1294 lines
68 KiB
C++
1294 lines
68 KiB
C++
/**
|
|
* Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
|
|
* SPDX-License-Identifier: Apache-2.0.
|
|
*/
|
|
|
|
#pragma once
|
|
#include <aws/machinelearning/MachineLearning_EXPORTS.h>
|
|
#include <aws/core/utils/memory/stl/AWSString.h>
|
|
#include <aws/core/utils/DateTime.h>
|
|
#include <aws/machinelearning/model/EntityStatus.h>
|
|
#include <aws/machinelearning/model/RealtimeEndpointInfo.h>
|
|
#include <aws/core/utils/memory/stl/AWSMap.h>
|
|
#include <aws/machinelearning/model/MLModelType.h>
|
|
#include <utility>
|
|
|
|
namespace Aws
|
|
{
|
|
template<typename RESULT_TYPE>
|
|
class AmazonWebServiceResult;
|
|
|
|
namespace Utils
|
|
{
|
|
namespace Json
|
|
{
|
|
class JsonValue;
|
|
} // namespace Json
|
|
} // namespace Utils
|
|
namespace MachineLearning
|
|
{
|
|
namespace Model
|
|
{
|
|
/**
|
|
* <p>Represents the output of a <code>GetMLModel</code> operation, and provides
|
|
* detailed information about a <code>MLModel</code>.</p><p><h3>See Also:</h3> <a
|
|
* href="http://docs.aws.amazon.com/goto/WebAPI/machinelearning-2014-12-12/GetMLModelOutput">AWS
|
|
* API Reference</a></p>
|
|
*/
|
|
class AWS_MACHINELEARNING_API GetMLModelResult
|
|
{
|
|
public:
|
|
GetMLModelResult();
|
|
GetMLModelResult(const Aws::AmazonWebServiceResult<Aws::Utils::Json::JsonValue>& result);
|
|
GetMLModelResult& operator=(const Aws::AmazonWebServiceResult<Aws::Utils::Json::JsonValue>& result);
|
|
|
|
|
|
/**
|
|
* <p>The MLModel ID<?oxy_insert_start author="annbech"
|
|
* timestamp="20160328T151251-0700">,<?oxy_insert_end> which is same as the
|
|
* <code>MLModelId</code> in the request.</p>
|
|
*/
|
|
inline const Aws::String& GetMLModelId() const{ return m_mLModelId; }
|
|
|
|
/**
|
|
* <p>The MLModel ID<?oxy_insert_start author="annbech"
|
|
* timestamp="20160328T151251-0700">,<?oxy_insert_end> which is same as the
|
|
* <code>MLModelId</code> in the request.</p>
|
|
*/
|
|
inline void SetMLModelId(const Aws::String& value) { m_mLModelId = value; }
|
|
|
|
/**
|
|
* <p>The MLModel ID<?oxy_insert_start author="annbech"
|
|
* timestamp="20160328T151251-0700">,<?oxy_insert_end> which is same as the
|
|
* <code>MLModelId</code> in the request.</p>
|
|
*/
|
|
inline void SetMLModelId(Aws::String&& value) { m_mLModelId = std::move(value); }
|
|
|
|
/**
|
|
* <p>The MLModel ID<?oxy_insert_start author="annbech"
|
|
* timestamp="20160328T151251-0700">,<?oxy_insert_end> which is same as the
|
|
* <code>MLModelId</code> in the request.</p>
|
|
*/
|
|
inline void SetMLModelId(const char* value) { m_mLModelId.assign(value); }
|
|
|
|
/**
|
|
* <p>The MLModel ID<?oxy_insert_start author="annbech"
|
|
* timestamp="20160328T151251-0700">,<?oxy_insert_end> which is same as the
|
|
* <code>MLModelId</code> in the request.</p>
|
|
*/
|
|
inline GetMLModelResult& WithMLModelId(const Aws::String& value) { SetMLModelId(value); return *this;}
|
|
|
|
/**
|
|
* <p>The MLModel ID<?oxy_insert_start author="annbech"
|
|
* timestamp="20160328T151251-0700">,<?oxy_insert_end> which is same as the
|
|
* <code>MLModelId</code> in the request.</p>
|
|
*/
|
|
inline GetMLModelResult& WithMLModelId(Aws::String&& value) { SetMLModelId(std::move(value)); return *this;}
|
|
|
|
/**
|
|
* <p>The MLModel ID<?oxy_insert_start author="annbech"
|
|
* timestamp="20160328T151251-0700">,<?oxy_insert_end> which is same as the
|
|
* <code>MLModelId</code> in the request.</p>
|
|
*/
|
|
inline GetMLModelResult& WithMLModelId(const char* value) { SetMLModelId(value); return *this;}
|
|
|
|
|
|
/**
|
|
* <p>The ID of the training <code>DataSource</code>.</p>
|
|
*/
|
|
inline const Aws::String& GetTrainingDataSourceId() const{ return m_trainingDataSourceId; }
|
|
|
|
/**
|
|
* <p>The ID of the training <code>DataSource</code>.</p>
|
|
*/
|
|
inline void SetTrainingDataSourceId(const Aws::String& value) { m_trainingDataSourceId = value; }
|
|
|
|
/**
|
|
* <p>The ID of the training <code>DataSource</code>.</p>
|
|
*/
|
|
inline void SetTrainingDataSourceId(Aws::String&& value) { m_trainingDataSourceId = std::move(value); }
|
|
|
|
/**
|
|
* <p>The ID of the training <code>DataSource</code>.</p>
|
|
*/
|
|
inline void SetTrainingDataSourceId(const char* value) { m_trainingDataSourceId.assign(value); }
|
|
|
|
/**
|
|
* <p>The ID of the training <code>DataSource</code>.</p>
|
|
*/
|
|
inline GetMLModelResult& WithTrainingDataSourceId(const Aws::String& value) { SetTrainingDataSourceId(value); return *this;}
|
|
|
|
/**
|
|
* <p>The ID of the training <code>DataSource</code>.</p>
|
|
*/
|
|
inline GetMLModelResult& WithTrainingDataSourceId(Aws::String&& value) { SetTrainingDataSourceId(std::move(value)); return *this;}
|
|
|
|
/**
|
|
* <p>The ID of the training <code>DataSource</code>.</p>
|
|
*/
|
|
inline GetMLModelResult& WithTrainingDataSourceId(const char* value) { SetTrainingDataSourceId(value); return *this;}
|
|
|
|
|
|
/**
|
|
* <p>The AWS user account from which the <code>MLModel</code> was created. The
|
|
* account type can be either an AWS root account or an AWS Identity and Access
|
|
* Management (IAM) user account.</p>
|
|
*/
|
|
inline const Aws::String& GetCreatedByIamUser() const{ return m_createdByIamUser; }
|
|
|
|
/**
|
|
* <p>The AWS user account from which the <code>MLModel</code> was created. The
|
|
* account type can be either an AWS root account or an AWS Identity and Access
|
|
* Management (IAM) user account.</p>
|
|
*/
|
|
inline void SetCreatedByIamUser(const Aws::String& value) { m_createdByIamUser = value; }
|
|
|
|
/**
|
|
* <p>The AWS user account from which the <code>MLModel</code> was created. The
|
|
* account type can be either an AWS root account or an AWS Identity and Access
|
|
* Management (IAM) user account.</p>
|
|
*/
|
|
inline void SetCreatedByIamUser(Aws::String&& value) { m_createdByIamUser = std::move(value); }
|
|
|
|
/**
|
|
* <p>The AWS user account from which the <code>MLModel</code> was created. The
|
|
* account type can be either an AWS root account or an AWS Identity and Access
|
|
* Management (IAM) user account.</p>
|
|
*/
|
|
inline void SetCreatedByIamUser(const char* value) { m_createdByIamUser.assign(value); }
|
|
|
|
/**
|
|
* <p>The AWS user account from which the <code>MLModel</code> was created. The
|
|
* account type can be either an AWS root account or an AWS Identity and Access
|
|
* Management (IAM) user account.</p>
|
|
*/
|
|
inline GetMLModelResult& WithCreatedByIamUser(const Aws::String& value) { SetCreatedByIamUser(value); return *this;}
|
|
|
|
/**
|
|
* <p>The AWS user account from which the <code>MLModel</code> was created. The
|
|
* account type can be either an AWS root account or an AWS Identity and Access
|
|
* Management (IAM) user account.</p>
|
|
*/
|
|
inline GetMLModelResult& WithCreatedByIamUser(Aws::String&& value) { SetCreatedByIamUser(std::move(value)); return *this;}
|
|
|
|
/**
|
|
* <p>The AWS user account from which the <code>MLModel</code> was created. The
|
|
* account type can be either an AWS root account or an AWS Identity and Access
|
|
* Management (IAM) user account.</p>
|
|
*/
|
|
inline GetMLModelResult& WithCreatedByIamUser(const char* value) { SetCreatedByIamUser(value); return *this;}
|
|
|
|
|
|
/**
|
|
* <p>The time that the <code>MLModel</code> was created. The time is expressed in
|
|
* epoch time.</p>
|
|
*/
|
|
inline const Aws::Utils::DateTime& GetCreatedAt() const{ return m_createdAt; }
|
|
|
|
/**
|
|
* <p>The time that the <code>MLModel</code> was created. The time is expressed in
|
|
* epoch time.</p>
|
|
*/
|
|
inline void SetCreatedAt(const Aws::Utils::DateTime& value) { m_createdAt = value; }
|
|
|
|
/**
|
|
* <p>The time that the <code>MLModel</code> was created. The time is expressed in
|
|
* epoch time.</p>
|
|
*/
|
|
inline void SetCreatedAt(Aws::Utils::DateTime&& value) { m_createdAt = std::move(value); }
|
|
|
|
/**
|
|
* <p>The time that the <code>MLModel</code> was created. The time is expressed in
|
|
* epoch time.</p>
|
|
*/
|
|
inline GetMLModelResult& WithCreatedAt(const Aws::Utils::DateTime& value) { SetCreatedAt(value); return *this;}
|
|
|
|
/**
|
|
