Packages

class SVDReg extends Error

The SVDReg class works on the principle of Gradient Descent for minimizing the error generated and L2 regularization, while predicting the missing value in the matrix. This is obtained by the dot product of 'u(i)' and 'v(j)' vectors: Dimensionality is reduced from 'n' features to 'k' factors.

predict (i, j) = u(i) dot v(j)

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Instance Constructors

  1. new SVDReg(a: MatrixD, k: Int)

    a

    the input m-by-n data matrix (m instances, n features/variables)

    k

    the number of factors (k <= n)

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
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  3. final def ==(arg0: Any): Boolean
    Definition Classes
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  4. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  5. def calc_objf: Double

    Calculate the value of the objective function after the 'u' and 'v' matrices are generated.

  6. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
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    Annotations
    @native() @throws( ... )
  7. final def eq(arg0: AnyRef): Boolean
    Definition Classes
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  8. def equals(arg0: Any): Boolean
    Definition Classes
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  9. def factor: Unit

    Factor the the input matrix 'a' to obtain the 'u' and the 'v' matrices.

  10. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  11. final 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
  12. final def getClass(): Class[_]
    Definition Classes
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    Annotations
    @native()
  13. def hashCode(): Int
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    @native()
  14. final def isInstanceOf[T0]: Boolean
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  15. final def ne(arg0: AnyRef): Boolean
    Definition Classes
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  16. final def notify(): Unit
    Definition Classes
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    @native()
  17. final def notifyAll(): Unit
    Definition Classes
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    @native()
  18. def nz_sqmean: Double

    Return the square root of the non-zero mean / k of the initial rating matrix.

  19. def predict(i: Int, j: Int): Double

    Predict the value for given row and column.

    Predict the value for given row and column.

    i

    the row id

    j

    the column id

  20. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  21. def toString(): String
    Definition Classes
    AnyRef → Any
  22. def update(h: Int): Double

    Update the 'u' and 'v' matrix to minimze sum of squared error and return the mean sum of squared errors.

    Update the 'u' and 'v' matrix to minimze sum of squared error and return the mean sum of squared errors.

    h

    the current column to update

  23. final def wait(): Unit
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    @throws( ... )
  24. final def wait(arg0: Long, arg1: Int): Unit
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    @throws( ... )
  25. final def wait(arg0: Long): Unit
    Definition Classes
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    @native() @throws( ... )

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