Double |
The DoublePCA type exposes the following members.
Name | Description | |
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DoublePCA | Default constructor. | |
DoublePCA(DataFrame) | Constructs a DoublePCA instance from the given data. | |
DoublePCA(DoubleMatrix) | Constructs a DoublePCA instance from the given data. | |
DoublePCA(DataFrame, Boolean, Boolean) | Constructs a DoublePCA instance from the given data, optionally centering and scaling the data before analysis takes place. | |
DoublePCA(DoubleMatrix, Boolean, Boolean) | Constructs a DoublePCA instance from the given data, optionally centering and scaling the data before analysis takes place. |
Name | Description | |
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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 zero-centered. | |
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 (1-norm). | |
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 zero-centered. | |
d_ | Eigenvalues. | |
means_ | Column means. Used for centering. | |
norms_ | Column 1-norms. 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. |