Commit cf0fc7d9 authored by M. François's avatar M. François

Dist version checked

parent f8e3beb5
......@@ -2,6 +2,181 @@ Welcome to Neural Filters’s documentation!
******************************************
NeuralFilterCell
================
This module implements a basic trainable all-pole first order filter
using pyTorch
Copyright (c) 2018 Idiap Research Institute, http://www.idiap.ch/
Written by Francois Marelli <Francois.Marelli@idiap.ch>
This file is part of neural_filters.
neural_filters is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License version 3 as
published by the Free Software Foundation.
neural_filters is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
General Public License for more details.
You should have received a copy of the GNU General Public License
along with neural_filters. If not, see <http://www.gnu.org/licenses/>.
class neural_filters.neural_filter.NeuralFilter(hidden_size)
A trainable first-order all-pole filter \frac{1}{1 - P z^{-1}}
* **hidden_size** (int) - the size of the data vector
forward(input_var, hidden=None)
Defines the computation performed at every call.
Should be overridden by all subclasses.
Note: Although the recipe for forward pass needs to be defined
within this function, one should call the "Module" instance
afterwards instead of this since the former takes care of
running the registered hooks while the latter silently ignores
them.
NeuralFilter2R
==============
This module implements a trainable all-pole second order filter with
real poles using pyTorch
Copyright (c) 2018 Idiap Research Institute, http://www.idiap.ch/
Written by Francois Marelli <Francois.Marelli@idiap.ch>
This file is part of neural_filters.
neural_filters is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License version 3 as
published by the Free Software Foundation.
neural_filters is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
General Public License for more details.
You should have received a copy of the GNU General Public License
along with neural_filters. If not, see <http://www.gnu.org/licenses/>.
class neural_filters.neural_filter_2R.NeuralFilter2R(hidden_size)
A trainable second-order all-(real)pole filter \frac{1}{1 - P_{1}
z^{-1}} \frac{1}{1 - P_{2} z^{-1}}
* **hidden_size** (int) - the size of data vector
forward(input_var, hx=(None, None))
Defines the computation performed at every call.
Should be overridden by all subclasses.
Note: Although the recipe for forward pass needs to be defined
within this function, one should call the "Module" instance
afterwards instead of this since the former takes care of
running the registered hooks while the latter silently ignores
them.
NeuralFilter2CD
===============
This module implements a trainable critically damped all-pole second
order filter with real poles using pyTorch
Copyright (c) 2018 Idiap Research Institute, http://www.idiap.ch/
Written by Francois Marelli <Francois.Marelli@idiap.ch>
This file is part of neural_filters.
neural_filters is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License version 3 as
published by the Free Software Foundation.
neural_filters is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
General Public License for more details.
You should have received a copy of the GNU General Public License
along with neural_filters. If not, see <http://www.gnu.org/licenses/>.
class neural_filters.neural_filter_2CD.NeuralFilter2CD(hidden_size)
A trainable second-order critically damped all-pole filter
\frac{1}{(1 - P z^{-1})^{2}}
* **hidden_size** (int) - the size of data vector
forward(input_var, hx=(None, None))
Defines the computation performed at every call.
Should be overridden by all subclasses.
Note: Although the recipe for forward pass needs to be defined
within this function, one should call the "Module" instance
afterwards instead of this since the former takes care of
running the registered hooks while the latter silently ignores
them.
NeuralFilter2CC
===============
This module implements a trainable all-pole second order filter with
complex conjugate poles using pyTorch
Copyright (c) 2018 Idiap Research Institute, http://www.idiap.ch/
Written by Francois Marelli <Francois.Marelli@idiap.ch>
This file is part of neural_filters.
neural_filters is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License version 3 as
published by the Free Software Foundation.
neural_filters is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
General Public License for more details.
You should have received a copy of the GNU General Public License
along with neural_filters. If not, see <http://www.gnu.org/licenses/>.
class neural_filters.neural_filter_2CC.NeuralFilter2CC(hidden_size)
A trainable second-order all-pole filter \frac{1}{1 - 2 P
\cos(\theta) z^{-1} + P^{2} z^{-2}}
* **hidden_size** (int) - the size of the data vector
forward(input_var, hidden=None)
Defines the computation performed at every call.
Should be overridden by all subclasses.
Note: Although the recipe for forward pass needs to be defined
within this function, one should call the "Module" instance
afterwards instead of this since the former takes care of
running the registered hooks while the latter silently ignores
them.
Indices and tables
******************
......
......@@ -17,11 +17,6 @@
# documentation root, use os.path.abspath to make it absolute, like shown here.
#
import os
import sys
sys.path.insert(0, os.path.abspath(os.path.join('..', '..', 'neural_filters')))
import neural_filters
# -- General configuration ------------------------------------------------
......
......@@ -10,7 +10,16 @@ Welcome to Neural Filters's documentation!
:maxdepth: 2
:caption: Contents:
.. automodule:: neural_filters
.. automodule:: neural_filters.neural_filter
:members:
.. automodule:: neural_filters.neural_filter_2R
:members:
.. automodule:: neural_filters.neural_filter_2CD
:members:
.. automodule:: neural_filters.neural_filter_2CC
:members:
Indices and tables
......
......@@ -2,7 +2,7 @@ from setuptools import setup, find_packages
setup(
name='neural-filters',
version='0.2',
version='1.0',
description='Linear filters for neural networks in pyTorch',
author='Idiap research institute - Francois Marelli',
author_email='francois.marelli@idiap.ch',
......
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