向量化一个循环 [英] Vectorizing a loop
问题描述
我找到了一种如何以矢量化的方式创建H_ID的方法。
N < - 50
###家用ID
#循环 - for
set.seed(20110224)
H_ID< - vector(integer,N)
H_ID [1] < - 1
for(i in 2:N)if (1)< .5)H_ID [i] < - H_ID [i-1] +1 else H_ID [i] < - H_ID [i-1]
print(H_ID)
#矢量化形式
set.seed(20110224)
r <-c(0,runif(N-1))
H_ID< cumsum(r< .5)
print(H_ID)
但是我不知道如何以矢量化方式创建P_ID 。
###人物ID
#loop-for
P_ID< - vector(integer如果(H_ID [i]> H_ID [i-1])P_ID [i] < - >,则对于(i in 2:N) 1 else P_ID [i] < - P_ID [i-1] +1
print(cbind(H_ID,P_ID))
#向量化格式
#
受马丁摩根的解决方案到一个密切相关的问题,这是一个真正的矢量化的方式来产生 P_ID
使用 cummax
函数。一旦注意到 P_ID
与!(r)的
: cumsum
密切相关, < 0.5)
set.seed(1)
N < - (0,runif(N-1))
H_ID< cumsum(r< .5)
r_< r> = 0.5#翻转生成H_ID的硬币。
z< - cumsum(r_)#这几乎是P_ID;只需要减去适当的量...
#...并且通过cummax
P_ID < - 1 + z - cummax(z *(!r_))
> cbind(H_ID,P_ID)
H_ID P_ID
[1,] 1 1
[2,] 1 2
[3,] 2 1
[4,] 3 1
[5,] 3 2
[6,] 3 3
[7,] 3 4
[8,] 4 1
[9,] 5 1
[10,] 5 2
我没有做详细的时序测试,但是它可能很快就会变得邪恶,因为这些都是内部的,矢量化的函数
I am creating some artificial data. I need to create household ID (H_ID) and personal ID (P_ID, in each household).
I found a way how to create H_ID in vectorized way.
N <- 50
### Household ID
# loop-for
set.seed(20110224)
H_ID <- vector("integer", N)
H_ID[1] <- 1
for (i in 2:N) if (runif(1) < .5) H_ID[i] <- H_ID[i-1]+1 else H_ID[i] <- H_ID[i-1]
print(H_ID)
# vectorised form
set.seed(20110224)
r <- c(0, runif(N-1))
H_ID <- cumsum(r < .5)
print(H_ID)
But I can not figure out how to create P_ID in vectorized way.
### Person ID
# loop-for
P_ID <- vector("integer", N)
P_ID[1] <- 1
for (i in 2:N) if (H_ID[i] > H_ID[i-1]) P_ID[i] <- 1 else P_ID[i] <- P_ID[i-1]+1
print(cbind(H_ID, P_ID))
# vectorised form
# ???
Inspired by Martin Morgan's solution to a closely related question, here's a truly vectorized way to generate the P_ID
using the cummax
function. It becomes clear once you note that P_ID
is closely related to the cumsum
of !(r < 0.5)
:
set.seed(1)
N <- 10
r <- c(0, runif(N-1))
H_ID <- cumsum(r < .5)
r_ <- r >= .5 # flip the coins that generated H_ID.
z <- cumsum(r_) # this is almost P_ID; just need to subtract the right amount...
# ... and the right amount to subtract is obtained via cummax
P_ID <- 1 + z - cummax( z * (!r_) )
> cbind(H_ID, P_ID)
H_ID P_ID
[1,] 1 1
[2,] 1 2
[3,] 2 1
[4,] 3 1
[5,] 3 2
[6,] 3 3
[7,] 3 4
[8,] 4 1
[9,] 5 1
[10,] 5 2
I haven't done detailed timing tests, but it's probably wicked fast, since these are all internal, vectorized functions
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