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Long Term Support Release
Differences Between: [Versions 310 and 401] [Versions 311 and 401] [Versions 39 and 401]
1 <?php 2 3 namespace PhpOffice\PhpSpreadsheet\Shared\Trend; 4 5 class LogarithmicBestFit extends BestFit 6 { 7 /** 8 * Algorithm type to use for best-fit 9 * (Name of this Trend class). 10 * 11 * @var string 12 */ 13 protected $bestFitType = 'logarithmic'; 14 15 /** 16 * Return the Y-Value for a specified value of X. 17 * 18 * @param float $xValue X-Value 19 * 20 * @return float Y-Value 21 */ 22 public function getValueOfYForX($xValue) 23 { 24 return $this->getIntersect() + $this->getSlope() * log($xValue - $this->xOffset); 25 } 26 27 /** 28 * Return the X-Value for a specified value of Y. 29 * 30 * @param float $yValue Y-Value 31 * 32 * @return float X-Value 33 */ 34 public function getValueOfXForY($yValue) 35 { 36 return exp(($yValue - $this->getIntersect()) / $this->getSlope()); 37 } 38 39 /** 40 * Return the Equation of the best-fit line. 41 * 42 * @param int $dp Number of places of decimal precision to display 43 * 44 * @return string 45 */ 46 public function getEquation($dp = 0) 47 { 48 $slope = $this->getSlope($dp); 49 $intersect = $this->getIntersect($dp); 50 51 return 'Y = ' . $slope . ' * log(' . $intersect . ' * X)'; 52 } 53 54 /** 55 * Execute the regression and calculate the goodness of fit for a set of X and Y data values. 56 * 57 * @param float[] $yValues The set of Y-values for this regression 58 * @param float[] $xValues The set of X-values for this regression 59 */ 60 private function logarithmicRegression(array $yValues, array $xValues, bool $const): void 61 { 62 $adjustedYValues = array_map( 63 function ($value) { 64 return ($value < 0.0) ? 0 - log(abs($value)) : log($value); 65 }, 66 $yValues 67 ); 68 69 $this->leastSquareFit($adjustedYValues, $xValues, $const); 70 } 71 72 /** 73 * Define the regression and calculate the goodness of fit for a set of X and Y data values. 74 * 75 * @param float[] $yValues The set of Y-values for this regression 76 * @param float[] $xValues The set of X-values for this regression 77 * @param bool $const 78 */ 79 public function __construct($yValues, $xValues = [], $const = true) 80 { 81 parent::__construct($yValues, $xValues); 82 83 if (!$this->error) { 84 $this->logarithmicRegression($yValues, $xValues, (bool) $const); 85 } 86 } 87 }
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