Packages

c

scalation.analytics.fda

PrincipalComponents_F

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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Instance Constructors

  1. new PrincipalComponents_F(xa: Functions, t: VectorD)

    xa

    the array of functions

    t

    the vector of time points

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
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  2. final def ##(): Int
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  3. final def ==(arg0: Any): Boolean
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  5. def clone(): AnyRef
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  6. final def eq(arg0: AnyRef): Boolean
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  7. def equals(arg0: Any): Boolean
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  8. 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.

    Definition Classes
    Reducer
    See also

    NMFactortorization

  9. 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

  10. final def getClass(): Class[_]
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  11. def hashCode(): Int
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  12. final def isInstanceOf[T0]: Boolean
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  13. final def ne(arg0: AnyRef): Boolean
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  14. final def notify(): Unit
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    @native() @HotSpotIntrinsicCandidate()
  15. final def notifyAll(): Unit
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    @native() @HotSpotIntrinsicCandidate()
  16. val pca: PrincipalComponents
  17. 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.

    Definition Classes
    PrincipalComponents_F → Reducer
  18. 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.

    Definition Classes
    PrincipalComponents_F → Reducer
  19. final def synchronized[T0](arg0: ⇒ T0): T0
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  20. def toString(): String
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  21. final def wait(arg0: Long, arg1: Int): Unit
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  22. final def wait(arg0: Long): Unit
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  23. final def wait(): Unit
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    @throws( ... )
  24. val x: MatrixD

Deprecated Value Members

  1. def finalize(): Unit
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    @throws( classOf[java.lang.Throwable] ) @Deprecated
    Deprecated

Inherited from Reducer

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