original_string: protected function get_evaluation_result_object(\stored_file $dataset, $phis, $maxdeviation) {
  
          // Average phi of all evaluations as final score.
          if (count($phis) === 1) {
              $avgphi = reset($phis);
          } else {
              $avgphi = \Phpml\Math\Statistic\Mean::arithmetic($phis);
          }
  
          // Standard deviation should ideally be calculated against the area under the curve.
          if (count($phis) === 1) {
              $modeldev = 0;
          } else {
              $modeldev = \Phpml\Math\Statistic\StandardDeviation::population($phis);
          }
  
          // Let's fill the results object.
          $resultobj = new \stdClass();
  
          // Zero is ok, now we add other bits if something is not right.
          $resultobj->status = \core_analytics\model::OK;
          $resultobj->info = array();
  
          // Convert phi to a standard score (from -1 to 1 to a value between 0 and 1).
          $resultobj->score = ($avgphi + 1) / 2;
  
          // If each iteration results varied too much we need more data to confirm that this is a valid model.
          if ($modeldev > $maxdeviation) {
              $resultobj->status = $resultobj->status + \core_analytics\model::NOT_ENOUGH_DATA;
              $a = new \stdClass();
              $a->deviation = $modeldev;
              $a->accepteddeviation = $maxdeviation;
              $resultobj->info[] = get_string('errornotenoughdatadev', 'mlbackend_php', $a);
          }
  
          if ($resultobj->score < \core_analytics\model::MIN_SCORE) {
              $resultobj->status = $resultobj->status + \core_analytics\model::LOW_SCORE;
              $a = new \stdClass();
              $a->score = $resultobj->score;
              $a->minscore = \core_analytics\model::MIN_SCORE;
              $resultobj->info[] = get_string('errorlowscore', 'mlbackend_php', $a);
          }
  
          if ($this->limitedsize === true) {
              $resultobj->info[] = get_string('datasetsizelimited', 'mlbackend_php', display_size($dataset->get_filesize()));
          }
  
          return $resultobj;
      }
code: protected function get_evaluation_result_object(\stored_file $dataset, $phis, $maxdeviation) {
  
          // Average phi of all evaluations as final score.
          if (count($phis) === 1) {
              $avgphi = reset($phis);
          } else {
              $avgphi = \Phpml\Math\Statistic\Mean::arithmetic($phis);
          }
  
          // Standard deviation should ideally be calculated against the area under the curve.
          if (count($phis) === 1) {
              $modeldev = 0;
          } else {
              $modeldev = \Phpml\Math\Statistic\StandardDeviation::population($phis);
          }
  
          // Let's fill the results object.
          $resultobj = new \stdClass();
  
          // Zero is ok, now we add other bits if something is not right.
          $resultobj->status = \core_analytics\model::OK;
          $resultobj->info = array();
  
          // Convert phi to a standard score (from -1 to 1 to a value between 0 and 1).
          $resultobj->score = ($avgphi + 1) / 2;
  
          // If each iteration results varied too much we need more data to confirm that this is a valid model.
          if ($modeldev > $maxdeviation) {
              $resultobj->status = $resultobj->status + \core_analytics\model::NOT_ENOUGH_DATA;
              $a = new \stdClass();
              $a->deviation = $modeldev;
              $a->accepteddeviation = $maxdeviation;
              $resultobj->info[] = get_string('errornotenoughdatadev', 'mlbackend_php', $a);
          }
  
          if ($resultobj->score < \core_analytics\model::MIN_SCORE) {
              $resultobj->status = $resultobj->status + \core_analytics\model::LOW_SCORE;
              $a = new \stdClass();
              $a->score = $resultobj->score;
              $a->minscore = \core_analytics\model::MIN_SCORE;
              $resultobj->info[] = get_string('errorlowscore', 'mlbackend_php', $a);
          }
  
          if ($this->limitedsize === true) {
              $resultobj->info[] = get_string('datasetsizelimited', 'mlbackend_php', display_size($dataset->get_filesize()));
          }
  
          return $resultobj;
      }
code_tokens: protected function get_evaluation_result_object ( \ stored_file $ dataset , $ phis , $ maxdeviation ) { // Average phi of all evaluations as final score. if ( count ( $ phis ) === 1 ) { $ avgphi = reset ( $ phis ) ; } else { $ avgphi = \ Phpml \ Math \ Statistic \ Mean :: arithmetic ( $ phis ) ; } // Standard deviation should ideally be calculated against the area under the curve. if ( count ( $ phis ) === 1 ) { $ modeldev = 0 ; } else { $ modeldev = \ Phpml \ Math \ Statistic \ StandardDeviation :: population ( $ phis ) ; } // Let's fill the results object. $ resultobj = new \ stdClass ( ) ; // Zero is ok, now we add other bits if something is not right. $ resultobj -> status = \ core_analytics \ model :: OK ; $ resultobj -> info = array ( ) ; // Convert phi to a standard score (from -1 to 1 to a value between 0 and 1). $ resultobj -> score = ( $ avgphi + 1 ) / 2 ; // If each iteration results varied too much we need more data to confirm that this is a valid model. if ( $ modeldev > $ maxdeviation ) { $ resultobj -> status = $ resultobj -> status + \ core_analytics \ model :: NOT_ENOUGH_DATA ; $ a = new \ stdClass ( ) ; $ a -> deviation = $ modeldev ; $ a -> accepteddeviation = $ maxdeviation ; $ resultobj -> info [ ] = get_string ( 'errornotenoughdatadev' , 'mlbackend_php' , $ a ) ; } if ( $ resultobj -> score < \ core_analytics \ model :: MIN_SCORE ) { $ resultobj -> status = $ resultobj -> status + \ core_analytics \ model :: LOW_SCORE ; $ a = new \ stdClass ( ) ; $ a -> score = $ resultobj -> score ; $ a -> minscore = \ core_analytics \ model :: MIN_SCORE ; $ resultobj -> info [ ] = get_string ( 'errorlowscore' , 'mlbackend_php' , $ a ) ; } if ( $ this -> limitedsize === true ) { $ resultobj -> info [ ] = get_string ( 'datasetsizelimited' , 'mlbackend_php' , display_size ( $ dataset -> get_filesize ( ) ) ) ; } return $ resultobj ; }
docstring: Returns the results objects from all evaluations.
  
  @param \stored_file $dataset
  @param array $phis
  @param float $maxdeviation
  @return \stdClass
docstring_tokens: Returns the results objects from all evaluations .