如何使用 Rcpp 来加速 for 循环？
How to use Rcpp to speed up a for loop?
我创建了一个 for 循环，我想使用 Rcpp 库加快它的速度。我不太熟悉 C++。你能帮我加快我的功能吗？
感谢您的帮助！
我已经将我的算法、代码、输入和输出以及会话信息包括在内。
这是我的算法：
如果当前价格高于前一个价格，则在名为 TR 的列中标记 (+1)
如果当前价格低于前一个价格，则在名为 TR 的列中标记 (-1)
如果当前价格与之前的价格相同，
在名为 TR
的列中标记与先前价格相同的内容
这是我的代码：
price <- c(71.91, 71.82, 71.81, 71.81, 71.81, 71.82, 71.81, 71.81, 71.81,
71.82, 71.81, 71.81, 71.8, 71.81, 71.8, 71.81, 71.8, 71.8, 71.8,
71.8, 71.81, 71.81, 71.81, 71.81, 71.81, 71.81, 71.81, 71.81,
71.81, 71.82, 71.81, 71.81, 71.81, 71.81, 71.81, 71.81, 71.8,
71.8, 71.81, 71.81, 71.81, 71.81, 71.82, 71.82, 71.81, 71.81,
71.81, 71.81, 71.81, 71.81, 71.81, 71.82, 71.82, 71.82, 71.82,
71.82, 71.82, 71.82, 71.81, 71.82, 71.82, 71.82, 71.82, 71.82,
71.82, 71.82, 71.82, 71.82, 71.81, 71.81, 71.81, 71.82, 71.82,
71.81, 71.82, 71.82, 71.82, 71.81, 71.82, 71.82, 71.82, 71.81,
71.81, 71.82, 71.82, 71.82, 71.82, 71.82, 71.82, 71.82, 71.82,
71.82, 71.82, 71.82, 71.82, 71.82, 71.82, 71.82, 71.82, 71.82,
71.82, 71.82, 71.82, 71.82, 71.82, 71.82, 71.82, 71.82, 71.82,
71.82, 71.82, 71.82, 71.82, 71.82, 71.82, 71.82, 71.82, 71.82,
71.82, 71.82, 71.82, 71.82, 71.82, 71.82, 71.82, 71.82, 71.82,
71.82, 71.82, 71.82, 71.82, 71.82, 71.82, 71.82, 71.82, 71.81,
71.82, 71.82, 71.82, 71.82, 71.83, 71.82, 71.82, 71.82, 71.81,
71.81, 71.81, 71.81, 71.81, 71.81, 71.81, 71.82, 71.82, 71.82,
71.81, 71.81, 71.81, 71.82, 71.82, 71.82, 71.82, 71.82, 71.82,
71.82, 71.82, 71.82, 71.82, 71.82, 71.83, 71.83, 71.83, 71.83,
71.83, 71.83, 71.83, 71.83, 71.83, 71.83, 71.83, 71.83, 71.83,
71.83, 71.83, 71.83, 71.83, 71.83, 71.83, 71.83, 71.83, 71.83,
71.83, 71.83, 71.83, 71.83, 71.83, 71.83, 71.83, 71.83, 71.83,
71.83)
TR <- numeric(length(price)-1)
TR <- c(NA,TR)
for (i in 1: (length(price)-1)){
if (price[i] == price[i+1]) {TR[i+1] = TR[i]}
if (price[i] < price[i+1]) {TR[i+1] = 1}
if (price[i] > price[i+1]) {TR[i+1] = -1}
}
这是我的输出： dput(TR) 产量
c(NA, -1, -1, -1, -1, 1, -1, -1, -1, 1, -1, -1, -1, 1, -1, 1,
-1, -1, -1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, -1, -1, -1, -1,
-1, -1, -1, -1, 1, 1, 1, 1, 1, 1, -1, -1, -1, -1, -1, -1, -1,
1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, -1, -1, -1,
1, 1, -1, 1, 1, 1, -1, 1, 1, 1, -1, -1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, -1, 1, 1, 1, 1, 1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
-1, 1, 1, 1, -1, -1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1)
这是我的会话信息：
> sessionInfo()
R version 3.1.2 (2014-10-31)
Platform: x86_64-w64-mingw32/x64 (64-bit)
locale:
