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|
<html>
<head>
<title>LIBSVM FAQ</title>
</head>
<body bgcolor="#ffffcc">
<a name="_TOP"><b><h1><a
href=http://www.csie.ntu.edu.tw/~cjlin/libsvm>LIBSVM</a> FAQ </h1></b></a>
<b>last modified : </b>
Sun, 1 Apr 2007 00:31:43 GMT
<class="categories">
<li><a
href="#_TOP">All Questions</a>(57)</li>
<ul><b>
<li><a
href="#/Q1:_Some_courses_which_have_used_libsvm_as_a_tool">Q1:_Some_courses_which_have_used_libsvm_as_a_tool</a>(1)</li>
<li><a
href="#/Q2:_Installation_and_running_the_program">Q2:_Installation_and_running_the_program</a>(8)</li>
<li><a
href="#/Q3:_Data_preparation">Q3:_Data_preparation</a>(3)</li>
<li><a
href="#/Q4:_Training_and_prediction">Q4:_Training_and_prediction</a>(28)</li>
<li><a
href="#/Q5:_Probability_outputs">Q5:_Probability_outputs</a>(3)</li>
<li><a
href="#/Q6:_Graphic_interface">Q6:_Graphic_interface</a>(3)</li>
<li><a
href="#/Q7:_Java_version_of_libsvm">Q7:_Java_version_of_libsvm</a>(4)</li>
<li><a
href="#/Q8:_Python_interface">Q8:_Python_interface</a>(5)</li>
<li><a
href="#/Q9:_MATLAB_interface">Q9:_MATLAB_interface</a>(2)</li>
</b></ul>
</li>
<ul><ul class="headlines">
<li class="headlines_item"><a href="#faq1">Some courses which have used libsvm as a tool</a></li>
<li class="headlines_item"><a href="#f201">Where can I find documents of libsvm ?</a></li>
<li class="headlines_item"><a href="#f202">What are changes in previous versions?</a></li>
<li class="headlines_item"><a href="#f203">I would like to cite libsvm. Which paper should I cite ? </a></li>
<li class="headlines_item"><a href="#f204">I would like to use libsvm in my software. Is there any license problem?</a></li>
<li class="headlines_item"><a href="#f205">Is there a repository of additional tools based on libsvm?</a></li>
<li class="headlines_item"><a href="#f206">On unix machines, I got "error in loading shared libraries" or "cannot open shared object file." What happened ? </a></li>
<li class="headlines_item"><a href="#f207">I have modified the source and would like to build the graphic interface "svm-toy" on MS windows. How should I do it ?</a></li>
<li class="headlines_item"><a href="#f208">I am an MS windows user but why only one (SVM_toy) of those precompiled .exe actually runs ? </a></li>
<li class="headlines_item"><a href="#f301">Why sometimes not all attributes of a data appear in the training/model files ?</a></li>
<li class="headlines_item"><a href="#f302">What if my data are non-numerical ?</a></li>
<li class="headlines_item"><a href="#f303">Why do you consider sparse format ? Will the training of dense data be much slower ?</a></li>
<li class="headlines_item"><a href="#f401">The output of training C-SVM is like the following. What do they mean?</a></li>
<li class="headlines_item"><a href="#f402">Can you explain more about the model file?</a></li>
<li class="headlines_item"><a href="#f403">Should I use float or double to store numbers in the cache ?</a></li>
<li class="headlines_item"><a href="#f404">How do I choose the kernel?</a></li>
<li class="headlines_item"><a href="#f405">Does libsvm have special treatments for linear SVM?</a></li>
<li class="headlines_item"><a href="#f406">The number of free support vectors is large. What should I do?</a></li>
<li class="headlines_item"><a href="#f407">Should I scale training and testing data in a similar way?</a></li>
<li class="headlines_item"><a href="#f408">Does it make a big difference if I scale each attribute to [0,1] instead of [-1,1]?</a></li>
<li class="headlines_item"><a href="#f409">The prediction rate is low. How could I improve it?</a></li>
<li class="headlines_item"><a href="#f410">My data are unbalanced. Could libsvm handle such problems?</a></li>
<li class="headlines_item"><a href="#f411">What is the difference between nu-SVC and C-SVC?</a></li>
<li class="headlines_item"><a href="#f412">The program keeps running (without showing any output). What should I do?</a></li>
<li class="headlines_item"><a href="#f413">The program keeps running (with output, i.e. many dots). What should I do?</a></li>
<li class="headlines_item"><a href="#f414">The training time is too long. What should I do?</a></li>
<li class="headlines_item"><a href="#f415">How do I get the decision value(s)?</a></li>
<li class="headlines_item"><a href="#f4151">How do I get the distance between a point and the hyperplane?</a></li>
<li class="headlines_item"><a href="#f416">On 32-bit machines, if I use a large cache (i.e. large -m) on a linux machine, why sometimes I get "segmentation fault ?"</a></li>
<li class="headlines_item"><a href="#f417">How do I disable screen output of svm-train and svm-predict ?</a></li>
<li class="headlines_item"><a href="#f418">I would like to use my own kernel but find out that there are two subroutines for kernel evaluations: k_function() and kernel_function(). Which one should I modify ?</a></li>
<li class="headlines_item"><a href="#f419">What method does libsvm use for multi-class SVM ? Why don't you use the "1-against-the rest" method ?</a></li>
<li class="headlines_item"><a href="#f420">After doing cross validation, why there is no model file outputted ?</a></li>
<li class="headlines_item"><a href="#f421">I would like to try different random partition for cross validation, how could I do it ?</a></li>
<li class="headlines_item"><a href="#f422">I would like to solve L2-SVM (i.e., error term is quadratic). How should I modify the code ?</a></li>
