- #WEKA JAR 64 BIT#
- #WEKA JAR CODE#
- #WEKA JAR DOWNLOAD#
Go back to the preprocess panel and press the Undo button to go back to the original dataset (or load the dataset in case you skipped the first example). Train an SVM using multiple affective lexicons, SentiStrength, and the average word-embedding vector Note: This example is also shown in video 1. Select the Percentage split option and start training the classifier. In this example, we are removing the attributes of type string. The FilteredClassfier allows directly passing a filter to the classifier. This is very convenient when training complex schemes with various parameters. Note: Weka allows copying and pasting the configuration of its objets. Right click on the panel right to the Choose button and click on the Edit Configuration option. Go to the classify panel and select the target class as the variable (Nom) class. Note: Select arff.gz files in the Files of Type option.Ĭhoose the TweetToSparseFeatureVector filter and configure it for calculating word n-grams, character n-grams, Brown word clusters, and POS tags: Open in the preprocess panel the dataset located in HOME/wekafiles/packages/AffectiveTweets/data/. It is strongly recommend to allocate as much memory as possible for large datasets or when calculating large dimensional features, such as word n-grams. Note: The -Xmx parameter allows incrementing the memory available for the Java virtual machine. Run WEKA and open the Explorer: java -Xmx4G -jar weka.jar Note: The following examples work with the newest version of the package. Add reference to from C:\Program Files\ikvm-.0\binĪt java.io.ObjectStreamClass.invokeReadObject(Object, ObjectInputStream )Īt java.io.ObjectInputStream.readSerialData(Object, ObjectStreamClass )Īt java.io.ObjectInputStream.readOrdinaryObject(Boolean )Īt java.io.ObjectInputStream.readObject0(Boolean )Īt java.io.faultReadFields(Object, ObjectStreamClass )Īt java.io.ObjectInputStream.readObject()Īt (InputStream stream)Īt (String filename)Īt () in C:\Users\saurabh\Documents\Visual Studio 2010\Projects\WEKAInitializer\WEKAInitializer\UnitTest1.The package can be used from the Weka GUI or the command line. Add reference to IKVM.Runtime from C:\Program Files\ikvm-.0\binģ. Run following command ikvmc -target:library weka.jarĢ. Change directory to location, where weka.jar file is located, in command promptĢ. Use IKVMC to convert the WEKA jar files to. Add "C:\WINDOWS\Microsoft.NET\Framework\v" to Path variable in system environment. Add "C:\Program Files\ikvm-.0\bin" to Path variable in system environment. Setup the environment for using IKVM tools. 
In command line prompt browse to C:\Program Files\IKVM and type IKVM and hit enter to test it. #WEKA JAR DOWNLOAD#
Download IKVM package from and put the folder extracted under C:\Program Files. Just in case, I have included steps that I have used to convert weka.jar file into weka.dll While running IKVM to convert weka.jar file into weka.dll file it gave me following warning which I ignored – unable to compile class “” I have tested running the model through a JAVA interface using weka.jar, and it has worked absolutely fine. #WEKA JAR CODE#
When I try to serialize my ANN model (generated using WEKA console) in my C# code (like below) using “weka.dll” I get “InvalidClassException” errorĬode – “ cl = ()(filepath + thrown - “java.io.InvalidClassException: local class incompatible: stream classdesc serialVersionUID = -1030230214076481435, local class serialVersionUID = 5670834184508236790}” – I have included the stack trace at the bottom of this message
#WEKA JAR 64 BIT#
I am running Weka(3.6.7) for Windows 圆4 in a 64 bit Intel® Core i7 3.40GHZ desktop machine and Windows 7. The exact steps worked for me and I was able to create a weka.dll file. I am running into following issue while trying to use weka.dll (.net version of weka.jar converted using IKVM) in my. I am posting on Pentaho forum for very first time.
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