function recommender_debug_table in Recommender API 6.2

File

./recommender.module, line 371

Code

function recommender_debug_table($app_name, $simpred, $un1, $un2) {
  $app_id = recommender_get_app_id($app_name);

  // load data
  if ($simpred == 0) {
    $sql = "SELECT mouse1_id un1, mouse2_id un2, similarity score FROM {recommender_similarity} WHERE app_id=%s ORDER BY mouse1_id, mouse2_id";
  }
  else {
    if ($simpred == 1) {
      $sql = "SELECT mouse_id un1, cheese_id un2, prediction score FROM {recommender_prediction} WHERE app_id=%s ORDER BY mouse_id, cheese_id";
    }
  }
  $result = db_query($sql, $app_id);

  // load users
  $users = array();
  $u_result = db_query("SELECT uid, name FROM {users}");
  while ($u = db_fetch_array($u_result)) {
    $users[$u['uid']] = $u['name'];
  }

  // load nodes
  $nodes = array();
  $n_result = db_query("SELECT nid, title FROM {node}");
  while ($n = db_fetch_array($n_result)) {
    $nodes[$n['nid']] = $n['title'];
  }
  $un1_content = $un1 ? $nodes : $users;
  $un2_content = $un2 ? $nodes : $users;
  $un1_header = $un1 ? 'node' : 'user';
  $un2_header = $un2 ? 'node' : 'user';
  $header = array(
    $un1_header,
    $un2_header,
    'score',
  );
  $rows = array();
  while ($record = db_fetch_array($result)) {
    $r = array();
    $r[] = l($un1_content[$record['un1']], "{$un1_header}/{$record['un1']}");
    $r[] = l($un2_content[$record['un2']], "{$un2_header}/{$record['un2']}");
    $r[] = $record['score'];
    $rows[] = $r;
  }
  return theme('table', $header, $rows);
}