* <p>The time that the <code>MLModel</code> was created. The time is expressed in
|
|
* epoch time.</p>
|
|
*/
|
|
inline GetMLModelResult& WithCreatedAt(Aws::Utils::DateTime&& value) { SetCreatedAt(std::move(value)); return *this;}
|
|
|
|
|
|
/**
|
|
* <p>The time of the most recent edit to the <code>MLModel</code>. The time is
|
|
* expressed in epoch time.</p>
|
|
*/
|
|
inline const Aws::Utils::DateTime& GetLastUpdatedAt() const{ return m_lastUpdatedAt; }
|
|
|
|
/**
|
|
* <p>The time of the most recent edit to the <code>MLModel</code>. The time is
|
|
* expressed in epoch time.</p>
|
|
*/
|
|
inline void SetLastUpdatedAt(const Aws::Utils::DateTime& value) { m_lastUpdatedAt = value; }
|
|
|
|
/**
|
|
* <p>The time of the most recent edit to the <code>MLModel</code>. The time is
|
|
* expressed in epoch time.</p>
|
|
*/
|
|
inline void SetLastUpdatedAt(Aws::Utils::DateTime&& value) { m_lastUpdatedAt = std::move(value); }
|
|
|
|
/**
|
|
* <p>The time of the most recent edit to the <code>MLModel</code>. The time is
|
|
* expressed in epoch time.</p>
|
|
*/
|
|
inline GetMLModelResult& WithLastUpdatedAt(const Aws::Utils::DateTime& value) { SetLastUpdatedAt(value); return *this;}
|
|
|
|
/**
|
|
* <p>The time of the most recent edit to the <code>MLModel</code>. The time is
|
|
* expressed in epoch time.</p>
|
|
*/
|
|
inline GetMLModelResult& WithLastUpdatedAt(Aws::Utils::DateTime&& value) { SetLastUpdatedAt(std::move(value)); return *this;}
|
|
|
|
|
|
/**
|
|
* <p>A user-supplied name or description of the <code>MLModel</code>.</p>
|
|
*/
|
|
inline const Aws::String& GetName() const{ return m_name; }
|
|
|
|
/**
|
|
* <p>A user-supplied name or description of the <code>MLModel</code>.</p>
|
|
*/
|
|
inline void SetName(const Aws::String& value) { m_name = value; }
|
|
|
|
/**
|
|
* <p>A user-supplied name or description of the <code>MLModel</code>.</p>
|
|
*/
|
|
inline void SetName(Aws::String&& value) { m_name = std::move(value); }
|
|
|
|
/**
|
|
* <p>A user-supplied name or description of the <code>MLModel</code>.</p>
|
|
*/
|
|
inline void SetName(const char* value) { m_name.assign(value); }
|
|
|
|
/**
|
|
* <p>A user-supplied name or description of the <code>MLModel</code>.</p>
|
|
*/
|
|
inline GetMLModelResult& WithName(const Aws::String& value) { SetName(value); return *this;}
|
|
|
|
/**
|
|
* <p>A user-supplied name or description of the <code>MLModel</code>.</p>
|
|
*/
|
|
inline GetMLModelResult& WithName(Aws::String&& value) { SetName(std::move(value)); return *this;}
|
|
|
|
/**
|
|
* <p>A user-supplied name or description of the <code>MLModel</code>.</p>
|
|
*/
|
|
inline GetMLModelResult& WithName(const char* value) { SetName(value); return *this;}
|
|
|
|
|
|
/**
|
|
* <p>The current status of the <code>MLModel</code>. This element can have one of
|
|
* the following values:</p> <ul> <li> <code>PENDING</code> - Amazon Machine
|
|
* Learning (Amazon ML) submitted a request to describe a
|
|
* <code>MLModel</code>.</li> <li> <code>INPROGRESS</code> - The request is
|
|
* processing.</li> <li> <code>FAILED</code> - The request did not run to
|
|
* completion. The ML model isn't usable.</li> <li> <code>COMPLETED</code> - The
|
|
* request completed successfully.</li> <li> <code>DELETED</code> - The
|
|
* <code>MLModel</code> is marked as deleted. It isn't usable.</li> </ul>
|
|
*/
|
|
inline const EntityStatus& GetStatus() const{ return m_status; }
|
|
|
|
/**
|
|
* <p>The current status of the <code>MLModel</code>. This element can have one of
|
|
* the following values:</p> <ul> <li> <code>PENDING</code> - Amazon Machine
|
|
* Learning (Amazon ML) submitted a request to describe a
|
|
* <code>MLModel</code>.</li> <li> <code>INPROGRESS</code> - The request is
|
|
* processing.</li> <li> <code>FAILED</code> - The request did not run to
|
|
* completion. The ML model isn't usable.</li> <li> <code>COMPLETED</code> - The
|
|
* request completed successfully.</li> <li> <code>DELETED</code> - The
|
|
* <code>MLModel</code> is marked as deleted. It isn't usable.</li> </ul>
|
|
*/
|
|
inline void SetStatus(const EntityStatus& value) { m_status = value; }
|
|
|
|
/**
|
|
* <p>The current status of the <code>MLModel</code>. This element can have one of
|
|
* the following values:</p> <ul> <li> <code>PENDING</code> - Amazon Machine
|
|
* Learning (Amazon ML) submitted a request to describe a
|
|
* <code>MLModel</code>.</li> <li> <code>INPROGRESS</code> - The request is
|
|
* processing.</li> <li> <code>FAILED</code> - The request did not run to
|
|
* completion. The ML model isn't usable.</li> <li> <code>COMPLETED</code> - The
|
|
* request completed successfully.</li> <li> <code>DELETED</code> - The
|
|
* <code>MLModel</code> is marked as deleted. It isn't usable.</li> </ul>
|
|
*/
|
|
inline void SetStatus(EntityStatus&& value) { m_status = std::move(value); }
|
|
|
|
/**
|
|
* <p>The current status of the <code>MLModel</code>. This element can have one of
|
|
* the following values:</p> <ul> <li> <code>PENDING</code> - Amazon Machine
|
|
* Learning (Amazon ML) submitted a request to describe a
|
|
* <code>MLModel</code>.</li> <li> <code>INPROGRESS</code> - The request is
|
|
* processing.</li> <li> <code>FAILED</code> - The request did not run to
|
|
* completion. The ML model isn't usable.</li> <li> <code>COMPLETED</code> - The
|
|
* request completed successfully.</li> <li> <code>DELETED</code> - The
|
|
* <code>MLModel</code> is marked as deleted. It isn't usable.</li> </ul>
|
|
*/
|
|
inline GetMLModelResult& WithStatus(const EntityStatus& value) { SetStatus(value); return *this;}
|
|
|
|
/**
|
|
* <p>The current status of the <code>MLModel</code>. This element can have one of
|
|
* the following values:</p> <ul> <li> <code>PENDING</code> - Amazon Machine
|
|
* Learning (Amazon ML) submitted a request to describe a
|
|
* <code>MLModel</code>.</li> <li> <code>INPROGRESS</code> - The request is
|
|
* processing.</li> <li> <code>FAILED</code> - The request did not run to
|
|
* completion. The ML model isn't usable.</li> <li> <code>COMPLETED</code> - The
|
|
* request completed successfully.</li> <li> <code>DELETED</code> - The
|
|
* <code>MLModel</code> is marked as deleted. It isn't usable.</li> </ul>
|
|
*/
|
|
inline GetMLModelResult& WithStatus(EntityStatus&& value) { SetStatus(std::move(value)); return *this;}
|
|
|
|
|
|
|
|
inline long long GetSizeInBytes() const{ return m_sizeInBytes; }
|
|
|
|
|
|
inline void SetSizeInBytes(long long value) { m_sizeInBytes = value; }
|
|
|
|
|
|
inline GetMLModelResult& WithSizeInBytes(long long value) { SetSizeInBytes(value); return *this;}
|
|
|
|
|
|
/**
|
|
* <p>The current endpoint of the <code>MLModel</code></p>
|
|
*/
|
|
inline const RealtimeEndpointInfo& GetEndpointInfo() const{ return m_endpointInfo; }
|
|
|
|
/**
|
|
* <p>The current endpoint of the <code>MLModel</code></p>
|
|
*/
|
|
inline void SetEndpointInfo(const RealtimeEndpointInfo& value) { m_endpointInfo = value; }
|
|
|
|
/**
|
|
* <p>The current endpoint of the <code>MLModel</code></p>
|
|
*/
|
|
inline void SetEndpointInfo(RealtimeEndpointInfo&& value) { m_endpointInfo = std::move(value); }
|
|
|
|
/**
|
|
* <p>The current endpoint of the <code>MLModel</code></p>
|
|
*/
|
|
inline GetMLModelResult& WithEndpointInfo(const RealtimeEndpointInfo& value) { SetEndpointInfo(value); return *this;}
|
|
|
|
/**
|
|
* <p>The current endpoint of the <code>MLModel</code></p>
|
|
*/
|
|
inline GetMLModelResult& WithEndpointInfo(RealtimeEndpointInfo&& value) { SetEndpointInfo(std::move(value)); return *this;}
|
|
|
|
|
|
/**
|
|
* <p>A list of the training parameters in the <code>MLModel</code>. The list is
|
|
* implemented as a map of key-value pairs.</p> <p>The following is the current set
|
|
* of training parameters: </p> <ul> <li> <p><code>sgd.maxMLModelSizeInBytes</code>
|
|
* - The maximum allowed size of the model. Depending on the input data, the size
|
|
* of the model might affect its performance.</p> <p> The value is an integer that
|
|
* ranges from <code>100000</code> to <code>2147483648</code>. The default value is
|
|
* <code>33554432</code>.</p> </li> <li><p><code>sgd.maxPasses</code> - The number
|
|
