original_string: public static function oneWayF(array $samples): array
      {
          $classes = count($samples);
          if ($classes < 2) {
              throw new InvalidArgumentException('The array must have at least 2 elements');
          }
  
          $samplesPerClass = array_map(function (array $class): int {
              return count($class);
          }, $samples);
          $allSamples = (int) array_sum($samplesPerClass);
          $ssAllSamples = self::sumOfSquaresPerFeature($samples);
          $sumSamples = self::sumOfFeaturesPerClass($samples);
          $squareSumSamples = self::sumOfSquares($sumSamples);
          $sumSamplesSquare = self::squaresSum($sumSamples);
          $ssbn = self::calculateSsbn($samples, $sumSamplesSquare, $samplesPerClass, $squareSumSamples, $allSamples);
          $sswn = self::calculateSswn($ssbn, $ssAllSamples, $squareSumSamples, $allSamples);
          $dfbn = $classes - 1;
          $dfwn = $allSamples - $classes;
  
          $msb = array_map(function ($s) use ($dfbn) {
              return $s / $dfbn;
          }, $ssbn);
          $msw = array_map(function ($s) use ($dfwn) {
              return $s / $dfwn;
          }, $sswn);
  
          $f = [];
          foreach ($msb as $index => $msbValue) {
              $f[$index] = $msbValue / $msw[$index];
          }
  
          return $f;
      }
code: public static function oneWayF(array $samples): array
      {
          $classes = count($samples);
          if ($classes < 2) {
              throw new InvalidArgumentException('The array must have at least 2 elements');
          }
  
          $samplesPerClass = array_map(function (array $class): int {
              return count($class);
          }, $samples);
          $allSamples = (int) array_sum($samplesPerClass);
          $ssAllSamples = self::sumOfSquaresPerFeature($samples);
          $sumSamples = self::sumOfFeaturesPerClass($samples);
          $squareSumSamples = self::sumOfSquares($sumSamples);
          $sumSamplesSquare = self::squaresSum($sumSamples);
          $ssbn = self::calculateSsbn($samples, $sumSamplesSquare, $samplesPerClass, $squareSumSamples, $allSamples);
          $sswn = self::calculateSswn($ssbn, $ssAllSamples, $squareSumSamples, $allSamples);
          $dfbn = $classes - 1;
          $dfwn = $allSamples - $classes;
  
          $msb = array_map(function ($s) use ($dfbn) {
              return $s / $dfbn;
          }, $ssbn);
          $msw = array_map(function ($s) use ($dfwn) {
              return $s / $dfwn;
          }, $sswn);
  
          $f = [];
          foreach ($msb as $index => $msbValue) {
              $f[$index] = $msbValue / $msw[$index];
          }
  
          return $f;
      }
code_tokens: public static function oneWayF ( array $ samples ) : array { $ classes = count ( $ samples ) ; if ( $ classes < 2 ) { throw new InvalidArgumentException ( 'The array must have at least 2 elements' ) ; } $ samplesPerClass = array_map ( function ( array $ class ) : int { return count ( $ class ) ; } , $ samples ) ; $ allSamples = ( int ) array_sum ( $ samplesPerClass ) ; $ ssAllSamples = self :: sumOfSquaresPerFeature ( $ samples ) ; $ sumSamples = self :: sumOfFeaturesPerClass ( $ samples ) ; $ squareSumSamples = self :: sumOfSquares ( $ sumSamples ) ; $ sumSamplesSquare = self :: squaresSum ( $ sumSamples ) ; $ ssbn = self :: calculateSsbn ( $ samples , $ sumSamplesSquare , $ samplesPerClass , $ squareSumSamples , $ allSamples ) ; $ sswn = self :: calculateSswn ( $ ssbn , $ ssAllSamples , $ squareSumSamples , $ allSamples ) ; $ dfbn = $ classes - 1 ; $ dfwn = $ allSamples - $ classes ; $ msb = array_map ( function ( $ s ) use ( $ dfbn ) { return $ s / $ dfbn ; } , $ ssbn ) ; $ msw = array_map ( function ( $ s ) use ( $ dfwn ) { return $ s / $ dfwn ; } , $ sswn ) ; $ f = [ ] ; foreach ( $ msb as $ index => $ msbValue ) { $ f [ $ index ] = $ msbValue / $ msw [ $ index ] ; } return $ f ; }
docstring: The one-way ANOVA tests the null hypothesis that 2 or more groups have
  the same population mean. The test is applied to samples from two or
  more groups, possibly with differing sizes.
  
  @param array[] $samples - each row is class samples
  
  @return float[]
docstring_tokens: The one - way ANOVA tests the null hypothesis that 2 or more groups have the same population mean . The test is applied to samples from two or more groups possibly with differing sizes .