class PrincipalComponents_F extends Reducer
The PrincipalComponents_F class performs the Principal Component Analysis 'PCA'
on data matrix 'x'. It can be used to reduce the dimensionality of the data.
First find the Principal Components 'PC's by calling 'findPCs' and then call
'reduce' to reduce the data (i.e., reduce matrix 'x' to a lower dimensionality
matrix).
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- new PrincipalComponents_F(xa: Functions, t: VectorD)
- xa
the array of functions
- t
the vector of time points
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- def factorReduce(): (MatriD, MatriD)
Reduce the original data matrix by factoring it into two lower dimensionality matrices that maintains most of the descriptive power of the original matrix.
Reduce the original data matrix by factoring it into two lower dimensionality matrices that maintains most of the descriptive power of the original matrix. Override to algorithms that use factoring.
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NMFactortorization
- def findPCs(k: Int): MatriD
Find the Principal Components/Features, the eigenvectors with the 'k' highest eigenvalues.
Find the Principal Components/Features, the eigenvectors with the 'k' highest eigenvalues.
- k
the number of Principal Components 'PC's to find
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- val pca: PrincipalComponents
- def recover(): MatriD
Approximately recover the original data by multiplying the reduced matrix by the inverse (via transpose) of the feature matrix and then adding back the means.
Approximately recover the original data by multiplying the reduced matrix by the inverse (via transpose) of the feature matrix and then adding back the means.
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- PrincipalComponents_F → Reducer
- def reduce(): MatriD
Multiply the zero mean data matrix by the feature matrix to reduce dimensionality.
Multiply the zero mean data matrix by the feature matrix to reduce dimensionality.
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- PrincipalComponents_F → Reducer
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