similar to: knn using custom distance metric

Displaying 20 results from an estimated 2000 matches similar to: "knn using custom distance metric"

2011 Sep 08
1
error in knn: too many ties in knn
Hello. I found the behavior of knn( http://stat.ethz.ch/R-manual/R-devel/library/class/html/knn.html) function looking very strange. Consider the toy example. > library(class) > train <- matrix(nrow=5000,ncol=2,data=rnorm(10000,0,1)) > test <- matrix(nrow=10,ncol=2,data=rnorm(20,0,1)) > cl <- rep(c(0,1),2500) > knn(train,test,cl,1) [1] 1 1 0 0 1 0 1 1 0 1 Levels: 0 1 It
2006 Jun 07
1
knn - 10 fold cross validation
Hi, I was trying to get the optimal 'k' for the knn. To do this I was using the following function : knn.cvk <- function(datmat, cl, k = 2:9) { datmatT <- (datmat) cv.err <- cl.pred <- c() for (i in k) { newpre <- as.vector(knn.cv(datmatT, cl, k = i)) cl.pred <- cbind(cl.pred, newpre) cv.err <- c(cv.err, sum(cl != newpre)) }
2007 Apr 11
1
Function knn.dist from knnflex library
Hello, I am feeling that this question can have a very simple answer, but I can't find it. I need to use the function knn.dist from knnflex library. Whatever I try, I get the error: Error in as.vector.dist(x, "character") : unused argument(s) ("character") First example: > a<-NULL > a<-rbind(a,c(5.2,-8.1)) > a<-rbind(a,c(8.8,-16.1)) >
2008 Oct 29
1
Help with impute.knn
ear all, This is my first time using this listserv and I am seeking help from the expert. OK, here is my question, I am trying to use impute.knn function in impute library and when I tested the sample code, I got the error as followingt: Here is the sample code: library(impute) data(khanmiss) khan.expr <- khanmiss[-1, -(1:2)] ## ## First example ## if(exists(".Random.seed"))
2011 Aug 31
8
!!!function to do the knn!!!
hi, r users i have a problem with KNN. i have 2 datasets, X0 and X1. >dim(X0) >1471*13 dim(X1) >5221*13 and for every instances in the dataset X1, i want to find the nearest neighbour(1nn) in the dataset X0. and i dont have the true classifications of dataset X1. but the function knn() need true classifications(cl) to do prediction. i just curious if there are some other function
2004 May 05
1
Segfault from knn.cv in class package (PR#6856)
The function knn.cv in the class package doesn't have error checking to ensure that the length of the classlabel argument is equal to the number of rows in the test set. If the classlabel is short, the result is often a segfault. > library(class) > dat <- matrix(rnorm(1000), nrow=10) > cl <- c(rep(1,5), rep(2,5)) > cl2 <- c(rep(1,5), rep(2,4)) > knn.cv(dat, cl) [1] 2
2005 Jul 06
1
Error message NA/NaN/Inf in foreign function call (arg 6) when using knn()
I am trying to use knn to do a nearest neighbor classification. I tried using my dataset and got an error message so I used a simple example to try and understand what I was doing wrong and got the same message. Here is what I typed into R: try [,1] [,2] [,3] [,4] r "A" "A" "T" "G" r "A" "A" "T" "G" f
2008 Sep 19
3
How to do knn regression?