[1] LC_COLLATE=English_United States.1252 LC_CTYPE=English_United States.1252
[3] LC_MONETARY=English_United States.1252 LC_NUMERIC=C
[5] LC_TIME=English_United States.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] data.table_1.9.4
loaded via a namespace (and not attached):
[1] chron_2.3-45 plyr_1.8.1 Rcpp_0.11.1 reshape2_1.4 stringr_0.6.2 tools_3.1.2
你可以直接翻译 for 循环：
library(Rcpp)
cppFunction(
"IntegerVector proc(NumericVector x) {
const int n = x.size();
IntegerVector y(n);
y[0] = NA_INTEGER;
for (int i=1; i < n; ++i) {
if (x[i] == x[i-1]) y[i] = y[i-1];
else if (x[i] > x[i-1]) y[i] = 1;
else y[i] = -1;
}
return y;
}")
与往常一样，与基础 R 中的 for 循环相比，使用 Rcpp 可以获得相当大的加速：
proc.for <- function(price) {
TR <- numeric(length(price)-1)
TR <- c(NA,TR)
for (i in 1: (length(price)-1)){
if (price[i] == price[i+1]) {TR[i+1] = TR[i]}
if (price[i] < price[i+1]) {TR[i+1] = 1}
if (price[i] > price[i+1]) {TR[i+1] = -1}
}
return(TR)
}
proc.aaron <- function(price) {
change <- sign(diff(price))
good <- change != 0
goodval <- change[good]
c(NA, goodval[cumsum(good)])
}
proc.jbaums <- function(price) {
TR <- sign(diff(price))
TR[TR==0] <- TR[which(TR != 0)][findInterval(which(TR == 0), which(TR != 0))]
TR
}
all.equal(proc(price), proc.for(price), proc.aaron(price), proc.jbaums(price))
# [1] TRUE
library(microbenchmark)
microbenchmark(proc(price), proc.for(price), proc.aaron(price), proc.jbaums(price))
# Unit: microseconds
# expr min lq mean median uq max neval
# proc(price) 1.871 2.5380 3.92111 3.1110 4.5880 15.318 100
# proc.for(price) 408.200 448.2830 542.19766 484.1265 546.3255 1821.104 100
# proc.aaron(price) 23.916 25.5770 33.53259 31.5420 35.8575 190.372 100
# proc.jbaums(price) 33.536 38.8995 46.80109 43.4510 49.3555 112.306 100
与 for 循环相比，我们看到加速超过 100 倍，与所提供向量的向量化替代方案相比，加速超过 10 倍。
对于更大的向量（此处测试长度为 100 万），加速甚至更显着：
price.big <- rep(price, times=5000)
all.equal(proc(price.big), proc.for(price.big), proc.aaron(price.big), proc.jbaums(price.big))
# [1] TRUE
microbenchmark(proc(price.big), proc.for(price.big), proc.aaron(price.big), proc.jbaums(price.big))
# Unit: milliseconds
# expr min lq mean median uq max neval
# proc(price.big) 1.442119 1.818494 5.094274 2.020437 2.771903 56.54321 100
# proc.for(price.big) 2639.819536 2699.493613 2949.962241 2781.636460 3062.277930 4472.35369 100
# proc.aaron(price.big) 91.499940 99.859418 132.519296 140.521212 147.462259 207.72813 100
# proc.jbaums(price.big) 117.242451 138.528214 170.989065 170.606048 180.337074 487.13615 100
现在，与 for 循环相比，我们的速度提高了 1000 倍，与矢量化 R 函数相比，速度提高了约 70 倍。即使在这种大小下，如果只调用一次函数，也不清楚 Rcpp 是否优于矢量化 R 解决方案，因为编译 Rcpp 代码肯定至少需要 100 毫秒。如果这是一段在您的分析中重复调用的代码，那么加速非常有吸引力。