* of times that the training process traverses the observations to build the
|
|
* <code>MLModel</code>. The value is an integer that ranges from <code>1</code> to
|
|
* <code>10000</code>. The default value is <code>10</code>.</p></li>
|
|
* <li><p><code>sgd.shuffleType</code> - Whether Amazon ML shuffles the training
|
|
* data. Shuffling data improves a model's ability to find the optimal solution for
|
|
* a variety of data types. The valid values are <code>auto</code> and
|
|
* <code>none</code>. The default value is <code>none</code>. We strongly recommend
|
|
* that you shuffle your data.</p></li> <li>
|
|
* <p><code>sgd.l1RegularizationAmount</code> - The coefficient regularization L1
|
|
* norm. It controls overfitting the data by penalizing large coefficients. This
|
|
* tends to drive coefficients to zero, resulting in a sparse feature set. If you
|
|
* use this parameter, start by specifying a small value, such as
|
|
* <code>1.0E-08</code>.</p> <p>The value is a double that ranges from
|
|
* <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not use L1
|
|
* normalization. This parameter can't be used when <code>L2</code> is specified.
|
|
* Use this parameter sparingly.</p> </li> <li>
|
|
* <p><code>sgd.l2RegularizationAmount</code> - The 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 <code>1.0E-08</code>.</p> <p>The
|
|
* value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>.
|
|
* The default is to not use L2 normalization. This parameter can't be used when
|
|
* <code>L1</code> is specified. Use this parameter sparingly.</p> </li> </ul>
|
|
*/
|
|
inline const Aws::Map<Aws::String, Aws::String>& GetTrainingParameters() const{ return m_trainingParameters; }
|
|
|
|
/**
|
|
* <p>A list of the training parameters in the <code>MLModel</code>. The list is
|
|
* implemented as a map of key-value pairs.</p> <p>The following is the current set
|
|
* of training parameters: </p> <ul> <li> <p><code>sgd.maxMLModelSizeInBytes</code>
|
|
* - The maximum allowed size of the model. Depending on the input data, the size
|
|
* of the model might affect its performance.</p> <p> The value is an integer that
|
|
* ranges from <code>100000</code> to <code>2147483648</code>. The default value is
|
|
* <code>33554432</code>.</p> </li> <li><p><code>sgd.maxPasses</code> - The number
|
|
* of times that the training process traverses the observations to build the
|
|
* <code>MLModel</code>. The value is an integer that ranges from <code>1</code> to
|
|
* <code>10000</code>. The default value is <code>10</code>.</p></li>
|
|
* <li><p><code>sgd.shuffleType</code> - Whether Amazon ML shuffles the training
|
|
* data. Shuffling data improves a model's ability to find the optimal solution for
|
|
* a variety of data types. The valid values are <code>auto</code> and
|
|
* <code>none</code>. The default value is <code>none</code>. We strongly recommend
|
|
* that you shuffle your data.</p></li> <li>
|
|
* <p><code>sgd.l1RegularizationAmount</code> - The coefficient regularization L1
|
|
* norm. It controls overfitting the data by penalizing large coefficients. This
|
|
* tends to drive coefficients to zero, resulting in a sparse feature set. If you
|
|
* use this parameter, start by specifying a small value, such as
|
|
* <code>1.0E-08</code>.</p> <p>The value is a double that ranges from
|
|
* <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not use L1
|
|
* normalization. This parameter can't be used when <code>L2</code> is specified.
|
|
* Use this parameter sparingly.</p> </li> <li>
|
|
* <p><code>sgd.l2RegularizationAmount</code> - The 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 <code>1.0E-08</code>.</p> <p>The
|
|
* value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>.
|
|
* The default is to not use L2 normalization. This parameter can't be used when
|
|
* <code>L1</code> is specified. Use this parameter sparingly.</p> </li> </ul>
|
|
*/
|
|
inline void SetTrainingParameters(const Aws::Map<Aws::String, Aws::String>& value) { m_trainingParameters = value; }
|
|
|
|
/**
|
|
* <p>A list of the training parameters in the <code>MLModel</code>. The list is
|
|
* implemented as a map of key-value pairs.</p> <p>The following is the current set
|
|
* of training parameters: </p> <ul> <li> <p><code>sgd.maxMLModelSizeInBytes</code>
|
|
* - The maximum allowed size of the model. Depending on the input data, the size
|
|
* of the model might affect its performance.</p> <p> The value is an integer that
|
|
* ranges from <code>100000</code> to <code>2147483648</code>. The default value is
|
|
* <code>33554432</code>.</p> </li> <li><p><code>sgd.maxPasses</code> - The number
|
|
* of times that the training process traverses the observations to build the
|
|
* <code>MLModel</code>. The value is an integer that ranges from <code>1</code> to
|
|
* <code>10000</code>. The default value is <code>10</code>.</p></li>
|
|
* <li><p><code>sgd.shuffleType</code> - Whether Amazon ML shuffles the training
|
|
* data. Shuffling data improves a model's ability to find the optimal solution for
|
|
* a variety of data types. The valid values are <code>auto</code> and
|
|
* <code>none</code>. The default value is <code>none</code>. We strongly recommend
|
|
* that you shuffle your data.</p></li> <li>
|
|
* <p><code>sgd.l1RegularizationAmount</code> - The coefficient regularization L1
|
|
* norm. It controls overfitting the data by penalizing large coefficients. This
|
|
* tends to drive coefficients to zero, resulting in a sparse feature set. If you
|
|
* use this parameter, start by specifying a small value, such as
|
|
* <code>1.0E-08</code>.</p> <p>The value is a double that ranges from
|
|
* <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not use L1
|
|
* normalization. This parameter can't be used when <code>L2</code> is specified.
|
|
* Use this parameter sparingly.</p> </li> <li>
|
|
* <p><code>sgd.l2RegularizationAmount</code> - The 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 <code>1.0E-08</code>.</p> <p>The
|
|
* value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>.
|
|
* The default is to not use L2 normalization. This parameter can't be used when
|
|
* <code>L1</code> is specified. Use this parameter sparingly.</p> </li> </ul>
|
|
*/
|
|
inline void SetTrainingParameters(Aws::Map<Aws::String, Aws::String>&& value) { m_trainingParameters = std::move(value); }
|
|
|
|
/**
|
|
* <p>A list of the training parameters in the <code>MLModel</code>. The list is
|
|
* implemented as a map of key-value pairs.</p> <p>The following is the current set
|
|
* of training parameters: </p> <ul> <li> <p><code>sgd.maxMLModelSizeInBytes</code>
|
|
* - The maximum allowed size of the model. Depending on the input data, the size
|
|
* of the model might affect its performance.</p> <p> The value is an integer that
|
|
* ranges from <code>100000</code> to <code>2147483648</code>. The default value is
|
|
* <code>33554432</code>.</p> </li> <li><p><code>sgd.maxPasses</code> - The number
|
|
* of times that the training process traverses the observations to build the
|
|
* <code>MLModel</code>. The value is an integer that ranges from <code>1</code> to
|
|
* <code>10000</code>. The default value is <code>10</code>.</p></li>
|
|
* <li><p><code>sgd.shuffleType</code> - Whether Amazon ML shuffles the training
|
|
* data. Shuffling data improves a model's ability to find the optimal solution for
|
|
* a variety of data types. The valid values are <code>auto</code> and
|
|
* <code>none</code>. The default value is <code>none</code>. We strongly recommend
|
|
* that you shuffle your data.</p></li> <li>
|
|
* <p><code>sgd.l1RegularizationAmount</code> - The coefficient regularization L1
|
|
* norm. It controls overfitting the data by penalizing large coefficients. This
|
|
* tends to drive coefficients to zero, resulting in a sparse feature set. If you
|
|
* use this parameter, start by specifying a small value, such as
|
|
* <code>1.0E-08</code>.</p> <p>The value is a double that ranges from
|
|
* <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not use L1
|
|
* normalization. This parameter can't be used when <code>L2</code> is specified.
