Comments (1)
doc_cnt and doc_ids have the same structure of online lda code by Matthew D. Hoffman (corresponds to wordcts, wordids in his code): https://github.com/blei-lab/onlineldavb/blob/master/onlineldavb.py
n_voca is the size of the vocabulary.
n_topics is the number of topics generally used in topic models.
from python-topic-model.
Related Issues (15)
- Explanations on Stochastic (Gibbs) EM implementation for sLDA HOT 5
- [Question] write a vectorized form of do_e_step method in lda_vb. HOT 3
- Probability distribution for a document
- Logging
- Adding regularizer term for topic vectors
- In the ptm code, some problem about at_model.py?
- Input datatypes for the RTM model
- Error calculation in collabotm.py
- How to create document links for RTM model
- Measure the similarity between documents
- Test code for models
- Per-document topic distribution (ATM)? HOT 1
- Flat topic distributions in author-topic model HOT 4
- Author-topic LDA HOT 1
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from python-topic-model.