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/* MELODIC - Multivariate exploratory linear optimized decomposition into
independent components
melhlprfns.cc - misc functions
Christian F. Beckmann, FMRIB Image Analysis Group
Copyright (C) 1999-2008 University of Oxford */
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/* Part of FSL - FMRIB's Software Library
http://www.fmrib.ox.ac.uk/fsl
fsl@fmrib.ox.ac.uk
Developed at FMRIB (Oxford Centre for Functional Magnetic Resonance
Imaging of the Brain), Department of Clinical Neurology, Oxford
University, Oxford, UK
LICENCE
FMRIB Software Library, Release 4.0 (c) 2007, The University of
Oxford (the "Software")
The Software remains the property of the University of Oxford ("the
University").
The Software is distributed "AS IS" under this Licence solely for
non-commercial use in the hope that it will be useful, but in order
that the University as a charitable foundation protects its assets for
the benefit of its educational and research purposes, the University
makes clear that no condition is made or to be implied, nor is any
warranty given or to be implied, as to the accuracy of the Software,
or that it will be suitable for any particular purpose or for use
under any specific conditions. Furthermore, the University disclaims
all responsibility for the use which is made of the Software. It
further disclaims any liability for the outcomes arising from using
the Software.
The Licensee agrees to indemnify the University and hold the
University harmless from and against any and all claims, damages and
liabilities asserted by third parties (including claims for
negligence) which arise directly or indirectly from the use of the
Software or the sale of any products based on the Software.
No part of the Software may be reproduced, modified, transmitted or
transferred in any form or by any means, electronic or mechanical,
without the express permission of the University. The permission of
the University is not required if the said reproduction, modification,
transmission or transference is done without financial return, the
conditions of this Licence are imposed upon the receiver of the
product, and all original and amended source code is included in any
transmitted product. You may be held legally responsible for any
copyright infringement that is caused or encouraged by your failure to
abide by these terms and conditions.
You are not permitted under this Licence to use this Software
commercially. Use for which any financial return is received shall be
defined as commercial use, and includes (1) integration of all or part
of the source code or the Software into a product for sale or license
by or on behalf of Licensee to third parties or (2) use of the
Software or any derivative of it for research with the final aim of
developing software products for sale or license to a third party or
(3) use of the Software or any derivative of it for research with the
final aim of developing non-software products for sale or license to a
third party, or (4) use of the Software to provide any service to an
external organisation for which payment is received. If you are
interested in using the Software commercially, please contact Isis
Innovation Limited ("Isis"), the technology transfer company of the
University, to negotiate a licence. Contact details are:
innovation@isis.ox.ac.uk quoting reference DE/1112. */
#ifndef __MELODICHLPR_h
#define __MELODICHLPR_h
#include "newimage/newimageall.h"
#include "newmatap.h"
#include "newmatio.h"
using namespace NEWIMAGE;
namespace Melodic{
void update_mask(volume<float>& mask, Matrix& Data);
void del_vols(volume4D<float>& in, int howmany);
Matrix smoothColumns(const Matrix& inp);
Matrix calc_FFT(const Matrix& Mat, const bool logpwr = 0);
Matrix convert_to_pbsc(Matrix& Mat);
