BestFit::getSlope() - Code Metrics - Inspection of "Merge pull request #3373 from PHPOffice/CalcEngine..." - PHPOffice/PhpSpreadsheet - Measure and Improve Code Quality continuously with Scrutinizer

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Push — master ( 9e1378...d02904 )

by Mark

created 2023-02-17 05:43 UTC

BestFit::getSlope() A

↳ Parent: BestFit

Complexity

Conditions	2
Paths	2

Size

Total Lines	7
Code Lines	3

Duplication

Lines	0
Ratio	0 %

Code Coverage

Tests	4
CRAP Score	2

Importance

Changes

Metric	Value
cc	2
eloc	3
c	0
b	0
f	0
nc	2
nop	1
dl	0
loc	7
rs	10
ccs	4
cts	4
cp	1
crap	2

<?php

namespace PhpOffice\PhpSpreadsheet\Shared\Trend;

abstract class BestFit
{
    /**
     * Indicator flag for a calculation error.
     *
     * @var bool
     */
    protected $error = false;

    /**
     * Algorithm type to use for best-fit.
     *
     * @var string
     */
    protected $bestFitType = 'undetermined';

    /**
     * Number of entries in the sets of x- and y-value arrays.
     *
     * @var int
     */
    protected $valueCount = 0;

    /**
     * X-value dataseries of values.
     *
     * @var float[]
     */
    protected $xValues = [];

    /**
     * Y-value dataseries of values.
     *
     * @var float[]
     */
    protected $yValues = [];

    /**
     * Flag indicating whether values should be adjusted to Y=0.
     *
     * @var bool
     */
    protected $adjustToZero = false;

    /**
     * Y-value series of best-fit values.
     *
     * @var float[]
     */
    protected $yBestFitValues = [];

    /** @var float */
    protected $goodnessOfFit = 1;

    /** @var float */
    protected $stdevOfResiduals = 0;

    /** @var float */
    protected $covariance = 0;

    /** @var float */
    protected $correlation = 0;

    /** @var float */
    protected $SSRegression = 0;

    /** @var float */
    protected $SSResiduals = 0;

    /** @var float */
    protected $DFResiduals = 0;

    /** @var float */
    protected $f = 0;

    /** @var float */
    protected $slope = 0;

    /** @var float */
    protected $slopeSE = 0;

    /** @var float */
    protected $intersect = 0;

    /** @var float */
    protected $intersectSE = 0;

    /** @var float */
    protected $xOffset = 0;

    /** @var float */
    protected $yOffset = 0;

    /** @return bool */
    public function getError()
    {
        return $this->error;
    }

    /** @return string */
    public function getBestFitType()
    {
        return $this->bestFitType;
    }

    /**
     * Return the Y-Value for a specified value of X.
     *
     * @param float $xValue X-Value
     *
     * @return float Y-Value
     */
    abstract public function getValueOfYForX($xValue);

    /**
     * Return the X-Value for a specified value of Y.
     *
     * @param float $yValue Y-Value
     *
     * @return float X-Value
     */
    abstract public function getValueOfXForY($yValue);

    /**
     * Return the original set of X-Values.
     *
     * @return float[] X-Values
     */
    public function getXValues()
    {
        return $this->xValues;
    }

    /**
     * Return the Equation of the best-fit line.
     *
     * @param int $dp Number of places of decimal precision to display
     *
     * @return string
     */
    abstract public function getEquation($dp = 0);

