diff options
Diffstat (limited to '')
7 files changed, 1190 insertions, 0 deletions
diff --git a/vendor/phpoffice/phpspreadsheet/src/PhpSpreadsheet/Shared/Trend/BestFit.php b/vendor/phpoffice/phpspreadsheet/src/PhpSpreadsheet/Shared/Trend/BestFit.php new file mode 100644 index 0000000..7d90323 --- /dev/null +++ b/vendor/phpoffice/phpspreadsheet/src/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;
+ }
+}
diff --git a/vendor/phpoffice/phpspreadsheet/src/PhpSpreadsheet/Shared/Trend/ExponentialBestFit.php b/vendor/phpoffice/phpspreadsheet/src/PhpSpreadsheet/Shared/Trend/ExponentialBestFit.php new file mode 100644 index 0000000..854915d --- /dev/null +++ b/vendor/phpoffice/phpspreadsheet/src/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);
+ }
+ }
+}
diff --git a/vendor/phpoffice/phpspreadsheet/src/PhpSpreadsheet/Shared/Trend/LinearBestFit.php b/vendor/phpoffice/phpspreadsheet/src/PhpSpreadsheet/Shared/Trend/LinearBestFit.php new file mode 100644 index 0000000..83bc179 --- /dev/null +++ b/vendor/phpoffice/phpspreadsheet/src/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);
+ }
+ }
+}
diff --git a/vendor/phpoffice/phpspreadsheet/src/PhpSpreadsheet/Shared/Trend/LogarithmicBestFit.php b/vendor/phpoffice/phpspreadsheet/src/PhpSpreadsheet/Shared/Trend/LogarithmicBestFit.php new file mode 100644 index 0000000..4f2c805 --- /dev/null +++ b/vendor/phpoffice/phpspreadsheet/src/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);
+ }
+ }
+}
diff --git a/vendor/phpoffice/phpspreadsheet/src/PhpSpreadsheet/Shared/Trend/PolynomialBestFit.php b/vendor/phpoffice/phpspreadsheet/src/PhpSpreadsheet/Shared/Trend/PolynomialBestFit.php new file mode 100644 index 0000000..5389501 --- /dev/null +++ b/vendor/phpoffice/phpspreadsheet/src/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;
+ }
+ }
+ }
+}
diff --git a/vendor/phpoffice/phpspreadsheet/src/PhpSpreadsheet/Shared/Trend/PowerBestFit.php b/vendor/phpoffice/phpspreadsheet/src/PhpSpreadsheet/Shared/Trend/PowerBestFit.php new file mode 100644 index 0000000..38c67f6 --- /dev/null +++ b/vendor/phpoffice/phpspreadsheet/src/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);
+ }
+ }
+}
diff --git a/vendor/phpoffice/phpspreadsheet/src/PhpSpreadsheet/Shared/Trend/Trend.php b/vendor/phpoffice/phpspreadsheet/src/PhpSpreadsheet/Shared/Trend/Trend.php new file mode 100644 index 0000000..f696b3e --- /dev/null +++ b/vendor/phpoffice/phpspreadsheet/src/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;
+ }
+ }
+}
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