/* gsmanager.h Mark Woolrich, Tim Behrens - FMRIB Image Analysis Group Copyright (C) 2002 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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Contact details are: innovation@isis.ox.ac.uk quoting reference DE/9564. */ #if !defined(gsmanager_h) #define gsmanager_h #include #include #include #include #include "gsoptions.h" #include "newimage/newimageall.h" #include "design.h" using namespace NEWIMAGE; using namespace MISCMATHS; namespace Gs { // Give this class a file containing class Gsmanager { public: // constructor Gsmanager() : opts(GsOptions::getInstance()), nmaskvoxels(0) { } // load data from file in from file and set up starting values void setup(); // initialise void initialise(); void run(); // saves results in logging directory void save(); // Destructor virtual ~Gsmanager() {} private: const Gsmanager& operator=(Gsmanager& par); Gsmanager(Gsmanager& des); void multitfit(const Matrix& x, ColumnVector& m, SymmetricMatrix& covar, float& v, bool fixmean=false) const; float log_likelihood(float beta, const ColumnVector& gam, const ColumnVector& y, const Matrix& z, const ColumnVector& S); float log_likelihood_outlier(float beta, float beta_outlier, const ColumnVector& gam, const ColumnVector& y, const Matrix& z, const ColumnVector& S, float global_prob_outlier, const ColumnVector& prob_outlier); float marg_posterior_energy(float x, const ColumnVector& y, const Matrix& z, const ColumnVector& S); float marg_posterior_energy_outlier(float logbeta, float logbeta_outlier, const ColumnVector& y, const Matrix& z, const ColumnVector& S, const ColumnVector& prob_outlier); float solveforbeta(const ColumnVector& y, const Matrix& z, const ColumnVector& S); bool pass_through_to_mcmc(float zlowerthresh, float zupperthresh, int px, int py, int pz); // functions to calc contrasts void ols_contrasts(const ColumnVector& gammean, const SymmetricMatrix& gamS, int px, int py, int pz); void fe_contrasts(const ColumnVector& gammean, const SymmetricMatrix& gamS, int px, int py, int pz); void flame1_contrasts(const ColumnVector& gammean, const SymmetricMatrix& gamS, int px, int py, int pz); void flame1_contrasts_with_outliers(const ColumnVector& mn, const SymmetricMatrix& covariance, int px, int py, int pz); void flame2_contrasts(const Matrix& gamsamples, int px, int py, int pz); void t_ols_contrast(const ColumnVector& gammean, const SymmetricMatrix& gamS, const RowVector& tcontrast, float& cope, float& varcope, float& t, float& dof, float& z, int px, int py, int pz); void f_ols_contrast(const ColumnVector& gammean, const SymmetricMatrix& gamS, const Matrix& fcontrast, float& f, float& dof1, float& dof2, float& z, int px, int py, int pz); void t_mcmc_contrast(const Matrix& gamsamples, const RowVector& tcontrast, float& cope, float& varcope, float& t, float& dof, float& z, int px, int py, int pz); void f_mcmc_contrast(const Matrix& gamsamples, const Matrix& fcontrast, float& f, float& dof1, float& dof2, float& z, int px, int py, int pz); // voxelwise functions to perform the different inference approaches void fixed_effects_onvoxel(const ColumnVector& Y, const Matrix& z, const ColumnVector& S, ColumnVector& gam, SymmetricMatrix& gamcovariance); void flame_stage1_onvoxel(const vector& Yg, const ColumnVector& Y, const vector& zg, const Matrix& z, const vector& Sg, const ColumnVector& S, ColumnVector& beta, ColumnVector& gam, SymmetricMatrix& gamcovariance, vector& marg, vector& weights_g, int& nparams, int px, int py, int pz); void flame_stage1_onvoxel_inferoutliers(const vector& Yg, const ColumnVector& Y, const vector& zg, const Matrix& z, const vector& Sg, const ColumnVector& S, ColumnVector& beta, ColumnVector& gam, SymmetricMatrix& gamcovariance, ColumnVector& global_prob_outlier, vector& prob_outlier_g, ColumnVector& prob_outlier, ColumnVector& beta_outlier, vector& marg, vector& weights_g, int& nparams, vector& no_outliers, int px, int py, int pz); void flame_stage1_inferoutliers(); void init_flame_stage1_inferoutliers(); // functions to perform the different inference approaches void fixed_effects(); void ols(); void flame_stage1(); void flame_stage2(); void do_kmeans(const Matrix& data,vector& z,const int k,Matrix& means); void randomise(vector< pair >& r); vector< pair > randomise(const int n); void regularise_flame2_contrasts(); volume mcmc_mask; // intermediates Design design; vector > beta_b; vector > beta_c; vector > beta_mean; vector > beta_outlier_mean; vector > global_prob_outlier_mean; vector > prob_outlier_mean; vector > weights; volume4D cov_pes; // outputs vector > pes; vector > ts; vector > tdofs; vector > zts; vector > zflame1upperts; vector > zflame1lowerts; vector > tcopes; vector > tvarcopes; vector > fs; vector > fdof1s; vector > fdof2s; vector > zfs; vector > zflame1upperfs; vector > zflame1lowerfs; // intermediates int ngs; int nevs; int ntpts; int xsize; int ysize; int zsize; GsOptions& opts; int nmaskvoxels; bool dofpassedin; }; bool compare(const pair &r1,const pair &r2); } #endif