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# Changelog
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+ ## tinytopics 0.5.0
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+
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+ ### Improvements
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+
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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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+
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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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+
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## tinytopics 0.4.1
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### Documentation
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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
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# Changelog
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+ ## tinytopics 0.5.0
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+
5
+ ### Improvements
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+
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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
9
+ distributions (#21 ).
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+
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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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+
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## tinytopics 0.4.1
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### Documentation
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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
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- search.highlight
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- search.suggest
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- toc.follow
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- - toc.integrate
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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" }
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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
@@ -227,7 +227,7 @@ notebook==7.3.1
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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
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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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