View source: R/preprocess_scale.R
preprocess_scale | R Documentation |
A utility to perform feature scaling on datasets using one of sixtechniques. Both scaling and inverse scaling are supported, andscalers can be saved and then applied to other datasets.
preprocess_scale( input, epsilon = NA, input_model = NA, inverse_scaling = FALSE, max_value = NA, min_value = NA, scaler_method = NA, seed = NA, verbose = FALSE )
input |
Matrix containing data (numeric matrix). |
epsilon |
regularization Parameter for pcawhitening, or zcawhitening, should be between -1 to 1. Default value "1e-06" (numeric). |
input_model |
Input Scaling model (ScalingModel). |
inverse_scaling |
Inverse Scaling to get original datase. Default value "FALSE" (logical). |
max_value |
Ending value of range for min_max_scaler. Default value "1" (integer). |
min_value |
Starting value of range for min_max_scaler. Default value "0" (integer). |
scaler_method |
method to use for scaling, the default is standard_scaler. Default value "standard_scaler" (character). |
seed |
Random seed (0 for std::time(NULL)). Default value "0" (integer). |
verbose |
Display informational messages and the full list of parameters and timers at the end of execution. Default value "FALSE" (logical). |
This utility takes a dataset and performs feature scaling using one of the six scaler methods namely: 'max_abs_scaler', 'mean_normalization', 'min_max_scaler' ,'standard_scaler', 'pca_whitening' and 'zca_whitening'. The function takes a matrix as "input" and a scaling method type which you can specify using "scaler_method" parameter; the default is standard scaler, and outputs a matrix with scaled feature.
The output scaled feature matrix may be saved with the "output" output parameters.
The model to scale features can be saved using "output_model" and later can be loaded back using"input_model".
A list with several components:
output |
Matrix to save scaled data to (numeric matrix). |
output_model |
Output scaling model (ScalingModel). |
mlpack developers
# So, a simple example where we want to scale the dataset "X" into "X_scaled" # with standard_scaler as scaler_method, we could run ## Not run: output <- preprocess_scale(input=X, scaler_method="standard_scaler") X_scaled <- output$output ## End(Not run) # A simple example where we want to whiten the dataset "X" into "X_whitened" # with PCA as whitening_method and use 0.01 as regularization parameter, we # could run ## Not run: output <- preprocess_scale(input=X, scaler_method="pca_whitening", epsilon=0.01) X_scaled <- output$output ## End(Not run) # You can also retransform the scaled dataset back using"inverse_scaling". An # example to rescale : "X_scaled" into "X"using the saved model "input_model" # is: ## Not run: output <- preprocess_scale(input=X_scaled, inverse_scaling=TRUE, input_model=saved) X <- output$output ## End(Not run) # Another simple example where we want to scale the dataset "X" into # "X_scaled" with min_max_scaler as scaler method, where scaling range is 1 # to 3 instead of default 0 to 1. We could run ## Not run: output <- preprocess_scale(input=X, scaler_method="min_max_scaler", min_value=1, max_value=3) X_scaled <- output$output ## End(Not run)
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