search for: euclidian

.... I uploaded a PDF showing the results Here is the code which produces the PDF file: ------------------------------------------------------------------------------------- s <- matrix(runif(1600,0,1000), nrow=20) a.res1 <- pvclust(t(s), method.hclust="complete", method.dist="euclidian", nboot=500) a.res2 <- pvclust(t(s), method.hclust="complete", method.dist="maximum", nboot=500) a.res3 <- pvclust(t(s), method.hclust="complete", method.dist="canberra", nboot=500) a.res4 <- pvclust(t(s), method.hclust="ward", method....

...sure about that -- I would have preferred a k dimensional space with a binary cluster in each dimension so a word can belong to 0..k clusters, but I digress... I can measure the strength of correlation between words fairly easily by counting cooccurance divided by frequency of each word, giving a euclidian distance, although this doesn't work especially well for rare words. However I don't have coordinates as such, and deriving them given distance is non-trivial. Now, as I understand k-means, it uses euclidian distance rather than coordiantes, the first step given in texts is to derive the...

...rough the previous clustering API to understand how it worked and it seems like the the approach for construction of the termlists which are used for distance metrics use TF-IDF weighting with cosine similarity, which is very similar to the approach I would need for this project. Just in this case, euclidian distance would be the metric. Would it be good to structure it in a way similar to the previous API with a few changes? For example, the Xapian::DocSimCosine::similarity( ) function in itself calculates the tf idf vectors and calculates the similarity. Instead would it be possible to have a custo...

..."CG", "L-BFGS-B", "SANN") in the optim function. So I would like to have a vector with c("Nelder-Mead", "BFGS", "CG", "L-BFGS-B", "SANN") Or for instance the 'method' in the dist function can be supplied with euclidian,maximum,manhatten, canberra,binary,minkowski. Is there someway to extract this so I would have a vector with c("euclidian","maximum","manhatten", "canberra","binary","minkowski"). Thanks

the first line of dist() says if (!is.na(pmatch(method, "euclidian"))) shouldn't that be "euclidean" ? --------------------- R version 2.5.1 (2007-06-27) i486-pc-linux-gnu locale: LC_CTYPE=en_US.UTF-8;LC_NUMERIC=C;LC_TIME=en_US.UTF-8;LC_COLLATE=en_US.UTF-8;LC_MONETARY=en_US.UTF-8;LC_MESSAGES=en_US.UTF-8;LC_PAPER=en_US.UTF-8;LC_NAME=C;LC_ADD...

I'm working on a project related to document clustering. I know that R has clustering algorithms such as clara, but only supports two distance metrics: euclidian and manhattan, which are not very useful for clustering documents. I was wondering how easy it would be to extend the clustering package in R to support other distance metrics, such as cosine distance, or if there was an API for custom distance metrics. Best regards, Raymond Pon pon3 at llnl.go...

...ich I'm not able to find a solution to. Since we are representing documents as Tf-idf vectors, they are really sparse vectors (a usual corpus can have around 5000 terms). So it gets really difficult to represent these sparse vectors in a way that would be computationally efficient to calculate euclidian distances. I had implemented a K-Medioids algorithm using PAM just to try it out, after modifying the API for whatever more was required, and that seems fine, since we are dealing with document vectors and not arbitrary vectors. But with KMeans, I am not able to figure out how to represent these ce...

Hey Parth, Thanks for the reply. I am considering implementing a cosine distance metric too, along with euclidian distance because of the dimensionality issue that comes in with K-Means and euclidian distance metric. That does help when we deal with sparse vectors for documents. The particular problem I'm having is representing centroids in an efficient way. For example, when we find the mean vector of a c...

Hi All, Just want to make sure, in function "bclust", do the following argument only have one option? argument "dist.method" has one option "Euclidian"; argument "hclust.method" has one option "average"; argument "base.method" has one option "kmeans". Thank you! [[alternative HTML version deleted]]

Hi, Few days ago I have asked about spatial join on the minimum distance between 2 sets of points with coordinates and attributes in 2 different data frames. Simon Knapp sent code to do it when calculating distance on a sphere using lat, long coordinates and I've change his code to use Euclidian distances since my data had UTM coordinates. Typically one data frame has around 30 000 points and the classification data frame has around 4000 points, and the aim is to add to each point from the first data frame all the attributes from the second data frame of the point that is closest to it....

