/** * 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 $const */ public function __construct($yValues, $xValues = array(), $const = true) { if (parent::__construct($yValues, $xValues) !== false) { $this->linearRegression($yValues, $xValues, $const); } }
/** * 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 $const */ public function __construct($order, $yValues, $xValues = array(), $const = true) { if (parent::__construct($yValues, $xValues) !== false) { if ($order < $this->valueCount) { $this->bestFitType .= '_' . $order; $this->order = $order; $this->polynomialRegression($order, $yValues, $xValues, $const); if ($this->getGoodnessOfFit() < 0.0 || $this->getGoodnessOfFit() > 1.0) { $this->_error = true; } } else { $this->_error = true; } } }