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 PCs 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: MatrixD)

    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
    AnyRef → Any
  4. final def asInstanceOf[T0]: T0

    Definition Classes
    Any
  5. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  6. def computeCov(): MatrixD

    Assuming mean centered data, compute the covariance matrix.

  7. def computeEigenVectors(eVal: VectorD): MatrixD

    Compute the unit eigenvectors for the covariance matrix.

    Compute the unit eigenvectors for the covariance matrix.

    eVal

    the vector of eigenvalues for the covariance matrrix

  8. final def eq(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  9. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any
  10. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  11. def findPCs(k: Int): MatrixD

    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 (PCs) to find

  12. def flaw(method: String, message: String): Unit

    Show the flaw by printing the error message.

    Show the flaw by printing the error message.

    method

    the method where the error occurred

    message

    the error message

    Definition Classes
    Error
  13. final def getClass(): Class[_]

    Definition Classes
    AnyRef → Any
  14. def hashCode(): Int

    Definition Classes
    AnyRef → Any
  15. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any
  16. def meanCenter(): VectorD

    Center the data about the means (i.

    Center the data about the means (i.e., subtract the means) and return the mean vector (i.e., the mean for each varaibale/dimension).

  17. final def ne(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  18. final def notify(): Unit

    Definition Classes
    AnyRef
  19. final def notifyAll(): Unit

    Definition Classes
    AnyRef
  20. def recover(): MatrixD

    Approximately recover the orginal data by multiplying the reduced matrix by the inverse (via transpose) of the feature matrix and then adding back the means.

    Approximately recover the orginal data by multiplying the reduced matrix by the inverse (via transpose) of the feature matrix and then adding back the means.

    Definition Classes
    PrincipalComponentsReducer
  21. def reduce(): MatrixD

    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
    PrincipalComponentsReducer
  22. final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes
    AnyRef
  23. def toString(): String

    Definition Classes
    AnyRef → Any
  24. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  25. final def wait(arg0: Long, arg1: Int): Unit

    Definition Classes
    AnyRef
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    @throws( ... )
  26. final def wait(arg0: Long): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from Error

Inherited from Reducer

Inherited from AnyRef

Inherited from Any

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