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lib/PhpSpreadsheet/Shared/Trend/PowerBestFit.php

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+<?php
+
+namespace PhpOffice\PhpSpreadsheet\Shared\Trend;
+
+class PowerBestFit extends BestFit
+{
+    /**
+     * Algorithm type to use for best-fit
+     * (Name of this Trend class).
+     */
+    protected string $bestFitType = 'power';
+
+    /**
+     * Return the Y-Value for a specified value of X.
+     *
+     * @param float $xValue X-Value
+     *
+     * @return float Y-Value
+     */
+    public function getValueOfYForX(float $xValue): float
+    {
+        return $this->getIntersect() * ($xValue - $this->xOffset) ** $this->getSlope();
+    }
+
+    /**
+     * Return the X-Value for a specified value of Y.
+     *
+     * @param float $yValue Y-Value
+     *
+     * @return float X-Value
+     */
+    public function getValueOfXForY(float $yValue): float
+    {
+        return (($yValue + $this->yOffset) / $this->getIntersect()) ** (1 / $this->getSlope());
+    }
+
+    /**
+     * Return the Equation of the best-fit line.
+     *
+     * @param int $dp Number of places of decimal precision to display
+     */
+    public function getEquation(int $dp = 0): string
+    {
+        $slope = $this->getSlope($dp);
+        $intersect = $this->getIntersect($dp);
+
+        return 'Y = ' . $intersect . ' * X^' . $slope;
+    }
+
+    /**
+     * Return the Value of X where it intersects Y = 0.
+     *
+     * @param int $dp Number of places of decimal precision to display
+     */
+    public function getIntersect(int $dp = 0): float
+    {
+        if ($dp != 0) {
+            return round(exp($this->intersect), $dp);
+        }
+
+        return exp($this->intersect);
+    }
+
+    /**
+     * 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 powerRegression(array $yValues, array $xValues, bool $const): void
+    {
+        $adjustedYValues = array_map(
+            fn ($value): float => ($value < 0.0) ? 0 - log(abs($value)) : log($value),
+            $yValues
+        );
+        $adjustedXValues = array_map(
+            fn ($value): float => ($value < 0.0) ? 0 - log(abs($value)) : log($value),
+            $xValues
+        );
+
+        $this->leastSquareFit($adjustedYValues, $adjustedXValues, $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
+     */
+    public function __construct(array $yValues, array $xValues = [], bool $const = true)
+    {
+        parent::__construct($yValues, $xValues);
+
+        if (!$this->error) {
+            $this->powerRegression($yValues, $xValues, (bool) $const);
+        }
+    }
+}