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Diffstat (limited to '')
| -rw-r--r-- | vendor/phpoffice/phpspreadsheet/src/PhpSpreadsheet/Shared/Trend/PolynomialBestFit.php | 200 |
1 files changed, 200 insertions, 0 deletions
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;
+ }
+ }
+ }
+}
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