| SequenceGraphTransform {Microsoft.VisualBasic.Data.GraphTheory} | .NET clr documentation |
Sequence Graph Transform (SGT) — Sequence Embedding for Clustering, Classification, and Search Sequence Graph Transform (SGT) is a sequence embedding function. SGT extracts the short- and long-term sequence features and embeds them in a finite-dimensional feature space. The long and short term patterns embedded in SGT can be tuned without any increase in the computation. >https://github.com/cran2367/sgt/blob/25bf28097788fbbf9727abad91ec6e59873947cc/python/sgt-package/sgt/sgt.py Compute embedding of a single or a collection of discrete item sequences. A discrete item sequence is a sequence made from a set discrete elements, also known as alphabet set. For example, suppose the alphabet set is the set of roman letters, {A, B, ..., Z}. This set is made of discrete elements. Examples of sequences from such a set are AABADDSA, UADSFJPFFFOIHOUGD, etc. Such sequence datasets are commonly found in online industry, for example, item purchase history, where the alphabet set is the set of all product items. Sequence datasets are abundant in bioinformatics as protein sequences. Using the embeddings created here, classification and clustering models can be built for sequence datasets. Read more inhttps://arxiv.org/pdf/1608.03533.pdf
# namespace Microsoft.VisualBasic.Data.GraphTheory
export class SequenceGraphTransform {
alphabets: string;
# the feature name is the combination of SequenceGraphTransform.alphabets
feature_names: string;
}