SHOGUN  v3.2.0
Interfaces.mainpage
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1 /*!
2 \page interfaces Interfaces
3 
4 SHOGUN comes as a stand-a-lone program accepting input via command-line or from
5 a *script* file, where standard file IO is used to read in /write out data
6 (optionally even tcp/ip). More importantly, it interfaces to established matrix
7 and statistics packages, such as Matlab(tm), Octave, R and Python. SHOGUN comes
8 in three flavours, the so called
9 \subpage staticinterfaces, the \subpage elwmsinterface
10 and the
11 \subpage modularinterfaces
12 
13 \li
14 The static interfaces are cross language compatible, i.e. the syntax for calling
15 shogun from python,R,matlab,octave is the same (with minor exceptions). However
16 only a singly object of each type is supported (e.g. only a single SVM
17 classifier). In case you are dealing with more complex models it is suggested to
18 use the object oriented, very flexible python-modular interface. Note that all
19 interfaces should work equally well, though some may not support more advanced
20 data types like byte matrices. For further information
21 \ref staticinterfaces "see Static Interfaces"
22 
23 \li
24 The special Eierlegende Wollmilchsau (elwms) interface is a chimera that in one
25 file interfaces to python,R,matlab,octave. Using it provides one with the
26 advantage of running e.g., python or R scripts within octave (where variables
27 are passed in memory). \ref elwmsinterface "see ELWMS Interface"
28 
29 \li
30 The modular interfaces are very flexible in nature, as they allow an arbitrary
31 number of objects potentially interacting together. For example several SVMs
32 with using multiple kernels or preprocessors, with the kernels relying on other
33 machine learning methods (like e.g. HMMs). The modular interfaces heavily rely
34 on swig and therefore currently only python is supported (though octave and R
35 support is on the way). For further information
36 \ref modularinterfaces "see Modular Interfaces"
37 
38 */

SHOGUN Machine Learning Toolbox - Documentation