845 lines
51 KiB
C
845 lines
51 KiB
C
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/**
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* Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
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* SPDX-License-Identifier: Apache-2.0.
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*/
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#pragma once
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#include <aws/machinelearning/MachineLearning_EXPORTS.h>
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#include <aws/machinelearning/MachineLearningRequest.h>
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#include <aws/core/utils/memory/stl/AWSString.h>
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#include <aws/machinelearning/model/MLModelType.h>
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#include <aws/core/utils/memory/stl/AWSMap.h>
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#include <utility>
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namespace Aws
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{
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namespace MachineLearning
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{
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namespace Model
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{
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/**
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*/
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class AWS_MACHINELEARNING_API CreateMLModelRequest : public MachineLearningRequest
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{
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public:
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CreateMLModelRequest();
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// Service request name is the Operation name which will send this request out,
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// each operation should has unique request name, so that we can get operation's name from this request.
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// Note: this is not true for response, multiple operations may have the same response name,
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// so we can not get operation's name from response.
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inline virtual const char* GetServiceRequestName() const override { return "CreateMLModel"; }
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Aws::String SerializePayload() const override;
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Aws::Http::HeaderValueCollection GetRequestSpecificHeaders() const override;
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/**
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* <p>A user-supplied ID that uniquely identifies the <code>MLModel</code>.</p>
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*/
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inline const Aws::String& GetMLModelId() const{ return m_mLModelId; }
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/**
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* <p>A user-supplied ID that uniquely identifies the <code>MLModel</code>.</p>
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*/
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inline bool MLModelIdHasBeenSet() const { return m_mLModelIdHasBeenSet; }
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/**
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* <p>A user-supplied ID that uniquely identifies the <code>MLModel</code>.</p>
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*/
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inline void SetMLModelId(const Aws::String& value) { m_mLModelIdHasBeenSet = true; m_mLModelId = value; }
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/**
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* <p>A user-supplied ID that uniquely identifies the <code>MLModel</code>.</p>
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*/
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inline void SetMLModelId(Aws::String&& value) { m_mLModelIdHasBeenSet = true; m_mLModelId = std::move(value); }
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/**
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* <p>A user-supplied ID that uniquely identifies the <code>MLModel</code>.</p>
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*/
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inline void SetMLModelId(const char* value) { m_mLModelIdHasBeenSet = true; m_mLModelId.assign(value); }
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/**
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* <p>A user-supplied ID that uniquely identifies the <code>MLModel</code>.</p>
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*/
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inline CreateMLModelRequest& WithMLModelId(const Aws::String& value) { SetMLModelId(value); return *this;}
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/**
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* <p>A user-supplied ID that uniquely identifies the <code>MLModel</code>.</p>
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*/
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inline CreateMLModelRequest& WithMLModelId(Aws::String&& value) { SetMLModelId(std::move(value)); return *this;}
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/**
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* <p>A user-supplied ID that uniquely identifies the <code>MLModel</code>.</p>
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*/
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inline CreateMLModelRequest& WithMLModelId(const char* value) { SetMLModelId(value); return *this;}
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/**
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* <p>A user-supplied name or description of the <code>MLModel</code>.</p>
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*/
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inline const Aws::String& GetMLModelName() const{ return m_mLModelName; }
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/**
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* <p>A user-supplied name or description of the <code>MLModel</code>.</p>
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*/
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inline bool MLModelNameHasBeenSet() const { return m_mLModelNameHasBeenSet; }
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/**
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* <p>A user-supplied name or description of the <code>MLModel</code>.</p>
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*/
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inline void SetMLModelName(const Aws::String& value) { m_mLModelNameHasBeenSet = true; m_mLModelName = value; }
