Minoru938/KmdPlus
This module contains a class for treating kernel mean descriptor (KMD), and a function for generating descriptors with summary statistics. This is an original implementation of KMD.
This tool helps materials scientists and researchers convert complex chemical compositions of mixture systems into a structured format called Kernel Mean Descriptors. It takes raw material composition data and generates these descriptors, which uniquely capture all features of the material's component distribution. Users can then perform analyses like PCA mapping, similar to those presented in published research.
No commits in the last 6 months.
Use this if you need a novel, high-fidelity way to represent the distribution of components within chemical compositions for materials science research, and want to explore these representations through techniques like PCA.
Not ideal if you are looking for simple, conventional material descriptors or if your primary interest is not in detailed chemical composition analysis.
Stars
8
Forks
1
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Sep 25, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/Minoru938/KmdPlus"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
GAA-UAM/scikit-fda
Functional Data Analysis Python package
mlr-org/mlr3
mlr3: Machine Learning in R - next generation
mlr-org/mlr3extralearners
Extra learners for use in mlr3.
mlr-org/mlr3book
Online version of Bischl, B., Sonabend, R., Kotthoff, L., & Lang, M. (Eds.). (2024). "Applied...
mlr-org/mlr3learners
Recommended learners for mlr3