*note: this will only work on Linux or Solaris workstations, as this will run GUI version of WEKAĥ) Please make the following two links to enable you to access the source code of weka and some sample datasets. How to install and run WEKA on CS account?:ģ) Create a symbolic link to our installed weka pack: WEKA also contains a large variety of tools that can be used for pre-processing datasets, so that you can focus on your algorithm without considering too much details as reading the data from files, implementing filtering algorithm and providing code to evaluate the results. (2) Implemented schemes for numeric prediction (1) Implemented schemes for classification There are three major implemented schemes in WEKA.

You can access the WEKA class library from your own Java program, and implement new machine learning algorithms.

It is written in Java and runs on almost every platform. The WekaDeepLearning4j package can be installed for training deep neural networks and word embeddings.Waka is a collection of machine learning algorithms for solving real-world data mining problems.Java -cp $WEKA_PATH/weka.jar -install-package snowball-stemmers Snowball-stemmers: This package allows using the Porter stemmer as well as other Snowball stemmers.Java -cp $WEKA_PATH/weka.jar -install-package RankCorrelation Java -cp $WEKA_PATH/weka.jar -install-package LibSVM Java -cp $WEKA_PATH/weka.jar -install-package LibLINEAR LibLinear: This package is required for running the examples.We recommend installing other useful packages for classification, regression and evaluation: The package must either be installed or JVM must be started in AffectiveTweets directory. The software can be tested using Junit test cases. Java -cp $WEKA_PATH/weka.jar -install-package dist/AffectiveTweets.zip

This is very useful if you want to modify the code or contribute with a new feature. You can also build the package from the Github version using the project’s build-package.xml file with the ant build tool. In case of having problems with the Weka packages repository, install the package as follows: java -cp $WEKA_PATH/weka.jar -install-package Make sure you have installed version 1.0.2 to run the examples. Install AffectiveTweets1.0.2 using the WekaPackageManager: java -cp $WEKA_PATH/weka.jar -install-package AffectiveTweets You can also build the developer branch from the SVN repository: # checkout weka Download the latest stable version or the developer branch of Weka.

Weka jar