82 lines
2.1 KiB
PHP
82 lines
2.1 KiB
PHP
<?php
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namespace PhpOffice\PhpSpreadsheet\Shared\Trend;
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class LinearBestFit extends BestFit
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{
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/**
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* Algorithm type to use for best-fit
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* (Name of this Trend class).
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*
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* @var string
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*/
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protected $bestFitType = 'linear';
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/**
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* Return the Y-Value for a specified value of X.
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*
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* @param float $xValue X-Value
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*
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* @return float Y-Value
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*/
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public function getValueOfYForX($xValue)
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{
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return $this->getIntersect() + $this->getSlope() * $xValue;
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}
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/**
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* Return the X-Value for a specified value of Y.
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*
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* @param float $yValue Y-Value
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*
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* @return float X-Value
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*/
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public function getValueOfXForY($yValue)
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{
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return ($yValue - $this->getIntersect()) / $this->getSlope();
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}
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/**
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* Return the Equation of the best-fit line.
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*
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* @param int $dp Number of places of decimal precision to display
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*
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* @return string
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*/
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public function getEquation($dp = 0)
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{
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$slope = $this->getSlope($dp);
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$intersect = $this->getIntersect($dp);
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return 'Y = ' . $intersect . ' + ' . $slope . ' * X';
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}
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/**
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* Execute the regression and calculate the goodness of fit for a set of X and Y data values.
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*
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* @param float[] $yValues The set of Y-values for this regression
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* @param float[] $xValues The set of X-values for this regression
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* @param bool $const
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*/
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private function linearRegression($yValues, $xValues, $const): void
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{
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$this->leastSquareFit($yValues, $xValues, $const);
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}
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/**
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* Define the regression and calculate the goodness of fit for a set of X and Y data values.
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*
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* @param float[] $yValues The set of Y-values for this regression
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* @param float[] $xValues The set of X-values for this regression
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* @param bool $const
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*/
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public function __construct($yValues, $xValues = [], $const = true)
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{
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parent::__construct($yValues, $xValues);
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if (!$this->error) {
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$this->linearRegression($yValues, $xValues, $const);
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}
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}
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}
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