fbpca | Fast Randomized PCA/SVD | Machine Learning library

by facebookarchive Python Version: Current License: Non-SPDX

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | fbpca Summary

fbpca is a Python library typically used in Artificial Intelligence, Machine Learning, Example Codes applications. fbpca has no bugs, it has no vulnerabilities, it has build file available and it has low support. However fbpca has a Non-SPDX License. You can download it from GitHub.

Fast Randomized PCA/SVD

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fbpca has a low active ecosystem.

It has 445 star(s) with 95 fork(s). There are 32 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 3 have been closed. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of fbpca is current.

Quality

fbpca has 0 bugs and 0 code smells.

Security

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

fbpca code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

fbpca has a Non-SPDX License.

Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

Reuse

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

Build file is available. You can build the component from source.

It has 985 lines of code, 26 functions and 3 files.

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

Top functions reviewed by kandi - BETA

kandi has reviewed fbpca and discovered the below as its top functions. This is intended to give you an instant insight into fbpca implemented functionality, and help decide if they suit your requirements.

Test eigenvalues
Generate eigens
Generate a diffSNorms
Multiply matrix A
Test PCA
Compute the principal components of A
Calculate the difference between two columns
Generate a diffSNormap
Test the test PCA
Test the test eigenvector
Generates the eigenvalue of a matrix A
Test the eigenvalue test
Test test eigenvalue
Test for the test_sparse function
Test the test_sparse
Run test_sparse
Run test on dense vectors
Test the diffSNorms
Test the test suite

Get all kandi verified functions for this library.

fbpca Key Features

No Key Features are available at this moment for fbpca.

fbpca Examples and Code Snippets

No Code Snippets are available at this moment for fbpca.

Community Discussions

Trending Discussions on fbpca

Why is my NumPy array taking much *less* memory than it should?

QUESTION

Why is my NumPy array taking much *less* memory than it should?

Asked 2019-Jul-25 at 11:37

I am working with large matrices, like the Movielens 20m dataset. I restructured the online file such that it matches the dimensions mentioned on the page (138000 by 27000), since the original file contains indices that are more of the size (138000 by 131000), but contain a lot of empty columns. Simply throwing out those empty columns and re-indexing yields the desired dimensions.

Anyways, the snippet to cast the sparse csv file to a dense format looks like this:

...

ANSWER

Answered 2019-Jul-25 at 11:37

I think your problem lies in the todense() call, which uses np.asmatrix(self.toarray(order=order, out=out)) internally. toarray creates its output with np.zeros. (See toarray, _process_toarray_args)

So your question can be reduced to: Why doesn't np.zeros allocate enough memory?

The answer is probably lazy-initialization and zero pages:

Why does numpy.zeros takes up little space
Linux kernel: Role of zero page allocation at paging_init time

So all zero-regions in your matrix are actually in the same physical memory block and only a write to all entries will force the OS to allocate enough physical memory.

Source https://stackoverflow.com/questions/51748215

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

Vulnerabilities

No vulnerabilities reported

Install fbpca

You can download it from GitHub.
You can use fbpca 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 .

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