Phpml\Classification\Linear\Perceptron Class Reference

Inheritance diagram for Phpml\Classification\Linear\Perceptron:

Public Member Functions
	__construct (float $learningRate=0.001, int $maxIterations=1000, bool $normalizeInputs=true)
	Initalize a perceptron classifier with given learning rate and maximum number of iterations used while training the perceptron.
	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)

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)
	Trains the perceptron model with Stochastic Gradient Descent optimization to get the correct set of weights.

Protected Attributes
bool	$enableEarlyStop = true
int	$featureCount = 0
array	$labels = []
float	$learningRate
int	$maxIterations
Normalizer	$normalizer
Optimizer GD StochasticGD null	$optimizer
array	$weights = []

Constructor & Destructor Documentation

__construct()

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

Initalize a perceptron classifier with given learning rate and maximum number of iterations used while training the perceptron.

Parameters

float	$learningRate	Value between 0.0(exclusive) and 1.0(inclusive)
int	$maxIterations	Must be at least 1

Exceptions

InvalidArgumentException

Member Function Documentation

output()

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

protected

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 = [] )

Implements Phpml\IncrementalEstimator.

predict()

Phpml\Estimator::predict ( array $samples )

inherited

Implemented in Phpml\Pipeline.

predictProbability()

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

protected

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 )

protected

Return values

mixed

runTraining()

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

protected

Trains the perceptron model with Stochastic Gradient Descent optimization to get the correct set of weights.

Reimplemented in Phpml\Classification\Linear\Adaline.

setEarlyStop()

Phpml\Classification\Linear\Perceptron::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.

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/Perceptron.php