dmBuildMetricSubspaces

Read a data model and generative data from files,
analyze the contained neural network in the data model for a level,
determine metric subspaces with density values above a level,
add obtained metric subspaces to the data model
and write it to original file.

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>.

Werner Mueller

ganDataModel

Build a Metric Subspaces Data Model for a Data Source

dmBuildMetricSubspaces function

<dl><dt>dataModelFileName</dt>
<dd>Name of data model file</dd>
<dt>level</dt>
<dd>Level</dd>
<dt>generativeDataFileName</dt>
<dd>Name of generative data file</dd></dl>

Arguments

Build metric subspaces for a level — dmBuildMetricSubspaces

<dl>

<dt>dataModelFileName</dt>
<dd>Name of data model file</dd>


<dt>level</dt>
<dd>Level</dd>


<dt>generativeDataFileName</dt>
<dd>Name of generative data file</dd>

</dl>

dmBuildMetricSubspaces: Build metric subspaces for a level

Description

Usage

Value

Arguments

Examples