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/**
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* <p>A user-supplied name or description of the <code>MLModel</code>.</p>
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*/
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inline void SetMLModelName(Aws::String&& value) { m_mLModelNameHasBeenSet = true; m_mLModelName = std::move(value); }
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/**
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* <p>A user-supplied name or description of the <code>MLModel</code>.</p>
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*/
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inline void SetMLModelName(const char* value) { m_mLModelNameHasBeenSet = true; m_mLModelName.assign(value); }
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/**
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* <p>A user-supplied name or description of the <code>MLModel</code>.</p>
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*/
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inline CreateMLModelRequest& WithMLModelName(const Aws::String& value) { SetMLModelName(value); return *this;}
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/**
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* <p>A user-supplied name or description of the <code>MLModel</code>.</p>
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*/
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inline CreateMLModelRequest& WithMLModelName(Aws::String&& value) { SetMLModelName(std::move(value)); return *this;}
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/**
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* <p>A user-supplied name or description of the <code>MLModel</code>.</p>
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*/
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inline CreateMLModelRequest& WithMLModelName(const char* value) { SetMLModelName(value); return *this;}
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/**
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* <p>The category of supervised learning that this <code>MLModel</code> will
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* address. Choose from the following types:</p> <ul> <li>Choose
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* <code>REGRESSION</code> if the <code>MLModel</code> will be used to predict a
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* numeric value.</li> <li>Choose <code>BINARY</code> if the <code>MLModel</code>
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* result has two possible values.</li> <li>Choose <code>MULTICLASS</code> if the
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* <code>MLModel</code> result has a limited number of values. </li> </ul> <p> For
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* more information, see the <a
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* href="http://docs.aws.amazon.com/machine-learning/latest/dg">Amazon Machine
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* Learning Developer Guide</a>.</p>
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*/
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inline const MLModelType& GetMLModelType() const{ return m_mLModelType; }
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/**
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* <p>The category of supervised learning that this <code>MLModel</code> will
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* address. Choose from the following types:</p> <ul> <li>Choose
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* <code>REGRESSION</code> if the <code>MLModel</code> will be used to predict a
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* numeric value.</li> <li>Choose <code>BINARY</code> if the <code>MLModel</code>
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* result has two possible values.</li> <li>Choose <code>MULTICLASS</code> if the
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* <code>MLModel</code> result has a limited number of values. </li> </ul> <p> For
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* more information, see the <a
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* href="http://docs.aws.amazon.com/machine-learning/latest/dg">Amazon Machine
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* Learning Developer Guide</a>.</p>
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*/
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inline bool MLModelTypeHasBeenSet() const { return m_mLModelTypeHasBeenSet; }
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/**
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* <p>The category of supervised learning that this <code>MLModel</code> will
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* address. Choose from the following types:</p> <ul> <li>Choose
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* <code>REGRESSION</code> if the <code>MLModel</code> will be used to predict a
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* numeric value.</li> <li>Choose <code>BINARY</code> if the <code>MLModel</code>
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* result has two possible values.</li> <li>Choose <code>MULTICLASS</code> if the
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* <code>MLModel</code> result has a limited number of values. </li> </ul> <p> For
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* more information, see the <a
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* href="http://docs.aws.amazon.com/machine-learning/latest/dg">Amazon Machine
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* Learning Developer Guide</a>.</p>
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*/
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inline void SetMLModelType(const MLModelType& value) { m_mLModelTypeHasBeenSet = true; m_mLModelType = value; }
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/**
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* <p>The category of supervised learning that this <code>MLModel</code> will
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* address. Choose from the following types:</p> <ul> <li>Choose
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* <code>REGRESSION</code> if the <code>MLModel</code> will be used to predict a
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* numeric value.</li> <li>Choose <code>BINARY</code> if the <code>MLModel</code>
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* result has two possible values.</li> <li>Choose <code>MULTICLASS</code> if the
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* <code>MLModel</code> result has a limited number of values. </li> </ul> <p> For
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* more information, see the <a
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* href="http://docs.aws.amazon.com/machine-learning/latest/dg">Amazon Machine
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* Learning Developer Guide</a>.</p>
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*/
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inline void SetMLModelType(MLModelType&& value) { m_mLModelTypeHasBeenSet = true; m_mLModelType = std::move(value); }
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/**
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* <p>The category of supervised learning that this <code>MLModel</code> will
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* address. Choose from the following types:</p> <ul> <li>Choose
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* <code>REGRESSION</code> if the <code>MLModel</code> will be used to predict a
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* numeric value.</li> <li>Choose <code>BINARY</code> if the <code>MLModel</code>
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* result has two possible values.</li> <li>Choose <code>MULTICLASS</code> if the
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* <code>MLModel</code> result has a limited number of values. </li> </ul> <p> For
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* more information, see the <a
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* href="http://docs.aws.amazon.com/machine-learning/latest/dg">Amazon Machine
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* Learning Developer Guide</a>.</p>
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*/
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inline CreateMLModelRequest& WithMLModelType(const MLModelType& value) { SetMLModelType(value); return *this;}
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/**
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* <p>The category of supervised learning that this <code>MLModel</code> will
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* address. Choose from the following types:</p> <ul> <li>Choose
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* <code>REGRESSION</code> if the <code>MLModel</code> will be used to predict a
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* numeric value.</li> <li>Choose <code>BINARY</code> if the <code>MLModel</code>
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* result has two possible values.</li> <li>Choose <code>MULTICLASS</code> if the
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* <code>MLModel</code> result has a limited number of values. </li> </ul> <p> For
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* more information, see the <a
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* href="http://docs.aws.amazon.com/machine-learning/latest/dg">Amazon Machine
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* Learning Developer Guide</a>.</p>
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*/
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inline CreateMLModelRequest& WithMLModelType(MLModelType&& value) { SetMLModelType(std::move(value)); return *this;}
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/**
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* <p>A list of the training parameters in the <code>MLModel</code>. The list is
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* implemented as a map of key-value pairs.</p> <p>The following is the current set
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* of training parameters: </p> <ul> <li> <p><code>sgd.maxMLModelSizeInBytes</code>
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* - The maximum allowed size of the model. Depending on the input data, the size
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* of the model might affect its performance.</p> <p> The value is an integer that
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* ranges from <code>100000</code> to <code>2147483648</code>. The default value is
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* <code>33554432</code>.</p> </li> <li><p><code>sgd.maxPasses</code> - The number
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* of times that the training process traverses the observations to build the
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* <code>MLModel</code>. The value is an integer that ranges from <code>1</code> to
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* <code>10000</code>. The default value is <code>10</code>.</p></li> <li>
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* <p><code>sgd.shuffleType</code> - Whether Amazon ML shuffles the training data.
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* Shuffling the data improves a model's ability to find the optimal solution for a
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* variety of data types. The valid values are <code>auto</code> and
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* <code>none</code>. The default value is <code>none</code>. We <?oxy_insert_start
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* author="laurama" timestamp="20160329T131121-0700">strongly recommend that you
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* shuffle your data.<?oxy_insert_end></p> </li> <li>
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* <p><code>sgd.l1RegularizationAmount</code> - The coefficient regularization L1
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* norm. It controls overfitting the data by penalizing large coefficients. This
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* tends to drive coefficients to zero, resulting in a sparse feature set. If you
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* use this parameter, start by specifying a small value, such as
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* <code>1.0E-08</code>.</p> <p>The value is a double that ranges from
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* <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not use L1
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* normalization. This parameter can't be used when <code>L2</code> is specified.
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* Use this parameter sparingly.</p> </li> <li>
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* <p><code>sgd.l2RegularizationAmount</code> - The coefficient regularization L2
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* norm. It controls overfitting the data by penalizing large coefficients. This
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* tends to drive coefficients to small, nonzero values. If you use this parameter,
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* start by specifying a small value, such as <code>1.0E-08</code>.</p> <p>The
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* value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>.
