Alter Version

vendor/PhpSpreadsheet/Shared/Trend/BestFit.php

@@ -0,0 +1,463 @@
+<?php
+
+namespace PhpOffice\PhpSpreadsheet\Shared\Trend;
+
+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 $xValue X-Value
+     *
+     * @return bool Y-Value
+     */
+    public function getValueOfYForX($xValue)
+    {
+        return false;
+    }
+
+    /**
+     * Return the X-Value for a specified value of Y.
+     *
+     * @param float $yValue Y-Value
+     *
+     * @return bool X-Value
+     */
+    public function getValueOfXForY($yValue)
+    {
+        return false;
+    }
+
+    /**
+     * 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 $dp Number of places of decimal precision to display
+     *
+     * @return bool
+     */
+    public function getEquation($dp = 0)
+    {
+        return false;
+    }
+
+    /**
+     * 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): 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) {
+                $SStot += ($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) {
+                $SSsex += ($this->xValues[$xKey] - $meanX) * ($this->xValues[$xKey] - $meanX);
+            } else {
+                $SSsex += $this->xValues[$xKey] * $this->xValues[$xKey];
+            }
+        }
+
+        $this->SSResiduals = $SSres;
+        $this->DFResiduals = $this->valueCount - 1 - $const;
+
+        if ($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 - $sumX ** 2) * ($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;
+            }
+        }
+    }
+
+    /**
+     * @param float[] $yValues
+     * @param float[] $xValues
+     * @param bool $const
+     */
+    protected function leastSquareFit(array $yValues, array $xValues, $const): void
+    {
+        // calculate sums
+        $x_sum = array_sum($xValues);
+        $y_sum = array_sum($yValues);
+        $meanX = $x_sum / $this->valueCount;
+        $meanY = $y_sum / $this->valueCount;
+        $mBase = $mDivisor = $xx_sum = $xy_sum = $yy_sum = 0.0;
+        for ($i = 0; $i < $this->valueCount; ++$i) {
+            $xy_sum += $xValues[$i] * $yValues[$i];
+            $xx_sum += $xValues[$i] * $xValues[$i];
+            $yy_sum += $yValues[$i] * $yValues[$i];
+
+            if ($const) {
+                $mBase += ($xValues[$i] - $meanX) * ($yValues[$i] - $meanY);
+                $mDivisor += ($xValues[$i] - $meanX) * ($xValues[$i] - $meanX);
+            } else {
+                $mBase += $xValues[$i] * $yValues[$i];
+                $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;
+        }
+
+        $this->calculateGoodnessOfFit($x_sum, $y_sum, $xx_sum, $yy_sum, $xy_sum, $meanX, $meanY, $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)
+    {
+        //    Calculate number of points
+        $nY = count($yValues);
+        $nX = count($xValues);
+
+        //    Define X Values if necessary
+        if ($nX == 0) {
+            $xValues = range(1, $nY);
+        } elseif ($nY != $nX) {
+            //    Ensure both arrays of points are the same size
+            $this->error = true;
+        }
+
+        $this->valueCount = $nY;
+        $this->xValues = $xValues;
+        $this->yValues = $yValues;
+    }
+}

vendor/PhpSpreadsheet/Shared/Trend/ExponentialBestFit.php

@@ -0,0 +1,122 @@
+<?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);
+        }
+    }
+}

vendor/PhpSpreadsheet/Shared/Trend/LinearBestFit.php

@@ -0,0 +1,81 @@
+<?php
+
+namespace PhpOffice\PhpSpreadsheet\Shared\Trend;
+
+class LinearBestFit extends BestFit
+{
+    /**
+     * Algorithm type to use for best-fit
+     * (Name of this Trend class).
+     *
+     * @var string
+     */
+    protected $bestFitType = 'linear';
+
+    /**
+     * 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;
+    }
+
+    /**
+     * 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);
+
+        return 'Y = ' . $intersect . ' + ' . $slope . ' * 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
+     * @param bool $const
+     */
+    private function linearRegression($yValues, $xValues, $const): void
+    {
+        $this->leastSquareFit($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) {
+            $this->linearRegression($yValues, $xValues, $const);
+        }
+    }
+}

