如何按行和列对 R 中的稀疏矩阵进行归一化?

How can I normalize a sparse matrix in R by both rows and columns?

我使用 R 包 "Matrix" 创建了一个稀疏矩阵。矩阵不是正方形,其尺寸为 4561 x 68825。

我希望标准化此矩阵,以便每个值 x 等于 x / 行总和 + 列总和。我在堆栈上找到了一个解决方案,我可以更改它来解决这个问题 here。但是,在链接问题中看到的解决方案中,该问题使用方阵,所以 Diaganal 可以是 used.In 我的情况,我的矩阵不是方阵,所以我无法使这个解决方案有效。

如何按行和列对 R 中的稀疏矩阵进行归一化?

如果您只想将每个单元格除以行总和和列总和的总和,这里有一个简单的方法:

test = matrix(1:20, 4, 5)
test
     [,1] [,2] [,3] [,4] [,5]
[1,]    1    5    9   13   17
[2,]    2    6   10   14   18
[3,]    3    7   11   15   19
[4,]    4    8   12   16   20

rs = rowSums(test)
cs = colSums(test)

for(j in 1:ncol(test)){
  for(i in 1:nrow(test)){
    test[i,j] = test[i,j]/(rs[i] + cs[j])
  }
}

test
           [,1]       [,2]      [,3]      [,4]      [,5]
[1,] 0.01818182 0.07042254 0.1034483 0.1262136 0.1428571
[2,] 0.03333333 0.07894737 0.1086957 0.1296296 0.1451613
[3,] 0.04615385 0.08641975 0.1134021 0.1327434 0.1472868
[4,] 0.05714286 0.09302326 0.1176471 0.1355932 0.1492537

希望对您有所帮助!

m_final <- t(t(m/rowSums(m)) + rowSums(t(m)))
m_final

输出为:

           [,1]     [,2]       [,3]
 [1,] 0.9748283 3.326324 -0.8274075
 [2,] 1.4574957 2.776025 -0.7597753
 [3,] 1.9265464 2.937874 -1.3906749
 [4,] 0.7105211 3.337394 -0.5741696
 [5,] 1.4808831 3.030777 -1.0379153
 [6,] 2.2123599 2.537209 -1.2758243
 [7,] 2.8672471 2.437124 -1.8306263
 [8,] 4.8144351 6.952963 -8.2936531
 [9,] 1.9486587 3.382196 -1.8571098
[10,] 0.8897446 3.329129 -0.7451281


#sample data:
set.seed(1)
m <- replicate(3,rnorm(10))
> m
            [,1]        [,2]        [,3]
 [1,] -0.6264538  1.51178117  0.91897737
 [2,]  0.1836433  0.38984324  0.78213630
 [3,] -0.8356286 -0.62124058  0.07456498
 [4,]  1.5952808 -2.21469989 -1.98935170
 [5,]  0.3295078  1.12493092  0.61982575
 [6,] -0.8204684 -0.04493361 -0.05612874
 [7,]  0.4874291 -0.01619026 -0.15579551
 [8,]  0.7383247  0.94383621 -1.47075238
 [9,]  0.5757814  0.82122120 -0.47815006
[10,] -0.3053884  0.59390132  0.41794156

编辑:
如果您想进行以下计算,则可以尝试

m/(row_sum + col_sum)

m/outer(rowSums(m), colSums(m), FUN = "+")