minisom
umap
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| minisom | umap | |
|---|---|---|
| 3 | 10 | |
| 1,387 | 6,946 | |
| - | - | |
| 8.4 | 8.3 | |
| 5 days ago | 4 days ago | |
| Python | Python | |
| MIT License | BSD 3-clause "New" or "Revised" License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
minisom
- How to use MiniSOM (Self Organizing Maps) Library
- [P][D] Self Organizing Maps
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[OC] Animation of a Self Organizing Map
I made this animation because I could not find a single decent demonstration of a SOM map on the internet, especially considering how popular it is becoming. I used the python library Pyvista for 3D plotting and creating the animation, and I used the minisom library for running the SOM.
umap
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[OC] Clustering Images with OpenAI CLIP, T-SNE, UMAP & Plotly
UMAP GitHub repository: https://github.com/lmcinnes/umap
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UMAP clustering in Ruby
Uniform Manifold Approximation and Projection (UMAP) is a well-known dimensionality reduction method along with t-SNE.
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Introducing the Semantic Graph
A number of excellent topic modeling libraries exist in Python today. BERTopic and Top2Vec are two of the most popular. Both use sentence-transformers to encode data into vectors, UMAP for dimensionality reduction and HDBSCAN to cluster nodes.
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Using the 80:20 rule, what top 20% of your tools, statistical tests, activities, etc. do you use to generate 80% of your results?
As with anything, it depends on the problem. But T-SNE and UMAP are often good.
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[D] In UMAP and PyNNDescent, the conversion of Cosine and Correlation measures to distance metric seems problematic
UMAP distances.py: umap/distances.py at master · lmcinnes/umap (github.com)
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I built an Image Search Engine using OpenAI CLIP and Images from Wikimedia
I used for this project Flask and OpenAI CLIP. For the vector search I used approximate nearest neighbors provided by spotify/annoy. I used Flask-SQLAlchemy with GeoAlchemy2 to query GPS coordinates. The embedding was done using UMAP.
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We Analyzed 425,909 Favicons
side note: instead of t-SNE consider UMAP - provides better results (and it's much faster) https://github.com/lmcinnes/umap
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Finding correlating features in a large dataset.
Sounds like a job for UMAP https://github.com/lmcinnes/umap ?
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The most perplexing bug I've ever seen
I am a fairly experienced python developer/researcher (about 10 years), and have found a bug that breaks all of my intuitions. I am messing with the [UMAP](https://github.com/lmcinnes/umap) repository and trying to add the option to disable some additional features. I've stripped everything from it but have a [quick test that will run my UMAP version and compare the outputs with what the original gave](https://github.com/Andrew-Draganov/probabilistic_dim_reduction/blob/master/umap/nndescent_umap_test.py). Managing my random seeds, same inputs, all that.
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Question about numpy method I found in github project
I'm currently reading through a project on github, https://github.com/lmcinnes/umap, and in `umap/umap_.py` at line 2287, they have this:
What are some alternatives?
somoclu - Massively parallel self-organizing maps: accelerate training on multicore CPUs, GPUs, and clusters
giotto-tda - A high-performance topological machine learning toolbox in Python
sparse-som - Efficient Self-Organizing Map for Sparse Data
annoy - Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk
susi - SuSi: Python package for unsupervised, supervised and semi-supervised self-organizing maps (SOM)
Traccar - Traccar GPS Tracking System
DBCV - Python implementation of Density-Based Clustering Validation
vaex - Out-of-Core hybrid Apache Arrow/NumPy DataFrame for Python, ML, visualization and exploration of big tabular data at a billion rows per second 🚀
som-tsp - Solving the Traveling Salesman Problem using Self-Organizing Maps
Openstreetmap - The Rails application that powers OpenStreetMap
n2d - A deep clustering algorithm. Code to reproduce results for our paper N2D: (Not Too) Deep Clustering via Clustering the Local Manifold of an Autoencoded Embedding.
CLIP - CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image