tctm | Text Classification using Topic Model | Topic Modeling library

by shalomeir Java Version: Current License: Non-SPDX

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | tctm Summary

tctm is a Java library typically used in Artificial Intelligence, Topic Modeling applications. tctm has no bugs, it has no vulnerabilities, it has build file available and it has low support. However tctm has a Non-SPDX License. You can download it from GitHub.

Text Clasification using Topic Model (TCTM) is a Java-based package for text classification tool using topic model. This application respond to Windows only until now. TCTM is made based on MALLET toolkit. This use a SVM classifier from SVM multiclass provided by Thorsten Joachims. This application’s input is a text documents set divided by training/test, and directory should be exist per class. First output is a topic model. Second one is bag of words(BOW) features. Third is joint with topic and BOW. Lastly, we provide a result by accuracy.

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tctm has a low active ecosystem.

It has 4 star(s) with 3 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 0 have been closed. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of tctm is current.

Quality

tctm has no bugs reported.

Security

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

License

tctm has a Non-SPDX License.

Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

Reuse

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

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

Top functions reviewed by kandi - BETA

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

Runs the program
Returns a classifierTrainer
Get the next split
Utility method for debugging purposes
This class is used by CRF
Trains the model
Create a CRF
Runs the tool
Write term weight list
Estimates a pipe
Performs a AdaBoost algorithm on the specified instances
Creates a new instance with properties from an instance
Gets infogains
Trains a maximum entropy model
Calculates the expgains
Cluster the KMeans algorithm
Evaluate instances list
Prints information about the Vectors
Calculates the per label information for each label
Returns the KLGain for a feature
Passes the current instance to the target instance
Returns a command - line wrapper for CRF
Maximize the given function
Optimized optimize algorithm
Generate a list of topic phrases
Writes the number of topics to the stream

Get all kandi verified functions for this library.

tctm Key Features

No Key Features are available at this moment for tctm.

tctm Examples and Code Snippets

No Code Snippets are available at this moment for tctm.

Community Discussions

Trending Discussions on tctm

Calculate the closest perpendicular point between a static line and a movable line

QUESTION

Calculate the closest perpendicular point between a static line and a movable line

Asked 2021-Jan-20 at 06:07

Description

Given is a blue line, which can be placed as pleased and is movable by mouse. By moving the line a gradient line (here in hotpink) is drawn from the original position. Now this line has the following requirements:

Assuming the left edge of the blue line being point 1 (red circle) and assuming the left edge of the original position of the blue line being point 2 (lime circle) assuming the right side of the blue line being point 3 (green circle) the angle from point 1 needs to be either 90 or -90 degress to point2/point3.

I believe the term for this is: the gradient hotpink line and the blue line are to be perpendicular. The direction of the blue line is not to change, only its position!

My issue

By moving the blue line I am able to calculate the degree to its original position and to draw the gradient hotpink line. Yet I am unable to calculate the closest point from the blue line which would make the gradient hotpink line perpendicular to both the original blue line and the new position of the blue line. If someone can point me to correct formula or proper term for this problem, I would be grateful.

Visual example (scrapped code parts)

Followed is a quick example which I scrapped together. One can move the blue line yet I can not force point 1 to keep a certain 90/-90 degree angle to point2/point3.

...

ANSWER

Answered 2021-Jan-18 at 11:42

To get the movement perpendicular to the blue line, do the following:

Get the vector of the blue lines orientation.
Rotate it 90 deg (its perpendicular to its surface).
Normalize it, e.g. make its length equal 1 (call it n).
Now get the movement vector of the mouse (current - start, call it m).
Now calculate n * dot(n,m). That is your movement vector in n direction.

Source https://stackoverflow.com/questions/65772897

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

Vulnerabilities

No vulnerabilities reported

Install tctm

Once you have downloaded and installed TCTM, the easiest way to get started is follow below step. 0. Check directory architecture that we want to classify. -input \data\sampleforTutorial\train\* (each directory should be a class(=label) name) \data\sampleforTutorial\test\*. See more : https://docs.google.com/presentation/d/1emBVYeGFYF4F2Nbp9quiSrksXE-M-iNL8_-bCoiQzgo/edit#slide=id.p.
Topic Modeling Make a model from a whole text corpus. edu.kaist.irlab.topics.tui.Text2VariedTopicModels --input data/sampleforTutorial/total/* --output-dir data/sampleforTutorial/topicmodel
Feature Set Generation Make a varied feature set from train and test data and topic models. edu.kaist.irlab.topics.tui.Text2VariedSvmLightFeatures --input-train-dir data/sampleforTutorial/train/* --input-test-dir data/sampleforTutorial/test/* --input-topic-dir data/sampleforTutorial/topicmodel/VTopicModel_Wi100_Di200 --output-dir data/sampleforTutorial/FeatureSet_Wi100_Di200
SVM Classification per feature set Excute SVM Multiclass Classification edu.kaist.irlab.classify.tui.ExecuteSvmMulticlass --input-dir data/sampleforTutorial/FeatureSet_Wi100_Di200/*

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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