Mehmet Hakan SatmanJuliaup release channel now points to v1.12.6โผ๏ธ
Don't forget to do
$ juliaup self update && juliaup update
Daniel LakelandOK, So progress on getting my son ready to learn Quarto for his term paper project. I have the quarto tar.gz distribution and julia via juliaup. Set up a manifest. Included typst in the manifest, and a number of other things. Then outside the container running codium. using the manifest #guix shell --emulate-fhs allows #julialang and quarto to run just fine. So he can edit his document and then at the terminal type #quarto render document.qmd and it runs julia and renders to pdf via typst.
RDNRE: https://floss.social/@rdnielsen/116365121149129752
The third and last of the series on unmixing using NMF is now posted at
https://dblog.vitumbre.tech/dart/unmixing-using-nmf-part-3-assessing-accuracy-of-end-members/
Part 3 illustrates the variability of results that can occur when repeatedly unmixing the same data set, and presents approaches to addressing the resultant uncertainty.
#DataAnalysis #DataExploration #Unmixing #NMF #Python #JuliaLang #RStats
RE: https://floss.social/@rdnielsen/116363795536617194
Part 2 of this series on unmixing is now available:
https://dblog.vitumbre.tech/dart/unmixing-using-nmf-part-2-evaluating-the-number-of-end-members/
Part 2 addresses the challenge of deciding how many end members are in a data set, recommends algorithms for Python, Julia, and R, and illustrates how several factors affect that determination.
#DataAnalysis #DataExploration #Unmixing #NMF #Python #JuliaLang #RStats
DataScienceLearningCommunityIt's #TidyTuesday y'all! Show us what you made on our Slack at https://dslc.io/join (find the #chat-tidytuesday channel)!
RT @jonthegeek https://fosstodon.org/@jonthegeek/116358180486819800
I just posted part 1 of a 3-part series on unmixing of data sets using non-negative matrix factorization.
Part 1 contains implementations in Python, Julia, and R, and includes an assessment of the relative accuracy of these implementations.
Parts 2 and 3 will follow shortly, and will contain more detail on the identification of, and accurate characterization of, unmixing end members.
#DataAnalysis #DataExploration #Python #JuliaLang #RStats #Unmixing #NMF
Unmixing Using Non-negative Matrix Factorization (NMF). Part 1: Introduction and Implementation
Data sets that can be represented as a matrix of cases (rows) and variables (columns) often have structure within them that is not immediately apparent. There are a number of techniques for identifying and characterizing hidden structure in data sets. Unmixing is a method that is appropriate when the data
Jon Harmonhttps://DSLC.io welcomes you to week 14 of #TidyTuesday! We're exploring Repair Cafes Worldwide!
๐ https://tidytues.day/2026/2026-04-07
๐ฐ https://insideclimatenews.org/news/11112025/todays-climate-repair-cafe-consumer-waste/
Submit a dataset! https://github.com/rfordatascience/tidytuesday/blob/main/.github/CONTRIBUTING.md
This study investigates the possibilities of applying classical MCDM methods to grey numbers by leveraging operator overloading and multiple dispatch features of Julia programming language.
GeoeconomicsImplication: #python #c et al were not designed with science and math in mind?
โArray indices in #julialang, as in Fortran and many other languages designed with scientific and mathematical work in mind, start at 1.โ
Coming from the comfort of RStudio #Rstats, learning package and environment management via #julialang has been informative. 1/4