这可以在 R 中轻松矢量化。
例如 diff 和 findInterval:
TR <- sign(diff(price))
TR[TR==0] <- TR[which(TR != 0)][findInterval(which(TR == 0), which(TR != 0))]
也许先尝试矢量化。尽管这可能不如 Rcpp 快，但它更直接。
f2 <- function(price) {
change <- sign(diff(price))
good <- change != 0
goodval <- change[good]
c(NA, goodval[cumsum(good)])
}
而且它仍然比 R for 循环有很大的加速。
f1 <- function(price) {
TR <- numeric(length(price)-1)
TR <- c(NA,TR)
for (i in 1: (length(price)-1)){
if (price[i] == price[i+1]) {TR[i+1] = TR[i]}
if (price[i] < price[i+1]) {TR[i+1] = 1}
if (price[i] > price[i+1]) {TR[i+1] = -1}
}
TR
}
microbenchmark(f1(price), f2(price), times=100)
## Unit: microseconds
## expr min lq mean median uq max neval cld
## f1(price) 550.037 592.9830 756.20095 618.7910 703.8335 3042.530 100 b
## f2(price) 36.915 39.3285 56.45267 45.5225 60.1965 184.536 100 a
你可以试试字节编译。查看使用与 Rcpp 代码相同的 if-else-if-else 逻辑的 R 循环也很有用。使用 R 3.1.2 我得到
f1 <- function(price) {
TR <- numeric(length(price)-1)
TR <- c(NA,TR)
for (i in 1: (length(price)-1)){
if (price[i] == price[i+1]) {TR[i+1] = TR[i]}
if (price[i] < price[i+1]) {TR[i+1] = 1}
if (price[i] > price[i+1]) {TR[i+1] = -1}
}
return(TR)
}
f2 <- function(price) {
TR <- numeric(length(price)-1)
TR <- c(NA,TR)
for (i in 1: (length(price)-1)){
if (price[i] == price[i+1]) {TR[i+1] = TR[i]}
else if (price[i] < price[i+1]) {TR[i+1] = 1}
else {TR[i+1] = -1}
}
return(TR)
}
library(compiler)
f1c <- cmpfun(f1)
f2c <- cmpfun(f2)
library(microbenchmark)
microbenchmark(f1(price), f2(price), f1c(price), f2c(price), times = 1000)
## Unit: microseconds
## expr min lq mean median uq max neval cld
## f1(price) 536.619 570.3715 667.3520 586.2465 609.9280 45046.462 1000 d
## f2(price) 328.592 351.2070 386.5895 365.0245 381.4850 1302.497 1000 c
## f1c(price) 167.570 182.4645 218.9537 192.4780 204.7810 7843.291 1000 b
## f2c(price) 96.644 107.4465 124.1324 113.5470 121.5365 1019.389 1000 a
R-devel 将于 4 月作为 R 3.2.0 发布，在用于此类标量计算的字节码引擎中有许多改进；我得到了
microbenchmark(f1(price), f2(price), f1c(price), f2c(price), times = 1000)
## Unit: microseconds
## expr min lq mean median uq max neval cld
## f1(price) 490.300 520.3845 559.19539 533.2050 548.6850 1330.219 1000 d
## f2(price) 298.375 319.7475 348.71384 330.4535 342.6405 1813.113 1000 c
## f1c(price) 61.947 66.3255 68.01555 67.7270 69.5470 138.308 1000 b
## f2c(price) 36.334 38.9500 40.45085 40.1830 41.8610 55.909 1000 a
这会让您进入与本示例中的矢量化解决方案相同的一般范围。字节码引擎仍有进一步改进的空间，应该会在未来的版本中实现。