<li class="headlines_item"><a href="#f424">How do I choose parameters for one-class svm as training data are in only one class?</a></li>
<li class="headlines_item"><a href="#f427">Why the code gives NaN (not a number) results?</a></li>
<li class="headlines_item"><a href="#f428">Why on windows sometimes grid.py fails?</a></li>
<li class="headlines_item"><a href="#f429">Why grid.py/easy.py sometimes generates the following warning message?</a></li>
<li class="headlines_item"><a href="#f430">Why the sign of predicted labels and decision values are sometimes reversed?</a></li>
<li class="headlines_item"><a href="#f425">Why training a probability model (i.e., -b 1) takes longer time</a></li>
<li class="headlines_item"><a href="#f426">Why using the -b option does not give me better accuracy?</a></li>
<li class="headlines_item"><a href="#f427">Why using svm-predict -b 0 and -b 1 gives different accuracy values?</a></li>
<li class="headlines_item"><a href="#f501">How can I save images drawn by svm-toy?</a></li>
<li class="headlines_item"><a href="#f502">I press the "load" button to load data points but why svm-toy does not draw them ?</a></li>
<li class="headlines_item"><a href="#f503">I would like svm-toy to handle more than three classes of data, what should I do ?</a></li>
<li class="headlines_item"><a href="#f601">What is the difference between Java version and C++ version of libsvm?</a></li>
<li class="headlines_item"><a href="#f602">Is the Java version significantly slower than the C++ version?</a></li>
<li class="headlines_item"><a href="#f603">While training I get the following error message: java.lang.OutOfMemoryError. What is wrong?</a></li>
<li class="headlines_item"><a href="#f604">Why you have the main source file svm.m4 and then transform it to svm.java?</a></li>
<li class="headlines_item"><a href="#f702">On MS windows, why does python fail to load the pyd file?</a></li>
<li class="headlines_item"><a href="#f703">How to modify the python interface on MS windows and rebuild the .pyd file ?</a></li>
<li class="headlines_item"><a href="#f704">Except the python-C++ interface provided, could I use Jython to call libsvm ?</a></li>
<li class="headlines_item"><a href="#f705">How could I install the python interface on Mac OS? </a></li>
<li class="headlines_item"><a href="#f706">I typed "make" on a unix system, but it says "Python.h: No such file or directory?"</a></li>
<li class="headlines_item"><a href="#f801">I compile the MATLAB interface without problem, but why errors occur while running it?</a></li>
<li class="headlines_item"><a href="#f802">Does the MATLAB interface provide a function to do scaling?</a></li>
</ul></ul>
<hr size="5" noshade />
<p/>
<a name="/Q1:_Some_courses_which_have_used_libsvm_as_a_tool"></a>
<a name="faq1"><b>Q: Some courses which have used libsvm as a tool</b></a>
<br/>
<ul>
<li><a href=http://lmb.informatik.uni-freiburg.de/lectures/svm_seminar/>Institute for Computer Science,
Faculty of Applied Science, University of Freiburg, Germany
</a>
<li> <a href=http://www.cs.vu.nl/~elena/ml.html>
Division of Mathematics and Computer Science.
Faculteit der Exacte Wetenschappen
Vrije Universiteit, The Netherlands. </a>
<li>
<a href=http://www.cae.wisc.edu/~ece539/matlab/>
Electrical and Computer Engineering Department,
University of Wisconsin-Madison
</a>
<li>
<a href=http://www.hpl.hp.com/personal/Carl_Staelin/cs236601/project.html>
Technion (Israel Institute of Technology), Israel.
<li>
<a href=http://www.cise.ufl.edu/~fu/learn.html>
Computer and Information Sciences Dept., University of Florida</a>
<li>
<a href=http://www.uonbi.ac.ke/acad_depts/ics/course_material/machine_learning/ML_and_DM_Resources.html>
The Institute of Computer Science,
University of Nairobi, Kenya.</a>
<li>
<a href=http://cerium.raunvis.hi.is/~tpr/courseware/svm/hugbunadur.html>
Applied Mathematics and Computer Science, University of Iceland.
<li>
<a href=http://chicago05.mlss.cc/tiki/tiki-read_article.php?articleId=2>
SVM tutorial in machine learning
summer school, University of Chicago, 2005.
</a>
</ul>
<p align="right">
<a href="#_TOP">[Go Top]</a>
<hr/>
<a name="/Q2:_Installation_and_running_the_program"></a>
<a name="f201"><b>Q: Where can I find documents of libsvm ?</b></a>
<br/>
<p>
In the package there is a README file which
details all options, data format, and library calls.
The model selection tool and the python interface
have a separate README under the directory python.
The guide
<A HREF="http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf">
A practical guide to support vector classification
</A> shows beginners how to train/test their data.
The paper <a href="http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf">LIBSVM
: a library for support vector machines</a> discusses the implementation of
libsvm in detail.
<p align="right">
<a href="#_TOP">[Go Top]</a>
<hr/>
<a name="/Q2:_Installation_and_running_the_program"></a>
<a name="f202"><b>Q: What are changes in previous versions?</b></a>
<br/>
<p>See <a href="http://www.csie.ntu.edu.tw/~cjlin/libsvm/log">the change log</a>.
<p> You can download earlier versions
<a href="http://www.csie.ntu.edu.tw/~cjlin/libsvm/oldfiles">here</a>.
<p align="right">
<a href="#_TOP">[Go Top]</a>
<hr/>
<a name="/Q2:_Installation_and_running_the_program"></a>
<a name="f203"><b>Q: I would like to cite libsvm. Which paper should I cite ? </b></a>
<br/>
<p>
Please cite the following document:
<p>
Chih-Chung Chang and Chih-Jen Lin, LIBSVM
: a library for support vector machines, 2001.
Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
<p>
The bibtex format is as follows
<pre>
@Manual{CC01a,
author = {Chih-Chung Chang and Chih-Jen Lin},
title = {{LIBSVM}: a library for support vector machines},
year = {2001},
note = {Software available at \url{http://www.csie.ntu.edu.tw/~cjlin/libsvm}}
}
</pre>
<p align="right">
<a href="#_TOP">[Go Top]</a>
<hr/>
<a name="/Q2:_Installation_and_running_the_program"></a>
<a name="f204"><b>Q: I would like to use libsvm in my software. Is there any license problem?</b></a>
<br/>
<p>
The libsvm license ("the modified BSD license")
is compatible with many
free software licenses such as GPL. Hence, it is very easy to
use libsvm in your software.
It can also be used in commercial products.
<p align="right">
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<hr/>
<a name="/Q2:_Installation_and_running_the_program"></a>
<a name="f205"><b>Q: Is there a repository of additional tools based on libsvm?</b></a>
<br/>
<p>
Yes, see <a href="http://www.csie.ntu.edu.tw/~cjlin/libsvmtools">libsvm
tools</a>
<p align="right">
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<hr/>
<a name="/Q2:_Installation_and_running_the_program"></a>
<a name="f206"><b>Q: On unix machines, I got "error in loading shared libraries" or "cannot open shared object file." What happened ? </b></a>
<br/>
<p>
This usually happens if you compile the code
on one machine and run it on another which has incompatible
libraries.
Try to recompile the program on that machine or use static linking.
<p align="right">
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<hr/>
<a name="/Q2:_Installation_and_running_the_program"></a>
<a name="f207"><b>Q: I have modified the source and would like to build the graphic interface "svm-toy" on MS windows. How should I do it ?</b></a>
<br/>
<p>
Build it as a project by choosing "Win32 Project."
On the other hand, for "svm-train" and "svm-predict"
you want to choose "Win32 Console Project."
After libsvm 2.5, you can also use the file Makefile.win.
See details in README.
<p>
If you are not using Makefile.win and see the following
link error
<pre>
LIBCMTD.lib(wwincrt0.obj) : error LNK2001: unresolved external symbol
_wWinMain@16
</pre>
you may have selected a wrong project type.
<p align="right">
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<hr/>
<a name="/Q2:_Installation_and_running_the_program"></a>
<a name="f208"><b>Q: I am an MS windows user but why only one (SVM_toy) of those precompiled .exe actually runs ? </b></a>
<br/>
<p>
You need to open a command window
and type svmtrain.exe to see all options.
Some examples are in README file.
<p align="right">
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<hr/>
<a name="/Q3:_Data_preparation"></a>
<a name="f301"><b>Q: Why sometimes not all attributes of a data appear in the training/model files ?</b></a>
<br/>
<p>
libsvm uses the so called "sparse" format where zero
values do not need to be stored. Hence a data with attributes
<pre>
1 0 2 0
</pre>
is represented as
<pre>
1:1 3:2
</pre>
<p align="right">
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<hr/>
<a name="/Q3:_Data_preparation"></a>
<a name="f302"><b>Q: What if my data are non-numerical ?</b></a>
<br/>
<p>
Currently libsvm supports only numerical data.
You may have to change non-numerical data to
numerical. For example, you can use several
binary attributes to represent a categorical
attribute.
<p align="right">
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<hr/>
<a name="/Q3:_Data_preparation"></a>
<a name="f303"><b>Q: Why do you consider sparse format ? Will the training of dense data be much slower ?</b></a>
<br/>
<p>
This is a controversial issue. The kernel
evaluation (i.e. inner product) of sparse vectors is slower
so the total training time can be at least twice or three times
of that using the dense format.
However, we cannot support only dense format as then we CANNOT
handle extremely sparse cases. Simplicity of the code is another
concern. Right now we decide to support
the sparse format only.
<p align="right">
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<hr/>
<a name="/Q4:_Training_and_prediction"></a>
<a name="f401"><b>Q: The output of training C-SVM is like the following. What do they mean?</b></a>
<br/>
<br>optimization finished, #iter = 219
<br>nu = 0.431030
<br>obj = -100.877286, rho = 0.424632
<br>nSV = 132, nBSV = 107
<br>Total nSV = 132
<p>
obj is the optimal objective value of the dual SVM problem.
rho is the bias term in the decision function
sgn(w^Tx - rho).
nSV and nBSV are number of support vectors and bounded support
vectors (i.e., alpha_i = C). nu-svm is a somewhat equivalent
form of C-SVM where C is replaced by nu. nu simply shows the
corresponding parameter. More details are in
<a href="http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf">
libsvm document</a>.
<p align="right">
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<hr/>
<a name="/Q4:_Training_and_prediction"></a>
<a name="f402"><b>Q: Can you explain more about the model file?</b></a>
<br/>
<p>
After the parameters, each line represents a support vector.
Support vectors are listed in the order of "labels" listed earlier.
(i.e., those from the first class in the "labels" list are
grouped first, and so on.)
If k is the total number of classes,
in front of each support vector, there are
k-1 coefficients
y*alpha where alpha are dual solution of the
following two class problems:
<br>
1 vs j, 2 vs j, ..., j-1 vs j, j vs j+1, j vs j+2, ..., j vs k
<br>
and y=1 in first j-1 coefficients, y=-1 in the remaining
k-j coefficients.
For example, if there are 4 classes, the file looks like:
<pre>
+-+-+-+--------------------+
|1|1|1| |
|v|v|v| SVs from class 1 |
|2|3|4| |
+-+-+-+--------------------+
|1|2|2| |
|v|v|v| SVs from class 2 |
|2|3|4| |
+-+-+-+--------------------+
|1|2|3| |
|v|v|v| SVs from class 3 |
|3|3|4| |
+-+-+-+--------------------+
|1|2|3| |
|v|v|v| SVs from class 4 |
|4|4|4| |
+-+-+-+--------------------+
</pre>
<p align="right">
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<hr/>
<a name="/Q4:_Training_and_prediction"></a>
<a name="f403"><b>Q: Should I use float or double to store numbers in the cache ?</b></a>
<br/>
<p>
We have float as the default as you can store more numbers
in the cache.
