Aws::Comprehend::Model::ClassifyDocumentResult Class Reference

#include <ClassifyDocumentResult.h>

Public Member Functions
	ClassifyDocumentResult ()
	ClassifyDocumentResult (const Aws::AmazonWebServiceResult< Aws::Utils::Json::JsonValue > &result)
ClassifyDocumentResult &	operator= (const Aws::AmazonWebServiceResult< Aws::Utils::Json::JsonValue > &result)
const Aws::Vector< DocumentClass > &	GetClasses () const
void	SetClasses (const Aws::Vector< DocumentClass > &value)
void	SetClasses (Aws::Vector< DocumentClass > &&value)
ClassifyDocumentResult &	WithClasses (const Aws::Vector< DocumentClass > &value)
ClassifyDocumentResult &	WithClasses (Aws::Vector< DocumentClass > &&value)
ClassifyDocumentResult &	AddClasses (const DocumentClass &value)
ClassifyDocumentResult &	AddClasses (DocumentClass &&value)
const Aws::Vector< DocumentLabel > &	GetLabels () const
void	SetLabels (const Aws::Vector< DocumentLabel > &value)
void	SetLabels (Aws::Vector< DocumentLabel > &&value)
ClassifyDocumentResult &	WithLabels (const Aws::Vector< DocumentLabel > &value)
ClassifyDocumentResult &	WithLabels (Aws::Vector< DocumentLabel > &&value)
ClassifyDocumentResult &	AddLabels (const DocumentLabel &value)
ClassifyDocumentResult &	AddLabels (DocumentLabel &&value)

Detailed Description

Definition at line 29 of file ClassifyDocumentResult.h.

Constructor & Destructor Documentation

ClassifyDocumentResult() [1/2]

Aws::Comprehend::Model::ClassifyDocumentResult::ClassifyDocumentResult

(

)

ClassifyDocumentResult() [2/2]

Aws::Comprehend::Model::ClassifyDocumentResult::ClassifyDocumentResult

(

const Aws::AmazonWebServiceResult< Aws::Utils::Json::JsonValue > &

result

)

Member Function Documentation

AddClasses() [1/2]

ClassifyDocumentResult& Aws::Comprehend::Model::ClassifyDocumentResult::AddClasses ( const DocumentClass &  value )

inline

The classes used by the document being analyzed. These are used for multi-class trained models. Individual classes are mutually exclusive and each document is expected to have only a single class assigned to it. For example, an animal can be a dog or a cat, but not both at the same time.

Definition at line 83 of file ClassifyDocumentResult.h.

AddClasses() [2/2]

ClassifyDocumentResult& Aws::Comprehend::Model::ClassifyDocumentResult::AddClasses ( DocumentClass &&  value )

inline

The classes used by the document being analyzed. These are used for multi-class trained models. Individual classes are mutually exclusive and each document is expected to have only a single class assigned to it. For example, an animal can be a dog or a cat, but not both at the same time.

Definition at line 91 of file ClassifyDocumentResult.h.

AddLabels() [1/2]

ClassifyDocumentResult& Aws::Comprehend::Model::ClassifyDocumentResult::AddLabels ( const DocumentLabel &  value )

inline

The labels used the document being analyzed. These are used for multi-label trained models. Individual labels represent different categories that are related in some manner and are not mutually exclusive. For example, a movie can be just an action movie, or it can be an action movie, a science fiction movie, and a comedy, all at the same time.

Definition at line 146 of file ClassifyDocumentResult.h.

AddLabels() [2/2]

ClassifyDocumentResult& Aws::Comprehend::Model::ClassifyDocumentResult::AddLabels ( DocumentLabel &&  value )

inline

The labels used the document being analyzed. These are used for multi-label trained models. Individual labels represent different categories that are related in some manner and are not mutually exclusive. For example, a movie can be just an action movie, or it can be an action movie, a science fiction movie, and a comedy, all at the same time.

Definition at line 155 of file ClassifyDocumentResult.h.

GetClasses()

const Aws::Vector<DocumentClass>& Aws::Comprehend::Model::ClassifyDocumentResult::GetClasses ( ) const

inline

The classes used by the document being analyzed. These are used for multi-class trained models. Individual classes are mutually exclusive and each document is expected to have only a single class assigned to it. For example, an animal can be a dog or a cat, but not both at the same time.

Definition at line 43 of file ClassifyDocumentResult.h.