所有解决方案在处理 NA/NaN 值的方式上有所不同，这对您来说可能重要也可能不重要。
我创建了一个 for 循环，我想使用 Rcpp 库加快它的速度。我不太熟悉 C++。你能帮我加快我的功能吗？ 感谢您的帮助！
我已经将我的算法、代码、输入和输出以及会话信息包括在内。
这是我的算法：
如果当前价格高于前一个价格，则在名为 TR 的列中标记 (+1)
如果当前价格低于前一个价格，则在名为 TR 的列中标记 (-1)
如果当前价格与之前的价格相同， 在名为 TR
的列中标记与先前价格相同的内容这是我的代码：
price <- c(71.91, 71.82, 71.81, 71.81, 71.81, 71.82, 71.81, 71.81, 71.81,
71.82, 71.81, 71.81, 71.8, 71.81, 71.8, 71.81, 71.8, 71.8, 71.8,
71.8, 71.81, 71.81, 71.81, 71.81, 71.81, 71.81, 71.81, 71.81,
71.81, 71.82, 71.81, 71.81, 71.81, 71.81, 71.81, 71.81, 71.8,
71.8, 71.81, 71.81, 71.81, 71.81, 71.82, 71.82, 71.81, 71.81,
71.81, 71.81, 71.81, 71.81, 71.81, 71.82, 71.82, 71.82, 71.82,
71.82, 71.82, 71.82, 71.81, 71.82, 71.82, 71.82, 71.82, 71.82,
71.82, 71.82, 71.82, 71.82, 71.81, 71.81, 71.81, 71.82, 71.82,
71.81, 71.82, 71.82, 71.82, 71.81, 71.82, 71.82, 71.82, 71.81,
71.81, 71.82, 71.82, 71.82, 71.82, 71.82, 71.82, 71.82, 71.82,
71.82, 71.82, 71.82, 71.82, 71.82, 71.82, 71.82, 71.82, 71.82,
71.82, 71.82, 71.82, 71.82, 71.82, 71.82, 71.82, 71.82, 71.82,
71.82, 71.82, 71.82, 71.82, 71.82, 71.82, 71.82, 71.82, 71.82,
71.82, 71.82, 71.82, 71.82, 71.82, 71.82, 71.82, 71.82, 71.82,
71.82, 71.82, 71.82, 71.82, 71.82, 71.82, 71.82, 71.82, 71.81,
71.82, 71.82, 71.82, 71.82, 71.83, 71.82, 71.82, 71.82, 71.81,
71.81, 71.81, 71.81, 71.81, 71.81, 71.81, 71.82, 71.82, 71.82,
71.81, 71.81, 71.81, 71.82, 71.82, 71.82, 71.82, 71.82, 71.82,
71.82, 71.82, 71.82, 71.82, 71.82, 71.83, 71.83, 71.83, 71.83,
71.83, 71.83, 71.83, 71.83, 71.83, 71.83, 71.83, 71.83, 71.83,
71.83, 71.83, 71.83, 71.83, 71.83, 71.83, 71.83, 71.83, 71.83,
71.83, 71.83, 71.83, 71.83, 71.83, 71.83, 71.83, 71.83, 71.83,
71.83)
TR <- numeric(length(price)-1)
TR <- c(NA,TR)
for (i in 1: (length(price)-1)){
if (price[i] == price[i+1]) {TR[i+1] = TR[i]}
if (price[i] < price[i+1]) {TR[i+1] = 1}
if (price[i] > price[i+1]) {TR[i+1] = -1}
}
这是我的输出： dput(TR) 产量
c(NA, -1, -1, -1, -1, 1, -1, -1, -1, 1, -1, -1, -1, 1, -1, 1,
-1, -1, -1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, -1, -1, -1, -1,
-1, -1, -1, -1, 1, 1, 1, 1, 1, 1, -1, -1, -1, -1, -1, -1, -1,
1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, -1, -1, -1,
1, 1, -1, 1, 1, 1, -1, 1, 1, 1, -1, -1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, -1, 1, 1, 1, 1, 1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
-1, 1, 1, 1, -1, -1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1)
这是我的会话信息：
> sessionInfo()
R version 3.1.2 (2014-10-31)
Platform: x86_64-w64-mingw32/x64 (64-bit)
locale:
[1] LC_COLLATE=English_United States.1252 LC_CTYPE=English_United States.1252
