jjb (version 0.1.1)

feature_scaling: Feature Scaling

Description

Scale features in a datasets.

Usage

feature_rescale(x, x_min = NULL, x_max = NULL)
feature_derescale(x_rescaled, x_min, x_max)
feature_norm(x, x_norm = NULL)
feature_denorm(x_norm_std, x_norm = NULL)
feature_standardize(x, x_mean = NULL, x_sd = NULL)
feature_destandardize(x_std, x_mean = NULL, x_sd = NULL)

Arguments

x

Numeric values

x_min

Minimum non-normalized numeric value

x_max

Maximum non-normalized numeric value

x_rescaled

Rescaled values of x.

x_norm

Euclidean norm of x

x_norm_std

Euclidean vector of normalized x values.

x_mean

Mean of x values

x_sd

Standard Deviation of x values

x_std

Z-transformed x values

Value

A numeric vector.

Feature Rescaling

Convert the original data \(x\) to \(x_{scaled}\):

$$x[scaled] = (x-x[min])/(x[max]-x[min])$$

To move from the rescaled value \(x_{scaled}\) to the original value \(x\) use:

$$x = x[scaled] * (x[max] - x[min]) + x[min]$$

Feature Standardization

Convert the original data \(x\) to \(x_{std}\):

$$x[std] = (x-avg[x])/(sigma[x])$$

To move from the standardized value \(x_{std}\) to the original value \(x\) use:

$$x = x[std] * sigma[x] + avg[x]$$

Feature Normalization

Convert the original data \(x\) to \(x_{norm}\):

$$x[norm] = (x)/||x||$$

To move from the normalized value \(x_{norm}\) to the original value \(x\) use:

$$x = x[norm] * ||x||$$

Details

The following functions provide a means to either scale features or to descale the features and return them to normal. These functions are ideal for working with optimizers.

Feature Scale	Feature Descale
feature_rescale	feature_derescale
feature_norm	feature_denorm

Examples

# NOT RUN {
# Rescaling Features
temperatures = c(94.2, 88.1, 32, 0)

temp_min = min(temperatures)
temp_max = max(temperatures)

temperatures_norm   = feature_rescale(temp_min, temp_max)
temperatures_denorm = feature_derescale(temperatures_norm, temp_min, temp_max)

all.equal(temperatures, temperatures_denorm)
 
# Norming Features
x = 1:10

x_norm = sqrt(sum(x^2))

x_norm_std = feature_norm(x, x_norm)

x_recover = feature_denorm(x_norm_std, x_norm)
all.equal(x, x_recover)

# Standardizing Features
x      = 1:10

x_mean = mean(x)
x_sd   = sd(x)

x_std  = feature_standardize(x, x_mean, x_sd)
x_recovery = feature_destandardize(x, x_mean, x_sd)

all.equal(x, x_recovery)
# }