tablepress / libraries / vendor / PhpSpreadsheet / Shared / Trend / ExponentialBestFit.php

ExponentialBestFit.php in TablePress – Tables in WordPress made easy 3.4, at libraries/vendor/PhpSpreadsheet/Shared/Trend/ExponentialBestFit.php

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1	<?php
2
3	namespace TablePress\PhpOffice\PhpSpreadsheet\Shared\Trend;
4
5	class ExponentialBestFit extends BestFit
6	{
7	/**
8	* Algorithm type to use for best-fit
9	* (Name of this Trend class).
10	*/
11	protected string $bestFitType = 'exponential';
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() * $this->getSlope() ** ($xValue - $this->xOffset);
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 $yValue): float
33	{
34	return log(($yValue + $this->yOffset) / $this->getIntersect()) / log($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 . ' * ' . $slope . '^X';
48	}
49
50	/**
51	* Return the Slope of the line.
52	*
53	* @param int $dp Number of places of decimal precision to display
54	*/
55	public function getSlope(int $dp = 0): float
56	{
57	if ($dp != 0) {
58	return round(exp($this->slope), $dp);
59	}
60
61	return exp($this->slope);
62	}
63
64	/**
65	* Return the Value of X where it intersects Y = 0.
66	*
67	* @param int $dp Number of places of decimal precision to display
68	*/
69	public function getIntersect(int $dp = 0): float
70	{
71	if ($dp != 0) {
72	return round(exp($this->intersect), $dp);
73	}
74
75	return exp($this->intersect);
76	}
77
78	/**
79	* Execute the regression and calculate the goodness of fit for a set of X and Y data values.
80	*
81	* @param float[] $yValues The set of Y-values for this regression
82	* @param float[] $xValues The set of X-values for this regression
83	*/
84	private function exponentialRegression(array $yValues, array $xValues, bool $const): void
85	{
86	$adjustedYValues = array_map(
87	fn ($value): float => ($value < 0.0) ? 0 - log(abs($value)) : log($value),
88	$yValues
89	);
90
91	$this->leastSquareFit($adjustedYValues, $xValues, $const);
92	}
93
94	/**
95	* Define the regression and calculate the goodness of fit for a set of X and Y data values.
96	*
97	* @param float[] $yValues The set of Y-values for this regression
98	* @param float[] $xValues The set of X-values for this regression
99	*/
100	public function __construct(array $yValues, array $xValues = [], bool $const = true)
101	{
102	parent::__construct($yValues, $xValues);
103
104	if (!$this->error) {
105	$this->exponentialRegression($yValues, $xValues, (bool) $const);
106	}
107	}
108	}
109