C-LDA | new generative model for topic segmentation | Topic Modeling library

by liliverpool Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | C-LDA Summary

C-LDA is a Python library typically used in Artificial Intelligence, Topic Modeling applications. C-LDA has no bugs, it has no vulnerabilities and it has low support. However C-LDA build file is not available. You can download it from GitHub.

A new generative model for topic segmentation in documents based on a Context-Aware Latent Dirichlet Allocation

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Quality

Security

License

Reuse

Support

C-LDA has a low active ecosystem.

It has 5 star(s) with 3 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

C-LDA has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of C-LDA is current.

Quality

C-LDA has no bugs reported.

Security

C-LDA has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

C-LDA 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

C-LDA releases are not available. You will need to build from source code and install.

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

Top functions reviewed by kandi - BETA

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

Run LDA
Creates a dictionary from data
Count the number of documents in the document
Computes the parameter estimation
Computes the number of texts in a list
Return the index number of words in the text
Compute the number of words in a list of texts
Return the number of contexts in text

Get all kandi verified functions for this library.

C-LDA Key Features

No Key Features are available at this moment for C-LDA.

C-LDA Examples and Code Snippets

No Code Snippets are available at this moment for C-LDA.

Community Discussions

Trending Discussions on C-LDA

Error on building Dockerfile to Image

Normalized topic document probabilities text2vec R

QUESTION

Error on building Dockerfile to Image

Asked 2018-Feb-26 at 20:12

I have the following Dockerfile. I'm trying to build it to an image, but somehow I receive the following error: ADD service /container/service ADD failed: stat /mnt/sda1/var/lib/docker/tmp/docker-builder005872257/service: no such file or directory at Step 6/9. I don't know why... Can anyone help me?

...

ANSWER

Answered 2018-Feb-25 at 20:49

it successfully build on my local machine.Can you delete the respective files or directories and try once. Also, check the permissions. Did you configure .dockerignore which will not allow to ADD those files. Or else try running with -f or --file command like,

Source https://stackoverflow.com/questions/48977751

QUESTION

Normalized topic document probabilities text2vec R

Asked 2018-Feb-22 at 12:59

I am trying to find out the topic document probabilities after running the lda model using text2vec package in R.

Following commands generate the model:

...

ANSWER

Answered 2018-Feb-22 at 12:59

According to the up to date documentation LDA fit_transform returns topic probabilities.

Source https://stackoverflow.com/questions/48889261

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

Vulnerabilities

No vulnerabilities reported

Install C-LDA

You can download it from GitHub.
You can use C-LDA 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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