[英]loop though a matrix in R
I try to loop trough a matrix but cant find a easy and elegant way instead of writing many (>10) equations... Can anyone help me please?我尝试通过矩阵循环,但找不到一种简单而优雅的方法,而不是编写许多(> 10)方程......有人可以帮我吗?
and I want to calculate the following:我想计算以下内容:
(0 * 0 * 4/24) + (0 * 1 * 6/24) + (0 * 2 * 3/24) + (1 * 0 * 3/24) + (1 * 1 * 4/24) + (1 * 2 * 4/24) (0 * 0 * 4/24) + (0 * 1 * 6/24) + (0 * 2 * 3/24) + (1 * 0 * 3/24) + (1 * 1 * 4/24) + (1 * 2 * 4/24)
instead of using而不是使用
__ btw: my code for the matrix __ 顺便说一句:我的矩阵代码
vals<- c(4/24, 6/24, 3/24, 3/24, 4/24, 4/24)
x <- c(0,1)
y <- c(0,1,2)
df <- matrix(vals, byrow = TRUE, nrow = 2, ncol = 3,
dimnames = list(x,y))
instead of calculation each step manually, I think there should be a for-loop method, but cant figure it out..而不是手动计算每个步骤,我认为应该有一个for循环方法,但无法弄清楚..
A possible solution:一个可能的解决方案:
c(x %*% df %*% y)
#> [1] 0.5
Another possible solution, based on outer
:另一种可能的解决方案,基于outer
:
sum(outer(x, y, Vectorize(\(x,y) x*y*df[x+1,y+1])))
#> [1] 0.5
x <- c(0, 1)
y <- c(0, 1, 2)
vals<- c(4/24, 6/24, 3/24, 3/24, 4/24, 4/24)
mat <- matrix(vals, byrow = TRUE, nrow = 2, ncol = 3,
dimnames = list(x,y)) ## not a data frame; don't call it "df"
There is even a better way than a for
loop:还有比for
循环更好的方法:
sum(tcrossprod(x, y) * mat)
#[1] 0.5
sum((x %o% y) * df)
x %o% y
gets the outer product of vectors x
and y
which is: x %o% y
得到向量x
和y
的外积,即:
#> [,1] [,2] [,3]
#> [1,] 0 0 0
#> [2,] 0 1 2
Since that has the same dimensions as df
, you can multiply the corresponding elements and get the sum: sum((x %o% y) * df)
由于它与df
具有相同的维度,因此您可以将相应的元素相乘并得到总和: sum((x %o% y) * df)
If you are new to R (as I am), here is the loop approach.如果您是 R 新手(就像我一样),这里是循环方法。
result = 0
for (i in 1:length(x)) {
for (j in 1:length(y)) {
result = result + x[i] * y[j] * df[i, j]
}
}
result
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