Definition adaptive_moment_estimation.h:14
void unscale_logarithmic(MatrixMap, Index)
Inverse of scale_logarithmic() for the given column.
void scale_standard_deviation(MatrixMap, Index, const Descriptives &)
Divides a column of the matrix by its standard deviation in place.
void unscale_minimum_maximum(MatrixMap, Index, const Descriptives &, float=-1.0f, float=1.0f)
Inverse of scale_minimum_maximum(): reconstructs original values for the given column.
void unscale_mean_standard_deviation(MatrixMap, Index, const Descriptives &)
Inverse of scale_mean_standard_deviation() for the given column.
void scale_mean_standard_deviation(MatrixMap, Index, const Descriptives &)
Standardises a column of the matrix in place using its descriptives' mean and standard deviation.
void scale_logarithmic(MatrixMap, Index)
Applies an element-wise logarithm to the given column.
void unscale_image_minimum_maximum(MatrixMap, Index)
Maps a column back from [-1, 1] to the [0, 255] image-pixel range.
void unscale_standard_deviation(MatrixMap, Index, const Descriptives &)
Inverse of scale_standard_deviation() for the given column.
void scale_minimum_maximum(MatrixMap, Index, const Descriptives &, float=-1.0f, float=1.0f)
Rescales a column to the [min_range, max_range] interval using its descriptives.
Map< MatrixR, Layout|AlignedMax > MatrixMap
Definition pch.h:186
Summary statistics (minimum, maximum, mean, standard deviation) for one variable.
Definition statistics.h:18