similar to: Design decision: Dynamically adding data question.

Hi All I have made my xapian indexer automatically create new indexes once it reaches X documents in each, and for each document that I add to each sub-index, I record its document_id and its index_id (relating to what index the document ended up in). writabledatabase_add_document() returns document_id:s beginning from 0 for each new index when you add new documents, like you would expect. So

Hi guys I have implemented xapian on a website, and it currently has about 2M items in its index. Its all been working quite nicely so far, until I tried removing some old items from the index (removing items when the index was smaller was no problems at all). When I try to remove them now (using writabledatabase_delete_document() via php), it halfway freezes up the machine, and the apache

OK, I'm indexing my data with the scriptindex. I want to be able to restrict the search by the category field. Do I need to do anything to the data itself? Like, literally prefix it with the characters "XC"? Below is my indexor for scriptindex and the my php code... document_id : field=ref unique=Q boolean=Q search_id : field=document_id index=S document_title : field=title

Hi all Is there a way to do adaptive query scoring (as in popular results returned by a query should get more weight because they are getting clicked more often) in xapian? Is this what the rset class should be used for? I could write a php app to do adaptive results scoring for separate words (just recording the clicks and then have a cron:ned script add weight to the document_id:s for the

Hello, this question is a bit out of the blue. I am a big R fan and user and in my new job I do some decision modeling (mostly health economics). For that decision trees are often used (I guess the most classic example is the investment decision A, B, and C with different probabilities, what is the expected payoff). We use a specialized software called TreeAge that some might know. The basic

I am new to ferret and am just reading about it in the O''reilly shortcuts as well as other web resources. My app is a Rails app and so I am looking into acts_as_ferret as well. There are some questions for which I couldn''t find answers in the material I have read so far so I''d appreciate any help on these from the list. A bit of a background. My app will have 10,000 -

Hi all, Ok folks, here''s how it is. The windows-api project will, from now on, consist of two parts. The first part is "win32-api". This is a replacement for ''Win32API''. This is a refactored version of the Win32API that currently ships with Ruby''s stdlib. It is a C extension. Changes include: * Name change - it''s now

As I''ve read in http://gnumonks.org/papers/netfilter-lk2000/presentation.html, there''s said about the "nat" netfilter table: "This table is different from the ''filter'' table, in that only the first packet of a new connection will traverse the table. The result of this traversal is then applied to all future packets of the same connection." I

Hi, I''d like to mark locally generated packets in the OUTPUT chain and do policy based routing (selecting one of two default gateways) based on the mark value. But when the packet hits the OUTPUT chain (in ''mangle'' table), the routing decision seems to be already made. AFAIK, locally generated packets do not pass the PREROUTING chain (so trying to mark them there

Dear mailinglist members, I have the following problem: I run a decision tree using the rpart function and, afterwords, I try to find to which leaf node a new register (not used to build the decision tree) belongs to. I will try to explain better: rpart.tree <- rpart(target.value ~., data) leaf.node <- new.function(rpart.tree, new.register) The new register has all the explanatory values

Hi, I am using SVM from e1071 package. I can get decision values very easily. But whenever, I try to get the probability measure, it returns NULL. I use the following codes to generate decision.values and probability. Is there anything wrong in it? predictor<-svm(train[,c(x1, x2, x3)], train[,x4], probability=TRUE) pred<-predict(predictor, test[,c(x1, x2, x3)], probability=TRUE,

I am new in using R. I want to know all about building decision tree model in R. Few options which I searched are rpart and rattle to build a decision tree.Both the functions are giving me splits which are statistically appropriate. But I am not able to figure out how to change those splits as per my business requirement. for example : the automatic split of Age by using rattle is > 30 and

Dear R, Short verse of the question: Is there R code which will calculate surrogate splits and/or delta impurity for decision trees at each node? Long Version: I have local, legacy code which I use to calculate my decision trees. I would like to switch to R, but as I understand it surrogate splits are not implemented. Surrogate splits and feature ranking are described in Breiman et al

Hi All, I need to access the node informations (viz. child nodes, split variable, split criterion etc.) for different trees packages like, CHAID, rpart, party etc. Is there an in-built function which I can use to get the requied info? Thanks a lot in advance!! Regards Ashish Kumar [[alternative HTML version deleted]]

Hi, I'm using the package randomForest to generate a classifier for the exemplary iris data set: data(iris) iris.rf<-randomForest(Species~.,iris) Is it possible to print all decision trees in the generated forest? If so, can the trees be also written to disk? What I actually need is to translate the decision trees in a random forest into equivalent C++ if-then-else constructs to

Hello everyone, I working in a public helath project and we have created a Decision Tree for categorical variables usign the package rpart. Our goal is to develop a model in order to predict presence/ausent of a diabetes and get a better understanding  of what are the important factors in a particular chilean population.  There are some importants variable that we have found.  Now we want to

As I am only familiar with the basics regarding decision trees I would like to ask, with the risk of sating a silly question: is it possible to perform recursive partitioning with the group median as the response/objective? For example, in stead of rpart focusing on means, could a similar tree be created with medians? Thanks

Hello, I have a question concerning decision trees coming from RWeka : library(RWeka) m =J48(Species~.,data=iris) How could such a decision tree be transferred into a matrix, pretty much in the same fashion, as it is done by getTree() in library(ofw) library(ofw) data(srbct) attach(srbct) ##ofwCART learn.cart.keep <- ofw(srbct,

Hi, As a number of people suggested, I downloaded the "e071" package and am using the function "svm" for my classification problem. I now want to take the classifier outside the R environment, for example to a C program, and use it. I know that the classifer is of the form WTX+b where W is defined by the support vectors. I did not see that W vector is output from svm. If am

Hi, while working with decision trees and unbalanced data, I came across the use of the Hellinger distance as an alternative to information gain [1,2], when dealing with skewed data. Does anybody know of R implementations of this approach to decision trees? Thanks, [1] http://www.cse.nd.edu/Reports/2008/TR-2008-06.pdf [2] http://csmr.ca.sandia.gov/~wpk/slides/wdmda-sem.pdf -- Rajarshi Guha NIH