希望你们知道很难在通用数据集上复制这样的内容。
基本上,我想做的是对两个不同大小的k的七个不同值的测试和训练集执行K-NN。
我的主要问题是,res应该是一个向量,用于存储相同火车集大小的所有精度值,但是每次迭代显示一个值,这不允许我绘制精度图,因为它们看上去是空的。
您知道如何解决此问题吗?
数据可直接在R上免费获得。
data("Sonar")
#Randomization of the sample
set.seed(123)
random <- sample(rep(1:dim(Sonar)[1]))
Sonar <- Sonar[random,]
head(Sonar)
for (i in c(50,100)){ #train/test set size
sonar.train <- Sonar[1:i,-61]
sonar.train.label <- Sonar[1:i,61]
sonar.test <- Sonar[(1+i) :208,-61]
sonar.test.label <- Sonar[(1+i) :208 ,61]
res <- rep(NA,7)
for (j in c(3,5,7,9,11,13,15)){ #values of k
mod = knn(train= sonar.train, test = sonar.test, cl = sonar.train.label, k = j) #classification for test set
err = sum(sonar.test.label==mod) #accuracy
res[match(j,c(3,5,7,9,11,13,15))] = err/length(mod) #put accuracy value in vector
print(res)
plot(x = c(3,5,7,9,11,13,15) ,y = res, type = "l" ,col = "blue", xlab = "Neighbours", ylab = "Accuracy") #plot the accuracy graphs for each of the two different train/test sets
res <- rep(NA,7)
}
}
#output
>
0.6835443 NA NA NA NA NA NA
NA 0.6582278 NA NA NA NA NA
NA NA 0.6075949 NA NA NA NA
NA NA NA 0.6265823 NA NA NA
NA NA NA NA 0.5949367 NA NA
NA NA NA NA NA 0.5949367 NA
NA NA NA NA NA NA 0.5506329
0.6759259 NA NA NA NA NA NA
NA 0.6111111 NA NA NA NA NA
NA NA 0.5648148 NA NA NA NA
NA NA NA 0.5833333 NA NA NA
NA NA NA NA 0.5925926 NA NA
NA NA NA NA NA 0.5740741 NA
NA NA NA NA NA NA 0.5740741
精度图显示为空,并且x轴上的k具有不同的标签。
感谢您阅读和帮助我!
您的内部循环应该填充res
中的值,每次迭代填充一次。但是,您似乎在循环的每次迭代结束时重置res
。这就是为什么它不保留任何先前的值。
这两行必须是内循环外(以及外循环内)
plot(x = c(3,5,7,9,11,13,15) ,y = res, type = "l" ,col = "blue", xlab = "Neighbours", ylab = "Accuracy") #plot the accuracy graphs for each of the two different train/test sets
res <- rep(NA,7)
[绘图功能和res
的重新初始化应该在内部循环之外,否则,您需要在每个内部循环内将res
重置为NA的向量。
新的for周期应如下
for (i in c(50,100)){ #train/test set size
sonar.train <- Sonar[1:i,-61]
sonar.train.label <- Sonar[1:i,61]
sonar.test <- Sonar[(1+i) :208,-61]
sonar.test.label <- Sonar[(1+i) :208 ,61]
res <- rep(NA,7)
for (j in c(3,5,7,9,11,13,15)){ #values of k
mod = knn(train= sonar.train, test = sonar.test, cl = sonar.train.label, k = j) #classification for test set
err = sum(sonar.test.label==mod) #accuracy
res[match(j,c(3,5,7,9,11,13,15))] = err/length(mod) #put accuracy value in vector
}
plot(x = c(3,5,7,9,11,13,15) ,y = res, type = "l" ,col = "blue", xlab = "Neighbours", ylab = "Accuracy", main = paste("i =", i)) #plot the accuracy graphs for each of the two different train/test sets
res <- rep(NA,7)
}
顺便说一句,我在绘图函数中添加了main = paste("i =", i)
,以便识别循环所指的是哪个迭代。
我只有在写完答案后才意识到@Aziz抢占了我几秒钟:D