# LIBSVM FAQ

last modified : Fri, 14 Mar 2008 23:36:32 GMT <class="categories">
- All Questions(66)

- Q1:_Some_sample_uses_of_libsvm(2)
- Q2:_Installation_and_running_the_program(8)
- Q3:_Data_preparation(6)
- Q4:_Training_and_prediction(30)
- Q5:_Probability_outputs(3)
- Q6:_Graphic_interface(3)
- Q7:_Java_version_of_libsvm(4)
- Q8:_Python_interface(5)
- Q9:_MATLAB_interface(5)

- Some courses which have used libsvm as a tool
- Some applications which have used libsvm
- Where can I find documents of libsvm ?
- What are changes in previous versions?
- I would like to cite libsvm. Which paper should I cite ?
- I would like to use libsvm in my software. Is there any license problem?
- Is there a repository of additional tools based on libsvm?
- On unix machines, I got "error in loading shared libraries" or "cannot open shared object file." What happened ?
- I have modified the source and would like to build the graphic interface "svm-toy" on MS windows. How should I do it ?
- I am an MS windows user but why only one (svm-toy) of those precompiled .exe actually runs ?
- Why sometimes not all attributes of a data appear in the training/model files ?
- What if my data are non-numerical ?
- Why do you consider sparse format ? Will the training of dense data be much slower ?
- Why sometimes the last line of my data is not read by svm-train?
- Is there a program to check if my data are in the correct format?
- May I put comments in data files?
- I don't know class labels of test data. What should I put in the first column of the test file?
- The output of training C-SVM is like the following. What do they mean?
- Can you explain more about the model file?
- Should I use float or double to store numbers in the cache ?
- How do I choose the kernel?
- Does libsvm have special treatments for linear SVM?
- The number of free support vectors is large. What should I do?
- Should I scale training and testing data in a similar way?
- Does it make a big difference if I scale each attribute to [0,1] instead of [-1,1]?
- The prediction rate is low. How could I improve it?
- My data are unbalanced. Could libsvm handle such problems?
- What is the difference between nu-SVC and C-SVC?
- The program keeps running (without showing any output). What should I do?
- The program keeps running (with output, i.e. many dots). What should I do?
- The training time is too long. What should I do?
- How do I get the decision value(s)?
- How do I get the distance between a point and the hyperplane?
- On 32-bit machines, if I use a large cache (i.e. large -m) on a linux machine, why sometimes I get "segmentation fault ?"
- How do I disable screen output of svm-train and svm-predict ?
- 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 ?
- What method does libsvm use for multi-class SVM ? Why don't you use the "1-against-the rest" method ?
- After doing cross validation, why there is no model file outputted ?
- Why my cross-validation results are different from those in the Practical Guide?
- But on some systems CV accuracy is the same in several runs. How could I use different data partitions?
- I would like to solve L2-loss SVM (i.e., error term is quadratic). How should I modify the code ?
- How do I choose parameters for one-class svm as training data are in only one class?
- Why the code gives NaN (not a number) results?
- Why on windows sometimes grid.py fails?
- Why grid.py/easy.py sometimes generates the following warning message?
- Why the sign of predicted labels and decision values are sometimes reversed?
- Why training a probability model (i.e., -b 1) takes longer time
- Why using the -b option does not give me better accuracy?
- Why using svm-predict -b 0 and -b 1 gives different accuracy values?
- How can I save images drawn by svm-toy?
- I press the "load" button to load data points but why svm-toy does not draw them ?
- I would like svm-toy to handle more than three classes of data, what should I do ?
- What is the difference between Java version and C++ version of libsvm?
- Is the Java version significantly slower than the C++ version?
- While training I get the following error message: java.lang.OutOfMemoryError. What is wrong?
- Why you have the main source file svm.m4 and then transform it to svm.java?
- On MS windows, why does python fail to load the pyd file?
- How to modify the python interface on MS windows and rebuild the .pyd file ?
- Except the python-C++ interface provided, could I use Jython to call libsvm ?
- How could I install the python interface on Mac OS?
- I typed "make" on a unix system, but it says "Python.h: No such file or directory?"
- I compile the MATLAB interface without problem, but why errors occur while running it?
- Does the MATLAB interface provide a function to do scaling?
- How could I use MATLAB interface for parameter selection?
- How could I generate the primal variable w of linear SVM?
- Is there an OCTAVE interface for libsvm?

