/** * 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 { $this->_error = true; } } }
function __construct($yValues, $xValues = array(), $const = True) { if (parent::__construct($yValues, $xValues) !== False) { $this->_logarithmic_regression($yValues, $xValues, $const); } }
function __construct($yValues, $xValues = array(), $const = True) { if (parent::__construct($yValues, $xValues) !== False) { $this->_exponential_regression($yValues, $xValues, $const); } }
/** * 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) { $this->exponentialRegression($yValues, $xValues, $const); } }
function __construct($yValues, $xValues = array(), $const = True) { if (parent::__construct ( $yValues, $xValues ) !== False) { $this->_power_regression ( $yValues, $xValues, $const ); } } // function __construct()
/** * 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); } }
/** * 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) { $this->logarithmicRegression($yValues, $xValues, $const); } }