    /**
     * Return the Slope of the line.
     *
     * @param int $dp Number of places of decimal precision to display
     *
     * @return float
     */
    public function getSlope($dp = 0)
    {
        if ($dp != 0) {
            return round($this->slope, $dp);
        }

        return $this->slope;
    }

    /**
     * Return the standard error of the Slope.
     *
     * @param int $dp Number of places of decimal precision to display
     *
     * @return float
     */
    public function getSlopeSE($dp = 0)
    {
        if ($dp != 0) {
            return round($this->slopeSE, $dp);
        }

        return $this->slopeSE;
    }

    /**
     * Return the Value of X where it intersects Y = 0.
     *
     * @param int $dp Number of places of decimal precision to display
     *
     * @return float
     */
    public function getIntersect($dp = 0)
    {
        if ($dp != 0) {
            return round($this->intersect, $dp);
        }

        return $this->intersect;
    }

    /**
     * Return the standard error of the Intersect.
     *
     * @param int $dp Number of places of decimal precision to display
     *
     * @return float
     */
    public function getIntersectSE($dp = 0)
    {
        if ($dp != 0) {
            return round($this->intersectSE, $dp);
        }

        return $this->intersectSE;
    }

    /**
     * Return the goodness of fit for this regression.
     *
     * @param int $dp Number of places of decimal precision to return
     *
     * @return float
     */
    public function getGoodnessOfFit($dp = 0)
    {
        if ($dp != 0) {
            return round($this->goodnessOfFit, $dp);
        }

        return $this->goodnessOfFit;
    }

    /**
     * Return the goodness of fit for this regression.
     *
     * @param int $dp Number of places of decimal precision to return
     *
     * @return float
     */
    public function getGoodnessOfFitPercent($dp = 0)
    {
        if ($dp != 0) {
            return round($this->goodnessOfFit * 100, $dp);
        }

        return $this->goodnessOfFit * 100;
    }

    /**
     * Return the standard deviation of the residuals for this regression.
     *
     * @param int $dp Number of places of decimal precision to return
     *
     * @return float
     */
    public function getStdevOfResiduals($dp = 0)
    {
        if ($dp != 0) {
            return round($this->stdevOfResiduals, $dp);
        }

        return $this->stdevOfResiduals;
    }

    /**
     * @param int $dp Number of places of decimal precision to return
     *
     * @return float
     */
    public function getSSRegression($dp = 0)
    {
        if ($dp != 0) {
            return round($this->SSRegression, $dp);
        }

        return $this->SSRegression;
    }

    /**
     * @param int $dp Number of places of decimal precision to return
     *
     * @return float
     */
    public function getSSResiduals($dp = 0)
    {
        if ($dp != 0) {
            return round($this->SSResiduals, $dp);
        }

        return $this->SSResiduals;
    }

    /**
     * @param int $dp Number of places of decimal precision to return
     *
     * @return float
     */
    public function getDFResiduals($dp = 0)
    {
        if ($dp != 0) {
            return round($this->DFResiduals, $dp);
        }

        return $this->DFResiduals;
    }

    /**
     * @param int $dp Number of places of decimal precision to return
     *
     * @return float
     */
    public function getF($dp = 0)
    {
        if ($dp != 0) {
            return round($this->f, $dp);
        }

        return $this->f;
    }

    /**
     * @param int $dp Number of places of decimal precision to return
     *
     * @return float
     */
    public function getCovariance($dp = 0)
    {
        if ($dp != 0) {
            return round($this->covariance, $dp);
        }

        return $this->covariance;
    }

    /**
     * @param int $dp Number of places of decimal precision to return
     *
     * @return float
     */
    public function getCorrelation($dp = 0)
    {
        if ($dp != 0) {
            return round($this->correlation, $dp);
        }

        return $this->correlation;
    }

    /**
     * @return float[]
     */
    public function getYBestFitValues()
    {
        return $this->yBestFitValues;
    }

    /** @var mixed */
    private static $scrutinizerZeroPointZero = 0.0;

    /**
     * @param mixed $x
     * @param mixed $y
     */
    private static function scrutinizerLooseCompare($x, $y): bool
    {
        return $x == $y;
    }

