faq.html

LIBSVM 是台湾大学林智仁(Chih-Jen Lin)博士等开发设计的一个操作简单、易于使用、快速有效的通用SVM 软件包

	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>
Then in the subroutine get_Q, after the for loop, add
<pre>
        if(i >= start && i < len) 
		data[i] += 0.5/C;
</pre>

<p>
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] += 0.5/C;
</pre>


<p>
For large linear L2-loss SVM, please use
<a href=../liblinear>LIBLINEAR</a>.
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  <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>
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<p>
You have pre-specified true positive rate in mind and then search for
parameters which achieve similar cross-validation accuracy.
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<a name="f427"><b>Q: Why the code gives NaN (not a number) results?</b></a>
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<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.
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<a name="f428"><b>Q: Why on windows sometimes grid.py fails?</b></a>
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<p>

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

<!--
<p>
If you are using earlier 
versions, 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>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.
-->
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<a name="f429"><b>Q: Why grid.py/easy.py sometimes generates the following warning message?</b></a>
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<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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<a name="f430"><b>Q: Why the sign of predicted labels and decision values are sometimes reversed?</b></a>
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<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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<a name="f431"><b>Q: I don't know class labels of test data. What should I put in the first column of the test file?</b></a>
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<p>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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<a name="f432"><b>Q: How can I use OpenMP to parallelize LIBSVM on a multicore/shared-memory computer?</b></a>
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<p>It is very easy if you are using GCC 4.2
or after. 

<p> In Makefile, add -fopenmp  to CFLAGS.

<p> In class SVC_Q of svm.cpp, modify the for loop
of get_Q to:
<pre>
#pragma omp parallel for private(j) 
			for(j=start;j&lt;len;j++)
</pre>
Note that j must be declared outside the for loop.
<p> In the subroutine svm_predict_values of svm.cpp, add one line to the for loop:
<pre>
#pragma omp parallel for private(i) 
		for(i=0;i&lt;l;i++)
			kvalue[i] = Kernel::k_function(x,model-&gt;SV[i],model-&gt;param);
</pre>

<p> Then rebuild the package. Kernel evaluations in training/testing will be parallelized. An example of running this modification on
an 8-core machine using the data set
<a href=../libsvmtools/datasets/binary/ijcnn1.bz2>ijcnn1</a>:

<p> 8 cores:
<pre>
%setenv OMP_NUM_THREADS 8
%time svm-train -c 16 -g 4 -m 400 ijcnn1
27.1sec
</pre>
1 core:
<pre>
%setenv OMP_NUM_THREADS 1
%time svm-train -c 16 -g 4 -m 400 ijcnn1
79.8sec
</pre>
For this data, kernel evaluations take 80% of training time.
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<a name="f433"><b>Q: How could I know which training instances are support vectors?</b></a>
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<p>
It's very simple. Please replace 
<pre>
			if(nonzero[i]) model->SV[p++] = x[i];
</pre>
in svm_train() of svm.cpp with
<pre>
			if(nonzero[i]) 
			{
				model->SV[p++] = x[i];
				info("%d\n", perm[i]);
			}
</pre>
If there are many requests, we may
provide a function to return indices
of support vectors. In the mean time,
if you need such information in your code,
you can add the array nonzero to the model
structure. This array has the same size as
the number of data, so alternatively you can
store only indices of support vectors.


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<a name="f425"><b>Q: Why training a probability model (i.e., -b 1) takes a longer time?</b></a>
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<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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<a name="f426"><b>Q: Why using the -b option does not give me better accuracy?</b></a>
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<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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<a name="f427"><b>Q: Why using svm-predict -b 0 and -b 1 gives different accuracy values?</b></a>
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<p>
Let's just consider two-class classification here. 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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<a name="f501"><b>Q: How can I save images drawn by svm-toy?</b></a>
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<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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<a name="f502"><b>Q: I press the "load" button to load data points but why svm-toy does not draw them ?</b></a>
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<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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<a name="f503"><b>Q: I would like svm-toy to handle more than three classes of data, what should I do ?</b></a>
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<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