Alter Version

vendor/PhpSpreadsheet/Shared/Trend/PolynomialBestFit.php

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+<?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;
+            }
+        }
+    }
+}