breeze.linalg

PCA

class PCA extends AnyRef

Perform Principal Components Analysis on input data. Handles scaling of the when computing the covariance matrix. Lazily produces the scores (the translation of the data to their new coordinates on the PC axes.

Input is a matrix that has data points as rows. Variable naming and documentation inspired and used directy from the 'princomp' function in R.

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Instance Constructors

  1. new PCA(x: DenseMatrix[Double], covmat: DenseMatrix[Double])

Value Members

  1. final def !=(arg0: Any): Boolean

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  2. final def ##(): Int

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  3. final def ==(arg0: Any): Boolean

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  4. final def asInstanceOf[T0]: T0

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  5. lazy val center: DenseVector[Double]

    The means of each column (axis) of the data.

  6. def clone(): AnyRef

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  7. val covmat: DenseMatrix[Double]

  8. lazy val cumuvar: DenseVector[Double]

    The cumulative proportion of variance explained by the first n principal components.

  9. lazy val eigenvalues: DenseVector[Double]

    Do SVD on the covariance matrix.

    Do SVD on the covariance matrix.

    eigenvalues: The vector of eigenvalues, from ranked from left to right with respect to how much of the variance is explained by the respective component.

    loadings: the matrix of variable loadings (i.e., a matrix whose rows contain the eigenvectors (note: in R, the eigenvectors are the columns)

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

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  11. def equals(arg0: Any): Boolean

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  12. def finalize(): Unit

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

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  15. final def isInstanceOf[T0]: Boolean

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  16. lazy val loadings: DenseMatrix[Double]

    Do SVD on the covariance matrix.

    Do SVD on the covariance matrix.

    eigenvalues: The vector of eigenvalues, from ranked from left to right with respect to how much of the variance is explained by the respective component.

    loadings: the matrix of variable loadings (i.e., a matrix whose rows contain the eigenvectors (note: in R, the eigenvectors are the columns)

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

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  18. lazy val nobs: Int

    The number of observations.

  19. final def notify(): Unit

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  20. final def notifyAll(): Unit

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  21. lazy val propvar: DenseVector[Double]

    The proportion of variance explained by each principal component.

  22. lazy val scores: DenseMatrix[Double]

    Translate the original data points to the PC axes.

  23. lazy val sdev: DenseVector[Double]

    The standard deviations of the principal components.

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

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  25. def toString(): String

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  26. final def wait(): Unit

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  28. final def wait(arg0: Long): Unit

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  29. val x: DenseMatrix[Double]

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