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