/**
* 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;
}
}
}
