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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.
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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.
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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.
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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.
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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