Inheritance diagram for Phpml\DimensionReduction\PCA:

Public Member Functions
	__construct (?float $totalVariance=null, ?int $numFeatures=null)
	PCA (Principal Component Analysis) used to explain given data with lower number of dimensions.

	fit (array $data)
	Takes a data and returns a lower dimensional version of this data while preserving $totalVariance or $numFeatures.

	transform (array $sample)
	Transforms the given sample to a lower dimensional vector by using the eigenVectors obtained in the last run of `fit`.

Public Attributes
int	$numFeatures = null
	Number of features to be preserved after the reduction.

float	$totalVariance = 0.9
	Total variance to be conserved after the reduction.

Protected Member Functions
	calculateMeans (array $data, int $n)

	eigenDecomposition (array $matrix)
	Calculates eigenValues and eigenVectors of the given matrix.

	normalize (array $data, int $n)
	Normalization of the data includes subtracting mean from each dimension therefore dimensions will be centered to zero.

	reduce (array $data)
	Returns the reduced data.

Protected Attributes
array	$eigValues = []
	Top eigenValues of the matrix.

array	$eigVectors = []
	Top eigenvectors of the matrix.

bool	$fit = false

array	$means = []

Constructor & Destructor Documentation

◆ __construct()

Phpml\DimensionReduction\PCA::__construct	(	?float	$totalVariance = null,
		?int	$numFeatures = null )

PCA (Principal Component Analysis) used to explain given data with lower number of dimensions.

This analysis transforms the data to a lower dimensional version of it by conserving a proportion of total variance within the data. It is a lossy data compression technique.

Parameters

float	$totalVariance	Total explained variance to be preserved
int	$numFeatures	Number of features to be preserved

Exceptions

InvalidArgumentException

Member Function Documentation

◆ eigenDecomposition()

Phpml\DimensionReduction\EigenTransformerBase::eigenDecomposition ( array $matrix )

protectedinherited

Calculates eigenValues and eigenVectors of the given matrix.

Returns top eigenVectors along with the largest eigenValues. The total explained variance of these eigenVectors will be no less than desired $totalVariance value

◆ fit()

Phpml\DimensionReduction\PCA::fit ( array $data )

Takes a data and returns a lower dimensional version of this data while preserving $totalVariance or $numFeatures.

$data is an n-by-m matrix and returned array is n-by-k matrix where k <= m

Reimplemented in Phpml\DimensionReduction\KernelPCA.

◆ transform()

Phpml\DimensionReduction\PCA::transform ( array $sample )

Transforms the given sample to a lower dimensional vector by using the eigenVectors obtained in the last run of fit.

Exceptions

InvalidOperationException

Reimplemented in Phpml\DimensionReduction\KernelPCA.

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

lib/mlbackend/php/phpml/src/Phpml/DimensionReduction/PCA.php

Public Member Functions

Public Attributes