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Diffstat (limited to '')
| -rw-r--r-- | vendor/phpoffice/phpspreadsheet/src/PhpSpreadsheet/Shared/Trend/BestFit.php | 463 |
1 files changed, 463 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;
+ }
+}
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