From 19985dbb8c0aa66dc4bf7905abc1148de909097d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Anton=20Luka=20=C5=A0ijanec?= Date: Tue, 11 Jan 2022 12:35:47 +0100 Subject: prvi-commit --- .../excel/PHPExcel/Shared/trend/bestFitClass.php | 432 +++++++++++++++++++++ .../Shared/trend/exponentialBestFitClass.php | 148 +++++++ .../PHPExcel/Shared/trend/linearBestFitClass.php | 111 ++++++ .../Shared/trend/logarithmicBestFitClass.php | 120 ++++++ .../Shared/trend/polynomialBestFitClass.php | 224 +++++++++++ .../PHPExcel/Shared/trend/powerBestFitClass.php | 142 +++++++ .../excel/PHPExcel/Shared/trend/trendClass.php | 156 ++++++++ 7 files changed, 1333 insertions(+) create mode 100644 admin/survey/excel/PHPExcel/Shared/trend/bestFitClass.php create mode 100644 admin/survey/excel/PHPExcel/Shared/trend/exponentialBestFitClass.php create mode 100644 admin/survey/excel/PHPExcel/Shared/trend/linearBestFitClass.php create mode 100644 admin/survey/excel/PHPExcel/Shared/trend/logarithmicBestFitClass.php create mode 100644 admin/survey/excel/PHPExcel/Shared/trend/polynomialBestFitClass.php create mode 100644 admin/survey/excel/PHPExcel/Shared/trend/powerBestFitClass.php create mode 100644 admin/survey/excel/PHPExcel/Shared/trend/trendClass.php (limited to 'admin/survey/excel/PHPExcel/Shared/trend') diff --git a/admin/survey/excel/PHPExcel/Shared/trend/bestFitClass.php b/admin/survey/excel/PHPExcel/Shared/trend/bestFitClass.php new file mode 100644 index 0000000..dd2c094 --- /dev/null +++ b/admin/survey/excel/PHPExcel/Shared/trend/bestFitClass.php @@ -0,0 +1,432 @@ +_error; + } // function getBestFitType() + + + public function getBestFitType() { + return $this->_bestFitType; + } // function getBestFitType() + + + /** + * Return the Y-Value for a specified value of X + * + * @param float $xValue X-Value + * @return float Y-Value + */ + public function getValueOfYForX($xValue) { + return False; + } // function getValueOfYForX() + + + /** + * Return the X-Value for a specified value of Y + * + * @param float $yValue Y-Value + * @return float X-Value + */ + public function getValueOfXForY($yValue) { + return False; + } // function getValueOfXForY() + + + /** + * Return the original set of X-Values + * + * @return float[] X-Values + */ + public function getXValues() { + return $this->_xValues; + } // function getValueOfXForY() + + + /** + * Return the Equation of the best-fit line + * + * @param int $dp Number of places of decimal precision to display + * @return string + */ + public function getEquation($dp=0) { + return False; + } // function getEquation() + + + /** + * Return the Slope of the line + * + * @param int $dp Number of places of decimal precision to display + * @return string + */ + public function getSlope($dp=0) { + if ($dp != 0) { + return round($this->_slope,$dp); + } + return $this->_slope; + } // function getSlope() + + + /** + * Return the standard error of the Slope + * + * @param int $dp Number of places of decimal precision to display + * @return string + */ + public function getSlopeSE($dp=0) { + if ($dp != 0) { + return round($this->_slopeSE,$dp); + } + return $this->_slopeSE; + } // function getSlopeSE() + + + /** + * Return the Value of X where it intersects Y = 0 + * + * @param int $dp Number of places of decimal precision to display + * @return string + */ + public function getIntersect($dp=0) { + if ($dp != 0) { + return round($this->_intersect,$dp); + } + return $this->_intersect; + } // function getIntersect() + + + /** + * Return the standard error of the Intersect + * + * @param int $dp Number of places of decimal precision to display + * @return string + */ + public function getIntersectSE($dp=0) { + if ($dp != 0) { + return round($this->_intersectSE,$dp); + } + return $this->_intersectSE; + } // function getIntersectSE() + + + /** + * 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 $this->_goodnessOfFit; + } // function getGoodnessOfFit() + + + public function getGoodnessOfFitPercent($dp=0) { + if ($dp != 0) { + return round($this->_goodnessOfFit * 100,$dp); + } + return $this->_goodnessOfFit * 100; + } // function getGoodnessOfFitPercent() + + + /** + * 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 $this->_stdevOfResiduals; + } // function getStdevOfResiduals() + + + public function getSSRegression($dp=0) { + if ($dp != 0) { + return round($this->_SSRegression,$dp); + } + return $this->_SSRegression; + } // function getSSRegression() + + + public function getSSResiduals($dp=0) { + if ($dp != 0) { + return round($this->_SSResiduals,$dp); + } + return $this->_SSResiduals; + } // function getSSResiduals() + + + public function getDFResiduals($dp=0) { + if ($dp != 0) { + return round($this->_DFResiduals,$dp); + } + return $this->_DFResiduals; + } // function getDFResiduals() + + + public function getF($dp=0) { + if ($dp != 0) { + return round($this->_F,$dp); + } + return $this->_F; + } // function getF() + + + public function getCovariance($dp=0) { + if ($dp != 0) { + return round($this->_covariance,$dp); + } + return $this->_covariance; + } // function getCovariance() + + + public function getCorrelation($dp=0) { + if ($dp != 0) { + return round($this->_correlation,$dp); + } + return $this->_correlation; + } // function getCorrelation() + + + public function getYBestFitValues() { + return $this->_yBestFitValues; + } // function getYBestFitValues() + + + protected function _calculateGoodnessOfFit($sumX,$sumY,$sumX2,$sumY2,$sumXY,$meanX,$meanY, $const) { + $SSres = $SScov = $SScor = $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) { + $SStot += ($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) { + $SSsex += ($this->_xValues[$xKey] - $meanX) * ($this->_xValues[$xKey] - $meanX); + } else { + $SSsex += $this->_xValues[$xKey] * $this->_xValues[$xKey]; + } + } + + $this->_SSResiduals = $SSres; + $this->_DFResiduals = $this->_valueCount - 1 - $const; + + if ($this->_DFResiduals == 0.0) { + $this->_stdevOfResiduals = 0.0; + } else { + $this->_stdevOfResiduals = sqrt($SSres / $this->_DFResiduals); + } + if (($SStot == 0.0) || ($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 * $sumX2 - pow($sumX,2)) * ($this->_valueCount * $sumY2 - pow($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 / $this->_DFResiduals; + } + } + } // function _calculateGoodnessOfFit() + + + protected function _leastSquareFit($yValues, $xValues, $const) { + // 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 = $yy_sum = 0.0; + for($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); + } else { + $mBase += $xValues[$i] * $yValues[$i]; + $mDivisor += $xValues[$i] * $xValues[$i]; + } + } + + // calculate slope +// $this->_slope = (($this->_valueCount * $xy_sum) - ($x_sum * $y_sum)) / (($this->_valueCount * $xx_sum) - ($x_sum * $x_sum)); + $this->_slope = $mBase / $mDivisor; + + // calculate intersect +// $this->_intersect = ($y_sum - ($this->_slope * $x_sum)) / $this->_valueCount; + if ($const) { + $this->_intersect = $meanY - ($this->_slope * $meanX); + } else { + $this->_intersect = 0; + } + + $this->_calculateGoodnessOfFit($x_sum,$y_sum,$xx_sum,$yy_sum,$xy_sum,$meanX,$meanY,$const); + } // function _leastSquareFit() + + + /** + * 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 + * @param boolean $const + */ + function __construct($yValues, $xValues=array(), $const=True) { + // Calculate number of points + $nY = count($yValues); + $nX = count($xValues); + + // Define X Values if necessary + if ($nX == 0) { + $xValues = range(1,$nY); + $nX = $nY; + } elseif ($nY != $nX) { + // Ensure both arrays of points are the same size + $this->_error = True; + return False; + } + + $this->_valueCount = $nY; + $this->_xValues = $xValues; + $this->_yValues = $yValues; + } // function __construct() + +} // class bestFit diff --git a/admin/survey/excel/PHPExcel/Shared/trend/exponentialBestFitClass.php b/admin/survey/excel/PHPExcel/Shared/trend/exponentialBestFitClass.php new file mode 100644 index 0000000..6cc8201 --- /dev/null +++ b/admin/survey/excel/PHPExcel/Shared/trend/exponentialBestFitClass.php @@ -0,0 +1,148 @@ +getIntersect() * pow($this->getSlope(),($xValue - $this->_Xoffset)); + } // function getValueOfYForX() + + + /** + * Return the X-Value for a specified value of Y + * + * @param float $yValue Y-Value + * @return float X-Value + **/ + public function getValueOfXForY($yValue) { + return log(($yValue + $this->_Yoffset) / $this->getIntersect()) / log($this->getSlope()); + } // function getValueOfXForY() + + + /** + * Return the Equation of the best-fit line + * + * @param int $dp Number of places of decimal precision to display + * @return string + **/ + public function getEquation($dp=0) { + $slope = $this->getSlope($dp); + $intersect = $this->getIntersect($dp); + + return 'Y = '.$intersect.' * '.$slope.'^X'; + } // function getEquation() + + + /** + * Return the Slope of the line + * + * @param int $dp Number of places of decimal precision to display + * @return string + **/ + public function getSlope($dp=0) { + if ($dp != 0) { + return round(exp($this->_slope),$dp); + } + return exp($this->_slope); + } // function getSlope() + + + /** + * Return the Value of X where it intersects Y = 0 + * + * @param int $dp Number of places of decimal precision to display + * @return string + **/ + public function getIntersect($dp=0) { + if ($dp != 0) { + return round(exp($this->_intersect),$dp); + } + return exp($this->_intersect); + } // function getIntersect() + + + /** + * Execute the regression and calculate the goodness of fit for a set of X and Y data values + * + * @param float[] $yValues The set of Y-values for this regression + * @param float[] $xValues The set of X-values for this regression + * @param boolean $const + */ + private function _exponential_regression($yValues, $xValues, $const) { + foreach($yValues as &$value) { + if ($value < 0.0) { + $value = 0 - log(abs($value)); + } elseif ($value > 0.0) { + $value = log($value); + } + } + unset($value); + + $this->_leastSquareFit($yValues, $xValues, $const); + } // function _exponential_regression() + + + /** + * Define the regression and calculate the goodness of fit for a set of X and Y data values + * + * @param float[] $yValues The set of Y-values for this regression + * @param float[] $xValues The set of X-values for this regression + * @param boolean $const + */ + function __construct($yValues, $xValues=array(), $const=True) { + if (parent::__construct($yValues, $xValues) !== False) { + $this->_exponential_regression($yValues, $xValues, $const); + } + } // function __construct() + +} // class exponentialBestFit \ No newline at end of file diff --git a/admin/survey/excel/PHPExcel/Shared/trend/linearBestFitClass.php b/admin/survey/excel/PHPExcel/Shared/trend/linearBestFitClass.php new file mode 100644 index 0000000..0fe62b1 --- /dev/null +++ b/admin/survey/excel/PHPExcel/Shared/trend/linearBestFitClass.php @@ -0,0 +1,111 @@ +getIntersect() + $this->getSlope() * $xValue; + } // function getValueOfYForX() + + + /** + * Return the X-Value for a specified value of Y + * + * @param float $yValue Y-Value + * @return float X-Value + **/ + public function getValueOfXForY($yValue) { + return ($yValue - $this->getIntersect()) / $this->getSlope(); + } // function getValueOfXForY() + + + /** + * Return the Equation of the best-fit line + * + * @param int $dp Number of places of decimal precision to display + * @return string + **/ + public function getEquation($dp=0) { + $slope = $this->getSlope($dp); + $intersect = $this->getIntersect($dp); + + return 'Y = '.$intersect.' + '.