    /**
     * @param float $sumX
     * @param float $sumY
     * @param float $sumX2
     * @param float $sumY2
     * @param float $sumXY
     * @param float $meanX
     * @param float $meanY
     * @param bool|int $const
     */
    protected function calculateGoodnessOfFit($sumX, $sumY, $sumX2, $sumY2, $sumXY, $meanX, $meanY, $const): void
    {
        $SSres = $SScov = $SStot = $SSsex = 0.0;
        foreach ($this->xValues as $xKey => $xValue) {
            $bestFitY = $this->yBestFitValues[$xKey] = $this->getValueOfYForX($xValue);

            $SSres += ($this->yValues[$xKey] - $bestFitY) * ($this->yValues[$xKey] - $bestFitY);
            if ($const === true) {
                $SStot += ($this->yValues[$xKey] - $meanY) * ($this->yValues[$xKey] - $meanY);
            } else {
                $SStot += $this->yValues[$xKey] * $this->yValues[$xKey];
            }
            $SScov += ($this->xValues[$xKey] - $meanX) * ($this->yValues[$xKey] - $meanY);
            if ($const === true) {
                $SSsex += ($this->xValues[$xKey] - $meanX) * ($this->xValues[$xKey] - $meanX);
            } else {
                $SSsex += $this->xValues[$xKey] * $this->xValues[$xKey];
            }
        }

        $this->SSResiduals = $SSres;
        $this->DFResiduals = $this->valueCount - 1 - ($const === true ? 1 : 0);

        if ($this->DFResiduals == 0.0) {
            $this->stdevOfResiduals = 0.0;
        } else {
            $this->stdevOfResiduals = sqrt($SSres / $this->DFResiduals);
        }
        // Scrutinizer thinks $SSres == $SStot is always true. It is wrong.
        if ($SStot == self::$scrutinizerZeroPointZero || self::scrutinizerLooseCompare($SSres, $SStot)) {
            $this->goodnessOfFit = 1;
        } else {
            $this->goodnessOfFit = 1 - ($SSres / $SStot);
        }

        $this->SSRegression = $this->goodnessOfFit * $SStot;
        $this->covariance = $SScov / $this->valueCount;
        $this->correlation = ($this->valueCount * $sumXY - $sumX * $sumY) / sqrt(($this->valueCount * $sumX2 - $sumX ** 2) * ($this->valueCount * $sumY2 - $sumY ** 2));
        $this->slopeSE = $this->stdevOfResiduals / sqrt($SSsex);
        $this->intersectSE = $this->stdevOfResiduals * sqrt(1 / ($this->valueCount - ($sumX * $sumX) / $sumX2));
        if ($this->SSResiduals != 0.0) {
            if ($this->DFResiduals == 0.0) {
                $this->f = 0.0;
            } else {
                $this->f = $this->SSRegression / ($this->SSResiduals / $this->DFResiduals);
            }
        } else {
            if ($this->DFResiduals == 0.0) {
                $this->f = 0.0;
            } else {
                $this->f = $this->SSRegression / $this->DFResiduals;
            }
        }
    }

    /** @return float|int */
    private function sumSquares(array $values)
    {
        return array_sum(
            array_map(
                function ($value) {
                    return $value ** 2;
                },
                $values
            )
        );
    }

    /**
     * @param float[] $yValues
     * @param float[] $xValues
     */
    protected function leastSquareFit(array $yValues, array $xValues, bool $const): void
    {
        // calculate sums
        $sumValuesX = array_sum($xValues);
        $sumValuesY = array_sum($yValues);
        $meanValueX = $sumValuesX / $this->valueCount;
        $meanValueY = $sumValuesY / $this->valueCount;
        $sumSquaresX = $this->sumSquares($xValues);
        $sumSquaresY = $this->sumSquares($yValues);
        $mBase = $mDivisor = 0.0;
        $xy_sum = 0.0;
        for ($i = 0; $i < $this->valueCount; ++$i) {
            $xy_sum += $xValues[$i] * $yValues[$i];

            if ($const === true) {
                $mBase += ($xValues[$i] - $meanValueX) * ($yValues[$i] - $meanValueY);
                $mDivisor += ($xValues[$i] - $meanValueX) * ($xValues[$i] - $meanValueX);
            } else {
                $mBase += $xValues[$i] * $yValues[$i];
                $mDivisor += $xValues[$i] * $xValues[$i];
            }
        }

