✨feat(hos_client_create, hos_client_destory): 多次调用destory不会导致重复释放

support/aws-sdk-cpp-master/aws-cpp-sdk-glue/include/aws/glue/model/FindMatchesMetrics.h

@@ -0,0 +1,260 @@
+/**
+ * Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+ * SPDX-License-Identifier: Apache-2.0.
+ */
+
+#pragma once
+#include <aws/glue/Glue_EXPORTS.h>
+#include <aws/glue/model/ConfusionMatrix.h>
+#include <utility>
+
+namespace Aws
+{
+namespace Utils
+{
+namespace Json
+{
+  class JsonValue;
+  class JsonView;
+} // namespace Json
+} // namespace Utils
+namespace Glue
+{
+namespace Model
+{
+
+  /**
+   * <p>The evaluation metrics for the find matches algorithm. The quality of your
+   * machine learning transform is measured by getting your transform to predict some
+   * matches and comparing the results to known matches from the same dataset. The
+   * quality metrics are based on a subset of your data, so they are not
+   * precise.</p><p><h3>See Also:</h3>   <a
+   * href="http://docs.aws.amazon.com/goto/WebAPI/glue-2017-03-31/FindMatchesMetrics">AWS
+   * API Reference</a></p>
+   */
+  class AWS_GLUE_API FindMatchesMetrics
+  {
+  public:
+    FindMatchesMetrics();
+    FindMatchesMetrics(Aws::Utils::Json::JsonView jsonValue);
+    FindMatchesMetrics& operator=(Aws::Utils::Json::JsonView jsonValue);
+    Aws::Utils::Json::JsonValue Jsonize() const;
+
+
+    /**
+     * <p>The area under the precision/recall curve (AUPRC) is a single number
+     * measuring the overall quality of the transform, that is independent of the
+     * choice made for precision vs. recall. Higher values indicate that you have a
+     * more attractive precision vs. recall tradeoff.</p> <p>For more information, see
+     * <a href="https://en.wikipedia.org/wiki/Precision_and_recall">Precision and
+     * recall</a> in Wikipedia.</p>
+     */
+    inline double GetAreaUnderPRCurve() const{ return m_areaUnderPRCurve; }
+
+    /**
+     * <p>The area under the precision/recall curve (AUPRC) is a single number
+     * measuring the overall quality of the transform, that is independent of the
+     * choice made for precision vs. recall. Higher values indicate that you have a
+     * more attractive precision vs. recall tradeoff.</p> <p>For more information, see
+     * <a href="https://en.wikipedia.org/wiki/Precision_and_recall">Precision and
+     * recall</a> in Wikipedia.</p>
+     */
+    inline bool AreaUnderPRCurveHasBeenSet() const { return m_areaUnderPRCurveHasBeenSet; }
+
+    /**
+     * <p>The area under the precision/recall curve (AUPRC) is a single number
+     * measuring the overall quality of the transform, that is independent of the
+     * choice made for precision vs. recall. Higher values indicate that you have a
+     * more attractive precision vs. recall tradeoff.</p> <p>For more information, see
+     * <a href="https://en.wikipedia.org/wiki/Precision_and_recall">Precision and
+     * recall</a> in Wikipedia.</p>
+     */
+    inline void SetAreaUnderPRCurve(double value) { m_areaUnderPRCurveHasBeenSet = true; m_areaUnderPRCurve = value; }
+
+    /**
+     * <p>The area under the precision/recall curve (AUPRC) is a single number
+     * measuring the overall quality of the transform, that is independent of the
+     * choice made for precision vs. recall. Higher values indicate that you have a
+     * more attractive precision vs. recall tradeoff.</p> <p>For more information, see
+     * <a href="https://en.wikipedia.org/wiki/Precision_and_recall">Precision and
+     * recall</a> in Wikipedia.</p>
+     */
+    inline FindMatchesMetrics& WithAreaUnderPRCurve(double value) { SetAreaUnderPRCurve(value); return *this;}
+
+
+    /**
+     * <p>The precision metric indicates when often your transform is correct when it
+     * predicts a match. Specifically, it measures how well the transform finds true
+     * positives from the total true positives possible.</p> <p>For more information,
