Class CreateMLModelRequest
- java.lang.Object
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- com.amazonaws.AmazonWebServiceRequest
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- com.amazonaws.services.machinelearning.model.CreateMLModelRequest
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- All Implemented Interfaces:
ReadLimitInfo
,Serializable
,Cloneable
public class CreateMLModelRequest extends AmazonWebServiceRequest implements Serializable, Cloneable
- See Also:
- Serialized Form
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Field Summary
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Fields inherited from class com.amazonaws.AmazonWebServiceRequest
NOOP
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Constructor Summary
Constructors Constructor Description CreateMLModelRequest()
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Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description CreateMLModelRequest
addParametersEntry(String key, String value)
CreateMLModelRequest
clearParametersEntries()
Removes all the entries added into Parameters.CreateMLModelRequest
clone()
Creates a shallow clone of this request.boolean
equals(Object obj)
String
getMLModelId()
A user-supplied ID that uniquely identifies theMLModel
.String
getMLModelName()
A user-supplied name or description of theMLModel
.String
getMLModelType()
The category of supervised learning that thisMLModel
will address.Map<String,String>
getParameters()
A list of the training parameters in theMLModel
.String
getRecipe()
The data recipe for creatingMLModel
.String
getRecipeUri()
The Amazon Simple Storage Service (Amazon S3) location and file name that contains theMLModel
recipe.String
getTrainingDataSourceId()
TheDataSource
that points to the training data.int
hashCode()
void
setMLModelId(String mLModelId)
A user-supplied ID that uniquely identifies theMLModel
.void
setMLModelName(String mLModelName)
A user-supplied name or description of theMLModel
.void
setMLModelType(MLModelType mLModelType)
The category of supervised learning that thisMLModel
will address.void
setMLModelType(String mLModelType)
The category of supervised learning that thisMLModel
will address.void
setParameters(Map<String,String> parameters)
A list of the training parameters in theMLModel
.void
setRecipe(String recipe)
The data recipe for creatingMLModel
.void
setRecipeUri(String recipeUri)
The Amazon Simple Storage Service (Amazon S3) location and file name that contains theMLModel
recipe.void
setTrainingDataSourceId(String trainingDataSourceId)
TheDataSource
that points to the training data.String
toString()
Returns a string representation of this object; useful for testing and debugging.CreateMLModelRequest
withMLModelId(String mLModelId)
A user-supplied ID that uniquely identifies theMLModel
.CreateMLModelRequest
withMLModelName(String mLModelName)
A user-supplied name or description of theMLModel
.CreateMLModelRequest
withMLModelType(MLModelType mLModelType)
The category of supervised learning that thisMLModel
will address.CreateMLModelRequest
withMLModelType(String mLModelType)
The category of supervised learning that thisMLModel
will address.CreateMLModelRequest
withParameters(Map<String,String> parameters)
A list of the training parameters in theMLModel
.CreateMLModelRequest
withRecipe(String recipe)
The data recipe for creatingMLModel
.CreateMLModelRequest
withRecipeUri(String recipeUri)
The Amazon Simple Storage Service (Amazon S3) location and file name that contains theMLModel
recipe.CreateMLModelRequest
withTrainingDataSourceId(String trainingDataSourceId)
TheDataSource
that points to the training data.-
Methods inherited from class com.amazonaws.AmazonWebServiceRequest
copyBaseTo, getCloneRoot, getCloneSource, getCustomQueryParameters, getCustomRequestHeaders, getGeneralProgressListener, getReadLimit, getRequestClientOptions, getRequestCredentials, getRequestCredentialsProvider, getRequestMetricCollector, getSdkClientExecutionTimeout, getSdkRequestTimeout, putCustomQueryParameter, putCustomRequestHeader, setGeneralProgressListener, setRequestCredentials, setRequestCredentialsProvider, setRequestMetricCollector, setSdkClientExecutionTimeout, setSdkRequestTimeout, withGeneralProgressListener, withRequestMetricCollector, withSdkClientExecutionTimeout, withSdkRequestTimeout
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Method Detail
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setMLModelId
public void setMLModelId(String mLModelId)
A user-supplied ID that uniquely identifies the
MLModel
.- Parameters:
mLModelId
- A user-supplied ID that uniquely identifies theMLModel
.
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getMLModelId
public String getMLModelId()
A user-supplied ID that uniquely identifies the
MLModel
.- Returns:
- A user-supplied ID that uniquely identifies the
MLModel
.
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withMLModelId
public CreateMLModelRequest withMLModelId(String mLModelId)
A user-supplied ID that uniquely identifies the
MLModel
.- Parameters:
mLModelId
- A user-supplied ID that uniquely identifies theMLModel
.- Returns:
- Returns a reference to this object so that method calls can be chained together.
