Click or drag to resize

DoublePCA Class

Class DoublePCA performs a principal component analysis on a given double-precision data matrix, or data frame.
Inheritance Hierarchy
SystemObject
  CenterSpace.NMath.CoreDoublePCA

Namespace: CenterSpace.NMath.Core
Assembly: NMath (in NMath.dll) Version: 7.4
Syntax
[SerializableAttribute]
public class DoublePCA : ICloneable

The DoublePCA type exposes the following members.

Constructors
 NameDescription
Protected methodDoublePCA Default constructor.
Public methodDoublePCA(DataFrame) Constructs a DoublePCA instance from the given data.
Public methodDoublePCA(DoubleMatrix) Constructs a DoublePCA instance from the given data.
Public methodDoublePCA(DataFrame, Boolean, Boolean) Constructs a DoublePCA instance from the given data, optionally centering and scaling the data before analysis takes place.
Public methodDoublePCA(DoubleMatrix, Boolean, Boolean) Constructs a DoublePCA instance from the given data, optionally centering and scaling the data before analysis takes place.
Top
Properties
 NameDescription
Public propertyCumulativeVarianceProportions Gets the cumulative variance proportions.
Public propertyData Gets the data matrix.
Public propertyEigenvalues Gets the eigenvalues of the covariance/correlation matrix, though the calculation is actually performed using the singular values of the data matrix.
Public propertyIsCentered Returns true if the data supplied at construction time was shifted to be zero-centered.
Public propertyIsScaled Returns true if the data supplied at construction time was scaled to have unit variance.
Public propertyItem Gets the specified principal component.
Public propertyLoadings Gets the loading matrix. Each column is a principal component.
Public propertyMeans Gets the column means of the data matrix.
Public propertyNorms Gets the column norms (1-norm).
Public propertyNumberOfObservations Gets the number of observations in the data matrix.
Public propertyNumberOfVariables Gets the number of variables in the data matrix.
Public propertyScores Gets the score matrix.
Public propertyStandardDeviations Gets the standard deviations of the principal components.
Public propertyVarianceProportions Gets the proportion of the total variance accounted for by each principal component.
Top
Methods
 NameDescription
Public methodClone Creates a deep copy of this principal component analysis.
Public methodThreshold Gets the number of principal components required to account for the given proportion of the total variance.
Top
Fields
 NameDescription
Protected fieldcenter_ If true, the data supplied at construction time will be shifted to be zero-centered.
Protected fieldd_ Eigenvalues.
Protected fieldmeans_ Column means. Used for centering.
Protected fieldnorms_ Column 1-norms. Used for scaling.
Protected fieldscale_ If true, the data supplied at construction time will be scaled to have unit variance.
Protected fieldscores_ Scores matrix.
Protected fieldv_ Right eigenvectors.
Protected fieldx_ The data matrix.
Top
Remarks
The first principal component accounts for as much of the variability in the data as possible, and each succeeding component accounts for as much of the remaining variability as possible.
The calculation is performed using a singular value decomposition of the data matrix. The data may optionally be centered and scaled before analysis takes place.
See Also