity is not present, it will be none by default. If granularity is none,
- * the timestamp property in time series point can be absent.
+ * Get the granularity property: Argument that indicates time granularity. If granularity is not present, the value
+ * is none by default. If granularity is none, the time stamp property in the time series point can be absent.
*
* @return the granularity value.
*/
@@ -106,9 +104,8 @@ public TimeGranularity getGranularity() {
}
/**
- * Set the granularity property: Optional argument, can be one of yearly, monthly, weekly, daily, hourly, minutely,
- * secondly, microsecond or none. If granularity is not present, it will be none by default. If granularity is none,
- * the timestamp property in time series point can be absent.
+ * Set the granularity property: Argument that indicates time granularity. If granularity is not present, the value
+ * is none by default. If granularity is none, the time stamp property in the time series point can be absent.
*
* @param granularity the granularity value to set.
* @return the UnivariateDetectionOptions object itself.
@@ -119,8 +116,8 @@ public UnivariateDetectionOptions setGranularity(TimeGranularity granularity) {
}
/**
- * Get the customInterval property: Custom Interval is used to set non-standard time interval, for example, if the
- * series is 5 minutes, request can be set as {"granularity":"minutely", "customInterval":5}.
+ * Get the customInterval property: A custom interval is used to set a nonstandard time interval. For example, if
+ * the series is 5 minutes, the request can be set as {"granularity":"minutely", "customInterval":5}.
*
* @return the customInterval value.
*/
@@ -129,8 +126,8 @@ public Integer getCustomInterval() {
}
/**
- * Set the customInterval property: Custom Interval is used to set non-standard time interval, for example, if the
- * series is 5 minutes, request can be set as {"granularity":"minutely", "customInterval":5}.
+ * Set the customInterval property: A custom interval is used to set a nonstandard time interval. For example, if
+ * the series is 5 minutes, the request can be set as {"granularity":"minutely", "customInterval":5}.
*
* @param customInterval the customInterval value to set.
* @return the UnivariateDetectionOptions object itself.
@@ -141,8 +138,8 @@ public UnivariateDetectionOptions setCustomInterval(Integer customInterval) {
}
/**
- * Get the period property: Optional argument, periodic value of a time series. If the value is null or does not
- * present, the API will determine the period automatically.
+ * Get the period property: Argument that indicates the periodic value of a time series. If the value is null or is
+ * not present, the API determines the period automatically.
*
* @return the period value.
*/
@@ -151,8 +148,8 @@ public Integer getPeriod() {
}
/**
- * Set the period property: Optional argument, periodic value of a time series. If the value is null or does not
- * present, the API will determine the period automatically.
+ * Set the period property: Argument that indicates the periodic value of a time series. If the value is null or is
+ * not present, the API determines the period automatically.
*
* @param period the period value to set.
* @return the UnivariateDetectionOptions object itself.
@@ -163,8 +160,8 @@ public UnivariateDetectionOptions setPeriod(Integer period) {
}
/**
- * Get the maxAnomalyRatio property: Optional argument, advanced model parameter, max anomaly ratio in a time
- * series.
+ * Get the maxAnomalyRatio property: Argument that indicates an advanced model parameter. It's the maximum anomaly
+ * ratio in a time series.
*
* @return the maxAnomalyRatio value.
*/
@@ -173,8 +170,8 @@ public Double getMaxAnomalyRatio() {
}
/**
- * Set the maxAnomalyRatio property: Optional argument, advanced model parameter, max anomaly ratio in a time
- * series.
+ * Set the maxAnomalyRatio property: Argument that indicates an advanced model parameter. It's the maximum anomaly
+ * ratio in a time series.
*
* @param maxAnomalyRatio the maxAnomalyRatio value to set.
* @return the UnivariateDetectionOptions object itself.
@@ -185,8 +182,8 @@ public UnivariateDetectionOptions setMaxAnomalyRatio(Double maxAnomalyRatio) {
}
/**
- * Get the sensitivity property: Optional argument, advanced model parameter, between 0-99, the lower the value is,
- * the larger the margin value will be which means less anomalies will be accepted.
+ * Get the sensitivity property: Argument that indicates an advanced model parameter between 0 and 99. The lower the
+ * value is, the larger the margin value is, which means fewer anomalies will be accepted.
*
* @return the sensitivity value.
*/
@@ -195,8 +192,8 @@ public Integer getSensitivity() {
}
/**
- * Set the sensitivity property: Optional argument, advanced model parameter, between 0-99, the lower the value is,
- * the larger the margin value will be which means less anomalies will be accepted.
+ * Set the sensitivity property: Argument that indicates an advanced model parameter between 0 and 99. The lower the
+ * value is, the larger the margin value is, which means fewer anomalies will be accepted.
*
* @param sensitivity the sensitivity value to set.
* @return the UnivariateDetectionOptions object itself.
@@ -207,7 +204,7 @@ public UnivariateDetectionOptions setSensitivity(Integer sensitivity) {
}
/**
- * Get the imputeMode property: Used to specify how to deal with missing values in the input series, it's used when
+ * Get the imputeMode property: Specifies how to deal with missing values in the input series. It's used when
* granularity is not "none".
*
* @return the imputeMode value.
@@ -217,7 +214,7 @@ public ImputeMode getImputeMode() {
}
/**
- * Set the imputeMode property: Used to specify how to deal with missing values in the input series, it's used when
+ * Set the imputeMode property: Specifies how to deal with missing values in the input series. It's used when
* granularity is not "none".
*
* @param imputeMode the imputeMode value to set.
@@ -229,8 +226,8 @@ public UnivariateDetectionOptions setImputeMode(ImputeMode imputeMode) {
}
/**
- * Get the imputeFixedValue property: Used to specify the value to fill, it's used when granularity is not "none"
- * and imputeMode is "fixed".
+ * Get the imputeFixedValue property: Specifies the value to fill. It's used when granularity is not "none" and
+ * imputeMode is "fixed".
*
* @return the imputeFixedValue value.
*/
@@ -239,8 +236,8 @@ public Double getImputeFixedValue() {
}
/**
- * Set the imputeFixedValue property: Used to specify the value to fill, it's used when granularity is not "none"
- * and imputeMode is "fixed".
+ * Set the imputeFixedValue property: Specifies the value to fill. It's used when granularity is not "none" and
+ * imputeMode is "fixed".
*
* @param imputeFixedValue the imputeFixedValue value to set.
* @return the UnivariateDetectionOptions object itself.
diff --git a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/UnivariateLastDetectionResult.java b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/UnivariateLastDetectionResult.java
index 7020f403d369..8aa2ab12d089 100644
--- a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/UnivariateLastDetectionResult.java
+++ b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/UnivariateLastDetectionResult.java
@@ -8,11 +8,11 @@
import com.fasterxml.jackson.annotation.JsonCreator;
import com.fasterxml.jackson.annotation.JsonProperty;
-/** The response of last anomaly detection. */
+/** Response of the last anomaly detection. */
@Immutable
public final class UnivariateLastDetectionResult {
/*
- * Frequency extracted from the series, zero means no recurrent pattern has been
+ * Frequency extracted from the series. Zero means no recurrent pattern has been
* found.
*/
@JsonProperty(value = "period", required = true)
@@ -32,46 +32,45 @@ public final class UnivariateLastDetectionResult {
/*
* Upper margin of the latest point. UpperMargin is used to calculate
- * upperBoundary, which equals to expectedValue + (100 - marginScale)*upperMargin.
+ * upperBoundary, which is equal to expectedValue + (100 - marginScale)*upperMargin.
* If the value of latest point is between upperBoundary and lowerBoundary, it
- * should be treated as normal value. By adjusting marginScale value, anomaly
- * status of latest point can be changed.
+ * should be treated as a normal value. Adjusting the marginScale value enables the anomaly
+ * status of the latest point to be changed.
*/
@JsonProperty(value = "upperMargin", required = true)
private double upperMargin;
/*
* Lower margin of the latest point. LowerMargin is used to calculate
- * lowerBoundary, which equals to expectedValue - (100 - marginScale)*lowerMargin.
- *
+ * lowerBoundary, which is equal to expectedValue - (100 - marginScale)*lowerMargin.
*/
@JsonProperty(value = "lowerMargin", required = true)
private double lowerMargin;
/*
- * Anomaly status of the latest point, true means the latest point is an anomaly
- * either in negative direction or positive direction.
