当前路径:vendor/phpoffice/phpspreadsheet/src/PhpSpreadsheet/Shared/Trend/ExponentialBestFit.php <?php namespace PhpOffice\PhpSpreadsheet\Shared\Trend; class ExponentialBestFit extends BestFit { /** * Algorithm type to use for best-fit * (Name of this Trend class). * * @var string */ protected $bestFitType = 'exponential'; /** * 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() * $this->getSlope() ** ($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 log(($yValue + $this->yOffset) / $this->getIntersect()) / log($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 . '^X'; } /** * Return the Slope of the line. * * @param int $dp Number of places of decimal precision to display * * @return float */ public function getSlope($dp = 0) { if ($dp != 0) { return round(exp($this->slope), $dp); } return exp($this->slope); } /** * Return the Value of X where it intersects Y = 0. * * @param int $dp Number of places of decimal precision to display * * @return float */ public function getIntersect($dp = 0) { if ($dp != 0) { return round(exp($this->intersect), $dp); } return exp($this->intersect); } /** * 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 $const */ private function exponentialRegression($yValues, $xValues, $const): void { foreach ($yValues 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) { $this->exponentialRegression($yValues, $xValues, $const); } } }
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地址:北京市海淀区大恒科技大厦五层 邮编:100080
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51Aspx.com 版权所有 CopyRight © 2006-2022. 京ICP备09089570号 | 京公网安备11010702000869号