|
|
* Use this parameter sparingly.</p> </li> <li>
|
|
* <p><code>sgd.l2RegularizationAmount</code> - The 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 <code>1.0E-08</code>.</p> <p>The
|
|
* value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>.
|
|
* The default is to not use L2 normalization. This parameter can't be used when
|
|
* <code>L1</code> is specified. Use this parameter sparingly.</p> </li> </ul>
|
|
*/
|
|
inline GetMLModelResult& WithTrainingParameters(const Aws::Map<Aws::String, Aws::String>& value) { SetTrainingParameters(value); return *this;}
|
|
|
|
/**
|
|
* <p>A list of the training parameters in the <code>MLModel</code>. The list is
|
|
* implemented as a map of key-value pairs.</p> <p>The following is the current set
|
|
* of training parameters: </p> <ul> <li> <p><code>sgd.maxMLModelSizeInBytes</code>
|
|
* - The maximum allowed size of the model. Depending on the input data, the size
|
|
* of the model might affect its performance.</p> <p> The value is an integer that
|
|
* ranges from <code>100000</code> to <code>2147483648</code>. The default value is
|
|
* <code>33554432</code>.</p> </li> <li><p><code>sgd.maxPasses</code> - The number
|
|
* of times that the training process traverses the observations to build the
|
|
* <code>MLModel</code>. The value is an integer that ranges from <code>1</code> to
|
|
* <code>10000</code>. The default value is <code>10</code>.</p></li>
|
|
* <li><p><code>sgd.shuffleType</code> - Whether Amazon ML shuffles the training
|
|
* data. Shuffling data improves a model's ability to find the optimal solution for
|
|
* a variety of data types. The valid values are <code>auto</code> and
|
|
* <code>none</code>. The default value is <code>none</code>. We strongly recommend
|
|
* that you shuffle your data.</p></li> <li>
|
|
* <p><code>sgd.l1RegularizationAmount</code> - The coefficient regularization L1
|
|
* norm. It controls overfitting the data by penalizing large coefficients. This
|
|
* tends to drive coefficients to zero, resulting in a sparse feature set. If you
|
|
* use this parameter, start by specifying a small value, such as
|
|
* <code>1.0E-08</code>.</p> <p>The value is a double that ranges from
|
|
* <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not use L1
|
|
* normalization. This parameter can't be used when <code>L2</code> is specified.
|
|
* Use this parameter sparingly.</p> </li> <li>
|
|
* <p><code>sgd.l2RegularizationAmount</code> - The 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 <code>1.0E-08</code>.</p> <p>The
|
|
* value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>.
|
|
* The default is to not use L2 normalization. This parameter can't be used when
|
|
* <code>L1</code> is specified. Use this parameter sparingly.</p> </li> </ul>
|
|
*/
|
|
inline GetMLModelResult& WithTrainingParameters(Aws::Map<Aws::String, Aws::String>&& value) { SetTrainingParameters(std::move(value)); return *this;}
|
|
|
|
/**
|
|
* <p>A list of the training parameters in the <code>MLModel</code>. The list is
|
|
* implemented as a map of key-value pairs.</p> <p>The following is the current set
|
|
* of training parameters: </p> <ul> <li> <p><code>sgd.maxMLModelSizeInBytes</code>
|
|
* - The maximum allowed size of the model. Depending on the input data, the size
|
|
* of the model might affect its performance.</p> <p> The value is an integer that
|
|
* ranges from <code>100000</code> to <code>2147483648</code>. The default value is
|
|
* <code>33554432</code>.</p> </li> <li><p><code>sgd.maxPasses</code> - The number
|
|
* of times that the training process traverses the observations to build the
|
|
* <code>MLModel</code>. The value is an integer that ranges from <code>1</code> to
|
|
* <code>10000</code>. The default value is <code>10</code>.</p></li>
|
|
* <li><p><code>sgd.shuffleType</code> - Whether Amazon ML shuffles the training
|
|
* data. Shuffling data improves a model's ability to find the optimal solution for
|
|
* a variety of data types. The valid values are <code>auto</code> and
|
|
* <code>none</code>. The default value is <code>none</code>. We strongly recommend
|
|
* that you shuffle your data.</p></li> <li>
|
|
* <p><code>sgd.l1RegularizationAmount</code> - The coefficient regularization L1
|
|
* norm. It controls overfitting the data by penalizing large coefficients. This
|
|
* tends to drive coefficients to zero, resulting in a sparse feature set. If you
|
|
* use this parameter, start by specifying a small value, such as
|
|
* <code>1.0E-08</code>.</p> <p>The value is a double that ranges from
|
|
* <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not use L1
|
|
* normalization. This parameter can't be used when <code>L2</code> is specified.
|
|
* Use this parameter sparingly.</p> </li> <li>
|
|
* <p><code>sgd.l2RegularizationAmount</code> - The 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 <code>1.0E-08</code>.</p> <p>The
|
|
* value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>.
|
|
* The default is to not use L2 normalization. This parameter can't be used when
|
|
* <code>L1</code> is specified. Use this parameter sparingly.</p> </li> </ul>
|
|
*/
|
|
inline GetMLModelResult& AddTrainingParameters(const Aws::String& key, const Aws::String& value) { m_trainingParameters.emplace(key, value); return *this; }
|
|
|
|
/**
|
|
* <p>A list of the training parameters in the <code>MLModel</code>. The list is
|
|
* implemented as a map of key-value pairs.</p> <p>The following is the current set
|
|
* of training parameters: </p> <ul> <li> <p><code>sgd.maxMLModelSizeInBytes</code>
|
|
* - The maximum allowed size of the model. Depending on the input data, the size
|
|
* of the model might affect its performance.</p> <p> The value is an integer that
|
|
* ranges from <code>100000</code> to <code>2147483648</code>. The default value is
|
|
* <code>33554432</code>.</p> </li> <li><p><code>sgd.maxPasses</code> - The number
|
|
* of times that the training process traverses the observations to build the
|
|
* <code>MLModel</code>. The value is an integer that ranges from <code>1</code> to
|
|
* <code>10000</code>. The default value is <code>10</code>.</p></li>
|
|
* <li><p><code>sgd.shuffleType</code> - Whether Amazon ML shuffles the training
|
|
* data. Shuffling data improves a model's ability to find the optimal solution for
|
|
* a variety of data types. The valid values are <code>auto</code> and
|
|
* <code>none</code>. The default value is <code>none</code>. We strongly recommend
|
|
* that you shuffle your data.</p></li> <li>
|
|
* <p><code>sgd.l1RegularizationAmount</code> - The coefficient regularization L1
|
|
* norm. It controls overfitting the data by penalizing large coefficients. This
|
|
* tends to drive coefficients to zero, resulting in a sparse feature set. If you
|
|
* use this parameter, start by specifying a small value, such as
|
|
* <code>1.0E-08</code>.</p> <p>The value is a double that ranges from
|
|
* <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not use L1
|
|
* normalization. This parameter can't be used when <code>L2</code> is specified.
|
|
* Use this parameter sparingly.</p> </li> <li>
|
|
* <p><code>sgd.l2RegularizationAmount</code> - The 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 <code>1.0E-08</code>.</p> <p>The
|
|
* value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>.
|
|
* The default is to not use L2 normalization. This parameter can't be used when
|
|
* <code>L1</code> is specified. Use this parameter sparingly.</p> </li> </ul>
|
|
*/
|
|
inline GetMLModelResult& AddTrainingParameters(Aws::String&& key, const Aws::String& value) { m_trainingParameters.emplace(std::move(key), value); return *this; }
|
|
|
|
/**
|
|
* <p>A list of the training parameters in the <code>MLModel</code>. The list is
|
|
* implemented as a map of key-value pairs.</p> <p>The following is the current set
|
|
* of training parameters: </p> <ul> <li> <p><code>sgd.maxMLModelSizeInBytes</code>
|
|
* - The maximum allowed size of the model. Depending on the input data, the size
|
|
* of the model might affect its performance.</p> <p> The value is an integer that
|
|
* ranges from <code>100000</code> to <code>2147483648</code>. The default value is
|
|
* <code>33554432</code>.</p> </li> <li><p><code>sgd.maxPasses</code> - The number
|
|
* of times that the training process traverses the observations to build the
|
|
* <code>MLModel</code>. The value is an integer that ranges from <code>1</code> to
|
|
* <code>10000</code>. The default value is <code>10</code>.</p></li>
|
|
* <li><p><code>sgd.shuffleType</code> - Whether Amazon ML shuffles the training
|
|
* data. Shuffling data improves a model's ability to find the optimal solution for
|
|
* a variety of data types. The valid values are <code>auto</code> and
|
|
* <code>none</code>. The default value is <code>none</code>. We strongly recommend
|
|
* that you shuffle your data.</p></li> <li>
|
|
* <p><code>sgd.l1RegularizationAmount</code> - The coefficient regularization L1
|
|
* norm. It controls overfitting the data by penalizing large coefficients. This
|
|
* tends to drive coefficients to zero, resulting in a sparse feature set. If you
|
|
* use this parameter, start by specifying a small value, such as
|
|
* <code>1.0E-08</code>.</p> <p>The value is a double that ranges from
|
|
* <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not use L1
|
|
* normalization. This parameter can't be used when <code>L2</code> is specified.
