ganDataModel
Build a Metric Subspaces Data Model for a Data Source
Neural networks are applied to create a density value function which approximates density values for a data source. The trained neural network is analyzed for different levels. For each level metric subspaces with density values above a level are determined. The obtained set of metric subspaces and the trained neural network are assembled into a data model. A prerequisite is the definition of a data source, the generation of generative data and the calculation of density values. These tasks are executed using package 'ganGenerativeData' <https://cran.r-project.org/package=ganGenerativeData>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
2.0.1 |
rolling source/ R- | ganDataModel_2.0.1.tar.gz |
505.3 KiB |
2.0.1 |
rolling linux/jammy R-4.5 | ganDataModel_2.0.1.tar.gz |
714.0 KiB |
2.0.1 |
latest source/ R- | ganDataModel_2.0.1.tar.gz |
505.3 KiB |
2.0.1 |
latest linux/jammy R-4.5 | ganDataModel_2.0.1.tar.gz |
714.0 KiB |
2.0.1 |
2026-04-23 source/ R- | ganDataModel_2.0.1.tar.gz |
505.3 KiB |
2.0.1 |
2026-04-09 windows/windows R-4.5 | ganDataModel_2.0.1.zip |
1.0 MiB |
1.1.7 |
2025-04-20 source/ R- | ganDataModel_1.1.7.tar.gz |
503.4 KiB |
Dependencies (latest)
Imports
- Rcpp (>= 1.0.3)
- tensorflow (>= 2.0.0)