PhpOffice\PhpSpreadsheet\Shared\Trend\LogarithmicBestFit

Complexity

Total Complexity

7

Size/Duplication

Total Lines	73
Duplicated Lines	0 %

Test Coverage

Coverage

66.67%

Importance

Changes

0

5 Methods

Rating	Name	Duplication	Size	Complexity
A	getEquation()	0	6	1
A	getValueOfXForY()	0	3	1
A	getValueOfYForX()	0	3	1
A	__construct()	0	6	2
A	logarithmicRegression()	0	8	2

<?php

namespace 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);
        }
    }
}