Possible improvement of the Bayesian filter

This code is a suggestion. It works nearly as the current spam.module rating. The difference is mainly in the SQL query, but should have some differences in the statistics as well. The total effect comes from clipping the query results to contain
only 'interesting' tokens and optionally limit the result with a hard shoulder ($max). The hard shoulder is probably an overkill,
since we are already filtering the 'unimportant' tokens, but might prove useful if the minimum drift is too small.

<code>
function _naiveBayes($tokens) {
  $probs = array();
  $drift = variable_get('spam_min_drift', 40);
  $max = variable_get('spam_max_tokens', 40);
  $num=0;

  //a rewrite of the original - it should not reduce the quality of the
  //predictions, while it has the potential to increase speed. Speed
  //difference depends on the speed of execution of asort() and the
  //number of interesting tokens considered the added comparison and  
  //evaluation in SQL
  //$drift - minimal drift from the median - a soft-ish shoulder of the
  //         filter
  //$max - maximum number of tokens to evaluate, a hard shoulder of the
  //         filter

  foreach($tokens as $token){
    //1. may be beneficial not to include $max or make it larger
    //2. the sql syntax should be relatively portable to postgres,
    // but haven't checked the details
    $result = db_query("SELECT probability FROM {spam_tokens} WHERE
token='%s' AND (ABS(50 - probablility) >= %d) AND last >= %d SORT BY
(ABS(50 - probability) LIMIT %d)", $token,$drift, $max);
    while($p=db_fetch_object($result)){
      if( $p->probability ) {
        $p->probability = variable_get('spam_unknown_probability',40);
      }
      $probs += $p->probability;
      $num++;
    }
  }

  $rating = ($probs + $weight) / $num;
  if ($rating > 99)
    $rating = 99;
  else if ($rating < 1)
    $rating = 1;

  return $rating;
}