Metadata-Version: 2.1
Name: deeptl
Version: 0.0.0
Summary: Deep Topological Learning: a deep learning library for complex topological tasks.
Home-page: https://github.com/pietrobarbiero/deep-topological-learning
Maintainer: P. Barbiero
Maintainer-email: barbiero@tutanota.com
License: Apache 2.0
Download-URL: https://github.com/pietrobarbiero/deep-topological-learning.git
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved
Classifier: Programming Language :: Python
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/x-rst
Requires-Dist: numpy
Requires-Dist: scikit-learn
Requires-Dist: tensorflow
Requires-Dist: tqdm
Provides-Extra: docs
Requires-Dist: sphinx ; extra == 'docs'
Requires-Dist: sphinx-gallery ; extra == 'docs'
Requires-Dist: sphinx-rtd-theme ; extra == 'docs'
Requires-Dist: numpydoc ; extra == 'docs'
Requires-Dist: matplotlib ; extra == 'docs'
Provides-Extra: tests
Requires-Dist: pytest ; extra == 'tests'
Requires-Dist: pytest-cov ; extra == 'tests'

Deep Topological Learning (DTL)
======================================================

DTL is a Python package providing an easy-to-use software
for learning complex topologies with neural networks.

DTL networks are based on a novel theory (`the duality theory`)
bridging
two research fields which are usually thought as disjointed:
supervised deep learning and unsupervised competitive
neighborhood-based learning.


Using DTL
-----------

.. code:: python

    from deeptl import DeepTopologicalClustering

    X, y = load_iris(return_X_y=True)

    # load and fit the neural model
    model = DeepTopologicalClustering()
    model.fit(X, N=40, num_epochs=400, lr=0.0008)

    # compute the final graphs
    model.compute_sample_graph()
    model.compute_graph()

    # plot the results
    model.plot_adjacency_matrix()
    model.plot_graph(y)
    model.plot_sample_graph(y)



.. image:: example.png
    :width: 200px
    :align: center
    :height: 200px
    :alt: result


Authors
-------

`Pietro Barbiero <http://www.pietrobarbiero.eu/>`__

Licence
-------

Copyright 2020 Pietro Barbiero.

Licensed under the Apache License, Version 2.0 (the "License"); you may
not use this file except in compliance with the License. You may obtain
a copy of the License at: http://www.apache.org/licenses/LICENSE-2.0.

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

See the License for the specific language governing permissions and
limitations under the License.