In general this is good enough but for few difficult
cases (e.g. C very very large) where solutions are huge
numbers, it might be possible that the numerical precision is not
enough using only float.
<p align="right">
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<hr/>
<a name="/Q4:_Training_and_prediction"></a>
<a name="f404"><b>Q: How do I choose the kernel?</b></a>
<br/>
<p>
In general we suggest you to try the RBF kernel first.
A recent result by Keerthi and Lin
(<a href=http://www.csie.ntu.edu.tw/~cjlin/papers/limit.ps.gz>
download paper here</a>)
shows that if RBF is used with model selection,
then there is no need to consider the linear kernel.
The kernel matrix using sigmoid may not be positive definite
and in general it's accuracy is not better than RBF.
(see the paper by Lin and Lin
(<a href=http://www.csie.ntu.edu.tw/~cjlin/papers/tanh.pdf>
download paper here</a>).
Polynomial kernels are ok but if a high degree is used,
numerical difficulties tend to happen
(thinking about dth power of (<1) goes to 0
and (>1) goes to infinity).
<p align="right">
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<hr/>
<a name="/Q4:_Training_and_prediction"></a>
<a name="f405"><b>Q: Does libsvm have special treatments for linear SVM?</b></a>
<br/>
<p>
No, at this point libsvm solves linear/nonlinear SVMs by the
same way.
Note that there are some possible
tricks to save training/testing time if the
linear kernel is used.
Hence libsvm is <b>NOT</b> particularly efficient for linear SVM,
especially for
using large C on
problems whose number of data is much larger
than number of attributes.
You can
<ul>
<li>
Use small C only. We have shown in the following paper
that after C is larger than a certain threshold,
the decision function is the same.
<p>
<a href="http://guppy.mpe.nus.edu.sg/~mpessk/">S. S. Keerthi</a>
and
<B>C.-J. Lin</B>.
<A HREF="papers/limit.ps.gz">
Asymptotic behaviors of support vector machines with
Gaussian kernel
</A>
.
<I><A HREF="http://mitpress.mit.edu/journal-home.tcl?issn=08997667">Neural Computation</A></I>, 15(2003), 1667-1689.
<li>
Check <a href=http://www.csie.ntu.edu.tw/~cjlin/bsvm>bsvm</a>,
which includes an efficient implementation for
linear SVMs.
More details can be found in the following study:
<p>
K.-M. Chung, W.-C. Kao,
T. Sun,
and
C.-J. Lin.
<A HREF="http://www.csie.ntu.edu.tw/~cjlin/papers/linear.pdf">
Decomposition Methods for Linear Support Vector Machines.
</A>
<I><A HREF="http://mitpress.mit.edu/journal-home.tcl?issn=08997667">Neural Computation</A></I>,
16(2004), 1689-1704.
</ul>
<p> On the other hand, you do not really need to solve
linear SVMs. See the previous question about choosing
kernels for details.
<p align="right">
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<hr/>
<a name="/Q4:_Training_and_prediction"></a>
<a name="f406"><b>Q: The number of free support vectors is large. What should I do?</b></a>
<br/>
<p>
This usually happens when the data are overfitted.
If attributes of your data are in large ranges,
try to scale them. Then the region
of appropriate parameters may be larger.
Note that there is a scale program
in libsvm.
<p align="right">
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<hr/>
<a name="/Q4:_Training_and_prediction"></a>
<a name="f407"><b>Q: Should I scale training and testing data in a similar way?</b></a>
<br/>
<p>
Yes, you can do the following:
<br> svm-scale -s scaling_parameters train_data > scaled_train_data
<br> svm-scale -r scaling_parameters test_data > scaled_test_data
<p align="right">
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<hr/>
<a name="/Q4:_Training_and_prediction"></a>
<a name="f408"><b>Q: Does it make a big difference if I scale each attribute to [0,1] instead of [-1,1]?</b></a>
<br/>
<p>
For the linear scaling method, if the RBF kernel is
used and parameter selection is conducted, there
is no difference. Assume Mi and mi are
respectively the maximal and minimal values of the
ith attribute. Scaling to [0,1] means
<pre>
x'=(x-mi)/(Mi-mi)
</pre>
For [-1,1],
<pre>
x''=2(x-mi)/(Mi-mi)-1.
</pre>
In the RBF kernel,
<pre>
x'-y'=(x-y)/(Mi-mi), x''-y''=2(x-y)/(Mi-mi).
</pre>
Hence, using (C,g) on the [0,1]-scaled data is the
same as (C,g/2) on the [-1,1]-scaled data.
<p> Though the performance is the same, the computational
time may be different. For data with many zero entries,
[0,1]-scaling keeps the sparsity of input data and hence
may save the time.
<p align="right">
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<hr/>
<a name="/Q4:_Training_and_prediction"></a>
<a name="f409"><b>Q: The prediction rate is low. How could I improve it?</b></a>
<br/>
<p>
Try to use the model selection tool grid.py in the python
directory find
out good parameters. To see the importance of model selection,
please
see my talk:
<A HREF="http://www.csie.ntu.edu.tw/~cjlin/talks/freiburg.pdf">
A practical guide to support vector
classification
</A>
<p align="right">
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<hr/>
<a name="/Q4:_Training_and_prediction"></a>
<a name="f410"><b>Q: My data are unbalanced. Could libsvm handle such problems?</b></a>
<br/>
<p>
Yes, there is a -wi options. For example, if you use
<p>
svm-train -s 0 -c 10 -w1 1 -w-1 5 data_file
<p>
the penalty for class "-1" is larger.
Note that this -w option is for C-SVC only.
<p align="right">
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<hr/>
<a name="/Q4:_Training_and_prediction"></a>
<a name="f411"><b>Q: What is the difference between nu-SVC and C-SVC?</b></a>
<br/>
<p>
Basically they are the same thing but with different
parameters. The range of C is from zero to infinity
but nu is always between [0,1]. A nice property
of nu is that it is related to the ratio of
support vectors and the ratio of the training
error.
<p align="right">
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<hr/>
<a name="/Q4:_Training_and_prediction"></a>
<a name="f412"><b>Q: The program keeps running (without showing any output). What should I do?</b></a>
<br/>
<p>
You may want to check your data. Each training/testing
data must be in one line. It cannot be separated.
In addition, you have to remove empty lines.
<p align="right">
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<hr/>
<a name="/Q4:_Training_and_prediction"></a>
<a name="f413"><b>Q: The program keeps running (with output, i.e. many dots). What should I do?</b></a>
<br/>
<p>
In theory libsvm guarantees to converge if the kernel
matrix is positive semidefinite.
After version 2.4 it can also handle non-PSD
kernels such as the sigmoid (tanh).
Therefore, this means you are
handling ill-conditioned situations
(e.g. too large/small parameters) so numerical
difficulties occur.
<p align="right">
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<hr/>
<a name="/Q4:_Training_and_prediction"></a>
<a name="f414"><b>Q: The training time is too long. What should I do?</b></a>
<br/>
<p>
For large problems, please specify enough cache size (i.e.,
-m).
Slow convergence may happen for some difficult cases (e.g. -c is large).
You can try to use a looser stopping tolerance with -e.
If that still doesn't work, you may want to train only a subset of the data.
You can use the program subset.py in the directory "tools"
to obtain a random subset.
<p>
If you are using polynomial kernels, please check the question on the pow() function.
<p align="right">
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<hr/>
<a name="/Q4:_Training_and_prediction"></a>
<a name="f415"><b>Q: How do I get the decision value(s)?</b></a>
<br/>
<p>
We print out decision values for regression. For classification,
we solve several binary SVMs for multi-class cases. You
can obtain values by easily calling the subroutine
svm_predict_values. Their corresponding labels
can be obtained from svm_get_labels.
Details are in
README of libsvm package.
<p>
We do not recommend the following. But if you would
like to get values for
TWO-class classification with labels +1 and -1
(note: +1 and -1 but not things like 5 and 10)
in the easiest way, simply add
<pre>
printf("%f\n", dec_values[0]*model->label[0]);
</pre>
after the line
<pre>
svm_predict_values(model, x, dec_values);
</pre>
of the file svm.cpp.
Positive (negative)
decision values correspond to data predicted as +1 (-1).
<p align="right">
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<hr/>
<a name="/Q4:_Training_and_prediction"></a>
<a name="f4151"><b>Q: How do I get the distance between a point and the hyperplane?</b></a>
<br/>
<p>
The distance is |decision_value| / |w|.
We have |w|^2 = w^Tw = alpha^T Q alpha = 2*(dual_obj + sum alpha_i).
Thus in svm.cpp please find the place
where we calculate the dual objective value
(i.e., the subroutine Solve())
and add a statement to print w^Tw.
<p align="right">
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<hr/>
<a name="/Q4:_Training_and_prediction"></a>
<a name="f416"><b>Q: On 32-bit machines, if I use a large cache (i.e. large -m) on a linux machine, why sometimes I get "segmentation fault ?"</b></a>
<br/>
<p>
On 32-bit machines, the maximum addressable
memory is 4GB. The Linux kernel uses 3:1
split which means user space is 3G and
kernel space is 1G. Although there are
3G user space, the maximum dynamic allocation
memory is 2G. So, if you specify -m near 2G,
the memory will be exhausted. And svm-train
will fail when it asks more memory.
For more details, please read
<a href=http://groups.google.com/groups?hl=en&lr=&ie=UTF-8&selm=3BA164F6.BAFA4FB%40daimi.au.dk>
this article</a>.
<p>
The easiest solution is to switch to a
64-bit machine.
Otherwise, there are two ways to solve this. If your
machine supports Intel's PAE (Physical Address
Extension), you can turn on the option HIGHMEM64G
in Linux kernel which uses 4G:4G split for
kernel and user space. If you don't, you can
try a software `tub' which can eliminate the 2G
boundary for dynamic allocated memory. The `tub'
is available at
<a href=http://www.bitwagon.com/tub.html>http://www.bitwagon.com/tub.html</a>.
<!--
This may happen only when the cache is large, but each cached row is
not large enough. <b>Note:</b> This problem is specific to
gnu C library which is used in linux.
The solution is as follows:
<p>
In our program we have malloc() which uses two methods
to allocate memory from kernel. One is
sbrk() and another is mmap(). sbrk is faster, but mmap
has a larger address
space. So malloc uses mmap only if the wanted memory size is larger
than some threshold (default 128k).
In the case where each row is not large enough (#elements < 128k/sizeof(float)) but we need a large cache ,
the address space for sbrk can be exhausted. The solution is to
lower the threshold to force malloc to use mmap
and increase the maximum number of chunks to allocate
with mmap.
<p>
Therefore, in the main program (i.e. svm-train.c) you want
to have
<pre>
#include <malloc.h>
</pre>
and then in main():
<pre>
mallopt(M_MMAP_THRESHOLD, 32768);
mallopt(M_MMAP_MAX,1000000);
</pre>
You can also set the environment variables instead
of writing them in the program:
<pre>
$ M_MMAP_MAX=1000000 M_MMAP_THRESHOLD=32768 ./svm-train .....
