两种计算机的单通道解决方案：

Question

假设您有一个包含列，日期，组ID，X和Y的表。

CREATE TABLE #sample
  (
     [Date]  DATETIME,
     GroupID INT,
     X       FLOAT,
     Y       FLOAT
  )

DECLARE @date DATETIME = getdate()

INSERT INTO #sample VALUES(@date, 1, 1,3)
INSERT INTO #sample VALUES(DATEADD(d, 1, @date), 1, 1,1)
INSERT INTO #sample VALUES(DATEADD(d, 2, @date), 1, 4,2)
INSERT INTO #sample VALUES(DATEADD(d, 3, @date), 1, 3,3)
INSERT INTO #sample VALUES(DATEADD(d, 4, @date), 1, 6,4)
INSERT INTO #sample VALUES(DATEADD(d, 5, @date), 1, 7,5)
INSERT INTO #sample VALUES(DATEADD(d, 6, @date), 1, 1,6)

并且您想要计算每个组的X和Y的相关性。目前我使用的CTE有点乱：

;WITH DataAvgStd
     AS (SELECT GroupID,
                AVG(X)   AS XAvg,
                AVG(Y)   AS YAvg,
                STDEV(X) AS XStdev,
                STDEV(Y) AS YSTDev,
                COUNT(*) AS SampleSize
         FROM   #sample
         GROUP  BY GroupID),
     ExpectedVal
     AS (SELECT s.GroupID,
                SUM(( X - XAvg ) * ( Y - YAvg )) AS ExpectedValue
         FROM   #sample s
                JOIN DataAvgStd das
                  ON s.GroupID = das.GroupID
         GROUP  BY s.GroupID)
SELECT das.GroupID,
       ev.ExpectedValue / ( das.SampleSize - 1 ) / ( das.XStdev * das.YSTDev )
       AS
       Correlation
FROM   DataAvgStd das
       JOIN ExpectedVal ev
         ON das.GroupID = ev.GroupID

DROP TABLE #sample

似乎应该有一种方法可以使用OVER和PARTITION一次性执行此操作而不需要任何子查询。理想情况下，TSQL会有一个函数，所以你可以写：

SELECT GroupID, CORR(X, Y) OVER(PARTITION BY GroupID)
FROM #sample
GROUP BY GroupID

Answer 1

即使您使用over()，使用此corellation公式也无法避免所有嵌套查询。问题是你不能在同一个查询中反复使用这两个组，也不能有嵌套的聚合函数，例如sum(x - avg(x))。因此，在最佳情况下，根据您的数据，您需要至少保留with。

您的代码看起来像那样

;WITH DataAvgStd
     AS (SELECT GroupID,
                STDEV(X) over(partition by GroupID) AS XStdev,
                STDEV(Y) over(partition by GroupID) AS YSTDev,
                COUNT(*) over(partition by GroupID) AS SampleSize,
                ( X - AVG(X) over(partition by GroupID)) * ( Y - AVG(Y) over(partition by GroupID)) AS ExpectedValue
         FROM   #sample s)         
SELECT distinct GroupID,
       SUM(ExpectedValue) over(partition by GroupID) / (SampleSize - 1 ) / ( XStdev * YSTDev ) AS Correlation
FROM DataAvgStd

另一种方法是使用等同公式进行相关，Wikipedia描述。

这可以写成

SELECT GroupID,
       Correlation=(COUNT(*) * SUM(X * Y) - SUM(X) * SUM(Y)) / 
                   (SQRT(COUNT(*) * SUM(X * X) - SUM(X) * SUM(x))
                    * SQRT(COUNT(*) * SUM(Y* Y) - SUM(Y) * SUM(Y)))
FROM #sample s
GROUP BY GroupID;

Answer 2

两种计算机的单通道解决方案：

Pearson相关系数有两种，一种用于样本，一种用于整个种群。这些都很简单，单通，我相信，两者的正确公式：

-- Methods for calculating the two Pearson correlation coefficients
SELECT  
        -- For Population
        (avg(x * y) - avg(x) * avg(y)) / 
        (sqrt(avg(x * x) - avg(x) * avg(x)) * sqrt(avg(y * y) - avg(y) * avg(y))) 
        AS correlation_coefficient_population,
        -- For Sample
        (count(*) * sum(x * y) - sum(x) * sum(y)) / 
        (sqrt(count(*) * sum(x * x) - sum(x) * sum(x)) * sqrt(count(*) * sum(y * y) - sum(y) * sum(y))) 
        AS correlation_coefficient_sample
    FROM (
        -- The following generates a table of sample data containing two columns with a luke-warm and tweakable correlation 
        -- y = x for 0 thru 99, y = x - 100 for 100 thru 199, etc.  Execute it as a stand-alone to see for yourself
        -- x and y are CAST as DECIMAL to avoid integer math, you should definitely do the same
        -- Try TOP 100 or less for full correlation (y = x for all cases), TOP 200 for a PCC of 0.5, TOP 300 for one near 0.33, etc.
        -- The superfluous "+ 0" is where you could apply various offsets to see that they have no effect on the results
        SELECT TOP 200
                CAST(ROW_NUMBER() OVER (ORDER BY [object_id]) - 1 + 0 AS DECIMAL) AS x, 
                CAST((ROW_NUMBER() OVER (ORDER BY [object_id]) - 1) % 100 AS DECIMAL) AS y 
            FROM sys.all_objects
    ) AS a

正如我在评论中所指出的，您可以尝试使用TOP 100或更低的示例进行完全关联（对于所有情况，y = x）; TOP 200产生的相关性非常接近0.5; TOP 300，约0.33;如果你愿意，有一个地方（“+ 0”）可以添加一个偏移;扰流警报，它没有任何影响。确保将值设置为DECIMAL - 整数数学可以显着影响这些计算。

Answer 3

SQL在嵌套聚合或窗口函数方面有点好笑，因此需要CTE或派生表。

如果必须在数据库服务器上实现，并且您正在寻找比CTE更具可读性的东西，那么您唯一的选择就是使用CLR滚动自己的聚合。

这里有一个很好的教程http://www.sqlservercentral.com/articles/SQLCLR/71942/，用于构建类似的CLR聚合。

有没有办法使用OVER子句而不是CTE来计算TSQL中的相关性？

3 个答案:

两种计算机的单通道解决方案：