similar to: nonparametric multiple sample comparison

Hi Rishabh, I think its better not to expose RankiList to Letor.h and make it better user friendly. So my suggestion is to convert RankList to the following statement in this method. std::map<Xapian::docid, double> letor_score(const Xapian::MSet & mset); So just convert the RankList in std::map<Xapian::docid, double> format in the methods where you need to return. Parth. On

Hi guys, I want to transfer the following code from R into .Call compatible form. How can i do that? Thanks!!! INT sim; for(i in 1:sim){ if(i>2) genemat <- genemat[,sample(1:ncol(genemat))] ranklist[,1] <- apply(genemat, 1, function(x){ (mean(x[cols]) - mean(x[-cols]))/sd(x)}) ranklist <- ranklist[order(ranklist[,1]),]

On Thu, Dec 04, 2014 at 08:28:32PM -0500, Manu Gupta wrote: > I was trying to build Han's code and was stuck here. It seems to me that > his code is trying to generate pdf for them. > > Is it possible to modify the make files to stop building the documentation. You can run the top-level configure with --disable-documentation. Cheers, Olly

> > > > The approach I was thinking would look something like this: > > * instead of Features, which is really a namespace implemented as a > class, we separate out the calculation of the different features > into distinct subclasses of Feature, whose only job is to calculate > a single feature. Currently the FeatureManager calls these (via >

Hi, I've read the code of letor module. And I have some questions about it: 1. In https://github.com/rishabhmehrotra/xapian/blob/master/xapian-letor/letor_internal.cc#L299, there is a write_to_file method, which save RankList into ?train.txt?. But the format for ?train.txt? is different from the one mentioned in http://trac.xapian.org/wiki/GSoC2011/LTR/Notes#QueryLevelNorm. And in

Hi all, Thank you for giving me the opportunity to work with Xapian :) I am Jiarong Wei, a third year undergraduate student in Zhejiang University, China. In GSoC 2014, I will work on Letor module with Hanxiao Sun. Here are some questions I encountered these days, 1. In letor.cc, we have two parts of functions: the training part and the ranking part. I?ll use SVMRanker as an example. The

Thanks for your reply! For the third question: In https://inex.mmci.uni-saarland.de/data/documentcollection.jsp, I can find inex2010-article.qrels in 2010 assessment, but can?t find query files. Could you send me the link? I have registered on INEX website. And I also need to download ``INEX 2009 collection without annotation tags: (unofficial)`` on

Hello, I applied to letor stabilisation project for gsoc. I'd like to use coming weeks to improve the workability of xapian-letor. For that, I'm planning to refactor code in current master and begin writing some tests for it. Before adding tests, I think it would be better if xapian-letor could be made consistent with how xapian-core is written. For that, I'd first like to

Hi, When I run the survfit function, I want to get the restricted mean value and the standard error also. I found out using the "print" function to do so, as shown below, print(km.fit,print.rmean=TRUE) Call: survfit(formula = Surv(diff, status) ~ 1, type = "kaplan-meier") records n.max n.start events *rmean *se(rmean) median 200.000

Hello, I create a plot from a coxph object called fit.ads4: plot(survfit(fit.ads4)) plot is located at: https://www.dropbox.com/s/9jswrzid7mp1u62/survfit%20plot.png I also create the following survfit statistics: > print(survfit(fit.ads4),print.rmean=T) Call: survfit(formula = fit.ads4) records n.max n.start events *rmean *se(rmean) median 0.95LCL 0.95UCL 203.0

One approach is to use the rms package's cph and Mean.cph functions. Mean.cph (cph calls coxph and can compute Kaplan-Meier and other survival estimates) can compute mean restricted life. Frank Dinesh W wrote > I am using survfit to generate a survival curve. My population is such > that my x axis is in days and i have a starting population of say 10,000 > of which say only 2000 are

Hi~ Manu I was glad to see you were interested in Xapian, especially the learning to rank project. There are two branch in my repo. The one named gsoc2014 is followed the thought of Parth's work in which we not assign the test set for each ranking and in another one gsoc2014-evaluation we assign the training set and test set explicitly so that we can get the evaluation score of each ranking

Hi, I'm not sure if this is the proper way to ask questions, sorry if not. But here's my problem: I'm trying to do a bootstrap estimate of the mean for some survival data. Is there a way to specifically call upon the rmean value, in order to store it in an object? I've used print(...,print.rmean=T) to print the summary of survfit, but I'm not sure how to access only rmean

Hi, Before starting my proposal, I wanted to know what is the expected output of Letor module. Is it for transfer learning (i.e you learn from one dataset and leverage it to predict the rankings of other dataset) or is it for supervised learning? For instance - Xapian currently powers the Gmane search which is by default based on BM25 weighting scheme and now suppose we want to use LETOR to rank

Hello, I have estimated a survival model with six strata: >model.b <- survfit(Surv(time=start.tijd,time2=eind.tijd2,event=va)~strata(product.code) , data=wu.wide) I would like to save the output of >print(model.b,print.n="records",show.rmean=FALSE) in a dataframe so that I can export it later. How do I do this? Note that summary(model.b) gives an error: Error in

Dear r-help, I have a matrix, suppose, 10x10, and I need the matrix 5x5, having in each cell a mean value of the cells from the initial matrix. Please, point me to a function in R, which can help me doing that. Digging the documentation and mail archives didn't give me a result. Thank you. --- Best regards, Vladimir mailto:wl at eimb.ru

Dear all, I'm trying to manage with user defined split function in rpart (file rpart\tests\usersplits.R in http://cran.r-project.org/src/contrib/rpart_3.1-34.tar.gz - see bottom of the email). Suppose to have the following data.frame (note that x's values are already sorted) > D y x 1 7 0.428 2 3 0.876 3 1 1.467 4 6 1.492 5 3 1.703 6 4 2.406 7 8 2.628 8 6 2.879 9 5 3.025 10 3 3.494

Dear Sirs, I want to estimate the survival mean of a few specific teams. I'm trying to calculate it through a Kaplan Meier estimator. For doing so, I load the "survival" package and run the following instructions: "options(survfit.print.mean=TRUE)" allows showing the mean and mean standard error "KM=survfit(Surv(Dias,Censura))"

Hello All, This time we are working on a weighting scheme "Learning to Rank" which involves machine learning and its a supervised ranking scheme unlike unsupervised schemes like BM25 under GSoC project. This mail intends to discuss the framework of the Learning to Rank in Xapian as a whole. I have thought of the following framework, pour in your insights or issues for the same. This is

The Wine maintenance release 2.0.5 is now available. What's new in this release (see below for details): - Various bug fixes The source is available from the following locations: https://dl.winehq.org/wine/source/2.0/wine-2.0.5.tar.xz http://mirrors.ibiblio.org/wine/source/2.0/wine-2.0.5.tar.xz Binary packages for various distributions will be available from: