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<?php namespace PhpOffice\PhpSpreadsheet\Shared\Trend; use PhpOffice\PhpSpreadsheet\Shared\JAMA\Matrix; class PolynomialBestFit extends BestFit { /** * Algorithm type to use for best-fit * (Name of this Trend class). * * @var string */ protected $bestFitType = 'polynomial'; /** * Polynomial order. * * @var int */ protected $order = 0; /** * Return the order of this polynomial. * * @return int */ public function getOrder() { return $this->order; } /** * Return the Y-Value for a specified value of X. * * @param float $xValue X-Value * * @return float Y-Value */ public function getValueOfYForX($xValue) { $retVal = $this->getIntersect(); $slope = $this->getSlope();> // @phpstan-ignore-next-lineforeach ($slope as $key => $value) { if ($value != 0.0) {< $retVal += $value * pow($xValue, $key + 1);> $retVal += $value * $xValue ** ($key + 1);} } return $retVal; } /** * Return the X-Value for a specified value of Y. * * @param float $yValue Y-Value * * @return float X-Value */ public function getValueOfXForY($yValue) { return ($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); $equation = 'Y = ' . $intersect;> // @phpstan-ignore-next-lineforeach ($slope as $key => $value) { if ($value != 0.0) { $equation .= ' + ' . $value . ' * X'; if ($key > 0) { $equation .= '^' . ($key + 1); } } } return $equation; } /** * Return the Slope of the line. * * @param int $dp Number of places of decimal precision to display *< * @return string> * @return float*/ public function getSlope($dp = 0) { if ($dp != 0) { $coefficients = []; foreach ($this->slope as $coefficient) { $coefficients[] = round($coefficient, $dp); }> // @phpstan-ignore-next-linereturn $coefficients; } return $this->slope; } public function getCoefficients($dp = 0) { return array_merge([$this->getIntersect($dp)], $this->getSlope($dp)); } /** * Execute the regression and calculate the goodness of fit for a set of X and Y data values. * * @param int $order Order of Polynomial for this regression * @param float[] $yValues The set of Y-values for this regression * @param float[] $xValues The set of X-values for this regression */< private function polynomialRegression($order, $yValues, $xValues)> private function polynomialRegression($order, $yValues, $xValues): void{ // calculate sums $x_sum = array_sum($xValues); $y_sum = array_sum($yValues); $xx_sum = $xy_sum = $yy_sum = 0; for ($i = 0; $i < $this->valueCount; ++$i) { $xy_sum += $xValues[$i] * $yValues[$i]; $xx_sum += $xValues[$i] * $xValues[$i]; $yy_sum += $yValues[$i] * $yValues[$i]; } /* * This routine uses logic from the PHP port of polyfit version 0.1 * written by Michael Bommarito and Paul Meagher * * The function fits a polynomial function of order $order through * a series of x-y data points using least squares. * */ $A = []; $B = []; for ($i = 0; $i < $this->valueCount; ++$i) { for ($j = 0; $j <= $order; ++$j) {< $A[$i][$j] = pow($xValues[$i], $j);> $A[$i][$j] = $xValues[$i] ** $j;} } for ($i = 0; $i < $this->valueCount; ++$i) { $B[$i] = [$yValues[$i]]; } $matrixA = new Matrix($A); $matrixB = new Matrix($B); $C = $matrixA->solve($matrixB); $coefficients = []; for ($i = 0; $i < $C->getRowDimension(); ++$i) { $r = $C->get($i, 0);< if (abs($r) <= pow(10, -9)) {> if (abs($r) <= 10 ** (-9)) {$r = 0; } $coefficients[] = $r; } $this->intersect = array_shift($coefficients); $this->slope = $coefficients; $this->calculateGoodnessOfFit($x_sum, $y_sum, $xx_sum, $yy_sum, $xy_sum, 0, 0, 0); foreach ($this->xValues as $xKey => $xValue) { $this->yBestFitValues[$xKey] = $this->getValueOfYForX($xValue); } } /** * Define the regression and calculate the goodness of fit for a set of X and Y data values. * * @param int $order Order of Polynomial for this regression * @param float[] $yValues The set of Y-values for this regression * @param float[] $xValues The set of X-values for this regression< * @param bool $const*/< public function __construct($order, $yValues, $xValues = [], $const = true)> public function __construct($order, $yValues, $xValues = []){ parent::__construct($yValues, $xValues); if (!$this->error) { if ($order < $this->valueCount) { $this->bestFitType .= '_' . $order; $this->order = $order; $this->polynomialRegression($order, $yValues, $xValues); if (($this->getGoodnessOfFit() < 0.0) || ($this->getGoodnessOfFit() > 1.0)) { $this->error = true; } } else { $this->error = true; } } } }