        // calculate slope
        $this->slope = $mBase / $mDivisor;

        // calculate intersect
        $this->intersect = ($const === true) ? $meanValueY - ($this->slope * $meanValueX) : 0.0;

        $this->calculateGoodnessOfFit($sumValuesX, $sumValuesY, $sumSquaresX, $sumSquaresY, $xy_sum, $meanValueX, $meanValueY, $const);
    }

    /**
     * Define the regression.
     *
     * @param float[] $yValues The set of Y-values for this regression
     * @param float[] $xValues The set of X-values for this regression
     */
    public function __construct($yValues, $xValues = [])
    {
        //    Calculate number of points
        $yValueCount = count($yValues);
        $xValueCount = count($xValues);

        //    Define X Values if necessary
        if ($xValueCount === 0) {
            $xValues = range(1, $yValueCount);
        } elseif ($yValueCount !== $xValueCount) {
            //    Ensure both arrays of points are the same size
            $this->error = true;
        }

        $this->valueCount = $yValueCount;
        $this->xValues = $xValues;
        $this->yValues = $yValues;
    }
}


1		<?php
2
3		namespace PhpOffice\PhpSpreadsheet\Shared\Trend;
4
5		abstract class BestFit
6		{
7		/**
8		* Indicator flag for a calculation error.
9		*
10		* @var bool
11		*/
12		protected $error = false;
13
14		/**
15		* Algorithm type to use for best-fit.
16		*
17		* @var string
18		*/
19		protected $bestFitType = 'undetermined';
20
21		/**
22		* Number of entries in the sets of x- and y-value arrays.
23		*
24		* @var int
25		*/
26		protected $valueCount = 0;
27
28		/**
29		* X-value dataseries of values.
30		*
31		* @var float[]
32		*/
33		protected $xValues = [];
34
35		/**
36		* Y-value dataseries of values.
37		*
38		* @var float[]
39		*/
40		protected $yValues = [];
41
42		/**
43		* Flag indicating whether values should be adjusted to Y=0.
44		*
45		* @var bool
46		*/
47		protected $adjustToZero = false;
48
49		/**
50		* Y-value series of best-fit values.
51		*
52		* @var float[]
53		*/
54		protected $yBestFitValues = [];
55
56		/** @var float */
57		protected $goodnessOfFit = 1;
58
59		/** @var float */
60		protected $stdevOfResiduals = 0;
61
62		/** @var float */
63		protected $covariance = 0;
64
65		/** @var float */
66		protected $correlation = 0;
67
68		/** @var float */
69		protected $SSRegression = 0;
70
71		/** @var float */
72		protected $SSResiduals = 0;
73
74		/** @var float */
75		protected $DFResiduals = 0;
76
77		/** @var float */
78		protected $f = 0;
79
80		/** @var float */
81		protected $slope = 0;
82
83		/** @var float */
84		protected $slopeSE = 0;
85
86		/** @var float */
87		protected $intersect = 0;
88
89		/** @var float */
90		protected $intersectSE = 0;
91
92		/** @var float */
93		protected $xOffset = 0;
94
95		/** @var float */
96		protected $yOffset = 0;
97
98		/** @return bool */
99		public function getError()
100		{
101		return $this->error;
102		}
103
104		/** @return string */
105		public function getBestFitType()
106		{
107		return $this->bestFitType;
108		}
109
110		/**
111		* Return the Y-Value for a specified value of X.
112		*
113		* @param float $xValue X-Value
114		*
115		* @return float Y-Value
116		*/
117		abstract public function getValueOfYForX($xValue);
118
119		/**
120		* Return the X-Value for a specified value of Y.
121		*
