randomized_telescopes | Efficient optimization of loops and limits

by PrincetonLIPS Python Version: Current License: No License

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

kandi X-RAY | randomized_telescopes Summary

randomized_telescopes is a Python library. randomized_telescopes has no bugs, it has no vulnerabilities and it has low support. However randomized_telescopes build file is not available. You can download it from GitHub.

Code for "Efficient optimization of loops and limits with randomized telescoping sums", Alex Beatson and Ryan Adams, ICML 2019.

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randomized_telescopes has a low active ecosystem.

It has 30 star(s) with 3 fork(s). There are 4 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 0 have been closed. On average issues are closed in 43 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of randomized_telescopes is current.

Quality

randomized_telescopes has 0 bugs and 0 code smells.

Security

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

randomized_telescopes code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

randomized_telescopes 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

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

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

randomized_telescopes saves you 1462 person hours of effort in developing the same functionality from scratch.

It has 3264 lines of code, 170 functions and 24 files.

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

Top functions reviewed by kandi - BETA

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

Runs the optimizer
Perform convergence update
Clips a list of torch torch torch Tensor
Clips an array by norm
Log a scalar
Make a problem
Integrate a given dxdt
Wrapper for pytorch
Performs a single step of the optimization
Perform softmax analysis
Calculate the log probability for the model
Evaluate the model
Get a batch of data
Creates a training dataset
Create a batch of data
Splits data into batches
Wrapper for GPU
Return the sample index
Get a batch from source
Log a scalar value
Loads the model
Clip an array by a norm
Get a batch of inputs
Assign model parameters to model
Tokenize a file
Sample the VMS estimate
Compute the embeddings
Calculates the log probability of each word
Train the model
Assign weights to the network

Get all kandi verified functions for this library.

randomized_telescopes Key Features

No Key Features are available at this moment for randomized_telescopes.

randomized_telescopes Examples and Code Snippets

No Code Snippets are available at this moment for randomized_telescopes.

Community Discussions

No Community Discussions are available at this moment for randomized_telescopes.Refer to stack overflow page for discussions.

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

Vulnerabilities

No vulnerabilities reported

Install randomized_telescopes

You can download it from GitHub.
You can use randomized_telescopes 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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