Skip to main content
Ctrl+K

alphacsc 0.4.1 documentation

  • Model descriptions
  • Examples Gallery
  • API Documentation
  • GitHub
  • PyPI
  • Model descriptions
  • Examples Gallery
  • API Documentation
  • GitHub
  • PyPI

Section Navigation

  • Univariate CSC
    • Selecting random state for CSC
    • CSC to learn LFP spiking atoms
    • Extracting cross-frequency coupling waveforms from rodent LFP data
    • Vanilla CSC on simulated data
  • Univariate and Multivariate CSC with ‘dicodile’ solver
    • Gait (steps) example
  • Multivariate CSC with rank 1 constraints
    • Extracting \(\mu\)-wave from the somato-sensory dataset
    • Extracting artifact and evoked response atoms from the MNE sample dataset
    • Extracting artifact and evoked response atoms from the sample dataset
  • Univariate CSC with an alpha-stable distribution
    • Alpha CSC on simulated data
    • Alpha CSC on empirical time-series with strong artifacts
  • Other shift-invariant dictionary learning algorithms
    • MoTIF on simulated data
    • SWM on simulated data
  • Examples Gallery

Examples Gallery#

Contents

  • Univariate CSC

  • Univariate and Multivariate CSC with ‘dicodile’ solver

  • Multivariate CSC with rank 1 constraints

  • Univariate CSC with an alpha-stable distribution

  • Other shift-invariant dictionary learning algorithms

Univariate CSC#

Selecting random state for CSC

Selecting random state for CSC

CSC to learn LFP spiking atoms

CSC to learn LFP spiking atoms

Extracting cross-frequency coupling waveforms from rodent LFP data

Extracting cross-frequency coupling waveforms from rodent LFP data

Vanilla CSC on simulated data

Vanilla CSC on simulated data

Univariate and Multivariate CSC with ‘dicodile’ solver#

Gait (steps) example

Gait (steps) example

Multivariate CSC with rank 1 constraints#

Extracting \mu-wave from the somato-sensory dataset

Extracting \mu-wave from the somato-sensory dataset

Extracting artifact and evoked response atoms from the MNE sample dataset

Extracting artifact and evoked response atoms from the MNE sample dataset

Extracting artifact and evoked response atoms from the sample dataset

Extracting artifact and evoked response atoms from the sample dataset

Univariate CSC with an alpha-stable distribution#

Alpha CSC on simulated data

Alpha CSC on simulated data

Alpha CSC on empirical time-series with strong artifacts

Alpha CSC on empirical time-series with strong artifacts

Other shift-invariant dictionary learning algorithms#

MoTIF on simulated data

MoTIF on simulated data

SWM on simulated data

SWM on simulated data

Download all examples in Python source code: auto_examples_python.zip

Download all examples in Jupyter notebooks: auto_examples_jupyter.zip

Gallery generated by Sphinx-Gallery

previous

Model descriptions

next

Univariate CSC

On this page
  • Univariate CSC
  • Univariate and Multivariate CSC with ‘dicodile’ solver
  • Multivariate CSC with rank 1 constraints
  • Univariate CSC with an alpha-stable distribution
  • Other shift-invariant dictionary learning algorithms

This Page

  • Show Source

© Copyright 2017-2018, Mainak Jas.

Created using Sphinx 7.4.7.

Built with the PyData Sphinx Theme 0.16.1.