/* 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 */ /* 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 5.0 (c) 2012, The University of Oxford (the "Software") The Software remains the property of the University of Oxford ("the University"). 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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/9564. */ #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& mask, Matrix& Data); void del_vols(volume4D& 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); void varnorm(Matrix& in, const RowVector& vars); 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); class basicGLM{ public: //constructor basicGLM(){} //destructor ~basicGLM(){} void olsfit(const Matrix& data, const Matrix& design, const Matrix& contrasts, int DOFadjust = -1); 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); } #endif