|
|
* Use this parameter sparingly.</p> </li> <li>
|
|
* <p><code>sgd.l2RegularizationAmount</code> - The 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 <code>1.0E-08</code>.</p> <p>The
|
|
* value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>.
|
|
* The default is to not use L2 normalization. This parameter can't be used when
|
|
* <code>L1</code> is specified. Use this parameter sparingly.</p> </li> </ul>
|
|
*/
|
|
inline GetMLModelResult& AddTrainingParameters(const Aws::String& key, Aws::String&& value) { m_trainingParameters.emplace(key, std::move(value)); return *this; }
|
|
|
|
/**
|
|
* <p>A list of the training parameters in the <code>MLModel</code>. The list is
|
|
* implemented as a map of key-value pairs.</p> <p>The following is the current set
|
|
* of training parameters: </p> <ul> <li> <p><code>sgd.maxMLModelSizeInBytes</code>
|
|
* - The maximum allowed size of the model. Depending on the input data, the size
|
|
* of the model might affect its performance.</p> <p> The value is an integer that
|
|
* ranges from <code>100000</code> to <code>2147483648</code>. The default value is
|
|
* <code>33554432</code>.</p> </li> <li><p><code>sgd.maxPasses</code> - The number
|
|
* of times that the training process traverses the observations to build the
|
|
* <code>MLModel</code>. The value is an integer that ranges from <code>1</code> to
|
|
* <code>10000</code>. The default value is <code>10</code>.</p></li>
|
|
* <li><p><code>sgd.shuffleType</code> - Whether Amazon ML shuffles the training
|
|
* data. Shuffling data improves a model's ability to find the optimal solution for
|
|
* a variety of data types. The valid values are <code>auto</code> and
|
|
* <code>none</code>. The default value is <code>none</code>. We strongly recommend
|
|
* that you shuffle your data.</p></li> <li>
|
|
* <p><code>sgd.l1RegularizationAmount</code> - The coefficient regularization L1
|
|
* norm. It controls overfitting the data by penalizing large coefficients. This
|
|
* tends to drive coefficients to zero, resulting in a sparse feature set. If you
|
|
* use this parameter, start by specifying a small value, such as
|
|
* <code>1.0E-08</code>.</p> <p>The value is a double that ranges from
|
|
* <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not use L1
|
|
* normalization. This parameter can't be used when <code>L2</code> is specified.
|
|
* Use this parameter sparingly.</p> </li> <li>
|
|
* <p><code>sgd.l2RegularizationAmount</code> - The 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 <code>1.0E-08</code>.</p> <p>The
|
|
* value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>.
|
|
* The default is to not use L2 normalization. This parameter can't be used when
|
|
* <code>L1</code> is specified. Use this parameter sparingly.</p> </li> </ul>
|
|
*/
|
|
inline GetMLModelResult& AddTrainingParameters(Aws::String&& key, Aws::String&& value) { m_trainingParameters.emplace(std::move(key), std::move(value)); return *this; }
|
|
|
|
/**
|
|
* <p>A list of the training parameters in the <code>MLModel</code>. The list is
|
|
* implemented as a map of key-value pairs.</p> <p>The following is the current set
|
|
* of training parameters: </p> <ul> <li> <p><code>sgd.maxMLModelSizeInBytes</code>
|
|
* - The maximum allowed size of the model. Depending on the input data, the size
|
|
* of the model might affect its performance.</p> <p> The value is an integer that
|
|
* ranges from <code>100000</code> to <code>2147483648</code>. The default value is
|
|
* <code>33554432</code>.</p> </li> <li><p><code>sgd.maxPasses</code> - The number
|
|
* of times that the training process traverses the observations to build the
|
|
* <code>MLModel</code>. The value is an integer that ranges from <code>1</code> to
|
|
* <code>10000</code>. The default value is <code>10</code>.</p></li>
|
|
* <li><p><code>sgd.shuffleType</code> - Whether Amazon ML shuffles the training
|
|
* data. Shuffling data improves a model's ability to find the optimal solution for
|
|
* a variety of data types. The valid values are <code>auto</code> and
|
|
* <code>none</code>. The default value is <code>none</code>. We strongly recommend
|
|
* that you shuffle your data.</p></li> <li>
|
|
* <p><code>sgd.l1RegularizationAmount</code> - The coefficient regularization L1
|
|
* norm. It controls overfitting the data by penalizing large coefficients. This
|
|
* tends to drive coefficients to zero, resulting in a sparse feature set. If you
|
|
* use this parameter, start by specifying a small value, such as
|
|
* <code>1.0E-08</code>.</p> <p>The value is a double that ranges from
|
|
* <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not use L1
|
|
* normalization. This parameter can't be used when <code>L2</code> is specified.
|
|
* Use this parameter sparingly.</p> </li> <li>
|
|
* <p><code>sgd.l2RegularizationAmount</code> - The 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 <code>1.0E-08</code>.</p> <p>The
|
|
* value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>.
|
|
* The default is to not use L2 normalization. This parameter can't be used when
|
|
* <code>L1</code> is specified. Use this parameter sparingly.</p> </li> </ul>
|
|
*/
|
|
inline GetMLModelResult& AddTrainingParameters(const char* key, Aws::String&& value) { m_trainingParameters.emplace(key, std::move(value)); return *this; }
|
|
|
|
/**
|
|
* <p>A list of the training parameters in the <code>MLModel</code>. The list is
|
|
* implemented as a map of key-value pairs.</p> <p>The following is the current set
|
|
* of training parameters: </p> <ul> <li> <p><code>sgd.maxMLModelSizeInBytes</code>
|
|
* - The maximum allowed size of the model. Depending on the input data, the size
|
|
* of the model might affect its performance.</p> <p> The value is an integer that
|
|
* ranges from <code>100000</code> to <code>2147483648</code>. The default value is
|
|
* <code>33554432</code>.</p> </li> <li><p><code>sgd.maxPasses</code> - The number
|
|
* of times that the training process traverses the observations to build the
|
|
* <code>MLModel</code>. The value is an integer that ranges from <code>1</code> to
|
|
* <code>10000</code>. The default value is <code>10</code>.</p></li>
|
|
* <li><p><code>sgd.shuffleType</code> - Whether Amazon ML shuffles the training
|
|
* data. Shuffling data improves a model's ability to find the optimal solution for
|
|
* a variety of data types. The valid values are <code>auto</code> and
|
|
* <code>none</code>. The default value is <code>none</code>. We strongly recommend
|
|
* that you shuffle your data.</p></li> <li>
|
|
* <p><code>sgd.l1RegularizationAmount</code> - The coefficient regularization L1
|
|
* norm. It controls overfitting the data by penalizing large coefficients. This
|
|
* tends to drive coefficients to zero, resulting in a sparse feature set. If you
|
|
* use this parameter, start by specifying a small value, such as
|
|
* <code>1.0E-08</code>.</p> <p>The value is a double that ranges from
|
|
* <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not use L1
|
|
* normalization. This parameter can't be used when <code>L2</code> is specified.
|
|
* Use this parameter sparingly.</p> </li> <li>
|
|
* <p><code>sgd.l2RegularizationAmount</code> - The 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 <code>1.0E-08</code>.</p> <p>The
|
|
* value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>.
|
|
* The default is to not use L2 normalization. This parameter can't be used when
|
|
* <code>L1</code> is specified. Use this parameter sparingly.</p> </li> </ul>
|
|
*/
|
|
inline GetMLModelResult& AddTrainingParameters(Aws::String&& key, const char* value) { m_trainingParameters.emplace(std::move(key), value); return *this; }
|
|
|
|
/**
|
|
* <p>A list of the training parameters in the <code>MLModel</code>. The list is
|
|
* implemented as a map of key-value pairs.</p> <p>The following is the current set
|
|
* of training parameters: </p> <ul> <li> <p><code>sgd.maxMLModelSizeInBytes</code>
|
|
* - The maximum allowed size of the model. Depending on the input data, the size
|
|
* of the model might affect its performance.</p> <p> The value is an integer that
|
|
* ranges from <code>100000</code> to <code>2147483648</code>. The default value is
|
|
* <code>33554432</code>.</p> </li> <li><p><code>sgd.maxPasses</code> - The number
|
|
* of times that the training process traverses the observations to build the
|
|
* <code>MLModel</code>. The value is an integer that ranges from <code>1</code> to
|
|
* <code>10000</code>. The default value is <code>10</code>.</p></li>
|
|
* <li><p><code>sgd.shuffleType</code> - Whether Amazon ML shuffles the training
|
|
* data. Shuffling data improves a model's ability to find the optimal solution for
|
|
* a variety of data types. The valid values are <code>auto</code> and
|
|
* <code>none</code>. The default value is <code>none</code>. We strongly recommend
|
|
* that you shuffle your data.</p></li> <li>
|
|
* <p><code>sgd.l1RegularizationAmount</code> - The coefficient regularization L1
|
|
* norm. It controls overfitting the data by penalizing large coefficients. This
|
|
* tends to drive coefficients to zero, resulting in a sparse feature set. If you
|
|
* use this parameter, start by specifying a small value, such as
|
|
* <code>1.0E-08</code>.</p> <p>The value is a double that ranges from
|
|
* <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not use L1
|
|
* normalization. This parameter can't be used when <code>L2</code> is specified.
|
|
* Use this parameter sparingly.</p> </li> <li>
|
|
* <p><code>sgd.l2RegularizationAmount</code> - The 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 <code>1.0E-08</code>.</p> <p>The
|
|
* value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>.
|
|
* The default is to not use L2 normalization. This parameter can't be used when
|
|
* <code>L1</code> is specified. Use this parameter sparingly.</p> </li> </ul>
|
|
*/
|
|
inline GetMLModelResult& AddTrainingParameters(const char* key, const char* value) { m_trainingParameters.emplace(key, value); return *this; }
|
|
|
|
|
|
/**
|
|
* <p>The location of the data file or directory in Amazon Simple Storage Service
|
|
* (Amazon S3).</p>
|
|
*/
|
|
inline const Aws::String& GetInputDataLocationS3() const{ return m_inputDataLocationS3; }
|
|
|
|
/**
|
|
* <p>The location of the data file or directory in Amazon Simple Storage Service
|
|
* (Amazon S3).</p>
|
|
*/
|
|
inline void SetInputDataLocationS3(const Aws::String& value) { m_inputDataLocationS3 = value; }
|
|
|
|
/**
|
|
* <p>The location of the data file or directory in Amazon Simple Storage Service
|
|
* (Amazon S3).</p>
|
|
*/
|
|
inline void SetInputDataLocationS3(Aws::String&& value) { m_inputDataLocationS3 = std::move(value); }
|
|
|
|
/**
|
|
* <p>The location of the data file or directory in Amazon Simple Storage Service
|
|
* (Amazon S3).</p>
|
|
*/
|
|
inline void SetInputDataLocationS3(const char* value) { m_inputDataLocationS3.assign(value); }
|
|
|
|
/**
|
|
* <p>The location of the data file or directory in Amazon Simple Storage Service
|
|
* (Amazon S3).</p>
|
|
*/
|
|
inline GetMLModelResult& WithInputDataLocationS3(const Aws::String& value) { SetInputDataLocationS3(value); return *this;}
|
|
|
|
/**
|
|
* <p>The location of the data file or directory in Amazon Simple Storage Service
|
|
* (Amazon S3).</p>
|
|
*/
|
|
inline GetMLModelResult& WithInputDataLocationS3(Aws::String&& value) { SetInputDataLocationS3(std::move(value)); return *this;}
|
|
|
|
/**
|
|
* <p>The location of the data file or directory in Amazon Simple Storage Service
|
|
* (Amazon S3).</p>
|
|
*/
|
|
inline GetMLModelResult& WithInputDataLocationS3(const char* value) { SetInputDataLocationS3(value); return *this;}
|
|
|
|
|
|
/**
|
|
* <p>Identifies the <code>MLModel</code> category. The following are the available
|
|
* types: </p> <ul> <li>REGRESSION -- Produces a numeric result. For example, "What
|
|
* price should a house be listed at?"</li> <li>BINARY -- Produces one of two
|
|
* possible results. For example, "Is this an e-commerce website?"</li>
|
|
* <li>MULTICLASS -- Produces one of several possible results. For example, "Is
|
|
* this a HIGH, LOW or MEDIUM risk trade?"</li> </ul>
|
|
*/
|
|
inline const MLModelType& GetMLModelType() const{ return m_mLModelType; }
|
|
|
|
/**
|
|
* <p>Identifies the <code>MLModel</code> category. The following are the available
|
|
* types: </p> <ul> <li>REGRESSION -- Produces a numeric result. For example, "What
|
|
* price should a house be listed at?"</li> <li>BINARY -- Produces one of two
|
|
* possible results. For example, "Is this an e-commerce website?"</li>
|
|
* <li>MULTICLASS -- Produces one of several possible results. For example, "Is
|
|
* this a HIGH, LOW or MEDIUM risk trade?"</li> </ul>
|
|
*/
|
|
inline void SetMLModelType(const MLModelType& value) { m_mLModelType = value; }
|
|
|
|
/**
|
|
* <p>Identifies the <code>MLModel</code> category. The following are the available
|
|
* types: </p> <ul> <li>REGRESSION -- Produces a numeric result. For example, "What
|
|
* price should a house be listed at?"</li> <li>BINARY -- Produces one of two
|
|
* possible results. For example, "Is this an e-commerce website?"</li>
|
|
* <li>MULTICLASS -- Produces one of several possible results. For example, "Is
|
|
* this a HIGH, LOW or MEDIUM risk trade?"</li> </ul>
|
|
*/
|
|
inline void SetMLModelType(MLModelType&& value) { m_mLModelType = std::move(value); }
|
|
|
|
/**
|
|
* <p>Identifies the <code>MLModel</code> category. The following are the available
|
|
* types: </p> <ul> <li>REGRESSION -- Produces a numeric result. For example, "What
|
|
* price should a house be listed at?"</li> <li>BINARY -- Produces one of two
|
|
* possible results. For example, "Is this an e-commerce website?"</li>
|
|
* <li>MULTICLASS -- Produces one of several possible results. For example, "Is
|
|
* this a HIGH, LOW or MEDIUM risk trade?"</li> </ul>
|
|
*/
|
|
inline GetMLModelResult& WithMLModelType(const MLModelType& value) { SetMLModelType(value); return *this;}
|
|
|
|
/**
|
|
* <p>Identifies the <code>MLModel</code> category. The following are the available
|
|
* types: </p> <ul> <li>REGRESSION -- Produces a numeric result. For example, "What
|
|
* price should a house be listed at?"</li> <li>BINARY -- Produces one of two
|
|
* possible results. For example, "Is this an e-commerce website?"</li>
|
|
* <li>MULTICLASS -- Produces one of several possible results. For example, "Is
|
|
* this a HIGH, LOW or MEDIUM risk trade?"</li> </ul>
|
|
*/
|
|
inline GetMLModelResult& WithMLModelType(MLModelType&& value) { SetMLModelType(std::move(value)); return *this;}
|
|
|
|
|
|
/**
|
|
* <p>The scoring threshold is used in binary classification
|
|
* <code>MLModel</code><?oxy_insert_start author="laurama"
|
|
* timestamp="20160329T114851-0700"> <?oxy_insert_end>models. It marks the boundary
|
|
* between a positive prediction and a negative prediction.</p> <p>Output values
|
|
* greater than or equal to the threshold receive a positive result from the
|
|
* MLModel, such as <code>true</code>. Output values less than the threshold
|
|
* receive a negative response from the MLModel, such as <code>false</code>.</p>
|
|
*/
|
|
inline double GetScoreThreshold() const{ return m_scoreThreshold; }
|
|
|
|
/**
|
|
* <p>The scoring threshold is used in binary classification
|
|
* <code>MLModel</code><?oxy_insert_start author="laurama"
|
|
* timestamp="20160329T114851-0700"> <?oxy_insert_end>models. It marks the boundary
|
|
* between a positive prediction and a negative prediction.</p> <p>Output values
|
|
* greater than or equal to the threshold receive a positive result from the
|
|
* MLModel, such as <code>true</code>. Output values less than the threshold
|
|
* receive a negative response from the MLModel, such as <code>false</code>.</p>
|
|
*/
|
|
inline void SetScoreThreshold(double value) { m_scoreThreshold = value; }
|
|
|
|
/**
|
|
* <p>The scoring threshold is used in binary classification
|
|
* <code>MLModel</code><?oxy_insert_start author="laurama"
|
|
* timestamp="20160329T114851-0700"> <?oxy_insert_end>models. It marks the boundary
|
|
* between a positive prediction and a negative prediction.</p> <p>Output values
|
|
* greater than or equal to the threshold receive a positive result from the
|
|
* MLModel, such as <code>true</code>. Output values less than the threshold
|
|
* receive a negative response from the MLModel, such as <code>false</code>.</p>
|
|
*/
|
|
inline GetMLModelResult& WithScoreThreshold(double value) { SetScoreThreshold(value); return *this;}
|
|
|
|
|
|
/**
|
|
* <p>The time of the most recent edit to the <code>ScoreThreshold</code>. The time
|
|
* is expressed in epoch time.</p>
|
|
*/
|
|
inline const Aws::Utils::DateTime& GetScoreThresholdLastUpdatedAt() const{ return m_scoreThresholdLastUpdatedAt; }
|
|
|
|
/**
|
|
* <p>The time of the most recent edit to the <code>ScoreThreshold</code>. The time
|
|
* is expressed in epoch time.</p>
|
|
*/
|
|
