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package and depencies

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This commit is contained in:
RafficMohammed
2023-01-08 02:57:24 +05:30
parent d5332eb421
commit 1d54b8bc7f
4309 changed files with 193331 additions and 172289 deletions
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105
vendor/phpoffice/phpspreadsheet/src/PhpSpreadsheet/Shared/Trend/BestFit.php vendored
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@@ -2,7 +2,7 @@
namespace PhpOffice\PhpSpreadsheet\Shared\Trend;
class BestFit
abstract class BestFit
{
/**
* Indicator flag for a calculation error.
@@ -96,24 +96,18 @@ class BestFit
*
* @param float $xValue X-Value
*
* @return bool Y-Value
* @return float Y-Value
*/
public function getValueOfYForX($xValue)
{
return false;
}
abstract public function getValueOfYForX($xValue);
/**
* Return the X-Value for a specified value of Y.
*
* @param float $yValue Y-Value
*
* @return bool X-Value
* @return float X-Value
*/
public function getValueOfXForY($yValue)
{
return false;
}
abstract public function getValueOfXForY($yValue);
/**
* Return the original set of X-Values.
@@ -130,12 +124,9 @@ class BestFit
*
* @param int $dp Number of places of decimal precision to display
*
* @return bool
* @return string
*/
public function getEquation($dp = 0)
{
return false;
}
abstract public function getEquation($dp = 0);
/**
* Return the Slope of the line.
@@ -341,20 +332,32 @@ class BestFit
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 == $y;
}
protected function calculateGoodnessOfFit($sumX, $sumY, $sumX2, $sumY2, $sumXY, $meanX, $meanY, $const): void
{
$SSres = $SScov = $SScor = $SStot = $SSsex = 0.0;
$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) {
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) {
if ($const === true) {
$SSsex += ($this->xValues[$xKey] - $meanX) * ($this->xValues[$xKey] - $meanX);
} else {
$SSsex += $this->xValues[$xKey] * $this->xValues[$xKey];
@@ -362,14 +365,15 @@ class BestFit
}
$this->SSResiduals = $SSres;
$this->DFResiduals = $this->valueCount - 1 - $const;
$this->DFResiduals = $this->valueCount - 1 - ($const === true ? 1 : 0);
if ($this->DFResiduals == 0.0) {
$this->stdevOfResiduals = 0.0;
} else {
$this->stdevOfResiduals = sqrt($SSres / $this->DFResiduals);
}
if (($SStot == 0.0) || ($SSres == $SStot)) {
// 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);
@@ -395,27 +399,39 @@ class BestFit
}
}
private function sumSquares(array $values)
{
return array_sum(
array_map(
function ($value) {
return $value ** 2;
},
$values
)
);
}
/**
* @param float[] $yValues
* @param float[] $xValues
* @param bool $const
*/
protected function leastSquareFit(array $yValues, array $xValues, $const): void
protected function leastSquareFit(array $yValues, array $xValues, bool $const): void
{
// calculate sums
$x_sum = array_sum($xValues);
$y_sum = array_sum($yValues);
$meanX = $x_sum / $this->valueCount;
$meanY = $y_sum / $this->valueCount;
$mBase = $mDivisor = $xx_sum = $xy_sum = $yy_sum = 0.0;
$sumValuesX = array_sum($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];
$xx_sum += $xValues[$i] * $xValues[$i];
$yy_sum += $yValues[$i] * $yValues[$i];
if ($const) {
$mBase += ($xValues[$i] - $meanX) * ($yValues[$i] - $meanY);
$mDivisor += ($xValues[$i] - $meanX) * ($xValues[$i] - $meanX);
if ($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];
@@ -426,13 +442,9 @@ class BestFit
$this->slope = $mBase / $mDivisor;
// calculate intersect
if ($const) {
$this->intersect = $meanY - ($this->slope * $meanX);
} else {
$this->intersect = 0;
}
$this->intersect = ($const === true) ? $meanValueY - ($this->slope * $meanValueX) : 0.0;
$this->calculateGoodnessOfFit($x_sum, $y_sum, $xx_sum, $yy_sum, $xy_sum, $meanX, $meanY, $const);
$this->calculateGoodnessOfFit($sumValuesX, $sumValuesY, $sumSquaresX, $sumSquaresY, $xy_sum, $meanValueX, $meanValueY, $const);
}
/**
@@ -440,23 +452,22 @@ class BestFit
*
* @param float[] $yValues The set of Y-values for this regression
* @param float[] $xValues The set of X-values for this regression
* @param bool $const
*/
public function __construct($yValues, $xValues = [], $const = true)
public function __construct($yValues, $xValues = [])
{
// Calculate number of points
$nY = count($yValues);
$nX = count($xValues);
$yValueCount = count($yValues);
$xValueCount = count($xValues);
// Define X Values if necessary
if ($nX == 0) {
$xValues = range(1, $nY);
} elseif ($nY != $nX) {
if ($xValueCount === 0) {
$xValues = range(1, $yValueCount);
} elseif ($yValueCount !== $xValueCount) {
// Ensure both arrays of points are the same size
$this->error = true;
}
$this->valueCount = $nY;
$this->valueCount = $yValueCount;
$this->xValues = $xValues;
$this->yValues = $yValues;
}