[3] LC_MONETARY=English_United States.1252 LC_NUMERIC=C
[5] LC_TIME=English_United States.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] data.table_1.9.4
loaded via a namespace (and not attached):
[1] chron_2.3-45 plyr_1.8.1 Rcpp_0.11.1 reshape2_1.4 stringr_0.6.2 tools_3.1.2
你可以直接翻译 for 循环：
library(Rcpp)
cppFunction(
"IntegerVector proc(NumericVector x) {
const int n = x.size();
IntegerVector y(n);
y[0] = NA_INTEGER;
for (int i=1; i < n; ++i) {
if (x[i] == x[i-1]) y[i] = y[i-1];
else if (x[i] > x[i-1]) y[i] = 1;
else y[i] = -1;
}
return y;
}")
与往常一样，与基础 R 中的 for 循环相比，使用 Rcpp 可以获得相当大的加速：
proc.for <- function(price) {
TR <- numeric(length(price)-1)
TR <- c(NA,TR)
for (i in 1: (length(price)-1)){
if (price[i] == price[i+1]) {TR[i+1] = TR[i]}
if (price[i] < price[i+1]) {TR[i+1] = 1}
if (price[i] > price[i+1]) {TR[i+1] = -1}
}
return(TR)
}
proc.aaron <- function(price) {
change <- sign(diff(price))
good <- change != 0
goodval <- change[good]
c(NA, goodval[cumsum(good)])
}
proc.jbaums <- function(price) {
TR <- sign(diff(price))
TR[TR==0] <- TR[which(TR != 0)][findInterval(which(TR == 0), which(TR != 0))]
TR
}
all.equal(proc(price), proc.for(price), proc.aaron(price), proc.jbaums(price))
# [1] TRUE
library(microbenchmark)
microbenchmark(proc(price), proc.for(price), proc.aaron(price), proc.jbaums(price))
# Unit: microseconds
# expr min lq mean median uq max neval
# proc(price) 1.871 2.5380 3.92111 3.1110 4.5880 15.318 100
# proc.for(price) 408.200 448.2830 542.19766 484.1265 546.3255 1821.104 100
# proc.aaron(price) 23.916 25.5770 33.53259 31.5420 35.8575 190.372 100
# proc.jbaums(price) 33.536 38.8995 46.80109 43.4510 49.3555 112.306 100
与 for 循环相比，我们看到加速超过 100 倍，与所提供向量的向量化替代方案相比，加速超过 10 倍。
对于更大的向量（此处测试长度为 100 万），加速甚至更显着：
price.big <- rep(price, times=5000)
all.equal(proc(price.big), proc.for(price.big), proc.aaron(price.big), proc.jbaums(price.big))
# [1] TRUE
microbenchmark(proc(price.big), proc.for(price.big), proc.aaron(price.big), proc.jbaums(price.big))
# Unit: milliseconds
# expr min lq mean median uq max neval
# proc(price.big) 1.442119 1.818494 5.094274 2.020437 2.771903 56.54321 100
# proc.for(price.big) 2639.819536 2699.493613 2949.962241 2781.636460 3062.277930 4472.35369 100
# proc.aaron(price.big) 91.499940 99.859418 132.519296 140.521212 147.462259 207.72813 100
# proc.jbaums(price.big) 117.242451 138.528214 170.989065 170.606048 180.337074 487.13615 100
现在，与 for 循环相比，我们的速度提高了 1000 倍，与矢量化 R 函数相比，速度提高了约 70 倍。即使在这种大小下，如果只调用一次函数，也不清楚 Rcpp 是否优于矢量化 R 解决方案，因为编译 Rcpp 代码肯定至少需要 100 毫秒。如果这是一段在您的分析中重复调用的代码，那么加速非常有吸引力。
