tinybig.expansion
This module provides the "data expansion functions" that can be used to build the RPN model within the tinyBIG toolkit.
Data Expansion Function
Formally, given the underlying data distribution mapping \(f: {R}^m \to {R}^n\) to be learned, the data expansion function \(\kappa\) projects input data into a new space shown as follows:
\[ \kappa: {R}^m \to {R}^{D}, \]
where the target dimension vector space dimension \(D\) is determined when defining \(\kappa\).
In practice, the function \(\kappa\) can either expand or compress the input to a higher- or lower-dimensional space. The corresponding function, \(\kappa\), can also be referred to as the data expansion function (if \(D > m\)) and data compression function (if \(D < m\)), respectively. Collectively, these can be unified under the term "data transformation functions".
Classes in this Module
This module contains the following categories of expansion functions:
- Basic expansion functions
- Polynomial expansion functions
- Trigonometric expansion functions
- RBF expansion functions
- Naive probabilistic expansion functions
- Combinatorial (probabilistic) expansion functions
- Nested expansion function
- Extended expansion function
Organization of this Module
Data Expansion/Transformation
- transformation (defined in the tinybig.module directory)
Basic Expansions
Polynomial Expansions
Trigonometric Expansions
RBF Expansions
Naive Probabilistic Expansions
- naive_normal_expansion
- naive_cauchy_expansion
- naive_chi2_expansion
- naive_gamma_expansion
- naive_exponential_expansion
- naive_laplace_expansion