inline void SetScoreThresholdLastUpdatedAt(const Aws::Utils::DateTime& value) { m_scoreThresholdLastUpdatedAt = value; }
|
|
|
|
/**
|
|
* <p>The time of the most recent edit to the <code>ScoreThreshold</code>. The time
|
|
* is expressed in epoch time.</p>
|
|
*/
|
|
inline void SetScoreThresholdLastUpdatedAt(Aws::Utils::DateTime&& value) { m_scoreThresholdLastUpdatedAt = std::move(value); }
|
|
|
|
/**
|
|
* <p>The time of the most recent edit to the <code>ScoreThreshold</code>. The time
|
|
* is expressed in epoch time.</p>
|
|
*/
|
|
inline GetMLModelResult& WithScoreThresholdLastUpdatedAt(const Aws::Utils::DateTime& value) { SetScoreThresholdLastUpdatedAt(value); return *this;}
|
|
|
|
/**
|
|
* <p>The time of the most recent edit to the <code>ScoreThreshold</code>. The time
|
|
* is expressed in epoch time.</p>
|
|
*/
|
|
inline GetMLModelResult& WithScoreThresholdLastUpdatedAt(Aws::Utils::DateTime&& value) { SetScoreThresholdLastUpdatedAt(std::move(value)); return *this;}
|
|
|
|
|
|
/**
|
|
* <p>A link to the file that contains logs of the <code>CreateMLModel</code>
|
|
* operation.</p>
|
|
*/
|
|
inline const Aws::String& GetLogUri() const{ return m_logUri; }
|
|
|
|
/**
|
|
* <p>A link to the file that contains logs of the <code>CreateMLModel</code>
|
|
* operation.</p>
|
|
*/
|
|
inline void SetLogUri(const Aws::String& value) { m_logUri = value; }
|
|
|
|
/**
|
|
* <p>A link to the file that contains logs of the <code>CreateMLModel</code>
|
|
* operation.</p>
|
|
*/
|
|
inline void SetLogUri(Aws::String&& value) { m_logUri = std::move(value); }
|
|
|
|
/**
|
|
* <p>A link to the file that contains logs of the <code>CreateMLModel</code>
|
|
* operation.</p>
|
|
*/
|
|
inline void SetLogUri(const char* value) { m_logUri.assign(value); }
|
|
|
|
/**
|
|
* <p>A link to the file that contains logs of the <code>CreateMLModel</code>
|
|
* operation.</p>
|
|
*/
|
|
inline GetMLModelResult& WithLogUri(const Aws::String& value) { SetLogUri(value); return *this;}
|
|
|
|
/**
|
|
* <p>A link to the file that contains logs of the <code>CreateMLModel</code>
|
|
* operation.</p>
|
|
*/
|
|
inline GetMLModelResult& WithLogUri(Aws::String&& value) { SetLogUri(std::move(value)); return *this;}
|
|
|
|
/**
|
|
* <p>A link to the file that contains logs of the <code>CreateMLModel</code>
|
|
* operation.</p>
|
|
*/
|
|
inline GetMLModelResult& WithLogUri(const char* value) { SetLogUri(value); return *this;}
|
|
|
|
|
|
/**
|
|
* <p>A description of the most recent details about accessing the
|
|
* <code>MLModel</code>.</p>
|
|
*/
|
|
inline const Aws::String& GetMessage() const{ return m_message; }
|
|
|
|
/**
|
|
* <p>A description of the most recent details about accessing the
|
|
* <code>MLModel</code>.</p>
|
|
*/
|
|
inline void SetMessage(const Aws::String& value) { m_message = value; }
|
|
|
|
/**
|
|
* <p>A description of the most recent details about accessing the
|
|
* <code>MLModel</code>.</p>
|
|
*/
|
|
inline void SetMessage(Aws::String&& value) { m_message = std::move(value); }
|
|
|
|
/**
|
|
* <p>A description of the most recent details about accessing the
|
|
* <code>MLModel</code>.</p>
|
|
*/
|
|
inline void SetMessage(const char* value) { m_message.assign(value); }
|
|
|
|
/**
|
|
* <p>A description of the most recent details about accessing the
|
|
* <code>MLModel</code>.</p>
|
|
*/
|
|
inline GetMLModelResult& WithMessage(const Aws::String& value) { SetMessage(value); return *this;}
|
|
|
|
/**
|
|
* <p>A description of the most recent details about accessing the
|
|
* <code>MLModel</code>.</p>
|
|
*/
|
|
inline GetMLModelResult& WithMessage(Aws::String&& value) { SetMessage(std::move(value)); return *this;}
|
|
|
|
/**
|
|
* <p>A description of the most recent details about accessing the
|
|
* <code>MLModel</code>.</p>
|
|
*/
|
|
inline GetMLModelResult& WithMessage(const char* value) { SetMessage(value); return *this;}
|
|
|
|
|
|
/**
|
|
* <p>The approximate CPU time in milliseconds that Amazon Machine Learning spent
|
|
* processing the <code>MLModel</code>, normalized and scaled on computation
|
|
* resources. <code>ComputeTime</code> is only available if the
|
|
* <code>MLModel</code> is in the <code>COMPLETED</code> state.</p>
|
|
*/
|
|
inline long long GetComputeTime() const{ return m_computeTime; }
|
|
|
|
/**
|
|
* <p>The approximate CPU time in milliseconds that Amazon Machine Learning spent
|
|
* processing the <code>MLModel</code>, normalized and scaled on computation
|
|
* resources. <code>ComputeTime</code> is only available if the
|
|
* <code>MLModel</code> is in the <code>COMPLETED</code> state.</p>
|
|
*/
|
|
inline void SetComputeTime(long long value) { m_computeTime = value; }
|
|
|
|
/**
|
|
* <p>The approximate CPU time in milliseconds that Amazon Machine Learning spent
|
|
* processing the <code>MLModel</code>, normalized and scaled on computation
|
|
* resources. <code>ComputeTime</code> is only available if the
|
|
* <code>MLModel</code> is in the <code>COMPLETED</code> state.</p>
|
|
*/
|
|
inline GetMLModelResult& WithComputeTime(long long value) { SetComputeTime(value); return *this;}
|
|
|
|
|
|
/**
|
|
* <p>The epoch time when Amazon Machine Learning marked the <code>MLModel</code>
|
|
* as <code>COMPLETED</code> or <code>FAILED</code>. <code>FinishedAt</code> is
|
|
* only available when the <code>MLModel</code> is in the <code>COMPLETED</code> or
|
|
* <code>FAILED</code> state.</p>
|
|
*/
|
|
inline const Aws::Utils::DateTime& GetFinishedAt() const{ return m_finishedAt; }
|
|
|
|
/**
|
|
* <p>The epoch time when Amazon Machine Learning marked the <code>MLModel</code>
|
|
* as <code>COMPLETED</code> or <code>FAILED</code>. <code>FinishedAt</code> is
|
|
* only available when the <code>MLModel</code> is in the <code>COMPLETED</code> or
|
|
* <code>FAILED</code> state.</p>
|
|
*/
|
|
inline void SetFinishedAt(const Aws::Utils::DateTime& value) { m_finishedAt = value; }
|
|
|
|
/**
|
|
* <p>The epoch time when Amazon Machine Learning marked the <code>MLModel</code>
|
|
* as <code>COMPLETED</code> or <code>FAILED</code>. <code>FinishedAt</code> is
|
|
* only available when the <code>MLModel</code> is in the <code>COMPLETED</code> or
|
|
* <code>FAILED</code> state.</p>
|
|
*/
|
|
inline void SetFinishedAt(Aws::Utils::DateTime&& value) { m_finishedAt = std::move(value); }
|
|
|
|
/**
|
|
* <p>The epoch time when Amazon Machine Learning marked the <code>MLModel</code>
|
|
* as <code>COMPLETED</code> or <code>FAILED</code>. <code>FinishedAt</code> is
|
|
* only available when the <code>MLModel</code> is in the <code>COMPLETED</code> or
|
|
* <code>FAILED</code> state.</p>
|
|
*/
|
|
inline GetMLModelResult& WithFinishedAt(const Aws::Utils::DateTime& value) { SetFinishedAt(value); return *this;}
|
|
|
|
/**
|
|
* <p>The epoch time when Amazon Machine Learning marked the <code>MLModel</code>
|
|
* as <code>COMPLETED</code> or <code>FAILED</code>. <code>FinishedAt</code> is
|
|
* only available when the <code>MLModel</code> is in the <code>COMPLETED</code> or
|
|
* <code>FAILED</code> state.</p>
|
|
*/
|
|
inline GetMLModelResult& WithFinishedAt(Aws::Utils::DateTime&& value) { SetFinishedAt(std::move(value)); return *this;}
|
|
|
|
|
|
/**
|
|
* <p>The epoch time when Amazon Machine Learning marked the <code>MLModel</code>
|
|
* as <code>INPROGRESS</code>. <code>StartedAt</code> isn't available if the
|
|
* <code>MLModel</code> is in the <code>PENDING</code> state.</p>
|
|
*/
|
|
inline const Aws::Utils::DateTime& GetStartedAt() const{ return m_startedAt; }
|
|
|
|
/**
|
|
* <p>The epoch time when Amazon Machine Learning marked the <code>MLModel</code>
