Phpml\Classification\Linear\Adaline Class Reference

Inheritance diagram for Phpml\Classification\Linear\Adaline:

Public Member Functions
	__construct (float $learningRate=0.001, int $maxIterations=1000, bool $normalizeInputs=true, int $trainingType=self::BATCH_TRAINING)
	Initalize an Adaline (ADAptive LInear NEuron) classifier with given learning rate and maximum number of iterations used while training the classifier
	getCostValues ()
	Returns the cost values obtained during the training.
	partialTrain (array $samples, array $targets, array $labels=[])
	predict (array $samples)
	setEarlyStop (bool $enable=true)
	Normally enabling early stopping for the optimization procedure may help saving processing time while in some cases it may result in premature convergence.
	train (array $samples, array $targets)
	trainBinary (array $samples, array $targets, array $labels)

Public Attributes
const	BATCH_TRAINING = 1
const	ONLINE_TRAINING = 2
	Online training: Stochastic gradient descent learning.

Protected Member Functions
	checkNormalizedSample (array $sample)
	Checks if the sample should be normalized and if so, returns the normalized sample.
	output (array $sample)
	Calculates net output of the network as a float value for the given input.
	outputClass (array $sample)
	Returns the class value (either -1 or 1) for the given input.
	predictProbability (array $sample, $label)
	Returns the probability of the sample of belonging to the given label.
	predictSampleBinary (array $sample)
	resetBinary ()
	runGradientDescent (array $samples, array $targets, Closure $gradientFunc, bool $isBatch=false)
	Executes a Gradient Descent algorithm for the given cost function.
	runTraining (array $samples, array $targets)
	Adapts the weights with respect to given samples and targets by use of gradient descent learning rule.

Protected Attributes
bool	$enableEarlyStop = true
int	$featureCount = 0
array	$labels = []
float	$learningRate
int	$maxIterations
Normalizer	$normalizer
Optimizer GD StochasticGD null	$optimizer
string int	$trainingType
	Training type may be either 'Batch' or 'Online' learning.
array	$weights = []

Constructor & Destructor Documentation

__construct()

Phpml\Classification\Linear\Adaline::__construct	(	float	$learningRate = 0.001,
		int	$maxIterations = 1000,
		bool	$normalizeInputs = true,
		int	$trainingType = self::BATCH_TRAINING )

Initalize an Adaline (ADAptive LInear NEuron) classifier with given learning rate and maximum number of iterations used while training the classifier

Learning rate should be a float value between 0.0(exclusive) and 1.0 (inclusive)
Maximum number of iterations can be an integer value greater than 0
If normalizeInputs is set to true, then every input given to the algorithm will be standardized by use of standard deviation and mean calculation

Exceptions

InvalidArgumentException

Member Function Documentation

output()

Phpml\Classification\Linear\Perceptron::output ( array $sample )

protectedinherited

Calculates net output of the network as a float value for the given input.

Return values

int|float

partialTrain()

Phpml\Classification\Linear\Perceptron::partialTrain ( array $samples, array $targets, array $labels = [] )

inherited

Implements Phpml\IncrementalEstimator.

predict()

Phpml\Estimator::predict ( array $samples )

inherited

Implemented in Phpml\Pipeline.

predictProbability()

Phpml\Classification\Linear\Perceptron::predictProbability ( array $sample, $label )

protectedinherited

Returns the probability of the sample of belonging to the given label.

The probability is simply taken as the distance of the sample to the decision plane.

Parameters

mixed

$label

predictSampleBinary()

Phpml\Classification\Linear\Perceptron::predictSampleBinary ( array $sample )

protectedinherited

Return values

mixed

runTraining()

Phpml\Classification\Linear\Adaline::runTraining ( array $samples, array $targets )

protected

Adapts the weights with respect to given samples and targets by use of gradient descent learning rule.

Reimplemented from Phpml\Classification\Linear\Perceptron.

setEarlyStop()

Phpml\Classification\Linear\Perceptron::setEarlyStop ( bool $enable = true )

inherited

Normally enabling early stopping for the optimization procedure may help saving processing time while in some cases it may result in premature convergence.

If "false" is given, the optimization procedure will always be executed for $maxIterations times

Return values

$this

train()

Phpml\Estimator::train ( array $samples, array $targets )

inherited

Implemented in Phpml\Classification\Ensemble\AdaBoost.

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The documentation for this class was generated from the following file:

• lib/mlbackend/php/phpml/src/Phpml/Classification/Linear/Adaline.php