R中的矢量化矩阵运算



我有一个R x C矩阵,填充到第k行,在这行下面为空。我需要做的是填充剩下的行。为了做到这一点,我有一个函数,它将2整行作为参数,处理这些行并输出2个新行(这些输出将以2个为一批的方式填充矩阵的空行)。我有一个固定的矩阵,包含所有要处理的行"对",但我的for循环对性能没有帮助:

# the processRows function:
processRows = function(r1, r2)
{
  # just change a little bit the two rows and return it in a compact way    
  nr1 = r1 * 0.1
  nr2 = -r2 * 0.1
  matrix (c(nr1, nr2), ncol = 2)
 }
# M is the matrix
# nrow(M) and k are even, so nLeft is even
M = matrix(1:48, ncol = 3)
# half to fill (can be more or less, but k is always even)
k = nrow(M)/2
# simulate empty rows to be filled
M[-(1:k), ] = 0
cat('before fill')
print(M)
# number of empty rows to fill
nLeft  = nrow(M) - k
nextRow = k + 1
# each row in idxList represents a 'pair' of rows to be processed 
# any pairwise combination of non-empty rows could happen
# make it reproducible
set.seed(1)
idxList = matrix (sample(1:k, k), ncol = 2, byrow = TRUE)
for ( i in 1 : (nLeft / 2))
{
   row1 = M[idxList[i, 1],]
   row2 = M[idxList[i, 2],]
   # the two columns in 'results' will become 2 rows in M
   results = processRows(row1, row2)
   # fill the matrix
   M[nextRow, ] = results[, 1]
   nextRow = nextRow + 1
   M[nextRow, ] = results[, 2]
   nextRow = nextRow + 1
 }
 cat('after fill')
 print(M)

好吧,这是您的代码。我们运行这个,这样我们就有了一个"真实"矩阵的副本,我们希望更快地复制这个矩阵。

#### Original Code (aka Gold Standard) ####
M = matrix(1:48, ncol = 3)
k = nrow(M)/2
M[-(1:k), ] = 0
nLeft  = nrow(M) - k
nextRow = k + 1
idxList = matrix(1:k, ncol = 2)
for ( i in 1 : (nLeft / 2))
{
    row1 = M[idxList[i, 1],]
    row2 = M[idxList[i, 2],]
    results = matrix(c(2*row1, 3*row2), ncol = 2)
    M[nextRow, ] = results[, 1]
    nextRow = nextRow + 1
    M[nextRow, ] = results[, 2]
    nextRow = nextRow + 1
}

现在是矢量化的代码。基本思想是,如果您有4行正在处理。与其一次将它们作为向量传递,不如一次传递。即:

(1:3) * 2
(1:3) * 2
(1:3) * 2
(1:3) * 2

与相同(但较慢)

c(1:3, 1:3, 1:3, 1:3) * 2

因此,首先,我们将使用相同的设置代码,然后将要处理的行创建为两个长向量(其中,所有4个原始行只是串在一起,如我上面的简单示例所示)。然后,我们得到这些结果,并将它们转换为具有适当维度的矩阵。最后一个技巧是只需两步就可以将结果分配回。您可以一次分配给矩阵的多行,因此我们使用seq()来获得奇数和偶数,因此将结果的第一列和第二列分别分配给。

#### Vectorized Code (testing) ####
M2 = matrix(1:48, ncol = 3)
k2 = nrow(M2)/2
M2[-(1:k2), ] = 0
nLeft2  = nrow(M2) - k2
nextRow2 = k2 + 1
idxList2 = matrix(1:k2, ncol = 2)
## create two long vectors of all rows to be processed
row12 <- as.vector(t(M2[idxList2[, 1],]))
row22 <- as.vector(t(M2[idxList2[, 2],]))
## get all results
results2 = matrix(c(2*row12, 3*row22), ncol = 2)
## add results back
M2[seq(nextRow2, nextRow2 + nLeft2-1, by = 2), ] <- matrix(results2[,1], nLeft2/2, byrow=TRUE)
M2[seq(nextRow2+1, nextRow2 + nLeft2, by = 2), ] <- matrix(results2[,2], nLeft2/2, byrow=TRUE)
## check that vectorized code matches your examples
all.equal(M, M2)

在我的机器上哪个给出:

> all.equal(M, M2)
[1] TRUE

最新更新