ganDataModel

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' .

Last 30 days

Last 365 days

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.