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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>.
| Version: | 1.1.6 |
| Imports: | Rcpp (≥ 1.0.3), tensorflow (≥ 2.0.0) |
| LinkingTo: | Rcpp |
| Published: | 2024-01-21 |
| Author: | Werner Mueller |
| Maintainer: | Werner Mueller <werner.mueller5 at chello.at> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | yes |
| SystemRequirements: | TensorFlow (https://www.tensorflow.org) |
| CRAN checks: | ganDataModel results |
| Reference manual: | ganDataModel.pdf |
| Package source: | ganDataModel_1.1.6.tar.gz |
| Windows binaries: | r-devel: ganDataModel_1.1.6.zip, r-release: ganDataModel_1.1.6.zip, r-oldrel: ganDataModel_1.1.6.zip |
| macOS binaries: | r-release (arm64): ganDataModel_1.1.6.tgz, r-oldrel (arm64): ganDataModel_1.1.6.tgz, r-release (x86_64): ganDataModel_1.1.6.tgz, r-oldrel (x86_64): ganDataModel_1.1.6.tgz |
| Old sources: | ganDataModel archive |
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