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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- 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
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- 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
- 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
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- 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
- 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
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- 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
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- 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
- 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
- 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
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- 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
- 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
- final def notify(): Unit
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- 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
- 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
- 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
- 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
- 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
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- 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
- 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
- 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
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