StandardScaler-class: class StandardScaler
In stschn/deepANN: Neural Network Toolbox
StandardScaler R Documentation
class StandardScaler
Description
A class for transforming features by removing the mean and scaling to unit standard deviation.
Usage
zscore(x, .mean, .sd)
inverse_zscore(x, .mean, .sd)
Super class
deepANN::Scaler -> StandardScaler
Public fields
mean('NULL' | numeric vector)
The mean value for each feature.
std('NULL' | numeric vector)
The standard deviation for each feature.
with_mean('NULL' | logical)
If true, center the data before scaling.
with_std('NULL' | logical)
If true, scale the data to unit standard deviation.
Methods
Public methods
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Method new()
Creates a new instance of this R6 class.
Usage
StandardScaler$new(with_mean = TRUE, with_std = TRUE)
Arguments
with_meanIf true, center the data before scaling.
with_stdif true, scale the data to unit standard deviation
Method fit()
Compute the mean and std to be used for later scaling.
Usage
StandardScaler$fit(X, axis = 1)
Arguments
XThe data used to compute the per-feature mean and std used for later scaling along the features axis.
axisThe axis along computation is applied.
Returns
The instance self itself.
Method transform()
Perform standardization by centering and scaling.
Usage
StandardScaler$transform(X, axis = 1, order = c("C", "F"))
Arguments
XThe data that will be transformed.
axisThe axis along scaling is applied.
orderThe order in which elements of data should be read during scaling. By default, the order is equivalent to the C-style ordering and means elements should be read in row-major order. In opposite, the Fortran-style ordering means elements should be read in column-major order.
Returns
The transformed X.
Method fit_transform()
Run fit() and transform().
Usage
StandardScaler$fit_transform(X, axis = 1, order = c("C", "F"))
Arguments
XThe data that will be transformed.
axisThe axis along scaling is applied.
orderThe order in which elements of data should be read during scaling. By default, the order is equivalent to the C-style ordering and means elements should be read in row-major order. In opposite, the Fortran-style ordering means elements should be read in column-major order.
Returns
The transformed X.
Method inverse_transform()
Scale back X to the original representation.
Usage
StandardScaler$inverse_transform(X, axis = 1, order = c("C", "F"))
Arguments
XThe data that will be transformed.
axisThe axis along scaling is applied.
orderThe order in which elements of data should be read during scaling. By default, the order is equivalent to the C-style ordering and means elements should be read in row-major order. In opposite, the Fortran-style ordering means elements should be read in column-major order.
Returns
The transformed X.
Method clone()
The objects of this class are cloneable with this method.
Usage
StandardScaler$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
Examples
a <- random_int(dim = c(6, 4), min = 1, max = 30)
scaler <- StandardScaler$new()
xt <- scaler$fit_transform(a)
scaler$inverse_transform(xt)
a <- random_int(dim = c(4, 3, 2), min = 1, max = 30)
scaler <- StandardScaler$new()
xt <- scaler$fit_transform(a, axis = c(1, 2))
scaler$inverse_transform(xt, axis = c(1, 2))
stschn/deepANN documentation built on June 25, 2024, 7:27 a.m.
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| StandardScaler | R Documentation |
class StandardScaler
Description
A class for transforming features by removing the mean and scaling to unit standard deviation.
Usage
zscore(x, .mean, .sd)
inverse_zscore(x, .mean, .sd)
Super class
deepANN::Scaler -> StandardScaler
Public fields
mean('NULL' | numeric vector)
The mean value for each feature.std('NULL' | numeric vector)
The standard deviation for each feature.with_mean('NULL' | logical)
If true, center the data before scaling.with_std('NULL' | logical)
If true, scale the data to unit standard deviation.
Methods
Public methods
Method new()
Creates a new instance of this R6 class.
Usage
StandardScaler$new(with_mean = TRUE, with_std = TRUE)
Arguments
with_meanIf true, center the data before scaling.
with_stdif true, scale the data to unit standard deviation
Method fit()
Compute the mean and std to be used for later scaling.
Usage
StandardScaler$fit(X, axis = 1)
Arguments
XThe data used to compute the per-feature mean and std used for later scaling along the features axis.
axisThe axis along computation is applied.
Returns
The instance self itself.
Method transform()
Perform standardization by centering and scaling.
Usage
StandardScaler$transform(X, axis = 1, order = c("C", "F"))Arguments
XThe data that will be transformed.
axisThe axis along scaling is applied.
orderThe order in which elements of data should be read during scaling. By default, the order is equivalent to the C-style ordering and means elements should be read in row-major order. In opposite, the Fortran-style ordering means elements should be read in column-major order.
Returns
The transformed X.
Method fit_transform()
Run fit() and transform().
Usage
StandardScaler$fit_transform(X, axis = 1, order = c("C", "F"))Arguments
XThe data that will be transformed.
axisThe axis along scaling is applied.
orderThe order in which elements of data should be read during scaling. By default, the order is equivalent to the C-style ordering and means elements should be read in row-major order. In opposite, the Fortran-style ordering means elements should be read in column-major order.
Returns
The transformed X.
Method inverse_transform()
Scale back X to the original representation.
Usage
StandardScaler$inverse_transform(X, axis = 1, order = c("C", "F"))Arguments
XThe data that will be transformed.
axisThe axis along scaling is applied.
orderThe order in which elements of data should be read during scaling. By default, the order is equivalent to the C-style ordering and means elements should be read in row-major order. In opposite, the Fortran-style ordering means elements should be read in column-major order.
Returns
The transformed X.
Method clone()
The objects of this class are cloneable with this method.
Usage
StandardScaler$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
Examples
a <- random_int(dim = c(6, 4), min = 1, max = 30)
scaler <- StandardScaler$new()
xt <- scaler$fit_transform(a)
scaler$inverse_transform(xt)
a <- random_int(dim = c(4, 3, 2), min = 1, max = 30)
scaler <- StandardScaler$new()
xt <- scaler$fit_transform(a, axis = c(1, 2))
scaler$inverse_transform(xt, axis = c(1, 2))
R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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