|
|
* as <code>INPROGRESS</code>. <code>StartedAt</code> isn't available if the
|
|
* <code>MLModel</code> is in the <code>PENDING</code> state.</p>
|
|
*/
|
|
inline void SetStartedAt(const Aws::Utils::DateTime& value) { m_startedAt = value; }
|
|
|
|
/**
|
|
* <p>The epoch time when Amazon Machine Learning marked the <code>MLModel</code>
|
|
* as <code>INPROGRESS</code>. <code>StartedAt</code> isn't available if the
|
|
* <code>MLModel</code> is in the <code>PENDING</code> state.</p>
|
|
*/
|
|
inline void SetStartedAt(Aws::Utils::DateTime&& value) { m_startedAt = std::move(value); }
|
|
|
|
/**
|
|
* <p>The epoch time when Amazon Machine Learning marked the <code>MLModel</code>
|
|
* as <code>INPROGRESS</code>. <code>StartedAt</code> isn't available if the
|
|
* <code>MLModel</code> is in the <code>PENDING</code> state.</p>
|
|
*/
|
|
inline GetMLModelResult& WithStartedAt(const Aws::Utils::DateTime& value) { SetStartedAt(value); return *this;}
|
|
|
|
/**
|
|
* <p>The epoch time when Amazon Machine Learning marked the <code>MLModel</code>
|
|
* as <code>INPROGRESS</code>. <code>StartedAt</code> isn't available if the
|
|
* <code>MLModel</code> is in the <code>PENDING</code> state.</p>
|
|
*/
|
|
inline GetMLModelResult& WithStartedAt(Aws::Utils::DateTime&& value) { SetStartedAt(std::move(value)); return *this;}
|
|
|
|
|
|
/**
|
|
* <p>The recipe to use when training the <code>MLModel</code>. The
|
|
* <code>Recipe</code> provides detailed information about the observation data to
|
|
* use during training, and manipulations to perform on the observation data during
|
|
* training.</p> <title>Note</title> <p>This parameter is provided as part of
|
|
* the verbose format.</p>
|
|
*/
|
|
inline const Aws::String& GetRecipe() const{ return m_recipe; }
|
|
|
|
/**
|
|
* <p>The recipe to use when training the <code>MLModel</code>. The
|
|
* <code>Recipe</code> provides detailed information about the observation data to
|
|
* use during training, and manipulations to perform on the observation data during
|
|
* training.</p> <title>Note</title> <p>This parameter is provided as part of
|
|
* the verbose format.</p>
|
|
*/
|
|
inline void SetRecipe(const Aws::String& value) { m_recipe = value; }
|
|
|
|
/**
|
|
* <p>The recipe to use when training the <code>MLModel</code>. The
|
|
* <code>Recipe</code> provides detailed information about the observation data to
|
|
* use during training, and manipulations to perform on the observation data during
|
|
* training.</p> <title>Note</title> <p>This parameter is provided as part of
|
|
* the verbose format.</p>
|
|
*/
|
|
inline void SetRecipe(Aws::String&& value) { m_recipe = std::move(value); }
|
|
|
|
/**
|
|
* <p>The recipe to use when training the <code>MLModel</code>. The
|
|
* <code>Recipe</code> provides detailed information about the observation data to
|
|
* use during training, and manipulations to perform on the observation data during
|
|
* training.</p> <title>Note</title> <p>This parameter is provided as part of
|
|
* the verbose format.</p>
|
|
*/
|
|
inline void SetRecipe(const char* value) { m_recipe.assign(value); }
|
|
|
|
/**
|
|
* <p>The recipe to use when training the <code>MLModel</code>. The
|
|
* <code>Recipe</code> provides detailed information about the observation data to
|
|
* use during training, and manipulations to perform on the observation data during
|
|
* training.</p> <title>Note</title> <p>This parameter is provided as part of
|
|
* the verbose format.</p>
|
|
*/
|
|
inline GetMLModelResult& WithRecipe(const Aws::String& value) { SetRecipe(value); return *this;}
|
|
|
|
/**
|
|
* <p>The recipe to use when training the <code>MLModel</code>. The
|
|
* <code>Recipe</code> provides detailed information about the observation data to
|
|
* use during training, and manipulations to perform on the observation data during
|
|
* training.</p> <title>Note</title> <p>This parameter is provided as part of
|
|
* the verbose format.</p>
|
|
*/
|
|
inline GetMLModelResult& WithRecipe(Aws::String&& value) { SetRecipe(std::move(value)); return *this;}
|
|
|
|
/**
|
|
* <p>The recipe to use when training the <code>MLModel</code>. The
|
|
* <code>Recipe</code> provides detailed information about the observation data to
|
|
* use during training, and manipulations to perform on the observation data during
|
|
* training.</p> <title>Note</title> <p>This parameter is provided as part of
|
|
* the verbose format.</p>
|
|
*/
|
|
inline GetMLModelResult& WithRecipe(const char* value) { SetRecipe(value); return *this;}
|
|
|
|
|
|
/**
|
|
* <p>The schema used by all of the data files referenced by the
|
|
* <code>DataSource</code>.</p> <title>Note</title> <p>This parameter is
|
|
* provided as part of the verbose format.</p>
|
|
*/
|
|
inline const Aws::String& GetSchema() const{ return m_schema; }
|
|
|
|
/**
|
|
* <p>The schema used by all of the data files referenced by the
|
|
* <code>DataSource</code>.</p> <title>Note</title> <p>This parameter is
|
|
* provided as part of the verbose format.</p>
|
|
*/
|
|
inline void SetSchema(const Aws::String& value) { m_schema = value; }
|
|
|
|
/**
|
|
* <p>The schema used by all of the data files referenced by the
|
|
* <code>DataSource</code>.</p> <title>Note</title> <p>This parameter is
|
|
* provided as part of the verbose format.</p>
|
|
*/
|
|
inline void SetSchema(Aws::String&& value) { m_schema = std::move(value); }
|
|
|
|
/**
|
|
* <p>The schema used by all of the data files referenced by the
|
|
* <code>DataSource</code>.</p> <title>Note</title> <p>This parameter is
|
|
* provided as part of the verbose format.</p>
|
|
*/
|
|
inline void SetSchema(const char* value) { m_schema.assign(value); }
|
|
|
|
/**
|
|
* <p>The schema used by all of the data files referenced by the
|
|
* <code>DataSource</code>.</p> <title>Note</title> <p>This parameter is
|
|
* provided as part of the verbose format.</p>
|
|
*/
|
|
inline GetMLModelResult& WithSchema(const Aws::String& value) { SetSchema(value); return *this;}
|
|
|
|
/**
|
|
* <p>The schema used by all of the data files referenced by the
|
|
* <code>DataSource</code>.</p> <title>Note</title> <p>This parameter is
|
|
* provided as part of the verbose format.</p>
|
|
*/
|
|
inline GetMLModelResult& WithSchema(Aws::String&& value) { SetSchema(std::move(value)); return *this;}
|
|
|
|
/**
|
|
* <p>The schema used by all of the data files referenced by the
|
|
* <code>DataSource</code>.</p> <title>Note</title> <p>This parameter is
|
|
* provided as part of the verbose format.</p>
|
|
*/
|
|
inline GetMLModelResult& WithSchema(const char* value) { SetSchema(value); return *this;}
|
|
|
|
private:
|
|
|
|
Aws::String m_mLModelId;
|
|
|
|
Aws::String m_trainingDataSourceId;
|
|
|
|
Aws::String m_createdByIamUser;
|
|
|
|
Aws::Utils::DateTime m_createdAt;
|
|
|
|
Aws::Utils::DateTime m_lastUpdatedAt;
|
|
|
|
Aws::String m_name;
|
|
|
|
EntityStatus m_status;
|
|
|
|
long long m_sizeInBytes;
|
|
|
|
RealtimeEndpointInfo m_endpointInfo;
|
|
|
|
Aws::Map<Aws::String, Aws::String> m_trainingParameters;
|
|
|
|
Aws::String m_inputDataLocationS3;
|
|
|
|
MLModelType m_mLModelType;
|
|
|
|
double m_scoreThreshold;
|
|
|
|
Aws::Utils::DateTime m_scoreThresholdLastUpdatedAt;
|
|
|
|
Aws::String m_logUri;
|
|
|
|
Aws::String m_message;
|
|
|
|
long long m_computeTime;
|
|
|
|
Aws::Utils::DateTime m_finishedAt;
|
|
|
|
Aws::Utils::DateTime m_startedAt;
|
|
|
|
Aws::String m_recipe;
|
|
|
|
Aws::String m_schema;
|
|
};
|
|
|
|
} // namespace Model
|
|
} // namespace MachineLearning
|
|
} // namespace Aws
|