+ * Anomaly status of the latest point. True means the latest point is an anomaly,
+ * either in the negative direction or in the positive direction.
*/
@JsonProperty(value = "isAnomaly", required = true)
private boolean isAnomaly;
/*
- * Anomaly status in negative direction of the latest point. True means the latest
+ * Anomaly status of the latest point in a negative direction. True means the latest
* point is an anomaly and its real value is smaller than the expected one.
*/
@JsonProperty(value = "isNegativeAnomaly", required = true)
private boolean isNegativeAnomaly;
/*
- * Anomaly status in positive direction of the latest point. True means the latest
+ * Anomaly status of the latest point in a positive direction. True means the latest
* point is an anomaly and its real value is larger than the expected one.
*/
@JsonProperty(value = "isPositiveAnomaly", required = true)
private boolean isPositiveAnomaly;
/*
- * The severity score for the last input point. The larger the value is, the more
- * sever the anomaly is. For normal points, the "severity" is always 0.
+ * Severity score for the last input point. The larger the value is, the more
+ * severe the anomaly is. For normal points, the severity is always 0.
*/
@JsonProperty(value = "severity")
private Double severity;
@@ -109,7 +108,7 @@ private UnivariateLastDetectionResult(
}
/**
- * Get the period property: Frequency extracted from the series, zero means no recurrent pattern has been found.
+ * Get the period property: Frequency extracted from the series. Zero means no recurrent pattern has been found.
*
* @return the period value.
*/
@@ -137,9 +136,9 @@ public double getExpectedValue() {
/**
* Get the upperMargin property: Upper margin of the latest point. UpperMargin is used to calculate upperBoundary,
- * which equals to expectedValue + (100 - marginScale)*upperMargin. If the value of latest point is between
- * upperBoundary and lowerBoundary, it should be treated as normal value. By adjusting marginScale value, anomaly
- * status of latest point can be changed.
+ * which is equal to expectedValue + (100 - marginScale)*upperMargin. If the value of latest point is between
+ * upperBoundary and lowerBoundary, it should be treated as a normal value. Adjusting the marginScale value enables
+ * the anomaly status of the latest point to be changed.
*
* @return the upperMargin value.
*/
@@ -149,7 +148,7 @@ public double getUpperMargin() {
/**
* Get the lowerMargin property: Lower margin of the latest point. LowerMargin is used to calculate lowerBoundary,
- * which equals to expectedValue - (100 - marginScale)*lowerMargin.
+ * which is equal to expectedValue - (100 - marginScale)*lowerMargin.
*
* @return the lowerMargin value.
*/
@@ -158,8 +157,8 @@ public double getLowerMargin() {
}
/**
- * Get the isAnomaly property: Anomaly status of the latest point, true means the latest point is an anomaly either
- * in negative direction or positive direction.
+ * Get the isAnomaly property: Anomaly status of the latest point. True means the latest point is an anomaly, either
+ * in the negative direction or in the positive direction.
*
* @return the isAnomaly value.
*/
@@ -168,7 +167,7 @@ public boolean isAnomaly() {
}
/**
- * Get the isNegativeAnomaly property: Anomaly status in negative direction of the latest point. True means the
+ * Get the isNegativeAnomaly property: Anomaly status of the latest point in a negative direction. True means the
* latest point is an anomaly and its real value is smaller than the expected one.
*
* @return the isNegativeAnomaly value.
@@ -178,7 +177,7 @@ public boolean isNegativeAnomaly() {
}
/**
- * Get the isPositiveAnomaly property: Anomaly status in positive direction of the latest point. True means the
+ * Get the isPositiveAnomaly property: Anomaly status of the latest point in a positive direction. True means the
* latest point is an anomaly and its real value is larger than the expected one.
*
* @return the isPositiveAnomaly value.
@@ -188,8 +187,8 @@ public boolean isPositiveAnomaly() {
}
/**
- * Get the severity property: The severity score for the last input point. The larger the value is, the more sever
- * the anomaly is. For normal points, the "severity" is always 0.
+ * Get the severity property: Severity score for the last input point. The larger the value is, the more severe the
+ * anomaly is. For normal points, the severity is always 0.
*
* @return the severity value.
