我在尝试实现一个k-NN算法，但结果始终是非常低的准确性值。一定存在逻辑错误，但我不确定错误在哪里。以下是代码：

start <- Sys.time()
AccEuc <- NULL
AccMan <- NULL
for(K in grid){
        cvAccEuc <- NULL
        cvAccMan <- NULL

        for (fold in 1:10){
                split = kfoldsplit(dfmerged,10,fold)
                train <- split[[1]][,-c(1,2)]
                valid <- split[[2]][,-c(1,2)]
                trainclass <- split[[1]][,2]
                validclass <- split[[2]][,2]

                combined=rbind(valid,train)
                eucdistcombined = as.matrix(dist(combined, method = "euclidian")) # 欧几里得距离
                mandistcombined = as.matrix(dist(combined, method = "manhattan")) # 曼哈顿距离

                lnvalid = dim(valid)[1]; lntrain = dim(train)[1]; lnall = lnvalid + lntrain

                eucdistcombined = eucdistcombined[1:lnvalid,(lnvalid+1):lnall]
                mandistcombined = mandistcombined[1:lnvalid,(lnvalid+1):lnall]

                neighbors_euc = t(apply(eucdistcombined, 1, order))
                neighbors_man = t(apply(mandistcombined, 1, order))

                idxeuc = t(apply(neighbors_euc, 1, function(x)( x <= K) ))
                idxman = t(apply(neighbors_man, 1, function(x)( x <= K) ))

                predseuc <- apply(idxeuc, 1, function(x) as.numeric(getmode( trainclass[as.vector(x)] )) )
                predsman <- apply(idxman, 1, function(x) as.numeric(getmode( trainclass[as.vector(x)] )) )

                cvAccEuc <- c( cvAccEuc, sum(validclass == predseuc)/dim(validclass)[1] )
                cvAccMan <- c( cvAccMan, sum(validclass == predsman)/dim(validclass)[1] )
        }

        AccEuc <- rbind(AccEuc, t(c(K,cvAccEuc)))
        AccMan <- rbind(AccMan, t(c(K,cvAccMan)))
}
Sys.time() - start

回答：

( x <= K) 应该替换为 x[1:K]。这里的 x 是包含 eucdistcombined/mandistcombined 行顺序值的行。 ( x <= K) 仅提供值小于K的索引，然而所需的是最小距离值的索引。应该使用 x[1:K] 来获取K个最近邻居。