122		* @param float $yValue Y-Value
123		*
124		* @return float X-Value
125		*/
126		abstract public function getValueOfXForY($yValue);
127
128		/**
129		* Return the original set of X-Values.
130		*
131		* @return float[] X-Values
132		*/
133	3	public function getXValues()
134		{
135	3	return $this->xValues;
136		}
137
138		/**
139		* Return the Equation of the best-fit line.
140		*
141		* @param int $dp Number of places of decimal precision to display
142		*
143		* @return string
144		*/
145		abstract public function getEquation($dp = 0);
146
147		/**
148		* Return the Slope of the line.
149		*
150		* @param int $dp Number of places of decimal precision to display
151		*
152		* @return float
153		*/
154	40	public function getSlope($dp = 0)
155		{
156	40	if ($dp != 0) {
157	2	return round($this->slope, $dp);
158		}
159
160	40	return $this->slope;
161		}
162
163		/**
164		* Return the standard error of the Slope.
165		*
166		* @param int $dp Number of places of decimal precision to display
167		*
168		* @return float
169		*/
170	3	public function getSlopeSE($dp = 0)
171		{
172	3	if ($dp != 0) {
173		return round($this->slopeSE, $dp);
174		}
175
176	3	return $this->slopeSE;
177		}
178
179		/**
180		* Return the Value of X where it intersects Y = 0.
181		*
182		* @param int $dp Number of places of decimal precision to display
183		*
184		* @return float
185		*/
186	38	public function getIntersect($dp = 0)
187		{
188	38	if ($dp != 0) {
189	2	return round($this->intersect, $dp);
190		}
191
192	38	return $this->intersect;
193		}
194
195		/**
196		* Return the standard error of the Intersect.
197		*
198		* @param int $dp Number of places of decimal precision to display
199		*
200		* @return float
201		*/
202	2	public function getIntersectSE($dp = 0)
203		{
204	2	if ($dp != 0) {
205		return round($this->intersectSE, $dp);
206		}
207
208	2	return $this->intersectSE;
209		}
210
211		/**
212		* Return the goodness of fit for this regression.
213		*
214		* @param int $dp Number of places of decimal precision to return
215		*
216		* @return float
217		*/
218	10	public function getGoodnessOfFit($dp = 0)
219		{
220	10	if ($dp != 0) {
221	3	return round($this->goodnessOfFit, $dp);
222		}
223
224	10	return $this->goodnessOfFit;
225		}
226
227		/**
228		* Return the goodness of fit for this regression.
229		*
230		* @param int $dp Number of places of decimal precision to return
231		*
232		* @return float
233		*/
234		public function getGoodnessOfFitPercent($dp = 0)
235		{
236		if ($dp != 0) {
237		return round($this->goodnessOfFit * 100, $dp);
238		}
239
240		return $this->goodnessOfFit * 100;
241		}
242
243		/**
244		* Return the standard deviation of the residuals for this regression.
245		*
246		* @param int $dp Number of places of decimal precision to return
247		*
248		* @return float
249		*/
250	7	public function getStdevOfResiduals($dp = 0)
251		{
252	7	if ($dp != 0) {
253		return round($this->stdevOfResiduals, $dp);
254		}
255
256	7	return $this->stdevOfResiduals;
257		}
258
259		/**
260		* @param int $dp Number of places of decimal precision to return
261		*
262		* @return float
263		*/
264	3	public function getSSRegression($dp = 0)
265		{
266	3	if ($dp != 0) {
267		return round($this->SSRegression, $dp);
268		}
269
270	3	return $this->SSRegression;
271		}
272
273		/**
274		* @param int $dp Number of places of decimal precision to return
