/** * \ingroup eigen * Eigenvectors * \param m \f$m\f$ * \return a variable matrix with the * eigenvectors of \f$m\f$ stored in its columns. */ dmatrix eigenvectors(const dmatrix &m) { if (m.rowsize() != m.colsize()) { cerr << "error -- non square matrix passed to dmatrix eigenvectors(const dmatrix& m)\n"; ad_exit(1); } int rmin = m.rowmin(); int rmax = m.rowmax(); dmatrix evecs(rmin, rmax, rmin, rmax); dvector evals(rmin, rmax); eigens(m, evecs, evals); return evecs; }
/** Eigenvalues. \param m Input matrix (unchanged on return). \return Vector of eigenvalues. */ dvector eigenvalues(const dmatrix& m) { if (m.rowsize()!=m.colsize()) { cerr << "error -- non square matrix passed to " "dvector eigen(const dmatrix& m)\n"; ad_exit(1); } dmatrix m1=symmetrize(m); m1.colshift(1); // set minimum column and row indices to 1 m1.rowshift(1); int n=m1.rowsize(); dvector diag(1,n); dvector off_diag(1,n); tri_dag(m1,diag,off_diag); get_eigen(diag,off_diag,m1); // eigenvalues are returned in diag // eigenvalues are returned in columns of z return diag; }
dvariable mult_likelihood(const dmatrix &o, const dvar_matrix &p, dvar_matrix &nu, const dvariable &log_vn) { // kludge to ensure observed and predicted matrixes are the same size if(o.colsize()!=p.colsize() || o.rowsize()!=p.rowsize()) { cerr<<"Error in multivariate_t_likelihood, observed and predicted matrixes" " are not the same size\n"; ad_exit(1); } dvariable vn = mfexp(log_vn); dvariable ff = 0.0; int r1 = o.rowmin(); int r2 = o.rowmax(); int c1 = o.colmin(); int c2 = o.colmax(); for(int i = r1; i <= r2; i++ ) { dvar_vector sobs = vn * o(i)/sum(o(i)); //scale observed numbers by effective sample size. ff -= gammln(vn); for(int j = c1; j <= c2; j++ ) { if( value(sobs(j)) > 0.0 ) ff += gammln(sobs(j)); } ff -= sobs * log(TINY + p(i)); dvar_vector o1=o(i)/sum(o(i)); dvar_vector p1=p(i)/sum(p(i)); nu(i) = elem_div(o1-p1,sqrt(elem_prod(p1,1.-p1)/vn)); } // exit(1); return ff; }
/** * Description not yet available. * \param */ dvar_matrix operator*(const dvar_matrix& m1, const dmatrix& cm2) { if (m1.colmin() != cm2.rowmin() || m1.colmax() != cm2.rowmax()) { cerr << " Incompatible array bounds in " "dmatrix operator*(const dvar_matrix& x, const dmatrix& m)\n"; ad_exit(21); } dmatrix cm1=value(m1); //dmatrix cm2=value(m2); dmatrix tmp(m1.rowmin(),m1.rowmax(), cm2.colmin(), cm2.colmax()); #ifdef OPT_LIB const size_t rowsize = (size_t)cm2.rowsize(); #else const int _rowsize = cm2.rowsize(); assert(_rowsize > 0); const size_t rowsize = (size_t)_rowsize; #endif try { double* temp_col = new double[rowsize]; temp_col-=cm2.rowmin(); for (int j=cm2.colmin(); j<=cm2.colmax(); j++) { for (int k=cm2.rowmin(); k<=cm2.rowmax(); k++) { temp_col[k] = cm2.elem(k,j); } for (int i=cm1.rowmin(); i<=cm1.rowmax(); i++) { double sum=0.0; dvector& temp_row = cm1(i); for (int k=cm1.colmin(); k<=cm1.colmax(); k++) { sum+=temp_row(k) * (temp_col[k]); // sum+=temp_row(k) * cm2(k,j); } tmp(i,j)=sum; } } temp_col+=cm2.rowmin(); delete [] temp_col; temp_col = 0; } catch (std::bad_alloc& e) { cerr << "Error[" << __FILE__ << ':' << __LINE__ << "]: Unable to allocate array.\n"; //ad_exit(21); throw e; } dvar_matrix vtmp=nograd_assign(tmp); save_identifier_string("TEST1"); //m1.save_dvar_matrix_value(); m1.save_dvar_matrix_position(); cm2.save_dmatrix_value(); cm2.save_dmatrix_position(); vtmp.save_dvar_matrix_position(); save_identifier_string("TEST6"); gradient_structure::GRAD_STACK1-> set_gradient_stack(dmcm_prod); return vtmp; }