/* Draws a histogram with N bars where N = dym_rows(freqs) Each bar is compartmented into K subbars, on top of each other, where K = dym_cols(freqs) and the j'th subcomponent of the i'th bar has height = dym_ref(freqs,i,j) and is on top of the j-1'th subcomponent. The i'th bar is centered on x=xlo + (i+0.5) * (xhi - xlo) / num_bars */ void ongr_plot_hist(frame *fr,ongr *on,dym *freqs,bool ratio) { int i; int num_bars = dym_rows(freqs); int num_classes = dym_cols(freqs); double xlo = on -> x_axis.lo; double xhi = on -> x_axis.hi; double bar_width = 0.9 * (xhi - xlo) / num_bars; for ( i = 0 ; i < num_bars ; i++ ) { double xmid = xlo + (i + 0.5) * (xhi - xlo) / num_bars; double x1 = xmid - bar_width/2; double x2 = xmid + bar_width/2; int j; double sumy = 0.0; double total_sum_y = real_max(1e-5,dym_sum_row(freqs,i)); for ( j = 0 ; j < num_classes ; j++ ) { double y1 = sumy; double dy = dym_ref(freqs,i,j) / ((ratio) ? (total_sum_y/100.0) : 1.0); double y2 = sumy + dy; int amut_col = color_code_to_ag_color(j); ongr_colored_bordered_rectangle(fr,on,x1,y1,x2,y2,amut_col); sumy = y2; } } }
/* Makes a dym consisting of a subset of the rows in x. The members of of the subset are those rows mentioned in "rows". Result will this have "ivec_size(rows)" rows and dym_cols(x) columns */ dym *mk_dym_from_subset_of_rows(dym *x,ivec *rows) { int num_rows = ivec_size(rows); int i; dym *result = mk_dym(num_rows,dym_cols(x)); for ( i = 0 ; i < num_rows ; i++ ) { int row = ivec_ref(rows,i); dyv *vec = mk_dyv_from_dym_row(x,row); copy_dyv_to_dym_row(vec,result,i); free_dyv(vec); } return result; }
lr_train *mk_lr_train_from_dym( dym *factors, dyv *outputs, lr_options *opts) { /* Set rows to NULL if you want all rows from ds to be used. */ int numrows, numatts; lr_train *lrt; numrows = dym_rows( factors); numatts = dym_cols( factors)+1; /* Number of factors including constant. */ /* Create lr lrt structure. */ lrt = AM_MALLOC(lr_train); /* Copy in opts. */ lrt->opts = mk_copy_lr_options( opts); /* Assign factors and outputs into lr structure. */ lrt->X = NULL; lrt->M = factors; /* Outputs. */ lrt->y = mk_copy_dyv( outputs); if (!dyv_is_binary( outputs)) { my_error( "mk_lr_train: Error: outputs are not binary.\n"); } /* Set log likelihood of saturated model. */ lrt->likesat = 0.0; /* Initialize remainder of lr struct */ lrt->numatts = numatts; lrt->numrows = numrows; /* Create lr_state member. */ lrt->lrs = mk_lr_state( lrt, opts); /* Now that the structure is complete, update n and u to prepare for iterations. */ lr_train_update_n(lrt); lr_train_update_u(lrt); return lrt; }