这可以在 R 中轻松矢量化。
例如 diff 和 findInterval:
TR <- sign(diff(price))
TR[TR==0] <- TR[which(TR != 0)][findInterval(which(TR == 0), which(TR != 0))]
也许先尝试矢量化。尽管这可能不如 Rcpp 快，但它更直接。
f2 <- function(price) {
change <- sign(diff(price))
good <- change != 0
goodval <- change[good]
c(NA, goodval[cumsum(good)])
}
而且它仍然比 R for 循环有很大的加速。
f1 <- function(price) {
TR <- numeric(length(price)-1)
TR <- c(NA,TR)
for (i in 1: (length(price)-1)){
if (price[i] == price[i+1]) {TR[i+1] = TR[i]}
if (price[i] < price[i+1]) {TR[i+1] = 1}
if (price[i] > price[i+1]) {TR[i+1] = -1}
}
TR
}
microbenchmark(f1(price), f2(price), times=100)
## Unit: microseconds
## expr min lq mean median uq max neval cld
## f1(price) 550.037 592.9830 756.20095 618.7910 703.8335 3042.530 100 b
## f2(price) 36.915 39.3285 56.45267 45.5225 60.1965 184.536 100 a
你可以试试字节编译。查看使用与 Rcpp 代码相同的 if-else-if-else 逻辑的 R 循环也很有用。使用 R 3.1.2 我得到
f1 <- function(price) {
TR <- numeric(length(price)-1)
TR <- c(NA,TR)
for (i in 1: (length(price)-1)){
if (price[i] == price[i+1]) {TR[i+1] = TR[i]}
if (price[i] < price[i+1]) {TR[i+1] = 1}
if (price[i] > price[i+1]) {TR[i+1] = -1}
}
return(TR)
}
f2 <- function(price) {
TR <- numeric(length(price)-1)
TR <- c(NA,TR)
for (i in 1: (length(price)-1)){
if (price[i] == price[i+1]) {TR[i+1] = TR[i]}
else if (price[i] < price[i+1]) {TR[i+1] = 1}
else {TR[i+1] = -1}
}
return(TR)
}
library(compiler)
f1c <- cmpfun(f1)
f2c <- cmpfun(f2)
library(microbenchmark)
microbenchmark(f1(price), f2(price), f1c(price), f2c(price), times = 1000)
## Unit: microseconds
## expr min lq mean median uq max neval cld
## f1(price) 536.619 570.3715 667.3520 586.2465 609.9280 45046.462 1000 d
## f2(price) 328.592 351.2070 386.5895 365.0245 381.4850 1302.497 1000 c
## f1c(price) 167.570 182.4645 218.9537 192.4780 204.7810 7843.291 1000 b
## f2c(price) 96.644 107.4465 124.1324 113.5470 121.5365 1019.389 1000 a
R-devel 将于 4 月作为 R 3.2.0 发布，在用于此类标量计算的字节码引擎中有许多改进；我得到了
microbenchmark(f1(price), f2(price), f1c(price), f2c(price), times = 1000)
## Unit: microseconds
## expr min lq mean median uq max neval cld
## f1(price) 490.300 520.3845 559.19539 533.2050 548.6850 1330.219 1000 d
## f2(price) 298.375 319.7475 348.71384 330.4535 342.6405 1813.113 1000 c
## f1c(price) 61.947 66.3255 68.01555 67.7270 69.5470 138.308 1000 b
## f2c(price) 36.334 38.9500 40.45085 40.1830 41.8610 55.909 1000 a
这会让您进入与本示例中的矢量化解决方案相同的一般范围。字节码引擎仍有进一步改进的空间，应该会在未来的版本中实现。
所有解决方案在处理 NA/NaN 值的方式上有所不同，这对您来说可能重要也可能不重要。