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* The default is to not use L2 normalization. This parameter can't be used when
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* <code>L1</code> is specified. Use this parameter sparingly.</p> </li> </ul>
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*/
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inline const Aws::Map<Aws::String, Aws::String>& GetParameters() const{ return m_parameters; }
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/**
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* <p>A list of the training parameters in the <code>MLModel</code>. The list is
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* implemented as a map of key-value pairs.</p> <p>The following is the current set
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* of training parameters: </p> <ul> <li> <p><code>sgd.maxMLModelSizeInBytes</code>
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* - The maximum allowed size of the model. Depending on the input data, the size
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* of the model might affect its performance.</p> <p> The value is an integer that
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* ranges from <code>100000</code> to <code>2147483648</code>. The default value is
|
|||
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* <code>33554432</code>.</p> </li> <li><p><code>sgd.maxPasses</code> - The number
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|||
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* of times that the training process traverses the observations to build the
|
|||
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* <code>MLModel</code>. The value is an integer that ranges from <code>1</code> to
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|||
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* <code>10000</code>. The default value is <code>10</code>.</p></li> <li>
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* <p><code>sgd.shuffleType</code> - Whether Amazon ML shuffles the training data.
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|||
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* Shuffling the data improves a model's ability to find the optimal solution for a
|
|||
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* variety of data types. The valid values are <code>auto</code> and
|
|||
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* <code>none</code>. The default value is <code>none</code>. We <?oxy_insert_start
|
|||
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* author="laurama" timestamp="20160329T131121-0700">strongly recommend that you
|
|||
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* shuffle your data.<?oxy_insert_end></p> </li> <li>
|
|||
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* <p><code>sgd.l1RegularizationAmount</code> - The coefficient regularization L1
|
|||
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* norm. It controls overfitting the data by penalizing large coefficients. This
|
|||
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* tends to drive coefficients to zero, resulting in a sparse feature set. If you
|
|||
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* use this parameter, start by specifying a small value, such as
|
|||
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* <code>1.0E-08</code>.</p> <p>The value is a double that ranges from
|
|||
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* <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not use L1
|
|||
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* normalization. This parameter can't be used when <code>L2</code> is specified.
|
|||
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* Use this parameter sparingly.</p> </li> <li>
|
|||
|
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* <p><code>sgd.l2RegularizationAmount</code> - The coefficient regularization L2
|
|||
|
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* norm. It controls overfitting the data by penalizing large coefficients. This
|
|||
|
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* tends to drive coefficients to small, nonzero values. If you use this parameter,
|
|||
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* start by specifying a small value, such as <code>1.0E-08</code>.</p> <p>The
|
|||
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* value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>.
|
|||
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* The default is to not use L2 normalization. This parameter can't be used when
|
|||
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* <code>L1</code> is specified. Use this parameter sparingly.</p> </li> </ul>
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|||
