Viewing file:  PolynomialBestFit.php (5.48 KB) -rw-r--r--
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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;
}
}
}
}
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