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diff --git a/vendor/phpoffice/phpspreadsheet/src/PhpSpreadsheet/Shared/Trend/LogarithmicBestFit.php b/vendor/phpoffice/phpspreadsheet/src/PhpSpreadsheet/Shared/Trend/LogarithmicBestFit.php
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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 = ' . $intersect . ' + ' . $slope . ' * log(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
+ * @param bool $const
+ */
+ private function logarithmicRegression($yValues, $xValues, $const): void
+ {
+ foreach ($xValues 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) {
+ $this->logarithmicRegression($yValues, $xValues, $const);
+ }
+ }
+}