Skip to main content

JAVA CLASSPATH setting



Source: http://weka.wikispaces.com/CLASSPATH

Win32 (2k and XP)

We assume that the mysql-connector-java-3.1.8-bin.jar archive is located in the following directory:
  • C:\Program Files\Weka-3-4
In the Control Panel click on System (or right click on My Computer and select Properties) and then go to the Advanced tab. There you will find a button called Environment Variables, click it.
Depending on, whether you're the only person using this computer or it is a lab computer shared by many, you can either create a new system-wide (you are the only user) environment variable or a user dependent one (recommended for multi-user machines). Enter the following name for the variable
  • CLASSPATH
and add this value
  • C:\Program Files\Weka-3-4\mysql-connector-java-3.1.8-bin.jar
If you want to add additional jars, you'll have to separate them with the path separator, the semicolon ; (no spaces!).

Unix/Linux

I assume, that the mysql jar is located in the following directory:
  • /home/johndoe/jars/
Open a shell and execute the following command, depending on the shell you're using:
  • bash
    export CLASSPATH=$CLASSPATH:/home/johndoe/jars/mysql-connector-java-3.1.8-bin.jar
  • c shell
    setenv CLASSPATH $CLASSPATH:/home/johndoe/jars/mysql-connector-java-3.1.8-bin.jar

Unix/Linux uses the colon : as path separator, in contrast to Win32, which uses the semicolon ;

Run before adding external jar Unix/Linux

 java -jar weka.jar

Run after adding external jar Unix/Linux 

You can not use same command java -jar , when you add external jar. You have to 
use java -classpath 
 
java -classpath $CLASSPATH:weka.jar:libsvm.jar weka.gui.GUIChooser  (linux )
 
java -classpath "%CLASSPATH%;weka.jar;libsvm.jar" weka.gui.GUIChooser (windows)

Comments

Post a Comment

Popular posts from this blog

MATLAB cross validation

// use built-in function samplesize = size( matrix , 1); c = cvpartition(samplesize,  'kfold' , k); % return the indexes on each fold ///// output in matlab console K-fold cross validation partition              N: 10    NumTestSets: 4      TrainSize: 8  7  7  8       TestSize: 2  3  3  2 ////////////////////// for i=1 : k    trainIdxs = find(training(c,i) ); %training(c,i);  // 1 means in train , 0 means in test    testInxs  = find(test(c,i)       ); % test(c,i);       // 1 means in test , 0 means in train    trainMatrix = matrix (  matrix(trainIdxs ), : );    testMatrix  = matrix (  matrix(testIdxs  ), : ); end //// now calculate performance %%  calculate performance of a partition     selectedKfoldSen=[];selectedKfoldSpe=[];selectedKfoldAcc=[];     indexSen=1;indexSpe=1;indexAcc=1;     if ( kfold == (P+N) )% leave one out         sensitivity = sum(cvtp) /( sum(cvtp) + sum(cvfn) )         specificity = sum(cvtn) /( sum(cvfp) + sum(cvtn) )         acc
5 comments
Read more

R tutorial

Install R in linux ============ In CRAN home page, the latest version is not available. So, in fedora, Open the terminal yum list R  --> To check the latest available version of r yum install R --> install R version yum update R --> update current version to latest one 0 find help ============ ?exact topic name (  i.e.   ?mean ) 0.0 INSTALL 3rd party package  ==================== install.packages('mvtnorm' , dependencies = TRUE , lib='/home/alamt/myRlibrary/')   #  install new package BED file parsing (Always use read.delim it is the best) library(MASS) #library(ggplot2) dirRoot="D:/research/F5shortRNA/TestRIKEN/Rscripts/" dirData="D:/research/F5shortRNA/TestRIKEN/" setwd(dirRoot) getwd() myBed="test.bed" fnmBed=paste(dirData, myBed, sep="") # ccdsHh19.bed   tmp.bed ## Read bed use read.delim - it is the  best mybed=read.delim(fnmBed, header = FALSE, sep = "\t", quote = &q
Post a Comment
Read more