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# $covarianceSamp
<a name="covarianceSamp"></a>

8.0.2 版的新功能。

Amazon DocumentDB 中的`$covarianceSamp`運算子會傳回兩個數值表達式的範例共變數。它是僅在`$setWindowFields`階段中使用的視窗運算子；它在 `$group`或 `$bucket`階段中無效。您可以在階段的 `output` 欄位下指定運算子，並選擇性地使用`window`文件定義視窗邊界。

**參數**
+ `$covarianceSamp` 採用雙元素陣列 `[ <expression1>, <expression2> ]`，其中每個元素都是數值表達式 （欄位路徑，例如 `"$x"`、表達式或常數）。它會傳回視窗中文件之間兩個表達式的範例共變數。

## 範例 (MongoDB Shell)
<a name="covarianceSamp-examples"></a>

下列範例會依 分割文件`series`、依 排序每個分割區`t`，並計算`y`整個分割區上 `x`和 的範例共變數。

**建立範例文件**

```
db.measurements.insertMany([
  { _id: 1, series: "A", t: 1, x: 1, y: 2 },
  { _id: 2, series: "A", t: 2, x: 2, y: 5 },
  { _id: 3, series: "A", t: 3, x: 3, y: 8 },
  { _id: 4, series: "B", t: 1, x: 1, y: 8 },
  { _id: 5, series: "B", t: 2, x: 2, y: 5 },
  { _id: 6, series: "B", t: 3, x: 3, y: 2 }
]);
```

**查詢範例**

```
db.measurements.aggregate([
  {
    $setWindowFields: {
      partitionBy: "$series",
      sortBy: { t: 1 },
      output: {
        covariance: {
          $covarianceSamp: [ "$x", "$y" ],
          window: { documents: ["unbounded", "unbounded"] }
        }
      }
    }
  }
]);
```

**輸出**

```
[
  { "_id": 1, "series": "A", "t": 1, "x": 1, "y": 2, "covariance": 3 },
  { "_id": 2, "series": "A", "t": 2, "x": 2, "y": 5, "covariance": 3 },
  { "_id": 3, "series": "A", "t": 3, "x": 3, "y": 8, "covariance": 3 },
  { "_id": 4, "series": "B", "t": 1, "x": 1, "y": 8, "covariance": -3 },
  { "_id": 5, "series": "B", "t": 2, "x": 2, "y": 5, "covariance": -3 },
  { "_id": 6, "series": "B", "t": 3, "x": 3, "y": 2, "covariance": -3 }
]
```

分割區中的每個文件都會收到相同的 `covariance`、該分割區中`y`所有文件的 `x` 和 範例共變數：`3`序列 `A`(x 和 y 一起增加） 和序列 `-3` `B`(y 隨 x 增加而減少）。範例共變數除以 `n - 1`而非 `n`，因此其大小大於相同資料的母體共變數。

## 程式碼範例
<a name="covarianceSamp-code"></a>

若要檢視在`$setWindowFields`階段中使用 `$covarianceSamp` 運算子的程式碼範例，請選擇您要使用之語言的標籤：

------
#### [ Node.js ]

```
const { MongoClient } = require('mongodb');

async function example() {
  const client = new MongoClient('mongodb://<username>:<password>@<cluster-endpoint>:27017/?tls=true&tlsCAFile=global-bundle.pem&replicaSet=rs0&readPreference=secondaryPreferred&retryWrites=false');

  try {
    await client.connect();
    const db = client.db('test');
    const measurements = db.collection('measurements');

    await measurements.insertMany([
      { _id: 1, series: "A", t: 1, x: 1, y: 2 },
      { _id: 2, series: "A", t: 2, x: 2, y: 5 },
      { _id: 3, series: "A", t: 3, x: 3, y: 8 },
      { _id: 4, series: "B", t: 1, x: 1, y: 8 },
      { _id: 5, series: "B", t: 2, x: 2, y: 5 },
      { _id: 6, series: "B", t: 3, x: 3, y: 2 }
    ]);

    const result = await measurements.aggregate([
      {
        $setWindowFields: {
          partitionBy: "$series",
          sortBy: { t: 1 },
          output: {
            covariance: {
              $covarianceSamp: [ "$x", "$y" ],
              window: { documents: ["unbounded", "unbounded"] }
            }
          }
        }
      }
    ]).toArray();

    console.log(result);
  } finally {
    await client.close();
  }
}

example();
```

------
#### [ Python ]

```
from pymongo import MongoClient

def example():
    client = MongoClient('mongodb://<username>:<password>@<cluster-endpoint>:27017/?tls=true&tlsCAFile=global-bundle.pem&replicaSet=rs0&readPreference=secondaryPreferred&retryWrites=false')

    try:
        db = client['test']
        measurements = db['measurements']

        measurements.insert_many([
            { '_id': 1, 'series': 'A', 't': 1, 'x': 1, 'y': 2 },
            { '_id': 2, 'series': 'A', 't': 2, 'x': 2, 'y': 5 },
            { '_id': 3, 'series': 'A', 't': 3, 'x': 3, 'y': 8 },
            { '_id': 4, 'series': 'B', 't': 1, 'x': 1, 'y': 8 },
            { '_id': 5, 'series': 'B', 't': 2, 'x': 2, 'y': 5 },
            { '_id': 6, 'series': 'B', 't': 3, 'x': 3, 'y': 2 }
        ])

        result = list(measurements.aggregate([
            {
                '$setWindowFields': {
                    'partitionBy': '$series',
                    'sortBy': { 't': 1 },
                    'output': {
                        'covariance': {
                            '$covarianceSamp': [ '$x', '$y' ],
                            'window': { 'documents': ['unbounded', 'unbounded'] }
                        }
                    }
                }
            }
        ]))

        print(result)
    finally:
        client.close()

example()
```

------