$slope.' * X'; + } // function getEquation() + + + /** + * Execute the regression and calculate the goodness of fit for a set of X and Y data values + * + * @param float[] $yValues The set of Y-values for this regression + * @param float[] $xValues The set of X-values for this regression + * @param boolean $const + */ + private function _linear_regression($yValues, $xValues, $const) { + $this->_leastSquareFit($yValues, $xValues,$const); + } // function _linear_regression() + + + /** + * Define the regression and calculate the goodness of fit for a set of X and Y data values + * + * @param float[] $yValues The set of Y-values for this regression + * @param float[] $xValues The set of X-values for this regression + * @param boolean $const + */ + function __construct($yValues, $xValues=array(), $const=True) { + if (parent::__construct($yValues, $xValues) !== False) { + $this->_linear_regression($yValues, $xValues, $const); + } + } // function __construct() + +} // class linearBestFit \ No newline at end of file diff --git a/admin/survey/excel/PHPExcel/Shared/trend/logarithmicBestFitClass.php b/admin/survey/excel/PHPExcel/Shared/trend/logarithmicBestFitClass.php new file mode 100644 index 0000000..acccc53 --- /dev/null +++ b/admin/survey/excel/PHPExcel/Shared/trend/logarithmicBestFitClass.php @@ -0,0 +1,120 @@ +getIntersect() + $this->getSlope() * log($xValue - $this->_Xoffset); + } // function getValueOfYForX() + + + /** + * Return the X-Value for a specified value of Y + * + * @param float $yValue Y-Value + * @return float X-Value + **/ + public function getValueOfXForY($yValue) { + return exp(($yValue - $this->getIntersect()) / $this->getSlope()); + } // function getValueOfXForY() + + + /** + * Return the Equation of the best-fit line + * + * @param int $dp Number of places of decimal precision to display + * @return string + **/ + public function getEquation($dp=0) { + $slope = $this->getSlope($dp); + $intersect = $this->getIntersect($dp); + + return 'Y = '.$intersect.' + '.$slope.' * log(X)'; + } // function getEquation() + + + /** + * Execute the regression and calculate the goodness of fit for a set of X and Y data values + * + * @param float[] $yValues The set of Y-values for this regression + * @param float[] $xValues The set of X-values for this regression + * @param boolean $const + */ + private function _logarithmic_regression($yValues, $xValues, $const) { + foreach($xValues as &$value) { + if ($value < 0.0) { + $value = 0 - log(abs($value)); + } elseif ($value > 0.0) { + $value = log($value); + } + } + unset($value); + + $this->_leastSquareFit($yValues, $xValues, $const); + } // function _logarithmic_regression() + + + /** + * Define the regression and calculate the goodness of fit for a set of X and Y data values + * + * @param float[] $yValues The set of Y-values for this regression + * @param float[] $xValues The set of X-values for this regression + * @param boolean $const + */ + function __construct($yValues, $xValues=array(), $const=True) { + if (parent::__construct($yValues, $xValues) !== False) { + $this->_logarithmic_regression($yValues, $xValues, $const); + } + } // function __construct() + +} // class logarithmicBestFit \ No newline at end of file diff --git a/admin/survey/excel/PHPExcel/Shared/trend/polynomialBestFitClass.php b/admin/survey/excel/PHPExcel/Shared/trend/polynomialBestFitClass.php new file mode 100644 index 0000000..eef0060 --- /dev/null +++ b/admin/survey/excel/PHPExcel/Shared/trend/polynomialBestFitClass.php @@ -0,0 +1,224 @@ +_order; + } // function getOrder() + + + /** + * Return the Y-Value for a specified value of X + * + * @param float $xValue X-Value + * @return float Y-Value + **/ + public function getValueOfYForX($xValue) { + $retVal = $this->getIntersect(); + $slope = $this->getSlope(); + foreach($slope as $key => $value) { + if ($value != 0.0) { + $retVal += $value * pow($xValue, $key + 1); + } + } + return $retVal; + } // function getValueOfYForX() + + + /** + * Return the X-Value for a specified value of Y + * + * @param float $yValue Y-Value + * @return float X-Value + **/ + public function getValueOfXForY($yValue) { + return ($yValue - $this->getIntersect()) / $this->getSlope(); + } // function getValueOfXForY() + + + /** + * Return the Equation of the best-fit line + * + * @param int $dp Number of places of decimal precision to display + * @return string + **/ + public function getEquation($dp=0) { + $slope = $this->getSlope($dp); + $intersect = $this->getIntersect($dp); + + $equation = 'Y = '.$intersect; + foreach($slope as $key => $value) { + if ($value != 0.0) { + $equation .= ' + '.$value.' * X'; + if ($key > 0) { + $equation .= '^'.($key + 1); + } + } + } + return $equation; + } // function getEquation() + + + /** + * Return the Slope of the line + * + * @param int $dp Number of places of decimal precision to display + * @return string + **/ + public function getSlope($dp=0) { + if ($dp != 0) { + $coefficients = array(); + foreach($this->_slope as $coefficient) { + $coefficients[] = round($coefficient,$dp); + } + return $coefficients; + } + return $this->_slope; + } // function getSlope() + + + public function getCoefficients($dp=0) { + return array_merge(array($this->getIntersect($dp)),$this->getSlope($dp)); + } // function getCoefficients() + + + /** + * Execute the regression and calculate the goodness of fit for a set of X and Y data values + * + * @param int $order Order of Polynomial for this regression + * @param float[] $yValues The set of Y-values for this regression + * @param float[] $xValues The set of X-values for this regression + * @param boolean $const + */ + private function _polynomial_regression($order, $yValues, $xValues, $const) { + // calculate sums + $x_sum = array_sum($xValues); + $y_sum = array_sum($yValues); + $xx_sum = $xy_sum = 0; + for($i = 0; $i < $this->_valueCount; ++$i) { + $xy_sum += $xValues[$i] * $yValues[$i]; + $xx_sum += $xValues[$i] * $xValues[$i]; + $yy_sum += $yValues[$i] * $yValues[$i]; + } + /* + * This routine uses logic from the PHP port of polyfit version 0.1 + * written by Michael Bommarito and Paul Meagher + * + * The function fits a polynomial function of order $order through + * a series of x-y data points using least squares. + * + */ + for ($i = 0; $i < $this->_valueCount; ++$i) { + for ($j = 0; $j <= $order; ++$j) { + $A[$i][$j] = pow($xValues[$i], $j); + } + } + for ($i=0; $i < $this->_valueCount; ++$i) { + $B[$i] = array($yValues[$i]); + } + $matrixA = new Matrix($A); + $matrixB = new Matrix($B); + $C = $matrixA->solve($matrixB); + + $coefficients = array(); + for($i = 0; $i < $C->m; ++$i) { + $r = $C->get($i, 0); + if (abs($r) <= pow(10, -9)) { + $r = 0; + } + $coefficients[] = $r; + } + + $this->_intersect = array_shift($coefficients); + $this->_slope = $coefficients; + + $this->_calculateGoodnessOfFit($x_sum,$y_sum,$xx_sum,$yy_sum,$xy_sum); + foreach($this->_xValues as $xKey => $xValue) { + $this->_yBestFitValues[$xKey] = $this->getValueOfYForX($xValue); + } + } // function _polynomial_regression() + + + /** + * Define the regression and calculate the goodness of fit for a set of X and Y data values + * + * @param int $order Order of Polynomial for this regression + * @param float[] $yValues The set of Y-values for this regression + * @param float[] $xValues The set of X-values for this regression + * @param boolean $const + */ + function __construct($order, $yValues, $xValues=array(), $const=True) { + if (parent::__construct($yValues, $xValues) !== False) { + if ($order < $this->_valueCount) { + $this->_bestFitType .= '_'.