# tablepress/3.4/libraries/vendor/PhpSpreadsheet/Shared/Trend/LogarithmicBestFit.php

TablePress – Tables in WordPress made easy, version 3.4. 81 lines.

- Page: https://pluginprobe.com/plugins/tablepress/3.4/code/libraries/vendor/PhpSpreadsheet/Shared/Trend/LogarithmicBestFit.php
- Raw: https://pluginprobe.com/plugins/tablepress/3.4/raw/libraries/vendor/PhpSpreadsheet/Shared/Trend/LogarithmicBestFit.php
- Modified: 2025-01-08T09:25:54+00:00

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```php
<?php

namespace TablePress\PhpOffice\PhpSpreadsheet\Shared\Trend;

class LogarithmicBestFit extends BestFit
{
	/**
	 * Algorithm type to use for best-fit
	 * (Name of this Trend class).
	 */
	protected string $bestFitType = 'logarithmic';

	/**
	 * 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() + $this->getSlope() * log($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(float $yValue): float
	{
		return exp(($yValue - $this->getIntersect()) / $this->getSlope());
	}

	/**
	 * Return the Equation of the best-fit line.
	 *
	 * @param int $dp Number of places of decimal precision to display
	 */
	public function getEquation(int $dp = 0): string
	{
		$slope = $this->getSlope($dp);
		$intersect = $this->getIntersect($dp);

		return 'Y = ' . $slope . ' * log(' . $intersect . ' * 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
	 */
	private function logarithmicRegression(array $yValues, array $xValues, bool $const): void
	{
		$adjustedYValues = array_map(
			fn ($value): float => ($value < 0.0) ? 0 - log(abs($value)) : log($value),
			$yValues
		);

		$this->leastSquareFit($adjustedYValues, $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
	 */
	public function __construct(array $yValues, array $xValues = [], bool $const = true)
	{
		parent::__construct($yValues, $xValues);

		if (!$this->error) {
			$this->logarithmicRegression($yValues, $xValues, (bool) $const);
		}
	}
}

```