PHP 클래스 MCordingley\Regression\Algorithm\GradientDescent\Gradient\Regularized

상속: implements MCordingley\Regression\Algorithm\GradientDescent\Gradient\Gradient

공개 메소드들

메소드	설명
__construct ( MCordingley\Regression\Algorithm\GradientDescent\Gradient\Gradient $gradient )
cost ( array $coefficients, array $features, float $outcome ) : float
gradient ( array $coefficients, array $features, float $outcome ) : array
ignoreFirstFeature ( boolean $ignoreFirst = true ) : self	Ignore the first feature when regularizing, as that is usually the bias (or intercept) term.
setLambda ( float $lambda ) : self	Sets the regularization cost parameter. Default value is 1.0
setLevel ( integer $level ) : self	Sets regularization level. e.g. L1 and L2. The default value is 2.

public __construct ( MCordingley\Regression\Algorithm\GradientDescent\Gradient\Gradient $gradient )
$gradient	MCordingley\Regression\Algorithm\GradientDescent\Gradient\Gradient

public cost ( array $coefficients, array $features, float $outcome ) : float
$coefficients	array
$features	array
$outcome	float
리턴	float

public gradient ( array $coefficients, array $features, float $outcome ) : array
$coefficients	array
$features	array
$outcome	float
리턴	array

Ignore the first feature when regularizing, as that is usually the bias (or intercept) term.

public ignoreFirstFeature ( boolean $ignoreFirst = true ) : self
$ignoreFirst	boolean
리턴	self

Sets the regularization cost parameter. Default value is 1.0

public setLambda ( float $lambda ) : self
$lambda	float
리턴	self

Sets regularization level. e.g. L1 and L2. The default value is 2.

public setLevel ( integer $level ) : self
$level	integer
리턴	self