Hello, I want to do regression or missing value imputation by knn. I searched r-help mailing list. This question was asked in 2005. ksmooth and loess were recommended. But my case is different. I have many predictors (p>20) and I really want try knn with a given k. ksmooth and loess use band width to define neighborhood size. This contrasts to knn's variable band width via fixing a
2005 Oct 06
1
how to use tune.knn() for dataset with missing values
Hi Everybody, i again have the problem in using tune.knn(), its giving an error saying missing values are not allowed.... again here is the script for BreastCancer Data, library(e1071) library(mda) trdata<-data.frame(train,row.names=NULL) attach(trdata) xtr <- subset(trdata, select = -Class) ytr <- Class bestpara <-tune.knn(xtr,ytr, k = 1:25, tunecontrol = tune.control(sampling
2011 Mar 02
2
*** caught segfault *** when using impute.knn (impute package)
hi, i am getting an error when calling the impute.knn function (see the screenshot below). what is the problem here and how can it be solved? screenshot: ################## *** caught segfault *** address 0x513c7b84, cause 'memory not mapped' Traceback: 1: .Fortran("knnimp", x, ximp = x, p, n, imiss = imiss, irmiss, as.integer(k), double(p), double(n), integer(p),
2005 Mar 21
1
How to do knn regression
How can I do a simple k nearest neighbor regression in R? My training data have 1 predictor and 1 outcome, both are numeric. I also need to use FPE and SC to find the optimal model. I know there is knn() in class package, but it's for knn classification. I also find a kknn package. What function should I use? Thanks in advance! Menghui
2005 Mar 15
1
KNN one factor predicting problem
Could anybody help me out please? > cl<-as.factor(traindata[,13]) > knn(traindata[1:295,2], newdata[1:32,2], cl,k=2, prob=TRUE) Error in knn(traindata[1:295, 2], newdata[1:32, 2], cl, k = m, prob = TRUE) : Dims of test and train differ Both traindata and newdata have 13 elements. Only one of the first 12 elemnets is needed to predict the 13 element. What's the problem of
2008 Aug 11
1
question about knn
Hello all, am a newby in R, am trying the knn function, and am doing just a stupid test : > knn(c(1,2,3,4,5,6), c(3), k=4 ,prob=TRUE,factor(c(1:6))) the result is unstable !! i have each time different result : [1] 5 attr(,"prob") [1] 0.1666667 Levels: 1 2 3 4 5 6 [1] 4 attr(,"prob") [1] 0.1666667 Levels: 1 2 3 4 5 6 [1] 1 attr(,"prob") [1] 0.1666667 Levels:
2011 Aug 30
1
ROC plot for KNN
Hi I need some help with ploting the ROC for K-nearest neighbors. Since KNN is a non-parametric classification methods, the predicted value will be either 0 or 1. It will not be able to test for different cutoff to plot ROC. What is the package or functions I should use to plot ROC for KNN? Thanks. Qian [[alternative HTML version deleted]]
2004 Mar 29
1
Interpreting knn Results
Maybe you should show your colleague how to access help pages in R? Right in ?knn, it says: prob: If this is true, the proportion of the votes for the winning class are returned as attribute 'prob'. so 1.0 mean all three NNs are of the `winning'; i.e., predicted, class, and 0.66667 means 2 out of the 3 NNs are of the winning class, etc. Andy > From: Ko-Kang
2008 Oct 01
1
knn class probabilities
Good Day, I'm using the knn function in the package class. With k set to 3, the function returns proportions of 1/3, 0.5, 0.6, 2/3, 3/4, and 1.0 for the test cases. I don't understand how with k set to 3 the proportions can be anything other than 1/3, 2/3, or 1.0 I've seen similar inconsistencies with k set to 5. R version 2.5.0 with redhat linux. Thanks in advance. Mike
2018 Apr 26
1
help with tdm matrix and knn
hello sir im working on text classification using java and r programming i start with exporting a document term matrix (tdm) from my java programme using corpus and now i try to apply knn algorithem using the matrix and r but i cant do that any help sir here my data here my script https://mega.nz/#!Q6J2ibAA!4PadiOKbP7rLodyiRrVsdKl-D2ZP7LYm0gaz94uBmF8 itry to post put icant whay!!
2004 Aug 24
1
help with knn from class library
Hi all, I made some computations with the knn function from the class library. If I execute this function several times (with the same parameters k, training set and test set), I obtain different results. I don't understand why the results for my test set are different. Could you give me some explanations? Is the solution for a k-nearest-neighbor classifier unique? Best regards.
2004 Feb 23
2
outputs of KNN prediction
Hello there: I got 13 variables in my training/target set, the first 12 variables are mixture of numerical and categorical variables. The last one is the one I need to predict, and it is a numerical variable. >train<-read.table("train.txt") >test<-read.table("test.txt") >cl<-factor(train[,13]) >pred<-knn(train, test, clk=3, prob=TRUE) >pred I got
2009 May 14
1
KNN script: Identity of specific K samples chosen?
I am currently doing some prediction work using the knn script in the 'class' package. Does anyone know a way of having R return the IDs (sample IDs, or column IDs of the training matrix) of the 'k' samples that are chosen by the algorithm as being nearest to a given test sample? I have searched/read everything I can about the script, however have not found anything other than the