Hello devs, I have filled out the repo link on TRAC as suggested. I'll also keep the journal updated on TRAC from now on. I am almost done with defining all the base classes required for the clusterer and have started coding the euclidian distance metric. This should be completed by tomorrow after which I'll be spending one day to test and make sure everything functions as expected, so that I can have feedback from the community on areas to improve on. Thanks -------------- next part -------------- An HTML attachment was scrubb...

This message is in MIME format. The first part should be readable text, while the remaining parts are likely unreadable without MIME-aware tools. Send mail to mime@docserver.cac.washington.edu for more info. ---559023410-162216788-916833047=:29339 Content-Type: TEXT/PLAIN; charset=US-ASCII On my R installation (0.62.4) there is no dist() function, so I attach one possibility. It provides

...-0.3974660 0.1541685 1 [2,] 2 0.5321347 0.2497867 1 [3,] 3 0.2550276 0.1691720 1 [4,] 4 -0.1162162 0.6754874 1 ... [55,] 55 1.1570996 1.1175119 2 [56,] 56 1.4816195 1.6836226 2 p.s. I use Euclidian to obtain/calculate distance matrix. Many thanks in advance HJ [[alternative HTML version deleted]]

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...e x with n people as rows and k variables as columns. Now, for each person (i.e., each row) we want to calculate a distance between him/her and EACH other person in x. In other words, we want to create a n x n matrix with distances (with zeros in the diagonal). However, we do not want to calculate Euclidian distances. We want to calculate Mahalanobis distances, which take into account the covariance among variables. Below is the piece of code we wrote ("covmat" in the function below is the variance-covariance matrix among variables in Data that has to be fed into mahalonobis function we are...

Hi everyone, I have the following question: given three objects let's say: a <- c( 2 , 5, 15, 16) b <- c(1 ,1, 8 , 8) c <- c (10, 10 11 ,11) m<-matrix(c(a,b,c),byrow=T,nrow=3) rownames(m)<-c("gene a", 'gene b','gene c') m gene.dist<-dist(m,method='euclidian') gene.dist which is the best way to plot their evolution in time? shoul I use a levelplot or just a normal plot? if I use a normal plot how do I plot evolution in time? -- View this message in context: http://r.789695.n4.nabble.com/best-way-to-plot-a-evolution-in-time-tp2267993p2267993.html...

...* We can check how many programs can be compiled by different high-level synthesis tools into FPGAs. We have tried LegUp and Vivado, for instance. * Our webpage contains a search box, so that you can get the closest programs to a given input program. Currently, we measure program distance as the Euclidian distance on Namolaru feature vectors. We do not currently provide inputs for those programs. It's possible to execute the so called "leaf-functions", e.g., functions that do not call other routines. We have thousands of them. However, we do not guarantee the absence of undefined beha...

Hi all, I am trying to compare two distance matrices with R. I would like to create a XY plot of these matrices and do some linear regression on it. But, I am a bit new to R, so i have a few questions (I searched in the documentation with no success). The first problem is loading a distance matrix into R. This matrix is the output of a the Phylip program Protdist and lookes like this: 5

...n terms of speed!). > > Thanks for the reply James! I went through the code in a little more detail and there are a few things I noticed and a few questions I have. First off, the distance metric used in the current implementation is the cosine measure. Though useful, K-means implicitly uses Euclidian distance as a measure of document similarity between two document term vectors. Hence, simply creating one more class for a distance metric by just inheriting the DocSim base class will be good. Using the tf-idf weights, we can find term weights and instead of using these vectors for cosine similar...

Hi R friends, I am posting this question even though I know that the nature of it is closer to general stats than R. Please let me know if you are aware of a list for general statistical questions: I am looking for a simple method to distinguish two groups of data in a long vector of numbers: list <- c(1,2,3,2,3,2,3,4,3,2,3,4,3,2,400,340,3,2,4,5,6,4,3,6,4,5,3) I would like to