1<?php
2
3namespace PhpOffice\PhpSpreadsheet\Shared\Trend;
4
5use PhpOffice\PhpSpreadsheet\Shared\JAMA\Matrix;
6
7class PolynomialBestFit extends BestFit
8{
9    /**
10     * Algorithm type to use for best-fit
11     * (Name of this Trend class).
12     *
13     * @var string
14     */
15    protected $bestFitType = 'polynomial';
16
17    /**
18     * Polynomial order.
19     *
20     * @var int
21     */
22    protected $order = 0;
23
24    /**
25     * Return the order of this polynomial.
26     *
27     * @return int
28     */
29    public function getOrder()
30    {
31        return $this->order;
32    }
33
34    /**
35     * Return the Y-Value for a specified value of X.
36     *
37     * @param float $xValue X-Value
38     *
39     * @return float Y-Value
40     */
41    public function getValueOfYForX($xValue)
42    {
43        $retVal = $this->getIntersect();
44        $slope = $this->getSlope();
45        // @phpstan-ignore-next-line
46        foreach ($slope as $key => $value) {
47            if ($value != 0.0) {
48                $retVal += $value * $xValue ** ($key + 1);
49            }
50        }
51
52        return $retVal;
53    }
54
55    /**
56     * Return the X-Value for a specified value of Y.
57     *
58     * @param float $yValue Y-Value
59     *
60     * @return float X-Value
61     */
62    public function getValueOfXForY($yValue)
63    {
64        return ($yValue - $this->getIntersect()) / $this->getSlope();
65    }
66
67    /**
68     * Return the Equation of the best-fit line.
69     *
70     * @param int $dp Number of places of decimal precision to display
71     *
72     * @return string
73     */
74    public function getEquation($dp = 0)
75    {
76        $slope = $this->getSlope($dp);
77        $intersect = $this->getIntersect($dp);
78
79        $equation = 'Y = ' . $intersect;
80        // @phpstan-ignore-next-line
81        foreach ($slope as $key => $value) {
82            if ($value != 0.0) {
83                $equation .= ' + ' . $value . ' * X';
84                if ($key > 0) {
85                    $equation .= '^' . ($key + 1);
86                }
87            }
88        }
89
90        return $equation;
91    }
92
93    /**
94     * Return the Slope of the line.
95     *
96     * @param int $dp Number of places of decimal precision to display
97     *
98     * @return float
99     */
100    public function getSlope($dp = 0)
101    {
102        if ($dp != 0) {
103            $coefficients = [];
104            foreach ($this->slope as $coefficient) {
105                $coefficients[] = round($coefficient, $dp);
106            }
107
108            // @phpstan-ignore-next-line
109            return $coefficients;
110        }
111
112        return $this->slope;
113    }
114
115    public function getCoefficients($dp = 0)
116    {
117        return array_merge([$this->getIntersect($dp)], $this->getSlope($dp));
118    }
119
120    /**
121     * Execute the regression and calculate the goodness of fit for a set of X and Y data values.
122     *
123     * @param int $order Order of Polynomial for this regression
124     * @param float[] $yValues The set of Y-values for this regression
125     * @param float[] $xValues The set of X-values for this regression
126     */
127    private function polynomialRegression($order, $yValues, $xValues): void
128    {
129        // calculate sums
130        $x_sum = array_sum($xValues);
131        $y_sum = array_sum($yValues);
132        $xx_sum = $xy_sum = $yy_sum = 0;
133        for ($i = 0; $i < $this->valueCount; ++$i) {
134            $xy_sum += $xValues[$i] * $yValues[$i];
135            $xx_sum += $xValues[$i] * $xValues[$i];
136            $yy_sum += $yValues[$i] * $yValues[$i];
137        }
138        /*
139         *    This routine uses logic from the PHP port of polyfit version 0.1
140         *    written by Michael Bommarito and Paul Meagher
141         *
142         *    The function fits a polynomial function of order $order through
143         *    a series of x-y data points using least squares.
144         *
145         */
146        $A = [];
147        $B = [];
148        for ($i = 0; $i < $this->valueCount; ++$i) {
149            for ($j = 0; $j <= $order; ++$j) {
150                $A[$i][$j] = $xValues[$i] ** $j;
151            }
152        }
153        for ($i = 0; $i < $this->valueCount; ++$i) {
154            $B[$i] = [$yValues[$i]];
155        }
156        $matrixA = new Matrix($A);
157        $matrixB = new Matrix($B);
158        $C = $matrixA->solve($matrixB);
159
160        $coefficients = [];
161        for ($i = 0; $i < $C->getRowDimension(); ++$i) {
162            $r = $C->get($i, 0);
163            if (abs($r) <= 10 ** (-9)) {
164                $r = 0;
165            }
166            $coefficients[] = $r;
167        }
168
169        $this->intersect = array_shift($coefficients);
170        $this->slope = $coefficients;
171
172        $this->calculateGoodnessOfFit($x_sum, $y_sum, $xx_sum, $yy_sum, $xy_sum, 0, 0, 0);
173        foreach ($this->xValues as $xKey => $xValue) {
174            $this->yBestFitValues[$xKey] = $this->getValueOfYForX($xValue);
175        }
176    }
177
178    /**
179     * Define the regression and calculate the goodness of fit for a set of X and Y data values.
180     *
181     * @param int $order Order of Polynomial for this regression
182     * @param float[] $yValues The set of Y-values for this regression
183     * @param float[] $xValues The set of X-values for this regression
184     */
185    public function __construct($order, $yValues, $xValues = [])
186    {
187        parent::__construct($yValues, $xValues);
188
189        if (!$this->error) {
190            if ($order < $this->valueCount) {
191                $this->bestFitType .= '_' . $order;
192                $this->order = $order;
193                $this->polynomialRegression($order, $yValues, $xValues);
194                if (($this->getGoodnessOfFit() < 0.0) || ($this->getGoodnessOfFit() > 1.0)) {
195                    $this->error = true;
196                }
197            } else {
198                $this->error = true;
199            }
200        }
201    }
202}
203