275		*
276		* @return float
277		*/
278	3	public function getSSResiduals($dp = 0)
279		{
280	3	if ($dp != 0) {
281		return round($this->SSResiduals, $dp);
282		}
283
284	3	return $this->SSResiduals;
285		}
286
287		/**
288		* @param int $dp Number of places of decimal precision to return
289		*
290		* @return float
291		*/
292	3	public function getDFResiduals($dp = 0)
293		{
294	3	if ($dp != 0) {
295		return round($this->DFResiduals, $dp);
296		}
297
298	3	return $this->DFResiduals;
299		}
300
301		/**
302		* @param int $dp Number of places of decimal precision to return
303		*
304		* @return float
305		*/
306	3	public function getF($dp = 0)
307		{
308	3	if ($dp != 0) {
309		return round($this->f, $dp);
310		}
311
312	3	return $this->f;
313		}
314
315		/**
316		* @param int $dp Number of places of decimal precision to return
317		*
318		* @return float
319		*/
320	5	public function getCovariance($dp = 0)
321		{
322	5	if ($dp != 0) {
323		return round($this->covariance, $dp);
324		}
325
326	5	return $this->covariance;
327		}
328
329		/**
330		* @param int $dp Number of places of decimal precision to return
331		*
332		* @return float
333		*/
334	4	public function getCorrelation($dp = 0)
335		{
336	4	if ($dp != 0) {
337		return round($this->correlation, $dp);
338		}
339
340	4	return $this->correlation;
341		}
342
343		/**
344		* @return float[]
345		*/
346		public function getYBestFitValues()
347		{
348		return $this->yBestFitValues;
349		}
350
351		/** @var mixed */
352		private static $scrutinizerZeroPointZero = 0.0;
353
354		/**
355		* @param mixed $x
356		* @param mixed $y
357		*/
358	36	private static function scrutinizerLooseCompare($x, $y): bool
359		{
360	36	return $x == $y;
361		}
362
363		/**
364		* @param float $sumX
365		* @param float $sumY
366		* @param float $sumX2
367		* @param float $sumY2
368		* @param float $sumXY
369		* @param float $meanX
370		* @param float $meanY
371		* @param bool\|int $const
372		*/
373	36	protected function calculateGoodnessOfFit($sumX, $sumY, $sumX2, $sumY2, $sumXY, $meanX, $meanY, $const): void
374		{
375	36	$SSres = $SScov = $SStot = $SSsex = 0.0;
376	36	foreach ($this->xValues as $xKey => $xValue) {
377	36	$bestFitY = $this->yBestFitValues[$xKey] = $this->getValueOfYForX($xValue);
378
379	36	$SSres += ($this->yValues[$xKey] - $bestFitY) * ($this->yValues[$xKey] - $bestFitY);
380	36	if ($const === true) {
381	31	$SStot += ($this->yValues[$xKey] - $meanY) * ($this->yValues[$xKey] - $meanY);
382		} else {
383	5	$SStot += $this->yValues[$xKey] * $this->yValues[$xKey];
384		}
385	36	$SScov += ($this->xValues[$xKey] - $meanX) * ($this->yValues[$xKey] - $meanY);
386	36	if ($const === true) {
387	31	$SSsex += ($this->xValues[$xKey] - $meanX) * ($this->xValues[$xKey] - $meanX);
388		} else {
389	5	$SSsex += $this->xValues[$xKey] * $this->xValues[$xKey];
390		}
391		}
392
393	36	$this->SSResiduals = $SSres;
394	36	$this->DFResiduals = $this->valueCount - 1 - ($const === true ? 1 : 0);
395
396	36	if ($this->DFResiduals == 0.0) {
397	1	$this->stdevOfResiduals = 0.0;
398		} else {
399	35	$this->stdevOfResiduals = sqrt($SSres / $this->DFResiduals);
400		}
401		// Scrutinizer thinks $SSres == $SStot is always true. It is wrong.
402	36	if ($SStot == self::$scrutinizerZeroPointZero \|\| self::scrutinizerLooseCompare($SSres, $SStot)) {
403		$this->goodnessOfFit = 1;
404		} else {
405	36	$this->goodnessOfFit = 1 - ($SSres / $SStot);
406		}
407
408	36	$this->SSRegression = $this->goodnessOfFit * $SStot;
409	36	$this->covariance = $SScov / $this->valueCount;
410	36	$this->correlation = ($this->valueCount * $sumXY - $sumX * $sumY) / sqrt(($this->valueCount * $sumX2 - $sumX ** 2) * ($this->valueCount * $sumY2 - $sumY ** 2));
411	36	$this->slopeSE = $this->stdevOfResiduals / sqrt($SSsex);
412	36	$this->intersectSE = $this->stdevOfResiduals * sqrt(1 / ($this->valueCount - ($sumX * $sumX) / $sumX2));
413	36	if ($this->SSResiduals != 0.0) {
414	25	if ($this->DFResiduals == 0.0) {
415		$this->f = 0.0;
416		} else {
417	25	$this->f = $this->SSRegression / ($this->SSResiduals / $this->DFResiduals);
418		}
419		} else {
420	12	if ($this->DFResiduals == 0.0) {
421	1	$this->f = 0.0;
422		} else {
423	11	$this->f = $this->SSRegression / $this->DFResiduals;
424		}
425		}
426		}
427
428		/** @return float\|int */
429	36	private function sumSquares(array $values)
430		{
431	36	return array_sum(
432	36	array_map(
433	36	function ($value) {
434	36	return $value ** 2;
435	36	},
436	36	$values
437	36	)
438	36	);
439		}
440
441		/**
442		* @param float[] $yValues
443		* @param float[] $xValues
444		*/
445	36	protected function leastSquareFit(array $yValues, array $xValues, bool $const): void
446		{
447		// calculate sums
448	36	$sumValuesX = array_sum($xValues);
449	36	$sumValuesY = array_sum($yValues);
450	36	$meanValueX = $sumValuesX / $this->valueCount;
451	36	$meanValueY = $sumValuesY / $this->valueCount;
452	36	$sumSquaresX = $this->sumSquares($xValues);
453	36	$sumSquaresY = $this->sumSquares($yValues);
454	36	$mBase = $mDivisor = 0.0;
455	36	$xy_sum = 0.0;
456	36	for ($i = 0; $i < $this->valueCount; ++$i) {
457	36	$xy_sum += $xValues[$i] * $yValues[$i];
458
459	36	if ($const === true) {
460	31	$mBase += ($xValues[$i] - $meanValueX) * ($yValues[$i] - $meanValueY);
461	31	$mDivisor += ($xValues[$i] - $meanValueX) * ($xValues[$i] - $meanValueX);
462		} else {
463	5	$mBase += $xValues[$i] * $yValues[$i];
464	5	$mDivisor += $xValues[$i] * $xValues[$i];
465		}
466		}
467
468		// calculate slope
469	36	$this->slope = $mBase / $mDivisor;
470
471		// calculate intersect
472	36	$this->intersect = ($const === true) ? $meanValueY - ($this->slope * $meanValueX) : 0.0;
473
474	36	$this->calculateGoodnessOfFit($sumValuesX, $sumValuesY, $sumSquaresX, $sumSquaresY, $xy_sum, $meanValueX, $meanValueY, $const);
475		}
476
477		/**
478		* Define the regression.
479		*
480		* @param float[] $yValues The set of Y-values for this regression
481		* @param float[] $xValues The set of X-values for this regression
482		*/
483	36	public function __construct($yValues, $xValues = [])
484		{
485		// Calculate number of points
486	36	$yValueCount = count($yValues);
487	36	$xValueCount = count($xValues);
488
489		// Define X Values if necessary
490	36	if ($xValueCount === 0) {
491		$xValues = range(1, $yValueCount);
492	36	} elseif ($yValueCount !== $xValueCount) {
493		// Ensure both arrays of points are the same size
494		$this->error = true;
495		}
496
497	36	$this->valueCount = $yValueCount;
498	36	$this->xValues = $xValues;
499	36	$this->yValues = $yValues;
500		}
501		}
502

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BestFit::getSlope() A

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