Alter Version

vendor/PhpSpreadsheet/Shared/Trend/ExponentialBestFit.php

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+<?php
+
+namespace PhpOffice\PhpSpreadsheet\Shared\Trend;
+
+class ExponentialBestFit extends BestFit
+{
+    /**
+     * Algorithm type to use for best-fit
+     * (Name of this Trend class).
+     *
+     * @var string
+     */
+    protected $bestFitType = 'exponential';
+
+    /**
+     * 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() * $this->getSlope() ** ($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 log(($yValue + $this->yOffset) / $this->getIntersect()) / log($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 = ' . $intersect . ' * ' . $slope . '^X';
+    }
+
+    /**
+     * Return the Slope of the line.
+     *
+     * @param int $dp Number of places of decimal precision to display
+     *
+     * @return float
+     */
+    public function getSlope($dp = 0)
+    {
+        if ($dp != 0) {
+            return round(exp($this->slope), $dp);
+        }
+
+        return exp($this->slope);
+    }
+
+    /**
+     * Return the Value of X where it intersects Y = 0.
+     *
+     * @param int $dp Number of places of decimal precision to display
+     *
+     * @return float
+     */
+    public function getIntersect($dp = 0)
+    {
+        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
+     * @param bool $const
+     */
+    private function exponentialRegression($yValues, $xValues, $const): void
+    {
+        foreach ($yValues as &$value) {
+            if ($value < 0.0) {
+                $value = 0 - log(abs($value));
+            } elseif ($value > 0.0) {
+                $value = log($value);
+            }
+        }
+        unset($value);
+
+        $this->leastSquareFit($yValues, $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) {
+            $this->exponentialRegression($yValues, $xValues, $const);
+        }
+    }
+}