naive-bayes-spam-filter | SPAM message filter using a naive Bayes classifier

by davidenunes Java Version: Current License: No License

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

kandi X-RAY | naive-bayes-spam-filter Summary

naive-bayes-spam-filter is a Java library. naive-bayes-spam-filter has no bugs, it has no vulnerabilities and it has low support. However naive-bayes-spam-filter build file is not available. You can download it from GitHub.

A SPAM message filter using a naive Bayes classifier written in Java.

Support

Quality

Security

License

Reuse

Support

naive-bayes-spam-filter has a low active ecosystem.

It has 14 star(s) with 16 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

naive-bayes-spam-filter has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of naive-bayes-spam-filter is current.

Quality

naive-bayes-spam-filter has 0 bugs and 0 code smells.

Security

naive-bayes-spam-filter has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

naive-bayes-spam-filter code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

naive-bayes-spam-filter 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

naive-bayes-spam-filter releases are not available. You will need to build from source code and install.

naive-bayes-spam-filter has no build file. You will be need to create the build yourself to build the component from source.

naive-bayes-spam-filter saves you 3168 person hours of effort in developing the same functionality from scratch.

It has 6814 lines of code, 85 functions and 51 files.

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

Top functions reviewed by kandi - BETA

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

Print a menu to stdout
Runs EM algorithm on the current model
Generates a table of thresholds from a file
Reads the email messages from the file
Returns a string representation of this dataset
Returns a string representation of the message object
Calculate the dimension of the dictionary
Gets the number of occurrences
Iterator implementation
Checks if the token contains the given token
Cross train
Returns a list with untagged map reads from the specified file
Train hash map
Remove a token from the token list

Get all kandi verified functions for this library.

naive-bayes-spam-filter Key Features

No Key Features are available at this moment for naive-bayes-spam-filter.

naive-bayes-spam-filter Examples and Code Snippets

No Code Snippets are available at this moment for naive-bayes-spam-filter.

Community Discussions

No Community Discussions are available at this moment for naive-bayes-spam-filter.Refer to stack overflow page for discussions.

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

Vulnerabilities

No vulnerabilities reported

Install naive-bayes-spam-filter

You can download it from GitHub.
You can use naive-bayes-spam-filter like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the naive-bayes-spam-filter component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .

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 .

Find more information at: