FloatPCA Class 
Namespace: CenterSpace.NMath.Core
The FloatPCA type exposes the following members.
Name  Description  

FloatPCA(FloatMatrix) 
Constructs a FloatPCA instance from the given data.
 
FloatPCA(FloatMatrix, Boolean, Boolean) 
Constructs a FloatPCA instance from the given data,
optionally centering and scaling the data before analysis takes place.

Name  Description  

CumulativeVarianceProportions 
Gets the cumulative variance proportions.
 
Data 
Gets the data matrix.
 
Eigenvalues 
Gets the eigenvalues of the covariance/correlation matrix, though the
calculation is actually performed using the singular values of the data
matrix.
 
IsCentered 
Returns true if the data supplied at construction time was
shifted to be zerocentered.
 
IsScaled 
Returns true if the data supplied at construction time was
scaled to have unit variance.
 
Item 
Gets the specified principal component.
 
Loadings 
Gets the loading matrix. Each column is a principal component.
 
Means 
Gets the column means of the data matrix.
 
Norms 
Gets the column norms (1norm).
 
NumberOfObservations 
Gets the number of observations in the data matrix.
 
NumberOfVariables 
Gets the number of variables in the data matrix.
 
Scores 
Gets the score matrix.
 
StandardDeviations 
Gets the standard deviations of the principal components.
 
VarianceProportions 
Gets the proportion of the total variance accounted for by each
principal component.

Name  Description  

Clone 
Creates a deep copy of this principal component analysis.
 
Threshold 
Gets the number of principal components required to account for the given
proportion of the total variance.

Name  Description  

center_ 
If true, the data supplied at construction time will be shifted to be
zerocentered.
 
d_ 
Eigenvalues.
 
means_ 
Column means. Used for centering.
 
norms_ 
Column 1norms. Used for scaling.
 
scale_ 
If true, the data supplied at construction time will be scaled to have
unit variance.
 
scores_ 
Scores matrix.
 
v_ 
Right eigenvectors.
 
x_ 
The data matrix.
