1 files changed, 90 insertions, 0 deletions
diff --git a/vendor/phpoffice/phpspreadsheet/src/PhpSpreadsheet/Shared/Trend/LogarithmicBestFit.php b/vendor/phpoffice/phpspreadsheet/src/PhpSpreadsheet/Shared/Trend/LogarithmicBestFit.php
new file mode 100644
index 0000000..4f2c805
--- /dev/null
+++ b/vendor/phpoffice/phpspreadsheet/src/PhpSpreadsheet/Shared/Trend/LogarithmicBestFit.php
@@ -0,0 +1,90 @@
+<?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);
+        }
+    }
+}