RowVector varnorm(Matrix& in, int dim = 30, float level = 1.6);
RowVector varnorm(Matrix& in, Matrix& Corr, int dim = 30, float level = 1.6);
Matrix SP2(const Matrix& in, const Matrix& weights, bool econ = 0);
RowVector Feta(int n1,int n2);
RowVector cumsum(const RowVector& Inp);
Matrix corrcoef(const Matrix& in1, const Matrix& in2);
Matrix corrcoef(const Matrix& in1, const Matrix& in2, const Matrix& part);
Matrix calc_corr(const Matrix& in, bool econ = 0);
Matrix calc_corr(const Matrix& in, const Matrix& weights, bool econ = 0);
float calc_white(const Matrix& tmpE, const RowVector& tmpD, const RowVector& PercEV, int dim, Matrix& param, Matrix& paramS, Matrix& white, Matrix& dewhite);
float calc_white(const Matrix& tmpE, const RowVector& tmpD, const RowVector& PercEV, int dim, Matrix& white, Matrix& dewhite);
void calc_white(const Matrix& tmpE, const RowVector& tmpD, int dim, Matrix& param, Matrix& paramS, Matrix& white, Matrix& dewhite);
void calc_white(const Matrix& tmpE, const RowVector& tmpD, int dim, Matrix& white, Matrix& dewhite);
void calc_white(const Matrix& Corr, int dim, Matrix& white, Matrix& dewhite);
void std_pca(const Matrix& Mat, Matrix& Corr, Matrix& evecs, RowVector& evals);
void std_pca(const Matrix& Mat, const Matrix& weights, Matrix& Corr, Matrix& evecs, RowVector& evals);
void em_pca(const Matrix& Mat, Matrix& evecs, RowVector& evals, int num_pc = 1, int iter = 20);
void em_pca(const Matrix& Mat, Matrix& guess, Matrix& evecs, RowVector& evals, int num_pc = 1, int iter = 20);
float rankapprox(const Matrix& Mat, Matrix& cols, Matrix& rows, int dim = 1);
RowVector krfact(const Matrix& Mat, Matrix& cols, Matrix& rows);
RowVector krfact(const Matrix& Mat, int colnum, Matrix& cols, Matrix& rows);
Matrix krprod(const Matrix& cols, const Matrix& rows);
Matrix krapprox(const Matrix& Mat, int size_col, int dim = 1);
void adj_eigspec(const RowVector& in, RowVector& out1, RowVector& out2, RowVector& out3, int& out4, int num_vox, float resels);
void adj_eigspec(const RowVector& in, RowVector& out1, RowVector& out2);
int ppca_dim(const Matrix& in, const Matrix& weights, Matrix& PPCA, RowVector& AdjEV, RowVector& PercEV, Matrix& Corr, Matrix& tmpE, RowVector &tmpD, float resels, string which);
int ppca_dim(const Matrix& in, const Matrix& weights, Matrix& PPCA, RowVector& AdjEV, RowVector& PercEV, float resels, string which);
int ppca_dim(const Matrix& in, const Matrix& weights, float resels, string which);
ColumnVector ppca_select(Matrix& PPCAest, int& dim, int maxEV, string which);
Matrix ppca_est(const RowVector& eigenvalues, const int N1, const float N2);
Matrix ppca_est(const RowVector& eigenvalues, const int N);
ColumnVector acf(const ColumnVector& in, int order);
ColumnVector pacf(const ColumnVector& in, int maxorder = 1);
Matrix est_ar(const Matrix& Mat, int maxorder);
ColumnVector gen_ar(const ColumnVector& in, int maxorder = 1);
Matrix gen_ar(const Matrix& in, int maxorder);
Matrix gen_arCorr(const Matrix& in, int maxorder);
public:
//constructor
basicGLM(){}
//destructor
~basicGLM(){}
void olsfit(const Matrix& data, const Matrix& design,
const Matrix& contrasts, int DOFadjust = 0);
inline Matrix& get_t(){return t;}
inline Matrix& get_z(){return z;}
inline Matrix& get_p(){return p;}
inline Matrix& get_f_fmf(){return f_fmf;}
inline Matrix& get_pf_fmf(){return pf_fmf;}
inline Matrix& get_cbeta(){return cbeta;}
inline Matrix& get_beta(){return beta;}
inline Matrix& get_varcb(){return varcb;}
inline Matrix& get_sigsq(){return sigsq;}
inline Matrix& get_residu(){return residu;}
inline int get_dof(){return dof;}
private:
Matrix beta;
Matrix residu;
Matrix sigsq;
Matrix varcb;
Matrix cbeta;
Matrix f_fmf, pf_fmf;
int dof;
Matrix t;
Matrix z;
Matrix p;
// Matrix glm_ols(const Matrix& dat, const Matrix& design);