Q: Some courses which have used libsvm as a tool

- Institute for Computer Science, Faculty of Applied Science, University of Freiburg, Germany
- Division of Mathematics and Computer Science. Faculteit der Exacte Wetenschappen Vrije Universiteit, The Netherlands.
- Electrical and Computer Engineering Department, University of Wisconsin-Madison
- Technion (Israel Institute of Technology), Israel.
- Computer and Information Sciences Dept., University of Florida
- The Institute of Computer Science, University of Nairobi, Kenya.
- Applied Mathematics and Computer Science, University of Iceland.
- SVM tutorial in machine learning summer school, University of Chicago, 2005.

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Q: Some applications which have used libsvm

- Realtime object recognition

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Q: Where can I find documents of libsvm ?

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 practical guide to support vector classification shows beginners how to train/test their data. The paper LIBSVM : a library for support vector machines discusses the implementation of libsvm in detail.

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Q: What are changes in previous versions?

See the change log.

You can download earlier versions here.

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Q: I would like to cite libsvm. Which paper should I cite ?

Please cite the following document:

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

The bibtex format is as follows

    @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}}
    }

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Q: I would like to use libsvm in my software. Is there any license problem?

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.

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Q: Is there a repository of additional tools based on libsvm?

Yes, see libsvm tools

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Q: On unix machines, I got "error in loading shared libraries" or "cannot open shared object file." What happened ?

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.

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Q: I have modified the source and would like to build the graphic interface "svm-toy" on MS windows. How should I do it ?

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.

If you are not using Makefile.win and see the following link error

    LIBCMTD.lib(wwincrt0.obj) : error LNK2001: unresolved external symbol
    _wWinMain@16

you may have selected a wrong project type.

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Q: I am an MS windows user but why only one (svm-toy) of those precompiled .exe actually runs ?

You need to open a command window and type svmtrain.exe to see all options. Some examples are in README file.

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Q: Why sometimes not all attributes of a data appear in the training/model files ?

libsvm uses the so called "sparse" format where zero values do not need to be stored. Hence a data with attributes

    1 0 2 0

is represented as

    1:1 3:2

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Q: What if my data are non-numerical ?

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.

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Q: Why do you consider sparse format ? Will the training of dense data be much slower ?

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.

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Q: Why sometimes the last line of my data is not read by svm-train?

We assume that you have '\n' in the end of each line. So please press enter in the end of your last line.

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Q: Is there a program to check if my data are in the correct format?

The svm-train program in libsvm conducts only a simple check of the input data. To do a detailed check, after libsvm 2.85, you can use the python script tools/checkdata.py. See tools/README for details.

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Q: May I put comments in data files?

No, for simplicity we don't support that. However, you can easily preprocess your data before using libsvm. For example, if you have the following data

    test.txt
    1 1:2 2:1 # proten A

then on unix machines you can do

    cut -d '#' -f 1 < test.txt > test.features
    cut -d '#' -f 2 < test.txt > test.comments
    svm-predict test.feature train.model test.predicts
    paste -d '#' test.predicts test.comments | sed 's/#/ #/' > test.results

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Q: I don't know class labels of test data. What should I put in the first column of the test file?

Any value is ok. In this situation, what you will use is the output file of svm-predict, which gives predicted class labels.

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Q: The output of training C-SVM is like the following. What do they mean?
optimization finished, #iter = 219
nu = 0.431030
obj = -100.877286, rho = 0.424632
nSV = 132, nBSV = 107
Total nSV = 132

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 libsvm document.

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Q: Can you explain more about the model file?

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:
1 vs j, 2 vs j, ..., j-1 vs j, j vs j+1, j vs j+2, ..., j vs k
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:

    +-+-+-+--------------------+
    |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|                    |
    +-+-+-+--------------------+

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Q: Should I use float or double to store numbers in the cache ?

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.

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Q: How do I choose the kernel?

In general we suggest you to try the RBF kernel first. A recent result by Keerthi and Lin ( download paper here) 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 ( download paper here). 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).

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Q: Does libsvm have special treatments for linear SVM?

No, libsvm solves linear/nonlinear SVMs by the same way. Some tricks may save training/testing time if the linear kernel is used, so libsvm is NOT particularly efficient for linear SVM, especially when C is large and the number of data is much larger than the number of attributes. You can either

- 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.

  S. S. Keerthi and C.-J. Lin. Asymptotic behaviors of support vector machines with Gaussian kernel . Neural Computation, 15(2003), 1667-1689.

- Check liblinear, which is designed for large-scale linear classification. More details can be found in the following study:

  C.-J. Lin, R. C. Weng, and S. S. Keerthi. Trust region Newton method for large-scale logistic regression. Technical report, 2007. A short version appears in ICML 2007.