GetLabels()

const Aws::Vector<DocumentLabel>& Aws::Comprehend::Model::ClassifyDocumentResult::GetLabels ( ) const

inline

The labels used the document being analyzed. These are used for multi-label trained models. Individual labels represent different categories that are related in some manner and are not mutually exclusive. For example, a movie can be just an action movie, or it can be an action movie, a science fiction movie, and a comedy, all at the same time.

Definition at line 101 of file ClassifyDocumentResult.h.

operator=()

ClassifyDocumentResult& Aws::Comprehend::Model::ClassifyDocumentResult::operator=

(

const Aws::AmazonWebServiceResult< Aws::Utils::Json::JsonValue > &

result

)

SetClasses() [1/2]

void Aws::Comprehend::Model::ClassifyDocumentResult::SetClasses ( const Aws::Vector< DocumentClass > &  value )

inline

The classes used by the document being analyzed. These are used for multi-class trained models. Individual classes are mutually exclusive and each document is expected to have only a single class assigned to it. For example, an animal can be a dog or a cat, but not both at the same time.

Definition at line 51 of file ClassifyDocumentResult.h.

SetClasses() [2/2]

void Aws::Comprehend::Model::ClassifyDocumentResult::SetClasses ( Aws::Vector< DocumentClass > &&  value )

inline

The classes used by the document being analyzed. These are used for multi-class trained models. Individual classes are mutually exclusive and each document is expected to have only a single class assigned to it. For example, an animal can be a dog or a cat, but not both at the same time.

Definition at line 59 of file ClassifyDocumentResult.h.

SetLabels() [1/2]

void Aws::Comprehend::Model::ClassifyDocumentResult::SetLabels ( const Aws::Vector< DocumentLabel > &  value )

inline

The labels used the document being analyzed. These are used for multi-label trained models. Individual labels represent different categories that are related in some manner and are not mutually exclusive. For example, a movie can be just an action movie, or it can be an action movie, a science fiction movie, and a comedy, all at the same time.

Definition at line 110 of file ClassifyDocumentResult.h.

SetLabels() [2/2]

void Aws::Comprehend::Model::ClassifyDocumentResult::SetLabels ( Aws::Vector< DocumentLabel > &&  value )

inline

The labels used the document being analyzed. These are used for multi-label trained models. Individual labels represent different categories that are related in some manner and are not mutually exclusive. For example, a movie can be just an action movie, or it can be an action movie, a science fiction movie, and a comedy, all at the same time.

Definition at line 119 of file ClassifyDocumentResult.h.

WithClasses() [1/2]

ClassifyDocumentResult& Aws::Comprehend::Model::ClassifyDocumentResult::WithClasses ( const Aws::Vector< DocumentClass > &  value )

inline

The classes used by the document being analyzed. These are used for multi-class trained models. Individual classes are mutually exclusive and each document is expected to have only a single class assigned to it. For example, an animal can be a dog or a cat, but not both at the same time.

Definition at line 67 of file ClassifyDocumentResult.h.

WithClasses() [2/2]

ClassifyDocumentResult& Aws::Comprehend::Model::ClassifyDocumentResult::WithClasses ( Aws::Vector< DocumentClass > &&  value )

inline

The classes used by the document being analyzed. These are used for multi-class trained models. Individual classes are mutually exclusive and each document is expected to have only a single class assigned to it. For example, an animal can be a dog or a cat, but not both at the same time.

Definition at line 75 of file ClassifyDocumentResult.h.

WithLabels() [1/2]

ClassifyDocumentResult& Aws::Comprehend::Model::ClassifyDocumentResult::WithLabels ( const Aws::Vector< DocumentLabel > &  value )

inline

The labels used the document being analyzed. These are used for multi-label trained models. Individual labels represent different categories that are related in some manner and are not mutually exclusive. For example, a movie can be just an action movie, or it can be an action movie, a science fiction movie, and a comedy, all at the same time.

Definition at line 128 of file ClassifyDocumentResult.h.

WithLabels() [2/2]

ClassifyDocumentResult& Aws::Comprehend::Model::ClassifyDocumentResult::WithLabels ( Aws::Vector< DocumentLabel > &&  value )

inline

The labels used the document being analyzed. These are used for multi-label trained models. Individual labels represent different categories that are related in some manner and are not mutually exclusive. For example, a movie can be just an action movie, or it can be an action movie, a science fiction movie, and a comedy, all at the same time.

Definition at line 137 of file ClassifyDocumentResult.h.

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The documentation for this class was generated from the following file:

• aws-cpp-sdk-comprehend/include/aws/comprehend/model/ClassifyDocumentResult.h