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setMLModelName
public void setMLModelName(String mLModelName)
A user-supplied name or description of the
MLModel
.- Parameters:
mLModelName
- A user-supplied name or description of theMLModel
.
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getMLModelName
public String getMLModelName()
A user-supplied name or description of the
MLModel
.- Returns:
- A user-supplied name or description of the
MLModel
.
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withMLModelName
public CreateMLModelRequest withMLModelName(String mLModelName)
A user-supplied name or description of the
MLModel
.- Parameters:
mLModelName
- A user-supplied name or description of theMLModel
.- Returns:
- Returns a reference to this object so that method calls can be chained together.
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setMLModelType
public void setMLModelType(String mLModelType)
The category of supervised learning that this
MLModel
will address. Choose from the following types:- Choose
REGRESSION
if theMLModel
will be used to predict a numeric value. - Choose
BINARY
if theMLModel
result has two possible values. - Choose
MULTICLASS
if theMLModel
result has a limited number of values.
For more information, see the Amazon Machine Learning Developer Guide.
- Parameters:
mLModelType
- The category of supervised learning that thisMLModel
will address. Choose from the following types:- Choose
REGRESSION
if theMLModel
will be used to predict a numeric value. - Choose
BINARY
if theMLModel
result has two possible values. - Choose
MULTICLASS
if theMLModel
result has a limited number of values.
For more information, see the Amazon Machine Learning Developer Guide.
- Choose
- See Also:
MLModelType
- Choose
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getMLModelType
public String getMLModelType()
The category of supervised learning that this
MLModel
will address. Choose from the following types:- Choose
REGRESSION
if theMLModel
will be used to predict a numeric value. - Choose
BINARY
if theMLModel
result has two possible values. - Choose
MULTICLASS
if theMLModel
result has a limited number of values.
For more information, see the Amazon Machine Learning Developer Guide.
- Returns:
- The category of supervised learning that this
MLModel
will address. Choose from the following types:- Choose
REGRESSION
if theMLModel
will be used to predict a numeric value. - Choose
BINARY
if theMLModel
result has two possible values. - Choose
MULTICLASS
if theMLModel
result has a limited number of values.
For more information, see the Amazon Machine Learning Developer Guide.
- Choose
- See Also:
MLModelType
- Choose
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withMLModelType
public CreateMLModelRequest withMLModelType(String mLModelType)
The category of supervised learning that this
MLModel
will address. Choose from the following types:- Choose
REGRESSION
if theMLModel
will be used to predict a numeric value. - Choose
BINARY
if theMLModel
result has two possible values. - Choose
MULTICLASS
if theMLModel
result has a limited number of values.
For more information, see the Amazon Machine Learning Developer Guide.
- Parameters:
mLModelType
- The category of supervised learning that thisMLModel
will address. Choose from the following types:- Choose
REGRESSION
if theMLModel
will be used to predict a numeric value. - Choose
BINARY
if theMLModel
result has two possible values. - Choose
MULTICLASS
if theMLModel
result has a limited number of values.
For more information, see the Amazon Machine Learning Developer Guide.
- Choose
- Returns:
- Returns a reference to this object so that method calls can be chained together.
- See Also:
MLModelType
- Choose
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setMLModelType
public void setMLModelType(MLModelType mLModelType)
The category of supervised learning that this
MLModel
will address. Choose from the following types:- Choose
REGRESSION
if theMLModel
will be used to predict a numeric value. - Choose
BINARY
if theMLModel
result has two possible values. - Choose
MULTICLASS
if theMLModel
result has a limited number of values.
For more information, see the Amazon Machine Learning Developer Guide.
- Parameters:
mLModelType
- The category of supervised learning that thisMLModel
will address. Choose from the following types:- Choose
REGRESSION
if theMLModel
will be used to predict a numeric value. - Choose
BINARY
if theMLModel
result has two possible values. - Choose
MULTICLASS
if theMLModel
result has a limited number of values.
For more information, see the Amazon Machine Learning Developer Guide.
- Choose
- See Also:
MLModelType
- Choose
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withMLModelType
public CreateMLModelRequest withMLModelType(MLModelType mLModelType)
The category of supervised learning that this
MLModel
will address. Choose from the following types:- Choose
REGRESSION
if theMLModel
will be used to predict a numeric value. - Choose
BINARY
if theMLModel
result has two possible values. - Choose
MULTICLASS
if theMLModel
result has a limited number of values.
For more information, see the Amazon Machine Learning Developer Guide.
- Parameters:
mLModelType
- The category of supervised learning that thisMLModel
will address. Choose from the following types:- Choose
REGRESSION
if theMLModel
will be used to predict a numeric value. - Choose
BINARY
if theMLModel
result has two possible values. - Choose
MULTICLASS
if theMLModel
result has a limited number of values.
For more information, see the Amazon Machine Learning Developer Guide.
