我正在尝试编写一个简单的例程来获取单位平方（二维）中某些区域的度量 我知道我可以使用标准数量之一。集成函数，但我想尝试优化我的代码，因为我感兴趣的函数类非常小。
这是我的代码：
#GRID DEFINITION
listgrid <- vector(mode="list", length=10201)
listgrid2 <- vector(mode="list", length=101)
for(i in 0:100)
{
listgrid2[[i+1]] <- c(NaN,i/100)
}
listgrid = rep(listgrid2,101)
for(j in 0:100)
{
for(k in 0:100)
{
listgrid[[j*101 + k + 1]][1] <- j/100
}
}
#Indicator Integration Routine
indint <- function(FUN){
valuegrid <- sapply(listgrid, FUN)
result <- ifelse(sum(valuegrid)== 0,0, (sum(valuegrid)+50.5)/10201)
return(result)
}
它只是定义一个网格然后它会尝试所有的点并检查它是零还是1并添加网格的元素。 50.5是偏差校正（非常粗糙，我知道）
问题在于，这既不快也不准确，我想可能是因为这个...... [/ p>
我想知道区域的区域并不复杂，它们有明确的界限。
你会如何解决这个问题？
谢谢。
答案 0 :(得分：2)
从我完成的小测试开始，你可能最好使用cubature包中的adaptIntegrate函数，因为它是用C语言编写的。
否则，如果您可以将函数从返回向量值更改为x和y的单独输入，则最好使用outer。
以下是一些测试代码，包括您的原始代码：
#GRID DEFINITION
listgrid <- vector(mode="list", length=10201)
listgrid2 <- vector(mode="list", length=101)
for(i in 0:100)
{
listgrid2[[i+1]] <- c(NaN,i/100)
}
listgrid = rep(listgrid2,101)
for(j in 0:100)
{
for(k in 0:100)
{
listgrid[[j*101 + k + 1]][1] <- j/100
}
}
#Indicator Integration Routine
indint <- function(FUN){
valuegrid <- sapply(listgrid, FUN)
result <- ifelse(sum(valuegrid)== 0,0, (sum(valuegrid)+50.5)/10201)
return(result)
}
#Matrix Grid
Row <- rep(seq(0, 1, .01), 101)
Col <- rep(seq(0, 1, .01), each = 101)
Grid <- cbind(Row, Col)
#Integrator using apply
QuickInt <- function(FUN) {
#FUN must be vector-valued and two-dimensional
return(sum(apply(Grid, 1, FUN)) / dim(Grid)[1])
}
#Integrator using mapply
QuickInt2 <- function(FUN) {
#FUN must take separate X and Y inpute and two-dimensional
return(sum(mapply(FUN, x = Row, y = Col)) / length (Row))
}
#Index for each axis for outer
Index <- seq(0, 1, .01)
#Integrator using outer
QuickInt3 <- function(FUN) {
#FUN must take separate X and Y inpute and two-dimensional
return(sum(outer(Index, Index, FUN) / (length(Index) * length(Index))))
}
以下是一些测试功能：
#Two test functions, both in vector input (_V) and independant input (_I) forms
test_f1_I <- function(x, y) x^2 + y^2
test_f1_V <- function(X) X[1]^2 + X[2]^2
test_f2_I <- function(x, y) sin(x) * cos(y)
test_f2_V <- function(X) sin(X[1]) * cos(X[2])
测试准确度：
#Accuracy Test
library(cubature)
TrueValues <- c(adaptIntegrate(test_f1_V, c(0, 0), c(1, 1))$integral, adaptIntegrate(test_f2_V, c(0, 0), c(1, 1))$integral)
InditV <- c(indint(test_f1_V), indint(test_f2_V))
Q1V <- c(QuickInt(test_f1_V), QuickInt(test_f2_V))
Q2V <- c(QuickInt2(test_f1_I), QuickInt2(test_f2_I))
Q3V <- c(QuickInt3(test_f1_I), QuickInt3(test_f2_I))
Rs <- rbind(TrueValues, InditV, Q1V, Q2V, Q3V)
Rs
> Rs
[,1] [,2]
TrueValues 0.6666667 0.3868223
InditV 0.6749505 0.3911174
Q1V 0.6700000 0.3861669
Q2V 0.6700000 0.3861669
Q3V 0.6700000 0.3861669
速度测试（R-3.1.2，Intel i7-2600K超频至4.6Ghz，16GB RAM，Windows 7 64位，R使用OpenBLAS 2.13编译）。
#Speed Test
library(microbenchmark)
Spd1 <- microbenchmark(adaptIntegrate(test_f1_V, c(0,0), c(1,1)),
indint(test_f1_V),
QuickInt(test_f1_V),
QuickInt2(test_f1_I),
QuickInt3(test_f1_I),
times = 100L,
unit = "relative",
control = list(order = 'block'))
Spd2 <- microbenchmark(adaptIntegrate(test_f2_V, c(0,0), c(1,1)),
indint(test_f2_V),
QuickInt(test_f2_V),
QuickInt2(test_f2_I),
QuickInt3(test_f2_I),
times = 100L,
unit = "relative",
control = list(order = 'block'))
> Spd1
Unit: relative
expr min lq mean median uq max neval
adaptIntegrate(test_f1_V, c(0, 0), c(1, 1)) 1.000000 1.000000 1.000000 1.000000 1.000000 1.00000 100
indint(test_f1_V) 491.861071 543.166516 544.559059 568.961534 500.777301 934.62521 100
QuickInt(test_f1_V) 1777.972641 2102.021092 2188.762796 2279.148477 2141.449811 1781.36640 100
QuickInt2(test_f1_I) 681.548730 818.746406 887.345323 875.833969 923.155066 972.86832 100
QuickInt3(test_f1_I) 4.689201 4.525525 4.795768 4.401223 3.706085 31.28801 100
> Spd2
Unit: relative
expr min lq mean median uq max neval
adaptIntegrate(test_f2_V, c(0, 0), c(1, 1)) 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000 100
indint(test_f2_V) 192.49010 216.29215 232.99091 226.67006 239.32413 464.76166 100
QuickInt(test_f2_V) 628.38419 770.34591 872.15128 870.58423 937.67521 1018.59902 100
QuickInt2(test_f2_I) 267.68011 306.74721 345.69155 332.67551 352.19696 726.86772 100
QuickInt3(test_f2_I) 12.32649 12.05382 12.15228 11.89353 11.75468 26.54075 100
答案 1 :(得分：1)
这是使用简单蒙特卡罗技术估算的矢量化方法。您可以使用n和replicate.n进行游戏，以获得所需的准确度/速度。
MC_estimator <- function(expr, n = 1E5, replicate.n = 50){
f <- function(){
# generate points randomly over the unit square
x <- runif(n, min = -1)
y <- runif(n, min = -1)
# select points only in unit circle
sel <- sqrt(x^2 + y^2) <= 1
x <- x[sel]
y <- y[sel]
selN <- length(x)
# select points in the desired area, find the proportion and multiply by total area of the sampled region for an estimate
eval(parse(text = paste("pi*sum(", expr, ")/selN")))
}
# replicate the procedure to bootstrap the estimate
mean(replicate(replicate.n, f()))
}
expr <- "y <= sin(x) & y >= x^2 & y <= 0.5 - x"
> system.time(MC_estimator(expr))
user system elapsed
1.20 0.01 1.23
> true <- 0.0370152
> estimated <- MC_estimator(expr)
> cat(round(100*(estimated - true)/true, 2), "% difference")
0.34 % difference