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*/
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inline bool ParametersHasBeenSet() const { return m_parametersHasBeenSet; }
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|||
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|||
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/**
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|||
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* <p>A list of the training parameters in the <code>MLModel</code>. The list is
|
|||
|
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* implemented as a map of key-value pairs.</p> <p>The following is the current set
|
|||
|
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* 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
|
|||
|
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* of the model might affect its performance.</p> <p> The value is an integer that
|
|||
|
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* 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 the 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 <?oxy_insert_start
|
|||
|
|
* author="laurama" timestamp="20160329T131121-0700">strongly recommend that you
|
|||
|
|
* shuffle your data.<?oxy_insert_end></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 SetParameters(const Aws::Map<Aws::String, Aws::String>& value) { m_parametersHasBeenSet = true; m_parameters = 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 the 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 <?oxy_insert_start
|
|||
|
|
* author="laurama" timestamp="20160329T131121-0700">strongly recommend that you
|
|||
|
|
* shuffle your data.<?oxy_insert_end></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 SetParameters(Aws::Map<Aws::String, Aws::String>&& value) { m_parametersHasBeenSet = true; m_parameters = 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 the 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 <?oxy_insert_start
|
|||
|
|
* author="laurama" timestamp="20160329T131121-0700">strongly recommend that you
|
|||
|
|
* shuffle your data.<?oxy_insert_end></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 CreateMLModelRequest& WithParameters(const Aws::Map<Aws::String, Aws::String>& value) { SetParameters(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 the 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 <?oxy_insert_start
|
|||
|
|
* author="laurama" timestamp="20160329T131121-0700">strongly recommend that you
|
|||
|
|
* shuffle your data.<?oxy_insert_end></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 CreateMLModelRequest& WithParameters(Aws::Map<Aws::String, Aws::String>&& value) { SetParameters(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 the 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 <?oxy_insert_start
|
|||
|
|
* author="laurama" timestamp="20160329T131121-0700">strongly recommend that you
|
|||
|
|
* shuffle your data.<?oxy_insert_end></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 CreateMLModelRequest& AddParameters(const Aws::String& key, const Aws::String& value) { m_parametersHasBeenSet = true; m_parameters.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 the 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 <?oxy_insert_start
|
|||
|
|
* author="laurama" timestamp="20160329T131121-0700">strongly recommend that you
|
|||
|
|
* shuffle your data.<?oxy_insert_end></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 CreateMLModelRequest& AddParameters(Aws::String&& key, const Aws::String& value) { m_parametersHasBeenSet = true; m_parameters.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 the 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 <?oxy_insert_start
|
|||
|
|
* author="laurama" timestamp="20160329T131121-0700">strongly recommend that you
|
|||
|
|
* shuffle your data.<?oxy_insert_end></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 CreateMLModelRequest& AddParameters(const Aws::String& key, Aws::String&& value) { m_parametersHasBeenSet = true; m_parameters.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 the 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 <?oxy_insert_start
|
|||
|
|
* author="laurama" timestamp="20160329T131121-0700">strongly recommend that you
|
|||
|
|
* shuffle your data.<?oxy_insert_end></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 CreateMLModelRequest& AddParameters(Aws::String&& key, Aws::String&& value) { m_parametersHasBeenSet = true; m_parameters.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 the 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 <?oxy_insert_start
|
|||
|
|
* author="laurama" timestamp="20160329T131121-0700">strongly recommend that you
|
|||
|
|
* shuffle your data.<?oxy_insert_end></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 CreateMLModelRequest& AddParameters(const char* key, Aws::String&& value) { m_parametersHasBeenSet = true; m_parameters.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 the 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 <?oxy_insert_start
|
|||
|
|
* author="laurama" timestamp="20160329T131121-0700">strongly recommend that you
|
|||
|
|
* shuffle your data.<?oxy_insert_end></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 CreateMLModelRequest& AddParameters(Aws::String&& key, const char* value) { m_parametersHasBeenSet = true; m_parameters.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 the 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 <?oxy_insert_start
|
|||
|
|
* author="laurama" timestamp="20160329T131121-0700">strongly recommend that you
|
|||
|
|
* shuffle your data.<?oxy_insert_end></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 CreateMLModelRequest& AddParameters(const char* key, const char* value) { m_parametersHasBeenSet = true; m_parameters.emplace(key, value); return *this; }
|
|||
|
|
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* <p>The <code>DataSource</code> that points to the training data.</p>
|
|||
|
|
*/
|
|||
|
|
inline const Aws::String& GetTrainingDataSourceId() const{ return m_trainingDataSourceId; }
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* <p>The <code>DataSource</code> that points to the training data.</p>
|
|||
|
|
*/
|
|||
|
|
inline bool TrainingDataSourceIdHasBeenSet() const { return m_trainingDataSourceIdHasBeenSet; }
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* <p>The <code>DataSource</code> that points to the training data.</p>
|
|||
|
|
*/
|
|||
|
|
inline void SetTrainingDataSourceId(const Aws::String& value) { m_trainingDataSourceIdHasBeenSet = true; m_trainingDataSourceId = value; }
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* <p>The <code>DataSource</code> that points to the training data.</p>
|
|||
|
|
*/
|
|||
|
|
inline void SetTrainingDataSourceId(Aws::String&& value) { m_trainingDataSourceIdHasBeenSet = true; m_trainingDataSourceId = std::move(value); }
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* <p>The <code>DataSource</code> that points to the training data.</p>
|
|||
|
|
*/
|
|||
|
|
inline void SetTrainingDataSourceId(const char* value) { m_trainingDataSourceIdHasBeenSet = true; m_trainingDataSourceId.assign(value); }
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* <p>The <code>DataSource</code> that points to the training data.</p>
|
|||
|
|
*/
|
|||
|
|
inline CreateMLModelRequest& WithTrainingDataSourceId(const Aws::String& value) { SetTrainingDataSourceId(value); return *this;}
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* <p>The <code>DataSource</code> that points to the training data.</p>
|
|||
|
|
*/
|
|||
|
|
inline CreateMLModelRequest& WithTrainingDataSourceId(Aws::String&& value) { SetTrainingDataSourceId(std::move(value)); return *this;}
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* <p>The <code>DataSource</code> that points to the training data.</p>
|
|||
|
|
*/
|
|||
|
|
inline CreateMLModelRequest& WithTrainingDataSourceId(const char* value) { SetTrainingDataSourceId(value); return *this;}
|
|||
|
|
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* <p>The data recipe for creating the <code>MLModel</code>. You must specify
|
|||
|
|
* either the recipe or its URI. If you don't specify a recipe or its URI, Amazon
|
|||
|
|
* ML creates a default.</p>
|
|||
|
|
*/
|
|||
|
|
inline const Aws::String& GetRecipe() const{ return m_recipe; }
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* <p>The data recipe for creating the <code>MLModel</code>. You must specify
|
|||
|
|
* either the recipe or its URI. If you don't specify a recipe or its URI, Amazon
|
|||
|
|
* ML creates a default.</p>
|
|||
|
|
*/
|
|||
|
|
inline bool RecipeHasBeenSet() const { return m_recipeHasBeenSet; }
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* <p>The data recipe for creating the <code>MLModel</code>. You must specify
|
|||
|
|
* either the recipe or its URI. If you don't specify a recipe or its URI, Amazon
|
|||
|
|
* ML creates a default.</p>
|
|||
|
|
*/
|
|||
|
|
inline void SetRecipe(const Aws::String& value) { m_recipeHasBeenSet = true; m_recipe = value; }
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* <p>The data recipe for creating the <code>MLModel</code>. You must specify
|
|||
|
|
* either the recipe or its URI. If you don't specify a recipe or its URI, Amazon
|
|||
|
|
* ML creates a default.</p>
|
|||
|
|
*/
|
|||
|
|
inline void SetRecipe(Aws::String&& value) { m_recipeHasBeenSet = true; m_recipe = std::move(value); }
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* <p>The data recipe for creating the <code>MLModel</code>. You must specify
|
|||
|
|
* either the recipe or its URI. If you don't specify a recipe or its URI, Amazon
|
|||
|
|
* ML creates a default.</p>
|
|||
|
|
*/
|
|||