+     * see <a href="https://en.wikipedia.org/wiki/Precision_and_recall">Precision and
+     * recall</a> in Wikipedia.</p>
+     */
+    inline double GetPrecision() const{ return m_precision; }
+
+    /**
+     * <p>The precision metric indicates when often your transform is correct when it
+     * predicts a match. Specifically, it measures how well the transform finds true
+     * positives from the total true positives possible.</p> <p>For more information,
+     * see <a href="https://en.wikipedia.org/wiki/Precision_and_recall">Precision and
+     * recall</a> in Wikipedia.</p>
+     */
+    inline bool PrecisionHasBeenSet() const { return m_precisionHasBeenSet; }
+
+    /**
+     * <p>The precision metric indicates when often your transform is correct when it
+     * predicts a match. Specifically, it measures how well the transform finds true
+     * positives from the total true positives possible.</p> <p>For more information,
+     * see <a href="https://en.wikipedia.org/wiki/Precision_and_recall">Precision and
+     * recall</a> in Wikipedia.</p>
+     */
+    inline void SetPrecision(double value) { m_precisionHasBeenSet = true; m_precision = value; }
+
+    /**
+     * <p>The precision metric indicates when often your transform is correct when it
+     * predicts a match. Specifically, it measures how well the transform finds true
+     * positives from the total true positives possible.</p> <p>For more information,
+     * see <a href="https://en.wikipedia.org/wiki/Precision_and_recall">Precision and
+     * recall</a> in Wikipedia.</p>
+     */
+    inline FindMatchesMetrics& WithPrecision(double value) { SetPrecision(value); return *this;}
+
+
+    /**
+     * <p>The recall metric indicates that for an actual match, how often your
+     * transform predicts the match. Specifically, it measures how well the transform
+     * finds true positives from the total records in the source data.</p> <p>For more
+     * information, see <a
+     * href="https://en.wikipedia.org/wiki/Precision_and_recall">Precision and
+     * recall</a> in Wikipedia.</p>
+     */
+    inline double GetRecall() const{ return m_recall; }
+
+    /**
+     * <p>The recall metric indicates that for an actual match, how often your
+     * transform predicts the match. Specifically, it measures how well the transform
+     * finds true positives from the total records in the source data.</p> <p>For more
+     * information, see <a
+     * href="https://en.wikipedia.org/wiki/Precision_and_recall">Precision and
+     * recall</a> in Wikipedia.</p>
+     */
+    inline bool RecallHasBeenSet() const { return m_recallHasBeenSet; }
+
+    /**
+     * <p>The recall metric indicates that for an actual match, how often your
+     * transform predicts the match. Specifically, it measures how well the transform
+     * finds true positives from the total records in the source data.</p> <p>For more
+     * information, see <a
+     * href="https://en.wikipedia.org/wiki/Precision_and_recall">Precision and
+     * recall</a> in Wikipedia.</p>
+     */
+    inline void SetRecall(double value) { m_recallHasBeenSet = true; m_recall = value; }
+
+    /**
+     * <p>The recall metric indicates that for an actual match, how often your
+     * transform predicts the match. Specifically, it measures how well the transform
+     * finds true positives from the total records in the source data.</p> <p>For more
+     * information, see <a
+     * href="https://en.wikipedia.org/wiki/Precision_and_recall">Precision and
+     * recall</a> in Wikipedia.</p>
+     */
+    inline FindMatchesMetrics& WithRecall(double value) { SetRecall(value); return *this;}
+
+
+    /**
+     * <p>The maximum F1 metric indicates the transform's accuracy between 0 and 1,
+     * where 1 is the best accuracy.</p> <p>For more information, see <a
+     * href="https://en.wikipedia.org/wiki/F1_score">F1 score</a> in Wikipedia.</p>
+     */
