Database Reference
In-Depth Information
Introducing Weka
You may ask, surely there are better command-line tools for clustering? And you are
right. One reason we include Weka in this chapter is to show you how you can work
around imperfections by building additional command-line tools. As you spend
more time on the command line and try out other command-line tools, chances are
that you come across one that seems very promising at first, but does not work as you
expected. A common imperfection, for example, is that the command-line tool does
not handle standard input or standard output correctly. In the next section, we'll
point out Weka's imperfections and demonstrate how we work around them.
Taming Weka on the Command Line
Weka can be invoked from the command line, but it's definitely not straightforward
or user friendly. Weka is programmed in Java, which means that you have to run
java
, specify the location of the
weka.jar
file, and specify the individual class you
want to call. For example, Weka has a class called
MexicanHat
, which generates a toy
data set. To generate 10 data points using this class, you would run:
$
java -cp ~/bin/weka.jar weka.datagenerators.classifiers.regression.MexicanHat
\
>
-n 10 | fold
%
%
Commandline
%
%
weka.datagenerators.classifiers.regression.MexicanHat -r weka.datagenerators.c
lassifiers.regression.MexicanHat-S_1_-n_10_-A_1.0_-R_-10..10_-N_0.0_-V_1.0 -S 1
-n 10 -A 1.0 -R -10..10 -N 0.0 -V 1.0
%
@relation weka.datagenerators.classifiers.regression.MexicanHat-S_1_-n_10_-A_1.0
_-R_-10..10_-N_0.0_-V_1.0
@attribute x numeric
@attribute y numeric
@data
4.617564,-0.215591
-1.798384,0.541716
-5.845703,-0.072474
-3.345659,-0.060572
9.355118,0.00744
-9.877656,-0.044298
9.274096,0.016186
8.797308,0.066736
8.943898,0.051718
8.741643,0.072209
