I can run it on Win2k Client.
I used the following command:
C:\DJFHQ\Info_Extraction\Weka>java -classpath weka-3-2-3\weka-3-2-3\weka.jar
I have WekaMetal.jar in the CLASSPATH. I also used WinZip to "unzip" the JAR
files for Weka 3.2.3 and WekaMetal after downloading them.
My version of Java is the following:
java version "1.3.0_02"
Java(TM) 2 Runtime Environment, Standard Edition (build 1
Java HotSpot(TM) Client VM (build 1.3.0_02, mixed mode)
PS I also noticed a possible typo in your quoted string which maybe should
"G:\Program Files\Weka-3-2-3\weka.jar" i.e. the "-3" is missing.
From: Chris Bacon [mailto:email@example.com]
Sent: Saturday, 17 August 2002 10:29 AM
Subject: [Wekalist] WekaMetal and Windows?
Has anyone been able to run WekaMetal on Windows? I've tried on
Server from a command prompt where I get this:
G:\Program Files\WekaMetal>java -jar WekaMetal.jar:"G:\Program
Exception in thread "main" java.util.zip.ZipException: The filename,
ame, or volume label syntax is incorrect
at java.util.zip.ZipFile.open(Native Method)
at java.util.zip.ZipFile.<init>(Unknown Source)
at java.util.jar.JarFile.<init>(Unknown Source)
at java.util.jar.JarFile.<init>(Unknown Source)
I've played around with the ClassPath, but it didn't help. I've also
from within IBM Websphere Application Developer, where it gets an IO error
because it can't find the cache files (ap.cache and dc.cache), even though
they're both in the same directory as the WekaMetal.jar file.
Any help would be appreciated.
Wekalist mailing list
I am applying ID3 to a medical data set and was wondering if there is a way to
get Weka to output the results of the ID3 algorithm graphically so that the
Specialist can interpret the results easily?
I would like to know whether someone has written/developed an Independent Component Analysis Module For Weka.
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Hello to all,
I've tried to use the K* weka alghorithm to solve a numeric prediction problem,
and it is seemed to me very good, while it is one of the few algorithms tha can
making numeric prediction working with both numerical and nominal attributes, so
I don't have the need to discretize the class before.
The problem is that I've seen that it's not possiblke to build a sort of
confusion matrix, and it's obvious, while it?s a numerical and not a class
problem. But I've the need to have an error-measure for each istance. I've seen
that it's possible to visualize all istances, and for each one to see the
effective value of the class and that of the predicted class.
I have for you some questions:
1) How can I compute classifier performances in this case?
2) How I should set, in the options window K* options in order to have a more
3) Is weka's grafic representation of test instances "editable" in any manner?
Thanks to all
when I run naiveBayes (under windows, using weka 3.4), I get the
following error when called from the command line
$ java weka.classifiers.bayes.NaiveBayes -t Nom2TrainMinEnt.0.25.arff -T
at weka.estimators.DiscreteEstimator.getProbability(Unknown Source)
at weka.classifiers.Evaluation.evaluateModelOnce(Unknown Source)
at weka.classifiers.Evaluation.evaluateModel(Unknown Source)
at weka.classifiers.bayes.NaiveBayes.main(Unknown Source)
when I run the J48 classifier, it runs w\o problems. I wonder if the
problem is in my file, but without the debug info, it is difficult to
figure out where the error is occurring. should I write a java app
wrapper for the classifier and try to catch the exception? that wont
really tell me that much more, though, I don't think.
thanks for any help\ideas,
I am trying to run PCA on a dataset of 40 examples with 971 attributes with
real number values. I keep getting an outOfMemoryError even when I increase
the values of -mx and -oss to 100000000 etc. I do this modification in the
CLI, then I open the GUI interface and run the experiments, and still get the
error and no results.
Currently I can only get PCA to work on a reduced dataset of half the examples
(485) I assume that Weka's implemention of PCA can only handle a upper limit
of 512 attributes but if I'm wrong in that regard let me know.
Also, previous versions of Weka allowed an AttributeselectionFilter in the
preprocessing stage allowing for PCA. Then the resulting set could be saved as
a different .arff file. newer versions only allow attribute selection in the
classification stage under the meta folder but there seems to be no way to
save the resulting principal components set.
I Don't mind using an older version but if there is a way to do it in 3.4.1
that'd be great.
Also if there is a way to run PCA on all my attributes it would be even
Any help would be greatly appreciated,
would there be a built in function to calculate the
fractal dimension of a multidimensional pointset? or
someone may kindly post a code?
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You can also use weka.filters.unsupervised.attribute.Remove in
weka.classifiers.meta.FilteredClassifier. That way the
FilteredClassifier can filter
out attributes that you want to ignore when building a classifier while
attributes will still be present when you visualize predictions, etc.
On Thursday, April 29, 2004, at 12:20 PM,
> From: Bernhard Pfahringer <bernhard(a)cs.waikato.ac.nz>
> Date: Wed Apr 28, 2004 8:55:24 PM Pacific/Auckland
> To: Tom Fawcett <tom.fawcett(a)comcast.net>
> Cc: wekalist(a)list.scms.waikato.ac.nz
> Subject: Re: [Wekalist] Ignoring attributes in an .arff file
> Hi Tom,
> sorry you cannot. All you can do is use a filter to delete columns
> you want to ignore, like weka.filters.unsupervised.attribute.Remove
> cheers, Bernhard