- Choose
- Returns:
- Returns a reference to this object so that method calls can be chained together.
- See Also:
MLModelType
- Choose
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getParameters
public Map<String,String> getParameters()
A list of the training parameters in the
MLModel
. The list is implemented as a map of key/value pairs.The following is the current set of training parameters:
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sgd.l1RegularizationAmount
- Coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in sparse feature set. If you use this parameter, start by specifying a small value such as 1.0E-08.The value is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L1 normalization. The parameter cannot be used when
L2
is specified. Use this parameter sparingly. -
sgd.l2RegularizationAmount
- Coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value such as 1.0E-08.The valuseis a double that ranges from 0 to MAX_DOUBLE. The default is not to use L2 normalization. This cannot be used when
L1
is specified. Use this parameter sparingly. -
sgd.maxPasses
- Number of times that the training process traverses the observations to build theMLModel
. The value is an integer that ranges from 1 to 10000. The default value is 10. -
sgd.maxMLModelSizeInBytes
- Maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432.
- Returns:
- A list of the training parameters in the
MLModel
. The list is implemented as a map of key/value pairs.The following is the current set of training parameters:
-
sgd.l1RegularizationAmount
- Coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in sparse feature set. If you use this parameter, start by specifying a small value such as 1.0E-08.The value is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L1 normalization. The parameter cannot be used when
L2
is specified. Use this parameter sparingly. -
sgd.l2RegularizationAmount
- Coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value such as 1.0E-08.The valuseis a double that ranges from 0 to MAX_DOUBLE. The default is not to use L2 normalization. This cannot be used when
L1
is specified. Use this parameter sparingly. -
sgd.maxPasses
- Number of times that the training process traverses the observations to build theMLModel
. The value is an integer that ranges from 1 to 10000. The default value is 10. -
sgd.maxMLModelSizeInBytes
- Maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432.
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setParameters
public void setParameters(Map<String,String> parameters)
A list of the training parameters in the
MLModel
. The list is implemented as a map of key/value pairs.The following is the current set of training parameters:
-
sgd.l1RegularizationAmount
- Coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in sparse feature set. If you use this parameter, start by specifying a small value such as 1.0E-08.The value is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L1 normalization. The parameter cannot be used when
L2
is specified. Use this parameter sparingly. -
sgd.l2RegularizationAmount
- Coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value such as 1.0E-08.The valuseis a double that ranges from 0 to MAX_DOUBLE. The default is not to use L2 normalization. This cannot be used when
L1
is specified. Use this parameter sparingly. -
sgd.maxPasses
- Number of times that the training process traverses the observations to build theMLModel
. The value is an integer that ranges from 1 to 10000. The default value is 10. -
sgd.maxMLModelSizeInBytes
- Maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432.
- Parameters:
parameters
- A list of the training parameters in theMLModel
. The list is implemented as a map of key/value pairs.The following is the current set of training parameters:
-
sgd.l1RegularizationAmount
- Coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in sparse feature set. If you use this parameter, start by specifying a small value such as 1.0E-08.The value is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L1 normalization. The parameter cannot be used when
L2
is specified. Use this parameter sparingly. -
sgd.l2RegularizationAmount
- Coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value such as 1.0E-08.The valuseis a double that ranges from 0 to MAX_DOUBLE. The default is not to use L2 normalization. This cannot be used when
L1
is specified. Use this parameter sparingly. -
sgd.maxPasses
- Number of times that the training process traverses the observations to build theMLModel
. The value is an integer that ranges from 1 to 10000. The default value is 10. -
sgd.maxMLModelSizeInBytes
- Maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432.
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withParameters
public CreateMLModelRequest withParameters(Map<String,String> parameters)
A list of the training parameters in the
MLModel
. The list is implemented as a map of key/value pairs.The following is the current set of training parameters:
-
sgd.l1RegularizationAmount
- Coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in sparse feature set. If you use this parameter, start by specifying a small value such as 1.0E-08.The value is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L1 normalization. The parameter cannot be used when
L2
is specified. Use this parameter sparingly. -
sgd.l2RegularizationAmount
- Coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value such as 1.0E-08.The valuseis a double that ranges from 0 to MAX_DOUBLE. The default is not to use L2 normalization. This cannot be used when
L1
is specified. Use this parameter sparingly. -
sgd.maxPasses
- Number of times that the training process traverses the observations to build theMLModel
. The value is an integer that ranges from 1 to 10000. The default value is 10. -
sgd.maxMLModelSizeInBytes
- Maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432.
