FastICA | python version of fast and robust ICA | Build Tool library

by Felix-Yan Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(5)Vulnerabilities Install Support

kandi X-RAY | FastICA Summary

FastICA is a Python library typically used in Utilities, Build Tool, Numpy applications. FastICA has no bugs, it has no vulnerabilities and it has low support. However FastICA build file is not available. You can download it from GitHub.

A python version of fast and robust ICA based on the paper of Aapo Hyvärinen.

Support

Quality

Security

License

Reuse

Support

FastICA has a low active ecosystem.

It has 13 star(s) with 9 fork(s). There are 3 watchers for this library.

It had no major release in the last 6 months.

FastICA has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of FastICA is current.

Quality

FastICA has 0 bugs and 0 code smells.

Security

FastICA has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

FastICA code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

FastICA does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

FastICA releases are not available. You will need to build from source code and install.

FastICA has no build file. You will be need to create the build yourself to build the component from source.

FastICA saves you 17 person hours of effort in developing the same functionality from scratch.

It has 49 lines of code, 0 functions and 1 files.

It has low code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi's functional review helps you automatically verify the functionalities of the libraries and avoid rework.
Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of FastICA

Get all kandi verified functions for this library.

FastICA Key Features

No Key Features are available at this moment for FastICA.

FastICA Examples and Code Snippets

No Code Snippets are available at this moment for FastICA.

Community Discussions

Trending Discussions on FastICA

How to plot ICA components extracted from EEG signal?

Error in passing a constant argument to mapply() function

Neuraxle's RandomSearch() successor

Trouble with the unmixing matrix from fastica toolbox in Matlab

Save intermenidte results of the sklearn pipeline

QUESTION

How to plot ICA components extracted from EEG signal?

Asked 2022-Mar-24 at 10:13

I'm following the answer to this question and this scikit-learn tutorial to remove artifacts from an EEG signal. They seem simple enough, and I'm surely missing something obvious here.

The components extracted don't have the same length as my signal. I have 88 channels of several hours of recordings, so the shape of my signal matrix is (88, 8088516). Yet the output of ICA is (88, 88). In addition to being so short, each component seems to capture very large, noisy-looking deflections (so out of 88 components only a couple actually look like signal, the rest look like noise). I also would have expected only a few components to look noisy. I suspect I'm doing something wrong here?

The matrix of (channels x samples) has shape (88, 8088516).

Sample code (just using a random matrix for minimum working purposes):

...

ANSWER

Answered 2022-Mar-24 at 10:13

You need to run the fit_transform on the transpose of your samples_matrix instead of the samples_matrix itself (so provide a 8088516 x 88 matrix instead of an 88x8088516 to the method).

Source https://stackoverflow.com/questions/71583770

QUESTION

Error in passing a constant argument to mapply() function

Asked 2022-Feb-06 at 12:56

I have created a function that run FastICA on a dataset with different number of components and it returns ICA signals (S matrix) but in a long format.

...

ANSWER

Answered 2022-Feb-06 at 08:50

I cannot reproduce the error, however, I think you misunderstood what mapply does.

When you apply mapply to a function FUN, with arguments that are lists or vectors (consider that R basic types like numbers or characters are always vectors), the function FUN is called iteratively on the first element of all the arguments, then on the second, etc. Arguments are recycled if necessary.

For instance:

Source https://stackoverflow.com/questions/71005061

QUESTION

Neuraxle's RandomSearch() successor

Asked 2020-May-16 at 02:18

I updated Neuraxle to the latest version (3.4).

I noticed the whole auto_ml.py was redone. I checked the documentation but there is nothing about it. On git it seems method RandomSearch() was replaced a long time ago by AutoML() method. However the parameters are different.

Does somebody knows how can I channel Boston Housing example pipeline to automatic parameter search in latest Neuraxle version (3.4)?

...

ANSWER

Answered 2020-May-16 at 02:18

Here is a solution to your problem, this is a new example that isn't yet published on the documentation site:

https://drive.google.com/drive/u/0/folders/12uzcNKU7n0EUyFzgitSt1wSaSvV4qJbs (go see the solution to the 2nd coding Kata from there)

Sample pipeline code from the link above:

Source https://stackoverflow.com/questions/60742991

QUESTION

Trouble with the unmixing matrix from fastica toolbox in Matlab

Asked 2020-Apr-28 at 17:53

I'm using the FastIca toolbox (https://research.ics.aalto.fi/ica/fastica/) but am confused about the orientation of the resulting W (separating/unmixing) matrix.

Let X be a n x B matrix where n is the number of signals in a data set and B is the number of time points sampled at.

I've been calculating the W matrix using:

...

ANSWER

Answered 2020-Apr-28 at 17:53

It should be Y=W*X. To be sure, you can reduce the number of component to estimate and then W should no longer be square:

Source https://stackoverflow.com/questions/61465345

QUESTION

Save intermenidte results of the sklearn pipeline

Asked 2020-Feb-06 at 19:51

I have a code example - sklearn pipeline that has two components (PCA and Random Forest), I want to use the intermediate results of the pipeline in order to bring some explainability. I know that it is possible to use .get_params() to see the intermediate steps, but is it possible to save or extract the intermediate results for additional actions? I want to apply additional functions of the PCA (1.1. and 1.2 sections in the code)

...

ANSWER

Answered 2020-Feb-06 at 19:51

We can assign get_params() to a variable which should return an object of type sklearn.decomposition.pca.PCA. With this, we are able to access all the methods and attributes of the decomposition.

Source https://stackoverflow.com/questions/60099242

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install FastICA

You can download it from GitHub.
You can use FastICA like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

Find more information at: