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

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

91 lines 2.4 KB

Raw Download Zip

1	<?php
2
3	namespace PhpOffice\PhpSpreadsheet\Shared\Trend;
4
5	class LogarithmicBestFit 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 = 'logarithmic';
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() + $this->getSlope() * log($xValue - $this->xOffset);
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 exp(($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 . ' * log(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 $const
60	*/
61	private function logarithmicRegression($yValues, $xValues, $const)
62	{
63	foreach ($xValues as &$value) {
64	if ($value < 0.0) {
65	$value = 0 - log(abs($value));
66	} elseif ($value > 0.0) {
67	$value = log($value);
68	}
69	}
70	unset($value);
71
72	$this->leastSquareFit($yValues, $xValues, $const);
73	}
74
75	/**
76	* Define the regression and calculate the goodness of fit for a set of X and Y data values.
77	*
78	* @param float[] $yValues The set of Y-values for this regression
79	* @param float[] $xValues The set of X-values for this regression
80	* @param bool $const
81	*/
82	public function __construct($yValues, $xValues = [], $const = true)
83	{
84	parent::__construct($yValues, $xValues);
85
86	if (!$this->error) {
87	$this->logarithmicRegression($yValues, $xValues, $const);
88	}
89	}
90	}
91