- Parameters:
parameters
- A list of the training parameters in theMLModel
. The list is implemented as a map of key/value pairs.The following is the current set of training parameters:
-
sgd.l1RegularizationAmount
- Coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in sparse feature set. If you use this parameter, start by specifying a small value such as 1.0E-08.The value is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L1 normalization. The parameter cannot be used when
L2
is specified. Use this parameter sparingly. -
sgd.l2RegularizationAmount
- Coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value such as 1.0E-08.The valuseis a double that ranges from 0 to MAX_DOUBLE. The default is not to use L2 normalization. This cannot be used when
L1
is specified. Use this parameter sparingly. -
sgd.maxPasses
- Number of times that the training process traverses the observations to build theMLModel
. The value is an integer that ranges from 1 to 10000. The default value is 10. -
sgd.maxMLModelSizeInBytes
- Maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432.
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- Returns:
- Returns a reference to this object so that method calls can be chained together.
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addParametersEntry
public CreateMLModelRequest addParametersEntry(String key, String value)
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clearParametersEntries
public CreateMLModelRequest clearParametersEntries()
Removes all the entries added into Parameters. <p> Returns a reference to this object so that method calls can be chained together.
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setTrainingDataSourceId
public void setTrainingDataSourceId(String trainingDataSourceId)
The
DataSource
that points to the training data.- Parameters:
trainingDataSourceId
- TheDataSource
that points to the training data.
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getTrainingDataSourceId
public String getTrainingDataSourceId()
The
DataSource
that points to the training data.- Returns:
- The
DataSource
that points to the training data.
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withTrainingDataSourceId
public CreateMLModelRequest withTrainingDataSourceId(String trainingDataSourceId)
The
DataSource
that points to the training data.- Parameters:
trainingDataSourceId
- TheDataSource
that points to the training data.- Returns:
- Returns a reference to this object so that method calls can be chained together.
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setRecipe
public void setRecipe(String recipe)
The data recipe for creating
MLModel
. You must specify either the recipe or its URI. If you don’t specify a recipe or its URI, Amazon ML creates a default.- Parameters:
recipe
- The data recipe for creatingMLModel
. You must specify either the recipe or its URI. If you don’t specify a recipe or its URI, Amazon ML creates a default.
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getRecipe
public String getRecipe()
The data recipe for creating
MLModel
. You must specify either the recipe or its URI. If you don’t specify a recipe or its URI, Amazon ML creates a default.- Returns:
- The data recipe for creating
MLModel
. You must specify either the recipe or its URI. If you don’t specify a recipe or its URI, Amazon ML creates a default.
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withRecipe
public CreateMLModelRequest withRecipe(String recipe)
The data recipe for creating
MLModel
. You must specify either the recipe or its URI. If you don’t specify a recipe or its URI, Amazon ML creates a default.- Parameters:
recipe
- The data recipe for creatingMLModel
. You must specify either the recipe or its URI. If you don’t specify a recipe or its URI, Amazon ML creates a default.- Returns:
- Returns a reference to this object so that method calls can be chained together.
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setRecipeUri
public void setRecipeUri(String recipeUri)
The Amazon Simple Storage Service (Amazon S3) location and file name that contains the
MLModel
recipe. You must specify either the recipe or its URI. If you don’t specify a recipe or its URI, Amazon ML creates a default.- Parameters:
recipeUri
- The Amazon Simple Storage Service (Amazon S3) location and file name that contains theMLModel
recipe. You must specify either the recipe or its URI. If you don’t specify a recipe or its URI, Amazon ML creates a default.
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getRecipeUri
public String getRecipeUri()
The Amazon Simple Storage Service (Amazon S3) location and file name that contains the
MLModel
recipe. You must specify either the recipe or its URI. If you don’t specify a recipe or its URI, Amazon ML creates a default.- Returns:
- The Amazon Simple Storage Service (Amazon S3) location and file
name that contains the
MLModel
recipe. You must specify either the recipe or its URI. If you don’t specify a recipe or its URI, Amazon ML creates a default.
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withRecipeUri
public CreateMLModelRequest withRecipeUri(String recipeUri)
The Amazon Simple Storage Service (Amazon S3) location and file name that contains the
MLModel
recipe. You must specify either the recipe or its URI. If you don’t specify a recipe or its URI, Amazon ML creates a default.- Parameters:
recipeUri
- The Amazon Simple Storage Service (Amazon S3) location and file name that contains theMLModel
recipe. You must specify either the recipe or its URI. If you don’t specify a recipe or its URI, Amazon ML creates a default.- Returns:
- Returns a reference to this object so that method calls can be chained together.
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toString
public String toString()
Returns a string representation of this object; useful for testing and debugging.- Overrides:
toString
in classObject
- Returns:
- A string representation of this object.
- See Also:
Object.toString()
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clone
public CreateMLModelRequest clone()
Description copied from class:AmazonWebServiceRequest
Creates a shallow clone of this request. Explicitly does not clone the deep structure of the request object.- Overrides:
clone
in classAmazonWebServiceRequest
- See Also:
Object.clone()
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