|
|
inline void SetRecipe(const char* value) { m_recipeHasBeenSet = true; m_recipe.assign(value); }
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* <p>The data recipe for creating the <code>MLModel</code>. You must specify
|
|||
|
|
* either the recipe or its URI. If you don't specify a recipe or its URI, Amazon
|
|||
|
|
* ML creates a default.</p>
|
|||
|
|
*/
|
|||
|
|
inline CreateMLModelRequest& WithRecipe(const Aws::String& value) { SetRecipe(value); return *this;}
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* <p>The data recipe for creating the <code>MLModel</code>. You must specify
|
|||
|
|
* either the recipe or its URI. If you don't specify a recipe or its URI, Amazon
|
|||
|
|
* ML creates a default.</p>
|
|||
|
|
*/
|
|||
|
|
inline CreateMLModelRequest& WithRecipe(Aws::String&& value) { SetRecipe(std::move(value)); return *this;}
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* <p>The data recipe for creating the <code>MLModel</code>. You must specify
|
|||
|
|
* either the recipe or its URI. If you don't specify a recipe or its URI, Amazon
|
|||
|
|
* ML creates a default.</p>
|
|||
|
|
*/
|
|||
|
|
inline CreateMLModelRequest& WithRecipe(const char* value) { SetRecipe(value); return *this;}
|
|||
|
|
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* <p>The Amazon Simple Storage Service (Amazon S3) location and file name that
|
|||
|
|
* contains the <code>MLModel</code> 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.</p>
|
|||
|
|
*/
|
|||
|
|
inline const Aws::String& GetRecipeUri() const{ return m_recipeUri; }
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* <p>The Amazon Simple Storage Service (Amazon S3) location and file name that
|
|||
|
|
* contains the <code>MLModel</code> 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.</p>
|
|||
|
|
*/
|
|||
|
|
inline bool RecipeUriHasBeenSet() const { return m_recipeUriHasBeenSet; }
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* <p>The Amazon Simple Storage Service (Amazon S3) location and file name that
|
|||
|
|
* contains the <code>MLModel</code> 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.</p>
|
|||
|
|
*/
|
|||
|
|
inline void SetRecipeUri(const Aws::String& value) { m_recipeUriHasBeenSet = true; m_recipeUri = value; }
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* <p>The Amazon Simple Storage Service (Amazon S3) location and file name that
|
|||
|
|
* contains the <code>MLModel</code> 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.</p>
|
|||
|
|
*/
|
|||
|
|
inline void SetRecipeUri(Aws::String&& value) { m_recipeUriHasBeenSet = true; m_recipeUri = std::move(value); }
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* <p>The Amazon Simple Storage Service (Amazon S3) location and file name that
|
|||
|
|
* contains the <code>MLModel</code> 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.</p>
|
|||
|
|
*/
|
|||
|
|
inline void SetRecipeUri(const char* value) { m_recipeUriHasBeenSet = true; m_recipeUri.assign(value); }
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* <p>The Amazon Simple Storage Service (Amazon S3) location and file name that
|
|||
|
|
* contains the <code>MLModel</code> 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.</p>
|
|||
|
|
*/
|
|||
|
|
inline CreateMLModelRequest& WithRecipeUri(const Aws::String& value) { SetRecipeUri(value); return *this;}
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* <p>The Amazon Simple Storage Service (Amazon S3) location and file name that
|
|||
|
|
* contains the <code>MLModel</code> 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.</p>
|
|||
|
|
*/
|
|||
|
|
inline CreateMLModelRequest& WithRecipeUri(Aws::String&& value) { SetRecipeUri(std::move(value)); return *this;}
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* <p>The Amazon Simple Storage Service (Amazon S3) location and file name that
|
|||
|
|
* contains the <code>MLModel</code> 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.</p>
|
|||
|
|
*/
|
|||
|
|
inline CreateMLModelRequest& WithRecipeUri(const char* value) { SetRecipeUri(value); return *this;}
|
|||
|
|
|
|||
|
|
private:
|
|||
|
|
|
|||
|
|
Aws::String m_mLModelId;
|
|||
|
|
bool m_mLModelIdHasBeenSet;
|
|||
|
|
|
|||
|
|
Aws::String m_mLModelName;
|
|||
|
|
bool m_mLModelNameHasBeenSet;
|
|||
|
|
|
|||
|
|
MLModelType m_mLModelType;
|
|||
|
|
bool m_mLModelTypeHasBeenSet;
|
|||
|
|
|
|||
|
|
Aws::Map<Aws::String, Aws::String> m_parameters;
|
|||
|
|
bool m_parametersHasBeenSet;
|
|||
|
|
|
|||
|
|
Aws::String m_trainingDataSourceId;
|
|||
|
|
bool m_trainingDataSourceIdHasBeenSet;
|
|||
|
|
|
|||
|
|
Aws::String m_recipe;
|
|||
|
|
bool m_recipeHasBeenSet;
|
|||
|
|
|
|||
|
|
Aws::String m_recipeUri;
|
|||
|
|
bool m_recipeUriHasBeenSet;
|
|||
|
|
};
|
|||
|
|
|
|||
|
|
} // namespace Model
|
|||
|
|
} // namespace MachineLearning
|
|||
|
|
} // namespace Aws
|