</pre>
More information can be found by
<pre>
$ info libc "Malloc Tunable Parameters"
</pre>
-->
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<hr/>
<a name="/Q4:_Training_and_prediction"></a>
<a name="f417"><b>Q: How do I disable screen output of svm-train and svm-predict ?</b></a>
<br/>
<p>
Simply update svm.cpp:
<pre>
#if 1
void info(char *fmt,...)
</pre>
to
<pre>
#if 0
void info(char *fmt,...)
</pre>
<p align="right">
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<hr/>
<a name="/Q4:_Training_and_prediction"></a>
<a name="f418"><b>Q: I would like to use my own kernel but find out that there are two subroutines for kernel evaluations: k_function() and kernel_function(). Which one should I modify ?</b></a>
<br/>
<p>
The reason why we have two functions is as follows:
For the RBF kernel exp(-g |xi - xj|^2), if we calculate
xi - xj first and then the norm square, there are 3n operations.
Thus we consider exp(-g (|xi|^2 - 2dot(xi,xj) +|xj|^2))
and by calculating all |xi|^2 in the beginning,
the number of operations is reduced to 2n.
This is for the training. For prediction we cannot
do this so a regular subroutine using that 3n operations is
needed.
The easiest way to have your own kernel is
to put the same code in these two
subroutines by replacing any kernel.
<p align="right">
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<hr/>
<a name="/Q4:_Training_and_prediction"></a>
<a name="f419"><b>Q: What method does libsvm use for multi-class SVM ? Why don't you use the "1-against-the rest" method ?</b></a>
<br/>
<p>
It is one-against-one. We chose it after doing the following
comparison:
C.-W. Hsu and C.-J. Lin.
<A HREF="http://www.csie.ntu.edu.tw/~cjlin/papers/multisvm.pdf">
A comparison of methods
for multi-class support vector machines
</A>,
<I>IEEE Transactions on Neural Networks</A></I>, 13(2002), 415-425.
<p>
"1-against-the rest" is a good method whose performance
is comparable to "1-against-1." We do the latter
simply because its training time is shorter.
<p align="right">
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<hr/>
<a name="/Q4:_Training_and_prediction"></a>
<a name="f420"><b>Q: After doing cross validation, why there is no model file outputted ?</b></a>
<br/>
<p>
Cross validation is used for selecting good parameters.
After finding them, you want to re-train the whole
data without the -v option.
<p align="right">
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<hr/>
<a name="/Q4:_Training_and_prediction"></a>
<a name="f421"><b>Q: I would like to try different random partition for cross validation, how could I do it ?</b></a>
<br/>
<p>
If you use GNU C library,
the default seed 1 is considered. Thus you always
get the same result of running svm-train -v.
To have different seeds, you can add the following code
in svm-train.c:
<pre>
#include <time.h>
</pre>
and in the beginning of the subroutine do_cross_validation(),
<pre>
srand(time(0));
</pre>
<p align="right">
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<hr/>
<a name="/Q4:_Training_and_prediction"></a>
<a name="f422"><b>Q: I would like to solve L2-SVM (i.e., error term is quadratic). How should I modify the code ?</b></a>
<br/>
<p>
It is extremely easy. Taking c-svc for example, only two
places of svm.cpp have to be changed.
First, modify the following line of
solve_c_svc from
<pre>
s.Solve(l, SVC_Q(*prob,*param,y), minus_ones, y,
alpha, Cp, Cn, param->eps, si, param->shrinking);
</pre>
to
<pre>
s.Solve(l, SVC_Q(*prob,*param,y), minus_ones, y,
alpha, INF, INF, param->eps, si, param->shrinking);
</pre>
Second, in the class of SVC_Q, declare C as
a private variable:
<pre>
double C;
</pre>
In the constructor we assign it to param.C:
<pre>
this->C = param.C;
</pre>
Than in the the subroutine get_Q, after the for loop, add
<pre>
if(i >= start && i < len)
data[i] += 1/C;
</pre>
For one-class svm, the modification is exactly the same. For SVR, you don't need an if statement like the above. Instead, you only need a simple assignment:
<pre>
data[real_i] += 1/C;
</pre>
<p align="right">
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<hr/>
<a name="/Q4:_Training_and_prediction"></a>
<a name="f424"><b>Q: How do I choose parameters for one-class svm as training data are in only one class?</b></a>
<br/>
<p>
You have pre-specified true positive rate in mind and then search for
parameters which achieve similar cross-validation accuracy.
<p align="right">
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<hr/>
<a name="/Q4:_Training_and_prediction"></a>
<a name="f427"><b>Q: Why the code gives NaN (not a number) results?</b></a>
<br/>
<p>
This rarely happens, but few users reported the problem.
It seems that their
computers for training libsvm have the VPN client
running. The VPN software has some bugs and causes this
problem. Please try to close or disconnect the VPN client.
<p align="right">
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<hr/>
<a name="/Q4:_Training_and_prediction"></a>
<a name="f428"><b>Q: Why on windows sometimes grid.py fails?</b></a>
<br/>
<p>
The error message is probably
<pre>
Traceback (most recent call last):
File "grid.py", line 349, in ?
main()
File "grid.py", line 344, in main
redraw(db)
File "grid.py", line 132, in redraw
gnuplot.write("set term windows\n")
IOError: [Errno 22] Invalid argument
</pre>
<p>There are some problems about using gnuplot on windows.
So far we have not found a good solution.
Please try to close gnuplot windows and rerun.
If the problem still occurs, comment the following
two lines in grid.py by inserting "#" in the beginning:
<pre>
redraw(db)
redraw(db,1)
</pre>
Then you get accuracy only but not cross validation contours.
<p align="right">
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<hr/>
<a name="/Q4:_Training_and_prediction"></a>
<a name="f429"><b>Q: Why grid.py/easy.py sometimes generates the following warning message?</b></a>
<br/>
<pre>
Warning: empty z range [62.5:62.5], adjusting to [61.875:63.125]
Notice: cannot contour non grid data!
</pre>
<p>Nothing is wrong and please disregard the
message. It is from gnuplot when drawing
the contour.
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<hr/>
<a name="/Q4:_Training_and_prediction"></a>
<a name="f430"><b>Q: Why the sign of predicted labels and decision values are sometimes reversed?</b></a>
<br/>
<p>Nothing is wrong. Very likely you have two labels +1/-1 and the first instance in your data
has -1.
Think about the case of labels +5/+10. Since
SVM needs to use +1/-1, internally
we map +5/+10 to +1/-1 according to which
label appears first.
Hence a positive decision value implies
that we should predict the "internal" +1,
which may not be the +1 in the input file.
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<hr/>
<a name="/Q5:_Probability_outputs"></a>
<a name="f425"><b>Q: Why training a probability model (i.e., -b 1) takes longer time</b></a>
<br/>
<p>
To construct this probability model, we internally conduct a
cross validation, which is more time consuming than
a regular training.
Hence, in general you do parameter selection first without
-b 1. You only use -b 1 when good parameters have been
selected. In other words, you avoid using -b 1 and -v
together.
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<hr/>
<a name="/Q5:_Probability_outputs"></a>
<a name="f426"><b>Q: Why using the -b option does not give me better accuracy?</b></a>
<br/>
<p>
There is absolutely no reason the probability outputs guarantee
you better accuracy. The main purpose of this option is
to provide you the probability estimates, but not to boost
prediction accuracy. From our experience,
after proper parameter selections, in general with
and without -b have similar accuracy. Occasionally there
are some differences.
It is not recommended to compare the two under
just a fixed parameter
set as more differences will be observed.
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<hr/>
<a name="/Q5:_Probability_outputs"></a>
<a name="f427"><b>Q: Why using svm-predict -b 0 and -b 1 gives different accuracy values?</b></a>
<br/>
<p>
After probability information is obtained in training,
we do not have
<p>
prob > = 0.5 if and only if decision value >= 0.
<p>
So predictions may be different with -b 0 and 1.
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<hr/>
<a name="/Q6:_Graphic_interface"></a>
<a name="f501"><b>Q: How can I save images drawn by svm-toy?</b></a>
<br/>
<p>
For Microsoft windows, first press the "print screen" key on the keyboard.
Open "Microsoft Paint"
(included in Windows)
and press "ctrl-v." Then you can clip
the part of picture which you want.
For X windows, you can
use the program "xv" or "import" to grab the picture of the svm-toy window.
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<hr/>
<a name="/Q6:_Graphic_interface"></a>
<a name="f502"><b>Q: I press the "load" button to load data points but why svm-toy does not draw them ?</b></a>
<br/>
<p>
The program svm-toy assumes both attributes (i.e. x-axis and y-axis
values) are in (0,1). Hence you want to scale your
data to between a small positive number and
a number less than but very close to 1.
Moreover, class labels must be 1, 2, or 3
(not 1.0, 2.0 or anything else).
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<hr/>
<a name="/Q6:_Graphic_interface"></a>
<a name="f503"><b>Q: I would like svm-toy to handle more than three classes of data, what should I do ?</b></a>
<br/>
<p>
Taking windows/svm-toy.cpp as an example, you need to
modify it and the difference
from the original file is as the following: (for five classes of
data)
<pre>
30,32c30
< RGB(200,0,200),
< RGB(0,160,0),
< RGB(160,0,0)
---
> RGB(200,0,200)
39c37
< HBRUSH brush1, brush2, brush3, brush4, brush5;
---
> HBRUSH brush1, brush2, brush3;
113,114d110
< brush4 = CreateSolidBrush(colors[7]);
< brush5 = CreateSolidBrush(colors[8]);
155,157c151
< else if(v==3) return brush3;
< else if(v==4) return brush4;
< else return brush5;
---
> else return brush3;
325d318
< int colornum = 5;
327c320
< svm_node *x_space = new svm_node[colornum * prob.l];
---
> svm_node *x_space = new svm_node[3 * prob.l];
333,338c326,331
< x_space[colornum * i].index = 1;
< x_space[colornum * i].value = q->x;
< x_space[colornum * i + 1].index = 2;
< x_space[colornum * i + 1].value = q->y;
< x_space[colornum * i + 2].index = -1;
< prob.x[i] = &x_space[colornum * i];
---
> x_space[3 * i].index = 1;
> x_space[3 * i].value = q->x;
> x_space[3 * i + 1].index = 2;
> x_space[3 * i + 1].value = q->y;
> x_space[3 * i + 2].index = -1;
> prob.x[i] = &x_space[3 * i];
397c390
< if(current_value > 5) current_value = 1;
---
> if(current_value > 3) current_value = 1;
</pre>
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<hr/>
<a name="/Q7:_Java_version_of_libsvm"></a>
<a name="f601"><b>Q: What is the difference between Java version and C++ version of libsvm?</b></a>
<br/>
<p>
They are the same thing. We just rewrote the C++ code
in Java.
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<hr/>
<a name="/Q7:_Java_version_of_libsvm"></a>
<a name="f602"><b>Q: Is the Java version significantly slower than the C++ version?</b></a>
<br/>
<p>
This depends on the VM you used. We have seen good
VM which leads the Java version to be quite competitive with
the C++ code. (though still slower)
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<hr/>
<a name="/Q7:_Java_version_of_libsvm"></a>
<a name="f603"><b>Q: While training I get the following error message: java.lang.OutOfMemoryError. What is wrong?</b></a>
<br/>
<p>
You should try to increase the maximum Java heap size.
For example,
<pre>
java -Xmx2048m -classpath libsvm.jar svm_train ...
</pre>
sets the maximum heap size to 2048M.
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<hr/>
<a name="/Q7:_Java_version_of_libsvm"></a>
<a name="f604"><b>Q: Why you have the main source file svm.m4 and then transform it to svm.java?</b></a>
<br/>
<p>
Unlike C, Java does not have a preprocessor built-in.
However, we need some macros (see first 3 lines of svm.m4).
</ul>
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<hr/>
<a name="/Q8:_Python_interface"></a>
<a name="f702"><b>Q: On MS windows, why does python fail to load the pyd file?</b></a>
<br/>
<p>
It seems the pyd file is version dependent. So far we haven't
found out a good solution. Please email us if you have any
good suggestions.
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<hr/>
<a name="/Q8:_Python_interface"></a>
<a name="f703"><b>Q: How to modify the python interface on MS windows and rebuild the .pyd file ?</b></a>
<br/>
<p>
To modify the interface, follow the instructions given in
<a href=
http://www.swig.org/Doc1.3/Python.html#Python>
http://www.swig.org/Doc1.3/Python.html#Python
</a>
<p>
If you just want to build .pyd for a different python version,
after libsvm 2.5, you can easily use the file Makefile.win.
See details in README.
Alternatively, you can use Visual C++:
<ol>
<li> Create a Win32 DLL project and set (Build->Active Configuration) to "Release."
<li> Add svm.cpp, svmc_wrap.c, python2x.lib to your project.
<li> Add the directories containing Python.h and svm.h to
the Additional include directories. (in Project
Settings->C/C++->Preprocessor)
<li> add __WIN32__ to Preprocessor definitions
<li> Make sure that in the "General" category of
Project->Settings->Settings For "Win32 Release", Output
directories should be "Release"
<li> Build the DLL.
<li> You may have to rename .dll to .pyd
</ol>
<!--
There do exist work arounds, however. In
http://aspn.activestate.com/ASPN/Mail/Message/python-list/983252
they presented a solution: 1) find the version of python in the
registry 2) perform LoadLibrary("pythonxx.dll") and 3) resolve the
reference to functions through GetProcAddress. It is said that SWIG
may help on this.
http://mailman.cs.uchicago.edu/pipermail/swig/2001-July/002744.html
presented a similar example.
-->
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<hr/>
<a name="/Q8:_Python_interface"></a>
<a name="f704"><b>Q: Except the python-C++ interface provided, could I use Jython to call libsvm ?</b></a>
<br/>
<p> Yes, here are some examples:
<pre>
$ export CLASSPATH=$CLASSPATH:~/libsvm-2.4/java/libsvm.jar
$ ./jython
Jython 2.1a3 on java1.3.0 (JIT: jitc)
Type "copyright", "credits" or "license" for more information.
>>> from libsvm import *
>>> dir()
['__doc__', '__name__', 'svm', 'svm_model', 'svm_node', 'svm_parameter',
'svm_problem']
>>> x1 = [svm_node(index=1,value=1)]
>>> x2 = [svm_node(index=1,value=-1)]
>>> param = svm_parameter(svm_type=0,kernel_type=2,gamma=1,cache_size=40,eps=0.001,C=1,nr_weight=0,shrinking=1)
>>> prob = svm_problem(l=2,y=[1,-1],x=[x1,x2])
>>> model = svm.svm_train(prob,param)
*
optimization finished, #iter = 1
nu = 1.0
obj = -1.018315639346838, rho = 0.0
nSV = 2, nBSV = 2
Total nSV = 2
>>> svm.svm_predict(model,x1)
1.0
>>> svm.svm_predict(model,x2)
-1.0
>>> svm.svm_save_model("test.model",model)
</pre>
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<hr/>
<a name="/Q8:_Python_interface"></a>
<a name="f705"><b>Q: How could I install the python interface on Mac OS? </b></a>
<br/>
<p> Instead of
LDFLAGS = -shared
in the Makefile, you need
<pre>
LDFLAGS = -framework Python -bundle
</pre>
<!--
LDFLAGS = -bundle -flat_namespace -undefined suppress
-->
The problem is that under MacOs there is no "shared libraries."
Instead they use "dynamic libraries."
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<hr/>
<a name="/Q8:_Python_interface"></a>
<a name="f706"><b>Q: I typed "make" on a unix system, but it says "Python.h: No such file or directory?"</b></a>
<br/>
<p>
Even though you may have python on your
system, very likely
python development tools
are not installed. Please check with
your system administrator.
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<hr/>
<a name="/Q9:_MATLAB_interface"></a>
<a name="f801"><b>Q: I compile the MATLAB interface without problem, but why errors occur while running it?</b></a>
<br/>
<p>
Your compiler version may not be supported/compatible for MATLAB.
Please check <a href=http://www.mathworks.com/support/tech-notes/1600/1601.html>this MATLAB page</a> first and then specify the version
number. For example, if g++ 3.3 is supported, replace
<pre>
CXX = g++
</pre>
in the Makefile with
<pre>
CXX = g++-3.3
</pre>
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<hr/>
<a name="/Q9:_MATLAB_interface"></a>
<a name="f802"><b>Q: Does the MATLAB interface provide a function to do scaling?</b></a>
<br/>
<p>
It is extremely easy to do scaling under MATLAB.
The following one-line code scale each feature to the range
of [0.1]:
<pre>
(data - repmat(min(data,[],1),size(data,1),1))./(repmat(max(data,[],1)-min(data,[],1),size(data,1),1))
</pre>
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<hr/>
<p align="middle">
<a href="http://www.csie.ntu.edu.tw/~cjlin/libsvm">LIBSVM home page</a>
</p>
</body>
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