GradientInduction | Framework of DataLog Neural Program Synthesis

by ZhengyaoJiang Python Version: Current License: GPL-3.0

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

kandi X-RAY | GradientInduction Summary

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

This project aims to build a general framework for DataLog neural program systhesis.

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Quality

Security

License

Reuse

Support

GradientInduction has a low active ecosystem.

It has 26 star(s) with 3 fork(s). There are 3 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 2 have been closed. On average issues are closed in 12 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of GradientInduction is current.

Quality

GradientInduction has 0 bugs and 0 code smells.

Security

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

GradientInduction code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

GradientInduction is licensed under the GPL-3.0 License. This license is Strong Copyleft.

Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

Reuse

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

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

GradientInduction saves you 336 person hours of effort in developing the same functionality from scratch.

It has 805 lines of code, 89 functions and 7 files.

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

Top functions reviewed by kandi - BETA

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

Train the Adam model
Evaluate the proof
Apply a rule
Unify a set of atoms
Calculate the loss of the model
Compute the gradient of the embedding
Start NTP
Return a new clause with all the atoms replaced
Return a new Atom with the same value
Convert a string to a Clause object
Generates the elimination matrix for the given clause
Return a dictionary that matches this variable
Return a new clause with the given head
Returns a list of all clauses satisfying the given head
Apply a proof state
Activate a clause
Substitute substitutions in an atom
Apply the rules to the proof state
Generates a list of clauses for the given intensional rule template
Generates a list of Atom objects
R Return a list of clauses that are pruned
Construct a NeuralProver from an ILP
Create an embeddings from a list of clauses
Start the DILP task
Train the model
Unify atoms
Create an Embeddings from a list of clauses
Convert a string into a Clause
Assign variable id to a given term

Get all kandi verified functions for this library.

GradientInduction Key Features

No Key Features are available at this moment for GradientInduction.

GradientInduction Examples and Code Snippets

No Code Snippets are available at this moment for GradientInduction.

Community Discussions

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

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

Vulnerabilities

No vulnerabilities reported

Install GradientInduction

You can download it from GitHub.
You can use GradientInduction 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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