我想找出向量中的三个最接近的数字。 像
v = c(10,23,25,26,38,50)
c = findClosest(v,3)
c
23 25 26
我尝试使用sort(colSums(as.matrix(dist(x))))[1:3]
,它的工作原理类似,但是它选择了总距离最小的三个数字而不是三个最接近的数字。
对于matlab已经有一个答案,但是我不知道如何将其转换为R:
%finds the index with the minimal difference in A
minDiffInd = find(abs(diff(A))==min(abs(diff(A))));
%extract this index, and it's neighbor index from A
val1 = A(minDiffInd);
val2 = A(minDiffInd+1);
How to find two closest (nearest) values within a vector in MATLAB?
答案 0 :(得分:11)
我的假设是,对于n
最接近的值,唯一重要的是v[i] - v[i - (n-1)]
之间的差异。也就是说,找到diff(x, lag = n - 1L)
的最小值。
findClosest <- function(x, n) {
x <- sort(x)
x[seq.int(which.min(diff(x, lag = n - 1L)), length.out = n)]
}
findClosest(v, 3L)
[1] 23 25 26
答案 1 :(得分:6)
让我们通过“最小L1距离之和的数字”来定义“最近的数字”。您可以结合使用diff
和加总和来实现所需的功能。
您可以编写一个简短得多的函数,但我逐步编写了此函数,以使其易于使用。
v <- c(10,23,25,26,38,50)
#' Find the n nearest numbers in a vector
#'
#' @param v Numeric vector
#' @param n Number of nearest numbers to extract
#'
#' @details "Nearest numbers" defined as the numbers which minimise the
#' within-group sum of L1 distances.
#'
findClosest <- function(v, n) {
# Sort and remove NA
v <- sort(v, na.last = NA)
# Compute L1 distances between closest points. We know each point is next to
# its closest neighbour since we sorted.
delta <- diff(v)
# Compute sum of L1 distances on a rolling window with n - 1 elements
# Why n-1 ? Because we are looking at deltas and 2 deltas ~ 3 elements.
withingroup_distances <- zoo::rollsum(delta, k = n - 1)
# Now it's simply finding the group with minimum within-group sum
# And working out the elements
group_index <- which.min(withingroup_distances)
element_indices <- group_index + 0:(n-1)
v[element_indices]
}
findClosest(v, 2)
# 25 26
findClosest(v, 3)
# 23 25 26
答案 2 :(得分:5)
一个想法是使用zoo
库进行滚动操作,即
library(zoo)
m1 <- rollapply(v, 3, by = 1, function(i)c(sum(diff(i)), c(i)))
m1[which.min(m1[, 1]),][-1]
#[1] 23 25 26
或使其成为一个函数
findClosest <- function(vec, n) {
require(zoo)
vec1 <- sort(vec)
m1 <- rollapply(vec1, n, by = 1, function(i) c(sum(diff(i)), c(i)))
return(m1[which.min(m1[, 1]),][-1])
}
findClosest(v, 3)
#[1] 23 25 26
答案 3 :(得分:5)
一个基本的R选项,想法是我们首先对向量进行sort
并在排序后的向量中将每个i
元素与i + n - 1
元素相减,然后选择差异最小的组。 / p>
closest_n_vectors <- function(v, n) {
v1 <- sort(v)
inds <- which.min(sapply(head(seq_along(v1), -(n - 1)), function(x)
v1[x + n -1] - v1[x]))
v1[inds: (inds + n - 1)]
}
closest_n_vectors(v, 3)
#[1] 23 25 26
closest_n_vectors(c(2, 10, 1, 20, 4, 5, 23), 2)
#[1] 1 2
closest_n_vectors(c(19, 23, 45, 67, 89, 65, 1), 2)
#[1] 65 67
closest_n_vectors(c(19, 23, 45, 67, 89, 65, 1), 3)
#[1] 1 19 23
在平局的情况下,由于我们使用的是which.min
,这将返回具有最小值的数字。
基准
由于我们已经获得了很多答案,因此值得对所有解决方案进行基准测试
set.seed(1234)
x <- sample(100000000, 100000)
identical(findClosest_antoine(x, 3), findClosest_Sotos(x, 3),
closest_n_vectors_Ronak(x, 3), findClosest_Cole(x, 3))
#[1] TRUE
microbenchmark::microbenchmark(
antoine = findClosest_antoine(x, 3),
Sotos = findClosest_Sotos(x, 3),
Ronak = closest_n_vectors_Ronak(x, 3),
Cole = findClosest_Cole(x, 3),
times = 10
)
#Unit: milliseconds
# expr min lq mean median uq max neval cld
#antoine 148.751 159.071 163.298 162.581 167.365 181.314 10 b
# Sotos 1086.098 1349.762 1372.232 1398.211 1453.217 1553.945 10 c
# Ronak 54.248 56.870 78.886 83.129 94.748 100.299 10 a
# Cole 4.958 5.042 6.202 6.047 7.386 7.915 10 a
答案 4 :(得分:0)
要在数据框中使用
data%>%
group_by(var1,var2)%>%
do(data.frame(findClosest(.$val,3)))