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<?php namespace PhpOffice\PhpSpreadsheet\Shared\Trend; class LogarithmicBestFit extends BestFit { /** * Algorithm type to use for best-fit * (Name of this Trend class). * * @var string */ protected $bestFitType = 'logarithmic'; /** * Return the Y-Value for a specified value of X. * * @param float $xValue X-Value * * @return float Y-Value */ public function getValueOfYForX($xValue) { return $this->getIntersect() + $this->getSlope() * log($xValue - $this->xOffset); } /** * Return the X-Value for a specified value of Y. * * @param float $yValue Y-Value * * @return float X-Value */ public function getValueOfXForY($yValue) { return exp(($yValue - $this->getIntersect()) / $this->getSlope()); } /** * Return the Equation of the best-fit line. * * @param int $dp Number of places of decimal precision to display * * @return string */ public function getEquation($dp = 0) { $slope = $this->getSlope($dp); $intersect = $this->getIntersect($dp); return 'Y = ' . $slope . ' * log(' . $intersect . ' * X)'; } /** * Execute the regression and calculate the goodness of fit for a set of X and Y data values. * * @param float[] $yValues The set of Y-values for this regression * @param float[] $xValues The set of X-values for this regression */ private function logarithmicRegression(array $yValues, array $xValues, bool $const): void { $adjustedYValues = array_map( function ($value) { return ($value < 0.0) ? 0 - log(abs($value)) : log($value); }, $yValues ); $this->leastSquareFit($adjustedYValues, $xValues, $const); } /** * Define the regression and calculate the goodness of fit for a set of X and Y data values. * * @param float[] $yValues The set of Y-values for this regression * @param float[] $xValues The set of X-values for this regression * @param bool $const */ public function __construct($yValues, $xValues = [], $const = true) { parent::__construct($yValues, $xValues); if (!$this->error) { $this->logarithmicRegression($yValues, $xValues, (bool) $const); } } }