Welcome to MAGE’s documentation¶
To install MAGE, clone the gitlab repository and run
cd /path/to/multiview_generator
pip install -e .
Read Me¶
MAGE : Multi-view Artificial Generation Engine¶
This package aims at generating customized mutli-view datasets to facilitate the development of new multi-view algorithms and their testing on simulated data representing specific tasks.
Getting started¶
This code has been originally developed on Ubuntu, but if the compatibility with Mac or Windows is mandatory for you, contact us so we adapt it.
Platform |
Last positive test |
|---|---|
Linux |
|
Mac |
Not verified yet |
Windows |
Not verified yet |
Prerequisites¶
To be able to use this project, you’ll need :
And the following python modules will be automatically installed :
matplotlib - Used to plot results,
sklearn - Used for the monoview classifiers,
h5py - Used to generate HDF5 datasets on hard drive and use them to spare RAM,
pandas - Used to manipulate data efficiently,
docutils - Used to generate documentation,
pyyaml - Used to read the config files,
plotly - Used to generate interactive HTML visuals,
tabulate - Used to generated the confusion matrix,
jupyter - Used for the tutorials
Installing¶
Once you cloned the project from the gitlab repository, you just have to use :
cd path/to/multiview_generator/
pip3 install -e .
In the multiview_generator directory to install MAGE and its dependencies.
Running the tests¶
To run the test suite of MAGE, run :
cd path/to/multiview_generator
pip install -e .[dev]
pytest
The coverage report is automatically generated and stored in the htmlcov/ directory
Building the documentation¶
To locally build the documentation run :
cd path/to/multiview_generator
pip install -e .[doc]
python setup.py build_sphinx
The locally built html files will be stored in path/to/multiview_generator/build/sphinx/html
Authors¶
Baptiste BAUVIN
Dominique BENIELLI
Sokol Koço