*/
diff --git a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/VariableState.java b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/VariableState.java
index bcdd13530bfe..4fb996fea8e4 100644
--- a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/VariableState.java
+++ b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/VariableState.java
@@ -8,7 +8,7 @@
import com.fasterxml.jackson.annotation.JsonProperty;
import java.time.OffsetDateTime;
-/** Variable Status. */
+/** Variable status. */
@Fluent
public final class VariableState {
/*
@@ -24,19 +24,19 @@ public final class VariableState {
private Double filledNARatio;
/*
- * Number of effective data points before applying fillNAMethod.
+ * Number of effective data points before fillNAMethod is applied.
*/
@JsonProperty(value = "effectiveCount")
private Integer effectiveCount;
/*
- * First valid timestamp with value of input data.
+ * First valid time stamp with a value of input data.
*/
@JsonProperty(value = "firstTimestamp")
private OffsetDateTime firstTimestamp;
/*
- * Last valid timestamp with value of input data.
+ * Last valid time stamp with a value of input data.
*/
@JsonProperty(value = "lastTimestamp")
private OffsetDateTime lastTimestamp;
@@ -85,7 +85,7 @@ public VariableState setFilledNARatio(Double filledNARatio) {
}
/**
- * Get the effectiveCount property: Number of effective data points before applying fillNAMethod.
+ * Get the effectiveCount property: Number of effective data points before fillNAMethod is applied.
*
* @return the effectiveCount value.
*/
@@ -94,7 +94,7 @@ public Integer getEffectiveCount() {
}
/**
- * Set the effectiveCount property: Number of effective data points before applying fillNAMethod.
+ * Set the effectiveCount property: Number of effective data points before fillNAMethod is applied.
*
* @param effectiveCount the effectiveCount value to set.
* @return the VariableState object itself.
@@ -105,7 +105,7 @@ public VariableState setEffectiveCount(Integer effectiveCount) {
}
/**
- * Get the firstTimestamp property: First valid timestamp with value of input data.
+ * Get the firstTimestamp property: First valid time stamp with a value of input data.
*
* @return the firstTimestamp value.
*/
@@ -114,7 +114,7 @@ public OffsetDateTime getFirstTimestamp() {
}
/**
- * Set the firstTimestamp property: First valid timestamp with value of input data.
+ * Set the firstTimestamp property: First valid time stamp with a value of input data.
*
* @param firstTimestamp the firstTimestamp value to set.
* @return the VariableState object itself.
@@ -125,7 +125,7 @@ public VariableState setFirstTimestamp(OffsetDateTime firstTimestamp) {
}
/**
- * Get the lastTimestamp property: Last valid timestamp with value of input data.
+ * Get the lastTimestamp property: Last valid time stamp with a value of input data.
*
* @return the lastTimestamp value.
*/
@@ -134,7 +134,7 @@ public OffsetDateTime getLastTimestamp() {
}
/**
- * Set the lastTimestamp property: Last valid timestamp with value of input data.
+ * Set the lastTimestamp property: Last valid time stamp with a value of input data.
*
* @param lastTimestamp the lastTimestamp value to set.
* @return the VariableState object itself.
diff --git a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/VariableValues.java b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/VariableValues.java
index 8372ef5d93d8..0e24780b548f 100644
--- a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/VariableValues.java
+++ b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/VariableValues.java
@@ -13,13 +13,13 @@
@Immutable
public final class VariableValues {
/*
- * Variable name of last detection request.
+ * Variable name of the last detection request.
*/
@JsonProperty(value = "variable", required = true)
private String variable;
/*
- * Timestamps of last detection request
+ * Time stamps of the last detection request.
*/
@JsonProperty(value = "timestamps", required = true)
private List timestamps;
@@ -48,7 +48,7 @@ public VariableValues(
}
/**
- * Get the variable property: Variable name of last detection request.
+ * Get the variable property: Variable name of the last detection request.
*
* @return the variable value.
*/
@@ -57,7 +57,7 @@ public String getVariable() {
}
/**
- * Get the timestamps property: Timestamps of last detection request.
+ * Get the timestamps property: Time stamps of the last detection request.
*
* @return the timestamps value.
