visualizer / vendor / phpoffice / phpspreadsheet / src / PhpSpreadsheet / Shared / Trend / LinearBestFit.php

LinearBestFit.php in Visualizer – Tables & Charts Manager with Built-in AI Generator 3.9.7, at vendor/phpoffice/phpspreadsheet/src/PhpSpreadsheet/Shared/Trend/LinearBestFit.php

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