pxz-hos-client-cpp-module/support/aws-sdk-cpp-master/aws-cpp-sdk-sagemaker/include/aws/sagemaker/model/ContinuousParameterRange.h

/**
 * Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
 * SPDX-License-Identifier: Apache-2.0.
 */

#pragma once
#include <aws/sagemaker/SageMaker_EXPORTS.h>
#include <aws/core/utils/memory/stl/AWSString.h>
#include <aws/sagemaker/model/HyperParameterScalingType.h>
#include <utility>

namespace Aws
{
namespace Utils
{
namespace Json
{
  class JsonValue;
  class JsonView;
} // namespace Json
} // namespace Utils
namespace SageMaker
{
namespace Model
{

  /**
   * <p>A list of continuous hyperparameters to tune.</p><p><h3>See Also:</h3>   <a
   * href="http://docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/ContinuousParameterRange">AWS
   * API Reference</a></p>
   */
  class AWS_SAGEMAKER_API ContinuousParameterRange
  {
  public:
    ContinuousParameterRange();
    ContinuousParameterRange(Aws::Utils::Json::JsonView jsonValue);
    ContinuousParameterRange& operator=(Aws::Utils::Json::JsonView jsonValue);
    Aws::Utils::Json::JsonValue Jsonize() const;


    /**
     * <p>The name of the continuous hyperparameter to tune.</p>
     */
    inline const Aws::String& GetName() const{ return m_name; }

    /**
     * <p>The name of the continuous hyperparameter to tune.</p>
     */
    inline bool NameHasBeenSet() const { return m_nameHasBeenSet; }

    /**
     * <p>The name of the continuous hyperparameter to tune.</p>
     */
    inline void SetName(const Aws::String& value) { m_nameHasBeenSet = true; m_name = value; }

    /**
     * <p>The name of the continuous hyperparameter to tune.</p>
     */
    inline void SetName(Aws::String&& value) { m_nameHasBeenSet = true; m_name = std::move(value); }

    /**
     * <p>The name of the continuous hyperparameter to tune.</p>
     */
    inline void SetName(const char* value) { m_nameHasBeenSet = true; m_name.assign(value); }

    /**
     * <p>The name of the continuous hyperparameter to tune.</p>
     */
    inline ContinuousParameterRange& WithName(const Aws::String& value) { SetName(value); return *this;}

    /**
     * <p>The name of the continuous hyperparameter to tune.</p>
     */
    inline ContinuousParameterRange& WithName(Aws::String&& value) { SetName(std::move(value)); return *this;}

    /**
     * <p>The name of the continuous hyperparameter to tune.</p>
     */
    inline ContinuousParameterRange& WithName(const char* value) { SetName(value); return *this;}


    /**
     * <p>The minimum value for the hyperparameter. The tuning job uses floating-point
     * values between this value and <code>MaxValue</code>for tuning.</p>
     */
    inline const Aws::String& GetMinValue() const{ return m_minValue; }

    /**
     * <p>The minimum value for the hyperparameter. The tuning job uses floating-point
     * values between this value and <code>MaxValue</code>for tuning.</p>
     */
    inline bool MinValueHasBeenSet() const { return m_minValueHasBeenSet; }

    /**
     * <p>The minimum value for the hyperparameter. The tuning job uses floating-point
     * values between this value and <code>MaxValue</code>for tuning.</p>
     */
    inline void SetMinValue(const Aws::String& value) { m_minValueHasBeenSet = true; m_minValue = value; }

    /**
     * <p>The minimum value for the hyperparameter. The tuning job uses floating-point
     * values between this value and <code>MaxValue</code>for tuning.</p>
     */
    inline void SetMinValue(Aws::String&& value) { m_minValueHasBeenSet = true; m_minValue = std::move(value); }

    /**
     * <p>The minimum value for the hyperparameter. The tuning job uses floating-point
     * values between this value and <code>MaxValue</code>for tuning.</p>
     */
    inline void SetMinValue(const char* value) { m_minValueHasBeenSet = true; m_minValue.assign(value); }

    /**
     * <p>The minimum value for the hyperparameter. The tuning job uses floating-point
     * values between this value and <code>MaxValue</code>for tuning.</p>
     */
    inline ContinuousParameterRange& WithMinValue(const Aws::String& value) { SetMinValue(value); return *this;}

    /**
     * <p>The minimum value for the hyperparameter. The tuning job uses floating-point
     * values between this value and <code>MaxValue</code>for tuning.</p>
     */
    inline ContinuousParameterRange& WithMinValue(Aws::String&& value) { SetMinValue(std::move(value)); return *this;}

    /**
     * <p>The minimum value for the hyperparameter. The tuning job uses floating-point
     * values between this value and <code>MaxValue</code>for tuning.</p>
     */
    inline ContinuousParameterRange& WithMinValue(const char* value) { SetMinValue(value); return *this;}


    /**
     * <p>The maximum value for the hyperparameter. The tuning job uses floating-point
     * values between <code>MinValue</code> value and this value for tuning.</p>
     */
    inline const Aws::String& GetMaxValue() const{ return m_maxValue; }

