public class PCA extends Object
Constructor and Description 

PCA(INDArray dataset)
Create a PCA instance with calculated data: covariance, mean, eigenvectors, and eigenvalues.

Modifier and Type  Method and Description 

INDArray 
convertBackToFeatures(INDArray data)
Take the data that has been transformed to the principal components about the mean and
transform it back into the original feature set.

INDArray 
convertToComponents(INDArray data)
Takes a set of data on each row, with the same number of features as the constructing data
and returns the data in the coordinates of the basis set about the mean.

static INDArray[] 
covarianceMatrix(INDArray in)
Returns the covariance matrix of a data set of many records, each with N features.

double 
estimateVariance(INDArray data,
int ndims)
Estimate the variance of a single record with reduced # of dimensions.

INDArray 
generateGaussianSamples(long count)
Generates a set of count random samples with the same variance and mean and eigenvector/values
as the data set used to initialize the PCA object, with same number of features N.

INDArray 
getCovarianceMatrix() 
INDArray 
getEigenvalues() 
INDArray 
getEigenvectors() 
INDArray 
getMean() 
static INDArray 
pca_factor(INDArray A,
double variance,
boolean normalize)
Calculates pca vectors of a matrix, for a given variance.

static INDArray 
pca_factor(INDArray A,
int nDims,
boolean normalize)
Calculates pca factors of a matrix, for a flags number of reduced features
returns the factors to scale observations
The return is a factor matrix to reduce (normalized) feature sets

static INDArray 
pca(INDArray A,
double variance,
boolean normalize)
Calculates pca reduced value of a matrix, for a given variance.

static INDArray 
pca(INDArray A,
int nDims,
boolean normalize)
Calculates pca vectors of a matrix, for a flags number of reduced features
returns the reduced feature set
The return is a projection of A onto principal nDims components
To use the PCA: assume A is the original feature set
then project A onto a reduced set of features.

static INDArray 
pca2(INDArray in,
double variance)
This method performs a dimensionality reduction, including principal components
that cover a fraction of the total variance of the system.

static INDArray[] 
principalComponents(INDArray cov)
Calculates the principal component vectors and their eigenvalues (lambda) for the covariance matrix.

INDArray 
reducedBasis(double variance)
Return a reduced basis set that covers a certain fraction of the variance of the data

public PCA(INDArray dataset)
dataset
 The set of data (records) of features, each row is a data record and each
column is a feature, every data record has the same number of features.public INDArray reducedBasis(double variance)
variance
 The desired fractional variance (0 to 1), it will always be greater than the value.public INDArray convertToComponents(INDArray data)
data
 Data of the same features used to construct the PCA objectpublic INDArray convertBackToFeatures(INDArray data)
data
 Data of the same features used to construct the PCA object but as the componentspublic double estimateVariance(INDArray data, int ndims)
data
 A single record with the same N features as the constructing data setndims
 The number of dimensions to include in calculationpublic INDArray generateGaussianSamples(long count)
count
 The number of samples to generatepublic static INDArray pca(INDArray A, int nDims, boolean normalize)
INDArray Areduced = A.mmul( factor ) ;
INDArray Aoriginal = Areduced.mmul( factor.transpose() ) ;
A
 the array of features, rows are results, columns are features  will be changednDims
 the number of components on which to project the featuresnormalize
 whether to normalize (adjust each feature to have zero mean)public static INDArray pca_factor(INDArray A, int nDims, boolean normalize)
A
 the array of features, rows are results, columns are features  will be changednDims
 the number of components on which to project the featuresnormalize
 whether to normalize (adjust each feature to have zero mean)pca(INDArray, int, boolean)
public static INDArray pca(INDArray A, double variance, boolean normalize)
A
 the array of features, rows are results, columns are features  will be changedvariance
 the amount of variance to preserve as a float 0  1normalize
 whether to normalize (set features to have zero mean)pca(INDArray, int, boolean)
public static INDArray pca_factor(INDArray A, double variance, boolean normalize)
A
 the array of features, rows are results, columns are features  will be changedvariance
 the amount of variance to preserve as a float 0  1normalize
 whether to normalize (set features to have zero mean)pca(INDArray, double, boolean)
public static INDArray pca2(INDArray in, double variance)
in
 A matrix of datapoints as rows, where column are features with fixed number Nvariance
 The desired fraction of the total variance requiredpublic static INDArray[] covarianceMatrix(INDArray in)
in
 A matrix of vectors of fixed length N (N features) on each rowpublic static INDArray[] principalComponents(INDArray cov)
cov
 The covariance matrix (calculated with the covarianceMatrix(in) method)public INDArray getCovarianceMatrix()
public INDArray getMean()
public INDArray getEigenvectors()
public INDArray getEigenvalues()
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