Packages

object LinearRegression extends Serializable

Linear Supertypes
Serializable, Serializable, AnyRef, Any
Ordering
  1. Alphabetic
  2. By Inheritance
Inherited
  1. LinearRegression
  2. Serializable
  3. Serializable
  4. AnyRef
  5. Any
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. All

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. def apply(AInv: DenseMatrix[Double], vb: Seq[DenseVector[Double]]): DenseMatrix[Double]

    Performs a multivariate linear regression on XA = B where the matrices A and B are given and X the matrix to solve for.

    Performs a multivariate linear regression on XA = B where the matrices A and B are given and X the matrix to solve for.

    AInv

    The (pseudo-) inverse of the matrix A

    vb

    The columns of matrix B

    returns

    Returns the matrix X that transforms A to B with minimal least squares errors.

  5. def apply(va: Seq[DenseVector[Double]], vb: Seq[DenseVector[Double]], fv: Seq[(DenseVector[Double]) ⇒ DenseVector[Double]], invThreshold: Double): DenseMatrix[Double]

    Performs a multivariate linear regression on XA = B where the matrices A and B are given and X the matrix to solve for.

    Performs a multivariate linear regression on XA = B where the matrices A and B are given and X the matrix to solve for.

    va

    The columns of matrix A

    vb

    The columns of matrix B

    returns

    Returns the matrix X that transforms A to B with minimal least squares errors.

  6. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  7. def buildMatrixFromColumns(va: Seq[DenseVector[Double]], fv: Seq[(DenseVector[Double]) ⇒ DenseVector[Double]] = vectorMappingLinearFit): DenseMatrix[Double]

    Builds the matrix A used in this linear regression implementation from its columns.

    Builds the matrix A used in this linear regression implementation from its columns. The columns can be horizontally extended by horizontally concatenating multiple value-mapped versions of them which facilitates the fitting of higher order functions to given data.

    The default behaviour of this function will generate a matrix of the following form:

    / | | ... | \ |va1 va2 ... va3| | | | ... | | \ 1 1 1 1 /

    from the matrix colums stored in va. This matrix can be used for fitting a multilinear function.

    va

    The columns

    fv

    A sequence of one or more functions that will be applied to the column vectors before concatenation.

    returns

    A matrix that can be used in the linear regression process.

  8. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  9. val defaultInvThreshold: Double

    Default threshold for inverting values in the process of computing the moore-penrose pseudo inverse.

  10. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  11. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  12. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  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. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  17. final def notify(): Unit
    Definition Classes
    AnyRef
  18. final def notifyAll(): Unit
    Definition Classes
    AnyRef
  19. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  20. def toString(): String
    Definition Classes
    AnyRef → Any
  21. val vectorMappingLinearFit: Seq[(DenseVector[Double]) ⇒ DenseVector[Double]]

    Default vector mapping for the A matrix used in linear fitting.

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

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped