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<?php namespace PhpOffice\PhpSpreadsheet\Shared\Trend;< class BestFit> abstract class BestFit{ /** * Indicator flag for a calculation error. * * @var bool */ protected $error = false; /** * Algorithm type to use for best-fit. * * @var string */ protected $bestFitType = 'undetermined'; /** * Number of entries in the sets of x- and y-value arrays. * * @var int */ protected $valueCount = 0; /** * X-value dataseries of values. * * @var float[] */ protected $xValues = []; /** * Y-value dataseries of values. * * @var float[] */ protected $yValues = []; /** * Flag indicating whether values should be adjusted to Y=0. * * @var bool */ protected $adjustToZero = false; /** * Y-value series of best-fit values. * * @var float[] */ protected $yBestFitValues = []; protected $goodnessOfFit = 1; protected $stdevOfResiduals = 0; protected $covariance = 0; protected $correlation = 0; protected $SSRegression = 0; protected $SSResiduals = 0; protected $DFResiduals = 0; protected $f = 0; protected $slope = 0; protected $slopeSE = 0; protected $intersect = 0; protected $intersectSE = 0; protected $xOffset = 0; protected $yOffset = 0; public function getError() { return $this->error; } public function getBestFitType() { return $this->bestFitType; } /** * Return the Y-Value for a specified value of X. * * @param float $xValue X-Value *< * @return bool Y-Value> * @return float Y-Value*/< public function getValueOfYForX($xValue) < { < return false; < }> abstract public function getValueOfYForX($xValue);/** * Return the X-Value for a specified value of Y. * * @param float $yValue Y-Value *< * @return bool X-Value> * @return float X-Value*/< public function getValueOfXForY($yValue) < { < return false; < }> abstract public function getValueOfXForY($yValue);/** * Return the original set of X-Values. * * @return float[] X-Values */ public function getXValues() { return $this->xValues; } /** * Return the Equation of the best-fit line. * * @param int $dp Number of places of decimal precision to display *< * @return bool> * @return string*/< public function getEquation($dp = 0) < { < return false; < }> abstract public function getEquation($dp = 0);/** * 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 ($dp != 0) { return round($this->slope, $dp); } return $this->slope; } /** * Return the standard error of the Slope. * * @param int $dp Number of places of decimal precision to display * * @return float */ public function getSlopeSE($dp = 0) { if ($dp != 0) { return round($this->slopeSE, $dp); } return $this->slopeSE; } /** * 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 ($dp != 0) { return round($this->intersect, $dp); } return $this->intersect; } /** * Return the standard error of the Intersect. * * @param int $dp Number of places of decimal precision to display * * @return float */ public function getIntersectSE($dp = 0) { if ($dp != 0) { return round($this->intersectSE, $dp); } return $this->intersectSE; } /** * Return the goodness of fit for this regression. * * @param int $dp Number of places of decimal precision to return * * @return float */ public function getGoodnessOfFit($dp = 0) { if ($dp != 0) { return round($this->goodnessOfFit, $dp); } return $this->goodnessOfFit; } /** * Return the goodness of fit for this regression. * * @param int $dp Number of places of decimal precision to return * * @return float */ public function getGoodnessOfFitPercent($dp = 0) { if ($dp != 0) { return round($this->goodnessOfFit * 100, $dp); } return $this->goodnessOfFit * 100; } /** * Return the standard deviation of the residuals for this regression. * * @param int $dp Number of places of decimal precision to return * * @return float */ public function getStdevOfResiduals($dp = 0) { if ($dp != 0) { return round($this->stdevOfResiduals, $dp); } return $this->stdevOfResiduals; } /** * @param int $dp Number of places of decimal precision to return * * @return float */ public function getSSRegression($dp = 0) { if ($dp != 0) { return round($this->SSRegression, $dp); } return $this->SSRegression; } /** * @param int $dp Number of places of decimal precision to return * * @return float */ public function getSSResiduals($dp = 0) { if ($dp != 0) { return round($this->SSResiduals, $dp); } return $this->SSResiduals; } /** * @param int $dp Number of places of decimal precision to return * * @return float */ public function getDFResiduals($dp = 0) { if ($dp != 0) { return round($this->DFResiduals, $dp); } return $this->DFResiduals; } /** * @param int $dp Number of places of decimal precision to return * * @return float */ public function getF($dp = 0) { if ($dp != 0) { return round($this->f, $dp); } return $this->f; } /** * @param int $dp Number of places of decimal precision to return * * @return float */ public function getCovariance($dp = 0) { if ($dp != 0) { return round($this->covariance, $dp); } return $this->covariance; } /** * @param int $dp Number of places of decimal precision to return * * @return float */ public function getCorrelation($dp = 0) { if ($dp != 0) { return round($this->correlation, $dp); } return $this->correlation; } /** * @return