<?php
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namespace PhpOffice\PhpSpreadsheet\Shared\Trend;
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class PowerBestFit 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 = 'power';
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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 $this->getIntersect() * ($xValue - $this->xOffset) ** $this->getSlope();
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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 $yValue): float
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{
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return (($yValue + $this->yOffset) / $this->getIntersect()) ** (1 / $this->getSlope());
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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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return 'Y = ' . $intersect . ' * X^' . $slope;
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}
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/**
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* Return the Value of X where it intersects Y = 0.
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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 getIntersect(int $dp = 0): float
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{
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if ($dp != 0) {
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return round(exp($this->intersect), $dp);
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}
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return exp($this->intersect);
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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 powerRegression(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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$yValues
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);
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$adjustedXValues = array_map(
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fn ($value): float => ($value < 0.0) ? 0 - log(abs($value)) : log($value),
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$xValues
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);
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$this->leastSquareFit($adjustedYValues, $adjustedXValues, $const);
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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, $xValues);
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if (!$this->error) {
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$this->powerRegression($yValues, $xValues, (bool) $const);
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}
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}
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}
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