Please also see our SVM guide on the discussion of using RBF and linear kernels.

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Q: The number of free support vectors is large. What should I do?

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.

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Q: Should I scale training and testing data in a similar way?

Yes, you can do the following:
svm-scale -s scaling_parameters train_data > scaled_train_data
svm-scale -r scaling_parameters test_data > scaled_test_data

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Q: Does it make a big difference if I scale each attribute to [0,1] instead of [-1,1]?

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

                    x'=(x-mi)/(Mi-mi)

For [-1,1],

                    x''=2(x-mi)/(Mi-mi)-1.

In the RBF kernel,

                    x'-y'=(x-y)/(Mi-mi), x''-y''=2(x-y)/(Mi-mi).

Hence, using (C,g) on the [0,1]-scaled data is the same as (C,g/2) on the [-1,1]-scaled data.

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.

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Q: The prediction rate is low. How could I improve it?

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 practical guide to support vector classification

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Q: My data are unbalanced. Could libsvm handle such problems?

Yes, there is a -wi options. For example, if you use

svm-train -s 0 -c 10 -w1 1 -w-1 5 data_file

the penalty for class "-1" is larger. Note that this -w option is for C-SVC only.

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Q: What is the difference between nu-SVC and C-SVC?

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.

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Q: The program keeps running (without showing any output). What should I do?

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.

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Q: The program keeps running (with output, i.e. many dots). What should I do?

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.

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Q: The training time is too long. What should I do?

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.

If you are using polynomial kernels, please check the question on the pow() function.

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Q: How do I get the decision value(s)?

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.

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

    		printf("%f\n", dec_values[0]*model->label[0]);

after the line

    		svm_predict_values(model, x, dec_values);

of the file svm.cpp. Positive (negative) decision values correspond to data predicted as +1 (-1).

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Q: How do I get the distance between a point and the hyperplane?

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.

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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 ?"

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 this article.

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 http://www.bitwagon.com/tub.html.

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Q: How do I disable screen output of svm-train and svm-predict ?

Simply update svm.cpp:

    #if 1
    void info(char *fmt,...)

to

    #if 0
    void info(char *fmt,...)

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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 ?

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.

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Q: What method does libsvm use for multi-class SVM ? Why don't you use the "1-against-the rest" method ?

It is one-against-one. We chose it after doing the following comparison: C.-W. Hsu and C.-J. Lin. A comparison of methods for multi-class support vector machines , IEEE Transactions on Neural Networks, 13(2002), 415-425.

"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.

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Q: After doing cross validation, why there is no model file outputted ?

Cross validation is used for selecting good parameters. After finding them, you want to re-train the whole data without the -v option.

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Q: Why my cross-validation results are different from those in the Practical Guide?

Due to random partitions of the data, on different systems CV accuracy values may be different.

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Q: But on some systems CV accuracy is the same in several runs. How could I use different data partitions?

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:

    #include <time.h>

and in the beginning of the subroutine do_cross_validation(),

    srand(time(0));

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Q: I would like to solve L2-loss SVM (i.e., error term is quadratic). How should I modify the code ?

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

    	s.Solve(l, SVC_Q(*prob,*param,y), minus_ones, y,
    		alpha, Cp, Cn, param->eps, si, param->shrinking);

to

    	s.Solve(l, SVC_Q(*prob,*param,y), minus_ones, y,
    		alpha, INF, INF, param->eps, si, param->shrinking);

Second, in the class of SVC_Q, declare C as a private variable:

    	double C;

In the constructor we assign it to param.C:

            this->C = param.C;

Then in the subroutine get_Q, after the for loop, add

            if(i >= start && i < len)
    		data[i] += 1/C;

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:

    	data[real_i] += 1/C;

For large linear L2-loss SVM, please use LIBLINEAR.

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Q: How do I choose parameters for one-class svm as training data are in only one class?

You have pre-specified true positive rate in mind and then search for parameters which achieve similar cross-validation accuracy.

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Q: Why the code gives NaN (not a number) results?

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.

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Q: Why on windows sometimes grid.py fails?

This problem shouldn't happen after version 2.85. If you are using earlier versions, please download the latest one.

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Q: Why grid.py/easy.py sometimes generates the following warning message?

    Warning: empty z range [62.5:62.5], adjusting to [61.875:63.125]
    Notice: cannot contour non grid data!

Nothing is wrong and please disregard the message. It is from gnuplot when drawing the contour.

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Q: Why the sign of predicted labels and decision values are sometimes reversed?

