;--------------------------------------------------------------------------------------------
; compute naive Bayesian Terms
;
; input: 
;        count_yes_all - total number of yes
;        count_no_all - total number of yes
;        count_yes_bin - total number of yes within this bin
;        count_no_bin - total number of no within this bin
;        max_r - maximum allowed value of r
;        prior_yes - prior probability of a yes
;
;        output
;        cond_yes = class conditional for yes
;        cond_no = class conditional for no
;        r - cond_no / cond_yes
;        post_prob = posterior probability
;
;--------------------------------------------------------------------------------------------
pro compute_class_cond, nb_rank, laplace_flag, $
                        count_yes_all, count_no_all, $
                        count_yes_bin, count_no_bin, $
                        max_r, prior_yes, cond_yes, cond_no, r, post_prob

    r = 1.0
    post_prob = prior_yes

    count_yes_bin = count_yes_bin + laplace_flag
    count_yes_all = count_yes_all + nb_rank * laplace_flag

    cond_yes = 0.0
    if (count_yes_all gt 0) then begin
      cond_yes = float(count_yes_bin)/float(count_yes_all)
    endif
      
    cond_no = 0.0
    if (count_no_all gt 0) then begin
       cond_no = float(count_no_bin)/float(count_no_all)
    endif

;print, 'yes = ', count_yes_all, count_yes_bin, cond_yes
;print, 'no = ', count_no_all, count_no_bin, cond_no

    if (cond_yes gt 0.0) then begin
        r = cond_no / cond_yes
    endif else begin
        r = max_r
    endelse

    post_prob = 1.0  / ( 1.0 + r/prior_yes - r)

    post_prob = min([1.0,max([0.0,post_prob])])


end