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individual-differences-multiview-representation-learning

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  • tffpy

    A Python package for time-frequency fading using Gabor multipliers based on the work in paper Time-frequency fading algorithms based on Gabor multipliers by A. Marina Krémé, Valentin Emiya, Caroline Chaux and Bruno Torré́sani, 2020.

    Install

    Install the current release with pip:

    pip install tffpy

    Download the data from this link.

    Then run function tffpy.utils.generate_config in order to create a configuration file and modify it to specify the path to your data folder. The location of the configuration file is given by function tffpy.utils.get_config_file.

    For additional details, see doc/install.rst.

    Usage

    See the documentation.

    Bugs

    Please report any bugs that you find through the tffpy GitLab project.

    You can also fork the repository and create a merge request.

    Source code

    The source code of tffpy is available via its GitLab project.

    You can clone the git repository of the project using the command:

    git clone git@gitlab.lis-lab.fr:skmad-suite/tff2020.git

    Copyright © 2020

    Contributors

    License

    Released under the GNU General Public License version 3 or later (see LICENSE.txt).