Packages

c

scalation.analytics

PrincipalComponents

class PrincipalComponents extends Reducer with Error

The PrincipalComponents 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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Error, Reducer, AnyRef, Any
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Instance Constructors

  1. new PrincipalComponents(x: MatriD)

    x

    the data matrix to reduce, stored column-wise

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean
    Definition Classes
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  4. final def asInstanceOf[T0]: T0
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  5. def clone(): AnyRef
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    protected[java.lang]
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    @native() @throws( ... )
  6. final def eq(arg0: AnyRef): Boolean
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  7. def equals(arg0: Any): Boolean
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  8. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
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    @throws( classOf[java.lang.Throwable] )
  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 flaw(method: String, message: String): Unit
    Definition Classes
    Error
  11. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
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    @native()
  12. def hashCode(): Int
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    @native()
  13. final def isInstanceOf[T0]: Boolean
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    Any
  14. final def ne(arg0: AnyRef): Boolean
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  15. final def notify(): Unit
    Definition Classes
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    @native()
  16. final def notifyAll(): Unit
    Definition Classes
    AnyRef
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    @native()
  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 → 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 → Reducer
  19. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  20. def toString(): String
    Definition Classes
    AnyRef → Any
  21. final def wait(): Unit
    Definition Classes
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    @throws( ... )
  22. final def wait(arg0: Long, arg1: Int): Unit
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    @throws( ... )
  23. final def wait(arg0: Long): Unit
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    @native() @throws( ... )

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