PHP 클래스 MCordingley\Regression\Algorithm\GradientDescent\Schedule\Fixed

Since the gradient of the error becomes shallower as the descent nears convergence, this will naturally shrink the updates into the error function's minimum. However, too large of a step size will lead to the descent diverging and too small of a step size will lead to an extremely long descent. Unfortunately, choosing a good step size is a matter of trial and error.

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

파일 보기 프로젝트 열기: mcordingley/regression 1 사용 예제들

공개 메소드들

메소드	설명
__construct ( float $stepSize = 0.01 )
step ( integer $featureIndex ) : float
update ( array $gradient )

메소드 상세

__construct() 공개 메소드

public __construct ( float $stepSize = 0.01 )
$stepSize	float

step() 공개 메소드

public step ( integer $featureIndex ) : float
$featureIndex	integer
리턴	float

update() 공개 메소드

public update ( array $gradient )
$gradient	array