1<?php 2 3namespace PhpOffice\PhpSpreadsheet\Shared\Trend; 4 5class ExponentialBestFit 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 = 'exponential'; 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 $this->getIntersect() * pow($this->getSlope(), ($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 log(($yValue + $this->yOffset) / $this->getIntersect()) / log($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 * Return the Slope of the line. 56 * 57 * @param int $dp Number of places of decimal precision to display 58 * 59 * @return float 60 */ 61 public function getSlope($dp = 0) 62 { 63 if ($dp != 0) { 64 return round(exp($this->slope), $dp); 65 } 66 67 return exp($this->slope); 68 } 69 70 /** 71 * Return the Value of X where it intersects Y = 0. 72 * 73 * @param int $dp Number of places of decimal precision to display 74 * 75 * @return float 76 */ 77 public function getIntersect($dp = 0) 78 { 79 if ($dp != 0) { 80 return round(exp($this->intersect), $dp); 81 } 82 83 return exp($this->intersect); 84 } 85 86 /** 87 * Execute the regression and calculate the goodness of fit for a set of X and Y data values. 88 * 89 * @param float[] $yValues The set of Y-values for this regression 90 * @param float[] $xValues The set of X-values for this regression 91 * @param bool $const 92 */ 93 private function exponentialRegression($yValues, $xValues, $const) 94 { 95 foreach ($yValues as &$value) { 96 if ($value < 0.0) { 97 $value = 0 - log(abs($value)); 98 } elseif ($value > 0.0) { 99 $value = log($value); 100 } 101 } 102 unset($value); 103 104 $this->leastSquareFit($yValues, $xValues, $const); 105 } 106 107 /** 108 * Define the regression and calculate the goodness of fit for a set of X and Y data values. 109 * 110 * @param float[] $yValues The set of Y-values for this regression 111 * @param float[] $xValues The set of X-values for this regression 112 * @param bool $const 113 */ 114 public function __construct($yValues, $xValues = [], $const = true) 115 { 116 parent::__construct($yValues, $xValues); 117 118 if (!$this->error) { 119 $this->exponentialRegression($yValues, $xValues, $const); 120 } 121 } 122} 123