Science Score: 36.0%
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○CITATION.cff file
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✓codemeta.json file
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✓.zenodo.json file
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○DOI references
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○Academic publication links
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✓Committers with academic emails
1 of 3 committers (33.3%) from academic institutions -
○Institutional organization owner
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○Scientific vocabulary similarity
Low similarity (17.1%) to scientific vocabulary
Repository
Basic Info
- Host: GitHub
- Owner: thomas-bottesch
- License: mit
- Language: C
- Default Branch: master
- Size: 631 KB
Statistics
- Stars: 1
- Watchers: 0
- Forks: 2
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
fcl - machine learning library
fcl is a machine learning library which is open source and commercially usable - MIT license (see LICENSE file). The machine learning core is programmed in C (C99) but fcl can be used from various languages:
- Matlab / Octave
- Python 2.x & 3.x (numpy and scipy matrices are supported)
- Command line interface
fcl was started as part of a phd thesis supported by:
Get the code
You can get the latest sources with the command:
git clone https://github.com/thomas-bottesch/fcl.git
Linux
On Ubuntu/Debian it might be necessary to install build essentials in order to compile fcl
sudo apt-get install build-essential
Compile the library
make
Use the library
# download the example datasets (in libsvm format)
./examples/datasets/get_datasets.sh
# now you can use the library to e.g. cluster an example dataset with k-means
./fcl kmeans fit ./examples/datasets/usps.scaled --file_model ./result_clusters --no_clusters 10
Have a look at the available options
./fcl --help
./fcl kmeans --help
./fcl kmeans fit --help
./fcl kmeans predict --help
Python 2/3
On Ubuntu/Debian install build essentials and the python dev package in order to create python bindings with cython
sudo apt-get install build-essential (also python2.7-dev / python3.4-dev or whatever python version you use)
Install via pip:
pip install fcl
Install packages required to run all the examples e.g. numpy, scipy and matplotlib (optional)
pip install -r python/requirements_examples.txt
Use the library (this example needs ./examples/datasets/get_datasets.sh to be executed first!)
from fcl import kmeans
km = kmeans.KMeans(no_clusters=2, verbose = True)
idx = km.fit_predict('./examples/datasets/sector.scaled')
# You can use a numpy/scipy matrix instead of a path as input to fit_predict
There exist various python examples in
examples/python/
Matlab/Octave
Easiest way to use fcl inside matlab/octave is to just compile the algorithm that you need. E.g.
execute matlab/kmeans/fcl_make_kmeans (from within Matlab/Octave)
Then this example can be run from any folder
% create sparse matrix
X = sprand(1000, 1000, 1/10);
IDX = fcl_kmeans(X, 10);
There exist various matlab/octave examples in
examples/matlab/
Citations
When using fcl in a scientific publication, it is appreciated if you cite the following paper (Bibtex):
@inproceedings{bottesch2016kmeans,
title={Speeding up k-means by approximating Euclidean distances via block vectors},
author={Bottesch, Thomas and B{\"u}hler, Thomas and K{\"a}chele, Markus},
booktitle={Proceedings of The 33rd International Conference on Machine Learning},
pages={2578--2586},
year={2016}
}
Owner
- Login: thomas-bottesch
- Kind: user
- Repositories: 2
- Profile: https://github.com/thomas-bottesch
GitHub Events
Total
- Watch event: 1
- Push event: 6
- Fork event: 1
- Create event: 2
Last Year
- Watch event: 1
- Push event: 6
- Fork event: 1
- Create event: 2
Committers
Last synced: over 2 years ago
Top Committers
| Name | Commits | |
|---|---|---|
| Thomas Bottesch | t****b@g****m | 48 |
| Thomas Bottesch | t****h@u****e | 29 |
| thomas-bottesch | t****h | 3 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 11 months ago
All Time
- Total issues: 1
- Total pull requests: 20
- Average time to close issues: about 2 months
- Average time to close pull requests: about 8 hours
- Total issue authors: 1
- Total pull request authors: 1
- Average comments per issue: 2.0
- Average comments per pull request: 0.65
- Merged pull requests: 11
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- xternalz (1)
Pull Request Authors
- thomas-bottesch (20)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
-
Total downloads:
- pypi 192 last-month
- Total dependent packages: 0
- Total dependent repositories: 1
- Total versions: 14
- Total maintainers: 1
pypi.org: fcl
fcl machine learning library
- Homepage: https://github.com/thomas-bottesch/fcl
- Documentation: https://fcl.readthedocs.io/
- License: MIT
-
Latest release: 0.0.15
published 11 months ago
Rankings
Maintainers (1)
Dependencies
- matplotlib >=1.5.0
- numpy >=1.10.4
- scikit-learn >=0.18.1
- scipy >=0.16.1
- wheel *
- actions/checkout v3 composite
- actions/setup-python v4 composite