normalizor: Feature Normalization

In EFAfactors: Determining the Number of Factors in Exploratory Factor Analysis

Feature Normalization

Description

This function normalizes a matrix of features using precomputed means and standard deviations. The function automatically runs load_scaler to read the standard deviations and means of the features, which are organized into a list object named scaler. These means and standard deviations are computed from the 10,000,000 datasets data.datasets for training the Pre-Trained Deep Neural Network (DNN).

Usage

normalizor(features)

Arguments

features

A numeric matrix where each row represents an observation and each column represents a feature.

Details

The function applies z-score normalization to each element in the features matrix. It uses the scaler object, which is expected to contain precomputed means and standard deviations for each feature. The normalized value for each element is computed as:

z = \frac{x - \mu}{\sigma}

where x is the original value, \mu is the mean, and \sigma is the standard deviation.

Value

A matrix of the same dimensions as features, where each feature has been normalized.

See Also

DNN_predictor, load_scaler, data.datasets, data.scaler

EFAfactors documentation built on Sept. 30, 2024, 1:06 a.m.