![]() Now, the installation is done! You can run WEKA either in command line or in graphic user interface. Please make sure that you also set the correct path to Java, so that the system can locate and run Java. If it displays an exception error message, then you will check if you set the environment variable correctly. Type "java 48.J48 " (note the change from previous versions, in case you are familiar with any), it should display a list of all learning options for J48. Please add "setenv CLASSPATH /home/course/cs573x/weka-3-4/weka.jar" into your shell configuration profile. Please add "export CLASSPATH=/home/course/cs573x/weka-3-4/weka.jar:$CLASSPATH" into your shell configuration profile. You need to indicate the location of the weka.jar file. The following step is to set the CLASSPATH environment variable prior to use the command line. ![]() ![]() WEKA JAR CODE*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. This is a GUI window that means we don’t know about any idea just click and use, yes like that this window displays four types of options, Explorer, Experimenter, KnowledgeExplorer and Simple CLI Command line. > ln -s /home/course/cs573x/weka-3-4/weka.jar If you installed a successful weka tool you get an image looks like on your desktop or by double clicking on the weka.jar file. WEKA JAR HOW TOHow 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 WEKA JAR ARCHIVE(1) Implemented schemes for classification The jar command (jar xf, Java Archive eXtract File) in line 27 in pkgbuild isn't part of the openjdks, therefore building there falls. 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. Based on the last 12-24 days, we will forecast the next days. This file contains daily high, low, opening, and closing data for Apple computer stocks from Januto December 31, 2011. It is written in Java and runs on almost every platform. Using Weka for stock value forecasting (Advanced) The following recipe uses Apple stock data (found in the dataset directory) taken from Yahoo finance. To run the code, add the path of the folder ParallelWeka to Matlab's paths on the local machine, and at the command line enter "runParallelWeka".Waka is a collection of machine learning algorithms for solving real-world data mining problems. Matlab must be installed on all machines hosting workers. * Copy the folder ParallelWeka to the same location on all worker machines. The jar file can be found in Weka's program directory. * Copy the Weka library weka.jar to the folder ParallelWeka. * Assign the configuration name to variable "config" in runParallelWeka.m. See "Configuring Parallel Processing.txt". * Define a parallel configuration in Matlab. This is useful if algorithms can be potentially long-running or non-terminating, or when there are a large number of experiments to ensure they finish within a reasonable amount of time.īefore the provided code can be executed: Parallel processing of Weka algorithms is handled here in Matlab as support for parallel processing in Weka does not seem to be fully fledged as yet.Ī timeout can be specified on algorithm/job processing time. The code given shows how Weka algorithms can be run in parallel across distributed computers using Matlab to take advantage of available hardware and quicken algorithm completion time on multiple data sets or parameter sets.
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