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breaking_cycles_in_noisy_hierarchies
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| pyDag | breaking_cycles_in_noisy_hierarchies | |
|---|---|---|
| 2 | 1 | |
| 24 | 71 | |
| - | - | |
| 0.0 | 10.0 | |
| over 1 year ago | over 1 year ago | |
| Python | Python | |
| Apache License 2.0 | BSD 3-clause "New" or "Revised" License |
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breaking_cycles_in_noisy_hierarchies
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I think that's a pretty good idea. Have you checked out the paper "Breaking Cycles in Noisy Hierarchies"? It looks pretty similar to what you're looking for and the code is open source: https://github.com/zhenv5/breaking_cycles_in_noisy_hierarchi...
What are some alternatives?
distance-metrics - Distance metrics are one of the most important parts of some machine learning algorithms, supervised and unsupervised learning, it will help us to calculate and measure similarities between numerical values expressed as data points
Competitive-Python - Python Algorithms Package used in competitive programming
docker-livy - Dockerizing and Consuming an Apache Livy environment
parallel-dfs-dag - A parallel implementation of DFS for Directed Acyclic Graphs (https://research.nvidia.com/publication/parallel-depth-first-search-directed-acyclic-graphs)
livyc - Apache Spark as a Service with Apache Livy Client
pathfinding - Pathfinding library for rust
pubsub2inbox - Pubsub2Inbox is a versatile, multi-purpose tool to handle Pub/Sub messages and turn them into email, API calls, GCS objects, files or almost anything.
breaking_cycles_in_noisy_hierarchi
p_tqdm - Parallel processing with progress bars
trueskill - An implementation of the TrueSkill rating system for Python
wbz - A parallel implementation of the bzip2 data compressor in python, this data compression pipeline is using algorithms like Burrows–Wheeler transform (BWT) and Move to front (MTF) to improve the Huffman compression. For now, this tool only will be focused on compressing .csv files, and other files on tabular format.
data-engineering-challenge-th - Dockerizing a Python Script for Web Scraping and consume the scraped data using FastApi (www.metroscubicos.com)