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scalation.analytics

MatrixTransform

object MatrixTransform

The MatrixTransform object is used to transform the columns of a data matrix 'x'. May also be used for a response vector 'y'. Such pre-processing of the data is required by some modeling techniques.

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  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. def center(x: MatriD, mu_x: VectoD): MatriD

    Center matrix 'x' to zero mean, column-wise, by subtracting the mean.

    Center matrix 'x' to zero mean, column-wise, by subtracting the mean.

    x

    the matrix to center

    mu_x

    the vector of column means of matrix x

  6. def clone(): AnyRef
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  7. def denormalize(x_n: MatriD, mu_sig: PairV): MatriD

    Denormalize the matrix 'x_n' from zero mean and unit standard deviation, column-wise, by multiplying by the standard deviation and adding the mean.

    Denormalize the matrix 'x_n' from zero mean and unit standard deviation, column-wise, by multiplying by the standard deviation and adding the mean.

    x_n

    the matrix to denormalize

  8. def denormalizeV(mu_sig: PairD)(x_n: VectoD): VectoD

    Denormalize the vector 'x_n' from zero mean and unit standard deviation, by multiplying by the standard deviation and adding the mean.

    Denormalize the vector 'x_n' from zero mean and unit standard deviation, by multiplying by the standard deviation and adding the mean.

    mu_sig

    the column vector's mean and standard deviation

    x_n

    the vector to denormalize

  9. final def eq(arg0: AnyRef): Boolean
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  10. def equals(arg0: Any): Boolean
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  11. def extreme(x: MatriD): PairV

    Return the extreme values (min, max) for matrix 'x', for each column.

    Return the extreme values (min, max) for matrix 'x', for each column.

    x

    the matrix whose extreme values are sought

  12. def extreme(x: VectoD): PairD

    Return the extreme values (min, max) for vector 'x'.

    Return the extreme values (min, max) for vector 'x'.

    x

    the vector whose extreme values are sought

  13. final def getClass(): Class[_]
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  14. def golden(x: VectoD): VectoD

    Transform the vector by taking the 'G_SECTION' power (weaker than a square root).

    Transform the vector by taking the 'G_SECTION' power (weaker than a square root).

    x

    the vector to make golden

  15. def hashCode(): Int
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  16. final def isInstanceOf[T0]: Boolean
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  17. def max(x: MatriD): VectorD

    Return the maximum value for each column in the matrix.

    Return the maximum value for each column in the matrix.

    x

    the given matrix

  18. def mean_stddev(x: VectoD): PairD

    Return the mean and standard deviation stats for vector 'x'

    Return the mean and standard deviation stats for vector 'x'

    x

    the vector whose stats are sought

  19. def min(x: MatriD): VectorD

    Return the minimum value for each column in the matrix.

    Return the minimum value for each column in the matrix.

    x

    the given matrix

  20. final def ne(arg0: AnyRef): Boolean
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  21. def normalize(x: MatriD, mu_sig: PairV): MatriD

    Normalize the matrix 'x' to zero mean and unit standard deviation, column-wise, by subtracting the mean and dividing by the standard deviation.

    Normalize the matrix 'x' to zero mean and unit standard deviation, column-wise, by subtracting the mean and dividing by the standard deviation.

    x

    the matrix to normalize

  22. def normalizeV(mu_sig: PairD)(x: VectoD): VectoD

    Normalize the vector 'x' to zero mean and unit standard deviation, by subtracting the mean and dividing by the standard deviation.

    Normalize the vector 'x' to zero mean and unit standard deviation, by subtracting the mean and dividing by the standard deviation.

    mu_sig

    the column vector's mean and standard deviation

    x

    the vector to normalize

  23. final def notify(): Unit
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  24. final def notifyAll(): Unit
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  25. def scale(x: MatriD, extremes: PairV, bounds: PairD): MatriD

    Scale matrix 'x' to the range 'lb to 'ub', column-wise: 'x -> x_s'.

    Scale matrix 'x' to the range 'lb to 'ub', column-wise: 'x -> x_s'.

    x

    the matrix to scale

    bounds

    the desired (lower, upper) bounds

  26. def scaleV(extremes: PairD, bounds: PairD)(x: VectoD): VectoD

    Scale vector 'x' to the range 'lb' to 'ub': 'x -> x_s'.

    Scale vector 'x' to the range 'lb' to 'ub': 'x -> x_s'.

    extremes

    the (minimum value, maximum value) in vector x

    bounds

    the desired (lower, upper) bounds

    x

    the vector to scale

  27. def setCol2One(x: MatriD, j: Int = 0): Unit

    Set column 'j' to all ones, e.g., for an intercept column.

    Set column 'j' to all ones, e.g., for an intercept column.

    x

    the given matrix

  28. def stddev(x: MatriD): VectorD

    Return the standard deviation for each column in the matrix.

    Return the standard deviation for each column in the matrix.

    x

    the given matrix

  29. def stddev(x: VectoD): Double

    Return the standard deviation for the given vector.

    Return the standard deviation for the given vector.

    x

    the given vector

  30. final def synchronized[T0](arg0: ⇒ T0): T0
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  31. def toString(): String
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  32. def uncenter(x_c: MatriD, mu_x: VectoD): MatriD

    Uncenter matrix 'x_c' from zero mean, column-wise, by adding the mean.

    Uncenter matrix 'x_c' from zero mean, column-wise, by adding the mean.

    x_c

    the matrix to uncenter

    mu_x

    the vector of column means of matrix x_c

  33. def unscale(x_s: MatriD, extremes: PairV, bounds: PairD): MatriD

    Unscale matrix 'x_s' from the range 'lb' to 'ub', column-wise: 'x_s -> x'.

    Unscale matrix 'x_s' from the range 'lb' to 'ub', column-wise: 'x_s -> x'.

    x_s

    the matrix to unscale

    bounds

    the scaled (lower, upper) bounds

  34. def unscaleV(extremes: PairD, bounds: PairD)(x_s: VectoD): VectoD

    Unscale vector 'x_s' from the range 'lb' to 'ub' to original range: 'x_s -> x'.

    Unscale vector 'x_s' from the range 'lb' to 'ub' to original range: 'x_s -> x'.

    extremes

    the (minimum value, maximum value) in original vector x

    bounds

    the scaled (lower, upper) bounds

    x_s

    the vector to unscale

  35. final def wait(arg0: Long, arg1: Int): Unit
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  36. final def wait(arg0: Long): Unit
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  37. final def wait(): Unit
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