pegasos | An sklearn-like python package for pegasos models | Machine Learning library

by ejlb Python Version: Current License: Apache-2.0

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

kandi X-RAY | pegasos Summary

pegasos is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch applications. pegasos has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

An sklearn-like python package for pegasos models

Support

Quality

Security

License

Reuse

Support

pegasos has a low active ecosystem.

It has 43 star(s) with 17 fork(s). There are 6 watchers for this library.

It had no major release in the last 6 months.

There are 2 open issues and 0 have been closed. On average issues are closed in 2279 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of pegasos is current.

Quality

pegasos has 0 bugs and 0 code smells.

Security

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

pegasos code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

pegasos is licensed under the Apache-2.0 License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

pegasos 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.

Installation instructions, examples and code snippets are available.

pegasos saves you 142 person hours of effort in developing the same functionality from scratch.

It has 355 lines of code, 30 functions and 12 files.

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

Top functions reviewed by kandi - BETA

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

Adds xi to the model
Compute the inner product of two arrays
Compute the inner product

Get all kandi verified functions for this library.

pegasos Key Features

No Key Features are available at this moment for pegasos.

pegasos Examples and Code Snippets

No Code Snippets are available at this moment for pegasos.

Community Discussions

Trending Discussions on pegasos

Python numpy floating point array precision

QUESTION

Python numpy floating point array precision

Asked 2018-Mar-05 at 16:18

I am trying to solve for SVM optimisation problem using Pegasos mini-batch algorithm (as in Fig 2) from this link: http://www.cs.huji.ac.il/~shais/papers/ShalevSiSrCo10.pdf

...

ANSWER

Answered 2018-Mar-05 at 16:18

If you need precision, you should use np.float64 (the normal floating point precision double).

If you are using Python 2, you are using integer divition in (1/t), (1/k), and (1/l). Write it as 1.0/, to force a floating point division.

Source https://stackoverflow.com/questions/49114732

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

Vulnerabilities

No vulnerabilities reported

Install pegasos

and nose for tests:.
scikit-learn >= 0.13.1
numpy >= 1.7.1
scipy >= 0.10.1

Support

sparse or dense matrix support. binary classification (multiclass via sklearn.multiclass). balanced class weightings via training loops. probabilistic predictions for logistic model. model serialisation via cPickle. See example.py for how to use the library.

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