$order; + $this->_order = $order; + $this->_polynomial_regression($order, $yValues, $xValues, $const); + if (($this->getGoodnessOfFit() < 0.0) || ($this->getGoodnessOfFit() > 1.0)) { + $this->_error = True; + } + } else { + $this->_error = True; + } + } + } // function __construct() + +} // class polynomialBestFit \ No newline at end of file diff --git a/admin/survey/excel/PHPExcel/Shared/trend/powerBestFitClass.php b/admin/survey/excel/PHPExcel/Shared/trend/powerBestFitClass.php new file mode 100644 index 0000000..22c23d7 --- /dev/null +++ b/admin/survey/excel/PHPExcel/Shared/trend/powerBestFitClass.php @@ -0,0 +1,142 @@ +getIntersect() * pow(($xValue - $this->_Xoffset),$this->getSlope()); + } // function getValueOfYForX() + + + /** + * Return the X-Value for a specified value of Y + * + * @param float $yValue Y-Value + * @return float X-Value + **/ + public function getValueOfXForY($yValue) { + return pow((($yValue + $this->_Yoffset) / $this->getIntersect()),(1 / $this->getSlope())); + } // function getValueOfXForY() + + + /** + * Return the Equation of the best-fit line + * + * @param int $dp Number of places of decimal precision to display + * @return string + **/ + public function getEquation($dp=0) { + $slope = $this->getSlope($dp); + $intersect = $this->getIntersect($dp); + + return 'Y = '.$intersect.' * X^'.$slope; + } // function getEquation() + + + /** + * Return the Value of X where it intersects Y = 0 + * + * @param int $dp Number of places of decimal precision to display + * @return string + **/ + public function getIntersect($dp=0) { + if ($dp != 0) { + return round(exp($this->_intersect),$dp); + } + return exp($this->_intersect); + } // function getIntersect() + + + /** + * Execute the regression and calculate the goodness of fit for a set of X and Y data values + * + * @param float[] $yValues The set of Y-values for this regression + * @param float[] $xValues The set of X-values for this regression + * @param boolean $const + */ + private function _power_regression($yValues, $xValues, $const) { + foreach($xValues as &$value) { + if ($value < 0.0) { + $value = 0 - log(abs($value)); + } elseif ($value > 0.0) { + $value = log($value); + } + } + unset($value); + foreach($yValues as &$value) { + if ($value < 0.0) { + $value = 0 - log(abs($value)); + } elseif ($value > 0.0) { + $value = log($value); + } + } + unset($value); + + $this->_leastSquareFit($yValues, $xValues, $const); + } // function _power_regression() + + + /** + * Define the regression and calculate the goodness of fit for a set of X and Y data values + * + * @param float[] $yValues The set of Y-values for this regression + * @param float[] $xValues The set of X-values for this regression + * @param boolean $const + */ + function __construct($yValues, $xValues=array(), $const=True) { + if (parent::__construct($yValues, $xValues) !== False) { + $this->_power_regression($yValues, $xValues, $const); + } + } // function __construct() + +} // class powerBestFit \ No newline at end of file diff --git a/admin/survey/excel/PHPExcel/Shared/trend/trendClass.php b/admin/survey/excel/PHPExcel/Shared/trend/trendClass.php new file mode 100644 index 0000000..59d1b1f --- /dev/null +++ b/admin/survey/excel/PHPExcel/Shared/trend/trendClass.php @@ -0,0 +1,156 @@ +getGoodnessOfFit(); + } + if ($trendType != self::TREND_BEST_FIT_NO_POLY) { + foreach(self::$_trendTypePolyOrders as $trendMethod) { + $order = substr($trendMethod,-1); + $bestFit[$trendMethod] = new PHPExcel_Polynomial_Best_Fit($order,$yValues,$xValues,$const); + if ($bestFit[$trendMethod]->getError()) { + unset($bestFit[$trendMethod]); + } else { + $bestFitValue[$trendMethod] = $bestFit[$trendMethod]->getGoodnessOfFit(); + } + } + } + // Determine which of our trend lines is the best fit, and then we return the instance of that trend class + arsort($bestFitValue); + $bestFitType = key($bestFitValue); + return $bestFit[$bestFitType]; + break; + default : + return false; + } + } // function calculate() + +} // class trendClass \ No newline at end of file -- cgit v1.2.3