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Merge pull request #22 from nanxstats/news
Release tinytopics 0.5.0
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CHANGELOG.md

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# Changelog
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## tinytopics 0.5.0
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### Improvements
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- Increased the speed of `generate_synthetic_data()` significantly by using
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direct mixture sampling, which leverages the properties of multinomial
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distributions (#21).
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This change makes simulating data at the scale of 100K x 100K
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more feasible. Although the approaches before and after are mathematically
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equivalent, the data generated with the same seed in previous versions and
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this version onward will be bitwise different.
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## tinytopics 0.4.1
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### Documentation

README.md

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![License](https://img.shields.io/pypi/l/tinytopics)
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Topic modeling via sum-to-one constrained neural Poisson NMF.
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Built with PyTorch, runs on both CPUs and GPUs.
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## Installation

docs/changelog.md

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# Changelog
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## tinytopics 0.5.0
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### Improvements
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- Increased the speed of `generate_synthetic_data()` significantly by using
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direct mixture sampling, which leverages the properties of multinomial
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distributions (#21).
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This change makes simulating data at the scale of 100K x 100K
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more feasible. Although the approaches before and after are mathematically
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equivalent, the data generated with the same seed in previous versions and
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this version onward will be bitwise different.
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## tinytopics 0.4.1
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### Documentation

docs/index.md

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![License](https://img.shields.io/pypi/l/tinytopics)
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Topic modeling via sum-to-one constrained neural Poisson NMF.
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Built with PyTorch, runs on both CPUs and GPUs.
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## Installation

mkdocs.yml

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- search.highlight
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- search.suggest
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- toc.follow
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- toc.integrate

pyproject.toml

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[project]
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name = "tinytopics"
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version = "0.4.1"
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version = "0.5.0"
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description = "Topic modeling via sum-to-one constrained neural Poisson non-negative matrix factorization"
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authors = [
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{ name = "Nan Xiao", email = "me@nanx.me" }

requirements-dev.lock

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# via mkdocstrings-python
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mkdocs-get-deps==0.2.0
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# via mkdocs
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mkdocs-material==9.5.47
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mkdocs-material==9.5.48
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mkdocs-material-extensions==1.3.1
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# via mkdocs-material
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mkdocstrings==0.27.0
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notebook-shim==0.2.4
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# via jupyterlab
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# via notebook
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numpy==2.1.3
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numpy==2.2.0
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# via contourpy
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# via imageio
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# via matplotlib

requirements.lock

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networkx==3.4.2
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# via scikit-image
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# via torch
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numpy==2.1.3
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numpy==2.2.0
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# via contourpy
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# via imageio
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# via matplotlib

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