vendor/PhpSpreadsheet/Shared/Trend/LogarithmicBestFit.php

@@ -0,0 +1,90 @@
+<?php
+
+namespace PhpOffice\PhpSpreadsheet\Shared\Trend;
+
+class LogarithmicBestFit extends BestFit
+{
+    /**
+     * Algorithm type to use for best-fit
+     * (Name of this Trend class).
+     *
+     * @var string
+     */
+    protected $bestFitType = 'logarithmic';
+
+    /**
+     * 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() + $this->getSlope() * log($xValue - $this->xOffset);
+    }
+
+    /**
+     * Return the X-Value for a specified value of Y.
+     *
+     * @param float $yValue Y-Value
+     *
+     * @return float X-Value
+     */
+    public function getValueOfXForY($yValue)
+    {
+        return exp(($yValue - $this->getIntersect()) / $this->getSlope());
+    }
+
+    /**
+     * Return the Equation of the best-fit line.
+     *
+     * @param int $dp Number of places of decimal precision to display
+     *
+     * @return string
+     */
+    public function getEquation($dp = 0)
+    {
+        $slope = $this->getSlope($dp);
+        $intersect = $this->getIntersect($dp);
+
+        return 'Y = ' . $intersect . ' + ' . $slope . ' * log(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
+     * @param bool $const
+     */
+    private function logarithmicRegression($yValues, $xValues, $const): void
+    {
+        foreach ($xValues 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) {
+            $this->logarithmicRegression($yValues, $xValues, $const);
+        }
+    }
+}

vendor/PhpSpreadsheet/Shared/Trend/PolynomialBestFit.php

@@ -0,0 +1,200 @@
+<?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 = $this->getSlope();
+        foreach ($slope as $key => $value) {
+            if ($value != 0.0) {
+                $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 = ' . $intersect;
+        foreach ($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 $dp Number of places of decimal precision to display
+     *
+     * @return string
+     */
+    public function getSlope($dp = 0)
+    {
+        if ($dp != 0) {
+            $coefficients = [];
+            foreach ($this->slope as $coefficient) {
+                $coefficients[] = round($coefficient, $dp);
+            }
+
+            return $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): 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] = $xValues[$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) <= 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 = [], $const = true)
+    {
+        parent::__construct($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;
+            }
+        }
+    }
+}

vendor/PhpSpreadsheet/Shared/Trend/PowerBestFit.php

@@ -0,0 +1,114 @@
+<?php
+
+namespace PhpOffice\PhpSpreadsheet\Shared\Trend;
+
+class PowerBestFit extends BestFit
+{
+    /**
+     * Algorithm type to use for best-fit
+     * (Name of this Trend class).
+     *
+     * @var string
+     */
+    protected $bestFitType = 'power';
+
+    /**
+     * Return the Y-Value for a specified value of X.
+     *
+     * @param float $xValue X-Value
+     *
+     * @return float Y-Value
+     */
+    public function getValueOfYForX($xValue)
+    {
+        return $this->getIntersect() * ($xValue - $this->xOffset) ** $this->getSlope();
+    }
+
+    /**
+     * 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->yOffset) / $this->getIntersect()) ** (1 / $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 . ' * X^' . $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 $const
+     */
+    private function powerRegression($yValues, $xValues, $const): void
+    {
+        foreach ($xValues as &$value) {
+            if ($value < 0.0) {
+                $value = 0 - log(abs($value));
+            } elseif ($value > 0.0) {
+                $value = log($value);
+            }
+        }
+        unset($value);
+        foreach ($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) {
+            $this->powerRegression($yValues, $xValues, $const);
+        }
+    }
+}

