<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">
<a href="#_TOP">[Go Top]</a>  
<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">
<a href="#_TOP">[Go Top]</a>  
<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 &lt;malloc.h&gt;
</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>
-->
<p align="right">
<a href="#_TOP">[Go Top]</a>  
<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">
<a href="#_TOP">[Go Top]</a>  
<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">
<a href="#_TOP">[Go Top]</a>  
<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">
<a href="#_TOP">[Go Top]</a>  
<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">
<a href="#_TOP">[Go Top]</a>  
<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 &lt;time.h&gt;
</pre>
and in the beginning of the subroutine do_cross_validation(),
<pre>
srand(time(0));
</pre>
<p align="right">
<a href="#_TOP">[Go Top]</a>  
<hr/>
  <a name="/Q4:_Training_and_prediction"></a>
<a name="f422"><b>Q: I would like to solve L2-loss 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>

<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] += 1/C;
</pre>


<p>
For large linear L2-loss SVM, please use
<a href=../liblinear>LIBLINEAR</a>.
<p align="right">
<a href="#_TOP">[Go Top]</a>  
<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">
<a href="#_TOP">[Go Top]</a>  
<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">
<a href="#_TOP">[Go Top]</a>  
<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">
<a href="#_TOP">[Go Top]</a>  
<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.  
<p align="right">
<a href="#_TOP">[Go Top]</a>  
<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.

<p align="right">
<a href="#_TOP">[Go Top]</a>  
<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.
<p align="right">
<a href="#_TOP">[Go Top]</a>