معرفی شرکت ها
alphacsc-0.4.0rc7
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشترتوضیحات
Convolutional dictionary learning for noisy signals.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | alphacsc-0.4.0rc7 |
| نام | alphacsc |
| نسخه کتابخانه | 0.4.0rc7 |
| نگهدارنده | ['Thomas Moreau'] |
| ایمیل نگهدارنده | [] |
| نویسنده | - |
| ایمیل نویسنده | thomas.moreau@inria.fr |
| آدرس صفحه اصلی | https://github.com/alphacsc/alphacsc.git |
| آدرس اینترنتی | https://pypi.org/project/alphacsc/ |
| مجوز | BSD (3-clause) |
===================================================
alphaCSC: Convolution sparse coding for time-series
===================================================
|Build Status| |codecov|
This is a library to perform shift-invariant `sparse dictionary learning
<https://en.wikipedia.org/wiki/Sparse_dictionary_learning>`_, also known as
convolutional sparse coding (CSC), on time-series data.
It includes a number of different models:
1. univariate CSC
2. multivariate CSC
3. multivariate CSC with a rank-1 constraint [1]_
4. univariate CSC with an alpha-stable distribution [2]_
A mathematical descriptions of these models is available `in the documentation
<https://alphacsc.github.io/models.html>`_.
Installation
============
To install this package, the easiest way is using ``pip``. It will install this
package and its dependencies. The ``setup.py`` depends on ``numpy`` and
``cython`` for the installation so it is advised to install them beforehand. To
install this package, please run one of the two commands:
(Latest stable version)
.. code::
pip install alphacsc
(Development version)
.. code::
pip install git+https://github.com/alphacsc/alphacsc.git#egg=alphacsc
(Dicodile backend)
.. code::
pip install numpy cython
pip install alphacsc[dicodile]
To use dicodile backend, do not forget to set ``MPI_HOSTFILE`` environment
variable.
If you do not have admin privileges on the computer, use the ``--user`` flag
with ``pip``. To upgrade, use the ``--upgrade`` flag provided by ``pip``.
To check if everything worked fine, you can run:
.. code::
python -c 'import alphacsc'
and it should not give any error messages.
Quickstart
==========
Here is an example to present briefly the API:
.. code:: python
import numpy as np
import matplotlib.pyplot as plt
from alphacsc import BatchCDL
# Define the different dimensions of the problem
n_atoms = 10
n_times_atom = 50
n_channels = 5
n_trials = 10
n_times = 1000
# Generate a random set of signals
X = np.random.randn(n_trials, n_channels, n_times)
# Learn a dictionary with batch algorithm and rank1 constraints.
cdl = BatchCDL(n_atoms, n_times_atom, rank1=True)
cdl.fit(X)
# Display the learned atoms
fig, axes = plt.subplots(n_atoms, 2, num="Dictionary")
for k in range(n_atoms):
axes[k, 0].plot(cdl.u_hat_[k])
axes[k, 1].plot(cdl.v_hat_[k])
axes[0, 0].set_title("Spatial map")
axes[0, 1].set_title("Temporal map")
for ax in axes.ravel():
ax.set_xticklabels([])
ax.set_yticklabels([])
plt.show()
Dicodile backend
================
AlphaCSC can use a `dicodile <https://github.com/tomMoral/dicodile>`_-based backend to perform sparse encoding in parallel.
To install dicodile, run ``pip install alphacsc[dicodile]``.
Known OpenMPI issues
--------------------
When self-installing OpenMPI (for instance to run `dicodile` on a single machine, or for continuous integration), running the `dicodile` solver might end up causing a deadlock (no output for a long time). It is often due to communication issue between the workers. This issue can often be solved by disabling Docker-related virtual NICs, for instance by running ``export OMPI_MCA_btl_tcp_if_exclude="docker0"``.
Bug reports
===========
Use the `github issue tracker <https://github.com/alphacsc/alphacsc/issues>`_ to report bugs.
Cite our work
=============
If you use this code in your project, please consider citing our work:
.. [1] Dupré La Tour, T., Moreau, T., Jas, M., & Gramfort, A. (2018).
`Multivariate Convolutional Sparse Coding for Electromagnetic Brain Signals
<https://arxiv.org/abs/1805.09654v2>`_. Advances in Neural Information
Processing Systems (NIPS).
.. [2] Jas, M., Dupré La Tour, T., Şimşekli, U., & Gramfort, A. (2017). `Learning
the Morphology of Brain Signals Using Alpha-Stable Convolutional Sparse Coding
<https://papers.nips.cc/paper/6710-learning-the-morphology-of-brain-signals-using-alpha-stable-convolutional-sparse-coding.pdf>`_.
Advances in Neural Information Processing Systems (NIPS), pages 1099--1108.
.. |Build Status| image:: https://github.com/alphacsc/alphacsc/workflows/unittests/badge.svg
.. |codecov| image:: https://codecov.io/gh/alphacsc/alphacsc/branch/master/graph/badge.svg
:target: https://codecov.io/gh/alphacsc/alphacsc
نیازمندی
| مقدار | نام |
|---|---|
| - | mne |
| - | numba |
| - | numpy |
| - | scipy |
| - | joblib |
| - | matplotlib |
| - | scikit-learn |
| - | flake8 |
| - | dicodile |
| - | pydata-sphinx-theme |
| - | numpydoc |
| - | sphinx-gallery |
| - | pactools |
| - | nibabel |
| - | pytest |
| - | pytest-cov |
نحوه نصب
نصب پکیج whl alphacsc-0.4.0rc7:
pip install alphacsc-0.4.0rc7.whl
نصب پکیج tar.gz alphacsc-0.4.0rc7:
pip install alphacsc-0.4.0rc7.tar.gz