object MatrixTransform
The MatrixTransform object is used to transform the columns of a data matrix 'x'.
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_x: VectoD, sig_x: VectoD): 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 normalize
- mu_x
the vector of column means of matrix x_n
- sig_x
the vector of column standard deviations of matrix x_n
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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
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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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ne(arg0: AnyRef): Boolean
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def
normalize(x: MatriD, mu_x: VectoD, sig_x: VectoD): MatriD
Normalize the matrix 'x' to zero mean and unit standard deviation, column-wise, by subtracting the mean and dividing by standard deviation
Normalize the matrix 'x' to zero mean and unit standard deviation, column-wise, by subtracting the mean and dividing by standard deviation
- x
the matrix to normalize
- mu_x
the vector of column means of matrix x
- sig_x
the vector of column standard deviations of matrix x
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def
scale(x: MatriD, min_x: VectoD, max_x: VectoD, bounds: (Double, Double)): 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
- min_x
the vector of column minima of matrix x
- max_x
the vector of column maxima of matrix x
- bounds
the desired (lower, upper) bounds
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def
scaleV(x: VectoD, min_x: Double, max_x: Double, bounds: (Double, Double)): 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'.
- x
the vector to scale
- min_x
the minimum value in vector x
- max_x
the maximum value in vector x
- bounds
the desired (lower, upper) bounds
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def
setCol2One(x: MatriD, j: Int = 0): Unit
Set column 'j' to all ones.
Set column 'j' to all ones.
- x
the given matrix
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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
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def
unscale(x_s: MatriD, min_x: VectoD, max_x: VectoD, bounds: (Double, Double)): 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
- min_x
the vector of column minima of original matrix x
- max_x
the vector of column maxima of original matrix x
- bounds
the scaled (lower, upper) bounds
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def
unscaleV(x_s: VectoD, min_x: Double, max_x: Double, bounds: (Double, Double)): 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'.
- x_s
the vector to unscale
- min_x
the minimum value in original vector x
- max_x
the maximum value in original vector x
- bounds
the scaled (lower, upper) bounds
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