The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
The HVT package is a collection of R functions to facilitate building
topology
preserving maps for rich multivariate data analysis, see
Figure 1
as an example of a 3D torus map generated from the
package. Tending towards a big data preponderance, a large number of
rows. A collection of R functions for this typical workflow is organized
below:
Data Compression: Vector quantization (VQ), HVQ (hierarchical vector quantization) using means or medians. This step compresses the rows (long data frame) using a compression objective.
Data Projection: Dimension projection of the compressed cells to 1D,2D or 3D with the Sammons Non-linear Algorithm. This step creates topology preserving map (also called an embedding) coordinates into the desired output dimension.
Tessellation: Create cells required for object visualization using the Voronoi Tessellation method, package includes heatmap plots for hierarchical Voronoi tessellations (HVT). This step enables data insights, visualization, and interaction with the topology preserving map useful for semi-supervised tasks.
Prediction: Scoring new data sets and recording their assignment using the map objects from the above steps, in a sequence of maps if required.
The HVT package allows creation of visually stunning tessellations, showcasing the power of topology preserving maps. below is an image depicting a captivating tessellation of a torus-
Figure 1: Heatmap Visualization of a Torus with 900 Cells
10th October, 2023
In this version of HVT package, the following new features have been introduced:
This package provides functionality to predict cells with layers
based on a sequence of maps using predictLayerHVT
.
06th December, 2022
This package provides functionality to predict based on a set of maps to monitor entities over time.
The creation of a predictive set of maps involves three steps -
Let us try to understand the steps with the help of the diagram below-
Figure 2: Flow diagram for predicting based on a sequence of maps using predictLayerHVT()
Following are the links to the vignettes for the HVT package:
HVT Vignette: Contains descriptions of the functions used for vector quantization and construction of hierarchical voronoi tessellations for data analysis.
HVT Model Diagnostics Vignette: Contains descriptions of functions used to perform model diagnostics and validation for HVT model.
HVT : Predicting Cells with Layers using predictLayerHVT: Contains descriptions of the functions used for predicting cells with layers based on a sequence of maps using predictLayerHVT.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.