float[] */ public function getYBestFitValues() { return $this->yBestFitValues; }< protected function calculateGoodnessOfFit($sumX, $sumY, $sumX2, $sumY2, $sumXY, $meanX, $meanY, $const)> protected function calculateGoodnessOfFit($sumX, $sumY, $sumX2, $sumY2, $sumXY, $meanX, $meanY, $const): void{ $SSres = $SScov = $SScor = $SStot = $SSsex = 0.0; foreach ($this->xValues as $xKey => $xValue) { $bestFitY = $this->yBestFitValues[$xKey] = $this->getValueOfYForX($xValue); $SSres += ($this->yValues[$xKey] - $bestFitY) * ($this->yValues[$xKey] - $bestFitY);< if ($const) {> if ($const === true) {$SStot += ($this->yValues[$xKey] - $meanY) * ($this->yValues[$xKey] - $meanY); } else { $SStot += $this->yValues[$xKey] * $this->yValues[$xKey]; } $SScov += ($this->xValues[$xKey] - $meanX) * ($this->yValues[$xKey] - $meanY);< if ($const) {> if ($const === true) {$SSsex += ($this->xValues[$xKey] - $meanX) * ($this->xValues[$xKey] - $meanX); } else { $SSsex += $this->xValues[$xKey] * $this->xValues[$xKey]; } } $this->SSResiduals = $SSres;< $this->DFResiduals = $this->valueCount - 1 - $const;> $this->DFResiduals = $this->valueCount - 1 - ($const === true ? 1 : 0);if ($this->DFResiduals == 0.0) { $this->stdevOfResiduals = 0.0; } else { $this->stdevOfResiduals = sqrt($SSres / $this->DFResiduals); } if (($SStot == 0.0) || ($SSres == $SStot)) { $this->goodnessOfFit = 1; } else { $this->goodnessOfFit = 1 - ($SSres / $SStot); } $this->SSRegression = $this->goodnessOfFit * $SStot; $this->covariance = $SScov / $this->valueCount;< $this->correlation = ($this->valueCount * $sumXY - $sumX * $sumY) / sqrt(($this->valueCount * $sumX2 - pow($sumX, 2)) * ($this->valueCount * $sumY2 - pow($sumY, 2)));> $this->correlation = ($this->valueCount * $sumXY - $sumX * $sumY) / sqrt(($this->valueCount * $sumX2 - $sumX ** 2) * ($this->valueCount * $sumY2 - $sumY ** 2));$this->slopeSE = $this->stdevOfResiduals / sqrt($SSsex); $this->intersectSE = $this->stdevOfResiduals * sqrt(1 / ($this->valueCount - ($sumX * $sumX) / $sumX2)); if ($this->SSResiduals != 0.0) { if ($this->DFResiduals == 0.0) { $this->f = 0.0; } else { $this->f = $this->SSRegression / ($this->SSResiduals / $this->DFResiduals); } } else { if ($this->DFResiduals == 0.0) { $this->f = 0.0; } else { $this->f = $this->SSRegression / $this->DFResiduals; } } }> private function sumSquares(array $values) /** > { * @param float[] $yValues > return array_sum( * @param float[] $xValues > array_map( * @param bool $const > function ($value) { */ > return $value ** 2; protected function leastSquareFit(array $yValues, array $xValues, $const) > }, { > $values // calculate sums > ) $x_sum = array_sum($xValues); > ); $y_sum = array_sum($yValues); > } $meanX = $x_sum / $this->valueCount; >< * @param bool $const< protected function leastSquareFit(array $yValues, array $xValues, $const)> protected function leastSquareFit(array $yValues, array $xValues, bool $const): void< $x_sum = array_sum($xValues); < $y_sum = array_sum($yValues); < $meanX = $x_sum / $this->valueCount; < $meanY = $y_sum / $this->valueCount; < $mBase = $mDivisor = $xx_sum = $xy_sum = $yy_sum = 0.0;> $sumValuesX = array_sum($xValues); > $sumValuesY = array_sum($yValues); > $meanValueX = $sumValuesX / $this->valueCount; > $meanValueY = $sumValuesY / $this->valueCount; > $sumSquaresX = $this->sumSquares($xValues); > $sumSquaresY = $this->sumSquares($yValues); > $mBase = $mDivisor = 0.0; > $xy_sum = 0.0;< $xx_sum += $xValues[$i] * $xValues[$i]; < $yy_sum += $yValues[$i] * $yValues[$i];< if ($const) { < $mBase += ($xValues[$i] - $meanX) * ($yValues[$i] - $meanY); < $mDivisor += ($xValues[$i] - $meanX) * ($xValues[$i] - $meanX);> if ($const === true) { > $mBase += ($xValues[$i] - $meanValueX) * ($yValues[$i] - $meanValueY); > $mDivisor += ($xValues[$i] - $meanValueX) * ($xValues[$i] - $meanValueX);$mDivisor += $xValues[$i] * $xValues[$i]; } } // calculate slope $this->slope = $mBase / $mDivisor; // calculate intersect< if ($const) { < $this->intersect = $meanY - ($this->slope * $meanX); < } else { < $this->intersect = 0; < }> $this->intersect = ($const === true) ? $meanValueY - ($this->slope * $meanValueX) : 0.0;< $this->calculateGoodnessOfFit($x_sum, $y_sum, $xx_sum, $yy_sum, $xy_sum, $meanX, $meanY, $const);> $this->calculateGoodnessOfFit($sumValuesX, $sumValuesY, $sumSquaresX, $sumSquaresY, $xy_sum, $meanValueX, $meanValueY, $const);} /** * Define the 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 $const*/< public function __construct($yValues, $xValues = [], $const = true)> public function __construct($yValues, $xValues = []){ // Calculate number of points< $nY = count($yValues); < $nX = count($xValues);> $yValueCount = count($yValues); > $xValueCount = count($xValues);// Define X Values if necessary< if ($nX == 0) { < $xValues = range(1, $nY); < } elseif ($nY != $nX) {> if ($xValueCount === 0) { > $xValues = range(1, $yValueCount); > } elseif ($yValueCount !== $xValueCount) {// Ensure both arrays of points are the same size $this->error = true; }< $this->valueCount = $nY;> $this->valueCount = $yValueCount;$this->xValues = $xValues; $this->yValues = $yValues; } }