similar to: 64-bit R failed to compile some packages (PR#7440)

Hi, been trying to build R-1.5.1 for som eof our users here. No probs with gcc-3.2 32-bit installtion, but thought we'd try it with the 64-bit just for the crack. Configuring thus CC=gcc3 CFLAGS="-m64" CXX=g++3 CXXFLAGS="-m64" F77=g773 FFLAGS="-m64" LDFLAGS="-m64 -L/usr/local/packages/gcc-3.2/lib/sparcv9" ./configure

# HG changeset patch # User john.levon@sun.com # Date 1161090130 25200 # Node ID afeef751f9dafa771726bcfb00b29a10b0baeabd # Parent 7147e57f18552bbb8d25155cc1c635315283ee31 On Solaris, GCC is configured to use Sun''s LD. Fix the build to use the correct flags, and link against libsocket where necessary. Signed-off-by: John Levon <john.levon@sun.com> diff --git a/config/Linux.mk

Hi, Thanks for the replies in the mailing list. I made some progress in building LLVM, but I still have a problem about ELF class. I am working with LLVM on a sparcv9 machine, while the gcc is configured to emit 32-bit binary by default. After executing "configure --with-llvmgccdir=... --enable-jit", I modified Makefile.config so it contains CXX = g++ -mcpu=v9 -m64 CC := gcc -mcpu=v9

All, The kohonen predict function is returning NA for SOM predictions regardless of data used... even the package example for a SOM using wine data is returning NA's Does anyone have a working example SOM. Also, what is the purpose of trainY, what would be the dependent data for an unsupervised SOM? As may be apparent to you by my questions, I am very new to kohonen maps and am very grateful

R version 2.9.2 (2009-08-24) - for windows > library(SOM) Error in library(SOM) : there is no package called 'SOM' Where can I get the SOM library from? Thanks in advance -- View this message in context: http://old.nabble.com/SOM-library---where-do-I-find-it-tp26415633p26415633.html Sent from the R help mailing list archive at Nabble.com.

Hi all, I originally posted this to the bioconductor group, but maybe it's better suited to the r-help... I'm using som() to partition samples of gene expression data into clusters. The point is to classify control vs. experimental cases (sample clustering). The original matrix was 22283 x 8. The 8 samples have 4 controls and 4 experimentals. I transposed the matrix so that its dim

Dear Lance, It is not work. Any idea? Regards, Eric -----Original Message----- From: Lance Fredrickson [mailto:lancethepants at gmail.com] Sent: Wednesday, February 17, 2016 12:53 AM To: tinc at tinc-vpn.org Subject: Re: Tinc Router Mode - PING RESULT is destination host unreachable On 2/16/2016 9:04 AM, Eric Yau wrote: > Hi Lars, > > Once I modify the firewall FORWARD rule to

Bug in "SOM" package in R. This simple code withot for loop runs:-------simple.r-------- library(som) A <- as.matrix(read.table("C:/code/h.txt", sep="\t", header=TRUE, row.names=1)) print(som(A,1,2)$visual) print(som(A,1,3)$visual) ---------------------------------------- The same code if inserted in for loop generates "Assertion failure" in

Dear C?dric, If I am using windows version of tinc, how can I check the connection list? Regards, Eric -----Original Message----- From: tinc-bounces at tinc-vpn.org [mailto:tinc-bounces at tinc-vpn.org] On Behalf Of tinc-request at tinc-vpn.org Sent: Wednesday, April 18, 2012 6:00 PM To: tinc at tinc-vpn.org Subject: tinc Digest, Vol 90, Issue 17 Send tinc mailing list submissions to tinc at

I am trying to understand how the SOM algorithm works using library(class) SOM function. I have a 1000*10 matrix and I want to be able to summarize the different types of 10-element vectors. In my real world case it is likely that most of the 1000 values are of one kind the rest of other (this is an oversimplification). Say for example: InputA<-matrix(cos(1:10),nrow=900,ncol=10,byrow=TRUE)

I have a data set with 10 variables, and about 8000 instances (or objects/rows/samples). In addition I have one more ('class') variable that I have about 10 instances for, but for which I wish to impute values for. I am a little confused how to go about doing this, mostly as I'm not well-versed in it. Do I train the SOM with a data object that contains just the first 10 variables

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somfunc<- function (file) { aa_som<-scale(file) final.som<-som(data=aa_som, rlen=10000, grid=somgrid(5,4, "hexagonal")) pdf(file="/home/cdu/changbin/file.pdf") #output graphic file. plot(final.som, main="Unsupervised SOM") dev.off() } I have many different files, if I want output pdf file with the same name as for each dataset I feed to the function

Hi All, I am still learning R, but making, IMO, great strides. I learned about Kohonen/Self-Organizing Maps in class and I would like to try to replicate some of the things we have seen in class. Below is my code. I am trying to create a u-matrix. In the documentation on page 9 it appears the type of plot, dist.neighbours should do the trick, however, I am getting an error: (Error in

Greetings... I'm struggling trying to set up a samba 3.0.23c PDC with ldap backend. The server is Fedora 5, OpenLdap version 2.3.19-4. I've got it so smbd and nmbd start properly and I can use a windows box and see the domain using srvmgr.exe and usrmgr.exe. I'm then able to signon from a windows XP computer with the command net use \\pdcserver\ipc$ /user:root rootpassword Some

Is there an implementation of the SOM ('Self Organizing Map') procedure in R ? I am aware of the implementations of Sammon mapping, multidimensional scaling and, somewhat peripherally, principal curves and projection pursuit .. but not SOM per se as far as I can see. Am I missing something? --------------------- for anyone interesed in SOM I found Samuel Kaski's thesis

Hello everyone, I'm new to this list, so let me introduce myself: my name is Jonck van der Kogel and I am a graduate student at the Erasmus University of Rotterdam. I am currently working on my thesis which is on the use of artificial intelligence for large data-sets. To do an analysis of a certain data-set I want to use Kohonen's SOM algorithm. However, as I understand it, the SOM

Hi all, I have a question about the SOM routine. You can either supply the initial representatives for the lattice yourself or else they are chosen randomly from the dataset. Is it possible to pass the random-seed as an argument somehow, when choosing the random initialisation of the lattice? As it is now, each time I run a SOM on a dataset with the same settings the resulting SOM will still

Hi I have a situation where I want to be able to count how many times a caller goes round a loop of "Please hold...", "please continue to hold". I have found an example on voip-info but I can't get it to work. Not sure if I've got some syntax wrong somewhere? All that happens at the moment, is I hit is the playback of "som-debug" at 9999. Any ideas would

Hi. Three years ago, I've read the question of availability of SOM library for R using Kohonen's SOM_PAK in this mailing-list. This answer was no availability. And no package dealing with SOM in CRAN. Is this situation same? Could you tell me availability this library? Best Regards.