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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Q: Why training a probability model (i.e., -b 1) takes longer time

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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Q: Why using the -b option does not give me better accuracy?

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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Q: Why using svm-predict -b 0 and -b 1 gives different accuracy values?

Let's just consider two-class classification here. After probability information is obtained in training, we do not have

prob > = 0.5 if and only if decision value >= 0.

So predictions may be different with -b 0 and 1.

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Q: How can I save images drawn by svm-toy?

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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Q: I press the "load" button to load data points but why svm-toy does not draw them ?

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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Q: I would like svm-toy to handle more than three classes of data, what should I do ?

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)

    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;

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Q: What is the difference between Java version and C++ version of libsvm?

They are the same thing. We just rewrote the C++ code in Java.

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Q: Is the Java version significantly slower than the C++ version?

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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Q: While training I get the following error message: java.lang.OutOfMemoryError. What is wrong?

You should try to increase the maximum Java heap size. For example,

    java -Xmx2048m -classpath libsvm.jar svm_train ...

sets the maximum heap size to 2048M.

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Q: Why you have the main source file svm.m4 and then transform it to svm.java?

Unlike C, Java does not have a preprocessor built-in. However, we need some macros (see first 3 lines of svm.m4).

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Q: On MS windows, why does python fail to load the pyd file?

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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Q: How to modify the python interface on MS windows and rebuild the .pyd file ?

To modify the interface, follow the instructions given in http://www.swig.org/Doc1.3/Python.html#Python

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++. Here is the example using Visual Studio .Net 2005:

1. Create a Win32 DLL project and set (in Project->$Project_Name Properties...->Configuration) to "Release." About how to create a new dynamic link library, please refer to http://msdn2.microsoft.com/en-us/library/ms235636(VS.80).aspx
2. Add svm.cpp, svmc_wrap.c, pythonXX.lib to your project.
3. Add the directories containing Python.h and svm.h to the Additional Include Directories(in Project->$Project_Name Properties...->C/C++->General)
4. Add __WIN32__ to Preprocessor definitions (in Project->$Project_Name Properties...->C/C++->Preprocessor)
5. Set Create/Use Precompiled Header to Not Using Precompiled Headers (in Project->$Project_Name Properties...->C/C++->Precompiled Headers)
6. Build the DLL.
7. You may have to rename .dll to .pyd

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Q: Except the python-C++ interface provided, could I use Jython to call libsvm ?

Yes, here are some examples:

    $ 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)

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Q: How could I install the python interface on Mac OS?

Instead of LDFLAGS = -shared in the Makefile, you need

    LDFLAGS = -framework Python -bundle

The problem is that under MacOs there is no "shared libraries." Instead they use "dynamic libraries."

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Q: I typed "make" on a unix system, but it says "Python.h: No such file or directory?"

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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Q: I compile the MATLAB interface without problem, but why errors occur while running it?

Your compiler version may not be supported/compatible for MATLAB. Please check this MATLAB page first and then specify the version number. For example, if g++ 3.3 is supported, replace

    CXX = g++

in the Makefile with

    CXX = g++-3.3

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Q: Does the MATLAB interface provide a function to do scaling?

It is extremely easy to do scaling under MATLAB. The following one-line code scale each feature to the range of [0.1]:

    (data - repmat(min(data,[],1),size(data,1),1))./(repmat(max(data,[],1)-min(data,[],1),size(data,1),1))

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Q: How could I use MATLAB interface for parameter selection?

One can do this by a simple loop. See the following example:

    bestcv = 0;
    for log2c = -1:3,
      for log2g = -4:1,
        cmd = ['-v 5 -c ', num2str(2^log2c), ' -g ', num2str(2^log2g)];
        cv = svmtrain(heart_scale_label, heart_scale_inst, cmd);
        if (cv >= bestcv),
          bestcv = cv; bestc = 2^log2c; bestg = 2^log2g;
        end
        fprintf('%g %g %g (best c=%g, g=%g, rate=%g)\n', log2c, log2g, cv, bestc, bestg, bestcv);
      end
    end

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Q: How could I generate the primal variable w of linear SVM?

Assume you have two labels -1 and +1. After obtaining the model from calling svmtrain, do the following to have w and b:

    w = model.SVs' * model.sv_coef;
    b = -model.rho;

    if model.Label(1) == -1
      w = -w;
      b = -b;
    end

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Q: Is there an OCTAVE interface for libsvm?

Yes, after libsvm 2.86, the matlab interface works on OCTAVE as well. Please type

    make octave

for installation.

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