*/
diff --git a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/package-info.java b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/package-info.java
index 89b355aedf89..03840f39504b 100644
--- a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/package-info.java
+++ b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/models/package-info.java
@@ -4,16 +4,16 @@
/**
* Package containing the data models for AnomalyDetector. The Anomaly Detector API detects anomalies automatically in
- * time series data. It supports two kinds of mode, one is for stateless using, another is for stateful using. In
- * stateless mode, there are three functionalities. Entire Detect is for detecting the whole series with model trained
- * by the time series, Last Detect is detecting last point with model trained by points before. ChangePoint Detect is
- * for detecting trend changes in time series. In stateful mode, user can store time series, the stored time series will
- * be used for detection anomalies. Under this mode, user can still use the above three functionalities by only giving a
- * time range without preparing time series in client side. Besides the above three functionalities, stateful model also
- * provide group based detection and labeling service. By leveraging labeling service user can provide labels for each
- * detection result, these labels will be used for retuning or regenerating detection models. Inconsistency detection is
- * a kind of group based detection, this detection will find inconsistency ones in a set of time series. By using
- * anomaly detector service, business customers can discover incidents and establish a logic flow for root cause
- * analysis.
+ * time series data. It supports both a stateless detection mode and a stateful detection mode. In stateless mode, there
+ * are three functionalities. Entire Detect is for detecting the whole series, with the model trained by the time
+ * series. Last Detect is for detecting the last point, with the model trained by points before. ChangePoint Detect is
+ * for detecting trend changes in the time series. In stateful mode, the user can store time series. The stored time
+ * series will be used for detection anomalies. In this mode, the user can still use the preceding three functionalities
+ * by only giving a time range without preparing time series on the client side. Besides the preceding three
+ * functionalities, the stateful model provides group-based detection and labeling services. By using the labeling
+ * service, the user can provide labels for each detection result. These labels will be used for retuning or
+ * regenerating detection models. Inconsistency detection is a kind of group-based detection that finds inconsistencies
+ * in a set of time series. By using the anomaly detector service, business customers can discover incidents and
+ * establish a logic flow for root cause analysis.
*/
package com.azure.ai.anomalydetector.models;
diff --git a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/package-info.java b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/package-info.java
index 0f8777c3c0d7..de8f5fee9994 100644
--- a/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/package-info.java
+++ b/sdk/anomalydetector/azure-ai-anomalydetector/src/main/java/com/azure/ai/anomalydetector/package-info.java
@@ -4,15 +4,16 @@
/**
* Package containing the classes for AnomalyDetector. The Anomaly Detector API detects anomalies automatically in time
- * series data. It supports two kinds of mode, one is for stateless using, another is for stateful using. In stateless
- * mode, there are three functionalities. Entire Detect is for detecting the whole series with model trained by the time
- * series, Last Detect is detecting last point with model trained by points before. ChangePoint Detect is for detecting
- * trend changes in time series. In stateful mode, user can store time series, the stored time series will be used for
- * detection anomalies. Under this mode, user can still use the above three functionalities by only giving a time range
- * without preparing time series in client side. Besides the above three functionalities, stateful model also provide
- * group based detection and labeling service. By leveraging labeling service user can provide labels for each detection
- * result, these labels will be used for retuning or regenerating detection models. Inconsistency detection is a kind of
- * group based detection, this detection will find inconsistency ones in a set of time series. By using anomaly detector
- * service, business customers can discover incidents and establish a logic flow for root cause analysis.
+ * series data. It supports both a stateless detection mode and a stateful detection mode. In stateless mode, there are
+ * three functionalities. Entire Detect is for detecting the whole series, with the model trained by the time series.
+ * Last Detect is for detecting the last point, with the model trained by points before. ChangePoint Detect is for
+ * detecting trend changes in the time series. In stateful mode, the user can store time series. The stored time series
+ * will be used for detection anomalies. In this mode, the user can still use the preceding three functionalities by
+ * only giving a time range without preparing time series on the client side. Besides the preceding three
+ * functionalities, the stateful model provides group-based detection and labeling services. By using the labeling
+ * service, the user can provide labels for each detection result. These labels will be used for retuning or
+ * regenerating detection models. Inconsistency detection is a kind of group-based detection that finds inconsistencies
+ * in a set of time series. By using the anomaly detector service, business customers can discover incidents and
+ * establish a logic flow for root cause analysis.
*/
package com.azure.ai.anomalydetector;