tomoto | High performance topic modeling for Ruby | Topic Modeling library

by ankane C++ Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(3)Vulnerabilities Install Support

kandi X-RAY | tomoto Summary

tomoto is a C++ library typically used in Artificial Intelligence, Topic Modeling applications. tomoto has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

:tomato: tomoto - high performance topic modeling - for Ruby.

Support

Quality

Security

License

Reuse

Support

tomoto has a low active ecosystem.

It has 38 star(s) with 1 fork(s). There are 1 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 61 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of tomoto is current.

Quality

tomoto has 0 bugs and 0 code smells.

Security

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

tomoto code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

tomoto is licensed under the MIT License. This license is Permissive.

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

Reuse

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

Installation instructions, examples and code snippets are available.

tomoto saves you 304 person hours of effort in developing the same functionality from scratch.

It has 885 lines of code, 81 functions and 46 files.

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

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tomoto Key Features

No Key Features are available at this moment for tomoto.

tomoto Examples and Code Snippets

No Code Snippets are available at this moment for tomoto.

Community Discussions

Trending Discussions on tomoto

How to split string and remove duplicate and concatenate second part of string

print isduplicate from array of array in javascript

How would I go about de-serializing a list of objects using Kotlin's serialization library?

QUESTION

How to split string and remove duplicate and concatenate second part of string

Asked 2021-May-30 at 17:10

I was trying to split the following string in XSLT and remove duplicate and concatenate the second part of the string in following manner.

...

ANSWER

Answered 2021-May-30 at 17:00

Assuming that your input actually looks more like:

Source https://stackoverflow.com/questions/67762570

QUESTION

print isduplicate from array of array in javascript

Asked 2020-Oct-13 at 17:18

Find duplicate value from nested array at 1st index if duplicate found show row first is duplicate if not should say this row is not not duplicate

...

ANSWER

Answered 2020-Oct-13 at 17:18

How's this?

For each set in the array, the first index is checked, and value and set index is recorded. Then, after all sets have been processed, an array of results is created from the recorded indexes.

Source https://stackoverflow.com/questions/64339570

QUESTION

How would I go about de-serializing a list of objects using Kotlin's serialization library?

Asked 2020-Sep-24 at 05:07

I've been running into the following exception at runtime with the debugger trying to de-serialize data from my Algolia index for my Kotlin Android recipe app I am trying to create by using the Kotlinx.Serialization library. The app compiles and runs fine, but no results show on the UI.

...

ANSWER

Answered 2020-Sep-24 at 05:07

As you can see this line:

Source https://stackoverflow.com/questions/64039382

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

Vulnerabilities

No vulnerabilities reported

Install tomoto

Add this line to your application’s Gemfile:. It can take 10-20 minutes to compile the extension.
Save the model to a file. Load the model from a file. Get topic probabilities for a document. Get the number of words for each topic. Get the log likelihood per word. Perform inference for unseen documents.