algopy | Research Prototype for Algorithmic Differentation | Machine Learning library

by b45ch1 Python Version: Current License: No License

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kandi X-RAY | algopy Summary

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

AlgoPy is a Research Prototype for Algorithmic Differentation in Python

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algopy has a low active ecosystem.

It has 68 star(s) with 13 fork(s). There are 9 watchers for this library.

It had no major release in the last 6 months.

There are 10 open issues and 15 have been closed. On average issues are closed in 162 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of algopy is current.

Quality

algopy has no bugs reported.

Security

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

License

algopy 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

algopy 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.

Top functions reviewed by kandi - BETA

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

Performs the search .
Plot the graph .
Compute the eigenvalue of the eigenvalues .
import from function f
compute the tensor product of a vector function
Compute the SVD decomposition of A .
Generate multiple indices
Compute a symmetric symmetric matrix .
Solve the implicit euler equation
Set up the build package .

Get all kandi verified functions for this library.

algopy Key Features

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algopy Examples and Code Snippets

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Community Discussions

Trending Discussions on algopy

Algorithmic Differentiation vs Multiple Explicit Components with Analytical Derivatives

QUESTION

Algorithmic Differentiation vs Multiple Explicit Components with Analytical Derivatives

Asked 2018-Oct-01 at 01:22

I have a problem composed of around 6 mathematical expressions - i.e. (f(g(z(y(x))))) where x are two independent arrays.

I can divide this expression into multiple explicit comps with analytical derivatives or use an algorithmic differentiation method to get the derivatives which reduces the system to a single explicit component.

As far as i understand it is not easy to tell in advance the possible computational performance difference between these 2 approaches. It might depend on the algorithmic differentiation tools capabilities on the reverse mode case but maybe the system will be very large with multiple explicit components that it would still be ok to use algo diff.

my questions is :

Is algo diff. a common tool being used by any of the developers/users ? I found AlgoPY but not sure about other python tools.

...

ANSWER

Answered 2018-Oct-01 at 01:22

As of OpenMDAO v2.4 the OpenMDAO development team has not heavily used AD tools on any pure-python components. We have experimented with it a bit and found roughly a 2x increase in computational vs hand differentiated components. While some computational cost increase is expected, I do not want to indicate that I expect 2x to be the final rule of thumb. We simply don't have enough data to provide such an estimate.

Python based AD tools are much less well developed than those for compiled languages. The dynamic typing and general language flexibility both make it much more challenging to write good AD tools.

We have interfaced OpenMDAO with compiled codes that use AD, such as CFD and FEA tools. In these cases you're always using the matrix-free derivative APIs for OpenMDAO (apply_linear and compute_jacvec_product).

If your component is small enough to fit in memory and fast enough to run on a single process, I suggest you hand differentiate your code. That will give you the best overall performance for now.

AD support for small serial components is something we'll look into supporting in the future, but we don't have anything to offer you in the near term (as of OpenMDAO v2.4)

Source https://stackoverflow.com/questions/52569917

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

Vulnerabilities

No vulnerabilities reported

Install algopy

You can download it from GitHub.
You can use algopy 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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