+    inline double GetF1() const{ return m_f1; }
+
+    /**
+     * <p>The maximum F1 metric indicates the transform's accuracy between 0 and 1,
+     * where 1 is the best accuracy.</p> <p>For more information, see <a
+     * href="https://en.wikipedia.org/wiki/F1_score">F1 score</a> in Wikipedia.</p>
+     */
+    inline bool F1HasBeenSet() const { return m_f1HasBeenSet; }
+
+    /**
+     * <p>The maximum F1 metric indicates the transform's accuracy between 0 and 1,
+     * where 1 is the best accuracy.</p> <p>For more information, see <a
+     * href="https://en.wikipedia.org/wiki/F1_score">F1 score</a> in Wikipedia.</p>
+     */
+    inline void SetF1(double value) { m_f1HasBeenSet = true; m_f1 = value; }
+
+    /**
+     * <p>The maximum F1 metric indicates the transform's accuracy between 0 and 1,
+     * where 1 is the best accuracy.</p> <p>For more information, see <a
+     * href="https://en.wikipedia.org/wiki/F1_score">F1 score</a> in Wikipedia.</p>
+     */
+    inline FindMatchesMetrics& WithF1(double value) { SetF1(value); return *this;}
+
+
+    /**
+     * <p>The confusion matrix shows you what your transform is predicting accurately
+     * and what types of errors it is making.</p> <p>For more information, see <a
+     * href="https://en.wikipedia.org/wiki/Confusion_matrix">Confusion matrix</a> in
+     * Wikipedia.</p>
+     */
+    inline const ConfusionMatrix& GetConfusionMatrix() const{ return m_confusionMatrix; }
+
+    /**
+     * <p>The confusion matrix shows you what your transform is predicting accurately
+     * and what types of errors it is making.</p> <p>For more information, see <a
+     * href="https://en.wikipedia.org/wiki/Confusion_matrix">Confusion matrix</a> in
+     * Wikipedia.</p>
+     */
+    inline bool ConfusionMatrixHasBeenSet() const { return m_confusionMatrixHasBeenSet; }
+
+    /**
+     * <p>The confusion matrix shows you what your transform is predicting accurately
+     * and what types of errors it is making.</p> <p>For more information, see <a
+     * href="https://en.wikipedia.org/wiki/Confusion_matrix">Confusion matrix</a> in
+     * Wikipedia.</p>
+     */
+    inline void SetConfusionMatrix(const ConfusionMatrix& value) { m_confusionMatrixHasBeenSet = true; m_confusionMatrix = value; }
+
+    /**
+     * <p>The confusion matrix shows you what your transform is predicting accurately
+     * and what types of errors it is making.</p> <p>For more information, see <a
+     * href="https://en.wikipedia.org/wiki/Confusion_matrix">Confusion matrix</a> in
+     * Wikipedia.</p>
+     */
+    inline void SetConfusionMatrix(ConfusionMatrix&& value) { m_confusionMatrixHasBeenSet = true; m_confusionMatrix = std::move(value); }
+
+    /**
+     * <p>The confusion matrix shows you what your transform is predicting accurately
+     * and what types of errors it is making.</p> <p>For more information, see <a
+     * href="https://en.wikipedia.org/wiki/Confusion_matrix">Confusion matrix</a> in
+     * Wikipedia.</p>
+     */
+    inline FindMatchesMetrics& WithConfusionMatrix(const ConfusionMatrix& value) { SetConfusionMatrix(value); return *this;}
+
+    /**
+     * <p>The confusion matrix shows you what your transform is predicting accurately
+     * and what types of errors it is making.</p> <p>For more information, see <a
+     * href="https://en.wikipedia.org/wiki/Confusion_matrix">Confusion matrix</a> in
+     * Wikipedia.</p>
+     */
+    inline FindMatchesMetrics& WithConfusionMatrix(ConfusionMatrix&& value) { SetConfusionMatrix(std::move(value)); return *this;}
+
+  private:
+
+    double m_areaUnderPRCurve;
+    bool m_areaUnderPRCurveHasBeenSet;
+
+    double m_precision;
+    bool m_precisionHasBeenSet;
+
+    double m_recall;
+    bool m_recallHasBeenSet;
+
+    double m_f1;
+    bool m_f1HasBeenSet;
+
+    ConfusionMatrix m_confusionMatrix;
+    bool m_confusionMatrixHasBeenSet;
+  };
+
+} // namespace Model
+} // namespace Glue
+} // namespace Aws