data_scaler_white.hpp File Reference

Implements data whitening (as result, each feature mean value is 0 with normalized stddev). More...

#include <iostream>
#include <cassert>
#include <sstream>
#include <cmath>
#include "error.hpp"
#include "global.hpp"
#include "data_scaler.hpp"

Go to the source code of this file.

Classes
class	FST::Data_Scaler_white< DATATYPE, IDXTYPE >
	Implements data whitening (as result, each feature mean value is 0 with normalized stddev). More...

Namespaces
namespace	FST
	The Feature Selection Toolbox 4 library: every library component – data access, criteria and classifiers, search algorithms, result trackers and the global infrastructure – lives in this namespace.

Detailed Description

Implements data whitening (as result, each feature mean value is 0 with normalized stddev).

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Feature Selection Toolbox 4 source code

Author

Petr Somol (somol.nosp@m.@uti.nosp@m.a.cas.nosp@m..cz) with collaborators, see Contacts at https://fst.utia.cz

Version 4 was developed with Anthropic Claude models as coding assistants: Claude Fable 5 for the 2026 development (with minor contributions by Claude Opus 5), Claude Fable 5.1 for the pre-publication reviews of the full codebase, and Claude Opus 4.5 for the December 2025 audit of the FST3 codebase; design, decisions, review and acceptance are human work

Date

March 2011 (last updated July 2026)

Version

4.2.0

Note

FST4 was developed using MinGW-W64 g++ 15.2 and requires

• a C++17 conforming compiler and standard library (no Boost, unlike FST3),

• (optionally) LibSVM (http://www.csie.ntu.edu.tw/~cjlin/libsvm/, version 3.37; not distributed with FST4 — drop svm.cpp/svm.h into ./libsvm/, see libsvm/readme.txt)

Note that LibSVM is required for SVM related tools only. Ready-to-run SVM examples: demos/demo12t.cfg, demo23.cfg, demo25t.cfg and demo32t.cfg (configurations of the fst4 command-line tool).