| 1 |
<?php |
| 2 |
|
| 3 |
namespace TablePress\PhpOffice\PhpSpreadsheet\Shared\Trend; |
| 4 |
|
| 5 |
class PowerBestFit extends BestFit |
| 6 |
{ |
| 7 |
/** |
| 8 |
* Algorithm type to use for best-fit |
| 9 |
* (Name of this Trend class). |
| 10 |
*/ |
| 11 |
protected string $bestFitType = 'power'; |
| 12 |
|
| 13 |
/** |
| 14 |
* Return the Y-Value for a specified value of X. |
| 15 |
* |
| 16 |
* @param float $xValue X-Value |
| 17 |
* |
| 18 |
* @return float Y-Value |
| 19 |
*/ |
| 20 |
public function getValueOfYForX(float $xValue): float |
| 21 |
{ |
| 22 |
return $this->getIntersect() * ($xValue - $this->xOffset) ** $this->getSlope(); |
| 23 |
} |
| 24 |
|
| 25 |
/** |
| 26 |
* Return the X-Value for a specified value of Y. |
| 27 |
* |
| 28 |
* @param float $yValue Y-Value |
| 29 |
* |
| 30 |
* @return float X-Value |
| 31 |
*/ |
| 32 |
public function getValueOfXForY(float $yValue): float |
| 33 |
{ |
| 34 |
return (($yValue + $this->yOffset) / $this->getIntersect()) ** (1 / $this->getSlope()); |
| 35 |
} |
| 36 |
|
| 37 |
/** |
| 38 |
* Return the Equation of the best-fit line. |
| 39 |
* |
| 40 |
* @param int $dp Number of places of decimal precision to display |
| 41 |
*/ |
| 42 |
public function getEquation(int $dp = 0): string |
| 43 |
{ |
| 44 |
$slope = $this->getSlope($dp); |
| 45 |
$intersect = $this->getIntersect($dp); |
| 46 |
|
| 47 |
return 'Y = ' . $intersect . ' * X^' . $slope; |
| 48 |
} |
| 49 |
|
| 50 |
/** |
| 51 |
* Return the Value of X where it intersects Y = 0. |
| 52 |
* |
| 53 |
* @param int $dp Number of places of decimal precision to display |
| 54 |
*/ |
| 55 |
public function getIntersect(int $dp = 0): float |
| 56 |
{ |
| 57 |
if ($dp != 0) { |
| 58 |
return round(exp($this->intersect), $dp); |
| 59 |
} |
| 60 |
|
| 61 |
return exp($this->intersect); |
| 62 |
} |
| 63 |
|
| 64 |
/** |
| 65 |
* Execute the regression and calculate the goodness of fit for a set of X and Y data values. |
| 66 |
* |
| 67 |
* @param float[] $yValues The set of Y-values for this regression |
| 68 |
* @param float[] $xValues The set of X-values for this regression |
| 69 |
*/ |
| 70 |
private function powerRegression(array $yValues, array $xValues, bool $const): void |
| 71 |
{ |
| 72 |
$adjustedYValues = array_map( |
| 73 |
fn ($value): float => ($value < 0.0) ? 0 - log(abs($value)) : log($value), |
| 74 |
$yValues |
| 75 |
); |
| 76 |
$adjustedXValues = array_map( |
| 77 |
fn ($value): float => ($value < 0.0) ? 0 - log(abs($value)) : log($value), |
| 78 |
$xValues |
| 79 |
); |
| 80 |
|
| 81 |
$this->leastSquareFit($adjustedYValues, $adjustedXValues, $const); |
| 82 |
} |
| 83 |
|
| 84 |
/** |
| 85 |
* Define the regression and calculate the goodness of fit for a set of X and Y data values. |
| 86 |
* |
| 87 |
* @param float[] $yValues The set of Y-values for this regression |
| 88 |
* @param float[] $xValues The set of X-values for this regression |
| 89 |
*/ |
| 90 |
public function __construct(array $yValues, array $xValues = [], bool $const = true) |
| 91 |
{ |
| 92 |
parent::__construct($yValues, $xValues); |
| 93 |
|
| 94 |
if (!$this->error) { |
| 95 |
$this->powerRegression($yValues, $xValues, (bool) $const); |
| 96 |
} |
| 97 |
} |
| 98 |
} |
| 99 |
|