vendor/PhpSpreadsheet/Shared/Trend/Trend.php

@@ -0,0 +1,120 @@
+<?php
+
+namespace PhpOffice\PhpSpreadsheet\Shared\Trend;
+
+class Trend
+{
+    const TREND_LINEAR = 'Linear';
+    const TREND_LOGARITHMIC = 'Logarithmic';
+    const TREND_EXPONENTIAL = 'Exponential';
+    const TREND_POWER = 'Power';
+    const TREND_POLYNOMIAL_2 = 'Polynomial_2';
+    const TREND_POLYNOMIAL_3 = 'Polynomial_3';
+    const TREND_POLYNOMIAL_4 = 'Polynomial_4';
+    const TREND_POLYNOMIAL_5 = 'Polynomial_5';
+    const TREND_POLYNOMIAL_6 = 'Polynomial_6';
+    const TREND_BEST_FIT = 'Bestfit';
+    const TREND_BEST_FIT_NO_POLY = 'Bestfit_no_Polynomials';
+
+    /**
+     * Names of the best-fit Trend analysis methods.
+     *
+     * @var string[]
+     */
+    private static $trendTypes = [
+        self::TREND_LINEAR,
+        self::TREND_LOGARITHMIC,
+        self::TREND_EXPONENTIAL,
+        self::TREND_POWER,
+    ];
+
+    /**
+     * Names of the best-fit Trend polynomial orders.
+     *
+     * @var string[]
+     */
+    private static $trendTypePolynomialOrders = [
+        self::TREND_POLYNOMIAL_2,
+        self::TREND_POLYNOMIAL_3,
+        self::TREND_POLYNOMIAL_4,
+        self::TREND_POLYNOMIAL_5,
+        self::TREND_POLYNOMIAL_6,
+    ];
+
+    /**
+     * Cached results for each method when trying to identify which provides the best fit.
+     *
+     * @var bestFit[]
+     */
+    private static $trendCache = [];
+
+    public static function calculate($trendType = self::TREND_BEST_FIT, $yValues = [], $xValues = [], $const = true)
+    {
+        //    Calculate number of points in each dataset
+        $nY = count($yValues);
+        $nX = count($xValues);
+
+        //    Define X Values if necessary
+        if ($nX == 0) {
+            $xValues = range(1, $nY);
+            $nX = $nY;
+        } elseif ($nY != $nX) {
+            //    Ensure both arrays of points are the same size
+            trigger_error('Trend(): Number of elements in coordinate arrays do not match.', E_USER_ERROR);
+        }
+
+        $key = md5($trendType . $const . serialize($yValues) . serialize($xValues));
+        //    Determine which Trend method has been requested
+        switch ($trendType) {
+            //    Instantiate and return the class for the requested Trend method
+            case self::TREND_LINEAR:
+            case self::TREND_LOGARITHMIC:
+            case self::TREND_EXPONENTIAL:
+            case self::TREND_POWER:
+                if (!isset(self::$trendCache[$key])) {
+                    $className = '\PhpOffice\PhpSpreadsheet\Shared\Trend\\' . $trendType . 'BestFit';
+                    self::$trendCache[$key] = new $className($yValues, $xValues, $const);
+                }
+
+                return self::$trendCache[$key];
+            case self::TREND_POLYNOMIAL_2:
+            case self::TREND_POLYNOMIAL_3:
+            case self::TREND_POLYNOMIAL_4:
+            case self::TREND_POLYNOMIAL_5:
+            case self::TREND_POLYNOMIAL_6:
+                if (!isset(self::$trendCache[$key])) {
+                    $order = substr($trendType, -1);
+                    self::$trendCache[$key] = new PolynomialBestFit($order, $yValues, $xValues, $const);
+                }
+
+                return self::$trendCache[$key];
+            case self::TREND_BEST_FIT:
+            case self::TREND_BEST_FIT_NO_POLY:
+                //    If the request is to determine the best fit regression, then we test each Trend line in turn
+                //    Start by generating an instance of each available Trend method
+                foreach (self::$trendTypes as $trendMethod) {
+                    $className = '\PhpOffice\PhpSpreadsheet\Shared\Trend\\' . $trendType . 'BestFit';
+                    $bestFit[$trendMethod] = new $className($yValues, $xValues, $const);
+                    $bestFitValue[$trendMethod] = $bestFit[$trendMethod]->getGoodnessOfFit();
+                }
+                if ($trendType != self::TREND_BEST_FIT_NO_POLY) {
+                    foreach (self::$trendTypePolynomialOrders as $trendMethod) {
+                        $order = substr($trendMethod, -1);
+                        $bestFit[$trendMethod] = new PolynomialBestFit($order, $yValues, $xValues, $const);
+                        if ($bestFit[$trendMethod]->getError()) {
+                            unset($bestFit[$trendMethod]);
+                        } else {
+                            $bestFitValue[$trendMethod] = $bestFit[$trendMethod]->getGoodnessOfFit();
+                        }
+                    }
+                }
+                //    Determine which of our Trend lines is the best fit, and then we return the instance of that Trend class
+                arsort($bestFitValue);
+                $bestFitType = key($bestFitValue);
+
+                return $bestFit[$bestFitType];
+            default:
+                return false;
+        }
+    }
+}