PHP Класс 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.

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

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Открытые методы

Метод	Описание
__construct ( float $stepSize = 0.01 )
step ( integer $featureIndex ) : float
update ( array $gradient )

Описание методов

__construct() публичный Метод

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

step() публичный Метод

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

update() публичный Метод

public update ( array $gradient )
$gradient	array