    /**
     * <p>The maximum value for the hyperparameter. The tuning job uses floating-point
     * values between <code>MinValue</code> value and this value for tuning.</p>
     */
    inline bool MaxValueHasBeenSet() const { return m_maxValueHasBeenSet; }

    /**
     * <p>The maximum value for the hyperparameter. The tuning job uses floating-point
     * values between <code>MinValue</code> value and this value for tuning.</p>
     */
    inline void SetMaxValue(const Aws::String& value) { m_maxValueHasBeenSet = true; m_maxValue = value; }

    /**
     * <p>The maximum value for the hyperparameter. The tuning job uses floating-point
     * values between <code>MinValue</code> value and this value for tuning.</p>
     */
    inline void SetMaxValue(Aws::String&& value) { m_maxValueHasBeenSet = true; m_maxValue = std::move(value); }

    /**
     * <p>The maximum value for the hyperparameter. The tuning job uses floating-point
     * values between <code>MinValue</code> value and this value for tuning.</p>
     */
    inline void SetMaxValue(const char* value) { m_maxValueHasBeenSet = true; m_maxValue.assign(value); }

    /**
     * <p>The maximum value for the hyperparameter. The tuning job uses floating-point
     * values between <code>MinValue</code> value and this value for tuning.</p>
     */
    inline ContinuousParameterRange& WithMaxValue(const Aws::String& value) { SetMaxValue(value); return *this;}

    /**
     * <p>The maximum value for the hyperparameter. The tuning job uses floating-point
     * values between <code>MinValue</code> value and this value for tuning.</p>
     */
    inline ContinuousParameterRange& WithMaxValue(Aws::String&& value) { SetMaxValue(std::move(value)); return *this;}

    /**
     * <p>The maximum value for the hyperparameter. The tuning job uses floating-point
     * values between <code>MinValue</code> value and this value for tuning.</p>
     */
    inline ContinuousParameterRange& WithMaxValue(const char* value) { SetMaxValue(value); return *this;}


    /**
     * <p>The scale that hyperparameter tuning uses to search the hyperparameter range.
     * For information about choosing a hyperparameter scale, see <a
     * href="https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-define-ranges.html#scaling-type">Hyperparameter
     * Scaling</a>. One of the following values:</p> <dl> <dt>Auto</dt> <dd> <p>Amazon
     * SageMaker hyperparameter tuning chooses the best scale for the
     * hyperparameter.</p> </dd> <dt>Linear</dt> <dd> <p>Hyperparameter tuning searches
     * the values in the hyperparameter range by using a linear scale.</p> </dd>
     * <dt>Logarithmic</dt> <dd> <p>Hyperparameter tuning searches the values in the
     * hyperparameter range by using a logarithmic scale.</p> <p>Logarithmic scaling
     * works only for ranges that have only values greater than 0.</p> </dd>
     * <dt>ReverseLogarithmic</dt> <dd> <p>Hyperparameter tuning searches the values in
     * the hyperparameter range by using a reverse logarithmic scale.</p> <p>Reverse
     * logarithmic scaling works only for ranges that are entirely within the range
     * 0&lt;=x&lt;1.0.</p> </dd> </dl>
     */
    inline const HyperParameterScalingType& GetScalingType() const{ return m_scalingType; }

    /**
     * <p>The scale that hyperparameter tuning uses to search the hyperparameter range.
     * For information about choosing a hyperparameter scale, see <a
     * href="https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-define-ranges.html#scaling-type">Hyperparameter
     * Scaling</a>. One of the following values:</p> <dl> <dt>Auto</dt> <dd> <p>Amazon
     * SageMaker hyperparameter tuning chooses the best scale for the
     * hyperparameter.</p> </dd> <dt>Linear</dt> <dd> <p>Hyperparameter tuning searches
     * the values in the hyperparameter range by using a linear scale.</p> </dd>
     * <dt>Logarithmic</dt> <dd> <p>Hyperparameter tuning searches the values in the
     * hyperparameter range by using a logarithmic scale.</p> <p>Logarithmic scaling
     * works only for ranges that have only values greater than 0.</p> </dd>
     * <dt>ReverseLogarithmic</dt> <dd> <p>Hyperparameter tuning searches the values in
     * the hyperparameter range by using a reverse logarithmic scale.</p> <p>Reverse
     * logarithmic scaling works only for ranges that are entirely within the range
     * 0&lt;=x&lt;1.0.</p> </dd> </dl>
     */
    inline bool ScalingTypeHasBeenSet() const { return m_scalingTypeHasBeenSet; }

    /**
     * <p>The scale that hyperparameter tuning uses to search the hyperparameter range.
     * For information about choosing a hyperparameter scale, see <a
     * href="https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-define-ranges.html#scaling-type">Hyperparameter
     * Scaling</a>. One of the following values:</p> <dl> <dt>Auto</dt> <dd> <p>Amazon
     * SageMaker hyperparameter tuning chooses the best scale for the
     * hyperparameter.</p> </dd> <dt>Linear</dt> <dd> <p>Hyperparameter tuning searches
     * the values in the hyperparameter range by using a linear scale.</p> </dd>
     * <dt>Logarithmic</dt> <dd> <p>Hyperparameter tuning searches the values in the
     * hyperparameter range by using a logarithmic scale.</p> <p>Logarithmic scaling
     * works only for ranges that have only values greater than 0.</p> </dd>
     * <dt>ReverseLogarithmic</dt> <dd> <p>Hyperparameter tuning searches the values in
     * the hyperparameter range by using a reverse logarithmic scale.</p> <p>Reverse
     * logarithmic scaling works only for ranges that are entirely within the range
     * 0&lt;=x&lt;1.0.</p> </dd> </dl>
     */
    inline void SetScalingType(const HyperParameterScalingType& value) { m_scalingTypeHasBeenSet = true; m_scalingType = value; }

    /**
     * <p>The scale that hyperparameter tuning uses to search the hyperparameter range.
     * For information about choosing a hyperparameter scale, see <a
     * href="https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-define-ranges.html#scaling-type">Hyperparameter
     * Scaling</a>. One of the following values:</p> <dl> <dt>Auto</dt> <dd> <p>Amazon
     * SageMaker hyperparameter tuning chooses the best scale for the
     * hyperparameter.</p> </dd> <dt>Linear</dt> <dd> <p>Hyperparameter tuning searches
     * the values in the hyperparameter range by using a linear scale.</p> </dd>
     * <dt>Logarithmic</dt> <dd> <p>Hyperparameter tuning searches the values in the
     * hyperparameter range by using a logarithmic scale.</p> <p>Logarithmic scaling
     * works only for ranges that have only values greater than 0.</p> </dd>
     * <dt>ReverseLogarithmic</dt> <dd> <p>Hyperparameter tuning searches the values in
     * the hyperparameter range by using a reverse logarithmic scale.</p> <p>Reverse
     * logarithmic scaling works only for ranges that are entirely within the range
     * 0&lt;=x&lt;1.0.</p> </dd> </dl>
     */
    inline void SetScalingType(HyperParameterScalingType&& value) { m_scalingTypeHasBeenSet = true; m_scalingType = std::move(value); }

    /**
     * <p>The scale that hyperparameter tuning uses to search the hyperparameter range.
     * For information about choosing a hyperparameter scale, see <a
     * href="https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-define-ranges.html#scaling-type">Hyperparameter
     * Scaling</a>. One of the following values:</p> <dl> <dt>Auto</dt> <dd> <p>Amazon
     * SageMaker hyperparameter tuning chooses the best scale for the
     * hyperparameter.</p> </dd> <dt>Linear</dt> <dd> <p>Hyperparameter tuning searches
     * the values in the hyperparameter range by using a linear scale.</p> </dd>
     * <dt>Logarithmic</dt> <dd> <p>Hyperparameter tuning searches the values in the
     * hyperparameter range by using a logarithmic scale.</p> <p>Logarithmic scaling
     * works only for ranges that have only values greater than 0.</p> </dd>
     * <dt>ReverseLogarithmic</dt> <dd> <p>Hyperparameter tuning searches the values in
     * the hyperparameter range by using a reverse logarithmic scale.</p> <p>Reverse
     * logarithmic scaling works only for ranges that are entirely within the range
     * 0&lt;=x&lt;1.0.</p> </dd> </dl>
     */
    inline ContinuousParameterRange& WithScalingType(const HyperParameterScalingType& value) { SetScalingType(value); return *this;}

    /**
     * <p>The scale that hyperparameter tuning uses to search the hyperparameter range.
     * For information about choosing a hyperparameter scale, see <a
     * href="https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-define-ranges.html#scaling-type">Hyperparameter
     * Scaling</a>. One of the following values:</p> <dl> <dt>Auto</dt> <dd> <p>Amazon
     * SageMaker hyperparameter tuning chooses the best scale for the
     * hyperparameter.</p> </dd> <dt>Linear</dt> <dd> <p>Hyperparameter tuning searches
     * the values in the hyperparameter range by using a linear scale.</p> </dd>
     * <dt>Logarithmic</dt> <dd> <p>Hyperparameter tuning searches the values in the
     * hyperparameter range by using a logarithmic scale.</p> <p>Logarithmic scaling
     * works only for ranges that have only values greater than 0.</p> </dd>
     * <dt>ReverseLogarithmic</dt> <dd> <p>Hyperparameter tuning searches the values in
     * the hyperparameter range by using a reverse logarithmic scale.</p> <p>Reverse
     * logarithmic scaling works only for ranges that are entirely within the range
     * 0&lt;=x&lt;1.0.</p> </dd> </dl>
     */
    inline ContinuousParameterRange& WithScalingType(HyperParameterScalingType&& value) { SetScalingType(std::move(value)); return *this;}

  private:

    Aws::String m_name;
    bool m_nameHasBeenSet;

    Aws::String m_minValue;
    bool m_minValueHasBeenSet;

    Aws::String m_maxValue;
    bool m_maxValueHasBeenSet;

    HyperParameterScalingType m_scalingType;
    bool m_scalingTypeHasBeenSet;
  };

} // namespace Model
} // namespace SageMaker
} // namespace Aws