https://github.com/foxriver76/thesis
https://github.com/foxriver76/thesis
Last synced: 8 months ago
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- Host: GitHub
- URL: https://github.com/foxriver76/thesis
- Owner: foxriver76
- License: mit
- Created: 2022-01-31T10:02:43.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2023-02-14T11:04:00.000Z (over 3 years ago)
- Last Synced: 2025-01-04T17:45:54.278Z (over 1 year ago)
- Language: Python
- Size: 32.5 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Learning high-dimensional data and processing in non-stationary environments
This repository contains the datasets, as well as the algorithms which has
been published and used in my PhD thesis.
## Structure
The following folder structure is used in this repository:
- prototype_lvq: Contains experiments and algorithms of Chapter 3
- random_projection: Contains experiments and algorithms of Chapter 4
- coresets: Contains experiments and algorithms of Chapter 5
- datasets: Contains all used datasets except from stream generators
Every folder has its own README.md file, which explains the content and strcuture.
Furthermore, usage examples are provided.
## Requirements
For execution of the experiments you need to have a running Python3.6 installation or higher.
Furthermore, ensure that the following packages are installed:
- scikit-multiflow
- sklearn
This can be simply done by executing `pip install .` in this directory, which will
execute the install script of this thesis.
## Execution
All files which are not represent a model implementation can be executed to generate resulsts
of the thesis. To do so, execute them via
```bash
python
```
Execution works from the root folder as well as from the chapters folder.
## LICENSE
The source code is licensed under the MIT license.